EPA/635/R-09/011F
www.epa.gov/iris
4IEF&
TOXICOLOGICAL REVIEW
OF
TRICHLOROETHYLENE
(CAS No. 79-01-6)
In Support of Summary Information on the
Integrated Risk Information System (IRIS)
September 2011
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental Protection
Agency policy and approved for publication. Mention of trade names or commercial products
does not constitute endorsement or recommendation for use.
11
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GUIDE TO READERS OF THIS DOCUMENT
Due to the length of the TCE toxicological review, it is recommended that
Chapters 1 and 6 be read prior to Chapters 2-5.
Chapter 1 is the standard introduction to an IRIS Toxicological Review, describing the
purpose of the assessment and the guidelines used in its development.
Chapter 2 is an exposure characterization that summarizes information about TCE
sources, releases, media levels, and exposure pathways for the general population (occupational
exposure is also discussed to a lesser extent).
Chapter 3 describes the toxicokinetics and physiologically based pharmacokinetic
(PBPK) modeling of TCE and metabolites (PBPK modeling details are in Appendix A).
Chapter 4 is the hazard characterization of TCE. Section 4.1 summarizes the evaluation
of epidemiologic studies of cancer and TCE (qualitative details in Appendix B; meta-analyses in
Appendix C). Each of the Sections 4.2-4.9 provides a self-contained summary and syntheses of
the epidemiologic and laboratory studies on TCE and metabolites, organized by tissue/type of
effects, in the following order: genetic toxicity, central nervous system (CNS), kidney, liver,
immune system, respiratory tract, reproduction and development, and other cancers. Additional
details are provided in Appendix D for CNS effects and in Appendix E for liver effects.
Section 4.10 summarizes the available data on susceptible lifestages and populations.
Section 4.11 describes the overall hazard characterization, including the weight of evidence for
noncancer effects and for carcinogenicity.
Chapter 5 is the dose-response assessment of TCE. Section 5.1 describes the dose-
response analyses for noncancer effects, and Section 5.2 describes the dose-response analyses for
cancer. Additional computational details are described in Appendix F for noncancer dose-
response analyses, Appendix G for cancer dose-response analyses based on rodent bioassays, and
Appendix H for cancer dose-response analyses based on human epidemiologic data.
Chapter 6 is the summary of the major conclusions in the characterization of TCE hazard
and dose response.
Appendix I contains the summary of EPA's response to major external peer review and
public comments.
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CONTENTS of TOXICOLOGICAL REVIEW for TRICHLOROETHYLENE
(CAS No. 79-01-6)
TOXICOLOGICAL REVIEW OF TRICHLOROETHYLENE i
DISCLAIMER ii
GUIDE TO READERS OF THIS DOCUMENT iii
CONTENTS of TOXICOLOGICAL REVIEW for TRICHLOROETHYLENE iv
LIST OF TABLES xiv
LIST OF FIGURES xxv
LIST OF ABBREVIATIONS AND ACRONYMS xxix
FOREWORD xxxv
AUTHORS, CONTRIBUTORS, AND REVIEWERS xxxvi
EXECUTIVE SUMMARY xlii
1. INTRODUCTION 1-1
2. EXPOSURE CHARACTERIZATION 2-1
2.1. ENVIRONMENTAL SOURCES 2-1
2.2. ENVIRONMENTAL FATE 2-6
2.2.1. Fate in Terrestrial Environments 2-6
2.2.2. Fate in the Atmosphere 2-6
2.2.3. Fate in Aquatic Environments 2-6
2.3. EXPOSURE CONCENTRATIONS 2-6
2.3.1. Outdoor Air—Measured Levels 2-6
2.3.2. Outdoor Air—Modeled Levels 2-8
2.3.3. Indoor Air 2-10
2.3.4. Water 2-12
2.3.5. Other Media 2-14
2.3.6. Biological Monitoring 2-15
2.4. EXPOSURE PATHWAYS AND LEVELS 2-16
2.4.1. General Population 2-16
2.4.1.1. Inhalation 2-16
2.4.1.2. Ingestion 2-17
iv
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2.4.1.3. Dermal 2-18
2.4.1.4. Exposure to TCE Related Compounds 2-19
2.4.2. Potentially Highly Exposed Populations 2-20
2.4.2.1. Occupational Exposure 2-20
2.4.2.2. Consumer Exposure 2-22
2.4.3. Exposure Standards 2-22
2.5. EXPOSURE SUMMARY 2-22
3. TOXICOKINETICS 3-1
3.1. ABSORPTION 3-2
3.1.1. Oral 3-2
3.1.2. Inhalation 3-4
3.1.3. Dermal 3-9
3.2. DISTRIBUTION AND BODY BURDEN 3-9
3.3. METABOLISM 3-16
3.3.1. Introduction 3-16
3.3.2. Extent of Metabolism 3-16
3.3.3. Pathways of Metabolism 3-20
3.3.3.1. CYP-Dependent Oxidation 3-20
3.3.3.2. GSH Conjugation Pathway 3-35
3.3.3.3. Relative roles of the CYP and GSH pathways 3-50
3.4. TCE EXCRETION 3-53
3.4.1. Exhaled Air 3-53
3.4.2. Urine 3-54
3.4.3. Feces 3-56
3.5. PBPK MODELING OF TCE AND ITS METABOLITES 3-57
3.5.1. Introduction 3-57
3.5.2. Previous PBPK Modeling of TCE for Risk Assessment
Application 3-57
3.5.3. Development and Evaluation of an Interim —Hanonized" TCE
PBPK Model 3-59
3.5.4. PBPK Model for TCE and Metabolites Used for This
Assessment 3-60
3.5.4.1. Introduction 3-60
3.5.4.2. Updated PBPK Model Structure 3-63
3.5.4.3. Specification of Baseline PBPK Model Parameter 3-65
3.5.4.4. Dose-Metric Predictions 3-67
3.5.5. Bayesian Estimation of PBPK Model Parameters, and Their
Uncertainty and Variability 3-68
3.5.5.1. UpdatedPharmacokinetic Database 3-68
3.5.5.2. Updated Hierarchical Population Statistical Model
and Prior Distributions 3-75
3.5.5.3. Use of Interspecies Scaling to Update Prior
Distributions in the Absence of Other Data 3-77
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3.5.5.4. Implementation 3-80
3.5.6. Evaluation of Updated PBPK Model 3-80
3.5.6.1. Convergence 3-80
3.5.6.2. Evaluation of Posterior Parameter Distributions 3-82
3.5.6.3. Comparison of Model Predictions With Data 3-103
3.5.6.4. Sensitivity Analysis With Respect to Calibration Data 3-126
3.5.6.5. Summary Evaluation of Updated PBPK Model 3-132
3.5.7. PBPK Model Dose-Metric Predictions 3-133
3.5.7.1. Characterization of Uncertainty and Variability 3-133
3.5.7.2. Local Sensitivity Analysis With Respect to Dose-
Metric Predictions 3-142
3.5.7.3. Implications for the Population Pharmacokinetics of
TCE 3-155
3.5.7.4. Key Limitations and Potential Implications of
Violating Key Assumptions 3-162
3.5.7.5. Overall Evaluation of PBPK Model-Based Internal
Dose Predictions 3-163
4. HAZARD CHARACTERIZATION 4-1
4.1. EPIDEMIOLOGIC STUDIES ON CANCER AND TCE—
METHODOLOGICAL OVERVIEW 4-1
4.2. GENETIC TOXICITY 4-29
4.2.1. TCE 4-30
4.2.1.1. DNA Binding Studies 4-30
4.2.1.2. Bacterial Systems—Gene Mutations 4-31
4.2.1.3. Fungal and Yeast Systems—Gene Mutations,
Conversions, and Recombination 4-35
4.2.1.4. Mammalian Systems Including Human Studies 4-37
4.2.1.5. Summary 4-48
4.2.2. TCA 4-49
4.2.2.1. Bacterial Systems—Gene Mutations 4-49
4.2.2.2. Mammalian Systems 4-51
4.2.2.3. Summary 4-55
4.2.3. DCA 4-56
4.2.3.1. Bacterial and Fungal Systems—Gene Mutations 4-56
4.2.3.2. Mammalian Systems 4-60
4.2.3.3. Summary 4-62
4.2.4. CH 4-62
4.2.4.1. DNA Binding Studies 4-62
4.2.4.2. Bacterial and Fungal Systems—Gene Mutations 4-69
4.2.4.3. Mammalian Systems 4-70
4.2.4 A. Summary 4-72
4.2.5. DCVCandDCVG 4-73
4.2.6. TCOH 4-78
4.2.7. Synthesis and Overall Summary 4-79
4.3. CENTRAL NERVOUS SYSTEM (CNS) TOXICITY 4-83
VI
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4.3.1. Alterations in Nerve Conduction 4-83
4.3.1.1. Trigeminal Nerve Function: Human Studies 4-83
4.3.1.2. Nerve Conduction Velocity—Human Studies 4-87
4.3.1.3. Trigeminal Nerve Function: Laboratory Animal
Studies 4-88
4.3.1.4. Discussion and Conclusions: TCE-Induced
Trigeminal Nerve Impairment 4-89
4.3.2. Auditory Effects 4-91
4.3.2.1. Auditory Function: Human Studies 4-91
4.3.2.2. Auditory Function: Laboratory Animal Studies 4-93
4.3.2.3. Summary and Conclusion of Auditory Effects 4-97
4.3.3. VestibularFunction 4-99
4.3.3.1. Vestibular Function: Human Studies 4-99
4.3.3.2. Vestibular Function: Laboratory Animal Data 4-99
4.3.3.3. Summary and Conclusions for the Vestibular
Function Studies 4-100
4.3.4. Visual Effects 4-101
4.3.4.1. Visual Effects: Human Studies 4-101
4.3.4.2. Visual Effects: Laboratory Animal Data 4-103
4.3.4.3. Summary and Conclusion of Visual Effects 4-105
4.3.5. Cognitive Function 4-106
4.3.5.1. Cognitive Effects: Human Studies 4-106
4.3.5.2. Cognitive Effects: Laboratory Animal Studies 4-109
4.3.5.3. Summary and Conclusions of Cognitive Function
Studies 4-110
4.3.6. PsychomotorEffects 4-111
4.3.6.1. Psychomotor Effects: Human Studies 4-111
4.3.6.2. Psychomotor Effects: Laboratory Animal Data 4-114
4.3.6.3. Summary and Conclusions for Psychomotor Effects 4-118
4.3.7. Mood Effects and Sleep Disorders 4-119
4.3.7.1. Effects on Mood: Human Studies 4-119
4.3.7.2. Effects on Mood: Laboratory Animal Findings 4-120
4.3.7.3. Sleep Disturbances 4-120
4.3.8. Developmental Neurotoxicity 4-120
4.3.8.1. Human Studies 4-120
4.3.8.2. Animal Studies 4-121
4.3.8.3. Summary and Conclusions for the Developmental
Neurotoxicity Studies 4-125
4.3.9. Mechanistic Studies of TCE Neurotoxicity 4-125
4.3.9.1. Dopamine Neuron Disruption 4-125
4.3.9.2. Neurochemical and Molecular Changes 4-127
4.3.10. Potential Mechanisms for TCE-Mediated Neurotoxicity 4-131
4.3.11. Overall Summary and Conclusions—Weight of Evidence 4-133
4.4. KIDNEY TOXICITY AND CANCER 4-137
4.4.1. Human Studies of Kidney 4-137
4.4.1.1. Nonspecific Markers of Nephrotoxicity 4-137
4.4.1.2. End-Stage Renal Disease (ESRD) 4-143
4.4.2. Human Studies of Kidney Cancer 4-143
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4.4.2.1. Studies of Job Titles and Occupations with Historical
TCE Usage 4-154
4.4.2.2. Cohort and Case-Controls Studies of TCE Exposure 4-155
4.4.2.3. Examination of Possible Confounding Factors 4-160
4.4.2.4. Susceptible Populations—Kidney Cancer and TCE
Exposure 4-164
4.4.2.5. Meta-Analysis for Kidney Cancer 4-166
4.4.3. Human Studies of Somatic Mutation of VHL Gene 4-172
4.4.4. Kidney Noncancer Toxicity in Laboratory Animals 4-177
4.4.5. Kidney Cancer in Laboratory Animals 4-185
4.4.5.1. Inhalation Studies of TCE 4-185
4.4.5.2. Gavage and Drinking Water Studies of TCE 4-187
4.4.5.3. Conclusions: Kidney Cancer in Laboratory Animals 4-188
4.4.6. Role of Metabolism in TCE Kidney Toxicity 4-188
4.4.6.1. In Vivo Studies of the Kidney Toxicity of TCE
Metabolites 4-189
4.4.6.2. In Vitro Studies of Kidney Toxicity of TCE and
Metabolites 4-197
4.4.6.3. Conclusi ons as to the Active Agents of TCE-Induced
Nephrotoxicity 4-199
4.4.7. Mode(s) of Action for Kidney Carcinogenicity 4-199
4.4.7.1. Hypothesized Mode of Action: Mutagenicity 4-200
4.4.7.2. Hypothesized Mode of Action: Cytotoxicity and
Regenerative Proliferation 4-209
4.4.7.3. Additional Hypothesized Modes of Action with
Limited Evidence or Inadequate Experimental
Support 4-212
4.4.7.4. Conclusions About the Hypothesized Modes of
Action 4-213
4.4.8. Summary: TCE Kidney Toxicity, Carcinogenicity, and Mode
of Action 4-216
4.5. LIVER TOXICITY AND CANCER 4-218
4.5.1. Liver Noncancer Toxicity in Humans 4-218
4.5.2. Liver Cancer in Humans 4-224
4.5.3. Experimental Studies of TCE in Rodents—Introduction 4-239
4.5.4. TCE-Induced Liver Noncancer Effects 4-241
4.5.4.1. Liver Weight 4-247
4.5.4.2. Cytotoxicity and Histopathology 4-251
4.5.4.3. Measures of DNA Synthesis, Cellular Proliferation,
and Apoptosis 4-258
4.5.4.4. Peroxisomal Proliferation and Related Effects 4-261
4.5.4.5. Oxidative Stress 4-263
4.5.4.6. Bile Production 4-264
4.5.4.7. Summary: TCE-Induced Noncancer Effects in
Laboratory Animals 4-265
4.5.5. TCE-Induced Liver Cancer in Laboratory Animals 4-266
4.5.5.1. Negative or Inconclusive Studies of Mice and Rats 4-266
4.5.5.2. Positive TCE Studies of Mice 4-273
Vlll
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4.5.5.3. Summary: TCE-Induced Cancer in Laboratory
Animals 4-275
4.5.6. Role of Metabolism in Liver Toxicity and Cancer 4-275
4.5.6.1. Pharmacokinetics of CH, TCA, and DCA from TCE
Exposure 4-276
4.5.6.2. Comparisons Between TCE and TCA, DCA, and CH
Noncancer Effects 4-277
4.5.6.3. Comparisons of TCE-Induced Carcinogenic
Responses with TCA, DCA, and CH Studies 4-296
4.5.6.4. Conclusions Regarding the Role of TCA, DCA, and
CH in TCE-Induced Effects in the Liver 4-320
4.5.7. Mode of Action for TCE Liver Carcinogenicity 4-321
4.5.7.1. Mutagenicity 4-321
4.5.7.2. PPARa Receptor Activation 4-323
4.5.7.3. Additional Proposed Hypotheses and Key Events
with Limited Evidence or Inadequate Experimental
Support 4-332
4.5.7.4. Mode-of-Action Conclusions 4-342
4.6. IMMUNOTOXICITY AND CANCERS OF THE IMMUNE
SYSTEM 4-354
4.6.1. Human Studies 4-354
4.6.1.1. Noncancer Immune-Related Effects 4-354
4.6.1.2. Cancers of the Immune System, Including Childhood
Leukemia 4-366
4.6.2. Animal Studies 4-401
4.6.2.1. Immunosuppression 4-401
4.6.2.2. Hypersensitivity 4-409
4.6.2.3. Autoimmunity 4-412
4.6.2.4. Cancers of the Immune System 4-424
4.6.3. Summary 4-427
4.6.3.1. Noncancer Effects 4-427
4.6.3.2. Cancer 4-429
4.7. RESPIRATORY TRACT TOXICITY AND CANCER 4-431
4.7.1. Epidemiologic Evidence 4-431
4.7.1.1. Chronic Effects: Inhalation 4-431
4.7.1.2. Cancer 4-431
4.7.2. Laboratory Animal Studies 4-443
4.7.2.1. Respiratory Tract Animal Toxicity 4-443
4.7.2.2. Respiratory Tract Cancer 4-451
4.7.3. Role of Metabolism in Pulmonary Toxicity 4-454
4.7.4. Mode of Action for Pulmonary Carcinogenicity 4-459
4.7.4.1. Mutagenicity via Oxidative Metabolism 4-459
4.7.4.2. Cytotoxicity Leading to Increased Cell Proliferation 4-461
4.7.4.3. Additional Hypothesized Modes of Action with
Limited Evidence or Inadequate Experimental
Support 4-462
IX
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4.7.4.4. Conclusions About the Hypothesized Modes of
Action 4-463
4.7.5. Summary and Conclusions 4-465
4.8. REPRODUCTIVE AND DEVELOPMENTAL TOXICITY 4-467
4.8.1. Reproductive Toxicity 4-467
4.8.1.1. Human Reproductive Outcome Data 4-467
4.8.1.2. Animal Reproductive Toxicity Studies 4-474
4.8.1.3. Discussion/Synthesis of Noncancer Reproductive
Toxicity Findings 4-487
4.8.2. Cancers of the Reproductive System 4-493
4.8.2.1. Human Data 4-493
4.8.2.2. Animal Studies 4-509
4.8.2.3. Mode of Action for Testicular Tumors 4-510
4.8.3. Developmental Toxicity 4-511
4.8.3.1. Human Developmental Data 4-511
4.8.3.2. Animal Developmental Toxicology Studies 4-534
4.8.3.3. Discussion/Synthesis of Developmental Data 4-556
4.9. OTHER SITE-SPECIFIC CANCERS 4-572
4.9.1. Esophageal Cancer 4-572
4.9.2. Bladder Cancer 4-583
4.9.3. CNS and Brain Cancers 4-590
4.10. SUSCEPTIBLE LIFESTAGES AND POPULATIONS 4-595
4.10.1.Lifestages 4-595
4.10.1.1.EarlyLifestages 4-595
4.10.1.2. Later Lifestages 4-605
4.10.2. Other Susceptibility Factors 4-606
4.10.2.1. Gender 4-606
4.10.2.2. Genetic Variability 4-611
4.10.2.3. Race/Ethnicity 4-613
4.10.2.4. Preexisting Health Status 4-613
4.10.2.5. Lifestyle Factors and Nutrition Status 4-614
4.10.2.6. Mixtures 4-617
4.10.3. Uncertainty of Database and Research Needs for Susceptible
Populations 4-617
4.11. HAZARD CHARACTERIZATION 4-619
4.11.1. Characterization of Noncancer Effects 4-619
4.11.1.1.Neurotoxicity 4-619
4.11.1.2. Kidney Toxicity 4-623
4.11.1.3. Liver Toxicity 4-624
4.11.1.4. Immunotoxicity 4-626
4.11.1.5. Respiratory Tract Toxicity 4-627
4.11.1.6. Reproductive Toxicity 4-628
4.11.1.7. Developmental Toxicity 4-629
4.11.2. Characterization of Carcinogenicity 4-632
4.11.2.1. Summary Evaluation of Epidemiologic Evidence of
TCE and Cancer 4-633
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4.11.2.2. Summary of Evidence for TCE Carcinogenicity in
Rodents 4-641
4.11.2.3. Summary of Additional Evidence on Biological
Plausibility 4-644
4.11.3. Characterization of Factors Impacting Susceptibility 4-649
5. DOSE-RESPONSE ASSESSMENT 5-1
5.1. DOSE-RESPONSE ANALYSES FOR NONCANCER ENDPOINTS 5-1
5.1.1. Modeling Approaches and UFs for Developing Candidate
Reference Values Based on Applied Dose 5-3
5.1.2. Candidate Critical Effects by Effect Domain 5-7
5.1.2.1. Candidate Critical Neurological Effects on the Basis
of Applied Dose 5-7
5.1.2.2. Candidate Critical Kidney Effects on the Basis of
Applied Dose 5-14
5.1.2.3. Candidate Critical Liver Effects on the Basis of
Applied Dose 5-21
5.1.2.4. Candidate Critical Body Weight Effects on the Basis
of Applied Dose 5-22
5.1.2.5. Candidate Critical Immunological Effects on the
Basis of Applied Dose 5-22
5.1.2.6. Candidate Critical Respiratory Tract Effects on the
Basis of Applied Dose 5-27
5.1.2.7. Candidate Critical Reproductive Effects on the Basis
of Applied Dose 5-27
5.1.2.8. Candidate Critical Developmental Effects on the
Basis of Applied Dose 5-38
5.1.2.9. Summary of cRfCs, cRfDs, and Candidate Critical
Effects 5-46
5.1.3. Application of PBPK Model to Inter- and Intraspecies
Extrapolation for Candidate Critical Effects 5-49
5.1.3.1. Selection of Dose-metrics for Different Endpoints 5-49
5.1.3.2. Methods for Inter- and Intraspecies Extrapolation
Using Internal Doses 5-57
5.1.3.3. Results and Discussion of p-RfCs and p-RfDs for
Candidate Critical Effects 5-75
5.1.4. Uncertainties in cRfCs and cRfDs 5-76
5.1.4.1. Qualitative Uncertainties 5-76
5.1.4.2. Quantitative Uncertainty Analy si s of PBPK Model -
Based Dose-metrics for LOAEL- or NOAEL-Based
PODs 5-79
5.1.5. Summary of Noncancer Reference Values 5-89
5.1.5.1. Preferred Candidate Reference Values (cRfCs, cRfD,
p-cRfCs, and p-cRfDs) for Candidate Critical Effects 5-89
5.1.5.2. RfC 5-95
5.1.5.3. RfD 5-98
5.2. DOSE-RESPONSE ANALYSIS FOR CANCER ENDPOINTS 5-101
XI
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5.2.1. Dose-Response Analyses: Rodent Bioassays 5-101
5.2.1.1. Rodent Dose-Response Analyses: Studies and
Modeling Approaches 5-102
5.2.1.2. Rodent Dose-Response Analyses: Dosimetry 5-110
5.2.1.3. Rodent Dose-Response Analyses: Results 5-118
5.2.1.4. Uncertainties in Dose-Response Analyses of Rodent
Bioassays 5-129
5.2.2. Dose-Response Analyses: Human Epidemiologic Data 5-139
5.2.2.1. Inhalation Unit Risk Estimate for RCC Derived from
Charbotel et al. (2006) Data 5-139
5.2.2.2. Adjustment of the Inhalation Unit Risk Estimate for
Multiple Sites 5-147
5.2.2.3. Route-to-Route Extrapolation Using PBPK Model 5-150
5.2.3. Summary of Unit Risk Estimates 5-153
5.2.3.1. Inhalation Unit Risk Estimate 5-153
5.2.3.2. Oral Slope Factor Estimate 5-154
5.2.3.3. Application of AD AFs 5-155
5.3. KEY RESEARCH NEEDS FOR TCE DOSE-RESPONSE
ANALYSES 5-164
6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF HAZARD
AND DOSE RESPONSE 6-1
6.1. HUMAN HAZARD POTENTIAL 6-1
6.1.1. Exposure (see Chapter 2) 6-1
6.1.2. Toxicokinetics and PBPK Modeling (see Chapter 3 and
Appendix A) 6-2
6.1.3. Noncancer Toxicity 6-4
6.1.3.1. Neurological Effects (see Sections 4.3 and 4.11.1.1
and Appendix D) 6-4
6.1.3.2. Kidney Effects (see Sections 4.4.1, 4.4.4, 4.4.6, and
4.11.1.2) 6-5
6.1.3.3. Liver Effects (see Sections 4.5.1, 4.5.3, 4.5.4, 4.5.6,
and 4.11.1.3, and Appendix E) 6-6
6.1.3.4. Immunological Effects (see Sections 4.6.1.1, 4.6.2,
and 4.11.1.4) 6-7
6.1.3.5. Respiratory Tract Effects (see Sections 4.7.1.1,
4.7.2.1,4.7.3, and 4.11.1.5) 6-8
6.1.3.6. Reproductive Effects (see Sections 4.8.1 and 4.11.1.6) 6-8
6.1.3.7. Developmental Effects (see Sections 4.8.3 and
4.11.1.7) 6-9
6.1.4. Carcinogenicity (see Sections 4.1, 4.2, 4.4.2, 4.4.5, 4.4.7, 4.5.2,
4.5.5, 4.5.6, 4.5.7, 4.6.1.2, 4.6.2.4, 4.7.1.2, 4.7.2.2, 4.7.4, 4.8.2,
4.9, and 4.11.2, and Appendices B and C) 6-11
6.1.5. Susceptibility (see Sections 4.10 and 4.11.3) 6-17
DOSE-RESPONSE ASSESSMENT 6-18
6.2.1. Noncancer Effects (see Section 5.1) 6-18
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6.2.1.1. Background and Methods 6-18
6.2.1.2. Uncertainties and Application of UFs (see Sections
5.1.1 and 5.1.4) 6-19
6.2.1.3. Noncancer Reference Values (see Section 5.1.5) 6-28
6.2.2. Cancer (see Section 5.2) 6-31
6.2.2.1. Background and Methods (rodent: see Section
5.2.1.1; human: see Section 5.2.2.1) 6-31
6.2.2.2. Inhalation Unit Risk Estimate (rodent: see Section
5.2.1.3; human: see Sections 5.2.2.1 and 5.2.2.2) 6-33
6.2.2.3. Oral Slope Factor Estimate (rodent: see Section
5.2.1.3; human: see Section 5.2.2.3) 6-35
6.2.2.4. Uncertainties in Cancer Dose-Response Assessment 6-36
6.2.2.5. Application of ADAFs (see Section 5.2.3.3) 6-41
6.3. OVERALL CHARACTERIZATION OF TCE HAZARD AND DOSE
RESPONSE 6-42
7. References 7-1
APPENDIX A: PBPK MODELING OF TCE AND METABOLITES-DETAILED
METHODS AND RESULTS A-l
APPENDIX B: SYSTEMATIC REVIEW OF EPIDEMIOLOGIC STUDIES ON
CANCER AND TRICHLOROETHYLENE (TCE) EXPOSURE B-1
APPENDIX C: MET A-ANALYSIS OF CANCER RESULTS FROM
EPIDEMIOLOGICAL STUDIES C-l
APPENDIX D: NEUROLOGICAL EFFECTS OF TRICHLOROETHYLENE D-l
APPENDIX E: ANALYSIS OF LIVER AND COEXPOSURE ISSUES FOR
THE TCE TOXICOLOGICAL REVIEW E-l
APPENDIX F: TCE NONCANCER DOSE-RESPONSE ANALYSES F-l
APPENDIX G: TCE CANCER DOSE-RESPONSE ANALYSES WITH RODENT
CANCER BIOASSAY DATA G-l
APPENDIX H: LIFETABLE ANALYSIS AND WEIGHTED LINEAR
REGRESSION BASED ON RESULTS FROM CHARBOTEL ET
AL H-l
APPENDIX I: EPA RESPONSE TO MAJOR PEER REVIEW AND PUBLIC
COMMENTS 1-1
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LIST OF TABLES
Table 2-1. TCE metabolites and related parent compounds21 2-1
Table 2-2. Chemical properties of TCE 2-2
Table 2-3. Properties and uses of TCE related compounds 2-3
Table 2-4. TRI releases of TCE (pounds/year) 2-4
Table 2-5. Concentrations of TCE in ambient air 2-7
Table 2-6. TCE ambient air monitoring data (ug/m3) 2-8
Table 2-7. Mean TCE air levels across monitors by land setting and use (1985-
1998) 2-8
Table 2-8. Concentrations of TCE in water based on pre-1990 studies 2-12
Table 2-9. Levels in food 2-15
Table 2-10. TCE levels in whole blood by population percentile 2-16
Table 2-11. Modeled 1999 annual exposure concentrations (ug/m3) for TCE 2-16
Table 2-12. Preliminary estimates of TCE intake from food ingestion 2-18
Table 2-13. Preliminary intake estimates of TCE and TCE-related chemicals 2-19
Table 2-14. Years of solvent use in industrial degreasing and cleaning operations 2-21
Table 2-15. TCE standards 2-22
Table 3-1. Blood:air partition coefficient values for humans 3-4
Table 3-2. Blood:air partition coefficient values for rats and mice 3-5
Table 3-3. Air and blood concentrations during exposure to TCE in humans 3-6
Table 3-4. Retention of inhaled TCE vapor in humans 3-6
Table 3-5. Uptake of TCE in volunteers following 4 hour exposure to 70 ppm 3-7
Table 3-6. Concentrations of TCE in maternal and fetal blood at birth 3-11
Table 3-7. Distribution of TCE to rat tissues3 following inhalation exposure 3-12
Table 3-8. Tissue:blood partition coefficient values for TCE 3-13
Table 3-9. Age-dependence of tissue:air partition coefficients in rats 3-14
Table 3-10. Predicted maximal concentrations of TCE in rat blood following a
6-hour inhalation exposure 3-14
Table 3-11. Tissue distribution of TCE metabolites following inhalation exposure 3-15
Table 3-12. Binding of [14C] from [14C]-TCE in rat liver and kidney at 72 hours
after oral administration of 200 mg/kg [14C]-TCE 3-16
Table 3-13. In vitro TCE oxidative metabolism in hepatocytes and microsomal
fractions 3-22
Table 3-14. In vitro kinetics of TCOH and TCA formation from CH in rat,
mouse, and human liver homogenates 3-25
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Table 3-15. In vitro kinetics of DCA metabolism in hepatic cytosol of mice, rats,
and humans 3-27
Table 3-17. Reported TCA plasma binding parameters'1 3-29
Table 3-18. Partition coefficients for TCE oxidative metabolites 3-30
Table 3-19. Urinary excretion of TCA by various species exposed to TCE (based
on data reviewed in Fisher et al., 1991) 3-32
Table 3-20. P450 isoform kinetics for metabolism of TCE to CH in human, rat,
and mouse recombinant P450s 3-33
Table 3-21. P450 isoform activities in human liver microsomes exhibiting
different affinities for TCE 3-34
Table 3-22. Comparison of peak blood concentrations in humans exposed to 100
ppm(537mg/m3)TCEfor4hours 3-38
Table 3-23. GSH conjugation of TCE (at 1-2 mM) in liver and kidney cellular
fractions in humans, male F344 rats, and male B6C3Fi mice from Lash
laboratory 3-39
Table 3-24. Kinetics of TCE metabolism via GSH conjugation in male F344 rat
kidney and human liver and kidney cellular and subcellular fractions from
Lash laboratory 3-40
Table 3-25. GSH conjugation of TCE (at 1.4-4 mM) in liver and kidney cellular
fractions in humans, male F344 rats, and male B6C3Fi mice from Green
and Dekant laboratories 3-41
Table 3-26. GGT activity in liver and kidney subcellular fractions of mice, rats,
and humans 3-47
Table 3-27. Multispecies comparison of whole-organ activity levels of GGT and
dipeptidase 3-47
Table 3-28. Comparison of hepatic in vitro oxidation and conjugation of TCEa 3-51
Table 3-29. Estimates of DCVG in blood relative to inhaled TCE dose in humans
exposed to 50 and 100 ppm (269 and 537 mg/m3) 3-52
Table 3-30. Concentrations of TCE in expired breath from inhalation-exposed
humans (Astrand and Ovrum, 1976) 3-53
Table 3-31. Conclusions from evaluation of Hack et al. (2006), and implications
for PBPK model development 3-61
Table 3-32. Discussion of changes to the Hack et al. (2006) PBPK model
implemented for this assessment 3-65
Table 3-33. PBPK model-based dose-metrics 3-67
Table 3-34. Rodent studies with pharmacokinetic data considered for analysis 3-69
Table 3-35. Human studies with pharmacokinetic data considered for analysis 3-73
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Table 3-36. Parameters for which scaling from mouse to rat, or from mouse and
rat to human, was used to update the prior distributions 3-78
Table 3-37. Prior and posterior uncertainty and variability in mouse PBPK model
parameters 3-84
Table 3-38. Prior and posterior uncertainty and variability in rat PBPK model
parameters 3-89
Table 3-39. Prior and posterior uncertainty and variability in human PBPK model
parameters 3-94
Table 3-40. CI widths (ratio of 97.5-2.5% estimates) and fold-shift in median
estimate for the PBPK model population median parameters, sorted in
order of decreasing CI widtha 3-98
Table 3-41. Estimates of the residual-error 3-104
Table 3-42. Summary comparison of updated PBPK model predictions and in
vivo data in mice 3-108
Table 3-43. Summary comparison of updated PBPK model predictions and in
vivo data used for—dabration" in rats 3-114
Table 3-44. Summary comparison of updated PBPK model predictions and in
vivo data used for—outof-sample" evaluation in rats 3-116
Table 3-45. Summary comparison of updated PBPK model predictions and in
vivo data used for —dabration" in humans 3-121
Table 3-46. Summary comparison of updated PBPK model predictions and in
vivo data used for —out)f-sample" evaluation in humans 3-123
Table 3-47. Summary of scaling parameters ordered by fraction of calibration
data of moderate or high sensitivity 3-131
Table 3-48. Posterior predictions for representative internal doses: mousea 3-144
Table 3-49. Posterior predictions for representative internal doses: rata 3-145
Table 3-50. Posterior predictions for representative internal doses: humana 3-146
Table 3-51. Degree of variance in dose-metric predictions due to incomplete
convergence (columns 2-4), combined uncertainty and population
variability (columns 5-7), uncertainty in particular human population
percentiles (columns 8-10), model fits to in vivo data (column 11); the
GSD is a -^old-change" from the central tendency 3-164
Table 4-1. Description of epidemiologic cohort and proportionate mortality ratio
(PMR) studies assessing cancer and TCE exposure 4-2
Table 4-2. Case-control epidemiologic studies examining cancer and TCE
exposure 4-8
Table 4-3. Geographic-based studies assessing cancer and TCE exposure 4-19
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Table 4-4. Standards of epidemiologic study design and analysis use for
identifying cancer hazard and TCE exposure 4-21
Table 4-5. Summary of criteria for meta-analysis study selection 4-24
Table 4-6. TCE genotoxicity: bacterial assays 4-33
Table 4-7. TCE genotoxicity: fungal and yeast systems 4-36
Table 4-8. TCE genotoxicity: mammalian systems—gene mutations and
chromosome aberrations 4-39
Table 4-9. TCE genotoxicity: mammalian systems—micronucleus, sister
chromatic exchanges 4-43
Table 4-10. TCE genotoxicity: mammalian systems—UDS, DNA strand
breaks/protein crosslinks, and cell transformation 4-46
Table 4-11. Genotoxicity of TCA—bacterial systems 4-50
Table 4-12. TCA Genotoxicity—mammalian systems (both in vitro and in vivo) 4-52
Table 4-13. Genotoxicity of DCA (bacterial systems) 4-57
Table 4-14. Genotoxicity of DCA—mammalian systems 4-58
Table 4-15. CH genotoxicity: bacterial, yeast, and fungal systems 4-63
Table 4-16. CH genotoxicity: mammalian systems—all genetic endpoints, in
vitro 4-65
Table 4-17. CH genotoxicity: mammalian systems—all genetic damage, in vivo 4-67
Table 4-18. TCE GSH conjugation metabolites genotoxicity 4-74
Table 4-19. Genotoxicity of TCOH 4-79
Table 4-20. Summary of human trigeminal nerve and nerve conduction velocity
studies 4-84
Table 4-21. Summary of animal trigeminal nerve studies 4-88
Table 4-22. Summary of human auditory function studies 4-92
Table 4-23. Summary of animal auditory function studies 4-94
Table 4-24. Summary of vestibular system studies 4-100
Table 4-25. Summary of human visual function studies 4-102
Table 4-26. Summary of animal visual system studies 4-104
Table 4-27. Summary of human cognition effect studies 4-107
Table 4-28. Summary of animal cognition effect studies 4-109
Table4-29. Summary of human CRT studies 4-112
Table 4-30. Summary of animal psychomotor function andRT studies 4-114
Table4-31. Summary of animal locomotor activity studies 4-116
Table 4-32. Summary of animal mood effect and sleep disorder studies 4-120
Table 4-33. Summary of human developmental neurotoxicity associated with
TCE exposures 4-121
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Table 4-34. Summary of mammalian in vivo developmental neurotoxicity
studies—oral exposures 4-122
Table 4-35. Summary of animal dopamine neuronal studies 4-127
Table 4-36. Summary of neurophysiological, neurochemical, and
neuropathological effects with TCE exposure 4-128
Table 4-37. Summary of in vitro ion channel effects with TCE exposure 4-130
Table 4-38. Summary of human kidney toxicity studies 4-139
Table 4-39. Summary of human studies on TCE exposure and kidney cancer 4-144
Table 4-40. Summary of case-control studies on kidney cancer and occupation or
job title 4-152
Table 4-41. Summary of lung and kidney cancer risks in active smokers 4-162
Table 4-42. Summary of human studies on somatic mutations of the VHL genea 4-174
Table 4-43. Inhalation studies of kidney noncancer toxicity in laboratory animals 4-178
Table 4-44. Oral and i.p. studies of kidney noncancer toxicity in laboratory
animals 4-179
Table 4-45. Summary of renal toxicity and tumor findings in gavage studies of
TCEbyNTP(1990)a 4-181
Table 4-46. Summary of renal toxicity and tumor findings in gavage studies of
TCEbyNCI(1976)a 4-182
Table 4-47. Summary of renal toxicity findings in gavage studies of TCE by
Maltonietal. (1988, 1986) 4-183
Table 4-48. Summary of renal toxicity and tumor incidence in gavage studies of
TCEbyNTP(1988)a 4-183
Table 4-49. Summary of renal toxicity and tumor findings in inhalation studies of
TCE by Maltonietal. (1988, 1986)a 4-184
Table 4-50. Summary of renal tumor findings in inhalation studies of TCE by
Henschleretal. (1980)aandFukudaetal. (1983)b 4-186
Table 4-51. Summary of renal tumor findings in gavage studies of TCE by
Henschler et al. (1984)a and Van Duuren et al. (1979)b 4-188
Table 4-52. Laboratory animal studies of kidney noncancer toxicity of TCE
metabolites 4-190
Table 4-53. Summary of histological changes in renal proximal tubular cells
induced by chronic exposure to TCE, DCVC, and TCOH 4-192
Table 4-54. Summary of major mode-of-action conclusions for TCE kidney
carcinogenesis 4-201
Table 4-55. Summary of human liver toxicity studies 4-220
Table 4-56. Selected results from epidemiologic studies of TCE exposure and
cirrhosis 4-222
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Table 4-57. Selected results from epidemiologic studies of TCE exposure and
liver cancer 4-226
Table 4-58. Oral studies of TCE-induced liver effects in mice and rats 4-242
Table 4-59. Inhalation and i.p. studies of TCE-induced liver effects in mice and
rats 4-245
Table 4-60. Summary of liver tumor findings in gavage studies of TCE by NTP
(1990)a 4-267
Table 4-61. Summary of liver tumor findings in gavage studies of TCE by NCI
(1976) 4-267
Table 4-62. Summary of liver tumor incidence in gavage studies of TCE by NTP
(1988) 4-268
Table 4-63. Summary of liver tumor findings in inhalation studies of TCE by
Maltonietal. (1988, 1986)a 4-269
Table 4-64. Summary of liver tumor findings in inhalation studies of TCE by
Henschler et al. (1980)a and Fukudaetal. (1983) 4-270
Table 4-65. Summary of liver tumor findings in gavage studies of TCE by
Henschler etal. (1984)a 4-271
Table 4-66. Potency indicators for mouse hepatocarcinogenicity and in vitro
transactivation of mouse PPARa for four PPARa agonists 4-331
Table 4-67. Potency indicators for rat hepatocarcinogenicity and common
short-term markers of PPARa activation for four PPARa agonists 4-332
Table 4-68. Summary of mode-of-action conclusions for TCE-induced liver
carcinogenesis 4-343
Table 4-69. Studies of immune parameters (IgE antibodies and cytokines) and
TCE in humans 4-356
Table 4-70. Case-control studies of autoimmune diseases with measures of TCE
exposure 4-365
Table 4-71. Incidence cohort studies of TCE exposure and lymphopoietic and
hematopoietic cancer risk 4-369
Table 4-72. Mortality cohort and PMR studies of TCE exposure and
lymphopoietic and hematopoietic cancer risk 4-374
Table 4-73. Case-control studies of TCE exposure and lymphopoietic cancer,
leukemia or multiple myeloma 4-383
Table 4-74. Geographic-based studies of TCE and NHL or leukemia in adults 4-389
Table 4-75. Selected results from epidemiologic studies of TCE exposure and
childhood leukemia 4-392
Table 4-76. Summary of TCE immunosuppression studies 4-402
Table 4-77. Summary of TCE hypersensitivity studies21 4-410
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Table 4-78. Summary of autoimmune-related studies of TCE and metabolites in
mice and rats (by sex, strain, and route of exposure)21 4-414
Table 4-79. Malignant lymphomas incidence in mice exposed to TCE in gavage
and inhalation exposure studies 4-425
Table 4-80. Leukemia incidence in rats exposed to TCE in gavage and inhalation
exposure studies 4-426
Table 4-81. Selected results from epidemiologic studies of TCE exposure and
lung cancer 4-432
Table 4-82. Selected results from epidemiologic studies of TCE exposure and
laryngeal cancer 4-439
Table 4-83. Animal toxicity studies of TCE 4-444
Table 4-84. Animal carcinogenicity studies of TCE 4-452
Table 4-85. Human reproductive effects 4-468
Table 4-86. Summary of mammalian in vivo reproductive toxicity studies—
inhalation exposures 4-474
Table 4-87. Summary of mammalian in vivo reproductive toxicity studies—oral
exposures 4-476
Table 4-88. Summary of adverse female reproductive outcomes associated with
TCE exposures 4-488
Table 4-89. Summary of adverse male reproductive outcomes associated with
TCE exposures 4-489
Table 4-90. Summary of human studies on TCE exposure and prostate cancer 4-494
Table 4-91. Summary of human studies on TCE exposure and breast cancer 4-497
Table 4-92. Summary of human studies on TCE exposure and cervical cancer 4-501
Table 4-93. Histopathology findings in reproductive organs 4-509
Table 4-94. Testicular tumors in male rats exposed to TCE, adjusted for reduced
survival21 4-510
Table 4-95. Developmental studies in humans 4-512
Table 4-96. Summary of mammalian in vivo developmental toxicity studies—
inhalation exposures 4-534
Table 4-97. Ocular defects observed (Narotsky et al., 1995) 4-535
Table 4-98. Summary of mammalian in vivo developmental toxicity studies—
oral exposures 4-536
Table 4-99. Types of congenital cardiac defects observed in TCE-exposed fetuses 4-544
Table 4-100. Types of heart malformations per 100 fetuses 4-545
Table 4-101. Congenital cardiac malformations 4-547
Table 4-102. Summary of adverse fetal and early neonatal outcomes associated
with TCE exposures 4-557
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Table 4-103. Summary of studies that identified cardiac malformations
associated with TCE exposures 4-558
Table 4-104. Events in cardiac valve formation in mammals andbirdsa 4-562
Table 4-105. Summary of other structural developmental outcomes associated
with TCE exposures 4-566
Table 4-106. Summary of developmental neurotoxicity associated with TCE
exposures 4-568
Table 4-107. Summary of developmental immunotoxicity associated with TCE
exposures 4-570
Table 4-108. Summary of childhood cancers associated with TCE exposures 4-571
Table 4-109. Selected observations from case-control studies of TCE exposure
and esophageal cancer 4-574
Table 4-110. Summary of human studies on TCE exposure and esophageal
cancer 4-577
Table 4-111. Summary of human studies on TCE exposure and bladder cancer 4-585
Table 4-112. Summary of human studies on TCE exposure and brain cancer 4-591
Table 4-113. Estimated lifestage-specific daily doses for TCE in watera 4-597
Table 5-1. Summary of studies of neurological effects suitable for dose-response
assessment 5-9
Table 5-2. Neurological effects in studies suitable for dose-response assessment,
and corresponding cRfCs and cRfDs 5-12
Table 5-3. Summary of studies of kidney, liver, and body weight effects suitable
for dose-response assessment 5-16
Table 5-4. Kidney, liver, and body weight effects in studies suitable for
dose-response assessment, and corresponding cRfCs and cRfDs 5-18
Table 5-5. Summary of studies of immunological effects suitable for
dose-response assessment 5-23
Table 5-6. Immunological effects in studies suitable for dose-response
assessment, and corresponding cRfCs and cRfDs 5-25
Table 5-7. Summary of studies of reproductive effects suitable for dose-response
assessment 5-28
Table 5-8. Reproductive effects in studies suitable for dose-response assessment,
and corresponding cRfCs and cRfDs 5-33
Table 5-9. Summary of studies of developmental effects suitable for
dose-response assessment 5-39
Table 5-10. Developmental effects in studies suitable for dose-response
assessment, and corresponding cRfCs and cRfDs 5-43
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Table 5-11. Ranges of cRfCs based on applied dose for various noncancer effects
associated with inhalation TCE exposure11 5-47
Table 5-12. Ranges of cRfDs based on applied dose for various noncancer effects
associated with oral TCE exposurea 5-48
Table 5-13. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs
(based on PBPK modeled internal dose-metrics) for candidate critical
neurological effects 5-61
Table 5-14. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs
(based on PBPK modeled internal dose-metrics) for candidate critical
kidney effects 5-63
Table 5-15. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs
(based on PBPK modeled internal dose-metrics) for candidate critical liver
effects 5-66
Table 5-16. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs
(based on PBPK modeled internal dose-metrics) for candidate critical
immunological effects 5-67
Table 5-17. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs
(based on PBPK modeled internal dose-metrics) for candidate critical
reproductive effects 5-69
Table 5-18. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs
(based on PBPK modeled internal dose-metrics) for candidate critical
developmental effects 5-72
Table 5-19. Comparison of —seniive individual" HECs or HEDs for
neurological effects based on PBPK modeled internal dose-metrics at
different levels of confidence and sensitivity, at the NOAEL or LOAEL 5-82
Table 5-20. Comparison of —seniive individual" HECs or HEDs for kidney and
liver effects based on PBPK modeled internal dose-metrics at different
levels of confidence and sensitivity, at the NOAEL or LOAEL 5-83
Table 5-21. Comparison of —seniive individual" HECs or HEDs for
immunological effects based on PBPK modeled internal dose-metrics at
different levels of confidence and sensitivity, at the NOAEL or LOAEL 5-85
Table 5-22. Comparison of —seniive individual" HECs or HEDs for
reproductive effects based on PBPK modeled internal dose-metrics at
different levels of confidence and sensitivity, at the NOAEL or LOAEL 5-86
Table 5-23. Comparison of —seniive individual" HECs or HEDs for
developmental effects based on PBPK modeled internal dose-metrics at
different levels of confidence and sensitivity, at the NOAEL or LOAEL 5-88
Table 5-24. Lowest p-cRfCs or cRfCs for different effect domains 5-90
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Table 5-25. Lowest p-cRfDs or cRfDs for different effect domains 5-92
Table 5-26. Lowest p-cRfCs for candidate critical effects for different types of
effect based on primary dose-metric 5-94
Table 5-27. Lowest p-cRfDs for candidate critical effects for different types of
effect based on primary dose-metric 5-94
Table 5-28. Summary of critical studies, effects, PODs, and UFs used to derive
theRfC 5-96
Table 5-29. Summary of supporting studies, effects, PODs, and UFs for the RfC 5-96
Table 5-30. Summary of critical studies, effects, PODs, and UFs used to derive
theRfD 5-99
Table 5-31. Summary of supporting studies, effects, PODs, and UFs for theRfD 5-100
Table 5-32. Inhalation bioassays 5-103
Table 5-33. Oral bioassays 5-104
Table 5-34. Specific dose-response analyses performed and dose-metrics used 5-107
Table 5-35. Mean PBPK model predictions for weekly internal dose in humans
exposed continuously to low levels of TCE via inhalation (ppm) or orally
(mg/kg/day) 5-118
Table 5-36. Summary of PODs and unit risk estimates for each
sex/species/bioassay/tumor type (inhalation) 5-119
Table 5-37. Summary of PODs and slope factor estimates for each
sex/species/bioassay/tumor type (oral) 5-121
Table 5-38. Comparison of survival-adjusted results for three oral male rat data
setsa 5-124
Table 5-39. Inhalation: most sensitive bioassay for each sex/species combination11 5-128
Table 5-40. Oral: most sensitive bioassay for each sex/species combination21 5-128
Table 5-41. Summary of PBPK model-based uncertainty analysis of unit risk
estimates for each sex/species/bioassay /tumor type (inhalation) 5-136
Table 5-42. Summary of PBPK model-based uncertainty analysis of slope factor
estimates for each sex/species/bioassay /tumor type (oral) 5-137
Table 5-43. Results from Charbotel et al. (2006) on relationship between TCE
exposure and RCC 5-140
Table 5-44. Extra risk estimates for RCC incidence from various levels of
lifetime exposure to TCE, using linear cumulative exposure model 5-142
Table 5-45. ECoi, LECoi, and unit risk estimates for RCC incidence, using linear
cumulative exposure model 5-143
Table 5-46. Relative contributions to extra risk for cancer incidence from TCE
exposure for multiple cancer types 5-149
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Table 5-47. Route-to-route extrapolation of site-specific inhalation unit risks to
oral slope factors 5-152
Table 5-48. Sample calculation for total lifetime cancer risk based on the kidney
unit risk estimate, potential risk for NHL and liver cancer, and potential
increased early-life susceptibility, assuming a constant lifetime exposure
to 1 ug/m3ofTCEinair 5-159
Table 5-49. Sample calculation for total lifetime cancer risk based on the kidney
cancer slope factor estimate, potential risk for NHL and liver cancer, and
potential increased early-life susceptibility, assuming a constant lifetime
exposure to 1 ug/L of TCE in drinking water 5-162
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LIST OF FIGURES
Figure 2-1. Molecular structure of TCE 2-2
Figure 2-2. Source contribution to TCE emissions 2-5
Figure 2-3. Annual emissions of TCE 2-5
Figure 2-4. Modeled ambient air concentrations of TCE 2-10
Figure 3-1. Gas uptake data from closed-chamber exposure of rats to TCE 3-8
Figure 3-2. Disposition of [14C]-TCE administered by gavage in mice 3-18
Figure 3-3. Disposition of [14C]-TCE administered by gavage in rats 3-19
Figure 3-4. Scheme for the oxidative metabolism of TCE 3-21
Figure 3-5. Scheme for GSH-dependent metabolism of TCE 3-36
Figure 3-6. Interorgan TCE transport and metabolism via the GSH pathway 3-45
Figure 3-7. Overall structure of PBPK model for TCE and metabolites used in this
assessment 3-64
Figure 3-8. Schematic of how posterior predictions were generated for comparison with
experimental data 3-104
Figure 3-9. Comparison of mouse data and PBPK model predictions from a random
posterior sample 3-106
Figure 3-10. Comparison of rat data and PBPK model predictions from a random
posterior sample 3-111
Figure 3-11. Comparison of urinary excretion data for NAcDCVC and predictions from
the Hack et al. (2006) and the updated PBPK models 3-119
Figure 3-12. Comparison of human data and PBPK model predictions from a random
posterior sample 3-120
Figure 3-13. Comparison of DCVG concentrations in human blood and predictions from
the updated model 3-126
Figure 3-14. Sensitivity analysis results: Number of mouse calibration data points with
SC in various categories for each scaling parameter 3-128
Figure 3-15. Sensitivity analysis results: Number of rat calibration data points with SC in
various categories for each scaling parameter 3-129
Figure 3-16. Sensitivity analysis results: Number of human calibration data points with
SC in various categories for each scaling parameter 3-130
Figure 3-17. PBPK model predictions for the fraction of intake that is metabolized under
continuous inhalation (A) and oral (B) exposure conditions in mice (white), rats
(diagonal hashing), and humans (horizontal hashing) 3-134
Figure 3-18. PBPK model predictions for the fraction of intake that is metabolized by
oxidation (in the liver and lung) under continuous inhalation (A) and oral (B)
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exposure conditions in mice (white), rats (diagonal hashing), and humans
(horizontal hashing) 3-135
Figure 3-19. PBPK model predictions for the fraction of intake that is metabolized by
GSH conjugation (in the liver and kidney) under continuous inhalation (A) and
oral (B) exposure conditions in mice (dotted line), rats (dashed line), and humans
(solid line) 3-136
Figure 3-20. PBPK model predictions for the fraction of intake that is bioactivated
DCVC in the kidney under continuous inhalation (A) and oral (B) exposure
conditions in rats (dashed line) and humans (solid line) 3-137
Figure 3-21. PBPK model predictions for fraction of intake that is oxidized in the
respiratory tract under continuous inhalation (A) and oral (B) exposure conditions
in mice (dotted line), rats (dashed line), and humans (solid line) 3-138
Figure 3-22. PBPK model predictions for the fraction of intake that is -ttntracked"
oxidation of TCE in the liver under continuous inhalation (A) and oral (B)
exposure conditions in mice (dotted line), rats (dashed line), and humans (solid
line) 3-139
Figure 3-23. PBPK model predictions for the weekly AUC of TCE in venous blood
(mg-hour/L-week) per unit exposure (ppm or mg/kg-day) under continuous
inhalation (A) and oral (B) exposure conditions in mice (dotted line), rats (dashed
line), and humans (solid line) 3-140
Figure 3-24. PBPK model predictions for the weekly AUC of TCOH in blood
(mg-hour/L-week) per unit exposure (ppm or mg/kg-day) under continuous
inhalation (A) and oral (B) exposure conditions in mice (dotted line), rats (dashed
line), and humans (solid line) 3-141
Figure 3-25. PBPK model predictions for the weekly AUC of TCA in the liver
(mg-hour/L-week) per unit exposure (ppm or mg/kg-day) under continuous
inhalation (A) and oral (B) exposure conditions in mice (dotted line), rats (dashed
line), and humans (solid line) 3-142
Figure 3-26. Sensitivity analysis results: SC for mouse scaling parameters with respect to
dose-metrics following 100 ppm (light bars) and 600 ppm (dark bars), 7
hours/day, 5 days/week inhalation exposures 3-149
Figure 3-27. Sensitivity analysis results: SC for mouse scaling parameters with respect to
dose-metrics following 300 mg/kg-day (light bars) and 1,000 mg/kg-day (dark
bars), 5 days/week gavage exposures 3-150
Figure 3-28. Sensitivity analysis results: SC for rat scaling parameters with respect to
dose-metrics following 100 ppm (light bars) and 600 ppm (dark bars), 7
hours/day, 5 days/week inhalation exposures 3-151
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Figure 3-29. Sensitivity analysis results: SC for rat scaling parameters with respect to
dose-metrics following 300 mg/kg-day (light bars) and 1,000 mg/kg-day (dark
bars), 5 days/week gavage exposures 3-152
Figure 3-30. Sensitivity analysis results: SC for female (light bars) and male (dark bars)
human scaling parameters with respect to dose-metrics following 0.001 ppm
continuous inhalation exposures 3-153
Figure 3-31. Sensitivity analysis results: SC for female (light bars) and male (dark bars)
human scaling parameters with respect to dose-metrics following 0.001
mg/kg-day continuous oral exposures 3-154
Figure 4-1. Meta-analysis of kidney cancer and overall TCE exposure 4-168
Figure 4-2. Meta-analysis of kidney cancer and TCE exposure—highest exposure
groups 4-170
Figure 4-3. Meta-analysis of liver and biliary tract cancer and overall TCE exposure 4-236
Figure 4-4. Meta-analysis of liver cancer and TCE exposure—highest exposure groups 4-237
Figure 4-5. Comparison of average fold-changes in relative liver weight to control and
exposure concentrations of 2 g/L or less in drinking water for TCA and DCA in
male B6C3Fi mice for 14-30 days 4-279
Figure 4-6. Comparisons of fold-changes in average relative liver weight and gavage
dose of (top panel) male B6C3Fi mice for 10-28 days of exposure and (bottom
panel) in male B6C3Fi and Swiss mice 4-281
Figure 4-7. Comparison of fold-changes in relative liver weight for data sets in male
B6C3Fi, Swiss, and NRMI mice between TCE studies [duration 28-42 days]) and
studies of direct oral TCA administration to B6C3Fi mice [duration 14-28 days]). ..4-283
Figure 4-8. Comparison of hepatomegaly as a function of AUC of TCA in liver, using
values for the TCA drinking water fractional absorption (Fabs) 4-285
Figure 4-9. Fold-changes in relative liver weight for data sets in male B6C3Fi, Swiss,
and NRMI mice reported by TCE studies of duration 28-42 days using internal
dose-metrics predicted by the PBPK model described in Section 3.5:
(A) dose-metric is the median estimate of the daily AUC of TCE in blood,
(B) dose-metric is the median estimate of the total daily rate of TCE oxidation 4-286
Figure 4-11. Dose-response relationship, expressed as (A) incidence and
(B) fold-increase over controls, for TCE hepatocarcinogenicity in Maltoni et al.
(1988, 1986) 4-301
Figure 4-12. Dose-response data for HCCs (A) incidence and (B) multiplicity, induced
by DCA from DeAngelo et al. (1999) 4-302
Figure 4-14. Reported incidence of HCCs induced by DCA and TCA in 104-week
studies 4-306
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Figure 4-15. Effects of dietary control on the dose-response curves for changes in liver
tumor incidences induced by CHin diet 4-309
Figure 4-16. Meta-analysis of NHL and overall TCE exposure 4-397
Figure 4-17. Meta-analysis of NHL and TCE exposure—highest exposure groups 4-398
Figure 5-1. Flow-chart of the process used to derive the RfD and RfC for noncancer
effects 5-3
Figure 5-2. Flow-chart for dose-response analyses of rodent noncancer effects using
PBPK model-based dose-metrics 5-58
Figure 5-3. Schematic of combined interspecies, intraspecies, and route-to-route
extrapolation from a rodent study LOAEL or NOAEL 5-59
Figure 5-4. Flow-chart for uncertainty analysis of FtECs and FtEDs derived using PBPK
model-based dose-metrics 5-80
Figure 5-5. Flow-chart for dose-response analyses of rodent bioassays using PBPK
model-based dose-metrics 5-117
Figure 5-6. Flow-chart for uncertainty analysis of dose-response analyses of rodent
bioassays using PBPK model-based dose-metrics 5-134
Figure 5-7. Flow-chart for route-to-route extrapolation of human site-specific cancer
inhalation unit risks to oral slope factors 5-151
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LIST OF ABBREVIATIONS AND ACRONYMS
[14C]TCE [14C]-radiolabeled TCE
1,2-DCVC S-(l,2-dichlorovinyl)-L-cysteine
17-P-HSD 17-p-hydroxy steroid dehydrogenase
8-OHdG 8-hydroxy-2' deoxyguanosine
AGO acyl CoA oxidase
ADAF age-dependent adjustment factor
ADME absorption, distribution, metabolism, and excretion
AIC Akaike's Information Criteria
ALL acute lymphoblastic leukemia
ALP alkaline phosphatase
ALT alanine aminotransferase
ANA antinuclear antibodies
ANCA antineutrophil-cytoplasmic antibody
ANO VA analy si s of vari ance
AOAA a beta-lyase inhibitor
ASD autism spectrum disorder
ASPEN Assessment System for Population Exposure Nationwide
AST aspartate aminotransferase
ATF-2 activating transcription factor 2
ATSDR Agency for Toxic Substances and Disease Registry
AUC area-under-the-curve
AV atrioventricular
AVC atrioventricular canal
AZ DHS Arizona Department of Health Services
BAER brainstem auditory-evoked response
BAL bronchoalveolar lavage
BMD benchmark dose
BMDL benchmark dose lower bound
BMDS BenchMark Dose Software
BMI body mass index
BMR benchmark response
BUN blood urea nitrogen
CA DHS California Department of Health Services
CH chloral hydrate
CI confidence interval
CLL chronic lymphocyte leukemia
CNS central nervous system
CC>2 carbon dioxide
CoA coenzyme A
cRfC candidate RfC
cRfD candidate RfD
CRT choice reaction time
CYP cytochrome P450
XXIX
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LIST OF ABBREVIATIONS AND ACRONYMS (continued)
DAL dichloroacetyl lysine
DASO2 diallyl sulfone
DBF dibutyl phthalate
DCA dichloroacetic acid
DCAA dichloroacetic anhydride
DCAC dichloroacetyl chloride
DCE dichloroethylene
DCVC S-dichlorovinyl-L-cysteine (collectively, the 1,2- and 2,2- isomers)
DCVG S-dichlorovinyl-L-glutathione (collectively, the 1,2- and 2,2- isomers)
DEHP di(2-ethylhexyl) phthalate
DHEAS dehydroepiandrosterone sulphate
DNA deoxyribonucleic acid
DNP dinitrophenol
DPM disintegrations per minute
dsDNA double-stranded DNA
ECX concentration of the chemical at which x% of the maximal effect is
produced
EEG electroencephalograph
EPA U.S. Environmental Protection Agency
ERG electroretinogram
ESRD end stage renal disease
FAA fumarylacetoacetate
FDVE fluoromethyl-2,2-difluoro-l-(trifluoromethyl)vinyl ether
FMO flavin mono-oxygenase
FOB functional observational battery
FSH follicle-stimulating hormone
G6PDH glucose 6-p dehydrogenase
GABA gamma-amino butyric acid
G-CSF granulocyte colony stimulating factor
GD gestation day
GGT y-glutamyl transpeptidase or y-transpeptidase
GI gastrointestinal
GIS geographic information system
GSD geometric standard deviation
GSH glutathione
GSSG oxidized GSH
GST glutathione-S-transferase
GT glutamyl transferase
H&E hematoxylin and eosin
H2O water
HCC hepatocellular carcinoma
hCG human chorionic gonadotropin
HC1 hydrochloric acid
HDL-C high density lipoprotein-cholesterol
FIEC human equivalent concentration
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LIST OF ABBREVIATIONS AND ACRONYMS (continued)
HED human equivalent dose
HgCb mercuric chloride
HH Hamberger and Hamilton
HPLC high-performance liquid chromatography
HPT hypothalamic-pituitary-testis
i.a. intra-arterial
i.p. intraperitoneal
i.v. intravenous
IARC International Agency for Research on Cancer
ICC intrahepatic cholangiocarcinoma
ICD International Classification of Disease
ICRP The International Commission on Radiological Protection
idPOD internal dose points of departure
IDR incidence density ratio
IFN interferon
IgE immunoglobulin E
IGF-II insulin-like growth factor-II (gene)
IL interleukin
IPCS International Programme on Chemical Safety
IUGR intrauterine growth restriction
JEM job-exposure matrix
ITEM job-task-exposure matrix
LC lethal concentration
LCL lower confidence limit
LDH lactate dehydrogenase
LECX lowest effective concentration corresponding to an extra risk of x%
LH luteinizing hormone
InPBC blood-air partition coefficient
InQCC cardiac output
InVMAXC VMAX for oxidation
InVPRC ventilation-perfusion ratio
LOAEL lowest-observed adverse effect level
LOH loss of heterozygosity
LORR loss of righting reflex
MA maleylacetone
MA DPH Massachusetts Department of Public Health
MAA maleylacetoacetate
MCA monochloroacetic acid
MCMC Markov chain Monte Carlo
MCP methylclofenapate
MDA malondialdehyde
MLE maximum likelihood estimate
MNU methyl nitrosourea
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LIST OF ABBREVIATIONS AND ACRONYMS (continued)
MS mass spectrometry
MSW multistage Weibull
NAcDCVC N-acetyl-S-(l,2-dichlorovinyl)-L-cysteine
NADH nicotinamide adenine dinucleotide
NADPH nicotinamide adenine dinucleotide phosphate-oxidase
NAG N-acetyl-p-D-glucosaminidase
NAS National Academy of Sciences
NAT N-acetyl transferase
NCI National Cancer Institute
NF-KB nuclear factor kappa-light-chain enhancer of activated B cells
NHL non-Hodgkin lymphoma
NK natural killer
NOAEL no-observed-adverse-effect level
NOEC no-observed-effect concentration
NOEL no-observed-effect level
NPMC nonpurified rat peritoneal mast cells
NRC National Research Council
NSATA National-Scale Air Toxics Assessment
NTP National Toxicology Program
NYS DOH New York State Department of Health
ODE ordinary differential equation
OECD Organization for Economic Co-operation and Development
OFT outflow tract
OP oscillatory potential
OR odds ratio
ORadj adjusted odds ratio
PAS periodic acid-Schiff
PBPK physiologically based pharmacokinetics
PCEs polychromatic erythrocytes
PCNA proliferating cell nuclear antigen
PCO palmitoyl-CoA oxidase
PCR polymerase chain reaction
p-cRfC PBPK model-based candidate RfCs
p-cRfD PBPK model-based candidate RfDs
PEG 400 polyethylene glycol 400
PFC plaque-forming cell
PFU plaque-forming units
PMR proportionate mortality ratio
PND postnatal day
PO2 partial pressure oxygen
POD point of departure
PPAR peroxisome proliferator activated receptor
RBL-2H3 rat basophilic leukemia
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LIST OF ABBREVIATIONS AND ACRONYMS (continued)
RCC renal cell carcinoma
RfC inhalation reference concentration
RfD oral reference dose
RNA ribonucleic acid
RR relative risk
RRm summary RR
RT reaction time
S9 metabolic activation system
SBA serum bile acids
SC sensitivity coefficient
SCE sister chromatid exchange
SD standard deviation
SDH sorbitol dehydrogenase
SE standard error
SEER Surveillance, Epidemiology, and End Results
SES socioeconomic status
SGA small for gestational age
SHBG sex-hormone binding globulin
SIR standardized incidence ratio
SMR standardized mortality ratio
SNP single nucleotide polymorphism
SRBC sheep red blood cells
SRT simple reaction time
SSB single-strand breaks
SSCP single stand conformation polymorphism
ssDNA single-stranded DNA
TaClo tetrahydro-beta-carbolines
TEARS thiobarbiturate acid-reactive substances
TCA trichloroacetic acid
TCAA trichloroacetaldehyde
TCAH trichloroacetaldehyde hydrate
TCE trichloroethylene
TCOG trichloroethanol-glucuronide conjugate
TCOH trichloroethanol
ThX T-helper Type X
TNF tumor necrosis factor
TRI Toxics Release Inventory
TSEP trigeminal somatosensory evoked potential
TTC total trichloro compounds
TWA time-weighted average
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LIST OF ABBREVIATIONS AND ACRONYMS (continued)
U.S. EPA U.S. Environmental Protection Agency
UCL upper confidence limit
UDS unscheduled DNA synthesis
UF uncertainty factor
USGS United States Geological Survey
U-TCA urinary-TCA
U-TTC urinary total trichloro-compounds
VEGF vascular endothelial growth factor
VEP visual evoked potential
VHL von Hippel-Lindau
VLivC liver volume
VOC volatile organic compound
VSCC voltage sensitive calcium channel
W wakefulness
WHO World Health Organization
YFF fluorescent Y-bodies
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FOREWORD
The purpose of this Toxicological Review is to provide scientific support and rationale
for the hazard and dose-response assessment in IRIS pertaining to chronic exposure to
trichloroethylene. It is not intended to be a comprehensive treatise on the chemical or
toxicological nature of trichloroethylene.
The intent of Chapter 6, Major Conclusions in the Characterization of Hazard and Dose
Response, is to present the major conclusions reached in the derivation of the reference dose,
reference concentration and cancer assessment, where applicable, and to characterize the overall
confidence in the quantitative and qualitative aspects of hazard and dose response by addressing
the quality of the data and related uncertainties. The discussion is intended to convey the
limitations of the assessment and to aid and guide the risk assessor in the ensuing steps of the
risk assessment process.
For other general information about this assessment or other questions relating to IRIS,
the reader is referred to EPA's IRIS Hotline at (202) 566-1676 (phone), (202) 566-1749 (fax), or
hotline.iris@epa.gov (email address).
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
CHEMICAL MANAGER
Weihsueh A. Chiu
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
AUTHORS
AmbujaBale
National Center for Environmental Assessment—Immediate Office
U.S. Environmental Protection Agency
Washington, DC
Stanley Bar one
National Center for Environmental Assessment—Immediate Office
U.S. Environmental Protection Agency
Washington, DC
Rebecca Brown
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
Jane C. Caldwell
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
Chao Chen
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
Weihsueh A. Chiu
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
Glinda Cooper
National Center for Environmental Assessment—Immediate Office
U.S. Environmental Protection Agency
Washington, DC
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AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)
Ghazi Dannan
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
Marina Evans
National Health and Environmental Effects Research Laboratory
(on detail to National Center for Environmental Assessment—Washington Office)
U.S. Environmental Protection Agency
Research Triangle Park, NC
John Fox
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
KathrynZ. Guyton
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
Maureen R. Gwinn
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
Jennifer Jinot
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
Nagalakshmi Keshava
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
John Lipscomb
National Center for Environmental Assessment—Cincinnati Office
U.S. Environmental Protection Agency
Cincinnati, OH
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AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)
Susan Makris
National Center for Environmental Assessment—Washington Office
U.S. Environmental Protection Agency
Washington, DC
Miles Okino
National Exposure Research Laboratory—Las Vegas Office
U.S. Environmental Protection Agency
Las Vegas, NV
Fred Power
National Exposure Research Laboratory—Las Vegas Office
U.S. Environmental Protection Agency
Las Vegas, NV
John Schaum
National Center for Environmental Assessment—Washington Office
Office of Research and Development
Washington, DC
Cheryl Siegel Scott
National Center for Environmental Assessment—Washington Office
Office of Research and Development
Washington, DC
REVIEWERS
This document has been reviewed by U.S. EPA scientists, reviewers from other Federal
agencies and White House offices, and the public, and peer reviewed by independent scientists
external to U.S. EPA. A summary and U.S. EPA's disposition of the comments received from
the independent external peer reviewers and from the public is included in Appendix I.
INTERNAL EPA REVIEWERS
Daniel Axelrad
National Center for Environmental Economics
Robert Benson
U.S. EPA Region 8
XXXVlll
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AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)
Ted Birner
National Center for Environmental Assessment—Immediate Office
Nancy Chiu
Office of Water
Joyce Donohue
Office of Water
David Farrar
National Center for Environmental Assessment—Cincinnati Office
Lynn Flowers
National Center for Environmental Assessment—Immediate Office
Brenda Foos
Office of Children's Health Protection and Environmental Education
Stiven Foster
Office of Solid Waste and Emergency Response
Susan Griffin
U.S. EPA Region 8
Samantha Jones
National Center for Environmental Assessment—Immediate Office
Leonid Kopylev
National Center for Environmental Assessment—Washington Office
Allan Marcus
National Center for Environmental Assessment—Immediate Office
Margaret McD enough
U.S. EPA Region 1
Gregory Miller
Office of Children's Health Protection and Environmental Education
Deirdre Murphy
Office of Air Quality Planning and Standards
Marian Olsen
U.S. EPA Region 2
XXXIX
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AUTHORS, CONTRIBUTORS, AND REVIEWERS (continued)
Peter Preuss
(formerly) National Center for Environmental Assessment—Immediate Office
Kathleen Raffaele
(formerly) National Center for Environmental Assessment—Washington Office
Nancy Rios-Jafolla
U.S. EPA Region 3
William Sette
Office of Solid Waste and Emergency Response
Bob Sonawane
National Center for Environmental Assessment—Washington Office
Suryanarayana Vulimiri
National Center for Environmental Assessment—Washington Office
Nina Ching Y. Wang
National Center for Environmental Assessment—Cincinnati Office
Paul White
National Center for Environmental Assessment—Washington Office
Marcia Bailey
U.S. EPA Region 10
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ACKNOWLEDGMENTS
Drafts of Section 3.3 (TCE metabolism) were prepared for the U.S. EPA by Syracuse
Research Corporation under contract. Additional support, including literature searches and
retrievals and drafts of Appendix D were prepared for the U.S. EPA by the Oak Ridge Institute
for Science and Education (ORISE) through interagency agreement number DW-89939822-01-0
with the U.S. Department of Energy (DOE). ORISE is managed by Oak Ridge Associated
Universities under a contract with DOE. The PBPK modeling sections of this report are
dedicated to the memory of Fred Power (1938-2007). His keen analytical mind will be greatly
missed, but his gentle heart and big smile will be missed even more.
Additionally, we gratefully acknowledge Terri Konoza and Ellen Lorang of NCEA for
their management of the document production and reference/citation management processes.
Technical editing support was provided by ICF International; IntelliTech Systems, Inc.; ECFlex,
Inc.; and Syracuse Research Corporation.
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EXECUTIVE SUMMARY
There is substantial potential for human exposure to trichloroethylene (TCE), as it has a
widespread presence in ambient air, indoor air, soil, and groundwater. At the same time, humans
are likely to be exposed to a variety of compounds that are either metabolites of TCE or which
have common metabolites or targets of toxicity. Once exposed, humans, as well as laboratory
animal species, rapidly absorb TCE, which is then distributed to tissues via systemic circulation,
extensively metabolized, and then excreted primarily in breath as unchanged TCE or carbon
dioxide, or in urine as metabolites.
Based on the available human epidemiologic data and experimental and mechanistic
studies, it is concluded that TCE poses a potential human health hazard for noncancer toxicity to
the central nervous system, kidney, liver, immune system, male reproductive system, and the
developing fetus. The evidence is more limited for TCE toxicity to the respiratory tract and
female reproductive system. Following U.S. Environmental Protection Agency (U.S. EPA,
2005b) Guidelines for Carcinogen Risk Assessment, TCE is characterized as -earcinogenic in
humans by all routes of exposure'' This conclusion is based on convincing evidence of a causal
association between TCE exposure in humans and kidney cancer. The human evidence of
carcinogenicity from epidemiologic studies of TCE exposure is strong for non-Hodgkin
Lymphoma but less convincing than for kidney cancer, and more limited for liver and biliary
tract cancer. Less human evidence is found for an association between TCE exposure and other
types of cancer, including bladder, esophageal, prostate, cervical, breast, and childhood
leukemia, breast. Further support for the characterization of TCE as -earcinogenic in humans by
all routes of exposure" is derived from positive results in multiple rodent cancer bioassays in rats
and mice of both sexes, similar toxicokinetics between rodents and humans, mechanistic data
supporting a mutagenic mode of action for kidney tumors, and the lack of mechanistic data
supporting the conclusion that any of the mode(s) of action for TCE-induced rodent tumors are
irrelevant to humans.
As TCE toxicity and carcinogenicity are generally associated with TCE metabolism,
susceptibility to TCE health effects may be modulated by factors affecting toxicokinetics,
including lifestage, gender, genetic polymorphisms, race/ethnicity, preexisting health status,
lifestyle, and nutrition status. In addition, while these some of these factors are known risk
factors for effects associated with TCE exposure, it is not known how TCE interacts with known
risk factors for human diseases.
For noncancer effects, the most sensitive types of effects, based either on human
equivalent concentrations/doses or on candidate inhalation reference concentrations (RfCs)/oral
reference doses (RfDs), appear to be developmental, kidney, and immunological (adult and
developmental) effects. The neurological and reproductive effects appear to be about an order of
xlii
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magnitude less sensitive, with liver effects another two orders of magnitude less sensitive. The
RfC of 0.0004 ppm (0.4 ppb or 2 ug/m3) is based on route-to-route extrapolated results from oral
studies for the critical effects of heart malformations (rats) and immunotoxicity (mice). This
RfC value is further supported by route-to-route extrapolated results from an oral study of toxic
nephropathy (rats). Similarly, the RfD for noncancer effects of 0.0005 mg/kg/day is based on
the critical effects of heart malformations (rats), adult immunological effects (mice), and
developmental immunotoxicity (mice), all from oral studies. This RfD value is further supported
by results from an oral study for the effect of toxic nephropathy (rats) and route-to-route
extrapolated results from an inhalation study for the effect of increased kidney weight (rats).
There is high confidence in these noncancer reference values, as they are supported by moderate-
to-high confidence estimates for multiple effects from multiple studies.
For cancer, the inhalation unit risk is 2 x 10"2 per ppm [4 x 10"6 per jig/m3], based on
human kidney cancer risks reported by Charbotel et al. (2006) and adjusted, using human
epidemiologic data, for potential risk for NHL and liver cancer. The oral unit risk for cancer is
5 x 10~2 per mg/kg/day, resulting from physiologically based pharmacokinetic model-based
route-to-route extrapolation of the inhalation unit risk based on the human kidney cancer risks
reported in Charbotel et al. (2006) and adjusted, using human epidemiologic data, for potential
risk for NHL and liver cancer. There is high confidence in these unit risks for cancer, as they are
based on good quality human data, as well as being similar to unit risk estimates based on
multiple rodent bioassays. There is both sufficient weight of evidence to conclude that TCE
operates through a mutagenic mode of action for kidney tumors and a lack of TCE-specific
quantitative data on early-life susceptibility. Generally, the application of age-dependent
adjustment factors (ADAFs) is recommended when assessing cancer risks for a carcinogen with
a mutagenic mode of action. However, because the ADAF adjustment applies only to the kidney
cancer component of the total risk, it is likely to have a minimal impact on the total cancer risk
except when exposures are primarily during early life.
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1. INTRODUCTION
This document presents background information and justification for the Integrated Risk
Information System (IRIS) Summary of the hazard and dose-response assessment of
trichloroethylene (TCE). IRIS Summaries may include oral reference dose (RfD) and inhalation
reference concentration (RfC) values for chronic and other exposure durations, and a
carcinogenicity assessment.
The RfD and RfC, if derived, provide quantitative information for use in risk assessments
for health effects known or assumed to be produced through a nonlinear (presumed threshold)
mode of action. The RfD (expressed in units of mg/kg-day) is defined as an estimate (with
uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human
population (including sensitive subgroups) that is likely to be without an appreciable risk of
deleterious effects during a lifetime. The inhalation RfC (expressed in units of ppm or ug/m3) is
analogous to the oral RfD, but provides a continuous inhalation exposure estimate. The
inhalation RfC considers toxic effects for both the respiratory system (portal-of-entry) and for
effects peripheral to the respiratory system (extrarespiratory or systemic effects). Reference
values are generally derived for chronic exposures (up to a lifetime), but may also be derived for
acute (<24 hours), short-term (>24 hours up to 30 days), and subchronic (>30 days up to 10% of
lifetime) exposure durations, all of which are derived based on an assumption of continuous
exposure throughout the duration specified. Unless specified otherwise, the RfD and RfC are
derived for chronic exposure duration.
The carcinogenicity assessment provides information on the carcinogenic hazard
potential of the substance in question and quantitative estimates of risk from oral and inhalation
exposure may be derived. The information includes a weight-of-evidence judgment of the
likelihood that the agent is a human carcinogen and the conditions under which the carcinogenic
effects may be expressed. Quantitative risk estimates may be derived from the application of a
low-dose extrapolation procedure. If derived, the oral slope factor is a plausible upper bound on
the estimate of risk per mg/kg-day of oral exposure. Similarly, an inhalation unit risk is a
plausible upper bound on the estimate of risk per ppm or ug/m3 in air breathed.
Development of these hazard identification and dose-response assessments for TCE has
followed the general guidelines for risk assessment as set forth by the National Research Council
(1983). U.S. Environmental Protection Agency (U.S. EPA) Guidelines and Risk Assessment
Forum Technical Panel Reports that may have been used in the development of this assessment
include the following: EPA Guidelines and Risk Assessment Forum technical panel reports that
may have been used in the development of this assessment include the following: Guidelines for
the Health Risk Assessment of Chemical Mixtures (U.S. EPA, 1986b), Guidelines for
Mutagenicity Risk Assessment (U.S. EPA, 1986a), Recommendations for and Documentation of
Biological Values for Use in Risk Assessment (U.S. EPA, 1988), Guidelines for Developmental
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Toxicity Risk Assessment (U.S. EPA, 1991), Interim Policy for Particle Size and Limit
Concentration Issues in Inhalation Toxicity (U.S. EPA, 1994b), Methods for Derivation of
Inhalation Reference Concentrations and Application of Inhalation Dosimetry (U.S. EPA,
1994a), Use of the Benchmark Dose Approach in Health Risk Assessment (U.S. EPA, 1995a),
Guidelines for Reproductive Toxicity Risk Assessment (U.S. EPA, 1996), Guidelines for
Neurotoxicity Risk Assessment (U.S. EPA, 1998a), Science Policy Council Handbook: Risk
Characterization (U.S. EPA, 2000a), Benchmark Dose Technical Guidance Document (U.S.
EPA, 2000b), Supplementary Guidance for Conducting Health Risk Assessment of Chemical
Mixtures (U.S. EPA, 2000c), A Review of the Reference Dose and Reference Concentration
Processes (U.S. EPA, 2002b), Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005b).
Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens
(U.S. EPA, 2005e), Science Policy Council Handbook: Peer Review (U.S. EPA, 2006b), and A
Framework for Assessing Health Risks of Environmental Exposures to Children (U.S. EPA,
The literature search strategy employed for this compound was based on the chemical
name, Chemical Abstracts Service Registry Number (CASRN), and multiple common
synonyms. Any pertinent scientific information submitted by the public to the IRIS Submission
Desk was also considered in the development of this document. Primary, peer-reviewed
literature identified through December 2010 was included where that literature was determined
to be critical to the assessment. The relevant literature included publications on trichloroethylene
which were identified through Toxicology Literature Online (TOXLINE), the U.S. National
Library of Medicine's MEDLINE, the Toxic Substance Control Act Test Submission Database
(TSCATS), the Registry of Toxic Effects of Chemical Substances (RTECS), the Chemical
Carcinogenesis Research Information System (CCRIS), the Developmental and Reproductive
Toxicology/Environmental Teratology Information Center (DART/ETIC), the Environmental
Mutagens Information Center (EMIC) and Environmental Mutagen Information Center Backfile
(EMICBACK) databases, the Hazardous Substances Data Bank (HSDB), the Genetic Toxicology
Data Bank (GENE-TOX), Chemical abstracts, and Current Contents. Other information,
including health assessments developed by other organizations, review articles, and independent
analyses of the health effects data were retrieved and may be included in the assessment where
appropriate. It should be noted that references have been added to the Toxicological Review
after the external peer review in response to peer reviewer's comments and for the sake of
completeness. These references have not changed the overall qualitative and quantitative
conclusions.
In addition to using peer-reviewed, published scientific literature, the preparation of this
toxicological review considered the advice to EPA from a 2002 SAB peer review report (SAB,
2002), a 2006 NRC consultation report (NRC, 2006), and a 2011 SAB peer review report (SAB,
2011), as well as comments from the public and other federal Agencies (weblinks).
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2. EXPOSURE CHARACTERIZATION
The purpose of this exposure characterization is to summarize information about TCE
sources, releases, media levels, and exposure pathways for the general population (occupational
exposure is also discussed to a lesser extent). It is not meant as a substitute for a detailed
exposure assessment for a particular risk assessment application. While this section primarily
addresses TCE, it also includes some information on a number of related compounds. These
related compounds include metabolites of TCE and other parent compounds that produce similar
metabolites as shown in Table 2-1. The first column in this table lists the principal TCE
metabolites in humans (trichloroethanol, trichloroethanol-glucuronide, and trichloroacetic acid)
as well as a number of minor metabolites (ATSDR, 1997c). The subsequent columns list parent
compounds that can produce some of the same metabolites. The metabolic reaction pathways
are much more complicated than implied here and it should be understood that this table is
intended only to provide a general understanding of which parent compounds lead to which TCE
metabolites. Exposure to the TCE-related compounds can alter or enhance TCE's metabolism
and toxicity by generating higher internal metabolite concentrations than would result from TCE
exposure by itself. This characterization is based largely on earlier work by Wu and Schaum
(2001, 2000), but also provides updates in a number of areas.
Table 2-1. TCE metabolites and related parent compounds3
TCE metabolites
Oxalic acid
Chloral
Chloral hydrate
Monochloroacetic acid
Dichloroacetic acid
Trichloroacetic acid
Trichloroethanol
Trichloroethanol-
glucuronide
Parent compounds
Tetrachloro-
ethylene
X
X
X
X
X
X
X
1,1-Dichloro-
ethane
X
X
1,1,1-Tri-
chloroethane
X
X
X
X
1,1,1,2-Tetra-
chloroethane
X
X
X
X
X
X
1,2-Dichloro-
ethylene
X
X
aX indicates that the parent compound can produce the corresponding metabolite (Hazardous Substances Data Bank,
http://toxnet.nlm. nih.gov./cgi-bin/sis/htmlgen?HSDBX
2.1. ENVIRONMENTAL SOURCES
TCE is a stable, colorless liquid with a chloroform-like odor and chemical formula
C2C13H as diagrammed in Figure 2-1 (Hawley and Lewis, 2001). Its chemical properties are
listed in Table 2-2.
2-1
-------
Cl
H
Cl
Cl
Figure 2-1. Molecular structure of TCE.
Table 2-2. Chemical properties of TCE
Property
Molecular weight
Boiling point
Melting point
Density
Solubility
Vapor pressure
Vapor density
Henry's law constant
Octanol/water partition coefficient
Air concentration conversion
Value
131.39
87.2°C
-84.7°C
1.4642 at 20°C
1, 280 mg/L water at 25°C
69.8 mniHG @ 25°C
4.53 (air =1)
9.85 x 10~3 atm-cu m/mol @ 25°C
log Kow = 2.61
1 ppb = 5.38 ug/m3
Reference
Lide et al. (19981
Lide et al. (19981
Lide et al. (19981
Budavari (19961
Horvath et al. (19991
Boublik et al. (19841
Budavari (19961
Leighton and Calo (19811
Hansch et al. (19951
HSDB (20021
TCE has been produced commercially since the 1920s in many countries by chlorination
of ethylene or acetylene. Its use in vapor degreasing began in the 1920s. In the 1930s, it was
introduced for use in dry cleaning. This use was largely discontinued in the 1950s and was
replaced with tetrachloroethylene (ATSDR. 1997c). More recently, 80-90% of TCE production
worldwide is used for degreasing metals (IARC, 1995a). It is also used in adhesives, paint-
stripping formulations, paints, lacquers, and varnishes (SRI, 1992). A number of past uses in
cosmetics, drugs, foods, and pesticides have now been discontinued including use as an
extractant for spice oleoresins, natural fats and oils, hops, and decaffeination of coffee (IARC,
), and as a carrier solvent for the active ingredients of insecticides and fungicides, and for
spotting fluids (ATSDR. 1997c: WHO. 1985). The production of TCE in the United States
peaked at 280 million kg (616 million pounds) in 1970 and declined to 60 million kg
(132 million pounds) in 1998 (USGS. 2006). In 1996, the United States imported 4.5 million kg
(10 million pounds) and exported 29.5 million kg (65 million pounds) (Chemical Marketing
Reporter, 1997). Table 2-3 summarizes the basic properties and principal uses of the TCE
related compounds.
2-2
-------
Table 2-3. Properties and uses of TCE related compounds
Tetrachloroethylene
1,1,1 -Trichloroethane
1 ,2-Dichloroethylene
1,U,2-
Tetrachloroethane
1 , 1 -Dichloroethane
Chloral
Chloral hydrate
Monochloroacetic acid
Dichloroacetic acid
Trichloroacetic acid
Oxalic acid
Dichlorovinyl cysteine
Trichloroethanol
Water
solubility
(mg/L)
150
4,400
3,000-6,000
1,100
5,500
High
High
High
High
High
220,000
Not available
Low
Vapor pressure
(mmHG)
18.5 @25°C
124 @25°C
273-395 @30°C
14 @25°C
234 @25°C
35 @20°C
NA
1 @43°C
<1 @20°C
1 @50°C
0.54 @105°C
Not available
NA
Uses
Dry cleaning, degreasing, solvent
Solvents, degreasing
Solvents, chemical intermediates
Solvents, but currently not produced in
United States
Solvents, chemical intermediates
Herbicide production
Pharmaceutical production
Pharmaceutical production
Pharmaceuticals, not widely used
Herbicide production
Scouring/cleaning agent, degreasing
Not available
Anesthetics and chemical intermediate
References
Wuand
Schaum
(2001)
Wuand
Schaum
(2001)
Wuand
Schaum
(2001)
HSDB,
2002; Wu
and Schaum
(2001)
Wuand
Schaum
(2001)
Wuand
Schaum
(2001)
Wuand
Schaum
(2001)
Wuand
Schaum
(2001)
Wuand
Schaum
(2001)
Wuand
Schaum
(2001)
HSDB
(2002)
Hawley and
Lewis
(2001)
Releases of TCE from nonanthropogenic activities are negligible (HSDB, 2002). Most of
the TCE used in the United States is released to the atmosphere, primarily from vapor degreasing
operations (ATSDR, 1997c). Releases to air also occur at treatment and disposal facilities, water
treatment facilities, and landfills (ATSDR, 1997c). TCE has also been detected in stack
emissions from municipal and hazardous waste incineration (ATSDR, 1997c). TCE is on the list
for reporting to U.S. EPA's Toxics Release Inventory (TRI). Reported releases into air
predominate over other types and have declined over the period 1994-2004 (see Table 2-4).
2-3
-------
Table 2-4. TRI releases of TCE (pounds/year)
Yr
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
On-site
fugitive air
15,018,818
12,498,086
10,891,223
9,276,150
6,769,810
5,861,635
5,485,493
4,968,282
4,761,104
3,963,054
3,040,460
2,733,983
2,816,241
On-site
stack air
15,929,943
13,784,853
10,995,228
8,947,909
6,504,289
4,784,057
4,375,516
3,453,451
3,436,289
3,121,718
3,144,980
2,893,168
2,795,184
Total on-site
air emissions
30,948,761
26,282,939
21,886,451
18,224,059
13,274,099
10,645,692
9,861,009
8,421,733
8,197,393
7,084,772
6,185,440
5,627,152
5,611,425
On-site
surface
water
discharges
1,671
1,477
541
568
882
1,034
593
406
579
595
216
533
482
Total on-site
underground
injection
288
550
1,291
986
593
0
47,877
98,220
140,190
90,971
123,637
86,817
0
Total on-
site
releases to
land
4,070
3,577
9,740
3,975
800
148,867
9,607
12,609
230
150,642
2
4,711
77,339
Total off-
site
disposal or
other
releases
96,312
74,145
89,527
182,423
136,766
192,385
171,952
133,531
139,398
66,894
71,780
60,074
90,758
Total on-
and off-
site
disposal or
other
releases
31,051,102
26,362,688
21,987,550
18,412,011
13,413,140
10,987,978
10,091,038
8,666,499
8,477,790
7,393,873
6,381,075
5,779,287
5,780,004
Source: EPA TRI Explorer, http://www.epa.gov/triexplorer/trends.htm.
Under the National-Scale Air Toxics Assessment (NSATA) program, EPA has developed
an emissions inventory for TCE (U.S. EPA, 2007a). The inventory includes sources in the
United States plus the Commonwealth of Puerto Rico and the U.S. Virgin Islands. The types of
emission sources in the inventory include large facilities, such as waste incinerators and factories
and smaller sources, such as dry cleaners and small manufacturers. Figures 2-2 and 2-3 show the
results of the 1999 emissions inventory for TCE. Figure 2-2 shows the percent contribution to
total emissions by source category. A variety of sources have TCE emissions with the largest
ones identified as halogenated solvent cleaners and metal parts and products. Figure 2-3 shows a
national map of the emission density (tons/square miles/year) for TCE. This map shows the
highest densities in the far west and northeastern regions of the United States. Emissions range
from 0 to 4.12 tons/square miles/year.
2-4
-------
Trichloroethylene Emissions
1999
2% Municipal Landfills
2% Pulp and Paper Production
2% Aerospace Industries 2% Printing. Coating & Dyeing Of Fabrics
2% Integrated Iron & Steel Manufacturing
2% Consumer and Commercial Products Use
4% Dry Cleaning
6% Miscellaneous Metal Parts 8 Products (Surface Coating)
19% Other Categories (293 categories)
59% Halogenated Solvent Cleaners
Figure 2-2. Source contribution to TCE emissions.
1999 County Emission Densities
Trichbroethylene - United States Counties
Distribution of U.S. Emission Densities
HlghtetlnU.S. ^^_ 4J2
95 • • 0.063
9a I I °'osa Pollutant Emission Density by County
„ Q jjj^ (tons/year / sq. mile )"
25 I I 0.00011 Source: US. EPA / (W3PS
I r, U.S. I 1 0.000 0 00 04-4 -\ 99 9 ^7^ Na f on al- Sea le Air Toxics Assessment
Figure 2-3. Annual emissions of TCE.
2-5
-------
2.2. ENVIRONMENTAL FATE
2.2.1. Fate in Terrestrial Environments
The dominant fate of TCE released to surface soils is volatilization. Because of its
moderate water solubility, TCE introduced into soil (e.g., landfills) also has the potential to
migrate through the soil into groundwater; this is confirmed by the relatively frequent detection
of TCE in groundwater. Biodegradation in soil and groundwater may occur at a relatively slow
rate (half-lives on the order of months to years) (Howard et al., 1991).
2.2.2. Fate in the Atmosphere
In the atmosphere, TCE is expected to be present primarily in the vapor phase, rather than
sorbed to particulate, because of its high vapor pressure. Some removal by scavenging during
wet precipitation is expected because of its moderate water solubility. The major degradation
process affecting vapor-phase TCE is photo-oxidation by hydroxyl radicals. Photolysis in the
atmosphere proceeds very slowly, if at all. TCE does not absorb ultraviolet light at wavelengths
of <290 nm and thus, will not directly photolyze. Based on measured rate data for the vapor
phase photo-oxidation reaction with hydroxyl radicals, the estimated half-life of TCE in the
atmosphere is on the order of 1-11 days with production of phosgene, dichloroacetyl chloride
(DCAC), and formyl chloride. Under smog conditions, degradation is more rapid (half-life on
the order of hours) (HSDB. 2002: Howard et al.. 1991).
2.2.3. Fate in Aquatic Environments
The dominant fate of TCE released to surface waters is volatilization (predicted half-life
of minutes to hours). Bioconcentration, biodegradation, and sorption to sediments and
suspended solids are not thought to be significant (HSDB, 2002). TCE is not hydrolyzed under
normal environmental conditions. However, slow photo-oxidation in water (half-life of
10.7 months) has been reported (HSDB. 2002: Howard et al.. 1991).
2.3. EXPOSURE CONCENTRATIONS
TCE levels in the various environmental media result from the releases and fate processes
discussed in Sections 2.1 and 2.2. No statistically based national sampling programs have been
conducted that would allow estimates of true national means for any environmental medium. A
substantial amount of air and groundwater data, however, has been collected as well as some
data in other media, as described below.
2.3.1. Outdoor Air—Measured Levels
TCE has been detected in the air throughout the United States. According to ATSDR
(1997c), atmospheric levels are highest in areas concentrated with industry and population, and
2-6
-------
lower in remote and rural regions. Table 2-5 shows levels of TCE measured in the ambient air at
a variety of locations in the United States.
Table 2-5. Concentrations of TCE in ambient air
Area
Rural
Whiteface Mountain, New York3
Badger Pass, California3
Reese River, Nevada3
Jetmar, Kansas3
All rural sites
Urban and suburban
New Jersey3
New York City, New York3
Los Angeles, California3
Lake Charles, Louisiana3
Phoenix, Arizone3
Denver, Colorado3
St. Louis, Missouri3
Portland, Oregon3
Philadelphia, Pennsylvania3
Southeast Chicago, Illinois'3
East St. Louis, Illinois'3
District of Columbia"
Urban Chicago, Illinois'1
Suburban Chicago, Illinois'1
300 cities in 42 states"
Several Canadian Citiesf
Several United States Citiesf
Phoenix, Arizona8
Tucson, Arizona8
All urban/suburban sites
Yr
1974
1977
1977
1978
1974-1978
1973-1979
1974
1976
1976-1978
1979
1980
1980
1984
1983-1984
1986-1990
1986-1990
1990-1991
pre-1993
pre-1993
pre-1986
1990
1990
1994-1996
1994-1996
1973-1996
Concentration (jig/m3)
Mean
0.5
0.06
0.06
0.07
9.1
3.8
1.7
8.6
2.6
1.07
0.6
1.5
1.9
1.0
2.1
1.94
0.82-1.16
0.52
2.65
0.28
6.0
0.29
0.23
Range
O.3-1.9
0.005-0.09
0.005-0.09
0.04-0.11
0.005-1.9
ND-97
0.6-5.9
0.14-9.5
0.4-11.3
0.06-16.7
0.15-2.2
0.1-1.3
0.6-3.9
1.6-2.1
1-16.65
0-1.53
0-1.47
0-97
eShah (1988).
fBunce (1994).
8Zielinska-Psuja (1998).
ND = nondetect
More recent ambient air measurement data for TCE were obtained from EPA's Air
Quality System database at the AirData Web site: http://www.epa.gov/air/data/index.html
(2007b). These data were collected from a variety of sources including state and local
environmental agencies. The data are not from a statistically based survey and cannot be
assumed to provide nationally representative values. The most recent data (2006) come from
258 monitors located in 37 states. The means for these monitors range from 0.03 to 7.73 ug/m3
2-7
-------
and have an overall average of 0.23 ug/m . Table 2-6 summarizes the data for the years
1999-2006. The data suggest that levels have remained fairly constant since 1999 at about
0.3 ug/m3. Table 2-7 shows the monitoring data organized by land setting (rural, suburban, or
urban) and land use (agricultural, commercial, forest, industrial, mobile, and residential). Urban
air levels are almost 4 times higher than rural areas. Among the land use categories, TCE levels
are highest in commercial/industrial areas and lowest in forest areas.
Table 2-6. TCE ambient air monitoring data (ug/m3)
Yr
1999
2000
2001
2002
2003
2004
2005
2006
Number of
monitors
162
187
204
259
248
256
313
258
Number of states
20
28
31
41
41
37
38
37
Mean
0.30
0.34
0.25
0.37
0.35
0.32
0.43
0.23
Standard
deviation
0.53
0.75
0.92
1.26
0.64
0.75
1.05
0.55
Median
0.16
0.16
0.13
0.13
0.16
0.13
0.14
0.13
Range
0.01-4.38
0.01-7.39
0.01-12.90
0.01-18.44
0.02-6.92
0.00-5.78
0.00-6.64
0.03-7.73
Source: EPA's Air Quality System database at the AirData Web site: http://www.epa.gov/air/data/index.html.
Table 2-7. Mean TCE air levels across monitors by land setting and use
(1985-1998)
Mean
concentration
(ug/m3)
n
Rural
0.42
93
Suburban
1.26
500
Urban
1.61
558
Agricul-
tural
1.08
31
Com-
mercial
1.84
430
Forest
0.1
17
Indus-
trial
1.54
186
Mobile
1.5
39
Resi-
dential
0.89
450
Source: EPA's Air Quality System database at the AirData Web site: http://www.epa.gov/air/data/index.html.
2.3.2. Outdoor Air—Modeled Levels
Under the National-Scale Air Toxics Assessment program, EPA has compiled emissions
data and modeled air concentrations/exposures for the Criteria Pollutants and Hazardous Air
Pollutants (U.S. EPA, 2007a). The results of the 1999 emissions inventory for TCE were
discussed earlier and results presented in Figures 2-2 and 2-3. A computer simulation model
known as the Assessment System for Population Exposure Nationwide (ASPEN) is used to
estimate toxic air pollutant concentrations (http://www.epa.gov/ttnatw01/nata/aspen.html). This
model is based on the EPA's Industrial Source Complex Long Term model which simulates the
behavior of the pollutants after they are emitted into the atmosphere. ASPEN uses estimates of
toxic air pollutant emissions and meteorological data from National Weather Service Stations to
2-8
-------
estimate air toxics concentrations nationwide. The ASPEN model takes into account important
determinants of pollutant concentrations, such as:
• rate of release;
• location of release;
• the height from which the pollutants are released;
• wind speeds and directions from the meteorological stations nearest to the release;
• breakdown of the pollutants in the atmosphere after being released (i.e., reactive decay);
• settling of pollutants out of the atmosphere (i.e., deposition); and
• transformation of one pollutant into another (i.e., secondary formation).
The model estimates toxic air pollutant concentrations for every census tract in the
continental United States, the Commonwealth of Puerto Rico and the U.S. Virgin Islands.
Census tracts are land areas defined by the U.S. Bureau of the Census and typically contain about
4,000 residents each. Census tracts are usually smaller than 2 square miles in size in cities but
much larger in rural areas.
Figure 2-4 shows the results of the 1999 ambient air concentration modeling for TCE.
The county median air levels range from 0 to 3.79 |ig/m3 and an overall median of 0.054 |ig/m3.
They have a pattern similar to the emission densities shown in Figure 2-3. These NSATA
modeled levels appear lower than the monitoring results presented above. For example, the 1999
air monitoring data (see Table 2-6) indicates a median outdoor air level of 0.16 ug/m3 which is
about 3 times as high as the modeled 1999 county median (0.054 |ig/m3). However, it should be
understood that the results from these two efforts are not perfectly comparable. The modeled
value is a median of county levels for the entire United States which includes many rural areas.
The monitors cover many fewer areas (n= 162 for 1999) and most are in nonrural locations. A
better analysis is provided by EPA (2007a) which presents a comparison of modeling results
from NSATA to measured values at the same locations. For 1999, it was found that
formaldehyde levels were underestimated at 79% of the sites (n = 92). Thus, while the NSATA
modeling results are useful for understanding geographic distributions, they may frequently
underestimate ambient levels.
2-9
-------
1999 Estimated County Median Ambient Concentrations
Trichbroethylene — United Stales Counties
V
HonSMj
P
Distribution of U.S. Ambient Concentrations
Highest In U.S.
95
90
Percentile 75
50
25
Lowest In U.S.
3.79
0.12
0.099 county Median Ambient Pollutant Concentration
gL™ ( micrograms / cubic meter )
Source: U.S. EPA / QAQPS
1999 MMA Naf on al—Scale Air Toxics Assessment
0.04S
0.019
Figure 2-4. Modeled ambient air concentrations of TCE.
2.3.3. Indoor Air
TCE can be released to indoor air from use of consumer products that contain it (i.e.,
adhesives and tapes), vapor intrusion (migration of volatile chemicals from the subsurface into
overlying buildings) and volatilization from the water supply. Where such sources are present, it
is likely that indoor levels will be higher than outdoor levels. A number of studies have
measured indoor levels of TCE:
• The 1987 EPA Total Exposure Assessment Methodology study (Wallace, 1987) showed
that the ratio of indoor to outdoor TCE concentrations for residences in Greensboro, NC,
was about 5:1.
• In two homes using well water with TCE levels averaging 22-128 ug/L, the TCE levels
in bathroom air ranged from <500-40,000 ug/m3 when the shower ran <30 minutes
(Andelman. 1985).
• Shah and Singh (1988) report an average indoor level of 7.2 ug/m3 based on over
2,000 measurements made in residences and workplaces during 1981-1984 from various
locations across the United States.
• Hers et al. (2001) provides a summary of indoor air TCE measurements at locations in
United States, Canada, and Europe with a range of <1-165 ug/m3.
2-10
-------
• Sapkota et al. (2005) measured TCE levels inside and outside of the Baltimore Harbor
Tunnel toll booths during the summer of 2001. Mean TCE levels were 3.11 ug/m3
indoors and 0.08 ug/m3 outdoors based on measurements on 7 days. The authors
speculated that indoor sources, possibly dry cleaning residues on uniforms, were the
primary source of the indoor TCE.
• Sexton et al. (2005) measured TCE levels inside and outside residences in
Minneapolis/St. Paul metropolitan area. Two day samples were collected over
three seasons in 1999. Mean TCE levels were 0.5 ug/m3 indoors (n = 292), 0.2 ug/m3
outdoors (n = 132) and 1.0 ug/m3 based on personal sampling (n = 288).
• Zhu et al. (2005) measured TCE levels inside and outside of residences in Ottawa,
Canada. Seventy-five homes were randomly selected and measurements were made
during the winter of 2002/2003. TCE was above detection limits in the indoor air of
33% of the residences and in the outdoor air of 19% of the residences. The mean levels
were 0.06 ug/m3 indoors and 0.08 ug/m3 outdoors. Given the high frequency of
nondetects, a more meaningful comparison can be made on basis of the 75th percentiles:
0.08 ug/m3 indoors and 0.01 ug/m3 outdoors.
TCE levels measured indoors have been directly linked to vapor intrusion at two sites in New
York:
• TCE vapor intrusion has occurred in buildings/residences near a former Smith Corona
manufacturing facility located in Cortlandville, New York. An extensive sampling
program conducted in 2006-2007 has detected TCE in groundwater (up to 22 ug/L),
subslab gas (up to 1,000 ug/m3), and indoor air (up to 34 ug/m3) (NYSDEC. 2007).
• Evidence of vapor intrusion of TCE has also been reported in buildings and residences in
Endicott, New York. Sampling in 2003 showed total volatile organic compounds
(VOCs) in soil gas exceeding 10,000 ug/m3 in some areas. Indoor air sampling detected
TCE levels ranging from 1 to 140 ug/m3 (Meyers, 2003).
Little et al. (1992) developed attenuation coefficients relating contaminants in soil gas
(assumed to be in chemical equilibrium with the groundwater) to possible indoor levels as a
result of vapor intrusion. On this basis they estimated that TCE groundwater levels of 540 ug/L,
(a high contamination level) could produce indoor air levels of 5-500 ug/m3. Vapor intrusion
can be an important contributor to indoor levels in situations where residences are located near
soils or groundwater with high contamination levels. EPA (2002c) recommends considering
vapor intrusion when volatiles are suspected to be present in groundwater or soil at a depth of
<100 feet. Hers et al. (2001) concluded that the contribution of VOCs from subsurface sources
relative to indoor sources is small for most chemicals and sites.
2-11
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2.3.4. Water
A number of early (pre-1990) studies measured TCE levels in natural water bodies
(levels in drinking water are discussed later in this section) as summarized in Table 2-8.
Table 2-8. Concentrations of TCE in water based on pre-1990 studies
Water type
Industrial effluent
Surface waters
Rainwater
Groundwater
Drinking water
Location
United States
United States
Portland,
Oregon
Minnesota
New Jersey
New York
Pennsylvania
Massachusetts
Arizona
United States
United States
United States
Massachusetts
New Jersey
California
California
North Carolina
North Dakota
Yr
1983
1983
1984
1983
1976
1980
1980
1976
1976
1977
1978
1984
1984
1985
1984
1984
1984
Mean
(jig/L)
0.006
23.4
66
5
5
Median
(jig/L)
0.5
0.1
Range
(Hg/L)
0.002-0.02
0.2-144
<1,530
<3,800
<27,300
<900
8.9-29
0.2-49
0-53
0.5-210
max. 267
max. 67
8-12
Number of
samples
NR
NR
NR
NR
NR
NR
NR
NR
NR
1130
486
486
48
48
Reference
IARC (1995a)
IARC (1995a)
Ligocki et al. (1985)
Sabel and Clark (19841
Burmaster et al. (1982)
Burmaster et al. (1982)
Burmaster et al. (1982)
Burmaster et al. (1982)
IARC (1995a)
IARC (1995a)
IARC (1995a)
IARC (1995a)
IARC (1995a)
Cohnetal. (1994b)
EPA, (1987)
EPA, (1987)
EPA, (1987)
EPA, (1987)
NR = not reported
According to IARC (1995a), the reported median concentrations of TCE in 1983-1984
were 0.5 ug/L in industrial effluents and 0.1 ug/L in ambient water. Results from an analysis of
the EPA STORET Data Base (1980-1982) showed that TCE was detected in 28% of
9,295 surface water reporting stations nationwide (ATSDR, 1997c). A more recent search of the
STORET database for TCE measurements nationwide during 2008 in streams, rivers and lakes
indicated three detects (0.03-0.04 ug/L) out of 150 samples (STORET Database,
http://www.epa.gov/storet/dbtop.html).
ATSDR (1997c) has reported that TCE is the most frequently reported organic
contaminant in groundwater and the one present in the highest concentration in a summary of
ground water analyses reported in 1982. It has been estimated that between 9 and 34% of the
drinking water supply sources tested in the United States may have some TCE contamination.
This estimate is based on available Federal and State surveys (ATSDR, 1997c).
Squillace et al. (2004) reported TCE levels in shallow groundwater based on data from
the National Water Quality Assessment Program managed by United States Geological Survey
(USGS). Samples from 518 wells were collected from 1996 to 2002. All wells were located in
2-12
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residential or commercial areas and had a median depth of 10 m. The authors reported that
approximately 8.3% of the well levels were above the detection limit (level not specified), 2.3%
were above 0.1 ug/L and 1.7% were above 0.2 ug/L.
As part of the Agency's first Six-Year Review, EPA obtained analytical results for over
200,000 monitoring samples reported at 23,035 public water systems (PWS) in 16 states (U.S.
EPA, 2003 c). Approximately 2.6% of the systems had at least one sample exceed a minimum
reporting level of 0.5 ug/L; almost 0.65% had at least one sample that exceeds the maximum
contaminant level of 5 ug/L. Based on average system concentrations estimated by EPA,
54 systems (0.23%) had an average concentration that exceeded the maximum contaminant level.
EPA's statistical analysis to extrapolate the sample result to all systems regulated for TCE
resulted in an estimate of 154 systems with average TCE concentrations that exceed the
maximum contaminant level.
TCE concentrations in ground water have been measured extensively in California. The
data were derived from a survey of water utilities with more than 200 service connections. The
survey was conducted by the California Department of Health Services (CDHS, 1986). From
January 1984 through December 1985, untreated water from wells in 819 water systems were
sampled for organic chemical contamination. The water systems use a total of 5,550 wells,
2,947 of which were sampled. TCE was found in 187 wells at concentrations up to 440 ug/L,
with a median concentration among the detects of 3.0 ug/L. Generally, the wells with the highest
concentrations were found in the heavily urbanized areas of the state. Los Angeles County
registered the greatest number of contaminated wells (149).
A second California study collected data on TCE levels in public drinking water
(Williams et al., 2002). The data were obtained from the C A DHS. The data spanned the years
1995-2001 and the number of samples for each year ranged from 3,447 to 4,226. The percent of
sources that were above the detection limit ranged from 9.6 to 11.7 per year (detection limits not
specified). The annual average detected concentrations ranged from 14.2 to 21.6 ug/L.
Although not reported, the overall average concentration of the samples (assuming an average of
20 ug/L among the samples above the detection limit, 10% detection rate and 0 for the
nondetects) would be about 2 ug/L.
The USGS (2006) conducted a national assessment of 55 VOCs, including TCE, in
ground water. A total of 3,500 water samples were collected during 1985-2001. Samples were
collected at the well head prior to any form of treatment. The types of wells sampled included
2,400 domestic wells and 1,100 public wells. Almost 20% of the samples contained one or more
of the VOCs above the assessment level of 0.2 ug/L. The detection frequency increased to over
50% when a subset of samples was analyzed with a low level method that had an assessment
level of 0.02 ug/L. The largest detection frequencies were observed in California, Nevada,
Florida, the New England States, and Mid-Atlantic states. The most frequently detected VOCs
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(>1% of samples) include TCE, tetrachloroethylene, 1,1,1-trichloroethane (methyl chloroform),
1,2 dichloroethylene, and 1,1-dichloroethane. Findings specific to TCE include the following:
• Detection frequency was 2.6% at 0.2 ug/L and was 3.8% at 0.02 ug/L.
• The median concentration was 0.15 ug/L with a range of 0.02-100 ug/L.
• The number of samples exceeding the maximum contaminant level (5 ug/L) was six at
domestic wells and nine at public wells.
USGS (2006) also reported that four solvents (TCE, tetrachloroethylene, 1,1,1-trichloro-
ethane and methylene chloride) occurred together in 5% of the samples. The most frequently
occurring two-solvent mixture was TCE and tetrachloroethylene. The report stated that the most
likely reason for this co-occurrence is the reductive dechlorination of tetrachloroethylene to
TCE.
2.3.5. Other Media
Levels of TCE were found in the sediment and marine animal tissue collected in
1980-1981 near the discharge zone of a Los Angeles County waste treatment plant.
Concentrations were 17 ug/L in the effluent, <0.5 ug/kg in dry weight in sediment, and
0.3-7 ug/kg wet weight in various marine animal tissue (IARC, 1995a). TCE has also been
found in a variety of foods. U.S. Food and Drug Administration (FDA) has limits on TCE use as
a food additive in decaffeinated coffee and extract spice oleoresins (see Table 2-15). Table 2-9
summarizes data from two sources:
• IARC (1995a) reports average concentrations of TCE in limited food samples collected in
the United States.
• Jones and Smith (2003) measured VOC levels in over 70 foods collected from 1996 to
2000 as part of the FDA's Total Diet Program. All foods were collected directly from
supermarkets. Analysis was done on foods in a ready-to-eat form. Sample sizes for most
foods were in the 2-5 range.
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Table 2-9. Levels in food
IARC (1995a)
Cheese 3.8 ug/kg
Butter and margarine 73.6 ug/kg
Peanut butter 0.5 ug/kg
Cereals 3 ug/kg
Grain-based foods 0.9 ug/kg
Fleming-Jones and Smith (2003)
Cheese 2-3 ug/kg
Butter 7-9 ug/kg
Margarine 2-21 ug/kg
Cheese pizza 2 ug/kg
Nuts 2-5 ug/kg
Peanut butter 4-70 ug/kg
Ground beef 3-6 ug/kg
Beef frankfurters 2-105 ug/kg
Hamburger 5-9 ug/kg
Cheeseburger 7 ug/kg
Chicken nuggets 2-5 ug/kg
Bologna 2-20 ug/kg
Pepperoni pizza 2 ug/kg
Banana 2 ug/kg
Avocado 2-75 ug/kg
Orange 2 ug/kg
Chocolate cake 3-57 ug/kg
Blueberry muffin 3-4 ug/kg
Sweet roll 3 ug/kg
Chocolate chip cookies 2-4 ug/kg
Apple pie 2-4 ug/kg
Doughnuts 3 ug/kg
Tuna 9- 11 ug/kg
Cereal 3 ug/kg
Popcorn 4-8 ug/kg
French fries 3 ug/kg
Potato chips 4-140 ug/kg
Coleslaw 3 ug/kg
2.3.6. Biological Monitoring
Biological monitoring studies have detected TCE in human blood and urine in the United
States and other countries such as Croatia, China, Switzerland, and Germany (IARC, 1995a).
Concentrations of TCE in persons exposed through occupational degreasing operations were
most likely to have detectable levels (IARC, 1995a). In 1982, eight of eight human breastmilk
samples from four United States urban areas had detectable levels of TCE. The levels of TCE
detected, however, are not specified (HSDB. 2002: ATSDR. 1997c).
The Third National Health and Nutrition Examination Survey (NHANES III) examined
TCE concentrations in blood in 677 nonoccupationally exposed individuals. The individuals
were drawn from the general U.S. population and selected on the basis of age, race, gender and
region of residence (IARC, 1995a: Ashley et al., 1994). The samples were collected during
1988-1994. TCE levels in whole blood were below the detection limit of 0.01 ug/L for about
90% of the people sampled (see Table 2-10). Assuming that nondetects equal half of the
detection limit, the mean concentration was about 0.017 ug/L.
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Table 2-10. TCE levels in whole blood by population percentile
Percentiles
Concentration (ug/L)
10
ND
20
ND
30
ND
40
ND
50
ND
60
ND
70
ND
80
ND
90
0.012
ND = Nondetect, i.e., below detection limit of 0.01 ug/L.
Sources: IARC (1995a); Ashley et al. (1994).
2.4. EXPOSURE PATHWAYS AND LEVELS
2.4.1. General Population
Because of the pervasiveness of TCE in the environment, most people are likely to have
some exposure via one or more of the following pathways: ingestion of drinking water,
inhalation of outdoor/indoor air, or ingestion of food (ATSDR, 1997c). As noted earlier, the
NHANES survey suggests that about 10% of the population has detectable levels of TCE in
blood. Each pathway is discussed below.
2.4.1.1. Inhalation
As discussed earlier, EPA has estimated emissions and modeled air concentrations for the
Criteria Pollutants and Hazardous Air Pollutants under the National-Scale Air Toxics
Assessment program (U.S. EPA, 2007a). This program has also estimated inhalation exposures
on a nationwide basis. The exposure estimates are based on the modeled concentrations from
outdoor sources and human activity patterns (U.S. EPA, 2005a). Table 2-11 shows the 1999
results for TCE.
Table 2-11. Modeled 1999 annual exposure concentrations (ug/m3) for TCE
Percentile
5
10
25
50
75
90
95
Mean
Exposure concentration (jig/m3)
Rural areas
0.030
0.034
0.038
0.044
0.053
0.070
0.097
0.058
Urban areas
0.048
0.054
0.065
0.086
0.122
0.189
0.295
0.130
Nationwide
0.038
0.043
0.056
0.076
0.113
0.172
0.262
0.116
Percentiles and mean are based on census tract values.
Source: http://www.epa.gov/ttn/atw/nata/ted/exporisk.htrnMndb.
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These modeled inhalation exposures would have a geographic distribution similar to that
of the modeled air concentrations as shown in Figure 2-4. Table 2-11 indicates that TCE
inhalation exposures in urban areas are generally about twice as high as rural areas. While these
modeling results are useful for understanding the geographic distribution of exposures, they
appear to underestimate actual exposures. This is based on the fact that, as discussed earlier, the
modeled ambient air levels are generally lower than measured values. Also, the modeled
exposures do not consider indoor sources. Indoor sources of TCE make the indoor levels higher
than ambient levels. This is particularly important to consider since people spend about 90% of
their time indoors (U.S. EPA, 1997). A number of measurement studies were presented earlier
that showed higher TCE levels indoors than outdoors. Sexton et al. (2005) measured TCE levels
in Minneapolis/St. Paul area and found means of 0.5 ug/m3 indoors (n = 292) and 1.0 ug/m3
based on personal sampling (n = 288). Using 1.0 ug/m3 and an average adult inhalation rate of
13m3 air/day (U.S. EPA, 1997) yields an estimated intake of 13 ug/day. This is consistent with
ATSDR (1997c), which reported an average daily air intake for the general population of
11-33 ug/day.
2.4.1.2. Ingestion
The median value from the nationwide survey of domestic and public wells by USGS for
1985-2001 is 0.15 ug/L. This value was selected for exposure estimation purposes because it
was the most current and most representative of the national population. Using this value and an
average adult water consumption rate of 1.4 L/d yields an estimated intake of 0.2 ug/day. [This is
from U.S. EPA (1997), but note that U.S. EPA (2004) indicates a mean per capita daily average
total water ingestion from all sources of 1.233 L]. This is lower than the ATSDR (1997c)
estimate water intake for the general population of 2-20 ug/day. The use of the USGS survey to
represent drinking water is uncertain in two ways. First, the USGS survey measured only
groundwater and some drinking water supplies use surface water. Second, the USGS measured
TCE levels at the well head, not the drinking water tap. Further discussion about the possible
extent and magnitude of TCE exposure via drinking water is presented below.
According to ATSDR (1997c), TCE is the most frequently reported organic contaminant
in ground water (1997c), and between 9 and 34% of the drinking water supply sources tested in
the United States may have some TCE contamination. Approximately 90% of the
155,000 public drinking water systems1 in the United States are ground water systems. The
drinking water standard for TCE only applies to community water systems (CWSs) and
approximately 78% of the 51,972 CWSs in the United States are ground water systems (U.S.
EPA, 2008a). Although commonly detected in water supplies, the levels are generally low
1 PWSs are defined as systems which provide water for human consumption through pipes or other constructed
conveyances to at least 15 service connections or serves an average of at least 25 people for at least 60 days a year.
EPA further specifies three types of PWSs, including CWS)—a PWS that supplies water to the same population
year-round.
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because, as discussed earlier, maximum contaminant level violations for TCE in public water
supplies are relatively rare for any extended period (U.S. EPA. 1998b). The USGS (2006)
survey found that the number of samples exceeding the maximum contaminant level (5 ug/L)
was six at domestic wells (n = 2,400) and nine at public wells (n = 1,100). Private wells,
however, are often not closely monitored and if located near TCE disposal/contamination sites
where leaching occurs, may have undetected contamination levels. About 10% of Americans
(27 million people) obtain water from sources other than public water systems, primarily private
wells (U.S. EPA, 1995b). TCE is a common contaminant at Superfund sites. As of September,
2011, EPA's Superfund program has identified 761 sites with TCE as a contaminant of concern in
groundwater, soil or both (CERCLIS Public Access Database). Studies have shown that many
people live near these sites: 41 million people live <4 miles from one or more of the nation's NPL
sites, and on average 3,325 people live within 1 mile of any given NPL site (ATSDR, 1996b).
Table 2-12 presents preliminary estimates of TCE intake from food. They are based on
average adult food ingestion rates and food data from Table 2-9. This approach suggests a total
ingestion intake of about 5 ug/d. It is important to consider this estimate as preliminary because
it is derived by applying data from very limited food samples to broad classes of food.
Table 2-12. Preliminary estimates of TCE intake from food ingestion
Fruit
Vegetables
Fish
Meat
Dairy products
Grains
Sweets
Total
Consumption rate
(g/kg-d)
3.4
4.3
2.1
8
4.1
0.5
Consumption rate
(g/d)
238
301
20
147
560
287
35
Concentration in
food (jig/kg)
2
3
10
5
3
3
3
Intake
(Hg/d)
0.48
0.90
0.20
0.73
1.68
0.86
0.10
4.96
"Consumption rates are per capita averages from EPA (1997).
bConsumption rates in g/d assume 70 kg body weight.
2.4.1.3. Dermal
TCE in bathing water and consumer products can result in dermal exposure. A modeling
study has suggested that a significant fraction of the total dose associated with exposure to
volatile organics in drinking water results from dermal absorption (Brown et al., 1984). EPA
(2004) used a prediction model based on octanol-water partitioning and molecular weight to
derive a dermal permeability coefficient for TCE in water of 0.012 cm/hour. EPA used this
value to compute the dermally absorbed dose from a 35 minute shower and compared it to the
dose from drinking 2 L of water at the same concentration. This comparison indicated that the
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dermal dose would be 17% of the oral dose. Much higher dermal permeabilities were reported
by Nakai et al. (1999) based on human skin in vitro testing. For dilute aqueous solutions of
TCE, they measured a permeability coefficient of 0.12 cm/hour (26°C). Nakai et al. (1999) also
measured a permeability coefficient of 0.018 cm/hour for tetrachloroethylene in water. Poet
et al. (2000) measured dermal absorption of TCE in humans from both water and soil matrices.
The absorbed dose was estimated by applying a physiologically based pharmacokinetic model to
TCE levels in breath. The permeability coefficient was estimated to be 0.015 cm/hour for TCE
in water and 0.007 cm/hour for TCE in soil (Poet et al.. 2000).
2.4.1.4. Exposure to TCE Related Compounds
Table 2-13 presents adult exposure estimates that have been reported for the TCE related
compounds. This table was originally compiled by Wu and Schaum (2001). The exposure/dose
estimates are taken directly from the listed sources or derived based on monitoring data
presented in the source documents. They are considered —prerhinary" because they are
generally based on very limited monitoring data. These preliminary estimates suggest that
exposures to most of the TCE related compounds are comparable to or greater than TCE itself.
Table 2-13. Preliminary intake estimates of TCE and TCE-related chemicals
Chemical
Trichloroethylene
Tetrachloroethylene
1,1,1 -Trichloroethane
1 ,2-Dichloroethylene
Cis-l,2-Dichloroethylene
1,1,1 ,2-Tetrachloroethane
1 , 1 -Dichloroethane
Chloral
Monochloroacetic acid
Dichloroacetic acid
Trichloroacetic acid
Population
General
General
Occupational
General
General
Occupational
General
General
General
General
General
General
General
General
General
General
General
General
General
Media
Air
Water
Air
Air
Water
Air
Air
Water
Air
Water
Air
Water
Air
Air
Water
Water
Water
Water
Water
Range of estimated
adult exposures
(Hg/d)
11-33
2-20b
2,232-9,489
80-200
0.1-0.2
5,897-219,685
10.8-108
0.38-4.2
1-6
2.2
5.4
0.5-5.4
142
4
2.47-469.38
0.02-36.4
2-2.4
10-266
8.56-322
Range of adult doses
(mg/kg-d)
1.57 x lQ-4-4.71 x 1Q-4
2.86 x lQ-5-2.86 x 10'4
3.19 x lQ-2-1.36 x 1Q-1
1.14 x !Q-3-2.86 x 1Q-3
1.43 x lQ-6-2.86 x 1Q-6
8.43 x lQ-2-3.14
1.54 x 10'4-1.54 x lO'3
5.5 x 10'6-6.0 x lO'5
1.43 x 10'5-8.57 x 10'5
3.14 x 10'5
7.71 x 10'5
7.14 x 10'6-7.71 x 10'5
2.03 x 1Q-3
5.71 x 1Q-5
3.53 x lQ-5-6.71 x 1Q-3
2.86 x lQ-7-5.20 x 1Q-4
2.86 x lQ-5-3.43 x 10'5
1.43 x 10'4-3.80 x lO'3
1.22 x 10'3-4.60 x lO'3
Data sources"
ATSDR (1997c)
ATSDR (1997c)
ATSDR (1997c)
ATSDR (1997a)
ATSDR (1997a)
ATSDR (1997a)
ATSDR (1995)
ATSDR (1995)
ATSDR (1996a)
ATSDR (1996a)
HSDB (1996)
HSDB (1996)
HSDB (2002)
ATSDR (1990)
ATSDR (1990)
HSDB (1996)
EPA (1994c)
IARC (1995a)
IARC (1995a)
"Originally compiled in Wu and Schaum (2001).
bNew data from USGS (2006) suggests much lower water intakes, i.e., 0.2 ug/d.
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2.4.2. Potentially Highly Exposed Populations
Some members of the general population may have elevated TCE exposures. ATSDR
(1997c) has reported that TCE exposures may be elevated for people living near waste facilities
where TCE may be released, residents of some urban or industrialized areas, people exposed at
work (discussed further below) and individuals using certain products (also discussed further
below). Because TCE has been detected in breast milk samples of the general population,
infants who ingest breast milk may be exposed, as well. Increased TCE exposure is also a
possible concern for bottle-fed infants because they ingest more water on a bodyweight basis
than adults (the average water ingestion rate for adults is 21 mL/kg-day and for infants under one
year old it is 44 mL/kg-day) (U.S. EPA, 1997). Also, because TCE can be present in soil,
children may be exposed through activities such as playing in or ingesting soil.
2.4.2.1. Occupational Exposure
Occupational exposure to TCE in the United States has been identified in various
degreasing operations, silk screening, taxidermy, and electronics cleaning (IARC, 1995a). The
major use of TCE is for metal cleaning or degreasing (IARC, 1995a). Degreasing is used to
remove oils, greases, waxes, tars, and moisture before galvanizing, electroplating, painting,
anodizing, and coating. The five primary industries using TCE degreasing are furniture and
fixtures; electronic and electric equipment; transport equipment; fabricated metal products; and
miscellaneous manufacturing industries (IARC, 1995a). Additionally, TCE is used in the
manufacture of plastics, appliances, jewelry, plumbing fixtures, automobile, textiles, paper, and
glass (IARC. 1995a).
Table 2-14 lists the primary types of industrial degreasing procedures and the years that
the associated solvents were used. Vapor degreasing has the highest potential for exposure
because vapors can escape into the work place. Hot dip tanks, where TCE is heated to close to
its boiling point of 87°C, are also major sources of vapor that can create exposures as high as
vapor degreasers. Cold dip tanks have a lower exposure potential, but they have a large surface
area which enhances volatilization. Small bench-top cleaning operations with a rag or brush and
open bucket have the lowest exposure potential. In combination with the vapor source, the size
and ventilation of the workroom are the main determinants of exposure intensity (NRC, 2006).
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Table 2-14. Years of solvent use in industrial degreasing and cleaning
operations
Years
-1934-1954
-1955-1968
-1969-1978
~1979-1990s
Vapor degreasers
Trichloroethylene
(poorly controlled)
TCE (poorly controlled,
tightened in 1960s)
TCE, (better controlled)
1,1,1 -Trichloroethane
(replaced TCE)
Cold dip tanks
Stoddard solvent3
TCE (replaced some
Stoddard solvent)
TCE, Stoddard solvent
1,1,1 -Trichloroethane
(replaced TCE),
Stoddard solvent
Rag or brush and bucket on bench top
Stoddard solvent (general use), alcohols
(electronics shop), carbon tetrachloride
(instrument shop).
Stoddard solvent, TCE (replaced some
Stoddard solvent), perchloroethylene,
1,1,1-trichloroethane (replaced carbon
tetrachloride, alcohols, ketones).
TCE, perchloroethylene, 1,1,1-trichloro-
ethane, alcohols, ketones, Stoddard solvent.
1,1,1 -Trichloroethane, perchloroethylene,
alcohols, ketones, Stoddard solvent.
aA mixture of straight and branched chain paraffins (48%), naphthenes (38%), and aromatic hydrocarbons (14%).
Sources: Stewart and Dosemeci (2005): Bakke et al. (2007).
Occupational exposure to TCE has been assessed in a number of epidemiologic and
industrial hygiene studies. Bakke et al. (2007) estimated that the arithmetic mean of TCE
occupational exposures across all industries and decades (mostly 1950s, 1970s, and 1980s) was
38.2 ppm (210 mg/m3). They also reported that the highest personal and area air levels were
found in vapor degreasing operations (arithmetic mean of 44.6 ppm or 240 mg/m3). Hein et al.
(2010) developed and evaluated statistical models to estimate the intensity of occupational
exposure to TCE (and other solvents) using a database of air measurement data and associated
exposure determinants. The measurement database was compiled from the published literature
and National Institute for Occupational Safety and Health (NIOSH) reports from 1940 to 1998
(n = 484) and were split between personal (47%)and area (53%) measurements. The predicted
arithmetic mean exposure intensity levels for the evaluated exposure scenarios ranged from
0.21 to 3,700 ppm (1.1-20,000 mg/m3) with a median of 30 ppm (160 mg/m3). Landrigan et al.
(1987) used air and biomonitoring techniques to quantify the exposure of degreasing workers
who worked around a heated, open bath of TRI. Exposures were found to be between 22 and
66 ppm (117-357 mg/m3) on average, with short-term peaks between 76 and 370 ppm
(413-2,000 mg/m3). High peak exposures have also been reported for cardboard workers who
were involved with degreasing using a heated and open process (Henschler et al., 1995).
Lacking industrial hygiene data and making some assumptions about plant environment and TCE
usage, Cherrie et al. (2001) estimated that cardboard workers at a plant in Germany had peak
exposures in the range of 200-4,000 ppm (1,100-22,000 mg/m3) and long-term average
exposures of 10-225 ppm (54-1,200 mg/m3). ATSDR (1997c) reports that the majority of
published worker exposure data show time-weighted average concentrations ranging from
<50 ppm-100 ppm (<270-540 mg/m3). NIOSH conducted a survey of various industries from
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1981 to 1983 and estimated that approximately 401,000 U.S. employees in 23,225 plants in the
United States were potentially exposed to TCE during this timeframe (ATSDR, 1997c: IARC,
1995a). Occupational exposure to TCE has likely declined since the 1950s and 1960s due to
decreased usage, better release controls, and improvements in worker protection. Reductions in
TCE use are illustrated in Table 2-14, which shows that by about 1980, common degreasing
operations had substituted other solvents for TCE.
2.4.2.2. Consumer Exposure
Consumer products reported to contain TCE include wood stains, varnishes, and finishes;
lubricants; adhesives; typewriter correction fluids; paint removers; and cleaners (ATSDR,
). Use of TCE has been discontinued in some consumer products (i.e., as an inhalation
anesthetic, fumigant, and an extractant for decaffeinating coffee) (ATSDR, 1997c).
2.4.3. Exposure Standards
Table 2-15 summarizes the federal regulations limiting TCE exposure.
Table 2-15. TCE standards
Standard
OSHA Permissible Exposure Limit: Table Z-2 8-hr
time-weighted average.
OSHA Permissible Exposure Limit: Table Z-2
Acceptable ceiling concentration (this cannot be
exceeded for any time period during an 8-hr shift
except as allowed in the maximum peak standard
below).
OSHA Permissible Exposure Limit: Table Z-2
Acceptable maximum peak above the acceptable
ceiling concentration for an 8-hr shift. Maximum
Duration: 5 minutes in any 2 hrs.
Maximum contaminant level under the Safe
Drinking Water Act.
FDA Tolerances for
decaffeinated ground coffee
decaffeinated soluble (instant) coffee
extract spice oleoresins.
Value
100 ppm
(538 mg/m3)
200 ppm
(1,076 mg/m3)
300 ppm
(1,614 mg/m3)
5 ppb (5 ug/L)
25 ppm (25 ug/g)
10 ppm (10 ug/g)
30 ppm (30 ug/g)
Reference
29 CFR 1910.1000 (7/1/2000)
29 CFR 1910.1000 (7/1/2000)
29 CFR 1910.1000 (7/1/2000)
40 CFR 141. 161
21 CFR 173.290 (4/1/2000)
OSHA = Occupational Safety and Health Administration
2.5. EXPOSURE SUMMARY
TCE is a volatile compound with moderate water solubility. Most TCE produced today
is used for metal degreasing. The highest environmental releases are to the air. Ambient air
monitoring data suggests that levels have remained fairly constant since 1999 at about 0.3 ug/m3.
Indoor levels are commonly three or more times higher than outdoor levels due to releases from
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building materials and consumer products. TCE is among the most common groundwater
contaminants and the median level based on a large survey by USGS for 1985-2001 is
0.15 ug/L. It has also been detected in a wide variety of foods in the 1-100 ug/kg range. None
of the environmental sampling has been done using statistically based national surveys.
However, a substantial amount of air and groundwater data have been collected allowing
reasonably well supported estimates of typical daily intakes by the general population:
inhalation—13 ug/day and water ingestion—0.2 ug/day. The limited food data suggests an
intake of about 5 ug/day, but this must be considered preliminary.
Much higher exposures have occurred to various occupational groups. For example, past
studies of aircraft workers have shown short term peak exposures in the hundreds of ppm
(>540,000 ug/m3) and long term exposures in the low tens of ppm (>54,000 ug/m3).
Occupational exposures have likely decreased in recent years due to better release controls and
improvements in worker protection.
Preliminary exposure estimates were presented for a variety of TCE related compounds
which include metabolites of TCE and other parent compounds that produce similar metabolites.
Exposure to the TCE related compounds can alter or enhance TCE's metabolism and toxicity by
generating higher internal metabolite concentrations than would result from TCE exposure by
itself. The preliminary estimates suggest that exposures to most of the TCE related compounds
are comparable to or greater than TCE itself.
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3. TOXICOKINETICS
TCE is a lipophilic compound that readily crosses biological membranes. Exposures may
occur via the oral, dermal, and inhalation routes, with evidence for systemic availability from
each route. TCE is rapidly and nearly completely absorbed from the gut following oral
administration, and studies with animals indicate that exposure vehicle may impact the time-
course of absorption: oily vehicles may delay absorption, whereas aqueous vehicles result in a
more rapid increase in blood concentrations.
Following absorption to the systemic circulation, TCE distributes from blood to solid
tissues by each organ's solubility. This process is mainly determined by the blood:tissue
partition coefficients, which are largely established by tissue lipid content. Adipose partitioning
is high, adipose tissue may serve as a reservoir for TCE, and accumulation into adipose tissue
may prolong internal exposures. TCE attains high concentrations relative to blood in the brain,
kidney, and liver—all of which are important target organs of toxicity. TCE is cleared via
metabolism mainly in three organs: the kidney, liver, and lungs.
The metabolism of TCE is an important determinant of its toxicity. Metabolites are
generally thought to be responsible for toxicity—especially for the liver and kidney. Initially,
TCE may be oxidized via cytochrome P450 (CYP) xenobiotic metabolizing isozymes or
conjugated with glutathione (GSH) by glutathione-S-transferase (GST) enzymes. While
CYP2E1 is generally accepted to be the CYP form most responsible for TCE oxidation at low
concentrations, other forms may also contribute, though their contributions may be more
important at higher, rather than lower, environmentally-relevant exposures.
Once absorbed, TCE is excreted primarily either in breath as unchanged TCE or carbon
dioxide (CO2), or in urine as metabolites. Minor routes of elimination include excretion of
metabolites in saliva, sweat, and feces. Following oral administration or upon cessation of
inhalation exposure, exhalation of unmetabolized TCE is a major elimination pathway. Initially,
elimination of TCE upon cessation of inhalation exposure demonstrates a steep concentration-
time profile: TCE is rapidly eliminated in the minutes and hours postexposure, and then the rate
of elimination via exhalation decreases. Following oral or inhalation exposure, urinary
elimination of parent TCE is minimal, with urinary elimination of the metabolites TCA and
TCOH accounting for the bulk of the absorbed dose of TCE.
Sections 3.1-3.4 below describe the absorption, distribution, metabolism, and excretion
(ADME) of TCE and its metabolites in greater detail. Section 3.5 then discusses PBPK
modeling of TCE and its metabolites.
5-1
-------
3.1. ABSORPTION
TCE is a low-molecular-weight lipophilic solvent; these properties explain its rapid
transfer from environmental media into the systemic circulation after exposure. As discussed
below, it is readily absorbed into the bloodstream following exposure via oral ingestion and
inhalation, with more limited data indicating dermal penetration.
3.1.1. Oral
Available reports on human exposure to TCE via the oral route are largely restricted to
case reports of occupational or intentional (suicidal) ingestions and suggest significant gastric
absorption (e.g., Briming et al.. 1998: Yoshida et al.. 1996: Perbellini et al.. 1991). Clinical
symptoms attributable to TCE or metabolites were observed in these individuals within a few
hours of ingestion (such as lack of consciousness), indicating absorption of TCE. In addition,
TCE and metabolites were measured in blood or urine at the earliest times possible after
ingestion, typically upon hospital admission, while urinary excretion of TCE metabolites was
followed for several days following exposure. Therefore, based on these reports, it is likely that
TCE is readily absorbed in the gastrointestinal (GI) tract; however, the degree of absorption
cannot be confidently quantified because the ingested amounts are not known.
Experimental evidence in mice and rats supports rapid and extensive absorption of TCE,
although variables such as stomach contents, vehicle, and dose may affect the degree of gastric
absorption. D'Souza et al. (1985) reported on bioavailability and blood kinetics in fasted and
nonfasted male Sprague-Dawley rats following intragastric administration of TCE at 5-25 mg/kg
in 50% polyethylene glycol (PEG 400) in water. TCE rapidly appeared in peripheral blood (at
the initial 0.5 minutes sampling) of fasted and nonfasted rats with peak levels being attained
shortly thereafter (6-10 minutes), suggesting that absorption is not diffusion limited, especially
in fasted animals. The presence of food in the GI tract, however, seems to influence TCE
absorption based on findings in the nonfasted animals of lesser bioavailability (60-80 vs. 90% in
fasted rats), smaller peak blood levels (two- to threefold lower than nonfasted animals), and a
somewhat longer terminal half-life (ti/2) (174 vs. 112 minutes in fasted rats).
Studies by Prout et al. (1985) and Dekant et al. O986b) have shown that up to 98% of
administered radiolabel was found in expired air and urine of rats and mice following gavage
administration of [14C]-radiolabeled TCE ([14C]-TCE). Prout et al. (1985) and Green and Prout
(1985) compared the degree of absorption, metabolites, and routes of elimination among
two strains each of male rats (Osborne-Mendel and Park Wistar) and male mice (B6C3Fi and
Swiss-Webster) following a single oral administration of 10, 500, or 1,000 [14C]-TCE.
Additional dose groups of Osborne-Mendel male rats and B6C3Fi male mice also received a
single oral dose of 2,000 mg/kg [14C]-TCE. At the lowest dose of 10 mg/kg, there were no major
differences between rats and mice in routes of excretion, with most of the administered
radiolabel (nearly 60-70%) being in the urine. At this dose, the expired air from all groups
O O
3-2
-------
contained 1-4% of unchanged TCE and 9-14% CC>2. Fecal elimination of the radiolabel ranged
from 8.3% in Osborne-Mendel rats to 24.1% in Park Wistar rats. However, at doses between
500 and 2,000 mg/kg, the rat progressively excreted a higher proportion of the radiolabel as
unchanged TCE in expired air, such that 78% of the administered high dose was found in expired
air (as unchanged TCE) while only 13% was excreted in the urine.
Following exposure to a chemical by the oral route, distribution is determined by delivery
to the first organ encountered in the circulatory pathway—the liver (i.e., the first-pass effect),
where metabolism and elimination may limit the proportion that may reach extrahepatic organs.
Lee et al. (1996) evaluated the efficiency and dose-dependency of presystemic elimination of
TCE in male Sprague-Dawley rats following administration into the carotid artery, jugular vein,
hepatic portal vein, or the stomach of TCE (0.17, 0.33, 0.71, 2, 8, 16, or 64 mg/kg) in a
5% aqueous Alkamus emulsion (polyethoxylated vegetable oil) in 0.9% saline. The first-pass
elimination, decreased from 57.5 to <1% with increasing dose (0.17-16 mg/kg), which implied
that hepatic TCE metabolism may be saturated at doses >16 mg/kg in the male rat. At doses of
>16 mg/kg, hepatic first-pass elimination was almost nonexistent indicating that, at relatively
large doses, virtually all of TCE passes through the liver without being extracted (Lee et al.,
1996). In addition to the hepatic first-pass elimination findings, pulmonary extraction, which
was relatively constant (at nearly 5-8% of dose) over the dose range, also played a role in
eliminating TCE.
In addition, oral absorption appears to be affected by both dose and vehicle used. The
majority of oral TCE studies have used either aqueous solution or corn oil as the dosing vehicle.
Most studies that relied on an aqueous vehicle delivered TCE as an emulsified suspension in
Tween 80® or PEG 400 in order to circumvent the water solubility problems. Lee et al. (2000a;
2000b) used Alkamus (a polyethoxylated vegetable oil emulsion) to prepare a 5% aqueous
emulsion of TCE that was administered by gavage to male Sprague-Dawley rats. The findings
confirmed rapid TCE absorption, but reported decreasing absorption rate constants (i.e., slower
absorption) with increasing gavage dose (2-432 mg/kg). The time to reach blood peak
concentrations increased with dose and ranged between 2 and 26 minutes postdosing. Other
pharmacokinetics data, including area under the blood concentration time curve (AUC) and
prolonged elevation of blood TCE levels at the high doses, indicated prolonged GI absorption
and delayed elimination due to metabolic saturation occurring at the higher TCE doses.
A study by Withey et al. (1983) evaluated the effect of dosing TCE with corn oil vs. pure
water as a vehicle by administering four VOCs separately in each dosing vehicle to male Wistar
rats. Based on its limited solubility in pure water, the dose for TCE was selected at 18 mg/kg
(administered in 5 mL/kg). Times to peak in blood reported for TCE averaged 5.6 minutes when
water was used. In comparison, the time to peak in blood was much longer (approximately
100 minutes) when the oil vehicle was used and the peaks were smaller, below the level of
detection, and not reportable.
3-3
-------
Time-course studies reporting times to peak in blood or other tissues have been
performed using both vehicles (Larson and Bull, 1992a, b; D'Souza et al., 1985; Green and
Prout 1985: Dekantetal.. 1984: Withevetal.. 1983). Related data for other solvents (Dix et al..
1997: Lilly et al.. 1994: Kimetal.. 1990a: Kimetal.. 1990b: Chiecoetal.. 1981) confirmed
differences in TCE absorption and peak height between the two administered vehicles. One
study has also evaluated the absorption of TCE from soil in rats (Kadry et al., 1991) and reported
absorption within 16 hours for clay and 24 hours for sandy soil. In summary, these studies
confirm that TCE is relatively quickly absorbed from the stomach, and that absorption is
dependent on the vehicle used.
3.1.2. Inhalation
TCE is a lipophilic volatile compound that is readily absorbed from inspired air. Uptake
from inhalation is rapid and the absorbed dose is proportional to exposure concentration and
duration, and pulmonary ventilation rate. Distribution into the body via arterial blood leaving the
lungs is determined by the net dose absorbed and eliminated by metabolism in the lungs.
Metabolic clearance in the lungs will be further discussed in Section 3.3, below. In addition to
metabolism, solubility in blood is the major determinant of the TCE concentration in blood
entering the heart and being distributed to the each body organ via the arterial blood. The
measure of TCE solubility in each organ is the partition coefficient, or the concentration ratio
between both organ phases of interest. The blood-to-air partition coefficient quantifies the
resulting concentration in blood leaving the lungs at equilibrium with alveolar air. The value of
the blood-to-air partition coefficient is used in PBPK modeling (see Section 3.5). The blood-to-
air partition has been measured in vitro using the same principles in different studies and found
to range between 8.1 and 11.7 in humans with somewhat higher values in mice and rats (13.3-
25.8) (see Tables 3-1-3-2, and references therein).
Table 3-1. Blood:air partition coefficient values for humans
Blood: air partition
coefficient
8.1 + 1.8
8.11
9.13 + 1.73 [6.47-11]
9.5
9.77
9.92
11. 15 + 0.74 [10.1-12.1]
U9 + 1 8 F7 Q 1 SI
11.0 ±1.6 [6.6-13. 5]
11.7 ±1.9 [6.7-16.8]
10.6 ±2.3 [3 14.4]
Reference/notes
Fiserova-Bergerova et al. (1984); mean + SD (SD converted from SE based on n = 5)
Gargas et al. (1989); (n = 3-15)
Fisher et al. (1998); mean + SD [range] of females (n = 6)
Sato and Nakaiima (1979); (n = 1)
Koizumi (1989)
Sato et al. (1977): (n=l)
Fisher et al. (1998); mean + SD [range] of males (n = 7)
Mahle et al. (2007); mean ± SD; 20 male pediatric patients aged 3-7 yrs (range; USAF.
2004)
Mahle et al. (2007); mean ± SD; 18 female pediatric patients aged 3-17 yrs (range;
USAF. 2004)
Mahle et al. (2007); mean ± SD; 32 male patients aged 23-82 vrs (range; USAF, 2004)
Mahle et al. (2007); mean ± SD; 27 female patients aged 23-82 vrs (range; USAF, 2004)
SE = standard error
3-4
-------
Table 3-2. Blood:air partition coefficient values for rats and mice
Blood: air partition
coefficient
Reference/notes
Rat
15+0.5
17.5
20.5 + 2.4
20.69 + 3.3
21.9
25.8
25.82 + 1.7
13.3 ±0.8 [11.6-15]
13.4 ±1.8 [11.8-17.2]
17.5 ±3.6 [11.7-23.1]
21.8 ±1.9 [16.9-23.5]
Fisher et al. (1998): mean + SD (SD converted from SE based on n = 3)
Rodriguez et al. (2007)
Barton et al. (1995); mean + SD (SD converted from SE based on n = 4)
Simmons et al. (2002): mean + SD (n = 7-10)
Gargas et al. (1989) (n = 3-15)
Koizumi (1989) (pooled n = 3)
Sato et al. (1977): mean + SD (n = 5)
Mahle et al. (2007): mean ± SD; 10 PND 10 male rat pups (range: USAF. 2004)
Mahle et al. (2007): mean ± SD; 10 PND 10 female rat pups (range: USAF. 2004)
Mahle et al. (2007); mean ± SD; 9 adult male rats (range: USAF. 2004)
Mahle et al. (2007); mean ± SD; 1 1 aged male rats (range: USAF. 2004)
Mouse
13.4
14.3
15.91
Fisher et al. (1991); male
Fisher et al. (1991); female
Abbas and Fisher (1997)
PND = postnatal day
TCE enters the human body quickly by inhalation, and, at high concentrations, it may
lead to death (Coopman et al., 2003), narcosis, unconsciousness, and acute kidney damage
(Carrieri et al., 2007). Controlled exposure studies in humans have shown absorption of TCE to
approach a steady state within a few hours after the start of inhalation exposure (Fernandez et al.,
1977; Monster et al., 1976; Vesterberg and Astrand, 1976; Vesterberg et al., 1976). Several
studies have calculated the net dose absorbed by measuring the difference between the inhaled
concentration and the exhaled air concentration. Soucek and Vlachova (1960) reported 58-70%
absorption of the amount inhaled for 5-hour exposures of 93-158 ppm. Bartonicek (1962)
obtained an average retention value of 58% after 5 hours of exposure to 186 ppm. Monster et al.
(1976) also took into account minute ventilation measured for each exposure, and calculated of
37-49% absorption in subjects exposed to 70 and 140 ppm. The impact of exercise, the increase
in workload, and its effect on breathing has also been measured in controlled inhalation
exposures. Astrand and Ovrum (1976) reported 50-58% uptake at rest and 25-46% uptake
during exercise from exposure to 100 or 200 ppm (540 or 1,080 mg/m3, respectively) of TCE for
30 minutes (see Table 3-3). These authors also monitored heart rate and pulmonary ventilation.
In contrast, Jakubowski and Wieczorek (1988) calculated about 40% retention in volunteers
exposed to TCE at 9 ppm (mean inspired concentration of 48-49 mg/m3) for 2 hours at rest, with
no change in retention during increased workload due to exercise (see Table 3-4).
5-5
-------
Table 3-3. Air and blood concentrations during exposure to TCE in humans
TCE
concentration
(mg/m3)
540
540
540
540
540
540
1,080
1,080
1,080
1,080
1,080
1,080
Work
load
(watt)
0
0
50
50
50
50
0
0
50
50
100
150
Exposure
series3
I
II
I
II
II
II
I
III
I
III
III
III
TCE concentration in
Alveolar air
(mg/m3)
124 ±9
127 ±11
245 ± 12
218±7
234 ±12
244 ± 16
280 ± 18
212 ±7
459 ± 44
407 ± 30
542 ± 33
651 ±53
Arterial
blood
(mg/kg)
l.liO.l
1.3±0.1
2.7 ±0.2
2.8 ±0.1
3.1±0.3
3. 3 ±0.3
2.6 ±0.0
2.1 ±0.2
6.0 ±0.2
5.2 ±0.5
7.5 ±0.7
9.0 ±1.0
Venous
blood
(mg/kg)
0.6 ±0.1
0.5 ±0.1
1.7 ±0.4
1.8 ±0.3
2.2 ±0.4
2.2 ±0.4
1.4 ±0.3
1.2±0.1
3. 3 ±0.8
2.9 ±0.7
4.8±1.1
7.4±1.1
Uptake as %
of amount
available
53 ±2
52 ±2
40 ±2
46 ±1
39 ±2
37 ±2
50 ±2
58 ±2
45 ±2
51±3
36 ±3
25 ±5
Amount
taken up
(mg)
79 ±4
81±7
160 ±5
179 ±2
157 ±2
147 ±9
156 ±9
186 ±7
702 ±31
378 ±18
418±39
419 ±84
aSeries I consisted of 30-minute exposure periods of rest, rest, 50 watts, and 50 watts; Series II consisted of
30-minute exposure periods of rest, 50 watts, 50 watts, 50 watts; and Series III consisted of 30-minute
exposure periods of rest, 50 watts, 100 watts, 150 watts.
Source: Astrand and Ovrum (1976)
Table 3-4. Retention of inhaled TCE vapor in humans
Workload
Rest
25 Watts
50 Watts
75 Watts
Inspired concentration
(mg/m3)
48±3a
49 ±1.3
49 ±1.6
48 ±1.9
Pulmonary ventilation
(m3/hr)
0.65 ±0.07
1.30 ±0.14
1.53 ±0.13
1.87 ±0.14
Retention
0.40 ±0.05
0.40 ±0.05
0.42 ± 0.06
0.41 ±0.06
Uptake (mg/hr)
12±1.1
25 ±2.9
31±2.8
37 ±4.8
aMean ± SD, n = 6 adult males.
Source: Jakubowski and Wieczorek (1988)
Environmental or occupational settings may result from a pattern of repeated exposure to
TCE. Monster et al. (1979a) reported 70-ppm TCE exposures in volunteers for 4 hours for
5 consecutive days, averaging a total uptake of 450 mg per 4 hours of exposure (see Table 3-5).
In dry-cleaning workers, Skender et al. (1991) reported initial blood concentrations of
0.38 jimol/L, increasing to 3.4 jimol/L 2 days after. Results of these studies support rapid
absorption of TCE via inhalation.
-------
Table 3-5. Uptake of TCE in volunteers following 4 hour exposure to 70 ppm
A
B
C
D
E
Mean
Body
weight
(kg)
80
82
82
67
90
Minute-volume
(L/min)
9.8 ±0.4
12.0 ±0.7
10.9 ±0.8
11.8±0.8
11.0 ±0.7
Percentage
retained
45 ±0.8
44 ±0.9
49 ±1.2
35 ±2.6
46±1.1
Uptake (mg/d)
404 ± 23
485 ±35
493 ± 28
385 ±38
481 ±25
Uptake (mg/kg-d)
5.1
5.9
6.0
5.7
5.3
5.6 ±0.4
Source: Monster et al. (1979b).
Direct measurement of retention after inhalation exposure in rodents is more difficult
because exhaled breath concentrations are challenging to obtain. The only available data are
from Dallas et al. (1991), who designed a nose-only exposure system for rats using a facemask
equipped with one-way breathing valves to obtain measurements of TCE in inspired and exhaled
air. In addition, indwelling carotid artery cannulae were surgically implanted to facilitate the
simultaneous collection of blood. After a 1-hour acclimatization period, rats were exposed to
50 or 500 ppm TCE for 2 hours, and the time course of TCE in blood and expired air was
measured during and for 3 hours following exposure. When air concentration data were
analyzed to reveal absorbed dose (minute volume multiplied by the concentration difference
between inspired and exhaled breath), it was demonstrated that the fractional absorption of either
concentration was >90% during the initial 5 minutes of exposure. Fractional absorption then
decreased to 69 and 71% at 50 and 500 ppm during the second hour of exposure. Cumulative
uptake appeared linear with respect to time over the 2-hour exposure, resulting in absorbed doses
of 8.4 and 73.3 mg/kg in rats exposed to 50 and 500 ppm, respectively. Given the 10-fold
difference in inspired concentration and the 8.7-fold difference in uptake, the authors interpreted
this information to indicate that metabolic saturation occurred at some concentration <500 ppm.
In comparing the absorbed doses to those developed for the 70-ppm-exposed human [see
Monster et al. (1979a)1, Dallas et al. (1991) concluded that on a systemic dose (mg/kg) basis, rats
receive a much higher TCE dose from a given inhalation exposure than do humans. In
particular, using the results cited above, the absorption per ppm-hour was 0.084 and
0.073 mg/kg-ppm-hour at 50 and 500 ppm in rats (Dallas et al., 1991) and 0.019 mg/kg-ppm-
hour at 70 ppm in humans (Monster et al., 1979a)—a difference of around fourfold. However,
rats have about a 10-fold higher alveolar ventilation rate per unit body weight than humans
(Brown etal., 1997), which more than accounts for the observed increase in absorption.
Other experiments, such as closed-chamber gas uptake experiments or blood
concentration measurements following open-chamber (fixed concentration) experiments,
measure absorption indirectly but are consistent with significant retention. Closed-chamber gas-
5-7
-------
uptake methods (Gargas et al., 1988) place laboratory animals or in vitro preparations into sealed
systems in which a known amount of TCE is injected to produce a predetermined chamber
concentration. As the animal retains a quantity of TCE inside its body, due to metabolism, the
closed-chamber concentration decreases with time when compared to the start of exposure.
Many different studies have made use of this technique in both rats and mice to calculate total
TCE metabolism (i.e., Simmons et al.. 2002: Fisher etal.. 1991: Andersen et al.. 1987a). This
inhalation technique is combined with PBPK modeling to calculate metabolic parameters, and
the results of these studies are consistent with rapid absorption of TCE via the respiratory tract.
Figure 3-1 shows an example from Simmons et al. (2002), in Long-Evans rats, that demonstrates
an immediate decline in chamber concentrations of TCE indicating absorption, with multiple
initial concentrations needed for each metabolic calculation. At concentrations below metabolic
saturation, a secondary phase of uptake appears, after 1 hour from starting the exposure,
indicative of metabolism. At concentrations >1,000 ppm, metabolism appears saturated, with
time-course curves having a flat phase after absorption. At intermediate concentrations, between
100 and 1,000 ppm, the secondary phase of uptake appears after distribution as continued
decreases in chamber concentration as metabolism proceeds. Using a combination of
experiments that include both metabolic linear decline and saturation obtained by using different
initial concentrations, both components of metabolism can be estimated from the gas uptake
curves, as shown in Figure 3-1.
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Symbols represent measured chamber concentrations. Source: Simmons et al.
(2002).
Figure 3-1 Gas uptake data from closed-chamber exposure of rats to TCE.
-------
Several other studies in humans and rodents have measured blood concentrations of TCE
or metabolites and urinary excretion of metabolites during and after inhalation exposure (e.g.,
Fisher etal.. 1998: 1991: 1990: Filser and Bolt 1979). While qualitatively indicative of
absorption, blood concentrations are also determined by metabolism, distribution, and excretion;
thus, comparisons between species may reflect similarities or differences in any of the
absorption, distribution, metabolism, and excretion processes.
3.1.3. Dermal
Skin membrane is believed to present a diffusional barrier for entrance of the chemical
into the body, and TCE absorption can be quantified using a permeability rate or permeability
constant, though not all studies performed such a calculation. Absorption through the skin has
been shown to be rapid by both vapor and liquid TCE contact with the skin. Human dermal
absorption of TCE vapors was investigated by Kezic et al. (2000). Volunteers were exposed to
3.18 x 104 ppm around each enclosed arm for 20 minutes. Adsorption was found to be rapid
(within 5 minutes), reaching a peak in exhaled breath around 30 minutes, with a calculated
dermal penetration rate averaging 0.049 cm/hour for TCE vapors.
With respect to dermal penetration of liquid TCE, Nakai et al. (1999) used surgically
removed skin samples exposed to TCE in aqueous solution in a chamber designed to measure the
difference between incoming and outgoing [14C]-TCE. The in vitro permeability constant
calculated by these researchers averaged 0.12 cm/hour. In vivo, Sato and Nakajima (1978)
exposed adult male volunteers dermally to liquid TCE for 30 minutes, with exhaled TCE
appearing at the initial sampling time of 5 minutes after start of exposure, with a maximum
observed at 15 minutes. In Kezic et al. (2001), volunteers were exposed dermally for 3 minutes
to neat liquid TCE, with TCE detected in exhaled breath at the first sampling point of 3 minutes,
and maximal concentrations observed at 5 minutes. Skin irritancy was reported in all subjects,
which may have increased absorption. A dermal flux of 430 ± 295 (mean ± standard error [SE])
nmol/cm2/minute was reported in these subjects, suggesting high interindividual variability.
Another species where dermal absorption for TCE has been reported is in guinea pigs.
Jakobson et al. (1982) applied liquid TCE to the shaved backs of guinea pigs and reported peak
blood TCE levels at 20 minutes after initiation of exposure. Bogen et al. (1992) estimated
permeability constants for dermal absorption of TCE in hairless guinea pigs of 0.16-
0.47 mL/cm2/hour across a range of concentrations (19-100,000 ppm).
3.2. DISTRIBUTION AND BODY BURDEN
TCE crosses biological membranes and quickly results in rapid systemic distribution to
tissues—regardless of the route of exposure. In humans, in vivo studies of tissue distribution are
limited to tissues taken from autopsies following accidental poisonings or from surgical patients
exposed environmentally, so the level of exposure is typically unknown. Tissue levels reported
3-9
-------
after autopsy show wide systemic distribution across all tested tissues, including the brain,
muscle, heart, kidney, lung, and liver (Coopman et al., 2003; Dehon et al., 2000; De Baere et al.,
1997; Fordetal., 1995). However, the reported levels themselves are difficult to interpret
because of the high exposures and differences in sampling protocols. In addition, human
populations exposed environmentally show detectable levels of TCE across different tissues,
including the liver, brain, kidney, and adipose tissues (Kroneld, 1989; Pellizzari etal., 1982;
McConnell et al.. 1975).
In addition, TCE vapors have been shown to cross the human placenta during childbirth
(Laham, 1970), with experiments in rats confirming this finding (Withey and Karpinski, 1985).
In particular, Laham (1970) reported determinations of TCE concentrations in maternal and fetal
blood following administration of TCE vapors (concentration unreported) intermittently and at
birth (see Table 3-6). TCE was present in all samples of fetal blood, with ratios of
concentrations in fetal:maternal blood ranging from approximately 0.5 to approximately 2. The
concentration ratio was <1.0 in six pairs, >1 in three pairs, and approximately 1 in one pair; in
general, higher ratios were observed at maternal concentrations <2.25 mg/100 mL. Because no
details of exposure concentration, duration, or time postexposure were given for samples taken,
these results are not suitable for use in PBPK modeling, but they do demonstrate the placental
transfer of TCE in humans. Withey and Karpinski (1985) exposed pregnant rats to TCE vapors
(302, 1,040, 1,559, or 2,088 ppm for 5 hours) on gestation day (GD) 17 and concentrations of
TCE in maternal and fetal blood were determined. At all concentrations, TCE concentration in
fetal blood was approximately one-third of the concentration in corresponding maternal blood.
Maternal blood concentrations approximated 15, 60, 80, and 110 |ig/g blood. When the position
along the uterine horn was examined, TCE concentrations in fetal blood decreased toward the tip
of the uterine horn. TCE appears to also distribute to mammary tissues and is excreted in milk.
Pellizzari et al. (1982) conducted a survey of environmental contaminants in human milk using
samples from cities in the northeastern region of the United States and one in the southern
region. No details of times postpartum, milk lipid content, or TCE concentration in milk or
blood were reported, but TCE was detected in 8 milk samples taken from 42 lactating women.
Fisher et al. (1990) exposed lactating rats to 600 ppm TCE for 4 hours and collected milk
immediately following the cessation of exposure. TCE was clearly detectable in milk, and, from
a visual interpretation of the graphic display of their results, concentrations of TCE in milk
approximated 110 |ig/mL milk.
3-10
-------
Table 3-6. Concentrations of TCE in maternal and fetal blood at birth
TCE concentration in blood (mg/100 mL)
Maternal
4.6
3.8
8
5.4
7.6
3.8
2
2.25
0.67
1.05
Fetal
2.4
2.2
5
3.6
5.2
3.3
1.9
3
1
2
Ratio of concentrations fetahmaternal
0.52
0.58
0.63
0.67
0.68
0.87
0.95
1.33
1.49
1.90
Source: Laham (1970).
In rodents, detailed tissue distribution experiments have been performed using different
routes of administration (Keys etal., 2003; Simmons et al., 2002; Greenberg et al., 1999; Abbas
and Fisher. 1997: Pfaffenberger et al., 1980: Savolainen et al.. 1977). Savolainen et al. (1977)
exposed adult male rats to 200 ppm TCE for 6 hours/day for a total of 5 days. Concentrations of
TCE in the blood, brain, liver, lung, and perirenal fat were measured 17 hours after cessation of
exposure on the fourth day and after 2, 3, 4, and 6 hours of exposure on the fifth day (see
Table 3-7). TCE appeared to be rapidly absorbed into blood and distributed to brain, liver, lungs,
and perirenal fat. TCE concentrations in these tissues reached near-maximal values within
2 hours of initiation of exposure on the fifth day. Pfaffenberger et al. (1980) dosed rats by
gavage with 1 or 10 mg TCE/kg/day in corn oil for 25 days to evaluate the distribution from
serum to adipose tissue. During the exposure period, concentrations of TCE in serum were
below the limit of detection (1 |ig/L) and were 280 and 20,000 ng/g fat in the 1 and 10 mg/day
dose groups, respectively. Abbas and Fisher (1997) and Greenberg et al. (1999) measured tissue
concentrations in the liver, lung, kidney, and fat of mice administered TCE by gavage (300-
2,000 mg/kg) and by inhalation exposure (100 or 600 ppm for 4 hours). In a study to investigate
the effects of TCE on neurological function, Simmons et al. (2002) conducted pharmacokinetic
experiments in rats exposed to 200, 2,000, or 4,000 ppm TCE vapors for 1 hour. Time-course
data were collected on blood, liver, brain, and fat. The data were used to develop a PBPK model
to explore the relationship between internal dose and neurological effect. Keys et al. (2003),
exposed groups of rats to TCE vapors of 50 or 500 ppm for 2 hours and sacrificed at different
time points during exposure. In addition to inhalation, this study also includes gavage and intra-
arterial (i.a.) dosing, with the following time course measured: liver, fat, muscle, blood, GI,
brain, kidney, heart, lung, and spleen. These pharmacokinetic data were presented with an
updated PBPK model for all routes.
3-11
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Table 3-7 Distribution of TCE to rat tissues3 following inhalation exposure
Exposure on
5th d
Ob
2
3
4
6
Tissue (concentration in nmol/g tissue)
Cerebrum
0
9.9 ±2.7
7.3 ±2.2
7.2 ± 1.7
7.4 ±2.1
Cerebellum
0
11.7 ±4.2
8.8 ±2.1
7.6 ±0.5
9.5 ±2.5
Lung
0.08
4.9 ±0.3
5.5 ±1.4
5.8 ±1.1
5.6 ±0.5
Liver
0.04
3.6
5.5 ±1.7
2.5 ±1.4
2.4 ±0.2
Perirenal fat
0.23 ±0.09
65.9 ±1.2
69.3 ±3. 3
69.5 ±6.3
75.4 ±14.9
Blood
0.35 ±0.1
7.5 ±1.6
6.6 ±0.9
6.0 ±0.2
6.8 ±1.2
aData presented as mean of two determinations ± range.
bSample taken 17 hours following cessation of exposure on day 4.
Source: Savolainen et al. (1977).
Besides the route of administration, another important factor contributing to body
distribution is the individual solubility of the chemical in each organ, as measured by a partition
coefficient. For volatile compounds, partition coefficients are measured in vitro using the vial
equilibration technique to determine the ratio of concentrations between organ and air at
equilibrium. Table 3-8 reports values developed by several investigators from mouse, rat, and
human tissues. In humans, partition coefficients in the following tissues have been measured:
brain, fat, kidney, liver, lung, and muscle; the organ having the highest TCE partition coefficient
is fat (63-70), while the lowest is the lung (0.5-1.7). The adipose tissue also has the highest
measured value in rodents, and is one of the considerations needed to be accounted for when
extrapolating across species. However, the rat adipose partition coefficient value is smaller (23-
36), when compared to humans (i.e., TCE is less lipophilic in rats than humans). For the mouse,
the measured fat partition coefficient averages 36, ranging between rats and humans. The value
of the partition coefficient plays a role in distribution for each organ and is computationally
described in computer simulations using a PBPK model. Due to its high lipophilicity in fat, as
compared to blood, the adipose tissue behaves as a storage compartment for this chemical,
affecting the slower component of the chemical's distribution. For example Monster et al.
(1979a) reported that, following repeated inhalation exposures to TCE, TCE concentrations in
expired breath postexposure were highest for the subject with the greatest amount of adipose
tissue (adipose tissue mass ranged 3.5-fold among subjects). The intersubject range in TCE
concentration in exhaled breath increased from approximately 2-fold at 20 hours to
approximately 10-fold 140 hours postexposure. Notably, they reported that this difference was
not due to differences in uptake, as body weight and lean body mass were most closely
associated with TCE retention. Thus, adipose tissue may play an important role in postexposure
distribution, but does not affect its rapid absorption.
3-12
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Table 3-8. Tissue:blood partition coefficient values for TCE
Species/
tissue
TCE partition coefficient
Tissue:blood
Tissue: air
References
Human
Brain
Fat
Kidney
Liver
Lung
Muscle
2.62
63.8-70.2
1.3-1.8
3.6-5.9
0.48-1.7
1.7-2.4
21.2
583-674.4
12-14.7
29.4-54
4.4-13.6
15.3-19.2
Fiserova-Bergerova et al. (1984)
Sato et al. (1977): Fiserova-Bergerova et al. (1984): Fisher et al.
(1998)
Fiserova-Bergerova et al. (1984): Fisher et al. (1998)
Fiserova-Bergerova et al. (1984): Fisher et al. (1998)
Fiserova-Bergerova et al. (1984): Fisher et al. (1998)
Fiserova-Bergerova et al. (1984): Fisher et al. (1998)
Rat
Brain
Fat
Heart
Kidney
Liver
Lung
Muscle
Spleen
Testis
Milk
0.71-1.29
22.7-36.1
1.1
1.0-1.55
1.03-2.43
1.03
0.46-0.84
1.15
0.71
7.10
14.6-33.3
447-661
28.4
17.7-40
20.5-62.7
26.6
6.9-21.6
29.7
18.3
Not reported
Sato et al. (1977): Simmons et al. (2002): Rodriguez et al. (2007)
Gargas et al. (1989): Sato et al. (1977): Simmons et al. (2002):
Rodriguez et al. (2007): Fisher et al. (1989): Koizumi (1989): Barton
et al. (1995)
Sato et al. (1977)
Sato et al., (1977): Barton et al., (1995): Rodriguez et al., (2007)
Gargas et al. (1989): Sato et al. (1977): Simmons et al. (2002):
Rodriguez et al. (2007): Fisher et al. (1989): Koizumi, (1989): Barton
et al. (1995)
Sato et al. (1977)
Gargas et al. (1989): Sato et al. (1977): Simmons et al. (2002):
Rodriguez et al. (2007): Fisher et al. (1989): Koizumi, (1989): Barton
et al. (1995)
Sato et al. (1977)
Sato et al. (1977)
Fisher et al. (1990)
Mouse
Fat
Kidney
Liver
Lung
Muscle
36.4
2.1
1.62
2.6
2.36
578.8
32.9
23.2
41.5
37.5
Abbas and Fisher (1997)
Abbas and Fisher (1997)
Fisher et al. (1991)
Abbas and Fisher (1997)
Abbas and Fisher (1997)
Mahle et al. (2007) reported age-dependent differences in partition coefficients in rats,
(see Table 3-9) that can have implications as to life-stage-dependent differences in tissue TCE
distribution. To investigate the potential impact of these differences, Rodriguez et al. (2007)
developed models for the postnatal day (PND) 10 rat pup; the adult and the aged rat, including
age-specific tissue volumes and blood flows; and age-scaled metabolic constants. The models
predict similar uptake profiles for the adult and the aged rat during a 6-hour exposure to
500 ppm; uptake by the PND 10 rat was higher (see Table 3-10). The effect was heavily
dependent on age-dependent changes in anatomical and physiological parameters (alveolar
3-13
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ventilation rates and metabolic rates); age-dependent differences in partition coefficient values
had minimal impact on predicted differences in uptake.
Table 3-9. Age-dependence of tissuerair partition coefficients in rats
Agea
PND 10 male
PND 10 female
Adult male
Aged male
Liver
22.1±2.3b
21.2 ±1.7
20.5 ±4.0
34.8 ± 8.7c'd
Kidney
15.2 ±1.3
15.0±1.1
17.6 ± 3. 9C
19.9±3.4C
Fat
398.7 ±89.2
424.5 ±67.5
631.4 ± 43. lc
757.5 ± 48.3c'd
Muscle
43. 9± 11.0
48.6 ±17.3
12.6 ±4.3
26.4±10.3c'd
Brain
11.0 ±0.6
11.6±1.2
17.4 ±2.6
25.0 ± 2.0c'd
an = 10, adult male and pooled male and female litters; n = 11, aged males.
bData are mean ± SD.
Statistically significant (p < 0.05) difference between either the adult or aged partition coefficient and the PND 10
male partition coefficient.
dStatistically significant (p < 0.05) difference between aged and adult partition coefficient.
Source: Mahle et al. (2007).
Table 3-10. Predicted maximal concentrations of TCE in rat blood following
a 6-hour inhalation exposure
Age
PND 10
Adult
Aged
Exposure concentration
50 ppm
Predicted peak
concentration (mg/L) in:a
Venous
blood
3.0
0.8
0.8
Brain
2.6
1.0
1.2
Predicted time to
reach 90% of
steady state (hr)b
4.1
3.5
6.7
500 ppm
Predicted peak
concentration (mg/L) in:a
Venous
blood
33
22
21
Brain
28
23
26
Predicted time to
reach 90% of
steady state (hr)b
4.2
11.9
23.3
aDuring a 6-hour exposure.
bUnder continuous exposure.
Source: Rodriguez et al. (2007).
Finally, TCE binding to tissues or cellular components within tissues can affect overall
pharmacokinetics. The binding of a chemical to plasma proteins, for example, affects the
availability of the chemical to other organs and the calculation of the total half-life. However,
most studies have evaluated binding using [14C]-TCE, from which one cannot distinguish
covalent binding of TCE from that of TCE metabolites. Nonetheless, several studies have
demonstrated binding of TCE-derived radiolabel to cellular components (Mazzullo et al., 1992;
Moslen et al., 1977). Bolt and Filser (1977) examined the total amount irreversibly bound to
tissues following 9-, 100-, and 1,000-ppm exposures via inhalation in closed-chambers. The
largest percent of in vivo radioactivity taken up occurred in the liver; albumin is the protein
3-14
-------
favored for binding (see Table 3-11). Banerjee and van Duuren (1978) evaluated the in vitro
binding of TCE to microsomal proteins from the liver, lung, kidney, and stomachs in rats and
mice. In both rats and mice, radioactivity was similar in stomach and lung, but about 30% lower
in kidney and liver.
Table 3-11. Tissue distribution of TCE metabolites following inhalation exposure
Tissue3
Lung
Liver
Spleen
Kidney
Small
intestine
Muscle
Percent of radioactivity taken up/g tissue
TCE = 9 ppm,
n = 4b
Total
metabolites
0.23 ± 0.026C
0.77 ±0.059
0.14 ±0.015
0.37 ±0.005
0.41 ±0.058
0.11 ±0.005
Irreversibly
bound
0.06 ± 0.002
0.28 ± 0.027
0.05 ± 0.002
0.09 ± 0.007
0.05 ±0.010
0.014 ±0.001
TCE = 100 ppm,
n = 4
Total
metabolites
0.24 ± 0.025
0.68 ±0.073
0.15 ±0.001
0.40 ± 0.029
0.38 ±0.062
0.11 ±0.013
Irreversibly
bound
0.06 ± 0.006
0.27 ±0.019
0.05 ± 0.004
0.09 ± 0.007
0.07 ± 0.008
0.012 ±0.001
TCE = 1,000 ppm,
n = 3
Total
metabolites
0.22 ±0.055
0.88 ±0.046
0.15 ±0.006
0.39 ±0.045
0.28 ±0.015
0.10±0.011
Irreversibly
bound
0.1 ±0.003
0.48 ± 0.020
0.08 ±0.003
0.14 ±0.016
0.09 ±0.015
0.027 ± 0.003
aMale Wistar rats, 250 g.
bn = number of animals.
°Values shown are means ± SD.
Source: Bolt and Filser (19TT).
Based on studies of the effects of metabolizing enzyme induction on binding, there is
some evidence that a major contributor to the observed binding is from TCE metabolites rather
than from TCE itself. Dekant et al. (1986b) studied the effect of enzyme modulation on the
binding of radiolabel from [14C]-TCE by comparing tissue binding after administration of
200 mg/kg via gavage in corn oil between control (naive) rats and rats pretreated with
phenobarbital (a known inducer of CYP2B family) or Aroclor 1254 (a known inducer of both
CYP1A and CYP2B families of isoenzymes) (see Table 3-12). The results indicate that
induction of total CYP content by 3-4-fold resulted in nearly 10-fold increase in radioactivity
(disintegrations per minute; [DPM]) bound in liver and kidney. By contrast, Mazzullo et al.
(1992) reported that phenobarbital pretreatment did not result in consistent or marked alterations
of in vivo binding of radiolabel to deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or
protein in rats and mice at 22 hours after an intraperitoneal (i.p.) injection of [14C]-TCE. On the
other hand, in vitro experiments by Mazzullo et al. (1992) reported reduction of TCE-radiolabel
binding to calf thymus DNA with introduction of a CYP inhibitor into incubations containing rat
liver microsomal protein. Moreover, increase/decrease of GSH levels in incubations containing
lung cytosolic protein led to a parallel increase/decrease in TCE-radiolabel binding to calf
thymus DNA.
3-15
-------
Table 3-12 Binding of [14C] from [14C]-TCE in rat liver and kidney at
72 hours after oral administration of 200 mg/kg [14C]-TCE
Tissue
Liver
Kidney
DPM/g tissue
Untreated
850 ± 100
680 ± 100
Phenobarbital
9,300 ±1,100
5,700 ± 900
Arochlor 1254
8,700 ± 1,000
7,300 ± 800
Source: Dekant et al. (1986bX
3.3. METABOLISM
This section focuses on both in vivo and in vitro studies of the biotransformation of TCE,
identifying metabolites that are deemed significant for assessing toxicity and carcinogenicity. In
addition, metabolism studies may be used to evaluate the flux of parent compound through the
known metabolic pathways. Sex-, species-, and interindividual differences in the metabolism of
TCE are discussed, as are factors that possibly contribute to this variability. Additional
discussion of variability and susceptibility is presented in Section 4.10.
3.3.1. Introduction
The metabolism of TCE has been studied mostly in mice, rats, and humans and has been
extensively reviewed (Lash et al.. 2000a: Lash et al.. 2000b: IARC, 1995b: US EPA. 1985). It is
now well accepted that TCE is metabolized in laboratory animals and in humans through at least
two distinct pathways: (1) oxidative metabolism via the CYP mixed-function oxidase system
and (2) GSH conjugation followed by subsequent further biotransformation and processing,
either through the cysteine conjugate beta lyase pathway or by other enzymes (Lash et al., 2000a:
Lash et al., 2000b). While the flux through the conjugative pathway is less, quantitatively, than
the flux through oxidation (Bloemen et al., 2001), GSH conjugation is an important route
lexicologically, giving rise to relatively potent toxic biotransformation products (Elfarra et al.,
1987:Elfarraetal., 1986).
Information about metabolism is important because, as discussed extensively in
Chapter 4, certain metabolites are thought to cause one or more of the same acute and chronic
toxic effects, including carcinogenicity, as TCE. Thus, in many of these cases, the toxicity of
TCE is generally considered to reside primarily in its metabolites rather than in the parent
compound itself.
3.3.2. Extent of Metabolism
TCE is extensively metabolized in animals and humans. The most comprehensive mass-
balance studies are in mice and rats (Dekant et al., 1986a: Dekant etal., 1986b: Green and Prout,
1985: Prout etal., 1985: Dekant etal., 1984) in which [14C]-TCE is administered by gavage at
3-16
-------
doses of 2-2,000 mg/kg, the data from which are summarized in Figures 3-2 and 3-3. In both
mice and rats, regardless of sex and strain, there is a general trend of increasing exhalation of
unchanged TCE with dose, suggesting saturation of a metabolic pathway. The increase is
smaller in mice (from 1-6 to 10-18%) than in rats (from 1-3 to 43-78%), suggesting greater
overall metabolic capacity in mice. The dose at which apparent saturation occurs appears to be
more sex- or strain-dependent in mice than in rats. In particular, the marked increase in exhaled
TCE occurred between 20 and 200 mg/kg in female NMRI mice, between 500 and 1,000 mg/kg
in B6C3Fi mice, and between 10 and 500 mg/kg in male Swiss-Webster mice. However,
because only one study is available in each strain, interlot or interindividual variability might
also contribute to the observed differences. In rats, all three strains tested showed marked
increase in unchanged TCE exhaled between 20 and 200 mg/kg or between 10 and 500 mg/kg.
Recovered urine, the other major source of excretion, had mainly TCA, TCOH, and
trichloroethanol-glucuronide conjugate (TCOG), but revealed no detectable TCE. The source of
radioactivity in feces was not analyzed, but it is presumed not to include substantial TCE given
the complete absorption expected from the corn oil vehicle. Therefore, at all doses tested in
mice, and at doses <200 mg/kg in rats, the majority of orally administered TCE is metabolized.
Pretreatment of rats with P450 inducers prior to a 200 mg/kg dose did not change the pattern of
recovery, but it did increase the amount recovered in urine by 10-15%, with a corresponding
decrease in the amount of exhaled unchanged TCE (Dekant et al., 1986b).
3-17
-------
100
90
80 H
70
p 60 H
£•
>
ts
.2 40
2
^ 30
20
10 H
0
m
i
i
2 mg/kg 20
mg/kg
F/NMRI
i
200
mg/kg
i
I
i
I
10 500 1000 2000
mg/kg mg/kg mg/kg mg/kg
M/B6C3F1
I
i
i
I
I
i
10 500 1000
mg/kg mg/kg mg/kg
M/Swiss-Webster
DCage
wash
• Carcass
DCO2
Exhaled
D Feces
E Urine
• TCE
Exhaled
Mouse Sex/Strain and Dose
Sources: Dekant et al. (1986b: 1984): Green and Prout (1985): Prout et al. (1985).
Figure 3-2 Disposition of [14C]-TCE administered by gavage in mice.
3-18
-------
% radioactivity recovered
-^NJco-t^cncn^ioocDO
3OOOOOOOOOO
u -
•
1
ra
!
^M
1
,-•
2 mg/kg 20 200
mg/kg mg/kg
F/Wistar
1
1
ffl
!
1 ,K
L\"
V
I f
IB!
i
X
mi
i
•* • .
3^
< * ,
10 500 1000 2000
mg/kg mg/kg mg/kg mg/kg
M/Osborne-Mendel
1
1
i *
H1
"•if
! /
1
i»
ij
* &
10 500 1000
mg/kg mg/kg mg/kg
M/Alderley-ParkWistar
Rat Sex/Strain and Dose
DCage
wash
• Carcass
Q1CO2
Exhaled
Q Feces
Q Urine
• TCE
Exhaled
Sources: Dekant et al. (1986b: 1984): Green and Prout (1985): Prout et al. (1985).
Figure 3-3 Disposition of [14C]-TCE administered by gavage in rats.
3-19
-------
The differences among these studies may reflect a combination of interindividual
variability and errors due to the difficulty in precisely estimating dose in inhalation studies, but
in all cases, <20% of the retained dose was exhaled unchanged and >50% was excreted in urine
as TCA and TCOH. Therefore, it is clear that TCE is extensively metabolized in humans. No
saturation was evident in any of these human recovery studies at the exposure levels tested.
3.3.3. Pathways of Metabolism
As mentioned in Section 3.3.1, TCE metabolism in animals and humans has been
observed to occur via two major pathways: P450-mediated oxidation and GSH conjugation.
Products of the initial oxidation or conjugation step are further metabolized to a number of other
metabolites. For P450 oxidation, all steps of metabolism occur primarily in the liver, although
limited oxidation of TCE has been observed in the lungs of mice, as discussed below. The GSH
conjugation pathway also begins predominantly in the liver, but lexicologically significant
metabolic steps occur extrahepatically—particularly in the kidney (Lash et al., 2006; Lash et al.,
1999a: Lash et al., 1998b: Lash et al., 1995). The mass-balance studies cited above found that at
exposures below the onset of saturation, >50% of TCE intake is excreted in urine as oxidative
metabolites (primarily as TCA and TCOH), so TCE oxidation is generally greater than TCE
conjugation. This is discussed in detail in Section 3.3.3.3.
3.3.3.1. CYP-Dependent Oxidation
Oxidative metabolism by the CYP, or CYP-dependent, pathway is quantitatively the
major route of TCE biotransformation (Lash et al., 2000a: Lash et al.. 2000b: US EPA. 1985).
The pathway is operative in humans and rodents and leads to several metabolic products, some
of which are known to cause toxicity and carcinogen!city (IARC, 1995c: US EPA, 1985).
Although several of the metabolites in this pathway have been clearly identified, others are
speculative or questionable. Figure 3-4 depicts the overall scheme of TCE P450 metabolism.
3-20
-------
H (TCE) Cl
OHCH,
(W-(Hydroxyacetyl)- N (CH2)2OH
aminoethanol) |-|
Cl
P450
TCE-O-P450
EHR
H' (TCE-0) VC|
OH
3c 1^ - —
(CH) OH
O
— *• cu c — \
(CHL)\
H
j
CO,
(TCOG) Q-glUC
(Glyoxylic
acid)
(MCA) OH
HO (OA) OH
Adapted from: Clewell et al. (2000): Cummings et al. (2001): Forkert et al.
(2006): Lash et al. (2000a: 2000b): long et al. (1998).
Figure 3-4. Scheme for the oxidative metabolism of TCE.
In brief, TCE oxidation via P450, primarily CYP2E1 (Guengerich and Shimada. 1991).
yields an oxygenated TCE-P450 intermediate. The TCE-P450 complex is a transition state that
goes on to form chloral or TCE oxide. In the presence of water, chloral rapidly equilibrates with
chloral hydrate (CH), which undergoes reduction and oxidation by alcohol dehydrogenase and
aldehyde dehydrogenase or aldehyde oxidase to form TCOH and TCA, respectively (Dekant et
al., 1986b: Green and Prout 1985: Miller and Guengerich, 1983). TCE oxide can rearrange to
DCAC. Table 3-13 summarizes available in vitro measurements of TCE oxidation, as assessed
by the formation of CH, TCOH, and TCA. Glucuronidation of TCOH forms TCOG, which is
readily excreted in urine. Alternatively, TCOG can be excreted in bile and passed to the small
intestine where it is hydrolyzed back to TCOH and reabsorbed (Bull, 2000). TCA is poorly
metabolized but may undergo dechlorination to form dichloroacetic acid (DCA). However, TCA
is predominantly excreted in urine, albeit at a relatively slow rate as compared to TCOG. Like
3-21
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the TCE-P450 complex, TCE oxide also seems to be a transient metabolite. Recent data suggest
that it is transformed to dichloroactyl chloride, which subsequently decomposes to form DCA
(Cai and Guengerich, 1999). As shown in Figure 3-4, several other metabolites, including oxalic
acid and TV-(hydroxyacetyl) aminoethanol, may form from the TCE oxide or the TCE-O-P450
intermediate and have been detected in the urine of rodents and humans following TCE
exposure. Pulmonary excretion of CC>2 has been identified in exhaled breath from rodents
exposed to [14C]-labeled TCE and is thought to arise from metabolism of DCA. The following
sections provide details as to pathways of TCE oxidation, including discussion of inter- and
intraspecies differences in metabolism.
Table 3-13. In vitro TCE oxidative metabolism in hepatocytes and
microsomal fractions
In vitro system
Human
hepatocytes
Human liver
microsomal
protein
Rat liver
microsomal
protein
Rat kidney
microsomal
protein
Mouse liver
microsomal
protein
KM
uM in medium
210±159b
(45-403)
16.7 ±2.45
(13.3-19.7)
30.9 ±3. 3
(27.0-36.3)
51.1±3.77
(46.7-55.7)
24.6
12 ±3
(9-14)
26 ±17
(13-45)
55.5
72 ±82
42 ±21
940
35.4
378 ±414
161 ±29
VMAX
nmol TCE
oxidized/min/mg MSP
or 10 hepatocytes
0.268 ±0.215
(0.101-0.691)
1.246 ±0.805
(0.490-3.309)
1.442 ±0.464
(0.890-2.353)
2.773 ±0.577
(2.078-3.455)
1.44
0.52±0.17
(0.37-0.79)
0.33 ±0.15
(0.19-0.48)
4.826
0.96 ±0.65
2.91 ±0.71
0.154
5.425
8.6±4.5
26.06 ±7.29
1,000 x
VMAX/KM
2.45 ±2.28
(0.46-5.57)
74.1 ±44.1
(38.9-176)
47.0 ±16.0
(30.1-81.4)
54.9 ±14.1
(37.3-69.1)
58.5
48 ±23
(26-79)
15 ±10 (11-29)
87.0
24 ±21
80 ±34
0.164
153
42 ±29
163 ±37
Source
Lipscomb et al. (1998b)
Lipscomb et al. (1997) (low KM)
Lipscomb et al. (1997) (mid KM)
Lipscomb et al. (1997) (high KM)
Lipscomb et al. (1998c) (pooled)
Elfarra et al. (1998) (males, high affinity)
Elfarra et al. (1998) (females, high affinity)
Lipscomb et al. (1998c) (pooled)
Elfarra et al. (1998) (males, high affinity)
Elfarra et al. (1998) (females, high affinity)
Cummings et al. (2001)
Lipscomb et al. (1998c) (pooled)
Elfarra et al. (1998) (males)
Elfarra et al. (1998) (females)
aKM for human hepatocytes converted from ppm in headspace to uM in medium using reported hepatocyte:air partition
coefficient (Lipscomb et al., 1998b).
Results presented as mean ± SD (minimum-maximum).
MSP = Microsomal protein.
3.3.3.1.1. Formation of TCE oxide
In previous studies of halogenated alkene metabolism, the initial step was the generation
of a reactive epoxide (Anders and Jakobson, 1985). Early studies in anesthetized human patients
3-22
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(Powell, 1945), dogs (Butler, 1949), and later reviews (e.g., Goeptar et al., 1995) suggest that the
TCE epoxide may be the initial reaction product of TCE oxidation.
Epoxides can form acyl chlorides or aldehydes, which can then form aldehydes,
carboxylic acids, or alcohols, respectively. Thus, earlier studies suggesting the appearance of
CH, TCA, and TCOH as the primary metabolites of TCE were considered consistent with the
oxidation of TCE to an epoxide intermediate (Butler, 1949; Powell, 1945). Following in vivo
exposures to 1,1-DCE, a halocarbon very similar in structure to TCE, mouse liver cytosol and
microsomes and lung Clara cells exhibited extensive P450-mediated epoxide formation (Forkert,
1999b: Forkert, 1999a: Forkert et al., 1999: Dowslev et al., 1996). Indeed, TCE oxide inhibits
purified CYP2E1 activity (Cai and Guengerich, 200Ib) similarly to TCE inhibition of CYP2E1
in human liver microsomes (Lipscomb et al., 1997).
Conversely, cases have been made against TCE oxide as an obligate intermediate to the
formation of chloral. Using liver microsomes and reconstituted P450 systems (Miller and
Guengerich, 1983, 1982) or isolated rat hepatocytes (Miller and Guengerich, 1983), it has been
suggested that chlorine migration and generation of a TCE-O-P450 complex (via the heme
oxygen) would better explain the observed destruction of the P450 heme, an outcome not likely
to be epoxide-mediated. Miller and Guengerich (1982) found CYP2E1 to generate an epoxide
but argued that the subsequent production of chloral was not likely related to the epoxide. Green
and Prout (1985) argued against epoxide (free form) formation in vivo in mice and rats,
suggesting that the expected predominant metabolites would be carbon monoxide, CC>2, MCA,
and DCA, rather than the observed predominant appearance of TCA, TCOH, and TCOG.
It appears likely that both a TCE-O-P450 complex and a TCE oxide are formed, resulting
in both CH and DCAC, respectively, though it appears that the former predominates. In
particular, it has been shown that DCAC can be generated from TCE oxide, dichloracetyl
chloride can be trapped with lysine (Cai and Guengerich, 1999), and dichloracetyl-lysine adducts
are formed in vivo (Forkert et al., 2006). Together, these data strongly suggest TCE oxide as an
intermediate metabolite, albeit short-lived, from TCE oxidation in vivo.
3.3.3.1.2. Formation of CH, TCOH and TCA
CH (in equilibrium with chloral) is a major oxidative metabolite produced from TCE as
has been shown in numerous in vitro systems, including human liver microsomes and purified
P450 CYP2E1 (Guengerich et al., 1991) as well as recombinant rat, mouse, and human P450s
including CYP2E1 (Forkert et al., 2005). However, in rats and humans, in vivo circulating CH is
generally absent from blood following TCE exposure. In mice, CH is detectable in blood and
tissues but is rapidly cleared from systemic circulation (Abbas and Fisher, 1997). The low
systemic levels of CH are due to its rapid transformation to other metabolites.
CH is further metabolized predominantly to TCOH (Shultz and Weiner, 1979; Sellers et
al., 1972) and/or CYP2E1 (Ni et al., 1996). The role for alcohol dehydrogenase was suggested
3-23
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by the observation that ethanol inhibited CH reduction to TCOH (Larson and Bull, 1989; Muller
etal., 1975; Sellers etal., 1972). For instance, Sellers et al. (1972) reported that co-exposure of
humans to ethanol and CH resulted in a higher percentage of urinary TCOH (24% of CH
metabolites) compared to TCA (19%). When ethanol was absent, 10 and 11% of CH was
metabolized to TCOH and TCA, respectively. However, because ethanol can be oxidized by
both alcohol dehydrogenase and CYP2E1, there is some ambiguity as to whether these
observations involve competition with one or the other of these enzymes. For instance, Ni et al.
(1996) reported that CYP2E1 expression was necessary for metabolism of CH to mutagenic
metabolites in a human lymphoblastoid cell line, suggesting a role for CYP2E1. Furthermore, Ni
et al. (1996) reported that cotreatment of mice with CH and pyrazole, a specific CYP2E1
inducer, resulted in enhanced liver microsomal lipid peroxidation, while treatment with
2,4-dichloro-6-phenoxyethylamine, an inhibitor of CYP2E1, suppressed lipid peroxidation,
suggesting CYP2E1 as a primary enzyme for CH metabolism in this system. Lipscomb et al.
(1996) suggested that two enzymes are likely responsible for CH reduction to TCOH based on
observation of biphasic metabolism for this pathway in mouse liver microsomes. This behavior
has also been observed in mouse liver cytosol, but was not observed in rat or human liver
microsomes. Moreover, CH metabolism to TCOH increased significantly both in the presence of
nicotinamide adenine dinucleotide (NADH) in the 700 x g supernatant of mouse, rat, and human
liver homogenate as well as with the addition of nicotinamide adenine dinucleotide phosphate-
oxidase (NADPH) in human samples, suggesting that two enzymes may be involved (Lipscomb
etal.. 1996).
TCOH formed from CH is available for oxidation to TCA (see below) or glucuronidation
via uridine 5'-diphospho-glucuronyltransferase to TCOG, which is excreted in urine or in bile
(Stenner et al., 1997). Biliary TCOG is hydrolyzed in the gut and available for reabsorption to
the liver as TCOH, where it can be glucuronidated again or metabolized to TCA. This
enterohepatic circulation appears to play a significant role in the generation of TCA from TCOH
and in the observed lengthy residence time of this metabolite, compared to TCE. Using jugular-,
duodenal-, and bile duct-cannulated rats, Stenner et al. (1997) showed that enterohepatic
circulation of TCOH from the gut back to the liver and subsequent oxidation to TCA was
responsible for 76% of TCA measured in the systemic blood.
Oxidation of CH and TCOH to TCA has been demonstrated in vivo in mice (Larson and
Bull 1992a: Dekantetal.. 1986b: Green and Prout 1985). rats (Stenner et al.. 1997: Pravecek et
al.. 1996: Templin et al.. 1995b: Larson and Bull 1992a: Dekantetal.. 1986b: Green and Prout.
1985), dogs (Templin et al., 1995b), and humans (Sellers etal., 1978). Urinary metabolite data
in mice and rats exposed to 200 mg/kg TCE (Larson and Bull, 1992a: Dekantetal., 1986b): and
humans following oral CH exposure (Sellers et al., 1978) show greater TCOH production
relative to TCA production. However, because of the much longer urinary half-life in humans of
TCA relative to TCOH, the total amount of TCA excreted may be similar to TCOH (Fisher et al.,
3-24
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1998; Monster et al., 1976). This is thought to be primarily due to conversion of TCOH to TCA,
either directly or via — bxak-conversion" of TCOH to CH, rather than due to the initial formation
of TCA from CH (Owens and Marshall 1955).
In vitro data are also consistent with CH oxidation to TCA being much less than CH
reduction to TCOH. For instance, Lipscomb et al. (1996) reported 1,832-fold differences in KM
values and 10-195-fold differences in clearance efficiency (VMAX/KM) for TCOH and TCA in all
three species (see Table 3-14). Clearance efficiency of CH to TCA in mice is very similar to
humans but is 13-fold higher than rats. Interestingly, Bronley-DeLancey et al. (2006) recently
reported that similar amounts of TCOH and TCA were generated from CH using cryopreserved
human hepatocytes. However, the intersample variation was extremely high, with measured
VMAX ranging from 8-fold greater TCOH to 5-fold greater TCA and clearance (VMAX/KM)
ranging from 13 -fold greater TCOH to 17-fold greater TCA. Moreover, because a comparison
with fresh hepatocytes or microsomal protein was not made, it is not clear to what extent these
differences are due to population heterogeneity or experimental procedures.
Table 3-14 In vitro kinetics of TCOH and TCA formation from CH in rat,
mouse, and human liver homogenates
Species
Rat
Moused
High affinity
Low affinity
Human
TCOH
Ka
M
0.52
0.19
0.12
0.51
1.34
V b
VMAX
24.3
11.3
6.3
6.1
34.7
VMAX/KM
46.7
59.5
52.5
12.0
25.9
TCA
Ka
M
16.4
3.5
Not applicable
Not applicable
23.9
v b
VMAX
4
10.6
Not applicable
Not applicable
65.2
VMAX/KM
0.24
3.0
Not applicable
Not applicable
2.7
aKM presented as mM CH in solution.
bVMAx presented as nmoles/mg supernatant protein/minute.
'Clearance efficiency represented by VMAX/KM.
dMouse kinetic parameters derived for observations over the entire range of CH exposure as well as discrete, bi-
phasic regions for CH concentrations below (high affinity) and above (low affinity) 1.0 mM.
Source: Lipscomb et al. (1996).
The metabolism of CH to TCA and TCOH involves several enzymes including CYP2E1,
alcohol dehydrogenase, and aldehyde dehydrogenase enzymes (Ni etal., 1996; Wang et al.,
1993; Guengerich et al., 1991; Miller and Guengerich, 1983; Shultz and Weiner, 1979). Because
these enzymes have preferred cofactors (NADPH, NADH, and NAD+), cellular cofactor ratio
and redox status of the liver may have an impact on the preferred pathway (Lipscomb et al.,
1996: Kawamoto et al., 1988a).
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3.3.3.1.3. Formation of DCA and other products
As discussed above, DCA could hypothetically be formed via multiple pathways. The
work reviewed by Guengerich (2004) suggested that one source of DCA may be through a TCE
oxide intermediary. Miller and Guengerich (1983) reported evidence of formation of the
epoxide, and Cai and Guengerich (1999) reported that a significant amount (about 35%) of DCA
is formed from aqueous decomposition of TCE oxide via hydrolysis in an almost pH-
independent manner. Because this reaction forming DCA from TCE oxide is a chemical process
rather than a process mediated by enzymes, and because evidence suggests that some epoxide
was formed from TCE oxidation, Guengerich (2004) notes that DCA would be an expected
product of TCE oxidation (see also Yoshioka et al., 2002). Alternatively, dechlorination of TCA
and oxidation of TCOH have been proposed as sources of DCA (Lash et al., 2000a). Merdink
et al. (2000) investigated dechlorination of TCA and reported trapping a DCA radical with the
spin-trapping agent phenyl-tert-butyl nitroxide, identified by gas chromatography/mass
spectroscopy, in both a chemical Fenton system and rodent microsomal incubations with TCA as
substrate. Dose-dependent catalysis of TCA to DCA was observed in cultured microflora from
B6C3Fi mice (Moghaddam et al., 1996). However, while antibiotic-treated mice lost the ability
to produce DCA in the gut, plasma DCA levels were unaffected by antibiotic treatment,
suggesting that the primary site of murine DCA production is other than the gut (Moghaddam et
al.. 1997).
However, direct evidence for DCA formation from TCE exposure remains equivocal. In
vitro studies in human and animal systems have demonstrated very little DCA production in the
liver (James et al., 1997). In vivo, DCA was detected in the blood of mice (Templin et al., 1993;
Larson and Bull, 1992a) and humans (Fisher et al., 1998) and in the urine of rats and mice
(Larson and Bull 1992b) exposed to TCE by aqueous gavage. However, the use of strong acids
in the analytical methodology produces ex vivo conversion of TCA to DCA in mouse blood
(Ketcha et al., 1996). This method may have resulted in the appearance of DCA as an artifact in
human plasma (Fisher et al., 1998) and mouse blood in vivo (Templin et al., 1995b). Evidence
for the artifact is suggested by DCA AUCs that were larger than would be expected from the
available TCA (Templin et al., 1995b). After the discovery of these analytical issues, Merdink
et al. (1998) reevaluated the formation of DCA from TCE, TCOH, and TCA in mice, with
particular focus on the hypothesis that DCA is formed from dechlorination of TCA. They were
unable to detect blood DCA in naive mice after administration of TCE, TCOH, or TCA. Low
levels of DCA were detected in the blood of children administered therapeutic doses of CH
(Henderson et al., 1997), suggesting TCA or TCOH as the source of DCA. Oral TCE exposure
in rats and dogs failed to produce detectable levels of DCA (Templin et al., 1995b).
Another difficulty in assessing the formation of DCA is its rapid metabolism at low
exposure levels. Degradation of DCA is mediated by GST-zeta (Saghir and Schultz, 2002; Tong
et al., 1998), apparently occurring primarily in the hepatic cytosol. DCA metabolism results in
3-26
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suicide inhibition of the enzyme, evidenced by decreased DCA metabolism in DCA-treated
animals (Gonzalez-Leon et al., 1999) and humans (Shroads et al., 2008) and loss of DCA
metabolic activity and enzymatic protein in liver samples from treated animals (Schultz et al.,
2002). This effect has been noted in young mice exposed to DCA in drinking water at doses
approximating 120 mg/kg-day (Schultz et al., 2002). The experimental data and
pharmacokinetic model simulations of several investigators (Li et al., 2008; Shroads et al., 2008;
Jia et al., 2006; Keys et al., 2004; Merdink et al., 1998) suggest that several factors prevent the
accumulation of measurable amounts of DCA: (1) its formation as a short-lived intermediate
metabolite and (2) its rapid elimination relative to its formation from TCA. While DCA
elimination rates appear approximately one order of magnitude higher in rats and mice than in
humans (James et al., 1997) (see Table 3-15), they still may be rapid enough so that even if DCA
were formed in humans, it would be metabolized too quickly to appear in detectable quantities in
blood.
Table 3-15. In vitro kinetics of DCA metabolism in hepatic cytosol of
mice, rats, and humans
Species
Mouse
Rat
Human
VMAX
(nmol/min/mg protein)
13.1
11.6
0.37
KM
(jiM)
350
280
71
VMAX/KM
37.4
41.4
5.2
Source: James et al. (1997).
A number of other metabolites, such as oxalic acid, MCA, glycolic acid, and glyoxylic
acid, are formed from DCA (Saghir and Schultz, 2002; Lash et al., 2000a). Unlike other
oxidative metabolites of TCE, DCA appears to be metabolized primarily via hepatic cytosolic
proteins. Since P450 activity resides almost exclusively in the microsomal and mitochondrial
cell fractions, DCA metabolism appears to be independent of P450. Rodent microsomal and
mitochondrial metabolism of DCA was measured to be <10% of cytosolic metabolism
(Lipscomb et al., 1995). DCA in the liver cytosol from rats and humans is transformed to
glyoxylic acid via a GSH-dependent pathway (James et al., 1997). In rats, the KM for GSH was
0.075 mM with a VMAX for glyoxylic acid formation of 1.7 nmol/mg protein/minute. While this
pathway may not involve GST (as evidenced by very low GST activity in this study), Tong et al.
(1998) showed GST-zeta, purified from rat liver, to be involved in metabolizing DCA to
glyoxylic acid, with a VMAX of 1,334 nmol/mg protein/minute and KM of 71.4 uM for glyoxylic
acid formation and a GSH KM of 59 uM.
3-27
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3.3.3.1.4. Tissue distribution of oxidative metabolism and metabolites
Oxidative metabolism of TCE, irrespective of the route of administration, occurs
predominantly in the liver, but TCE metabolism via the P450 (CYP) system also occurs at other
sites because CYP isoforms are present to some degree in most tissues of the body. For
example, both the lung and kidneys exhibit CYP enzyme activities (Forkert et al., 2005;
Cummingsetal.. 2001: 1997a: Green etal.. 1997b). Green et al. (1997b) detected TCE
oxidation to chloral in microsomal fractions of whole-lung homogenates from mice, rats, and
humans, with the activity in mice the greatest and in humans the least. The rates were slower
than in the liver (which also has a higher microsomal protein content as well as greater tissue
mass) by 1.8-, 10-, and >10-fold in mice, rats, and humans, respectively. While qualitatively
informative, these rates were determined at a single concentration of about 1 mM TCE. A full
kinetic analysis was not performed, so clearance and maximal rates of metabolism could not be
determined. With the kidney, Cummings et al. (2001) performed a full kinetic analysis using
kidney microsomes and found that clearance rates (VMAX/KM) for oxidation were > 100-fold
smaller than average rates found in the liver (see Table 3-13). In human kidney microsomes,
Amet et al.(1997) reported that CYP2E1 activity was weak and near detection limits, with no
CYP2E1 detectable using immunoblot analysis. Cummings and Lash (2000) reported detecting
oxidation of TCE in only one of four kidney microsome samples, and only at the highest tested
concentration of 2 mM, with a rate of 0.13 nmol/minute/mg protein. This rate contrasts with the
VMAX values for human liver microsomal protein of 0.19-3.5 nmol/minute/mg protein reported
in various experiments (see Table 3-13). Extrahepatic oxidation of TCE may play an important
role for generation of toxic metabolites in situ. The roles of local metabolism in kidney and lung
toxicity are discussed in detail in Sections 4.4 and 4.7, respectively.
With respect to further metabolism beyond oxidation of TCE, CH has been shown to be
metabolized to TCA and TCOH in lysed whole blood of mice and rats and fractionated human
blood (Lipscomb et al., 1996) (see Table 3-16). TCOH production is similar in mice and rats and
is approximately twofold higher in rodents than in human blood. However, TCA formation in
human blood is two- or threefold higher than in mouse or rat blood, respectively. In human
blood, TCA is formed only in the erythrocytes. TCOH formation occurs in both plasma and
erythrocytes, but fourfold more TCOH is found in plasma than in an equal volume of packed
erythrocytes. While blood metabolism of CH may contribute further to its low circulating levels
in vivo the metabolic capacity of blood (and kidney) may be substantially lower than liver.
Regardless, any CH reaching the blood may be rapidly metabolized to TCA and TCOH. DCA
and TCA are known to bind to plasma proteins. Schultz et al. (1999) measured DCA binding in
rats at a single concentration of about 100 jiM and found a binding fraction of <10%. However,
these data are not greatly informative for TCE exposure in which DCA levels are significantly
lower than 100 jiM. In addition, the limitation to a single concentration in this experiment
precludes fitting a binding curve, as can be done for TCA with Templin et al. (1995a: 1995b:
3-28
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1993). Schultz et al. (19991 Lumpkin et al. (20031 and Yu et al. (2003). all of which measured
TCA binding in various species and at various concentration ranges. Of these, Templin et al.
(1995a: 1995b) and Lumpkin et al. (2003) measured levels in humans, mice, and rats. Lumpkin
et al. (2003) studied the widest concentration range, spanning reported TCA plasma
concentrations from experimental studies. Table 3-17 shows derived binding parameters.
However, these data are not entirely consistent among researchers; two- to fivefold differences in
BMAX and Kd are noted in some cases, although some differences existed in the rodent strains and
experimental protocols used. In general, however, at lower concentrations, the bound fraction
appears greater in humans than in rats and mice. Typical human TCE exposures, even in
controlled experiments with volunteers, lead to TCA blood concentrations well below the
reported Kd (see Table 3-17, below), so the TCA binding fraction should be relatively constant.
However, in rats and mice, experimental exposures may lead to peak concentrations similar to,
or above, the reported Kd (e.g., Yu et al., 2000; Templin et al., 1993), meaning that the bound
fraction should temporarily decrease following such exposures.
Table 3-16. TCOH and TCA formed from CH in vitro in lysed whole blood
of rats and mice or fractionated blood of humans (nmoles formed in 400 uL
samples over 30 minutes)
TCOH
TCA
Rat
45.4 ±4.9
0.14 ±0.2
Mouse
46.7 ±1.0
0.21 ±0.3
Human
Erythrocytes
15.7 ± 1.4
0.42 ±0.0
Plasma
4.48 ±0.2
Not detected
Source: Lipscomb et al. (1996).
Table 3-17. Reported TCA plasma binding parameters3
A
BMAX (HM)
Kd(uM)
A+
BMAX/K
-------
Limited data are available on tissue:blood partitioning of the oxidative metabolites CH,
TCA, TCOH, and DCA, as shown in Table 3-18. As these chemicals are all water soluble and
not lipophilic, it is not surprising that their partition coefficients are close to one (within about
twofold). It should be noted that the TCA tissue:blood partition coefficients reported in
Table 3-18 were measured at concentrations 1.6-3.3 M, over 1,000-fold higher than the reported
Kd. Therefore, these partition coefficients should reflect the equilibrium between tissue and free
blood concentrations. In addition, only one in vitro measurement has been reported of
blood:plasma concentration ratios for TCA: Schultz et al. (1999) reported a value of 0.76 in rats.
Table 3-18 Partition coefficients for TCE oxidative metabolites
Species/tissue"
Tissue:blood partition coefficient
CH
TCA
TCOH
DCA
Humanb
Kidney
Liver
Lung
Muscle
-
-
-
-
0.66
0.66
0.47
0.52
2.15
0.59
0.66
0.91
-
-
-
-
Mouse0
Kidney
Liver
Lung
Muscle
0.98
1.42
1.65
1.35
0.74
1.18
0.54
0.88
1.02
1.3
0.78
1.11
0.74
1.08
1.23
0.37
aTCA and TCOH partition coefficients have not been reported for rats.
bFisher et al. (1998).
cAbbas and Fisher (19971.
3.3.3.1.5. Species-, sex-, and age-dependent differences of oxidative metabolism
The ability to describe species- and sex-dependent variations in TCE metabolism is
important for species extrapolation of bioassay data and identification of human populations that
are particularly susceptible to TCE toxicity. In particular, information on the variation in the
initial oxidative step of CH formation from TCE is desirable, because this is the rate-limiting
step in the eventual formation and distribution of the putative toxic metabolites TCA and DCA
(Lipscomb et al.. 1997).
Inter- and intraspecies differences in TCE oxidation have been investigated in vitro using
cellular or subcellular fractions, primarily of the liver. The available in vitro metabolism data on
TCE oxidation in the liver (see Table 3-13) show substantial inter- and intraspecies variability.
Across species, microsomal data show that mice apparently have greater capacity (VMAX) than
rat or humans, but the variability within species can be 2-10-fold. Part of the explanation may
be related to CYP2E1 content. Although liver P450 content is similar across species, mice and
rats exhibit higher levels of CYP2E1 content (0.85 and 0.89 nmol/mg protein, respectively)
3-30
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(Davis et al., 2002; Nakajima et al., 1993) than humans (approximately 0.25-0.30 nmol/mg
protein) (Davis et al., 2002; Elfarra et al., 1998). Thus, the data suggest that rodents would have
a higher capacity than humans to metabolize TCE, but this is difficult to verify in vivo because
very high exposure concentrations in humans would be necessary to assess the maximum
capacity of TCE oxidation.
With respect to the KM of liver microsomal TCE oxidative metabolism, where KM is
indicative of affinity (the lower the numerical value of KM, the higher the affinity), the trend
appears to be that mice and rats have higher KM values (i.e., lower affinity) than humans, but
with substantial overlap due to interindividual variability. Note that, as shown in Table 3-13, the
ranking of rat and mouse liver microsomal KM values between the two reports by Lipscomb et al.
(1998c) and Elfarra et al. (1998) is not consistent. However, both studies clearly show that KM is
the lowest (i.e., affinity is highest) in humans. Because clearance at lower concentrations is
determined by the ratio VMAX to KM, the lower apparent KM in humans may partially offset the
lower human VMAX, and lead to similar oxidative clearances in the liver at environmentally
relevant doses. However, differences in activity measured in vitro may not translate into in vivo
differences in metabolite production, as the rate of metabolism in vivo depends also on the rate
of delivery to the tissue via blood flow (Lipscomb et al., 2003). The interaction of enzyme
activity and blood flow is best investigated using PBPK models and is discussed, along with
descriptions of in vivo data, in Section 3.5.
Data on sex- and age-dependence in oxidative TCE metabolism are limited but suggest
relatively modest differences in humans and animals. In an extensive evaluation of CYP-
dependent activities in human liver microsomal protein and cryopreserved hepatocytes,
Parkinson et al. (2004) identified no age- or gender-related differences in CYP2E1 activity. In
liver microsomes from 23 humans, the KM values for females was lower than males, but VMAX
values were very similar (Lipscomb etal., 1997). Appearance of total trichloro compounds
(TTCs) in urine following i.p. dosing with TCE was 28% higher in female rats than in males
(Verma and Rana, 2003). The oxidation of TCE in male and female rat liver microsomes was
not significantly different; however, pregnancy resulted in a decrease of 27-39% in the rate of
CH production in treated microsomes from females (Nakajima et al., 1992b). Formation of CH
in liver microsomes in the presence of 0.2 or 5.9 mM TCE exhibited some dependency on age of
rats, with formation rates in both sexes of 1.1-1.7 nmol/mg protein/minute in 3-week-old
animals and 0.5-1.0 nmol/mg protein/minute in 18-week-old animals (Nakajima et al., 1992b).
Fisher et al. (1991) reviewed data available at that time on urinary metabolites to
characterize species differences in the amount of urinary metabolism accounted for by TCA (see
Table 3-19). They concluded that TCA seemed to represent a higher percentage of urinary
metabolites in primates than in other mammalian species, indicating a greater proportion of
oxidation leading ultimately to TCA relative to TCOG.
3-31
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Table 3-19. Urinary excretion of TCA by various species exposed to TCE
(based on data reviewed in (Fisher et al., 1991)
Species"
Baboonb'c
Chimpanzee13
Monkey, Rhesusb'°
Mice, NMRId
Mice, B6C3F!b
Rabbit, Japanese
Whiteb'c
Rat, Wistard
Rat, Osborne-Mendef
Rat, Holtzman3
Percentage of
urinary excretion of
TCA
Male
16
24
19
-
7-12
0.5
-
6-7
7
Female
-
22
-
8-20
-
-
14-17
-
-
Dose route
Intramuscular
injection
Intramuscular
injection
Intramuscular
injection
Oral intubation
Oral intubation
i.p. injection
Oral intubation
Oral intubation
i.p. injection
TCE dose
(mg TCE/kg)
50
50
50
2-200
10-2,000
200
2-200
10-2,000
10 mg TCE/rat
References
Mueller et al. (1982)
Mueller et al. (1982)
Mueller et al. (1982)
Dekant et al. (1986b)
Green and Prout (1985)
Nomiyama and Nomiyama
(1979)
Dekant et al. (1986b)
Green and Prout (1985)
Nomiyama and Nomiyama
(1979)
aThe human data tabulated in Fisher et al. (1991) from Nomiyama and Nomiyama (1971) were not included here
because they were relative to urinary excretion of TTCs—not as fraction of intake as was the case for the other data
included here.
Percentage urinary excretion determined from accumulated amounts of TCOH and TCA in urine 3-6 days
postexposure.
°Sex not specified.
Percentage urinary excretion determined from accumulated amounts of TCOH, DCA, oxalic acid, and
jV-(hydroxyacetyl)aminoethanol in urine 3 days postexposure.
3.3.3.1.6. CYP isoforms and genetic polymorphisms
A number of studies have identified multiple P450 isozymes as having a role in the
oxidative metabolism of TCE. These isozymes include CYP2E1 (Nakajima et al., 1992a:
Guengerich et al., 1991; Guengerich and Shimada, 1991; Nakajima et al., 1990; Nakajima et al.,
1988), CYP3A4 (Shimada et al.. 1994), CYP1A1/2, CYP2C11/6 (Nakaiima et al., 1993:
Nakajima et al.. 1992a), CYP2F, and CYP2B1 (Forkert et al.. 2005). Recent studies in CYP2E1-
knockout mice have shown that in the absence of CYP2E1, mice still have substantial capacity
for TCE oxidation (Forkert et al., 2006; Kim and Ghanayem, 2006). However, CYP2E1 appears
to be the predominant (i.e., higher affinity) isoform involved in oxidizing TCE (Forkert et al.,
2005; Nakajima et al., 1992a: Guengerich et al., 1991; Guengerich and Shimada, 1991). In rat
liver, CYP2E1 catalyzed TCE oxidation more than CYP2C11/6 (Nakaiima et al., 1992a). In rat
recombinant-derived P450s, the CYP2E1 had a lower KM (higher affinity) and higher VMAX/KM
ratio (intrinsic clearance) than CYP2B1 or CYP2F4 (Forkert et al., 2005). Interestingly, there
was substantial differences in KM between rat and human CYP2Els and between rat CYP2F4
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and mouse CYP2F2, suggesting that species-specific isoforms have different kinetic behavior
(see Table 3-20).
Table 3-20. P450 isoform kinetics for metabolism of TCE to CH in human,
rat, and mouse recombinant P450s
Experiment
Human rCYP2El
RatrCYP2El
RatrCYP2Bl
Rat rCYP2F4
Mouse rCYP2F2
KM
jiM
196 ± 40
14 ±3
131 ±36
64 ±9
114 ±17
VMAX
pmol/min/pmol P450
4 ±0.2
11 ±0.3
9 ±0.5
17 ±0.5
13 ±0.4
VMAX/KM
0.02
0.79
0.07
0.27
0.11
Source: Forkert et al. (2005).
The presence of multiple P450 isoforms in human populations affects the variability in
individuals' ability to metabolize TCE. Studies using microsomes from human liver or from
human lymphoblastoid cell lines expressing CYP2E1, CYP1 Al, CYP1A2, or CYP3A4 have
shown that CYP2E1 is responsible for >60% of oxidative TCE metabolism (Lipscomb et al.,
1997). Similarities between metabolism of chlorzoxazone (a CYP2E1 substrate) in liver
microsomes from 28 individuals (Peter et al., 1990) and TCE metabolism helped identify
CYP2E1 as the predominant (high affinity) isoform for TCE oxidation. Additionally, Lash et al.
(2000a) suggested that, at concentrations above the KM value for CYP2E1, CYP1A2, and
CYP2A4 may also metabolize TCE in humans; however, their contribution to the overall TCE
metabolism was considered low compared to that of CYP2E1. Given the difference in
expression of known TCE-metabolizing P450 isoforms (see Table 3-21) and the variability in
P450-mediated TCE oxidation (Lipscomb etal., 1997), significant variability may exist in
individual human susceptibility to TCE toxicity.
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Table 3-21. P450 isoform activities in human liver microsomes exhibiting
different affinities for TCE
Affinity group
Low KM
Mid KM
High KM
CYP isoform activity (pmol/min/mg protein)3
CYP2E1
520 ± 295
820 ± 372
1,317 ±592
CYP1A2
241 ± 146
545 ± 200
806 ± 442
CYP3A4
2.7 ±2.7
2.9 ±2.8
1.8±1.1
"Activities of CYP1A2, CYP2E1, and CYP3A4 were measured with phenacetin, chlorzoxazone, and testosterone as
substrates, respectively. Data are means ± SD from 10, 9, and 4 samples for the low-, mid-, and high-KM groups,
respectively. Only CYP3A4 activities are not significantly different (p < 0.05) from one another by Kruskal-Wallis
one-way analysis of variance.
Source: Lash et al. QOOOa).
Differences in content and/or intrinsic catalytic properties (KM, VMAX) of specific
enzymes among species, strains, and individuals may play an important role in the observed
differences in TCE metabolism and resulting toxicities. Lipscomb et al. (1997) reported
observing three statistically distinct groups of KM values for TCE oxidation using human
microsomes. The mean ± standard deviation (SD) (uM TCE) for each of the three groups was
16.7 ± 2.5 (n = 10), 30.9 ± 3.3 (n = 9), and 51.1 ± 3.8 (n = 4). Within each group, there were no
significant differences in sex or ethnicity. However, the overall observed KM values in female
microsomes (21.9 ±3.5 uM, n = 10) were significantly lower than males (33.1 ±3.5 uM,
n = 13). Interestingly, in human liver microsomes, different groups of individuals with different
affinities for TCE oxidation appeared to also have different activities for other substrates not
only with respect to CYP2E1 but also CYP1A2 (Lash et al.. 2000a) (see Table 3-21). Genetic
polymorphisms in humans have been identified in the CYP isozymes thought to be responsible
for TCE metabolism (Pastino et al., 2000), but no data exist correlating these polymorphisms
with enzyme activity. It is relevant to note that repeat polymorphism (Hu et al., 1999) or
polymorphism in the regulatory sequence (McCarver et al., 1998) were not involved in the
constitutive expression of human CYP2E1; however, it is unknown if these types of
polymorphisms may play a role in the inducibility of the respective gene.
Individual susceptibilities to TCE toxicity may also result from variations in enzyme
content, either at baseline or due to enzyme induction/inhibition, which can lead to alterations in
the amounts of metabolites formed. Certain physiological and pathological conditions or
exposure to other chemicals (e.g., ethanol and acetominophen) can induce, inhibit, or compete
for enzymatic activity. Given the well-established (or well-characterized) role of the liver to
oxidatively metabolize TCE (by CYP2E1), increasing the CYP2E1 content or activity (e.g., by
enzyme induction) may not result in further increases in TCE oxidation. Indeed, Kaneko et al.
(1994) reported that enzyme induction by ethanol consumption in humans increased TCE
metabolism only at high concentrations (500 ppm, 2,687 mg/m3) in inspired air. However, other
3-34
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interactions between ethanol and the enzymes that oxidatively metabolize TCE metabolites can
result in altered metabolic fate of TCE metabolites. In addition, enzyme inhibition or
competition can decrease TCE oxidation and subsequently alter the TCE toxic response via, for
instance, increasing the proportion undergoing GSH conjugation Lash et al. (2000a). TCE itself
is a competitive inhibitor of CYP2E1 activity (Lipscomb etal., 1997), as shown by reduced
/7-nitrophenol hydroxylase activity in human liver microsomes, and may therefore alter the
toxicity of other chemicals metabolized through that pathway. On the other hand, suicidal CYP
heme destruction by the TCE-oxygenated CYP intermediate has also been shown (Miller and
Guengerich, 1983).
3.3.3.2. GSH Conjugation Pathway
Historically, the conjugative metabolic pathways have been associated with xenobiotic
detoxification. This is true for GSH conjugation of many compounds. However, several
halogenated alkanes and alkenes, including TCE, are bioactivated to cytotoxic metabolites by the
GSH conjugate processing pathway (mercapturic acid) pathways (Elfarra et al., 1987; Elfarra et
al., 1986). In the case of TCE, production of reactive species several steps downstream from the
initial GSH conjugation is believed to cause cytotoxicity and carcinogenicity, particularly in the
kidney. Since the GSH conjugation pathway is in competition with the P450 oxidative pathway
for TCE biotransformation, it is important to understand the role of various factors in
determining the flux of TCE through each pathway. Figure 3-5 depicts the present
understanding of TCE metabolism via GSH conjugation.
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H (TCE) CI
CI2 C2 H
CI2C2H
Acylase CI2 C2
r
(DCVT)
(DCVCS)
CI2 C2 H
(NAcDCVCS)
Adapted from: Lash et al. (2000a); Cummings and Lash (2000): NRC (2006).
Figure 3-5. Scheme for GSH-dependent metabolism of TCE.
3.3.3.2.1. Formation of S-(l,2-dichlorovinyl)glutathione or S-(2,2-dichlorovinyl)-
glutathione (DCVG)
The conjugation of TCE to GSH produces S-(l,2-dichlorovinyl)glutathione or its isomer
S-(2,2-dichlorovinyl)glutathione (collectively, S-dichlorovinyl-glutathione, DCVG). There is
some uncertainty as to which GST isoforms mediate TCE conjugation. Lash and colleagues
studied TCE conjugation in renal tissue preparations, isolated renal tubule cells from male F344
rats and purified GST alpha-class isoforms 1-1, 1-2, and 2-2 (Cummings and Lash, 2000:
Cummings et al., 2000b: Lash et al., 2000b). The results demonstrated high conjugative activity
in the renal cortex and proximal tubule cells. Although the isoforms studied had similar VMAX
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values, the KM value for GST 2-2 was significantly lower than the other forms, indicating that
this form will catalyze TCE conjugation at lower (more physiologically relevant) substrate
concentrations. In contrast, using purified rat and human enzymes, Hissink et al. (2002) reported
in vitro activity for DCVG formation only for mu- and pi-class GST isoforms, and none towards
alpha-class isoforms; however, the rat mu-class GST 3-3 was several-fold more active than the
human mu-class GST Ml-1. Although GSTs are present in tissues throughout the body, the
majority of TCE GSH conjugation is thought to occur in the liver (Lash et al., 2000a). Using in
vitro studies with renal preparations, it has been demonstrated that GST catalyzed conjugation of
TCE is increased following the inhibition of CYP-mediated oxidation (Cummings and Lash,
2000).
In F344 rats, following gavage doses of 263-1,971 mg/kg TCE in 2 mL corn oil, DCVG
was observed in the liver and kidney of females only, in blood of both sexes (Lash et al., 2006),
and in bile of males (Dekant, 1990). The data from Lash et al. (2006) are difficult to interpret
because the time courses seem extremely erratic, even for the oxidative metabolites TCOH and
TCA. Moreover, a comparison of blood levels of TCA and TCOH with other studies in rats at
similar doses reveals differences of over 1,000-fold in reported concentrations. For instance, at
the lowest dose of 263 mg/kg, the peak blood levels of TCE and TCA in male F344 rats were
10.5 and 1.6 |ig/L, respectively (Lash et al., 2006). By contrast, Larson and Bull (1992a)
reported peak blood TCE and TCA levels in male Sprague-Dawley rats over 1,000-fold higher—
around 10 and 13 mg/L, respectively—following oral doses of 197 mg/kg as a suspension in 1%
aqueous Tween 80®. The results of Larson and Bull (1992a) are similar to Lee et al. (2000b),
who reported peak blood TCE levels of 20-50 mg/L after male Sprague-Dawley rats received
oral doses of 144-432 mg/kg in a 5% aqueous Alkamus emulsion (polyethoxylated vegetable
oil), and to Stenner et al. (1997), who reported peak blood levels of TCA in male F344 rats of
about 5 mg/L at a slightly lower TCE oral dose of 100 mg/kg administered to fasted animals in
2% Tween 80®. Thus, while useful qualitatively as an indicator of the presence of DCVG in rats,
the quantitative reliability of reported concentrations, for metabolites of either oxidation or GSH
conjugation, may be questionable.
In humans, DCVG was readily detected at in human blood following onset of a 4-hour
TCE inhalation exposure to 50 or 100 ppm (269 or 537 mg/m3) (Lash et al., 1999b). At 50 ppm,
peak blood levels ranged from 2.5 to 30 uM, while at 100 ppm, the mean (± SE, n = 8) peak
blood levels were 46.1 ± 14.2 uM in males and 13.4 ± 6.6 uM in females. Although on average,
male subjects had threefold higher peak blood levels of DCVG than females, DCVG blood levels
in half of the male subjects were similar to or lower than those of female subjects. This suggests
a polymorphism in GSH conjugation of TCE rather than a true gender difference (Lash et al.,
1999b) as also has been indicated by Hissink et al. (2002) for the human mu-class GST Ml-1
enzyme. Interestingly, as shown in Table 3-22, the peak blood levels of DCVG are similar on a
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molar basis to peak levels of TCE, TCA, and TCOH in the same subjects, as reported in
Fisher et al. (1998).
Table 3-22. Comparison of peak blood concentrations in humans exposed to
100 ppm (537 mg/m3) TCE for 4 hours
Chemical species
TCE
TCA
TCOH
DCVG
Peak blood concentration (mean ± SD, uM)
Males
23 ±11
56 ±9.8
21 ±5.0
46.1 ±14.2
Females
14 ±4.7
59 ±12
15 ±5.6
13.4 ±6.6
Sources: Fisher et al. (1998): Lash et al. (1999aX
Tables 3-23-3-25 summarize DCVG formation from TCE conjugation from in vitro
studies of liver and kidney cellular and subcellular fractions in mouse, rat, and human (tissue-
distribution and species- and gender-differences in DCVG formation are discussed below). As
shown by these tables, different investigators have reported considerably different rates for TCE
conjugation in human liver and kidney cell fractions. For instance, values in Table 3-23 from
Lash et al. (1999b) are between 2 and 5 orders of magnitude higher than those reported by Green
et al. (1997a) or Dekant et al. (1990) (see Table 3-25). In addition, Green et al. (1997a) and
Dekant et al. (1990) reported a difference in the relative importance of rat liver cytosol and rat
liver microsomes for GSH conjugation, with Green et al. (1997a) reporting activity in the cytosol
and none in the microsomes and Dekant et al. (1990) reporting the opposite.
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Table 3-23. GSH conjugation of TCE (at 1-2 mM) in liver and kidney
cellular fractions in humans, male F344 rats, and male B6C3Fi mice from
Lash laboratory
Species and cellular/subcellular fraction (TCE
concentration)
DCVG formation
(nmol/hr/mg protein or 106 cells)3
Male
Female
Human
Hepatocytes (0.9 mM) (pooled)
Liver cytosol (1 mM) (individual samples)
Liver cytosol (2 mM) (pooled)
Liver microsomes (1 mM) (individual samples)
Liver microsomes (1 mM) (pooled)
Kidney cytosol (2 mM) (pooled)
Kidney microsomes (1 mM) (pooled)
11±3
156 ±16
174 ± 13
346
108 ± 24
83 ± 11
146
42
320
Rat
Liver cytosol (2 mM)
Liver microsomes (2 mM)
Kidney cortical cells (2 mM)
Kidney cytosol (2 mM)
Kidney microsomes (2 mM)
7.30 ±2.8
10.3 ±2.8
0.48 ±0.02
0.45 ±0.22
Not detected
4.86 ±0.14
7.24 ± 0.24
0.65 ±0.15
0.32 ±0.02
0.61 ±0.06
Mouse
Liver cytosol (2 mM)
Liver microsomes (2 mM)
Kidney cytosol (2 mM)
Kidney microsomes (2 mM)
24.5 ±2.4
40.0 ±3.1
5.6 ±0.24
5.47 ±1.41
21.7 ±0.9
25.6 ±0.8
3.7 ±0.48
16.7 ±4.7
aMean±SE.
Sources: Lash et al. (1999a; 1998a: 1995): Cummings and Lash (2000).
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Table 3-24. Kinetics of TCE metabolism via GSH conjugation in male F344
rat kidney and human liver and kidney cellular and subcellular fractions
from Lash laboratory
Tissue and cellular fraction
KM
(uM TCE)
VMAX
(nmol
DCVG/min/mg
protein or 106
hepatocytes)
1,000 x
VMAX/KM
Rat
Kidney proximal tubular cells: low affinity
Kidney proximal tubular cells: high affinity
2,910
460
0.65
0.47
0.22
1.0
Human
Liver hepatocytes3
Liver cytosol: low affinity
Liver cytosol: high affinity
Liver microsomes: low affinity
Liver microsomes: high affinity
Kidney proximal tubular cells: low affinity
Kidney proximal tubular cells: high affinity
Kidney cytosol
Kidney microsomes
37-106
333
22.7
250
29.4
29,400
580
26.3
167
0.16-0.26
8.77
4.27
3.1
1.42
1.35
0.11
0.81
6.29
2.4-4.5
2.6
190
12
48
0.046
0.19
31
38
aKinetic analyses of first 6-9 (out of 10) data points from Figure 1 from Lash et al. (1999b) using Lineweaver-Burk
or Eadie-Hofstee plots and linear regression (R2 = 0.50-0.95). Regression with best R2 used first 6 data points and
Eadie-Hofstee plot, with resulting KM and VMAX of 106 and 0.26, respectively.
Sources: Lash et al. (1999b): Cummings and Lash (2000): (Cummings et al.. 2000b).
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Table 3-25. GSH conjugation of TCE (at 1.4-4 mM) in liver and kidney
cellular fractions in humans, male F344 rats, and male B6C3Fi mice from
Green and Dekant laboratories
Species and cellular/subcellular fraction (TCE
concentration)
DCVG formation
(nmol/hr/mg protein) (substrate concentration in
mM)a
Dekant et al. (1990)
Green et al. (1997a)
Human
Liver cytosol
Liver microsomes
Kidney cytosol
Kidney microsomes
-
-
-
-
0.00019 ±0.00014
Not determined
Not determined
Not determined
Rat
Liver cytosol
Liver microsomes
Kidney cytosol
Kidney microsomes
O.002
0.002
-
-
0.00162 ± 0.00002
Not determined
Not determined
Not determined
Mouse
Liver cytosol
Liver microsomes
Kidney cytosol
Kidney microsomes
-
-
-
-
0.0025
Not determined
Not determined
Not determined
"Where available, mean± SD.
Sources: Dekant et al. (1990). Green et al. (1997a).
The reasons for such discrepancies are unclear, but they may be related to different
analytical methods (Lash et al., 2000a). In particular, Lash et al. (1999b) employed the —Reed
method," which used ion-exchange high-performance liquid chromatography (HPLC) of
derivatized analytes. This HPLC method is characterized by variability and an overall decline in
retention times over the life of the HPLC column due to derivatization of amine groups on the
column (Lash et al., 1999a). Although data are limited, the GSH pathway metabolite levels
reported by methods that utilize [14C]-TCE and radiochemical detection followed by mass
spectrometry (MS) identification of the metabolites are lower. In particular, Green et al. (1997a)
and Dekant et al. (1990) both used HPLC with radiochemical detection. Peak identity was
confirmed by Green et al. (1997a) using liquid chromatography (LC)/MS and by GC/MS
following hydrolysis by Dekant et al. (1990). In addition, studies using HPLC-MS/MS
techniques with stable isotope-labeled DCVG and dichlorovinyl cysteine (DCVC) standards
have also been used to detect GSH pathway metabolite levels Kim et al. (2009). Based on the in
vitro work presented in Table 3-23 using the —Reed isthod," one would expect mouse serum
DCVG levels to be -4-6 times lower than humans. However, using the HPLC-MS/MS
technique of Kim et al. (2009), the peak DCVG serum levels are -1,000 times lower in mouse
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serum than determined by Lash et al. (1999b) in human serum. Although advances in LC
technology, and differences in exposure routes (inhalation vs. oral, with different first pass),
exposure doses, and the degree of competition with TCE oxidation (greater in mouse than in
human) should be considered, this much-larger-than-expected difference is consistent with the
suggestion that the —Red method" provides an overestimation of DCVG levels in humans. This
could occur if the -Reed method" identifies nonspecific derivatives as DCVG or other GSH
pathway metabolites. However, the degree of overestimation is unclear, and differing results in
humans may be attributable to true interindividual variation (especially since GSTs are known to
be polymorphic). Overall, there remains significant uncertainty in the quantitative estimation of
DCVG formation from TCE both in vivo and in vitro.
3.3.3.2.2. Formation of S-(l,2-dichlorovinyl) cysteine or S-(2,2-dichlorovinyl) cysteine
(DCVC)
The cysteine conjugate, isomers S-(l,2-dichlorovinyl) cysteine (1,2-DCVC) or
S-(2,2-dichlorovinyl) cysteine (2,2-DCVC) (collectively S-dichlorovinyl-cysteine, DCVC), is
formed from DCVG in a two-step sequence. DCVG is first converted to the cysteinylglycine
conjugate S-(l,2-dichlorovinyl)-L-cysteinylglycine or its isomer S-(2,2-dichlorovinyl)-
L-cysteinylglycine by y-glutamyl transpeptidase (GGT) in the renal brush border (Lash et al.,
1988: Elfarra and Anders. 1984).
Cysteinylglycine dipeptidases in the renal brush border and basolateral membrane
convert DCVG to DCVC via glycine cleavage (Goeptar et al.. 1995: Lashetal.. 1995). This
reaction can also occur in the bile or gut, as DCVG excreted into the bile is converted to DCVC
and reabsorbed into the liver where it may undergo further acetylation.
3.3.3.2.3. Formation of N-Acetyl-S-(l,2-dichlorovinyl)-L-cysteine or N-Acetyl-
S-(2,2-dichlorovinyl)-L-cysteine(NAcDCVC)
N-acetylation of DCVC can either occur in the kidney, as demonstrated in rat kidney
microsomes (Duffel and Jakoby, 1982), or in the liver (Birner et al., 1997). Subsequent release
of DCVC from the liver to blood may result in distribution to the kidney resulting in increased
internal kidney exposure to the acetylated metabolite over and above what the kidney already is
capable of generating. In the kidney, N-Acetyl-S-(l,2-dichlorovinyl)-L-cysteine or N-Acetyl-
S-(2,2-dichlorovinyl)-L-cysteine (collectively N-Acetyl-S-dichlorovinyl-L-cysteine, NAcDCVC)
may undergo deacetylation, which is considered a rate-limiting-step in the production of
proximal tubule damage (Wolfgang et al., 1989a: Zhang and Stevens, 1989). As a polar
mercapturate, NAcDCVC may be excreted in the urine as evidenced by findings in mice (Birner
et al., 1993), rats (Bernauer et al., 1996; Commandeur and Vermeulen, 1990), and humans who
were exposed to TCE (Bernauer et al., 1996; Birner etal., 1993), suggesting a common
GSH-mediated metabolic pathway for DCVC among species.
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3.3.3.2.4. Beta lyase metabolism of DCVC
The enzyme cysteine conjugate p-lyase catalyzes the breakdown of 1,2-DCVC to reactive
nephrotoxic metabolites (Goeptar et al., 1995). This reaction involves removal of pyruvate and
ammonia and production of S-dichlorovinyl thiol (DCVT), an unstable intermediate, which
rearranges to other reactive alkylation metabolites that form covalent bonds with cellular
nucleophiles (Goeptar et al., 1995; Dekant etal., 1988). The rearrangement of DCVT to
enethiols and their acetylating agents has been described in trapping experiments (Dekant et al.,
1988) and proposed to be responsible for nucleophilic adduction and toxicity in the kidney. The
quantification of acid-labile adducts was proposed as a metric for TCE flux through the GSH
pathway. However, the presence of analytical artifacts precluded such analysis. In fact,
measurement of acid-labile adduct products resulted in higher values in mice than in rats (Eyre et
al.. 1995b, a).
DCVC metabolism to reactive species via a P-lyase pathway has been observed in vitro
by Green et al. (1997a), who reported greater P-lyase activity in rats than in mice or humans.
However, in vitro DCVC metabolism by the competing enzyme TV-acetyl transferase was also
reported to be greater in rats than mice and humans. In vivo, P-lyase activity in humans and rats
(reaction rates were not reported) was demonstrated using a surrogate substrate, 2-(fluoro-
methoxy)-l,l,3,3,3-pentafluoro-l-propene (Iyer etal., 1998). p-lyase-mediated reactive adducts
have been described in several extrarenal tissues, including rat and human liver and intestinal
microflora (Larsen and Stevens, 1986; Tomisawa et al., 1986; Stevens, 1985; Tomisawa et al.,
1984: Stevens and Jakobv, 1983: Dohn and Anders, 1982: Tateishi et al., 1978) and rat brain
(Alberati-Giani et al., 1995: Malherbe et al., 1995).
In the kidneys, glutamine transaminase K appears to be primarily responsible for P-lyase
metabolism of DCVC (Perry etal., 1993: Lash et al., 1990: Jones etal., 1988: Stevens et al.,
1988: Lash etal., 1986: Stevens etal., 1986). p-Lyase transformation of DCVC appears to be
regulated by 2-keto acids. DCVC toxicity in isolated rat proximal tubular cells was significantly
increased with the addition of a-keto-y-methiolbutyrate or phenylpyruvate (Elfarra et al., 1986).
The presence of a-keto acid cofactors is necessary to convert the inactive form of the P-lyase
enzyme (containing pyridoxamine phosphate) to the active form (containing pyridoxal
phosphate) (Goeptar et al., 1995).
Both low- and high-molecular-weight enzymes with P-lyase activities have been
identified in rat kidney cytosol and mitochondria (Abraham et al., 1995a: Abraham et al., 1995b:
Stevens etal., 1988: Lash etal., 1986). While glutamine transaminase K and kynureninase-
associated P-lyase activities have been identified in rat liver (Alberati-Giani et al., 1995: Stevens,
1985), they are quite low compared to renal glutamine transaminase K activity and do not result
in hepatotoxicity in DCVG- or DCVC-treated rats (Elfarra and Anders, 1984). Similar isoforms
of P-lyase have also been reported in mitochondrial fractions of brain tissue (Cooper, 2004).
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The kidney enzyme, L-a-hydroxy (L-amino) acid oxidase, is capable of forming an
iminium intermediate and keto acid analogues (pyruvate or S-(l,2-dichlorovinyl)-2-oxo-
3-mercaptopropionate) of DCVC, which decomposes to dichlorovinylthiol (Lash et al., 1990;
Stevens et al., 1989). In rat kidney homogenates, this enzyme activity resulted in as much as
35% of GSH pathway-mediated bioactivation. However, this enzyme is not present in humans,
an important consideration for extrapolation of renal effects across species.
3.3.3.2.5. DCVC and NAcDCVC
A second pathway for bioactivation of TCE S-conjugates involves sulfoxidation of either
the cysteine or mercapturic acid conjugates (Krause et al., 2003; Lash etal., 2003; Birner et al.,
1998: Werner etal.. 1996. 1995a: Werner etal.. 1995b: Lash etal.. 1994: Park etal.. 1992:
Sausen and Elfarra, 1990). Sulfoxidation of DCVC was mediated mainly by flavin
monooxygenase 3 (FMO3), rather than CYP, in rabbit liver microsomes (Ripp etal., 1997) and
human liver microsomes (Krause et al., 2003). Krause et al. (2003) also reported DCVC
sulfoxidation by human cDNA-expressed FMO3, as well as detection of FMO3 protein in human
kidney samples. While Krause et al. (2003) were not able to detect sulfoxidation in human
kidney microsomes, the authors noted FMO3 expression in the kidney was lower and more
variable than that in the liver. However, sulfoxidation products in tissues or urine have not been
reported in vivo.
Sulfoxidation of NAcDCVC, by contrast, was found to be catalyzed predominantly, if not
exclusively, by CYP3 A enzymes (Werner et al., 1996), whose expressions are highly
polymorphic in humans. Sulfoxidation of other haloalkyl mercapturic acid conjugates has also
been shown to be catalyzed by CYP3 A (Altuntas et al., 2004: Werner etal., 1995a: Werner et al.,
1995b). While Lash et al. (2000a) suggested that this pathway would be quantitatively minor
because of the relatively low CYP3A levels in the kidney, no direct data exist to establish the
relative toxicological importance of this pathway relative to bioactivation of DCVC by p-lyase or
FMO3. However, the contribution of CYP3A in S-conjugate sulfoxidation to nephrotoxicity in
vivo was recently demonstrated by Sheffels et al. (2004) with fluoromethyl-2,2-difluoro-
l-(trifluoromethyl)vinyl ether (FDVE). In particular, in vivo production and urinary excretion of
FDVE-mercapturic acid sulfoxide metabolites were unambiguously established by mass
spectrometry, and CYP inducers/inhibitors increased/decreased nephrotoxicity in vivo while
having no effect on urinary excretion of metabolites produced through P-lyase (Sheffels et al.,
2004). These data suggest that, by analogy, sulfoxidation of NAcDCVC may be an important
bioactivating pathway.
3.3.3.2.6. Tissue distribution of GSH metabolism
The sites of enzymatic metabolism of TCE to the various GSH pathway-mediated
metabolites are significant in determining target tissue toxicity along this pathway. Figure 3-6
3-44
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presents a schematic of interorgan transport and metabolism of TCE along the GSH pathway.
TCE is taken up either by the liver or kidney and conjugated to DCVG. The primary factors
affecting TCE flux via this pathway include high hepatic GST activity, efficient transport of
DCVG from the liver to the plasma or bile, high renal brush border and low hepatic GGT
activities, and the capability for GSH conjugate uptake into the renal basolateral membranes with
limited or no uptake into liver cell plasma membranes.
TCE
DCVG
DCVC
NAcDCVC
Blood/
Plasma
Rest of
Body
Liver
A
Small
Intestine
Kidney
h Pilr
^^^^^•Metj
*
-
Blood/
Plasma
Rest of
Body
Liver
4 i
i 1
Small
Intestine
Kidney
41
W
>
Blood/
Plasma
*
Rest of
Body
Liver
1
Bile
»
4
Small ;j
Intestine
Kidney *
i 1
Urine w
d flow
flow DCVT
nerular filtration
abolism
1
-
W
Blood/
Plasma
Rest of
Body
Liver
Small ;V
Intestine t
1
Kidney '*
1 j 1
^^^v LjrinG ^^^v
T T T T
DCVCS NAcDCVC
j
See Figure 3-5 for enzymes involved in metabolic steps. Source: Lash et al.
(2000a; 2000b); NRC (2006).
Figure 3-6 Interorgan TCE transport and metabolism via the GSH
pathway.
As discussed previously, GST activity is present in many different cell types. However,
the liver is the major tissue for GSH conjugation. GST activities in rat and mouse cytosolic
fractions were measured using l-chloro-2,4-dinitrobenzene, a GST substrate that is nonspecific
3-45
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for particular isoforms (Lash et al., 1998b). Specific activities (normalized for protein content)
in whole-kidney cytosol were slightly less than those in the liver (0.64 compared to 0.52 mU/mg
protein for males and females). However, the much larger mass of the liver compared to the
kidney indicates that far more total GST activity resides in the liver. This is consistent with in
vitro data on TCE conjugation to DCVG, discussed previously (see Tables 3-23 and 3-24). For
instance, in humans, rats, and mice, liver cytosol exhibits greater DCVG production than kidney
cytosol. Distinct high- and low-affinity metabolic profiles were observed in the liver but not in
the kidney (see Table 3-24). In microsomes, human liver and kidney had similar rates of DCVG
production, while for rats and mice, the production in the liver was substantially greater.
According to studies by Lash et al. (1998a: 1998b), the activity of GGT, the first step in the
conversion of DCVG to DCVC, is much higher in the kidney than the liver of mice, rats, and
humans, with most of the activity being concentrated in the microsomal, rather than the
cytosolic, fraction of the cell (see Table 3-26). In rats, this activity is quite high in the kidney but
is below the level of detection in the liver, while the relative kidney-to-liver levels in humans and
mice were higher by 18- and up to 2,300-fold, respectively. Similar qualitative findings were
also reported in another study (Hinchman and Ballatori, 1990) when total organ GGT levels were
compared in several species (see Table 3-27). Cysteinylglycine dipeptidase was also
preferentially higher in the kidney than the liver of all tested species although the interorgan
differences in this activity (one-ninefold) seemed to be less dramatic than for GGT (see
Table 3-27). High levels of both GGT and dipeptidases have also been reported in the small
intestine of rat (Kozak and Tate, 1982) and mouse (Habib et al., 1996), as well as GGT in the
human jejunum (Fairman et al., 1977). No specific human intestinal Cysteinylglycine
dipeptidase has been identified; however, a related enzyme (EC 3.4.13.11) from human kidney
microsomes has been purified and studied (Adachi et al., 1989), while several human intestinal
dipeptidases have been characterized including a membrane dipeptidase (EC 3.4.13.19), which
has a wide dipeptide substrate specificity including Cysteinylglycine (Ristoff and Larsson, 2007;
Hooper etal., 1994).
3-46
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Table 3-26. GGT activity in liver and kidney subcellular fractions of mice,
rats, and humans
Species
Mouse
Rat
Human
Sex
Male
Female
Male
Female
Male
Tissue
Liver
Kidney
Liver
Kidney
Liver
Kidney
Liver
Kidney
Liver
Kidney
Cellular fraction
Cytosol
Microsomes
Cytosol
Microsomes
Cytosol
Microsomes
Cytosol
Microsomes
Cytosol
Microsomes
Cytosol
Microsomes
Cytosol
Microsomes
Cytosol
Microsomes
Cytosol
Microsomes
Cytosol
Microsomes
Activity (mil/nig)
0.07 ± 0.04
0.05 ± 0.04
1.63 ±0.85
92.6 ± 15.6
0.10 ±0.10
0.03 ± 0.03
0.79 ±0.79
69.3 ± 14.0
<0.02
O.02
<0.02
1,570 ±100
<0.02
O.02
<0.02
1,840 ± 40
8.89 ±3. 58
29
13.2 ±1.0
960 ± 77
Sources: Lash et al. (1999a; 1998a)
Table 3-27. Multispecies comparison of whole-organ activity levels of GGT
and dipeptidase
Species
Rat
Mouse
Rabbit
Guinea pig
Pig
Macaque
Whole organ enzyme activity (umol substrate/organ)
Kidney
GGT
1,010 ±41
60.0 ±4.2
1,119 ±186
148 ± 13
3,800 ± 769
988
Dipeptidase
20.2 ±1.1
3.0 ±0.3
112±17
77 ±10
2,428 ± 203
136
Liver
GGT
7.1 ±1.4
0.47 ±0.05
71.0±9.1
46.5 ±4.2
1,600 ± 255
181
Dipeptidase
6.1 ±0.4
1.7 ±0.2
12.6 ±1.0
13.2 ±1.5
2,178 ±490
71
Source: Hinchman and Ballatori (1990).
3.3.3.2.7. Sex- and species-dependent differences in GSH metabolism
Diverse sex and species differences appear to exist in TCE metabolism via the GSH
pathway. In rodents, rates of TCE conjugation to GSH in male rats and mice are higher than
3-47
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females (see Table 3-23). Verma and Rana (2003) reported twofold higher GST activity values
in liver cytosol of female rats, compared to males, given 15 i.p. injections of TCE over 30 days
period. This effect may be due to sex-dependent variation in induction, as GST activities in male
and female controls were similar. DCVG formation rates by liver and kidney subcellular
fractions were much higher in both sexes of mice than in rats and, except for mouse kidney
microsomes, the rates were generally higher in males than in females of the same species (see
Table 3-23).
In terms of species differences, comparisons at 1-2 mM TCE concentrations (see
Table 3-23) suggest that, in liver and kidney cytosol, the greatest DCVG production rate was in
humans, followed by mice and then rats. However, different investigators have reported
considerably different rates for TCE conjugation in human liver and kidney cell fractions. For
instance, values in Table 3-23 from Lash et al. (1999b) are between 2 and 5 orders of magnitude
higher than those reported by Green et al. (1997a). The rates of DCVG formation by liver
cytosol from male F344 rat, male B6C3Fi mouse, and human were 1.62, 2.5, and 0.19 pmol/
minute/mg protein, respectively, while there was no measurable activity in liver microsomes or
subcellular kidney fractions (Green et al., 1997a). The reasons for such discrepancies are unclear
but may be related to different analytical methods employed such as detection of radiolabled
substrate vs. derivatized analytes (Lash et al., 2000a).
Expression of GGT activity does not appear to be influenced by sex (see Table 3-26); but
species differences in kidney GGT activity are notable with rat subcellular fractions exhibiting
the highest levels and mice and humans exhibiting about 4-6 and 50%, respectively, of rat levels
(Lash et al., 1999a: Lash et al., 1998a). Table 3-27 shows measures of whole-organ GGT and
dipeptidase activities in rats, mice, guinea pigs, rabbits, pigs, and monkeys. These data show
that the whole kidney possesses higher activities than liver for these enzymes, despite the
relatively larger mass of the liver.
As discussed above, the three potential bioactivating pathways subsequent to the
formation of DCVC are catalyzed by p-lyase, FMO3, or CYP3A. Lash et al. (2000a) compared
in vitro P-lyase activities and kinetic constants (when available) for kidney of rats, mice, and
humans. They reported that variability of these values spans up to two orders of magnitude
depending on substrate, analytical method used, and research group. Measurements of rat,
mouse, and human P-lyase activities collected by the same researchers following tetrachloro-
ethylene exposure (Green et al., 1990) resulted in higher KM and lower VMAX values for mice and
humans than rats. Further, female rats exhibited higher KM and lower VMAX values than males.
With respect to FMO3, Ripp et al. (1999) found that this enzyme appeared catalytically
similar across multiple species, including humans, rats, dogs, and rabbits, with respect to several
substrates, including DCVC, but that there were species differences in expression. Specifically,
in male liver microsomes, rabbits had 3-fold higher methionine S-oxidase activity than mice and
dogs had 1.5-fold higher activity than humans and rats. Species differences were also noted in
3-48
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male and female kidney microsomes; rats exhibited two- to sixfold higher methionine S-oxidase
activity than the other species. Krause et al. (2003) detected DCVC sulfoxidation in incubations
with human liver microsomes but did not in an incubation with a single sample of human kidney
microsomes. However, FMO3 expression in the 26 human kidney samples was found to be
highly variable, with a range of five- to sixfold (Krause et al., 2003).
No data on species differences in CYP3A-mediated sulfoxidation of NAcDCVC are
available. However, Altuntas et al. (2004) examined sulfoxidation of cysteine and mercapturic
acid conjugates of fluoromethyl-2,2-difluoro-l-(trifluoromethyl)vinyl ether (FDVE) in rat and
human liver and kidney microsomes. They reported that the formation of sulfoxides from the
mercapturates TV-Ac-FF VC and (Z)-TV-Ac-FFVC (FFVC is (E,Z)-S-(l-fluoro-2-fluoromethoxy-
2-(trifluoromethyl)vinyl-Lcysteine) were greatest in rat liver microsomes, and 2-30-fold higher
than in human liver microsomes (which had high variability). Sulfoxidation of 7V-Ac-FFVC
could not be detected in either rat or human kidney microsomes, but sulfoxidation of
(Z)-TV-Ac-FFVC was detected in both rat and human kidney microsomes at rates comparable to
human liver microsomes. Using human- and rat-expressed CYP3A, Altuntas et al. (2004)
reported that rates of sulfoxidation of (Z)-TV-Ac-FFVC were comparable in human CYP3A4 and
rat CYP3 Al and CYP3 A2, but that only rat CYP3 Al and A2 catalyzed sulfoxidation of
7V-Ac-FFVC. As the presence or absence of the species differences in mercapturate sulfoxidation
appears to be highly chemical-specific, no clear inferences can be made as to whether species
differences exist for sulfoxidation of NAcDCVC
Also relevant to assess the flux through the various pathways are the rates of 7V-acety-
lation and de-acetylation of DCVC. This is demonstrated by the results of Elfarra and Hwang
(1990) using S-(2-benzothiazolyl)-L-cysteine as a marker for p-lyase metabolism in rats, mice,
hamsters, and guinea pigs. Guinea pigs exhibited about twofold greater flux through the P-lyase
pathway, but this was not attributable to higher P-lyase activity. Rather, guinea pigs have
relatively low 7V-acetylation and high deacetylation activities, leading to a high level of substrate
recirculation (Lauetal., 1995). Thus, a high 7V-deacetylase:7V-acetylase activity ratio may favor
DCVC recirculation and subsequent metabolism to reactive species. In human, Wistar rat,
Fischer rat, and mouse cytosol, deacetylation rates for NAcDCVC varied less than threefold
(0.35, 0.41, 0.61, and 0.94 nmol DCVC formed/minute/mg protein in humans, rats, and mice)
(Birner et al., 1993). However, similar experiments have not been carried out for 7V-acetylation
of DCVC, so the balance between its 7V-acetylation and de-acetylation has not been established.
3.3.3.2.8. Human variability and susceptibility in GSH conjugation
Knowledge of human variability in metabolizing TCE through the GSH pathway is
limited to in vitro comparisons of variance in GST activity rates. Unlike CYP-mediated
oxidation, quantitative differences in the polymorphic distribution or activity levels of GST
isoforms in humans are not presently known. However, the available data (Lash et al., 1999a:
3-49
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Lash et al., 1999b) do suggest that significant variation in GST-mediated conjugation of TCE
exists in humans. In particular, at a single substrate concentration of 1 mM, the rate of GSH
conjugation of TCE in human liver cytosol from 9 male and 11 females spanned a range of
2.4-fold (34.7-83.6 nmol DCVG formed/20-minute/mg protein) (Lash et al.. 1999a). In liver
microsomes from 5 males and 15 females, the variation in activity was 6.5-fold (9.9-64.6 nmol
DCVG formed/20 minute/mg protein). No sex-dependent variation was identified. Despite
being less pronounced than the known variability in human CYP-mediated oxidation, the impact
on risk assessment of the variability in GSH conjugation to TCE is currently unknown especially
in the absence of data on variability for 7V-acetylation and bioactivation via p-lyase, FMO3, or
CYP3A in the human kidney.
3.3.3.3. Relative roles of the CYP and GSH pathways
In vivo mass balance studies in rats and mice, discussed above, have shown
unequivocally that in these species, CYP oxidation of TCE predominates over GSH conjugation.
In these species, at doses of 2-2,000 mg/kg of [14C]-TCE, the sum of radioactivity in exhaled
TCE, urine, and exhaled CC>2 constitutes 69-94% of the dose, with the vast majority of the
radioactivity in urine (95-99%) attributable to oxidative metabolites (Dekant et al., 1986b: Green
andProut 1985; Prout etal., 1985; Dekant etal., 1984). The rest of the radioactivity was found
mostly in feces and the carcass. More rigorous quantitative limits on the amount of GSH
conjugation based on in vivo data such as these can be obtained using PBPK models, discussed
in Section 3.5.
Comprehensive mass-balance studies are unavailable in humans. DCVG and DCVC in
urine have not been detected in any species, while the amount of urinary NAcDCVC from
human exposures is either below detection limits or very small from a total mass balance point of
view (Bloemen et al.. 2001: Lash et al.. 1999b: Bernauer et al.. 1996: Birner et al.. 1993). For
instance, the ratio of primary oxidative metabolites (TCA + TCOH) to NAcDCVC in urine of
rats and humans exposed to 40-160 ppm (215-860 mg/m3) TCE heavily favored oxidation,
resulting in ratios of 986-2,562:1 in rats and 3,292-7,163:1 in humans (Bernauer et al., 1996).
Bloemen et al. (2001) reported that, at most, 0.05% of an inhaled TCE dose would be excreted as
NAcDCVC, and concluded that this suggested that TCE metabolism by GSH conjugation was of
minor importance. While it is a useful biomarker of exposure and an indicator of GSH
conjugation, NAcDCVC may capture only a small fraction of TCE flux through the GSH
conjugation pathway due to the dominance of bioactivating pathways (Lash et al., 2000a).
A number of lines of evidence suggest that the amount of TCE conjugation to GSH in
humans, while likely smaller than the amount of oxidation, may be much more substantial than
analysis of urinary mercapturates would suggest. In Table 3-28, in vitro estimates of the VMAX,
KM, and clearance (VMAX/KM) for hepatic oxidation and conjugation of TCE are compared in a
manner that accounts for differences in cytosolic and microsomal partitioning and protein
3-50
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content. Surprisingly, the range of in vitro kinetic estimates for oxidation and conjugation of
TCE substantially overlap, suggesting similar flux through each pathway, though with high
interindividual variation. The microsomal and cytosolic protein measurements of GSH
conjugation should be caveated by the observation by Lash et al. (1999b) that GSH conjugation
of TCE was inhibited by -50% in the presence of oxidation. Note that this comparison cannot be
made in rats and mice because in vitro kinetic parameters for GSH conjugation in the liver are
not available in those species (only activity at 1 or 2 mM have been measured).
Table 3-28. Comparison of hepatic in vitro oxidation and conjugation of
TCEa
Cellular or
subcellular
fraction
Hepatocytes
Liver
microsomes
Liver cytosol
v b
VMAX
(nmol TCE metabolized/min/g
tissue)
Oxidation
10.0-68.4
6.1-111
-
-
GSH
conjugation
16-25
45
380
KM
(uM in blood)
Oxidation
22.1-198
2.66-11.1*
71.0-297**
-
-
GSH
conjugation
16-47
5.9*
157**
4.5*
22.7**
VMAX/KM
(mL/min/g tissue)
Oxidation
0.087-1.12
1.71-28.2*
0.064-1.06**
-
-
GSH
conjugation
0.55-1.0
7.6*
0.29**
84*
16.7**
aWhen biphasic metabolism was reported, only high affinity pathway is shown here.
bConversion assumptions for VMAX: hepatocellularity of 99 million cells/g liver (Barter et al.. 2007): liver
microsomal protein content of 32 mg protein/g tissue (Barter et al.. 2007): and liver cytosolic protein content of 89
mg protein/g tissue (based on rats: Prasanna et al. (1989): van Bree et al. (1990).
Conversion assumptions for KM:
For hepatocytes, KM in headspace converted to KM in blood using blood:air partition coefficient of 9.5 (reported
range of measured values 6.5-12.1, Table 3-1);
For microsomal protein, option (*) assumes KM in medium is equal to KM in tissue, and converts to KM in blood by
using a liverblood partition coefficient of 5 (reported ranges of measured values 3.6-5.9, Table 3-8), and option
(**) converts KM in medium to KM in air using the measured microsomal protein:air partition coefficient of 1.78
(Lipscomb etal.. 1997). and then converts to KM in blood by using the blood:air partition coefficient of 9.5; and
For cytosolic protein, option (*) assumes KM in medium is equal to KM in tissue, and converts to KM in blood by
using a liverblood partition coefficient of 5 (reported ranges of measured values 3.6-5.9, Table 3-8), and option
(**) assumes KM in medium is equal to KM in blood, so no conversion is necessary.
Furthermore, as shown earlier in Table 3-22, the human in vivo data of Lash et al.
(1999b) show blood concentrations of DCVG similar, on a molar basis, to those of TCE, TCA,
or TCOH, suggesting substantial conjugation of TCE. In addition, these data give a lower limit
as to the amount of TCE conjugated. In particular, by multiplying the peak blood concentration
of DCVG by the blood volume, a minimum amount of DCVG in the body at that time can be
derived (i.e., assuming the minimal empirical distribution volume equal to the blood volume).
As shown in Table 3-29, this lower limit amounts to about 0.4-3.7% of the inhaled TCE dose.
Since this is the minimum amount of DCVG in the body at a single time point, the total amount
of DCVG formed is likely to be substantially greater, owing to possible distribution outside of
3-51
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the blood as well as the metabolism and/or excretion of DCVG. Lash et al. (1999b) found that
levels of urinary mercapturates were near or below the level of detection of 0.19 jiM, results that
are consistent with those of Bloemen et al. (2001), who reported urinary concentrations below
0.04 jiM at two- to fourfold lower cumulative exposures. Taken together, these results confirm
the suggestion by Lash et al. (2000a) that NAcDCVC is a poor quantitative marker for the flux
through the GSH pathway.
Table 3-29. Estimates of DCVG in blood relative to inhaled TCE dose in
humans exposed to 50 and 100 ppm (269 and 537 mg/m3) (Lash et al., 1999b)
Sex exposure
Estimated inhaled TCE dose
(mmol)a
Estimated peak amount of DCVG in blood
(mmol)b
Males
50 ppm x 4 hrs
100 ppm x 4 hrs
3.53
7.07
0.11+0.08
0.26 + 0.08
Females
50 ppm x 4 hrs
100 ppm x 4 hrs
2.36
4.71
0.010 + 0
0.055+0.027
alnhaled dose estimated by (50 or 100 ppm)/(24,450 ppm/mM) x (240 minutes) x QP, where alveolar ventilation rate
QP is 7.2 L/minute for males and 4.8 L/minute for females. QP is calculated as (VT - VD) x fR with the following
respiratory parameters: tidal volume VT (0.75 L for males, 0.46 L for females), dead space VD (0.15 L for males,
0.12 L for females), and respiration frequency fR (12 minutes"1 for males, 14 minutes"1 for females) [assumed sitting,
awake from The International Commission on Radiological Protection (ICRP. 2003)1.
bPeak amount of DCVG in blood estimated by multiplying the peak blood concentration by the estimated blood
volume: 5.6 L in males and 4.1 L in females (ICRP. 2003).
Sources: Fisher et al. (1998): Lash et al. (1999b).
However, as discussed in Section 3.3.3.2.1, data from other laboratories have reported
substantially lower amounts of GSH conjugation in vitro. The reasons for such discrepancies are
unclear, but they may be related to different analytical methods (Lash et al., 2000a). More recent
in vivo data from Kim et al. (2009) in mice reported -1,000 times lower DCVG in mouse serum
as compared to the levels of DCVG reported by Lash et al. (1999b) in human blood. These data
are consistent with the suggestion that the —Reednethod" employed by Lash et al. (1999b)
overestimated DCVG levels in humans. However, the degree of overestimation is unclear, as is
the degree to which differences may be attributable to true inter-species or inter-individual
variability.
In summary, TCE oxidation is likely to be greater quantitatively than conjugation with
GSH in mice, rats, and humans. Some evidence suggests that the flux through the GSH pathway,
particularly in humans, may be greater by an order of magnitude or more than the <0.1%
typically excreted of NAcDCVC in urine. This is evidenced both by a direct comparison of in
vitro rates of oxidation and conjugation, as well as by in vivo data on the amount of DCVG in
3-52
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blood. PBPK models can be used to more quantitatively synthesize these data and put more
rigorous limits on the relative amounts of TCE oxidation and conjugation with GSH. Such
analyses are discussed in Section 3.5. However, these data are not consistent with studies in
other laboratories using different analytical methods, which report 2-5 orders of magnitude
lower estimates of GSH conjugation. Because the reason for these differences have not been
fully determined, substantial uncertainty remains in the degree of GSH conjugation, particularly
in humans.
3.4. TCE EXCRETION
This section discusses the major routes of excretion of TCE and its metabolites in exhaled
air, urine, and feces. Unmetabolized TCE is eliminated primarily via exhaled air. As discussed
in Section 3.3, the majority of TCE absorbed into the body is eliminated by metabolism. With
the exception of CC>2, which is eliminated solely via exhalation, most TCE metabolites have low
volatility and, therefore, are excreted primarily in urine and feces. Although trace amounts of
TCE metabolites have also been detected in sweat and saliva (Bartonicek, 1962), these excretion
routes are likely to be relatively minor.
3.4.1. Exhaled Air
In humans, pulmonary elimination of unchanged TCE and other volatile compounds is
related to ventilation rate, cardiac output, and the solubility of the compound in blood and tissue,
which contribute to final exhaled air concentration of TCE. In their study of the impact of
workload on TCE absorption and elimination, Astrand and Ovrum (1976) characterized the
postexposure elimination of TCE in expired breath. TCE exposure (540 or 1,080 mg/m3; 100 or
200 ppm) was for a total of 2 hours, at workloads of 0-150 watts. Elimination profiles were
roughly equivalent among groups, demonstrating a rapid decline in TCE concentrations in
expired breath postexposure (see Table 3-30).
Table 3-30. Concentrations of TCE in expired breath from inhalation-
exposed humans (Astrand, 1982)
Time postexposure
0 min
30 min
60 min
90 min
120 min
300 min
420 min
19hrs
Alveolar air
r
459 ± 44
70 ±5
40 ±4
35 ±9
31±8
8±1
5 ±0.5
2 ±0.3
II
244 ± 16
51±3
28 ±2
21 ±1
16 ±1
9±2
4 ±0.5
2 ±0.2
III
651 ±53
105 ±18
69 ±8
55 ±2
45 ±1
14 ±2
8 ±1.3
4 ±0.5
aRoman numerals refer to groups assigned different workloads; concentrations are in mg/m3 for expired air.
3-53
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The lung clearance of TCE represents the volume of air from which all TCE can be
removed per unit time, and is a measure of the rate of excretion via the lungs. Monster et al.
(1976) reported lung clearances ranging from 3.8 to 4.9 L/minute in four adults exposed at rest to
70 and 140 ppm of TCE for 4 hours. Pulmonary ventilation rates in these individuals at rest
ranged from 7.7 to 12.3 L/minute. During exercise, when ventilation rates increased to 29-
30 L/minute, lung clearance was correspondingly higher, 7.7-12.3 L/minute. Under single and
repeated exposure conditions, Monster et al. (1979: 1976) reported that 7-17% of absorbed TCE
was excreted in exhaled breath. Pulmonary elimination of unchanged TCE at the end of
exposure is a first-order diffusion process across the lungs from blood into alveolar air, and it can
be thought of as the reversed equivalent of its uptake from the lungs. Exhaled pulmonary
excretion occurs in several distinct (delayed) phases corresponding to release from different
tissue groups, at different times. Sato et al. (1977) detected three first-order phases of pulmonary
excretion in the first 10 hours after exposure to 100 ppm for 4 hours, with fitted half-times of
pulmonary elimination of 0.04, 0.67, and 5.6 hours, respectively. Opdam (1989) sampled
alveolar air up to 20-310 hours after 29-62-minute exposures to 6-38 ppm, and reported
terminal half-lives of 8-44 hours at rest. Chiu et al. (2007) sampled alveolar air up to 100 hours
after 6-hour exposures to 1 ppm and reported terminal half-lives of 14-23 hours. The long
terminal half-time of TCE pulmonary excretion indicates that considerable time is necessary to
completely eliminate the compound, primarily due to the high partitioning to adipose tissues (see
Section 3.2).
As discussed above, several studies (Green and Prout 1985; Prout et al., 1985; Dekant et
al., 1984) have investigated the disposition of [14C]-TCE in rats and mice following gavage
administrations (see Section 3.3.2). These studies have reported CC>2 as an exhalation excretion
product in addition to unchanged TCE. With low doses, the amount of TCE excreted unchanged
in exhaled breath is relatively low. With increasing dose in rats, a disproportionately increased
amount of radiolabel is expired as unchanged TCE. This may indicate saturation of metabolic
activities in rats at doses >200 mg/kg, which is perhaps only minimally apparent in the data from
mice. In addition, exhaled air TCE concentration has been measured after constant inhalation
exposure for 2 hours to 50 or 500 ppm in rats (Dallas et al., 1991), and after dermal exposure in
rats and humans (Poet et al., 2000). Exhaled TCE data from rodents and humans have been
integrated into the PBPK model presented in Section 3.5.
Finally, TCOH is also excreted in exhaled breath, though at a rate about 10,000-fold
lower than unmetabolized TCE (Monster, 1979; Monster et al., 1976).
3.4.2. Urine
Urinary excretion after TCE exposure consists predominantly of the metabolites, TCA
and TCOH, with minor contributions from other oxidative metabolites and GSH conjugates.
3-54
-------
Measurements of unchanged TCE in urine have been at or below detection limits (e.g., Chiu et
al., 2007; Fisher etal., 1998). The recovery of urinary oxidative metabolites in mice, rats, and
humans was addressed earlier (see Section 3.3.2) and will not be discussed here. Because of
their relatively long elimination half-life, urinary oxidative metabolites have been used as an
occupational biomarker of TCE exposure for many decades (Carrieri et al., 2007; Ikeda and
Imamura, 1973). Ikeda and Imamura (1973) measured TTCs, TCOH, and TCA in urine over
3 consecutive postexposure days for four exposure groups totaling 24 adult males and one
exposure group comprising 6 adult females. The elimination half-lives for TTC were 26.1-
48.8 hours in males and 50.7 hours in females. The elimination half-lives for TCOH were
15.3 hours in the only group of males studied and 42.7 hours in females. The elimination half-
lives for TCA were 39.7 hours in the only group of males studied and 57.6 hours in females.
These authors compared their results to previously published elimination half-lives for TTC,
TCOH, and TCA. Following experimental exposures of groups of two-five adults, elimination
half-lives were 31-50 hours for TTC, 19-29 hours for TCOH, and 36-55 hours for TCA
(Nomiyama and Nomiyama, 1971; Ogata etal., 1971; Stewart et al., 1970; Bartonicek, 1962).
The urinary elimination half-lives of TCE metabolites in a subject who worked with and was
addicted to sniffing TCE for 6-8 years approximated 49.7 hours for TCOH, 72.6 hours for TCA,
and 72.6 hours for TTC (Ikeda etal.. 1971).
The quantitative relationship between urinary concentrations of oxidative metabolites and
exposure in an occupational setting was investigated by Ikeda (1977). This study examined the
urinary elimination of TCE and metabolites in urine of 51 workers from 10 workshops. The
concentration of TCA and TCOH in urine demonstrated a marked concentration-dependence,
with concentrations of TCOH being approximately twice as high as those for TCA. Urinary
half-life values were calculated for six males and six females from five workshops; males were
intermittently exposed to 200 ppm and females were intermittently exposed to 50 ppm
(269 mg/m3). Urinary elimination half-lives for TTC, TCOH, and TCA were 26.1, 15.3, and
39.7 hours in males, respectively, and 50.7, 42.7 and 57.6 hours in females, respectively, which
were similar to the range of values previously reported. These authors estimated that urinary
elimination of parent TCE during exposure might account for one-third of the systemically
absorbed dose. Importantly, urinary TCA exhibited marked saturation at exposures >50 ppm.
Because neither TTC nor urinary TCOH (in the form of the glucuronide TCOG) showed such an
effect, this saturation cannot be due to TCE oxidation itself, but must rather be from one of the
metabolic processes forming TCA from TCOH. Unfortunately, since biological monitoring
programs usually measure only urinary TCA, rather than TTC, urinary TCA levels above around
150 mg/L cannot distinguish between exposures at 50 ppm and at much higher concentrations.
It is interesting to attempt to extrapolate on a cumulative exposure basis the Ikeda (1977)
results for urinary metabolites obtained after occupational exposures at 50 ppm to the controlled
exposure study by Chiu et al. (2007) at 1.2 ppm for 6 hours (the only controlled exposure study
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for which urinary concentrations, rather than only cumulative excretion, are available). Ikeda
(1977) reported that measurements were made during the second half of the week, so one can
postulate a cumulative exposure duration of 20-40 hours. At 50 ppm, Ikeda (1977) report a
urinary TCOH concentration of about 290 mg/L, so that per ppm-hour, the expected urinary
concentration would be 290/(50 x 20 ~ 40) = 0.145 ~ 0.29 mg/L-ppm-hour. The cumulative
exposure in Chiu et al. (2007) is 1.2 x 6 = 7.2 ppm-hour, so the expected urinary TCOH
concentration would be 7.2 x (0.145 ~ 0.29) = 1.0-2.1 mg/L. This estimate is somewhat
surprisingly consistent with the actual measurements of Chiu et al. (2007) during the first day
postexposure, which ranged from 0.8 to -1.2 mg/L TCOH in urine.
On the other hand, extrapolation of TCA concentrations was less consistent. At 50 ppm,
Ikeda (1977) report a urinary TCA concentration of about 140 mg/L, so that per ppm-hour, the
expected urinary concentration would be 140/(50 x 20 - 40) = 0.07 - 0.14 mg/L-ppm-hour. The
cumulative exposure in Chiu et al. (2007) is 1.2 x 6 = 7.2 ppm-hour, so the expected urinary
TCA concentration would be 7.2 x (0.07 - 0.14) = 0.5 - 1.0 mg/L, whereas Chiu et al. (2007)
reported urinary TCA concentrations on the first day after exposure of 0.03-0.12 mg/L.
However, as noted in Chiu et al. (2007), relative urinary excretion of TCA was 3-10-fold lower
in Chiu et al. (2007) than other studies at exposures of 50-140 ppm, which may explain part of
the discrepancies. However, this may be due, in part, to saturation of many urinary TCA
measurements, and, furthermore, interindividual variance, observed to be substantial in Fisher
et al. (1998), cannot be ruled out.
Urinary elimination kinetics have been reported to be much faster in rodents than in
humans. For instance, adult rats were exposed to 50, 100, or 250 ppm (269, 537, or
1,344 mg/m3) via inhalation for 8 hours or were administered an i.p. injection (1.47 g/kg) and the
urinary elimination of TTCs was followed for several days (Ikeda and Imamura, 1973). These
authors calculated urinary elimination half-lives of 14.3-15.6 hours for female rats and 15.5-
16.6 hours for male rats; the route of administration did not appear to influence half-life value.
In other rodent experiments using orally administered radiolabeled TCE, urinary elimination was
complete within 1 or 2 days after exposure (Green and Prout, 1985; Prout et al., 1985; Dekant et
al., 1984).
3.4.3. Feces
Fecal elimination accounts for a small percentage of TCE as shown by limited
information in the available literature. Bartonicek (1962) exposed seven volunteers to 1.042 mg
TCE/L air for 5 hours and examined TCOH and TCA in feces on the 3rd and 7th day following
exposure. The mean amount of TCE retained during exposure was 1,107 mg, representing 51-
64% (mean 58%) of administered dose. On the 3rd day following TCE exposure, TCOH and
TCA in feces demonstrated mean concentrations of 17.1 and 18.5 mg/100 g feces, similar to
concentrations in urine. However, because of the 10-fold smaller daily rate of excretion of feces
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relative to urine, this indicates fecal excretion of these metabolites is much less significant than
urinary excretion. Neither TCOH nor TCA was detected in feces on the 7th day following
exposure.
In rats and mice, total radioactivity has been used to measure excretion in feces after
gavage TCE administration in corn oil, but since the radiolabel was not characterized, it is not
possible to determine whether the radiolabel in feces represented unabsorbed parent compound,
excreted parent compound, and/or excreted metabolites. Dekant et al. (1984) reported that mice
eliminated 5% of the total administered TCE, while rats eliminated 2% after gavage. Dekant
et al. (1986b) reported a dose-response-related increase in fecal elimination with dose, ranging
between 0.8 and 1.9% in rats and between 1.6 and 5% in mice after gavage in corn oil. Due to
the relevant role of CYP2E1 in the metabolism of TCE (see Section 3.3.3.1.6), Kim and
Ghanayem (2006) compared fecal elimination in both wild-type and CYP2E1 knockout mice and
reported fecal elimination ranging between 4.1 and 5.2% in wild-type and between 2.1 and 3.8%
in knockout mice exposed by gavage in aqueous solution.
3.5. PBPK MODELING OF TCE AND ITS METABOLITES
3.5.1. Introduction
PBPK models are extremely useful tools for quantifying the relationship between
external measures of exposure and internal measures of lexicologically relevant dose. In
particular, for the purposes of this assessment, PBPK models are evaluated for the following:
(1) providing additional quantitative insights into the ADME of TCE and metabolites described
in the sections above; (2) cross-species pharmacokinetic extrapolation of rodent studies of both
cancer and noncancer effects; (3) exposure-route extrapolation; and (4) characterization of
human pharmacokinetic variability. The following sections first describe and evaluate previous
and current TCE PBPK modeling efforts, then discuss the insights into ADME (1, above), and
finally present conclusions as to the utility of the model to predict internal doses for use in dose-
response assessment (2-4, above).
3.5.2. Previous PBPK Modeling of TCE for Risk Assessment Application
TCE has an extensive number of both in vivo pharmacokinetic and PBPK modeling
studies [see Chiu et al. (2006b) supplementary material, for a review]. Models previously
developed for occupational or industrial hygiene applications are not discussed here but are
reviewed briefly in Clewell et al. (2000). Models designed for risk assessment applications have
focused on descriptions of TCE and its major oxidative metabolites, TCA, TCOH, and TCOG.
Most of these models were extensions of the —first generatin" of models developed by Fisher
and coworkers (Allen and Fisher, 1993; Fisher et al., 1991) in rats, mice, and humans. These
models, in turn, are based on a Ramsey and Andersen (1984) structure with flow-limited tissue
compartments and equilibrium gas exchange, saturable Michaelis-Menten kinetics for oxidative
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metabolism, and lumped volumes for the major circulating oxidative metabolites TCA and
TCOH. Fisher and coworkers updated their models with new in vivo and in vitro experiments
performed in mice (Greenberg et al., 1999; Abbas and Fisher, 1997) and volunteers (Fisher et al.,
1998) and summarized their findings in Fisher (2000). Clewell et al. (2000) added enterohepatic
recirculation of TCOG and pathways for local oxidative metabolism in the lung and GST
metabolism in the liver. While Clewell et al. (2000) does not include the updated Fisher (2000)
data, they have used a wider set of in vivo and in vitro mouse, rat, and human data than previous
models. Finally, Bois (2000a, b) performed reestimations of PBPK model parameters for the
Fisher and Clewell models using a Bayesian population approach [Gelman (1996), and discussed
further below].
As discussed in Rhomberg (2000), the choice as to whether to use the Fisher, Clewell,
and/or Bois models for cross-species extrapolation of rodent cancer bioassays led to quantitative
results that differed by as much as an order of magnitude. There are a number of differences in
modeling approaches that can explain their differing results. First, the Clewell et al. (2000)
model differed structurally in its use of single-compartment volume-of-distribution models for
metabolites as opposed to the Fisher (Fisher, 2000) models, which use multiple physiologic
compartments. Also, the Clewell et al. (2000) model, but not the Fisher models, includes
enterohepatic recirculation of TCOH/TCOG (although reabsorption was set to zero in some
cases). In addition to structural differences in the models, the input parameter values for these
various models were calibrated using different subsets of the overall in vivo database [see Chiu
et al. (2006b), supplementary material, for a review]. The Clewell et al. (2000) model is based
primarily on a variety of data published before 1995; the Fisher (2000) models were based
primarily on new studies conducted by Fisher and coworkers (after 1997); and the Bois (2000a,
b) reestimations of the parameters for the Clewell et al. (2000) and Fisher (2000) models used
slightly different data sets than the original authors. The Bois (2000a, b) reanalyses also led to
somewhat different parameter estimates than the original authors, both because of the different
data sets used as well as because the methodology used by Bois allowed many more parameters
to be estimated simultaneously than were estimated in the original analyses.
Given all of these methodological differences, it is not altogether surprising that the
different models led to different quantitative results. Even among the Fisher models themselves,
Fisher (2000) noted some inconsistencies, including differing estimates for metabolic parameters
between mouse gavage and inhalation experiments. These authors included possible
explanations for these inconsistencies: the impact of corn oil vehicle use during gavage (Staats et
al., 1991) and the impact of a decrease in ventilation rate in mice due to sensory irritation during
the inhalation of solvents [e.g., Stadler and Kennedy (1996)1.
As discussed in a report by the National Research Council (NRC, 2006), several
additional PBPK models relevant to TCE pharmacokinetics have been published since 2000 and
are reviewed briefly here. Poet et al. (2000) incorporated dermal exposure to TCE in PBPK
3-58
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models in rats and humans, and published in vivo data in both species from dermal exposure
(Poet et al.. 2000: Thrall and Poet 2000). Albanese et al. (2002) published a series of models
with more complex descriptions of TCE distribution in adipose tissue but did not show
comparisons with experimental data. Simmons et al. (2002) developed a PBPK model for TCE
in the Long-Evans rat that focused on neurotoxicity endpoints and compared model predictions
with experimentally determined TCE concentrations in several tissues, including the brain. Keys
et al. (2003) investigated the lumping and unlumping of various tissue compartments in a series
of PBPK models in the rat and compared model predictions with TCE tissue concentrations in a
multitude of tissues. Although none of these TCE models included metabolite descriptions, the
experimental data were available for either model or evaluation. Finally, Keys et al. (2004)
developed a model for DCA in the mouse that included a description of suicide inhibition of
GST-zeta, but this model was not been linked to TCE.
3.5.3. Development and Evaluation of an Interim "Harmonized" TCE PBPK Model
Throughout 2004, EPA and the U.S. Air Force jointly sponsored an integration of the
Fisher, Clewell, and Bois modeling efforts (Hack et al., 2006). In brief, a single interim PBPK
model structure combining features from both the Fisher and Clewell models was developed and
used for all three species of interest (mice, rats, and humans). An effort was made to combine
structures in as simple a manner as possible; the evaluation of most alternative structures was left
for future work. The one level of increased complexity introduced was inclusion of species- and
dose-dependent TCA plasma binding, although only a single in vitro study of Lumpkin et al.
(2003) was used as parameter inputs. As part of this joint effort, a hierarchical Bayesian
population analysis using Markov chain Monte Carlo (MCMC) sampling [similar to the Bois
(2000a, b) analyses] was performed on the revised model with a cross-section of the combined
database of kinetic data to provide estimates of parameter uncertainty and variability (Hack et al.,
2006). Particular attention was given to using data from each of the different efforts, but owing
to time and resource constraints, a combined analysis of all data was not performed. The results
from this effort suggested that a single model structure could provide reasonable fits to a variety
of data evaluated for TCE and its major oxidative metabolites TCA, TCOH, and TCOG.
However, in many cases, different parameter values—particularly for metabolism—were
required for different studies, indicating significant interindividual or interexperimental
variability. In addition, these authors concluded that dosimetry of DCA, conjugative
metabolites, and metabolism in the lung remained highly uncertain (Hack et al., 2006).
Subsequently, EPA conducted a detailed evaluation of the Hack et al. (2006) model that
included: (1) additional model runs to improve convergence; (2) evaluation of posterior
distributions for population parameters; and (3) comparison of model predictions both with the
data used in the Hack et al. (2006) analysis as well as with additional data sets identified in the
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literature. Appendix A provides the details and conclusions of this evaluation, briefly
summarized in Table 3-31, along with their pharmacokinetic implications.
3.5.4. PBPK Model for TCE and Metabolites Used for This Assessment
3.5.4.1. Introduction
Based on the recommendations of the NRC (2006) as well as additional analysis and
evaluation of the Hack et al. (2006) PBPK model, an updated PBPK model for TCE and
metabolites was developed for use in this risk assessment. The updated model is reported in
Evans et al. (2009) and Chiu et al. (2009), and the discussion below provides some details in
additional to the information in the published articles.
This updated model included modification of some aspects of the Hack et al. (2006)
PBPK model structure, incorporation of additional in vitro and in vivo data for estimating model
parameters, and an updated hierarchical Bayesian population analysis of PBPK model
uncertainty and variability. In the subsections below, the updated PBPK model and baseline
parameter values are described, as well as the approach and results of the analysis of PBPK
model uncertainty and variability. Appendix A provides more detailed descriptions of the model
and parameters, including background on hierarchical Bayesian analyses, model equations,
statistical distributions for parameter uncertainty and variability, data sources for these parameter
values, and the PBPK model code. Additional computer codes containing input files to the
MCSim program are available electronically.
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Table 3-31. Conclusions from evaluation of Hack et al. (2006), and implications for PBPK model development
Conclusion from evaluation of Hack et al. (2006) model
Implications for PBPK model parameters, structure, or data
For some model parameters, posterior distributions were somewhat inconsistent
with the prior distributions.
• For parameters with strongly informative priors (e.g., tissue volumes and
flows), this may indicate errors in the model.
• For many parameters, the prior distributions were based on visual fits to the
same data. If the posteriors are inconsistent, then the priors were
-^appropriately" informative, and, thus, the same data were used twice.
Reevaluation of all prior distributions.
• Update priors for parameters with independent data (physiological
parameters, partition coefficients, in vitro metabolism), looking across all
available data sets.
• For priors without independent data (e.g., many metabolism parameters),
use less informative priors (e.g., log-uniform distributions with wide
bounds) to prevent bias.
Evaluate modifications to the model structure, as discussed below.
A number of data sets involve TCE (i.a., portal vein), TCA (oral, i.v.), and
TCOH (oral, i.v.) dosing routes that are not currently in the model, but could be
useful for calibration.
Additional dosing routes can be added easily.
TCE concentrations in blood, air, and tissues well-predicted only in rats, not in
mice and humans. Specifically:
• In mice, the oral uptake model could not account for the time-course of
several data sets. Blood TCE concentrations after inhalation were
consistently overpredicted.
• In rats, tissue concentrations measured in data not used for calibration were
accurately predicted.
• In humans, blood and air TCE concentrations were consistently
overpredicted in the majority of (but not all) data sets.
In mice, uptake from the stomach compartment (currently zero), but
previously included in Abbas and Fisher (1997). may improve the model
fit.
In mice and humans, additional extrahepatic metabolism, either
presystemic (e.g., in the lung) or postsystemic (e.g., in the kidney) and/or
a wash-in/wash-out effect may improve the model fit.
Total metabolism appears well-predicted in rats and mice based on closed-
chamber data, but required significantly different VMAX values between dose
groups. Total recovery in humans (60-70%) is less than the model would
predict. In all three species, the ultimate disposition of metabolism is uncertain.
In particular, there are uncertainties in attributing the —rissing" metabolism to
• GSH pathway (e.g., urinary mercapturates may only capture a fraction of the
total flux; moreover, in Bernauer et al. (1996). excretion was still on-going
at end of collection period; model does not accurately depict time-course of
mercapturate excretion).
• Other hepatic oxidation (currently attributed to DCA).
• Extrahepatic systemic metabolism (e.g., kidney).
• Presystemic metabolism in the lung.
• Additional metabolism of TCOH or TCA (see below).
Calibration of GSH pathway may be improved by utilizing in vitro data
on liver and kidney GSH metabolism, adding a DCVG compartment to
improve the prediction of the time-course for mercapturate excretion,
and/or using the Lash et al. (1999b) blood DCVG in humans
(necessitating the addition of a DCVG compartment).
Presystemic lung metabolism can only be evaluated if added to the
model (in vitro data exist to estimate the VMAX for such metabolism). In
addition, a wash-in/wash-out effect (e.g., suggested by Greenberg et al.,
(1999) can be evaluated using a continuous breathing model that
separately tracks inhaled and exhaled air, with adsorption/desporption in
the respiratory tract.
Additional elimination pathways for TCOH and TCA can be added for
evaluation.
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Table 3-31. Conclusions from evaluation of Hack et al. (2006), and implications for PBPK model development
(continued)
Conclusion from evaluation of Hack et al. (2006) model
Implications for PBPK model parameters, structure, or data
TCA blood/plasma concentrations were well-predicted following TCE
exposures in all species. However, there may be inaccuracies in the total flux of
TCA production, as well as its disposition.
• In TCA dosing studies, the majority (>50%), but substantially <100%, was
recovered in urine, suggesting significant metabolism of TCA. Although
urinary TCA was well-predicted in mice and humans (but not in rats), if
TCA metabolism is significant, then the current model underestimates the
flux of TCE metabolism to TCA.
• An improved TCOH/TCOG model may also provide better estimates of
TCA kinetics (see below).
TCOH/TCOG concentrations and excretion were inconsistently predicted,
particularly after TCOH dosing.
• In mice and rats, first-order clearance for TCOH glucuronidation was
predicted to be greater than hepatic blood flow, which is consistent with a
first-pass effect that is not currently accounted for.
• In humans, the estimated clearance rate for TCOH glucuronidation was
substantially smaller than hepatic blood flow. However, the presence of
substantial TCOG in blood (as opposed to free TCOH) in the Chiu et al.
(2007) data are consistent with greater glucuronidation than predicted by
the model.
• In TCOH dosing studies, substantially <100% was recovered in urine as
TCOG and TCA, suggesting another metabolism or elimination pathway.
• Additional elimination pathways for TCOH and TCA can be added for
evaluation.
• The addition of a liver compartment for TCOH and TCOG would
permit hepatic first-pass effects to be accounted for, as appears
necessary for mice and rats.
i.v. = intravenous
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3.5.4.2. Updated PBPK Model Structure
The updated TCE PBPK model is illustrated in Figure 3-7, with detailed descriptions of
the model structure, equations, and parameters found in Appendix A (see Section A.4), and the
major changes from the Hack et al. (2006) model described here. The TCE submodel was
augmented by the addition of kidney and venous blood compartments, and an updated
respiratory tract model that included both metabolism and the possibility of local storage in the
respiratory tissue. In particular, in the updated lung, separate processes describing inhalation and
exhalation allowed for adsorption and desorption from tracheobronchial epithelium (wash-in/
wash-out), with the possibility of local metabolism as well. In addition, conjugative metabolism
in the kidney was added, motivated by the in vitro data on TCE conjugation described in
Sections 3.3.3.2-3.3.3.3. With respect to oxidation, a portion of the lung metabolism was
assumed to produce systemically available oxidative metabolites, including TCOH and TCA,
with the remaining fraction assumed to be locally cleared. This is clearly a lumping of a
multistep process, but the lack of data precludes the development of a more sequential model.
TCE oxidation in the kidney was not included because it was not likely to constitute a substantial
flux of total TCE oxidation given the much lower CYP activity in the kidney relative to the liver
(Cummings and Lash, 2000; Cummings et al., 1999) and the greater tissue mass of the liver.2 In
addition, liver compartments were added to the TCOH and TCOG submodels to account
properly for first-pass hepatic metabolism, which is important for consistency across routes of
exposure. Furthermore, additional clearance pathways of TCOH and TCA were added to their
respective submodels. With respect to TCE conjugation, in humans, an additional DCVG
compartment was added between TCE conjugation and production of DCVC. In addition, it
should be noted that the urinary clearance of DCVC represents a lumping of 7V-acetylation of
DCVC, deacetylation of NAcDCVC, and urinary excretion NAcDCVC, and that the
bioactivation of DCVC represents a lumping of thiol production from DCVC by beta-lyase,
sulfoxidation of DCVC by FMO3, and sulfoxidation of NAcDCVC by CYP3A. Such lumping
was used because these processes are not individually identifiable given the available data.
2The extraction ratio for kidney oxidation is likely to be very low, as shown by the following calculation in rats and
humans. In rats, the in vitro kidney oxidative clearance (VMAX/KM) rate (see Table 3-13, converting units) is
1.64 x 10~7 L/minutes/mg microsomal protein. Converting units using 16 mg microsomal protein to g tissue (Bong
et al., 1985) gives a clearance rate per unit tissue mass of 2.6 x 10~6 L/minutes/g kidney. This is more than
1,000-fold smaller than the kidney specific blood flow rate of 6.3 x 10"3 L/minutes/g kidney (Brown etal. 1997). In
humans, an in vitro clearance rate of 6.5 x 10"8 L/minutes/mg microsomal protein is derived from the only detectable
in vitro oxidation rate from Cummings and Lash (2000) of 0.13 nmol/minutes/mg protein at 2 mM. Using the same
conversion from microsomal protein to tissue mass gives a clearance rate of 1.0 * 10"6 L/minutes/g kidney, more
than 1,000-fold smaller than the kidney specific blood flow of 3.25 x 10"3 L/minutes/g kidney (Brown etal.. 1997).
No data on kidney metabolism are available in mice, but the results are likely to be similar. Therefore, even
accounting for uncertainties of up to an order of magnitude in the in-vitro-to-in-vivo conversion, kidney oxidation
should contribute negligibly to total metabolism of TCE.
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Boxes with underlined labels are additions or modifications of the Hack et al.
(2006) model, which are discussed in Table 3-32.
Figure 3-7. Overall structure of PBPK model for TCE and metabolites used
in this assessment.
3-64
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Table 3-32 Discussion of changes to the Hack et al. (2006) PBPK model
implemented for this assessment
Change to Hack et al.
(2006) PBPK model
TCE respiratory tract
compartments and
metabolism
TCE kidney compartment
TCE venous blood
compartment
TCOH and TCOG liver
compartments
TCOH and TCA -ether"
elimination pathways
DCVG compartment
(human model only)
Discussion
In vitro data indicate that the lung (at least in the mouse) has a significant capacity for
oxidizing TCE. However, in the Hack et al. (2006) model, respiratory metabolism was
blood flow-limited. The model structure used was inconsistent with other PBPK
models in which the same mechanism for respiratory metabolism is assumed (e.g.,
styrene, Sarangapani et al. (2003). In these models, the main source of exposure in the
respiratory tract tissue is from the respiratory lumen — not from the tracheobronchial
blood flow. In addition, a wash-in/wash-out effect has also been postulated. The
current structure, which invokes a —
-------
noninformative prior was used. Section 3.5.5.4, below, discusses the updating of these
noninformative priors using interspecies scaling.
In keeping with standard practice, many of the PBPK model parameters were -scaled" by
body or organ weights, cardiac output, or allometrically by an assumed (fixed) power of body
weight. Metabolic capacity and cardiac output were scaled by the 3/4 power of body weight and
rate coefficients were scaled by the -1A power of body weight, in keeping with general
expectations as to the relationship between metabolic rates and body size (West et al., 2002; U.S.
EPA, 1992). So as to ensure a consistent model structure across species as well as improve the
performance of the MCMC algorithm, parameters were further scaled to the baseline point-
estimates where available, as was done by Hack et al. (2006). For example, to obtain the actual
liver volume (VLivC) in L, a point estimate is first obtained by multiplying the fixed, species-
specific baseline point estimate for the fractional liver volume by a fixed body weight (measured
or species-specific default) with density of 1 kg/L assumed to convert from kg to L. Then, any
deviation from this point estimate is represented by multiplying by a separate —sded" parameter
VLivC that has a value of 1 if there is no deviation from the point estimate. These —saled"
parameters are those estimated by the MCMC algorithm, and for which population means and
variances are estimated.
Baseline physiological parameters were reestimated based on the updated tissue lumping
(e.g., separate blood and kidney compartments) using the standard references, International
Commission on Radiological Protection (ICRP, 2003) and Brown et al. (1997). For a few of
these parameters, such as hematocrit and respiratory tract volumes in rodents, additional
published sources were used as available, but no attempt was made to compile a comprehensive
review of available measurements. In addition, a few parameters, such as the slowly perfused
volume, were calculated rather than sampled in order to preserve total mass or flow balances.
For chemical-specific distribution and metabolism parameters, in vitro data from various
sources were used. Where multiple measurements had been made, as was the case for many
partition coefficients, TCA plasma protein binding parameters, and TCE metabolism, different
results were pooled together, with their uncertainty reflected appropriately in the prior
distribution. Such in vitro measurements were available for most chemical partition coefficients,
except for those for TCOG (TCOH used as a proxy) and DCVG. There were also such data to
develop baseline values for the oxidative metabolism of TCE in the liver (VMAX and KM), the
relative split in TCE oxidation between formation of TCA and TCOH, and the VMAX for TCE
oxidation in the lung. For GSH conjugation, the geometric means of the in vitro data from Lash
et al. (1999a)and Green et al. (1997a) were used as central estimates, with a wide enough
uncertainty range to encompass both (widely disparate) estimates. Thus, the prior distribution
for these parameters was only mildly informative, and the results are primarily determined by the
available in vivo data. All other metabolism parameters were not given baseline values and
needed to be estimated from the in vivo data.
3-66
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3.5.4.4. Dose-Metric Predictions
The purpose of this PBPK model is to make predictions of internal dose in rodents used
in toxicity studies or in humans in the general population, and not in the groups or individuals for
which pharmacokinetic data exist. Therefore, to evaluate its predictive utility for risk
assessment, a number of dose-metrics were selected for simulation in a —grieric" mouse, rat, or
human, summarized in Table 3-33. The parent dose-metric was AUC in blood. TCE
metabolism dose-metrics (i.e., related to the amount metabolized) included both total
metabolism, metabolism splits between oxidation vs. conjugation, oxidation in the liver vs. the
lung, the amount of oxidation in the liver to products other than TCOH and TCA, and the
amount of TCA produced. These metabolism rate dose-metrics are scaled by body weight in the
case of TCA produced, by the metabolizing tissue volume and by body weight to the % power in
the cases of the lung and —otM oxidation in the liver, and by body weight to the 3/4 power only
in other cases. With respect to the oxidative metabolites, liver concentrations of TCA and blood
concentrations of free TCOH were used. With respect to conjugative metabolites, the dose-
metrics considered were total GSH metabolism scaled by body weight to the 3/4 power, and the
amount of DCVC bioactivated (rather than excreted in urine) per unit body weight to the
3/4 power and per unit kidney mass.
Table 3-33. PBPK model-based dose-metrics
Abbreviation
ABioactDCVCBW34
ABioactDCVCKid
AMetGSHBW34
AMetLivlBW34
AMetLivOtherBW34
AMetLivOtherLiv
AMetLngBW34
AMetLngResp
AUCCBld
AUCCTCOH
AUCLivTCA
TotMetabBW34
TotOxMetabBW34
TotTCAInBW
Description
Amount of DCVC bioactivated in the kidney (mg) per unit body weight'7' (kgy<)
Amount of DCVC bioactivated in the kidney (mg) per unit kidney mass (kg)
Amount of TCE conjugated with GSH (mg) per unit body weight'7' (kg'7')
Amount of TCE oxidized in the liver per unit body weight'7' (kg'7')
Amount of TCE oxidized to metabolites other than TCA and TCOH in the liver (mg) per
unit body weight'7' (kg'7')
Amount of TCE oxidized to metabolites other than TCA and TCOH in the liver (mg) per
unit liver mass (kg)
Amount of TCE oxidized in the respiratory tract (mg) per unit body weight'7' (kg'7')
Amount of TCE oxidized in the respiratory tract (mg) per unit respiratory tract tissue
mass (kg)
Area under the curve of the venous blood concentration of TCE (mg-hr/L)
Area under the curve of the blood concentration of TCOH (mg-hr/L)
Area under the curve of the liver concentration of TCA (mg-hr/L)
Total amount of TCE metabolized (mg) per unit body weight'7' (kg'7')
Total amount of TCE oxidized (mg) per unit body weight'7' (kg'7')
Total amount of TCA produced (mg) per unit body weight (kg)
All dose-metrics are converted to daily or weekly averages based on simulations lasting
10 weeks for rats and mice and 100 weeks for humans. These simulation times were the shortest
3-67
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for which additional simulation length did not add substantially to the average (i.e., less than a
few percent change with a doubling of simulation time).
3.5.5. Bayesian Estimation of PBPK Model Parameters, and Their Uncertainty and
Variability
3.5.5.1. Updated Pharmacokinetic Database
An extensive search was made for data not previously considered in the PBPK modeling
of TCE and metabolites, with a few studies identified or published subsequent to the review by
Chiu et al. (2006b). The studies considered for analysis are listed in Tables 3-34 and 3-35, along
with an indication of whether and how they were used.3
The least amount of data was available for mice, so an effort was made to include as
many studies as feasible for use in calibrating the PBPK model parameters. Exceptions include
mouse studies with CH or DCA dosing, since those metabolites are not included in the PBPK
model. In addition, the Birner et al. (1993) data only reported urine concentrations, not the
amount excreted in urine. Because there is uncertainty as to total volume of urine excreted, and
over what time period, these data were not used. Moreover, many other studies had urinary
excretion data, so this exclusion should have minimal impact. Several data sets not included by
Hack et al. (2006) were used here. Of particular importance was the inclusion of TCA and
TCOH dosing data from Abbas et al. (1997), Green and Prout (1985), Larson and Bull (1992a\
and Templin et al. (1993). A substantial amount of data is available in rats, so some data that
appeared to be redundant were excluded from the calibration set and saved for comparison with
posterior predictions (a -v-alidation" set). In particular, those used for "validation" are one
closed-chamber experiment (Andersen et al., 1987b), several data sets with only TCE blood data
(Leeet al., 1996; Jakob son et al., 1986; D'Souza et al., 1985), and selected time courses from
Fisher et al. (1991) and Lee et al. (2000a; 2000b), and one unpublished data set (Bruckner et al.,
unpublished). The Andersen et al. (1987b) data were selected randomly from the available
closed-chamber data, while the other data sets were selected because they were unpublished or
because they were more limited in scope (e.g., TCE blood only) and so were not as efficient for
use in the computationally-intensive calibration stage. As with the mouse analyses, TCA and
TCOH dosing data were incorporated to better calibrate those pathways.
3Additional in vivo data on TCE or metabolites published after the PBPK modeling was completed (Kim et al..
2009: LiuetaL 2009: Sweeney et al.. 2009) were evaluated separately, and discussed in Appendix A.
3-68
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Table 3-34. Rodent studies with pharmacokinetic data considered for analysis
Reference
Species
(strain)
Sex
TCE exposures
Other exposures
Calibration
Validation
Not
used
Comments
Mouse studies
Abbas et al. (1996)
Abbas and Fisher
(1997)
Abbas et al. (1997)
Barton et al. (1999)
Birner et al. (1993)
Fisher and Allen,
(1993)
Fisher et al. (1991)
Green and Prout
(1985)
Greenberg et al.
(1999)
Larson and Bull
(1992b)
Larson and Bull
(1992a)
Merdink et al. (1998)
Mouse
(B6C3FO
Mouse
(B6C3FO
Mouse
(B6C3FO
Mouse
(B6C3FO
Mouse (NMRI)
Mouse
(B6C3FO
Mouse
(B6C3FO
Mouse
(B6C3FO
Mouse
(B6C3FO
Mouse
(B6C3FO
Mouse
(B6C3FO
Mouse
(B6C3FO
M
M
M
M
M+F
M+F
M+F
M
M
M
M
M
-
Oral (corn oil)
-
-
Gavage
Gavage (corn oil)
Inhalation
Gavage (corn oil)
Inhalation
Oral (aqueous)
i.v.
CH i.v.
-
TCOH, TCA i.v.
DCA i.v. and oral
(aqueous)
-
-
-
TCA i.v.
-
DCA, TCA oral
(aqueous)
-
CH i.v.
Va
V
V
Va
V
Va
V
V
V
V
V
V
CH not in model.
DCA not in model.
Only urine concentrations
available, not amount.
Only data on TCA dosing was
used, since DCA is not in the
model.
Only data on TCE dosing was
used, since CH is not in the
model.
5-69
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Table 3-34. Rodent studies with pharmacokinetic data considered for analysis (continued)
Reference
Prout et al. (1985)
Templin et al. (1993)
Species
(strain)
Mouse
(B6C3FJ,
Swiss)
Mouse
(B6C3FO
Sex
M
M
TCE exposures
Gavage (corn oil)
Oral (aqueous)
Other exposures
TCA oral
Calibration
Va
Va
Validation
Not
used
Comments
Rat studies
Andersen et al.
(1997)
Barton et al. (1995)
Bernauer et al.
(19%)
Birner et al. (1993)
Birner et al. (1997)
Bruckner et al.
unpublished
Dallas et al. (1991)
D'Souza et al. (1985)
Fisher et al. (1989)
Fisher et al. (1991)
Rat (F344)
Rat (Sprague-
Dawley)
Rat (Wistar)
Rat (Wistar,
F344)
Rat (Wistar)
Rat (Sprague-
Dawley)
Rat (Sprague-
Dawley)
Rat (Sprague-
Dawley)
Rat (F344)
Rat (F344)
M
M
M
M+F
M+F
M
M
M
F
M+F
Inhalation
Inhalation
Inhalation
Gavage (ns)
Inhalation
Inhalation
i.v., oral (aqueous)
Inhalation
Inhalation
-
-
-
-
DCVC i.v.
-
-
—
Va
V
V
Va
Va
V
V
V
V
V
V
Initial chamber concentrations
unavailable, so not used.
Only urine concentrations
available, not amount.
Single dose, route does not
recapitulate how DCVC is
formed from TCE, excreted
NAcDCVC -100-fold greater
than that from relevant TCE
exposures (Bernauer et al..
1996).
Not published, so not used for
calibration. Similar to Keys
et al. (2003) data.
Only TCE blood
measurements, and>10-fold
greater than other similar
studies.
Experiment with blood only
data not used for calibration.
5-70
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Table 3-34. Rodent studies with pharmacokinetic data considered for analysis (continued)
Reference
Green and Prout
(1985)
Hissink et al. (2002)
Jakobson et al.
(1986)
Kaneko et al. (1994)
Keys et al. (2003)
Kimmerle and Eben
(1973b)
Larson and Bull
(1992b)
Larson and Bull
(1992a)
Lash et al. (2006)
Lee et al. (1996)
Lee et al. (2000a;
2000b)
Merdink et al. (1999)
Poet et al. (2000)
Prout et al. (1985)
Species
(strain)
Rat (Osborne-
Mendel)
Rat (Wistar)
Rat (Sprague-
Dawley)
Rat (Wistar)
Rat (Sprague-
Dawley)
Rat (Wistar)
Rat (F344)
Rat (Sprague-
Dawley)
Rat (F344)
Rat (Sprague-
Dawley)
Rat (Sprague-
Dawley)
Rat (F344)
Rat (F344)
Rat (Osborne-
Mendel,
Wistar)
Sex
M
M
F
M
M
M
M
M
M+F
M
M
M
M
M
TCE exposures
Gavage (corn oil)
Gavage (corn oil),
i.v.
Inhalation
Inhalation
Inhalation,
oral (aqueous), i.a.
Inhalation
-
Oral (aqueous)
Gavage (corn oil)
Arterial, venous,
portal, stomach
injections
Stomach injection,
i.v., p.v.
-
Dermal
Gavage (corn oil)
Other exposures
TCA gavage
(aqueous)
-
Various
pretreatments
(oral)
Ethanol
pretreatment
(oral)
-
-
DCA, TCA oral
(aqueous)
-
-
p-Nitrophenol
pretreatment (i.a.)
CH, TCOH i.v.
-
Calibration
V
V
V
V
V
V
Va
V
V
Va
Validation
V
V
V
Not
used
V
V
Comments
Pretreatments not included.
Only blood TCE data
available.
Pretreatments not included.
Only TCA dosing data used,
since DCA is not in the model.
Highly inconsistent with other
studies.
Only blood TCE data
available.
Pretreatments not included.
Only experiments with blood
and liver data used for
calibration.
TCOH dosing used; CH not in
model.
Dermal exposure not in model.
5-71
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Table 3-34. Rodent studies with pharmacokinetic data considered for analysis (continued)
Reference
Saghir et al. (2002)
Simmons et al.
(20021
Stenner et al. (1997)
Templin et al.
(1995b)
Thrall et al. (2000)
Yu et al. (20001
Species
(strain)
Rat (F344)
Rat (Long-
Evans)
Rat (F344)
Rat (F344)
Rat (F344)
Rat (F344)
Sex
M
M
M
M
M
M
TCE exposures
-
Inhalation
intraduodenal
Oral (aqueous)
i.v., i.p.
-
Other exposures
DCA i.v., oral
(aqueous)
-
TCOH, TCA i.v.
-
With toluene
TCA i.v.
Calibration
V
V
Va
V
Validation
Not
used
V
V
Comments
DCA not in model.
Only exhaled breath data
available from i.v. study; i.p.
dosing not in model.
"Part or all of the data in the study was used for calibration in Hack et al. (2006).
p.v. = intraperivenous
5-72
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Table 3-35. Human studies with pharmacokinetic data considered for analysis
Reference
Bartonicek (1962)
Bernauer et al. (1996)
Bloemen et al. (2001)
Chiu et al. (2007)
Ertle et al. (1972)
Fernandez et al. (1977)
Fisher et al. (1998)
Kimmerle and Eben
(1973a)
Lapare et al. (1995)
Lash et al. (1999b)
Monster et al. (1976)
Monster et al. (1979)
Muller et al. (1972)
Species
(number of
individuals)
Human (n = 8)
Human
Human (n = 4)
Human (n = 6)
Human
Human
Human (n = 17)
Human (n = 12)
Human (n = 4)
Human
Human (n = 4)
Human
Human
Sex
M+F
M
M
M
M
M
M+F
M+F
M+F
M+F
M
M
ns
TCE
exposures
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Other
exposures
-
"
-
CH oral
-
-
-
-
-
-
Calibration
Va
V
Va
V
V
>/"
Validation
V
V
V
V»
Va
Not
used
V
V
Comments
Sparse data, so not included for
calibration to conserve computational
resources.
Grouped data, but unique in that
includes NAcDCVC urine data.
Sparse data, so not included for
calibration to conserve computational
resources.
Very similar to Muller data.
Complex exposure patterns, and only
grouped data available for urine, so
used for validation.
Grouped only, but unique in that
DCVG blood data available (same
individuals as Fisher et al. (1998)].
Experiments with exercise not
included.
Grouped data only.
Same data also included in Muller
et al. (1975).
5-73
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Table 3-35. Human studies with pharmacokinetic data considered for analysis (continued)
Reference
Muller et al. (1974)
Muller et al. (1975)
Paykoc et al. (1945)
Poet et al. (2000)
Sato et al. (1977)
Stewart et al. (1970)
Treibig et al. (1976)
Vesterberg and Astrand
(1976)
Species
(number of
individuals)
Human
Human
Human (n = 3)
Human
Human
Human
Human
Human
Sex
M
M
ns
M+F
M
ns
ns
M
TCE
exposures
Inhalation
Inhalation
-
Dermal
Inhalation
Inhalation
Inhalation
Inhalation
Other
exposures
CH, TCA,
TCOH oral
Ethanol oral
TCA i.v.
-
-
-
-
-
Calibration
V
V
Validation
Va
Va
V
Va
Va
Not
used
V
Comments
TCA and TCOH dosing data used for
calibration, since it is rare to have
metabolite dosing data. TCE dosing
data used for validation, since only
grouped data available. CH not in
model.
Grouped data only.
Dermal exposure not in model.
All experiments included exercise, so
were not included.
"Part or all of the data in the study was used for calibration in Hack et al. (2006).
bGrouped data from this study was used for calibration in Hack et al. (2006). but individual data were used here.
5-74
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The human pharmacokinetic database of controlled exposure studies is extensive, but also
more complicated. For the majority of the studies, only grouped or aggregated data were
available, and most of those data were saved for -validation" since there remained a large
number of studies for which individual data were available. However, some data that may be
uniquely informative are only available in grouped form, in particular DCVG blood
concentrations, NAcDCVC urinary excretion, and data from TCA and TCOH dosing. While
there are analytic uncertainties as to the DCVG blood measurements, discussed above in
Section 3.3.3.2.1, they were nonetheless included here because they are the only in vivo data
available on this measurement in humans. The uncertainty associated with their use is discussed
below (see Section 3.5.7.3.2).
In addition, several human data sets, while having individual data, involved sparse
collection at only one or a few time points per exposure (Bloemen et al., 2001; Bartonicek, 1962)
and were subsequently excluded to conserve computational resources. Lapare et al. (1995),
which involved multiple, complex exposure patterns over the course of a month and was missing
the individual urine data, was also excluded due to the relatively low amount of data given the
large computational effort required to simulate the data. Several studies also investigated the
effects of exercise during exposure on human TCE toxicokinetics. The additional parameters in
a model including exercise would include those for characterizing the changes in cardiac output,
alveolar ventilation, and regional blood flow as well as their interindividual variability, and
would have further increased the computational burden. Therefore, it was decided that such data
would be excluded from this analysis. Even with these exclusions, data on a total of
42 individuals, some involving multiple exposures, were included in the calibration.
3.5.5.2. Updated Hierarchical Population Statistical Model and Prior Distributions
While the individual animals of a common strain and sex within a study are likely to vary
to some extent, this variability was not included as part of the hierarchical population model for
several reasons. First, generally, only aggregated pharmacokinetic data (arithmetic mean and SD
or SE) are available from rodent studies. While methods exist for addressing between-animal
variability with aggregated data (e.g., Chiu and Bois, 2007), they require a higher level of
computational intensity. Second, dose-response data are generally also only separated by sex
and strain, and otherwise aggregated. Thus, in analyzing dose-response data (see Chapter 5), one
usually has no choice but to treat all of the animals in a particular study of a particular strain and
sex as identical units. In the Hack et al. (2006) model, each simulation was treated as a separate
observational unit, so different dosing levels within the same study were treated separately and
assigned different PBPK model parameters. However, the animals within a study are generally
inbred and kept under similarly controlled conditions, whereas animals in different studies—
even if of the same strain and sex—likely have differences in genetic lineage, diet, and handling.
Thus, animals within a study are likely to be much more homogeneous than animals between
3-75
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studies. As a consequence, in the revised model, for rodents, different animals of the same sex
and strain in the same study (or series of studies conducted simultaneously) were treated as
identical, and grouped together as a single —subjet." Thus, the predictions from the population
model in rodents simulate -a-verage" pharmacokinetics for a particular "lot" of rodents of a
particular species, strain, and sex. Between-animal variability is not explicitly modeled, but it is
incorporated in a —resdual" error term as part of the likelihood function (see Appendix A,
Section A.4.3.4). Therefore, a high degree of within-study variability would be reflected in a
high posterior value in the variance of the re si dual-err or.
In humans, however, interindividual variability is of interest, and, furthermore,
substantial individual data are available in humans. However, in some studies, the same
individual was exposed more than once, so those data should be grouped together [in the Hack
et al. (2006) model, they were treated as different -^idivi duals"]. Because the primary interest
here is chronic exposure, and because it would add substantially to the computational burden,
interoccasion variability—changes in pharmacokinetic parameters in a single individual over
time—is not addressed. Therefore, each individual is considered a single —subejct," and the
predictions from the population model in humans are the —laerage" across different occasions for
a particular individual (adult). Between-occasion variability is not explicitly modeled, but it is
incorporated in a —resdual" error term as part of the likelihood function (see Appendix A,
Section A.4.3.4). Therefore, a high degree of between-occasion variability would be reflected in
a high posterior value in the variance of the residual-error.
As discussed in Section 3.3.3.1, sex and (in rodents) strain differences in oxidative
metabolism were modest or minimal. While some sex-differences have been noted in GSH
metabolism (see Sections 3.3.3.2.7 and 3.3.3.2.8), almost all of the available in vivo data are in
males, making it more difficult to statistically characterize that difference with PBPK modeling.
Therefore, within a species, different sexes and (in rodents) strains were considered to be drawn
from a single, species-level population. For humans, each individual was considered to be drawn
from a single (adult) human population.
Thus, from here forward, the term -subject" will be used to refer to both a particular —dt"
of a particular rodents' species, strain, and sex for, and a particular human individual. The term
—poputtion" will, therefore, refer to the collection of rodent —lots of the same species and the
collection of human individuals.
Figure A-l in Appendix A illustrates the hierarchical structure. Informative prior
distributions reflecting the uncertainty in the population mean and variance, detailed in
Appendix A, were updated from those used in Hack et al. (2006) based on an extensive analysis
of the available literature. The population variability of the scaling parameter across subjects is
assumed to be distributed as a truncated normal distribution, a standard assumption in the
absence of specific data suggesting otherwise. Because of the truncation of extreme values, the
sensitivity to this choice is expected to be small as long as the true underlying distribution is uni-
3-76
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modal and symmetric. In addition, most scaling parameters, being strictly positive in their
original units, were log-transformed—so these parameters have lognormal distributions in their
original units. The uncertainty distribution for the population parameters was assumed to be a
truncated normal distribution for population mean parameters and an inverse gamma distribution
for population variance parameters—both standard choices in hierarchical models.
Section 3.5.5.3, next, discusses specification of prior distributions in the case where no data
independent of the calibration data exist.
3.5.5.3. Use of Interspecies Scaling to Update Prior Distributions in the Absence of
Other Data
For many metabolic parameters, little or no in vitro or other prior information is available
to develop prior distributions. Initially, for such parameters, noninformative priors in the form of
log-uniform distributions with a range spanning at least 104 were specified. However, in the
time available for analysis (up to about 100,000 iterations), only for the mouse did all of these
parameters achieve adequate convergence. This suggests that some of these parameters are
poorly identified for the rat and human. Additional preliminary runs indicated replacing the log-
uniform priors with lognormal priors and/or requiring more consistency between species could
improve identifiability sufficiently for adequate convergence. However, an objective method of
—catering" the lognormal distributions that did not rely on the in vivo data (e.g., via visual
fitting or limited optimization) being calibrated against was necessary in order to minimize
potential bias.
Therefore, the approach taken was to consider three species sequentially, from mouse to
rat to human, and to use interspecies scaling to update the prior distributions across species. This
sequence was chosen because the models are essentially —nsted" in this order, the rat model
adds to the mouse model the —dowrtaream" GSH conjugation pathways, and the human model
adds to the rat model the intermediary DCVG compartment. Therefore, for those parameters
with little or no independent data only, the mouse posteriors were used to update the rat priors,
and both the mouse and rat posteriors were used to update the human priors. Table 3-36 contains
a list of the parameters for which this scaling was used to update prior distributions. The scaling
relationship is defined by the —scaled paraieters" listed in Appendix A (see Section A.4.1,
Table A-4), and generally follows standard practice. For instance, VMAX and clearance rates
scale by body weight to the 3/4 power, whereas KM values are assumed to not scale, and rate
constants (inverse time units) scale by body weight to the -Vi power.
3-77
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Table 3-36. Parameters for which scaling from mouse to rat, or from mouse and rat to human, was used to
update the prior distributions
Parameter with no or highly uncertain a priori data
Respiratory lumen— ^tissue diffusion flow rate
TCOG body^lood partition coefficient
TCOG liver/body partition coefficient
Fraction of hepatic TCE oxidation not to TCA+TCOH
VMAX for hepatic TCE GSH conjugation
KM for hepatic TCE GSH conjugation
VMAX for renal TCE GSH conjugation
KM for renal TCE GSH conjugation
VMAX for Tracheo-bronchial TCE oxidation
KM for Tracheo-bronchial TCE oxidation
Fraction of respiratory oxidation entering systemic circulation
VMAX for hepatic TCOH^TCA
KM for hepatic TCOH^TCA
VMAX for hepatic TCOH^TCOG
KM for hepatic TCOH^TCOG
Rate constant for hepatic TCOH— Bother
Rate constant for TCA plasma— mrine
Rate constant for hepatic TCA— Bother
Rate constant for TCOG liver— >bile
Lumped rate constant for TCOG bile— >TCOH liver
Rate constant for TCOG— nirine
Lumped rate constant for DCVC^Urinary NAcDCVC
Rate constant for DCVC bioactivation
Mouse — >
rat
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
Rat^
human
A/
A/
Mouse+
rat^
human
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
Comments
No a priori information
Prior centered on TCOH data, but highly uncertain
Prior centered on TCOH data, but highly uncertain
No a priori information
Rat data on at 1 and 2 mM. Human data at more
concentrations, so VMAX and KM can be estimated
Rat data on at 1 and 2 mM. Human data at more
concentrations, so VMAX and KM can be estimated
Prior based on activity at a single concentration
No a priori information
No a priori information
No a priori information
No a priori information
No a priori information
No a priori information
No a priori information
Prior centered at glomerular filtration rate, but highly
uncertain
No a priori information
No a priori information
No a priori information
Prior centered at glomerular filtration rate, but highly
uncertain
Not included in mouse model
Not included in mouse model
aSee Appendix A, Table A-4 for scaling relationships.
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The scaling model is given explicitly as follows. If 9, are the —sded" parameters
(usually also natural-log-transformed) that are actually estimated, and A is the —uruersal"
(species-independent) parameter, then 0,• = A + s;, where et is the species-specific —departre"
from the scaling relationship, assumed to be normally distributed with variance (^e2. Therefore,
the mouse model gives an initial estimate of "A," which is used to update the prior distribution
for 9r = A + sr in the rat. The rat and mouse together then give a -better" estimate of A, which is
used to update the prior distribution for 0/, = A + s/, in the human, with the assumed distribution
for eh. The mathematical details are given in Appendix A, but three key points in this model are
worth noting here:
• It is known that interspecies scaling is not an exact relationship, and that, therefore, in
any particular case, it may either over- or underestimate. Therefore, the variance in the
new priors reflect a combination of (1) the uncertainty in the —previus" species'
posteriors as well as (2) a —predictin error" that is distributed lognormally with
geometric standard deviation (GSD) of 3.16-fold, so that the 95% confidence range about
the central estimate spans 100-fold. This choice was dictated partially by practicality, as
larger values of the GSD used in preliminary runs did not lead to adequate convergence
within the time available for analysis.
• The rat posterior is a product of its prior (which is based on the mouse posterior) and its
likelihood. Therefore, using the rat and mouse posteriors together to update the human
priors would use the mouse posterior —vfrice." Therefore, the rat posterior is
disaggregated into its prior and its likelihood using a lognormal approximation (since the
prior is lognormal), and only the (approximate) likelihood is used along with the mouse
posterior to develop the human prior.
• The model transfers the marginal distributions for each parameter across species, so
correlations between parameters are not retained. This is a restriction on the software
used for conducting MCMC analyses. However, assuming independence will lead to a
—borader" joint distribution, given the same marginal distributions. Therefore, this
assumption tends to reduce the weight of the interspecies scaling as compared to the
species-specific calibration data.
To summarize, in order to improve rate of the convergence of the MCMC analyses in rats
and humans, a sequential approach was used for fitting scaling parameters without strong prior
species-specific information. In particular, an additional assumption was made that across
species., these scaling parameters were, in absence of other information, expected to have a
common underlying value. These assumptions are generally based on allometric scaling
principles—with partition coefficients and concentrations scaling directly and rate constants
scaling by body weight to the -Vi power (so clearances and maximum metabolic capacities would
scale by body weight to the % power). These assumptions are used consistently throughout the
parameter calibration process. Therefore, after running the mouse model, the posterior
distribution for these parameters was used, with an additional error term, as priors for the rat
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model. Subsequently, after the mouse and rat model were run, their posterior distributions were
combined, with an additional error term, to use a priors for the human model. With this
methodology for updating the prior distributions, adequate convergence was achieved for the rat
and human after 110,000-140,000 iterations (discussed further below).
3.5.5.4. Implementation
The PBPK model was coded in for use in the MCSim software (version 5.0.0), which was
developed particularly for implementing MCMC simulations. As a quality control check, results
were checked against the original Hack et al. (2006) model, with the original structures restored
and parameter values made equivalent, and the results were within the error tolerances of the
ordinary differential equation (ODE) solver after correcting an error in the Hack et al. (2006)
model for calculating the TCA liver plasma flow. In addition, the model was translated to
MatLab (version 7.2.0.232) with simulation results checked and found to be within the error
tolerances of the ODE solver used ("odelSs"). Mass balances were also checked using the
baseline parameters, as well as parameters from preliminary MCMC simulations, and found to
be within the error tolerances of the ODE solver. Appendix A contains the MCSim model code.
3.5.6. Evaluation of Updated PBPK Model
3.5.6.1. Convergence
As in previous similar analyses (David et al., 2006; Hack et al., 2006; Bois, 2000b, a;
Gelman etal., 1996), the potential scale reduction factor -#" is used to determine whether
different independent MCMC chains have converged to a common distribution. The R
diagnostic is calculated for each parameter in the model, and represents the factor by which the
SD or other measure of scale of the posterior distribution (such as a confidence interval [CI])
may potentially be reduced with additional samples (Gelman et al., 2003). This convergence
diagnostic declines to 1 as the number of simulation iterations approaches infinity, so values
close to 1 indicate approximate convergence, with values of <1.1 commonly considered adequate
(Gelman et al., 2003). However, as an additional diagnostic, the convergence of model dose-
metric predictions was also assessed. Specifically, dose-metrics for a number of generic
exposure scenarios similar to those used in long-term bioassays were generated, and their natural
log (due to their approximate lognormal posterior distributions) was assessed for convergence
using the potential scale reduction factor -£." This is akin to the idea of utilizing sensitivity
analysis so that effort is concentrated on calibrating the most sensitive parameters for the purpose
of interest. In addition, predictions of interest that do not adequately converge can be flagged as
such, so that the statistical uncertainty associated with the limited sample size can be considered.
The mouse model had the most rapid reduction in potential scale reduction factors.
Initially, four chains of 42,500 iterations each were run, with the first 12,500 discarded as -burn-
in" iterations. The initial decision for determining —bur-in" was determined by visual
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inspection. At this point, evaluating the 30,000 remaining iterations, all of the population
parameters except for the VMAX for DC VG formation had R < 1.2, with only the first-order
clearance rate for DCVG formation and the VMAX and KM for TCOH glucuronidation having
R > 1.1. For the samples used for inference, all of these initial iterations were treated as —burn
in" iterations, and each chain was then restarted and run for an additional 68,700-
71,400 iterations (chains were terminated at the same time, so the number of iterations per chains
was slightly different). For these iterations, all values of R were <1.03. Dose-metric predictions
calculated for exposure scenarios of 10-600 ppm either continuously or 7 hours/day,
5 days/week and 10-3,000 mg/kg-day either continuously or by gavage 5 days/week. These
predictions were all adequately converged, with all values of R < 1.03.
As discussed above, for parameters with little or no a priori information, the posterior
distributions from the mouse model were used to update prior distributions for the rat model,
accounting for both the uncertainty reflected in the mouse posteriors as well as the uncertainty in
interspecies extrapolation. Four chains were run to 111,960-128,000 iterations each (chains
were terminated at the same time and run on computers with slightly different processing speeds,
so the number of iterations per chains was slightly different). As is standard, about the
first —half of the chains (i.e., the first 64,000 iterations) were discarded as —burrin" iterations,
and the remaining iterations were used for inferences. For these remaining iterations, the
diagnostic R was <1.1 for all population parameters except the fraction of oxidation not
producing TCA or TCOH (R = 1.44 for population mean, R = 1.35 for population variance), the
KM for TCOH -> TCA (R = 1.19 for population mean), the VMAX and KM for TCOH
glucuronidation (R = 1.23 and 1.12, respectively for population mean, and R = 1.13 for both
population variances), and the rate of "other" metabolism of TCOH (R = 1.29 for population
mean and R = 1.18 for population variance). Due to resource constraints, chains needed to be
stopped at this point. However, these are similar to the degree of convergence reported in Hack
et al. (2006). Dose-metric predictions calculated for two inhalation exposure scenarios (10-
600 ppm continuously or 7 hours/day, 5 days/week) and two oral exposure scenarios (10-
3,000 mg/kg-day continuously or by gavage 5 days/week).
All dose-metric predictions hadR < 1.04, except for the amount of—othdroxidative
metabolism (i.e., not producing TCA or TCOH), which had R = 1.12-1.16, depending on the
exposure scenario. The poorer convergence of this dose-metric is expected given that a key
determining parameter, the fraction of oxidation not producing TCA or TCOH, had the poorest
convergence among the population parameters.
For the human model, a set of four chains was run for 74,160-84,690 iterations using
—pMiminary" updated prior distributions based on the mouse posteriors and preliminary runs of
the rat model. Once the rat chains were completed, final updated prior distributions were
calculated and the last iteration of the preliminary runs were used as starting points for the final
runs. The center of the final updated priors shifted by <25% of the SD of either the preliminary
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or revised priors, so that the revised median was between the 40th and 60th percentile of the
preliminary median, and vice versa. The SDs changed by <5%. Therefore, the use of the
preliminary chains as a starting point should introduce no bias, as long as an appropriate burn-in
period is used for the final runs.
The final chains were run for an additional 59,140-61,780 iterations, at which point, due
to resource constraints, chains needed to be stopped. After the first 20,000 iterations, visual
inspection revealed the chains were no longer dependent on the starting point. These iterations
were therefore discarded as -burn-in" iterations, and for the remaining -40,000 iterations used
for inferences. All population mean parameters had R< l.l except for the respiratory tract
diffusion constant (R = 1.20), the liverblood partition coefficient for TCOG (R = 1.23), the rate
of TCE clearance in the kidney producing DCVG (R = 1.20), and the rate of elimination of
TCOG in bile (R = 1.46). All population variances also had R < 1.1 except for the variance for
the fraction of oxidation not producing TCOH or TCA (R = 1.10). Dose-metric predictions were
assessed for continuous exposure scenarios at 1-60 ppm in air or 1-300 mg/kg-day orally. These
predictions were all adequately converged with all values of R < 1.02.
3.5.6.2. Evaluation of Posterior Parameter Distributions
Posterior distributions of the population parameters need to be checked as to whether
they appear reasonable given the prior distributions. Inconsistency between the prior and
posterior distributions may indicate insufficiently broad (i.e., due to overconfidence) or
otherwise incorrectly specified priors, a misspecification of the model structure (e.g., leading to
pathological parameter estimates), or an error in the data. As was done with the evaluation of
Hack et al. (2006) in Appendix A, parameters were flagged if the interquartile regions of their
prior and posterior distributions did not overlap.
Appendix A contains detailed tables of the —saipled" parameters, and their prior and
posterior distributions. Because these parameters are generally scaled one or more times to
obtain a physically meaningful parameter, they are difficult to interpret. Therefore, in
Tables 3-37-3-39, the prior and posterior population distributions for the PBPK model
parameters obtained after scaling are summarized. Since it is desirable to characterize the
contributions from both uncertainty in population parameters and variability within the
population, the following procedure is adopted. First, 500 sets of population parameters (i.e.,
population mean and variance for each scaling parameter) are either generated from the prior
distributions via Monte Carlo or extracted from the posterior MCMC samples—these represent
the uncertainty in the population parameters. To minimize autocorrelation, for the posteriors, the
samples were obtained by —limning" the chains to the appropriate degree. From each of these
sets of population parameters, 100 sets of—sublet"-level parameters were generated by Monte
Carlo—each of these represents the population variability, given a particular set of population
parameters. Thus, a total of 50,000 subjects, representing 100 (variability) each for 500 different
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populations (uncertainty), were generated. For each of the 500 populations, the scaling
parameters are converted to PBPK model parameters, and the population median and GSD is
calculated—representing the central tendency and variability for that population. Then, the
median and the 95% CIs for the population median and GSD are calculated, and presented in the
tables that follow. Thus, these tables summarize separately the uncertainty in population
distribution as well as the variability in the population, while also accounting for correlations
among the population-level parameters. Finally, Table 3-40 shows the change in the CI in the
population median for the PBPK model parameters between the prior and posterior distributions,
as well as the shift in the central estimate (median) of the population median PBPK model
parameter.
The prior and posterior distributions for most physiological parameters were similar. The
posterior distribution was substantially narrower (i.e., less uncertainty) than the prior distribution
only in the case of the diffusion rate from the respiratory lumen to the respiratory tissue, which
also was to be expected given the very wide, noninformative prior for that parameter.
For distribution parameters, there were only relatively minor changes between prior and
posterior distributions for TCE and TCOH partition coefficients. The posterior distributions for
several TCA partition coefficients and plasma binding parameters were substantially narrower
than their corresponding priors, but the central estimates were similar, meaning that values at the
high and low extremes were not likely. For TCOG as well, partition coefficient posterior
distributions were substantially narrower, which was expected given the greater uncertainty in
the prior distributions (TCOH partition coefficients were used as a proxy).
Again, posterior distributions indicated that the high and low extremes were not likely.
Finally, posterior distribution for the distribution volume for DCVG was substantially narrower
than the prior distribution, which only provided a lower bound given by the blood volume. In
this case, the upper bounds were substantially lower in the posterior.
Posterior distributions for oral absorption parameters in mice and rats (there were no oral
studies in humans) were also informed by the data, as reflected in their being substantially more
narrow than the corresponding priors. Finally, with a few exceptions, TCE and metabolite
kinetic parameters showed substantially narrower posterior distributions than prior distributions,
indicating that they were fairly well specified by the in vivo data. The exceptions were the VMAX
for hepatic oxidation in humans (for which there was substantial in vitro data) and the VMAX for
respiratory metabolism in mice and rats (although the posterior distribution for the KM for this
pathway was substantially narrower than the corresponding prior).
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Table 3-37. Prior and posterior uncertainty and variability in mouse PBPK model parameters
Parameter description
Cardiac output (L/hr)
Alveolar ventilation (L/hr)
Scaled fat blood flow
Scaled gut blood flow
Scaled liver blood flow
Scaled slowly perfused
blood flow
Scaled rapidly perfused
blood flow
Scaled kidney blood flow
Respiratory lumen:tissue
diffusive clearance rate
(L/hr)
Fat fractional compartment
volume
Gut fractional compartment
volume
Liver fractional
compartment volume
Rapidly perfused fractional
compartment volume
Fractional volume of
respiratory lumen
Fractional volume of
respiratory tissue
Kidney fractional
compartment volume
Blood fractional
compartment volume
PBPK parameter
QC
QP
QFatC
QGutC
QLivC
QSlwC
QRapC
QKidC
DResp
VFatC
VGutC
VLivC
VRapC
VRespLumC
VRespEffC
VKidC
VBldC
Prior population
median: median (2.5%,
97.5%)
0.84 (0.59, 1.2)
2.1(1.3,3.5)
0.07(0.03,0.11)
0.14(0.11,0.17)
0.02 (0.016, 0.024)
0.22(0.14,0.29)
0.46 (0.37, 0.56)
0.092(0.054,0.13)
0.017 (0.000032, 15)
0.071(0.032,0.11)
0.049(0.041,0.057)
0.054(0.038,0.071)
0.1(0.087,0.11)
0.0047 (0.004, 0.0053)
0.0007 (0.0006, 0.00079)
0.017(0.015,0.019)
0.049 (0.042, 0.056)
Posterior population
median: median (2.5%,
97.5%)
1 (0.79, 1.3)
2.1(1.5,2.7)
0.072(0.044,0.1)
0.16(0.14,0.17)
0.021 (0.017, 0.024)
0.21(0.15,0.28)
0.45 (0.37, 0.52)
0.091(0.064,0.12)
2.5(1.4,5.1)
0.089(0.061,0.11)
0.048 (0.042, 0.055)
0.047 (0.037, 0.06)
0.099(0.09,0.11)
0.0047 (0.0041, 0.0052)
7e-04 (0.00062, 0.00078)
0.017(0.015,0.019)
0.048 (0.043, 0.054)
Prior population GSD:
median (2.5%, 97.5%)
1.17(1.1,1.4)
1.27(1.17, 1.54)
1.65 (1.22, 2.03)
1.15(1.09, 1.19)
1.15(1.09, 1.19)
1.3(1.15, 1.38)
1.15(1.11, 1.2)
1.34(1.14, 1.45)
1.37 (1.25, 1.62)
1.59(1.19, 1.93)
1.11(1.07, 1.14)
1.22(1.12, 1.29)
1.08(1.05, 1.11)
1.09(1.06, 1.12)
1.09(1.06, 1.12)
1.08(1.05, 1.11)
1.1(1.06, 1.13)
Posterior population
GSD: median (2.5%,
97.5%)
1.35(1.15, 1.54)
1.45 (1.28, 1.66)
1.64(1.3, 1.99)
1.12(1.07, 1.19)
1.15(1.09, 1.19)
1.3(1.17, 1.39)
1.17(1.12, 1.2)
1.34(1.18, 1.44)
1.53 (1.37, 1.73)
1.4(1.19, 1.78)
1.11(1.08, 1.14)
1.23(1.17,1.3)
1.09(1.06, 1.11)
1.09(1.07, 1.12)
1.1(1.07, 1.12)
1.09(1.06, 1.11)
1.1(1.08, 1.13)
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Table 3-37. Prior and posterior uncertainty and variability in mouse PBPK model parameters (continued)
Parameter description
Slowly perfused fractional
compartment volume
Plasma fractional
compartment volume
TCA body fractional
compartment volume [not
incl. blood+liver]
TCOH/G body fractional
compartment volume [not
incl. liver]
TCE blood:air partition
coefficient
TCE fatblood partition
coefficient
TCE gutblood partition
coefficient
TCE liverblood partition
coefficient
TCE rapidly perfused:blood
partition coefficient
TCE respiratory tissue :air
partition coefficient
TCE kidney :blood partition
coefficient
TCE slowly perfused:blood
partition coefficient
TCA blood:plasma
concentration ratio
Free TCA body :blood
plasma partition coefficient
Free TCA liverblood
plasma partition coefficient
Protein: TCA dissociation
constant (umole/L)
PBPK parameter
VSlwC
VPlasC
VBodC
VBodTCOHC
PB
PFat
PGut
PLiv
PRap
PResp
PKid
PSlw
TCAPlas
PBodTCA
PLivTCA
kDissoc
Prior population
median: median (2.5%,
97.5%)
0.55 (0.5, 0.59)
0.026(0.016,0.036)
0.79 (0.77, 0.8)
0.84 (0.82, 0.85)
15(10,23)
36 (21, 62)
1.9(0.89,3.8)
1.7(0.89,3.5)
1.8 (0.98, 3.7)
2.7(1.2,5)
2.2 (0.96, 4.6)
2.4(1.2,4.9)
0.76 (0.4, 16)
0.77 (0.27, 17)
1.1(0.36,21)
100 (13, 790)
Posterior population
median: median (2.5%,
97.5%)
0.54(0.51,0.57)
0.022 (0.016, 0.029)
0.79 (0.78, 0.81)
0.84 (0.83, 0.85)
14(11, 17)
36 (26, 49)
1.5 (0.94, 2.6)
2.2(1.3,3.3)
1.8(1.1,3)
2.5(1.5,4.2)
2.6(1.7,4)
2.2(1.4,3.5)
1.1(0.75, 1.8)
0.87 (0.59, 1.5)
1.1(0.64, 1.9)
130 (24, 520)
Prior population GSD:
median (2.5%, 97.5%)
1.05 (1.04, 1.07)
1.24(1.15, 1.35)
1.01 (1.01, 1.02)
1.01 (1.01, 1.02)
1.22(1.12, 1.42)
1.26(1.14, 1.52)
1.36(1.2, 1.75)
1.37(1.2, 1.75)
1.37(1.2, 1.76)
1.36(1.19, 1.78)
1.36(1.2, 1.77)
1.38(1.2, 1.78)
1.21 (1.09, 1.58)
1.41(1.23, 1.8)
1.41(1.23, 1.8)
2.44 (1.73, 5.42)
Posterior population
GSD: median (2.5%,
97.5%)
1.05 (1.04, 1.07)
1.27(1.19, 1.36)
1.01(1.01, 1.02)
1.01(1.01, 1.02)
1.44(1.28, 1.53)
1.32(1.16, 1.56)
1.36(1.2, 1.79)
1.39(1.21, 1.84)
1.37(1.2, 1.77)
1.37(1.19, 1.74)
1.51(1.25, 1.88)
1.39(1.21,1.8)
1.23(1.1, 1.73)
1.39(1.24,1.9)
1.4(1.24, 1.87)
2.64 (1.75, 5.45)
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Table 3-37. Prior and posterior uncertainty and variability in mouse PBPK model parameters (continued)
Parameter description
Maximum binding
concentration (umole/L)
TCOH body:blood partition
coefficient
TCOH liverbody partition
coefficient
TCOG body :blood partition
coefficient
TCOG liverbody partition
coefficient
DCVG effective volume of
distribution
TCE stomach absorption
coefficient (/hr)
TCE stomach-duodenum
transfer coefficient (/hr)
TCE duodenum absorption
coefficient (/hr)
TCA stomach absorption
coefficient (/hr)
VMAX for hepatic TCE
oxidation (mg/hr)
KM for hepatic TCE
oxidation (mg/L)
Fraction of hepatic TCE
oxidation not to
TCA+TCOH
Fraction of hepatic TCE
oxidation to TCA
VMAX for hepatic TCE GSH
conjugation (mg/hr)
KM for hepatic TCE GSH
conjugation (mg/L)
PBPK parameter
BMAX
PBodTCOH
PLivTCOH
PBodTCOG
PLivTCOG
VDCVG
kAS
kTSD
kAD
kASTCA
VMAX
KM
FracOther
FracTCA
VMAXDCVG
KMDCVG
Prior population
median: median (2.5%,
97.5%)
87 (9.6, 790)
1.1(0.61,2.1)
1.3(0.73,2.3)
0.95 (0.016, 77)
1.3 (0.019, 92)
0.033 (0.0015, 15)
1.7 (0.0049, 450)
1.4(0.043,51)
1.2 (0.0024, 200)
0.63 (0.0027, 240)
3.9(1.4, 15)
34 (1.6, 620)
0.43 (0.0018, 1)
0.086 (0.00022, 0.66)
3.7 (0.0071, 2,800)
250 (0.0029, 6,500,000)
Posterior population
median: median (2.5%,
97.5%)
140 (28, 690)
0.89 (0.65, 1.3)
1.9(1.2,2.6)
0.48(0.18, 1.1)
1.3 (0.64, 2.6)
0.027(0.0016,4.1)
1.7(0.37, 13)
4.5(0.51,26)
0.27 (0.067, 1.6)
4 (0.2, 74)
2.5(1.6,4.2)
2.7(1.4,8)
0.023(0.0037,0.15)
0.13(0.084,0.21)
0.6(0.01,480)
2200(0.17,2,300,000)
Prior population GSD:
median (2.5%, 97.5%)
2.72 (1.92, 5.78)
1.29(1.16, 1.66)
1.3(1.16, 1.61)
1.36(1.19,2.05)
1.36(1.18,2.13)
1.28 (1.08, 1.97)
4.74 (2.29, 23.4)
3.84 (2.09, 10.6)
4.33(2.14,26)
4.26 (2.27, 23.4)
2.02 (1.56, 2.85)
1.25(1.15, 1.61)
1.23(1,2.13)
1.48(1.12,2.56)
1.55(1.33,2.52)
1.81 (1.47, 3.62)
Posterior population
GSD: median (2.5%,
97.5%)
2.88(1.93,5.89)
1.31(1.17, 1.61)
1.35(1.18, 1.68)
1.41(1.22,2.19)
1.56(1.28,2.52)
1.31(1.1,2.19)
4.28 (2.39, 13.4)
4.79 (2.53, 10.9)
4.17(2.34, 14.4)
5.15(2.56,22)
1.86(1.59,2.47)
2.08 (1.48, 3.49)
1.49(1.25,2.83)
1.4(1.21, 1.96)
1.61(1.37,2.91)
1.93 (1.49, 3.68)
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Table 3-37. Prior and posterior uncertainty and variability in mouse PBPK model parameters (continued)
Parameter description
VMAX for renal TCE GSH
conjugation (mg/hr)
KM for renal TCE GSH
conjugation (mg/L)
VMAX for tracheo -bronchial
TCE oxidation (mg/hr)
KM for tracheo-bronchial
TCE oxidation (mg/L)
Fraction of respiratory
metabolism to systemic circ.
VMAX for hepatic
TCOH^TCA (mg/hr)
KM for hepatic
TCOH^TCA (mg/L)
VMAX for hepatic
TCOH^TCOG (mg/hr)
KM for hepatic
TCOH^TCOG (mg/L)
Rate constant for hepatic
TCOH^other (/hr)
Rate constant for TCA
plasma— mrine (/hr)
Rate constant for hepatic
TCA^other (/hr)
Rate constant for TCOG
liver— >bile (/hr)
Lumped rate constant for
TCOGbile^TCOH liver
(/hr)
Rate constant for
TCOG^urine (/hr)
Rate constant for hepatic
DCVG^DCVC (/hr)
PBPK parameter
VMAxKidDCVG
KMKidDCVG
VMAxClara
KMClara
FracLungSys
VMAXTCOH
KMTCOH
VMAXGIUC
KMGluc
kMetTCOH
kUrnTCA
kMetTCA
kBile
kEHR
kUrnTCOG
kDCVG
Prior population
median: median (2.5%,
97.5%)
0.34(0.00051, 180)
150 (0.0053, 6,200,000)
0.24 (0.03, 3.9)
1.5(0.0018,630)
0.34 (0.0016, 1)
0.064 (0.000014, 380)
1.4 (0.00018, 5,300)
0.11(0.000013,310)
1.8(0.0018,610)
0.19(0.000039, 1,400)
32 (0.38, 1700)
0.12(0.0004, 130)
0.3 (0.0004, 160)
0.21 (0.00036, 150)
1 (0.00015, 6,200)
0.24 (0.0004, 160)
Posterior population
median: median (2.5%,
97.5%)
0.027 (0.0012, 13)
160 (0.078, 280,000)
0.42(0.1, 1.5)
0.011(0.0024,0.09)
0.78(0.18,0.99)
0.12(0.048,0.28)
0.92 (0.26, 2.7)
4.6(1.9, 16)
30 (5.3, 130)
8.8(1.9,23)
3.2(1.2,7.1)
1.5(0.63,2.9)
2.4 (0.74, 8.4)
0.039(0.0026,0.11)
12 (2.6, 77)
0.81 (0.0033, 46)
Prior population GSD:
median (2.5%, 97.5%)
1.49 (1.26, 2.49)
1.79 (1.43, 3.45)
2.32 (1.74, 3.66)
1.47(1.25,2.58)
1.24(1,2.1)
1.5(1.24,2.61)
1.48(1.24,2.41)
1.48(1.26,2.53)
1.48 (1.25, 2.48)
1.47(1.25,2.36)
1.57(1.34,2.61)
1.48(1.25,2.32)
1.48 (1.24, 2.29)
1.47 (1.23, 2.29)
1.71(1.4,3.13)
1.48(1.25,2.39)
Posterior population
GSD: median (2.5%,
97.5%)
1.54 (1.28, 2.72)
1.91(1.5,3.91)
4.13(2.27,6.79)
1.63 (1.28, 5.02)
1.11(1, 1.72)
1.6(1.28,2.92)
1.49(1.26,2.4)
1.47(1.26,2.14)
1.8(1.3,4.72)
1.54 (1.26, 2.92)
1.84 (1.44, 2.94)
1.51(1.26,2.27)
1.51(1.26,2.39)
1.53(1.28,2.94)
3.44 (1.89, 9.49)
1.52(1.25,2.5)
5-87
-------
Table 3-37. Prior and posterior uncertainty and variability in mouse PBPK model parameters (continued)
Parameter description
Lumped rate constant for
DCVC^urinary NAcDCVC
(/hr)
Rate constant for DCVC
bioactivation (/hr)
PBPK parameter
kNAT
kKidBioact
Prior population
median: median (2.5%,
97.5%)
0.29 (0.0004, 160)
0.18(0.0004, 150)
Posterior population
median: median (2.5%,
97.5%)
0.37 (0.0021, 34)
0.23 (0.0024, 33)
Prior population GSD:
median (2.5%, 97.5%)
1.5(1.25,2.49)
1.48(1.25,2.51)
Posterior population
GSD: median (2.5%,
97.5%)
1.53(1.25,2.77)
1.53(1.25,3.03)
5-88
-------
Table 3-38. Prior and posterior uncertainty and variability in rat PBPK model parameters
Parameter description
Cardiac output (L/hr)
Alveolar ventilation (L/hr)
Scaled fat blood flow
Scaled gut blood flow
Scaled liver blood flow
Scaled slowly perfused
blood flow
Scaled rapidly perfused
blood flow
Scaled kidney blood flow
Respiratory lumen:tissue
diffusive clearance rate
(L/hr)
Fat fractional
compartment volume
Gut fractional
compartment volume
Liver fractional
compartment volume
Rapidly perfused
fractional compartment
volume
Fractional volume of
respiratory lumen
Fractional volume of
respiratory tissue
Kidney fractional
compartment volume
PBPK
parameter
QC
QP
QFatC
QGutC
QLivC
QSlwC
QRapC
QKidC
DResp
VFatC
VGutC
VLivC
VRapC
VRespLumC
VRespEffC
VKidC
Prior population
median: median
(2.5%, 97.5%)
5.3 (4.2, 6.9)
10(5.1, 18)
0.071(0.032,0.11)
0.15(0.12,0.18)
0.021 (0.017, 0.026)
0.33 (0.21, 0.46)
0.28(0.15,0.42)
0.14(0.12,0.16)
9.9 (0.48, 85)
0.069(0.031,0.11)
0.032 (0.027, 0.037)
0.034 (0.026, 0.042)
0.087(0.076,0.1)
0.0046 (0.0037, 0.0057)
0.0005 (0.00039,
0.00061)
0.0069 (0.0056, 0.0082)
Posterior population
median: median (2.5%,
97.5%)
6.1(5.2,7.4)
7.5 (5.8, 10)
0.081(0.06,0.1)
0.17(0.15,0.19)
0.022 (0.018, 0.025)
0.31 (0.23,0.4)
0.28(0.18,0.36)
0.14(0.12,0.16)
21 (9.5, 46)
0.069 (0.046, 0.091)
0.032 (0.028, 0.036)
0.033 (0.028, 0.039)
0.088 (0.079, 0.097)
0.0047 (0.0039, 0.0055)
5e-04 (0.00041, 0.00058)
0.007 (0.006, 0.008)
Prior population GSD:
median (2.5%, 97.5%)
1.12(1.07, 1.28)
1.32(1.18, 1.71)
1.66(1.21,2.02)
1.15(1.09, 1.19)
1.15(1.09, 1.2)
1.31(1.15, 1.4)
1.38 (0.0777, 1.72)
1.11(1.07, 1.14)
1.41 (1.26, 1.77)
1.61(1.2, 1.93)
1.11(1.07, 1.14)
1.16(1.09, 1.21)
1.1(1.06, 1.13)
1.16(1.1, 1.21)
1.16(1.09, 1.21)
1.13(1.08, 1.17)
Posterior population
GSD: median (2.5%,
97.5%)
1.26(1.12, 1.36)
1.52(1.33,1.84)
1.5(1.3, 1.86)
1.13(1.08,1.18)
1.15(1.1, 1.19)
1.32(1.22, 1.41)
1.42 (0.0856, 1.75)
1.11(1.08, 1.14)
1.59(1.41, 1.9)
1.59(1.34, 1.88)
1.11(1.08, 1.14)
1.17(1.12, 1.21)
1.1 (1.07, 1.13)
1.16(1.11, 1.21)
1.16(1.11, 1.2)
1.13(1.09, 1.17)
5-89
-------
Table 3-38. Prior and posterior uncertainty and variability in rat PBPK model parameters (continued)
Parameter description
Blood fractional
compartment volume
Slowly perfused fractional
compartment volume
Plasma fractional
compartment volume
TCA body fractional
compartment volume [not
incl. blood+liver]
TCOH/G body fractional
compartment volume [not
incl. liver]
TCE blood:air partition
coefficient
TCE fatblood partition
coefficient
TCE gutblood partition
coefficient
TCE liverblood partition
coefficient
TCE rapidly perfused:blood
partition coefficient
TCE respiratory tissue :air
partition coefficient
TCE kidney :blood partition
coefficient
TCE slowly perfused:blood
partition coefficient
TCA blood:plasma
concentration ratio
Free TCA body :blood
plasma partition coefficient
Free TCA liverblood
plasma partition coefficient
PBPK parameter
VBldC
VSlwC
VPlasC
VBodC
VBodTCOHC
PB
PFat
PGut
PLiv
PRap
PResp
PKid
PSlw
TCAPlas
PBodTCA
PLivTCA
Prior population
median: median (2.5%,
97.5%)
0.073 (0.063, 0.085)
0.6 (0.55, 0.63)
0.039 (0.025, 0.054)
0.79 (0.78, 0.81)
0.87 (0.86, 0.87)
22 (14, 33)
27 (16, 46)
1.3 (0.69, 3)
1.5(1.2,1.9)
1.3 (0.66, 2.7)
0.97(0.48,2.1)
1.3 (0.77, 2.2)
0.57 (0.35, 0.97)
0.78 (0.6, 0.96)
0.7(0.18,2.2)
0.84 (0.25, 3.3)
Posterior population
median: median (2.5%,
97.5%)
0.074 (0.066, 0.082)
0.6 (0.57, 0.62)
0.04 (0.032, 0.049)
0.79 (0.78, 0.8)
0.87 (0.86, 0.87)
19 (16, 24)
31 (24,42)
1.1(0.79, 1.7)
1.6(1.3, 1.8)
1.3(0.82,2.1)
1 (0.62, 1.6)
1.2 (0.9, 1.7)
0.73 (0.54, 0.97)
0.78 (0.71, 0.86)
0.76 (0.46, 1.3)
1.1(0.61,2.1)
Prior population GSD:
median (2.5%, 97.5%)
1.1(1.06, 1.13)
1.05 (1.04, 1.06)
1.24(1.15, 1.35)
1.01(1.01, 1.01)
1.01 (1, 1.01)
1.26(1.19, 1.35)
1.32 (1.22, 1.44)
1.36(1.21, 1.79)
1.15(1.11, 1.2)
1.35(1.18, 1.82)
1.37(1.19, 1.77)
1.31(1.19, 1.5)
1.32(1.23, 1.43)
1.12(1.06, 1.22)
1.72(1.39,2.81)
1.71(1.39,2.78)
Posterior population
GSD: median (2.5%,
97.5%)
1.1(1.07, 1.13)
1.05 (1.04, 1.06)
1.22(1.16, 1.33)
1.01(1.01, 1.01)
1.01(1, 1.01)
1.3 (1.22, 1.38)
1.32(1.23, 1.43)
1.36(1.2, 1.68)
1.15(1.11,1.2)
1.37(1.2, 1.76)
1.36(1.19, 1.78)
1.3 (1.2, 1.45)
1.33 (1.25, 1.46)
1.11(1.07, 1.17)
1.65(1.4,2.19)
1.66(1.38,2.37)
5-90
-------
Table 3-38. Prior and posterior uncertainty and variability in rat PBPK model parameters (continued)
Parameter description
Protein: TC A dissociation
constant (umole/L)
Maximum binding
concentration (umole/L)
TCOH body:blood partition
coefficient
TCOH liverbody partition
coefficient
TCOG body :blood partition
coefficient
TCOG liverbody partition
coefficient
DCVG effective volume of
distribution
TCE stomach absorption
coefficient (/hr)
TCE stomach-duodenum
transfer coefficient (/hr)
TCE duodenum absorption
coefficient (/hr)
TCA stomach absorption
coefficient (/hr)
VMAX for hepatic TCE
oxidation (mg/hr)
KM for hepatic TCE
oxidation (mg/L)
Fraction of hepatic TCE
oxidation not to
TCA+TCOH
Fraction of hepatic TCE
oxidation to TCA
VMAX for hepatic TCE GSH
conjugation (mg/hr)
KM for hepatic TCE GSH
conjugation (mg/L)
PBPK parameter
kDissoc
BMAX
PBodTCOH
PLivTCOH
PBodTCOG
PLivTCOG
VDCVG
kAS
kTSD
kAD
kASTCA
VMAX
KM
FracOther
FracTCA
VMAXDCVG
KMDCVG
Prior population
median: median (2.5%,
97.5%)
270 (95, 790)
320 (80, 1300)
1 (0.33, 4)
1.3(0.39,4.5)
0.48 (0.021, 14)
1.3 (0.078, 39)
0.27 (0.27, 0.27)
0.73 (0.0044, 400)
1.4 (0.04, 45)
0.96 (0.0023, 260)
0.83 (0.0024, 240)
5.8 (2, 19)
18 (1.9, 240)
0.027 (0.0018, 0.59)
0.2 (0.027, 0.76)
2(0.015, 1,100)
1,500 (1.2, 1,800,000)
Posterior population
median: median (2.5%,
97.5%)
280 (140, 530)
320 (130, 750)
1.1(0.51,2.1)
1.2 (0.59, 2.8)
1.6 (0.091, 16)
10 (2.7, 41)
0.27 (0.27, 0.27)
2.5 (0.32, 19)
3.2(0.31, 19)
0.17(0.038, 1)
1.4(0.13, 13)
5.3 (3.9, 7.7)
0.74 (0.54, 1.4)
0.29 (0.047, 0.56)
0.046 (0.023, 0.087)
5.8(0.16,340)
6300 (120, 720,000)
Prior population GSD:
median (2.5%, 97.5%)
1.62(1.31,2.43)
1.89(1.5,2.64)
1.71(1.37,2.69)
1.71(1.37,2.8)
1.39(1.2, 1.97)
1.4(1.2,2.14)
1 (1, 1)
4.16(2.21,20)
3.92(2.13, 10.4)
4.17(2.15,20.8)
4.15(2.2, 18.7)
1.97 (1.54, 2.92)
2.76 (1.89, 6.46)
1.42(1.15,2.33)
1.35(1.11,2.14)
1.52(1.3,2.67)
1.83(1.45,3.15)
Posterior population
GSD: median (2.5%,
97.5%)
1.6(1.31,2.31)
1.84(1.49,2.57)
1.76(1.38,2.45)
1.78(1.37,2.75)
1.42(1.21,2.52)
1.42(1.21,2.3)
1(1,1)
9.3(4.07,31.1)
5.54 (2.77, 10.7)
4.07(2.51, 11.9)
4.21 (2.4, 11.4)
1.69(1.47,2.15)
1.84(1.51,2.7)
2.15(1.32,5.06)
1.84(1.36,2.8)
1.57(1.32,2.93)
1.88(1.48,3.49)
5-91
-------
Table 3-38. Prior and posterior uncertainty and variability in rat PBPK model parameters (continued)
Parameter description
VMAX for renal TCEGSH
conjugation (mg/hr)
KM for renal TCE GSH
conjugation (mg/L)
VMAX for tracheo -bronchial
TCE oxidation (mg/hr)
KM for tracheo-bronchial
TCE oxidation (mg/L)
Fraction of respiratory
metabolism to systemic circ.
VMAX for hepatic
TCOH^TCA (mg/hr)
KM for hepatic
TCOH^TCA (mg/L)
VMAX for hepatic
TCOH^TCOG (mg/hr)
KM for hepatic
TCOH^TCOG (mg/L)
Rate constant for hepatic
TCOH^other (/hr)
Rate constant for TCA
plasma— mrine (/hr)
Rate constant for hepatic
TCA^other (/hr)
Rate constant for TCOG
liver— >bile (/hr)
Lumped rate constant for
TCOGbile^TCOH liver
(/hr)
Rate constant for
TCOG^urine (/hr)
Rate constant for hepatic
DCVG^DCVC (/hr)
PBPK parameter
VMAxKidDCVG
KMKidDCVG
VMAxClara
KMClara
FracLungSys
VMAXTCOH
KMTCOH
VMAxGluc
KMGluc
kMetTCOH
kUrnTCA
kMetTCA
kBile
kEHR
kUrnTCOG
kDCVG
Prior population
median: median (2.5%,
97.5%)
0.038 (0.00027, 13)
470 (0.47, 530,000)
0.2 (0.0077, 2.4)
0.016 (0.0014, 0.58)
0.82 (0.027, 1)
0.75 (0.037, 20)
1 (0.029, 23)
27 (0.83, 620)
31(1,570)
4.2(0.17, 150)
1.9 (0.21, 47)
0.76 (0.037, 19)
1.4(0.052,31)
0.013 (0.00055, 0.64)
11 (0.063, 1,000)
30,000 (30,000, 30,000)
Posterior population
median: median (2.5%,
97.5%)
0.0024 (0.0005, 0.014)
0.25 (0.038, 2.2)
0.17(0.042,0.69)
0.025(0.005,0.15)
0.73 (0.06, 0.98)
0.71 (0.27, 2.2)
19 (3.6, 94)
11(4.1,32)
6.3 (1.2, 20)
3 (0.57, 15)
0.92(0.51, 1.7)
0.47(0.17, 1.2)
14 (2.7, 39)
1.7 (0.34, 7.4)
12 (0.45, 370)
30,000 (30,000, 30,000)
Prior population GSD:
median (2.5%, 97.5%)
1.52(1.3,2.81)
1.84 (1.47, 4.27)
2.26(1.71,3.3)
1.47 (1.26, 2.39)
1.09(1, 1.71)
1.51 (1.25,2.64)
1.52(1.26,2.7)
1.5(1.25,2.59)
1.5(1.25,2.74)
1.49 (1.27, 2.67)
1.56(1.33,2.81)
1.5 (1.26, 2.74)
1.5(1.25,2.8)
1.5(1.25,2.49)
1.74 (1.42, 2.99)
1 (1, 1)
Posterior population
GSD: median (2.5%,
97.5%)
1.56 (1.29, 2.72)
1.93 (1.49, 3.57)
4.35(1.99,6.7)
1.65 (1.28, 10.5)
1.13(1.01, 1.86)
1.68(1.3,3.23)
1.72(1.26,3.93)
2.3(1.41,5.19)
2.04(1.3,8.4)
1.72(1.3,8.31)
1.58(1.36,2.25)
1.52(1.27,2.45)
1.63(1.29,4.1)
1.67(1.26,5.91)
1.86(1.43,3.54)
1(1,1)
5-92
-------
Table 3-38. Prior and posterior uncertainty and variability in rat PBPK model parameters (continued)
Parameter description
Lumped rate constant for
DCVC^urinary NAcDCVC
(/hr)
Rate constant for DCVC
bioactivation (/hr)
PBPK parameter
kNAT
kKidBioact
Prior population
median: median (2.5%,
97.5%)
0.15(0.00024,84)
0.12(0.00023,83)
Posterior population
median: median (2.5%,
97.5%)
0.0029 (0.00066, 0.015)
0.0092 (0.0012, 0.043)
Prior population GSD:
median (2.5%, 97.5%)
1.49(1.24,2.8)
1.48 (1.24, 2.68)
Posterior population
GSD: median (2.5%,
97.5%)
1.54(1.26,2.45)
1.52(1.25,2.5)
5-93
-------
Table 3-39. Prior and posterior uncertainty and variability in human PBPK model parameters
Parameter description
Cardiac output (L/hr)
Alveolar ventilation (L/hr)
Scaled fat blood flow
Scaled gut blood flow
Scaled liver blood flow
Scaled slowly perfused
blood flow
Scaled rapidly perfused
blood flow
Scaled kidney blood flow
Respiratory lumen:tissue
diffusive clearance rate
(L/hr)
Fat fractional compartment
volume
Gut fractional compartment
volume
Liver fractional
compartment volume
Rapidly perfused fractional
compartment volume
Fractional volume of
respiratory lumen
Fractional volume of
respiratory tissue
Kidney fractional
compartment volume
PBPK parameter
QC
QP
QFatC
QGutC
QLivC
QSlwC
QRapC
QKidC
DResp
VFatC
VGutC
VLivC
VRapC
VRespLumC
VRespEffC
VKidC
Prior population
median: median (2.5%,
97.5%)
390 (280, 560)
380 (220, 640)
0.051(0.021,0.078)
0.19(0.15,0.23)
0.063 (0.029, 0.099)
0.22(0.13,0.3)
0.29(0.18,0.4)
0.19(0.16,0.22)
560 (44, 3300)
0.19(0.088,0.31)
0.02 (0.018, 0.022)
0.026(0.018,0.032)
0.087 (0.079, 0.096)
0.0024 (0.0018, 0.003)
0.00018 (0.00014,
0.00022)
0.0043 (0.0034, 0.0052)
Posterior population
median: median (2.5%,
97.5%)
330 (280, 390)
440 (360, 530)
0.043 (0.033, 0.055)
0.16(0.14,0.18)
0.039 (0.026, 0.055)
0.17(0.14,0.21)
0.39 (0.34, 0.43)
0.19(0.18,0.21)
270 (130, 470)
0.16(0.12,0.21)
0.02(0.019,0.021)
0.026 (0.022, 0.03)
0.088 (0.083, 0.093)
0.0024 (0.0021, 0.0027)
0.00018 (0.00015, 0.00021)
0.0043 (0.0038, 0.0048)
Prior population GSD:
median (2.5%, 97.5%)
1.17(1.1, 1.39)
1.27(1.17, 1.52)
1.64(1.23,2)
1.16(1.1, 1.21)
1.62 (1.22, 1.92)
1.34(1.18, 1.45)
1.31(1.14, 1.57)
1.1(1.07, 1.13)
1.37(1.25, 1.61)
1.66(1.23, 1.93)
1.07 (1.04, 1.08)
1.21(1.12, 1.28)
1.07 (1.05, 1.09)
1.18(1.1, 1.23)
1.18(1.1, 1.24)
1.15(1.09, 1.19)
Posterior population
GSD: median (2.5%,
97.5%)
1.39(1.26, 1.54)
1.58(1.44, 1.73)
1.92 (1.72, 2.09)
1.16(1.12,1.2)
1.8(1.62, 1.98)
1.39(1.31, 1.46)
1.22(1.16,1.3)
1.1(1.07, 1.12)
1.71(1.52,2.35)
1.65(1.4, 1.9)
1.06(1.05, 1.08)
1.2(1.13, 1.26)
1.06(1.05, 1.08)
1.17(1.12, 1.22)
1.17(1.13, 1.23)
1.14(1.1, 1.19)
5-94
-------
Table 3-39. Prior and posterior uncertainty and variability in human PBPK model parameters (continued)
Parameter description
Blood fractional
compartment volume
Slowly perfused fractional
compartment volume
Plasma fractional
compartment volume
TCA body fractional
compartment volume [not
incl. blood+liver]
TCOH/G body fractional
compartment volume [not
incl. liver]
TCE blood:air partition
coefficient
TCE fat:blood partition
coefficient
TCE gut:blood partition
coefficient
TCE liverblood partition
coefficient
TCE rapidly perfused:blood
partition coefficient
TCE respiratory tissue:air
partition coefficient
TCE kidney :blood partition
coefficient
TCE slowly perfused:blood
partition coefficient
TCA blood:plasma
concentration ratio
Free TCA body :blood
plasma partition coefficient
Free TCA liverblood
plasma partition coefficient
Protein:TCA dissociation
constant (umole/L)
PBPK parameter
VBldC
VSlwC
VPlasC
VBodC
VBodTCOHC
PB
PFat
PGut
PLiv
PRap
PResp
PKid
PSlw
TCAPlas
PBodTCA
PLivTCA
kDissoc
Prior population
median: median (2.5%,
97.5%)
0.077 (0.066, 0.088)
0.45 (0.33, 0.55)
0.044(0.037,0.051)
0.75 (0.74, 0.77)
0.83 (0.82, 0.84)
9.6 (6.5, 13)
68 (46, 98)
2.6(1.3,5.3)
4(1.9,8.5)
2.6(1.2,5.7)
1.3 (0.65, 2.7)
1.6(1.1,2.3)
2.1(1.2,3.5)
0.78 (0.55, 15)
0.45(0.19,8.1)
0.59 (0.24, 10)
180 (160, 200)
Posterior population
median: median (2.5%,
97.5%)
0.078 (0.072, 0.084)
0.48 (0.43, 0.52)
0.044 (0.04, 0.048)
0.75 (0.74, 0.76)
0.83 (0.83, 0.83)
9.2 (8.2, 10)
57 (49, 66)
2.9(1.9,4.1)
4.1 (2.7,5.9)
2.4(1.8,3.2)
1.3 (0.9, 1.9)
1.6(1.3,1.9)
2.3(1.9,2.8)
0.65 (0.6, 0.77)
0.44 (0.33, 0.55)
0.55 (0.39, 0.77)
180 (170, 190)
Prior population GSD:
median (2.5%, 97.5%)
1.1(1.06, 1.13)
1.18(1.1, 1.24)
1.11(1.08, 1.14)
1.01(1.01, 1.01)
1.01(1, 1.01)
1.18(1.13, 1.26)
1.18(1.11, 1.33)
1.37(1.2, 1.78)
1.37(1.22, 1.81)
1.37(1.21, 1.78)
1.36(1.19,1.81)
1.17(1.1, 1.33)
1.28(1.14,1.53)
1.08(1.03,1.53)
1.36(1.19,1.75)
1.36(1.18,1.76)
1.05(1.03,1.09)
Posterior population
GSD: median (2.5%,
97.5%)
1.1(1.07, 1.13)
1.16(1.12, 1.22)
1.11(1.08, 1.14)
1.01(1.01, 1.01)
1.01(1, 1.01)
1.21(1.16, 1.28)
1.18(1.11, 1.3)
1.41(1.21, 1.77)
1.33(1.19, 1.6)
1.5(1.25, 1.87)
1.32(1.2, 1.56)
1.15(1.09, 1.25)
1.51 (1.36, 1.66)
1.52(1.23,2.03)
1.67(1.38,2.2)
1.65(1.37,2.16)
1.04(1.03, 1.07)
5-95
-------
Table 3-39. Prior and posterior uncertainty and variability in human PBPK model parameters (continued)
Parameter description
Maximum binding
concentration (umole/L)
TCOH body:blood partition
coefficient
TCOH liverbody partition
coefficient
TCOG body :blood partition
coefficient
TCOG liverbody partition
coefficient
DCVG effective volume of
distribution
TCE stomach absorption
coefficient (/hr)
TCE stomach-duodenum
transfer coefficient (/hr)
TCE duodenum absorption
coefficient (/hr)
TCA stomach absorption
coefficient (/hr)
TCOH stomach absorption
coefficient (/hr)
VMAX for hepatic TCE
oxidation (mg/hr)
KM for hepatic TCE
oxidation (mg/L)
Fraction of hepatic TCE
oxidation not to
TCA+TCOH
Fraction of hepatic TCE
oxidation to TCA
VMAX for hepatic TCE GSH
conjugation (mg/hr)
KM for hepatic TCE GSH
conjugation (mg/L)
VMAX for renal TCE GSH
conjugation (mg/hr)
PBPK parameter
BMAX
PBodTCOH
PLivTCOH
PBodTCOG
PLivTCOG
VDCVG
kAS
kTSD
kAD
kASTCA
kASTCOH
VMAX
KM
FracOther
FracTCA
VMAXDCVG
KMDCVG
VMAxKidDCVG
Prior population
median: median (2.5%,
97.5%)
830(600, 1100)
0.89 (0.51, 1.7)
0.58(0.32, 1.1)
0.67 (0.036, 16)
1.8(0.11,28)
73 (5.2, 36000)
1.4(1.4, 1.4)
1.4(1.4, 1.4)
0.75 (0.75, 0.75)
0.58 (0.0022, 210)
0.49 (0.0024, 210)
430 (130, 1500)
3.7 (0.22, 63)
0.12(0.0066,0.7)
0.19(0.036,0.56)
100 (0.0057, 690,000)
3.1(0.21,42)
220 (0.028, 6,700,000)
Posterior population
median: median (2.5%,
97.5%)
740 (630, 880)
1.5(1.3, 1.7)
0.63 (0.45, 0.87)
0.72 (0.3, 1.8)
3.1(0.87,8.1)
6.1(5.4,7.3)
1.4(1.4, 1.4)
1.4(1.4, 1.4)
0.75 (0.75, 0.75)
3 (0.061, 180)
7.6(0.11, 150)
190 (130, 290)
0.18(0.078,0.4)
0.11(0.024,0.23)
0.035 (0.024, 0.05)
340(110, 1,100)
3.6(1.2, 11)
2.1(0.17,9.3)
Prior population GSD:
median (2.5%, 97.5%)
1.17(1.1,1.3)
1.29(1.16, 1.64)
1.29(1.16, 1.65)
1.38(1.2,2.42)
1.38(1.19,2.04)
1.27(1.08, 1.95)
1 (1, 1)
1 (1, 1)
1 (1, 1)
4.26(2.13, 17.6)
4.19(2.22,21.5)
1.98(1.69,2.31)
2.74(2.1,5.62)
1.4(1.11,2.38)
2.55(1.51,3.96)
1.91(1.55,3.76)
1.52(1.26,2.91)
1.86(1.51,3.33)
Posterior population
GSD: median (2.5%,
97.5%)
1.16(1.1, 1.28)
1.34(1.25, 1.47)
1.29(1.17, 1.5)
7.83 (4.86, 12.6)
4.94 (2.73, 8.58)
1.1(1.07, 1.16)
1(1,1)
1(1,1)
1(1,1)
5.16(2.57,22.3)
5.02 (2.44, 18.5)
2.02 (1.77, 2.38)
4.02 (2.9, 5.64)
2.71(1.37,5.33)
2.25 (1.89, 2.87)
6.18(3.35, 11.3)
4.2 (2.48, 8.01)
4.02(1.57,33.9)
5-96
-------
Table 3-39. Prior and posterior uncertainty and variability in human PBPK model parameters (continued)
Parameter description
KM for renal TCEGSH
conjugation (mg/L)
VMAX for tracheo-bronchial
TCE oxidation (mg/hr)
KM for tracheo-bronchial
TCE oxidation (mg/L)
Fraction of respiratory
metabolism to systemic circ.
VMAX for hepatic
TCOH^TCA (mg/hr)
KM for hepatic
TCOH^TCA (mg/L)
VMAX for hepatic
TCOH^TCOG (mg/hr)
KM for hepatic
TCOH^TCOG (mg/L)
Rate constant for hepatic
TCOH^other (/hr)
Rate constant for TCA
plasma— mrine (/hr)
Rate constant for hepatic
TCA^other (/hr)
Rate constant for TCOG
liver— >bile (/hr)
Lumped rate constant for
TCOG bile^TCOH liver
(/hr)
Rate constant for
TCOG^urine (/hr)
Rate constant for hepatic
DCVG^DCVC (/hr)
Lumped rate constant for
DCVC^urinary
NAcDCVC (/hr)
Rate constant for DCVC
bioactivation (/hr)
PBPK parameter
KMKidDCVG
VMAxClara
KMClara
FracLungSys
VMAXTCOH
KMTCOH
VMAXGIUC
KMGluc
kMetTCOH
kUrnTCA
kMetTCA
kBile
kEHR
kUrnTCOG
kDCVG
kNAT
kKidBioact
Prior population
median: median (2.5%,
97.5%)
2.7(0.14,41)
25 (1, 260)
0.019 (0.0017, 0.5)
0.75(0.051,0.99)
42 (0.77, 2,200)
5 (0.23, 81)
720 (12, 50,000)
10 (0.53, 190)
0.83 (0.035, 10)
0.26 (0.038, 4)
0.19(0.01,2.6)
1.2 (0.059, 16)
0.074 (0.004, 1.4)
2.9 (0.061, 260)
0.044 (0.000063, 22)
0.00085 (0.000055, 0.041)
0.0022 (0.000095, 0.079)
Posterior population
median: median (2.5%,
97.5%)
0.76 (0.29, 5.8)
18(3.8,41)
0.31(0.057, 1.4)
0.96 (0.86, 0.99)
9.2 (5.5, 20)
2.2(1.3,4.5)
900 (340, 2,000)
130 (47, 290)
0.25 (0.042, 0.7)
0.11(0.083,0.15)
0.096(0.038,0.19)
2.5(1.1,6.9)
0.053 (0.033, 0.087)
2.4 (0.83, 7)
2.5(1.9,3.4)
0.0001 (0.000047, 0.0007)
0.023 (0.0062, 0.061)
Prior population GSD:
median (2.5%, 97.5%)
1.5(1.27,2.56)
2.25(1.85,3.25)
1.48(1.25,2.39)
1.12(1, 1.75)
1.83(1.46,3.43)
1.49(1.25,2.57)
1.83(1.48,3.5)
1.5(1.25,2.6)
1.5 (1.26, 3)
1.48(1.29,2.29)
1.48(1.26,2.57)
1.47(1.25,2.75)
1.52(1.26,2.64)
1.75(1.4,3.31)
1.48(1.25,2.83)
1.51(1.25,2.34)
1.51(1.25,2.57)
Posterior population
GSD: median (2.5%,
97.5%)
1.49(1.27,2.32)
2.9(2.12,6.49)
10.8(1.99,37.6)
1.02(1, 1.1)
3.15(2.3,5.44)
2.58(1.75,4.5)
2.29 (1.84, 4.57)
1.58(1.26,3.69)
5.13 (2.72, 16.7)
1.86(1.58,2.28)
2.52(1.79,4.34)
1.56(1.27,3.21)
1.72(1.35,2.51)
18.7(11.6,31.8)
1.51(1.3, 1.86)
1.47 (1.24, 2.48)
1.52(1.25,2.69)
5-97
-------
Table 3-40. CI widths (ratio of 97.5-2.5% estimates) and fold-shift in median estimate for the PBPK model
population median parameters, sorted in order of decreasing CI width3
Mouse
PBPK
parameter
KMDCVG
KMKidDCVG
VMAXDCVG
VMAxKidDCVG
kASTCA
kTSD
kEHR
FracOther
KMClara
kAS
kUrnTCOG
BMAX
KMGluc
kAD
kDissoc
VMAxClara
kMetTCOH
kBile
KMTCOH
VMAxGluc
Width of CI on population
median
Prior
2,230,000,000
1,170,000,000
400,000
357,000
89,300
1,190
412,000
567
351,000
91,900
4,0500,000
81.8
344,000
84,900
60.3
131
35,500,000
390,000
29,600,000
23,600,000
Posterior
13,400,000
3,540,000
46,200
11,000
374
51.1
42.1
39.5
37.5
35.9
29.9
24.4
24.3
23.8
21.8
15
12.1
11.3
10.5
8.28
Fold-shift
in
population
median
x8.8
xl.05
-6.18
-12.8
x6.3
x3.26
-5.43
-18.5
-134
xl
xll.8
xl.66
xl6.3
-4.53
xl.33
xl.75
x47.4
x8.23
-1.47
x41.1
Rat
PBPK
parameter
KMDCVG
VMAXDCVG
kUrnTCOG
PBodTCOG
kASTCA
kTSD
kAS
KMKidDCVG
kKidBioact
KMClara
VMAxKidDCVG
kMetTCOH
kAD
KMTCOH
kNAT
kEHR
KMGluc
VMAxClara
FracLungSys
PLivTCOG
Width of CI on
population median
Prior
1,500,000
71,100
16,700
666
98,200
1,130
91,000
1,130,000
366,000
406
48,500
891
115,000
781
351,000
1,160
562
305
36.7
501
Posterior
5,800
2,130
822
172
95.7
61.8
60.2
58.6
35.6
29.9
27.5
26.4
26.3
26
22.7
21.9
17.1
16.5
16.3
14.8
Fold-shift
in
population
median
x4.29
x2.86
xl.04
x3.43
xl.69
x2.29
x3.41
-1880
-13.3
xl.53
-15.6
-1.41
-5.53
x!8.7
-50.2
xl34
-4.98
-1.21
-1.12
x8.07
Human
PBPK
parameter
kASTCA
kASTCOH
VMAX"
KidDCVG
KMClara
KMKidDCVG
kMetTCOH
kNAT
VMAxClara
kKidBioact
VMAXDCVG
FracOther
PLivTCOG
KMDCVG
kUrnTCOG
kBile
KMGluc
PBodTCOG
VMAxGluc
KM
kMetTCA
Width of CI on
population median
Prior
94,300
85,900
236,000,000
289
287
289
756
255
833
122,000,000
106
253
198
4,290
274
365
454
4,330
288
248
Posterior
3,040
1,420
55.1
23.9
20
16.6
15.1
10.6
9.91
9.78
9.75
9.32
9.13
8.5
6.54
6.07
5.85
5.71
5.1
4.89
Fold-shift
in
population
median
x5.18
x!5.6
-105
xl6.2
-3.48
-3.28
-8.14
-1.41
xlO.5
x3.29
-1.09
xl.77
xl.18
-1.19
x2.01
xl3.4
xl.08
xl.25
-20.5
-1.94
5-98
-------
Table 3-40. CI widths (ratio of 97.5-2.5% estimates) and fold-shift in median estimate for the PBPK model
population median parameters, sorted in order of decreasing CI width3 (continued)
Mouse
PBPK
parameter
PBodTCOG
VMAXTCOH
KM
kUrnTCA
FracLungSys
kMetTCA
PLivTCOG
DResp
PLivTCA
PResp
PRap
PGut
VMAX
PBodTCA
PSlw
PLiv
FracTCA
TCAPlas
PKid
QFatC
PLivTCOH
PBodTCOH
QKidC
PFat
QSlwC
VPlasC
WatC
QP
VLivC
Width of CI on population
median
Prior
4,770
27,100,000
386
4,540
608
316,000
4,860
475,000
58.3
4
3.78
4.33
10.7
62.6
4.04
3.87
3,060
40.6
4.78
3.62
3.19
3.41
2.39
3.01
2.04
2.18
3.49
2.75
1.85
Posterior
6.27
5.78
5.76
5.76
5.55
4.59
3.99
3.64
2.88
2.85
2.79
2.77
2.67
2.55
2.54
2.5
2.49
2.38
2.37
2.26
2.13
2.01
.91
.89
.88
.87
.83
.82
1.6
Fold-shift
in
population
median
-1.95
xl.8
-12.5
-10.2
x2.27
xi2
xl.04
x!47
xl
-1.07
-1.03
-1.25
-1.58
xl.14
-1.06
xl.26
xl.49
xl.46
xl.2
xl.02
xl.48
-1.27
-1.01
-1.01
-1.02
-1.17
xl.25
-1.02
-1.16
Rat
PBPK
parameter
kBile
FracOther
VMAXTCOH
VnAxGluc
kMetTCA
BMAX
DResp
PLivTCOH
PBodTCOH
kDissoc
FracTCA
PLivTCA
kUrnTCA
PBodTCA
PResp
KM
PRap
PGut
VMAX
QRapC
WatC
PKid
QP
PSlw
PFat
QSlwC
QFatC
VPlasC
PB
Width of CI on
population median
Prior
588
331
550
740
507
16.2
180
11.5
12.1
8.38
28.1
13.3
219
12
4.32
123
4.01
4.35
9.5
2.77
3.58
2.89
3.59
2.76
2.91
2.19
3.47
2.17
2.37
Posterior
14.8
11.9
8.25
7.79
6.93
5.79
4.81
4.7
4.03
3.85
3.85
3.49
3.28
2.8
2.6
2.56
2.53
2.16
.98
.97
.96
.85
.79
.79
.77
.69
.66
.55
.51
Fold-shift
in
population
median
x9.67
xlO.7
-1.06
-2.4
-1.61
xl
x2.12
-1.09
xl.03
xl.04
-4.27
xl.37
-2
xl.09
xl.04
-24
-1.01
-1.17
-1.11
-1
-1
-1.11
-1.38
xl.28
xl.16
-1.06
xl.14
xl.03
-1.15
Human
PBPK
parameter
DResp
VMAXTCOH
KMTCOH
kEHR
VMAX
PResp
PLiv
QLivC
PGut
FracTCA
PLivTCA
PLivTCOH
kDCVG
kUrnTCA
VFatC
PRap
QFatC
PBodTCA
PSlw
PKid
QP
QSlwC
QC
BMAX
VLivC
PFat
VDCVG
VRespEffC
PBodTCOH
Width of CI on
population median
Prior
74.3
2,900
359
339
11.5
4.1
4.44
3.46
4.21
15.5
42.6
3.52
344,000
105
3.49
4.66
3.7
42.9
2.9
2.05
2.97
2.25
2.04
1.92
1.79
2.13
6,820
1.66
3.32
Posterior
3.71
3.62
3.48
2.62
2.27
2.16
2.14
2.11
2.1
2.06
1.98
1.93
1.8
1.79
1.76
1.74
1.7
1.7
1.5
.49
.48
.48
.39
.38
.36
.34
.34
.33
.32
Fold-shift
in
population
median
-2.06
-4.56
-2.33
-1.39
-2.33
-1.01
xl.02
-1.62
xl.ll
-5.37
-1.07
xl.08
x55.7
-2.32
- .21
- .09
- .19
- .04
x .11
- .01
x .16
- .26
- .19
- .12
x .01
- .2
-12
- .02
x .68
5-99
-------
Table 3-40. CI widths (ratio of 97.5-2.5% estimates) and fold-shift in median estimate for the PBPK model
population median parameters, sorted in order of decreasing CI width3 (continued)
Mouse
PBPK
parameter
QC
PB
QLivC
QRapC
VGutC
VBldC
VRespLumC
VRespEffC
QGutC
VKidC
VRapC
VSlwC
VBodC
VBodTCOHC
Width of CI on population
median
Prior
2.1
2.3
.55
.51
.38
.34
.32
.31
.52
.29
1.3
.19
.05
.04
Posterior
.59
.54
.42
.41
1.3
.27
.26
.26
.24
.24
.23
.11
.03
.03
Fold-shift
in
population
median
xl.2
-1.07
xl.02
-1.03
-1.01
-1.02
-1.01
-1
xl.15
-1
-1.01
-1.01
xl.Ol
xl.Ol
Rat
PBPK
parameter
QC
VRespEffC
VRespLumC
VLivC
PLiv
QLivC
VKidC
QKidC
VGutC
VBldC
VRapC
QGutC
rCAPlas
VSlwC
VBodC
VBodTCOHC
Width of CI on
population median
Prior
1.64
1.56
1.56
1.57
1.67
1.53
1.47
1.39
1.38
1.34
1.34
1.53
1.6
1.15
1.04
1.02
Posterior
.43
.43
.41
1.4
.37
.34
.33
.28
.28
.25
.23
.22
.21
.09
.03
.01
Fold-shift
in
population
median
xl.15
-1
xl
-1.05
xl.05
xl.04
xl.Ol
xl
-1.01
xl.Ol
xl
xl.14
-1.01
xl
xl
xl
Human
PBPK
parameter
VRespLumC
TCAPlas
VKidC
PB
QRapC
QGutC
VSlwC
VPlasC
QKidC
VBldC
FracLungSys
VRapC
kDissoc
VGutC
VBodC
VBodTCOHC
Width of CI on
population median
Prior
1.65
26.9
1.54
2.04
2.22
.59
.66
.39
.36
.34
9.4
.22
.23
.22
.04
.02
Posterior
.31
.29
.28
.28
.25
.23
.21
1.2
.17
.17
.14
.12
.12
.11
.02
.01
Fold-shift
in
population
median
-
- .21
- .01
- .04
x .34
- .19
x .07
x .01
-
x .02
x .29
X
- .01
x .01
-
-
aShifts in the median estimate greater than threefold are in bold to denote larger shifts between the prior and posterior distributions
5-100
-------
However, for some parameters, the posterior distributions in the population medians had
CIs >100-fold. In mice, the absorption parameter for TCA still had posterior CI of 400-fold,
reflecting the fact that the absorption rate is poorly estimated from the few available studies with
TCA dosing. In addition, mouse metabolism parameters for GSH conjugation have posterior CIs
>10,000-fold, reflecting the lack of any direct data on GSH conjugation in mice. In rats, two
parameters related to TCOH and TCOG had CIs between 100- and 1,000-fold, reflecting the
poor identifiability of these parameters given the available data. In humans, only the oral
absorption parameters for TCA and TCOH had CIs >100-fold, reflecting the fact that the
absorption rate is poorly estimated from the few available studies with TCOH and TCA dosing.
In terms of general consistency between prior and posterior distributions, in most cases,
the central estimate of the population median shifted by less than threefold. In almost all of the
cases that the shift was greater (see bold entries in Table 3-40), the prior distribution had a wide
distribution, with CI greater (sometimes substantially greater) than 100-fold. The only exception
was the fraction of TCE oxidation directly producing TCA, which shifted by fourfold in rats and
fivefold in mice, with prior CIs of 28- and 16-fold, respectively. These shifts are still relatively
modest in comparison to the prior CI, and moreover, the posterior CI is quite narrow (fourfold in
rats, twofold in humans), suggesting that the parameter is well identified by the in vivo data.
In addition, there were only a few cases in which the interquartile regions of the prior and
posterior distributions did not overlap. In most of these cases, including the diffusion rate from
respiratory lumen to tissue, the KM values for renal TCE GSH conjugation and respiratory TCE
oxidation, and several metabolite kinetic parameters, the prior distributions themselves were
noninformative. For a noninformative prior, the lack of overlap would only be an issue if the
posterior distributions were affected by the truncation limit, which was not the case. The only
other parameter for which there was a lack of interquartile overlap between the prior and
posterior distribution was the KM for hepatic TCE oxidation in mice and in rats, though the prior
and posterior 95% CIs did overlap within each species. As discussed Section 3.3, there is some
uncertainty in the extrapolation of in vitro KM values to in vivo values (within the same species).
In addition, in mice, it has been known for some time that KM values appear to be discordant
among different studies (Greenberg et al., 1999; Abbas and Fisher, 1997; Fisher et al., 1991).
In terms of estimates of population variability, for the vast majority of parameters, the
posterior estimate of the population GSD was either twofold or less, indicating modest
variability. In some cases, while the posterior population GSD was greater than twofold, it was
similar to the prior estimate of the population GSD, indicating limited additional informative
data on variability. This was the case for oral absorption parameters, which are expected to be
highly variable because the current model lumps parameters for different oral dosing vehicles
together, and a relatively wide prior distribution was given. In addition, in some cases, this was
due to in vitro data showing a higher degree of variability. Examples of this include TCA
plasma binding parameters in the mouse, and the VMAX for hepatic oxidation and the fraction of
3-101
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oxidation to TCA in humans. In a few other cases, the in vivo data appeared to indicate greater
than twofold variability between subjects, and these are discussed in more detail below.
In the mouse, the two parameters for which this is the case are the VMAX for respiratory
tract oxidation and the urinary excretion rate for TCOG. In the first case, the variability is driven
by the need for a higher respiratory tract VMAX for males in the Fisher et al. (1991) study as
compared to other studies. In the second case, it is driven by the relatively low estimate of
urinary excretion of TCOG in the Abbas and Fisher (1997), Abbas et al. (1997), and Greenberg
et al. (1999) studies as compared with the relatively high estimate in Green and Prout (1985) and
Prout et al. (1985).
In the rat, the two parameters for which the in vivo data suggest greater than twofold
variability are the fraction of oxidation not producing TCA or TCOH, and the VMAX for
respiratory tract oxidation. In the first case, this is driven by three studies that appeared to
require greater (Bernauer et al., 1996; Kimmerle and Eben, 1973b) or lower (Hissink et al., 2002)
estimates for this parameter as compared with the other studies. Nonetheless, the degree of
variability is not much greater than twofold, with a central estimate population GSD of 2.15-fold.
In the case of the VMAX for respiratory tract oxidation, two studies appeared to require higher
(Fisher et al., 1989) or lower (Simmons et al., 2002) values for this parameter as compared with
the other studies.
In humans, as would be expected, more parameters appeared to exhibit greater than
twofold variability. In terms of distribution, the partition coefficients for TCOG had rather large
posterior estimates for the population GSD of eightfold for the body and fivefold for the liver. In
terms of the body, a few of the subjects in Fisher et al. (1998) and all of the subjects in Monster
et al. (1976) appeared to require much higher partition coefficients for TCOG. For the liver, the
variability did not have a discernable trend across studies. In addition, almost all of the
metabolism and clearance parameters had posterior estimates for population variability of greater
than a twofold GSD. The largest of these was the urinary excretion rate for TCOG, with a GSD
of 19-fold. In this case, the variability was driven by individuals in the Chiu et al. (2007) 1 ppm
study, who were predicted to have much lower rate of urinary excretion as compared to that
estimated in the other, higher exposure studies.
In sum, the Bayesian analysis of the updated PBPK model and data exhibited no major
inconsistencies in prior and posterior parameter distributions. The most significant issue in terms
of population central estimates was the KM for hepatic oxidative metabolism, for which the
posterior estimates were low compared to, albeit somewhat uncertain, in vitro estimates, and it
could be argued that a wider prior distribution would have been better. However, the central
estimates were not at or near the truncation boundary, so it is unlikely that wider priors would
change the results substantially. In terms of population variability, in rodents, the estimates of
variability were generally modest, which is consistent with more homogeneous and controlled
experimental subjects and conditions, whereas the estimates of human population variability
3-102
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were greater—particularly for metabolism and clearance. Overall, there were no indications
based on this evaluation of prior and posterior distributions either that prior distributions were
overly restrictive or that model specification errors led to pathological parameter estimates.
3.5.6.3. Comparison of Model Predictions With Data
Comparisons of model predictions and data for each species are discussed in the sub-
sections below. First, as an overall summary, for each species and each output measurement, the
data and predictions generated from a random sample of the MCMC chain are scatter-plotted to
show the general degree of consistency between data and predictions. Next, as with the Hack
et al. (2006) model, the sampled subject-specific parameters were used to generate predictions
for comparison to the calibration data (see Figure 3-8). Thus, the predictions for a particular data
set are conditioned on the posterior parameter distributions for same data set. Because these
parameters were -optimized" for each experiment, these subject-specific predictions should be
accurate by design—and, on the whole, were so. In addition, the —residuabrror" estimate for
each measurement (see Table 3-41) provides some quantitative measure of the degree to which
there were deviations due to intrastudy variability and model misspecification, including any
difficulties fitting multiple dose levels in the same study using the same model parameters.
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MCMC outputs
Posterior
Posterior I2
Posterior subject-
specific
Posterior population
prediction^
Yjki
-------
Table 3-41. Estimates of the residual-error (continued)
Measurement
abbreviation
CPlasTCA
CBldTCA
CLivTCA
AUrnTCA
AUrnTCA_collect
CTCOH
CLivTCOH
TotCTCOH
ABileTCOG
CTCOG
CTCOGTCOH
CLivTCOGTCOH
AUrnTCOGTCOH
AUrnTCOGTCOH_
collect
AUrnTCTotMole
CDCVGmol
AUrnNDCVC
Measurement description
TCA concentration in plasma (mg/L)
TCA concentration in blood (mg/L)
TCA concentration in liver (mg/L)
Cumulative amount of TCA excreted in urine (mg)
Cumulative amount of TCA collected in urine
(noncontinuous sampling) (mg)
Free TCOH concentration in blood (mg/L)
Free TCOH concentration in liver (mg/L)
Total TCOH concentration in blood (mg/L)
Cumulative amount of bound TCOH excreted in bile
(mg)
Bound TCOH concentration in blood
Bound TCOH concentration in blood in free TCOH
equivalents
Bound TCOH concentration in liver in free TCOH
equivalents (mg/L)
Cumulative amount of total TCOH excreted in urine
(mg)
Cumulative amount of total TCOH collected in urine
(noncontinuous sampling) (mg)
Cumulative amount of TCA+total TCOH excreted in
urine (mmol)
DCVG concentration in blood (mmol/L)
Cumulative amount of NAcDCVC excreted in urine
(mg)
GSD for "residual" error
(median estimate)3
Mouse
1.40
1.49
1.34
1.34
-
1.54
1.59
1.85
-
-
1.49
1.63
1.26
-
-
-
-
Rat
1.13-1.21
1.13-1.59
1.67
1.18-1.95
-
1.14-1.64
-
1.49
2.13
2.76
-
-
1.12-2.27
-
1.12-1.54
-
1.17
Human
1.12-1.17
1.12-1.49
-
1.11-1.54
2-2.79
1.14-2.1
-
1.2-1.69
-
-
-
-
1.11-1.13
1.3-1.63
-
1.53
1.17
"Values higher than twofold are in bold.
Next, only samples of the population parameters (means and variances) were used, and
new subjects were sampled from appropriate distribution using these population means and
variances (see Figure 3-8). That is, the predictions were only conditioned on the population-
level parameters distributions, representing an "average" over all of the data sets, and not on the
specific predictions for that data set. These —new subjects then represent the predicted
population distribution, incorporating variability in the population as well as uncertainty in the
population means and variances. Because of the limited amount of mouse data, all available data
for that species were utilized for calibration, and there were no data available for —outof-
sample" evaluation (often referred to as -v-alidation data," but this term is not used here due to
ambiguities as to its definition). In rats, several studies that contained primarily blood TCE data,
which were abundant, were used for out-of-sample evaluation. In humans, there were substantial
individual and aggregated (mean of individuals in a study) data that were available for out-of-
sample evaluation, as computational intensity limited the number of individuals who could be
used in the MCMC-based calibration.
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3.5.6.3.1. Mouse model and data
Each panel of Figure 3-9 shows a scatter plot of the calibration data and a random
posterior prediction for each of the measured endpoint. The endpoint abbreviations are listed in
Table 3-41, as are the implied GSDs for the —esidual" errors, which include intrastudy
variability, interindividual variability, and measurement and model errors. The residual-error
GSDs are also shown as grey dotted lines in Figure 3-9. Table 3-42 provides an evaluation of
the predictions of the mouse model for each data set, with figures showing individual time-
course data and predictions in Appendix A.
AExhpost Data
CPIasTCA Data
Each panel shows results for a different measurement. The solid line represents
prediction = data, and the grey dotted lines show prediction = data * GSDerr and
data + GSDerr, where GSDerr is the median estimate of the residual-error GSD
shown in Table 3-41.
Figure 3-9. Comparison of mouse data and PBPK model predictions from a
random posterior sample.
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1CT1 1 101 102 103
ClivTCA Data
AUrnTCA Data
10'z 10"1 1 10' 102 103
CTCOH Data
M
10"1 1 101 1Q2 103
CLivTCOH Data
TotCTCOH Data
10"' 1
CTCOGTCOH Data
1CT1 1 101 102
CLivTCOGTCOH Data
AUrnTCOGTCOH Data
Each panel shows results for a different measurement. The solid line represents
prediction = data, and the grey dotted lines show prediction = data * GSDerr and
data + GSDerr, where GSDerr is the median estimate of the residual-error GSD
shown in Table 3-41.
Figure 3-9 (continued). Comparison of mouse data and PBPK model
predictions from a random posterior sample.
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Table 3-42. Summary comparison of updated PBPK model predictions and
in vivo data in mice
Study
Exposure(s)
Discussion
Abbas and Fisher
(1997)
TCE gavage
(corn oil)
Generally, model predictions were quite good, especially with respect to
tissue concentrations of TCE, TCA, and TCOH. There were some
discrepancies in TCA and TCOG urinary excretion and TCA and TCOG
concentrations in blood due to the requirement (unlike in Hack et al. 2006)
that all experiments in the same study utilize the same parameters. Thus, for
instance, TCOG urinary excretion was accurately predicted at 300 mg/kg,
underpredicted at 600 mg/kg, overpredicted at 1,200 mg/kg, and
underpredicted again at 2,000 mg/kg, suggesting significant
intraexperimental variability (not addressed in the model). Population
predictions were quite good, with the almost all of the data within the 95%
CI of the predictions, and most within the interquartile region.
Abbas et al. (1997)
TCOH, TCA i.v.
Both subject-specific and population predictions were quite good. Urinary
excretion, which was overpredicted by the Hack et al. (2006) model, was
accurately predicted due to the allowance of additional -ttntracked"
clearance. In the case of population predictions, almost all of the data were
within the 95% CI of the predictions, and most within the interquartile
region.
Fisher and Allen
(1993)
TCE gavage
(corn oil)
Both subject-specific and population predictions were quite good. Some
discrepancies in the time-course of TCE blood concentrations were evidence
across doses in the subject-specific predictions, but not in the population
predictions, suggesting significant intrasubject variability (not addressed in
the model).
Fisher et al. (1991)
TCE inhalation
Blood TCE levels during and following inhalation exposures were still
overpredicted at the higher doses. However, there was the stringent
requirement (absent in Hack et al.. 2006) that the model utilize the same
parameters for all doses and in both the closed and open-chamber
experiments. Moreover, the Hack et al. (2006) model required significant
differences in the parameters for the different closed-chamber experiments,
while predictions here were accurate utilizing the same parameters across
different initial concentrations. These conclusions were the same for
subject-specific and population predictions (e.g., TCE blood levels remained
overpredicted in the later case).
Green and Prout
(1985)
TCE gavage
(corn oil)
Both subject-specific and population predictions were adequate, though the
data collection was sparse. In the case of population predictions, almost all
of the data were within the 95% CI of the predictions, and about half within
the interquartile region.
Greenberg et al.
(1999)
TCE inhalation
Model predictions were quite good across a wide variety of measures that
included tissue concentrations of TCE, TCA, and TCOH. However, as with
the Hack et al. (2006) predictions, TCE blood levels were overpredicted by
up to twofold. Population predictions were quite good, with the exception of
TCE blood levels. Almost all of the other data was within the 95% CI of the
predictions, and most within the interquartile region.
Larson and Bull
(1992a)
TCE gavage
(aqueous)
Both subject-specific and population predictions were quite good, though the
data collection was somewhat sparse. In the case of population predictions,
all of the data were within the 95% CI of the predictions.
Larson and Bull
(1992b)
TCA gavage
(aqueous)
Both subject-specific and population predictions were quite good. In the
case of population predictions, most of the data were within the interquartile
region.
Merdink et al.
(1998)
TCE i.v.
Both subject-specific and population predictions were quite good, though the
data collection was somewhat sparse. In the case of population predictions,
all of the data were within the 95% CI of the predictions.
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Table 3-42. Summary comparison of updated PBPK model predictions and
in vivo data in mice (continued)
Study
Prout et al. (1985)
Templin et al.
(1993)
Exposure(s)
TCE gavage
(corn oil)
TCE gavage
(aqueous)
Discussion
Both subject-specific and population predictions were adequate, though
there was substantial scatter in the data due to the use of single animals at
each data point.
Both subject-specific and population predictions were quite good. With
respect to population predictions, almost all of the other data was within the
95% CI of the predictions, and most within the interquartile region.
In terms of total metabolism, closed-chamber data (see Figure 3-9, panel A) were fit
accurately with the updated model, with a small residual-error GSD of 1.18. While the previous
analyses of Hack et al. (2006) allowed for each chamber experiment to be fit with different
parameters, the current analysis made the more restrictive assumption that all experiments in a
single study utilize the same parameters. Furthermore, the accuracy of closed-chamber
predictions did not require the very high values for cardiac output that were used by Fisher et al.
(1991), confirming the suggestion (discussed in Appendix A) that additional respiratory
metabolism would resolve this discrepancy. The accurate model means that uncertainty with
respect to possible wash-in/wash-out, respiratory metabolism, and extrahepatic metabolism could
be well characterized. For instance, the absence of in vivo data on GSH metabolism in mice
means that this pathway remains relatively uncertain; however, the current model should be
reliable for estimating lower and upper bounds on the GSH pathway flux.
In terms of the parent compound TCE (see Figure 3-9, panels B-G), the parent PBPK
model (for TCE) appears to now be robust, with the exception of the remaining overprediction of
TCE in blood following inhalation exposure. As expected, the venous-blood TCE concentration
had the largest residual-error, with a GSD of 2.7, reflecting largely the difficulty in fitting TCE
blood levels following inhalation exposure. In addition, the fat and kidney TCE concentrations
also are somewhat uncertain, with a GSD for the residual-error of 2.5 and 2.2, respectively.
These tissues were only measured in two studies, Abbas and Fisher (1997) and Greenberg et al.
(1999), and the residual-error reflects the difficulties in simultaneously fitting the model to the
different dose levels with the same parameters. Residual-error GSDs for other TCE
measurements were less than twofold. Thus, most of the problems previously encountered with
the Abbas and Fisher (1997) gavage data were solved by allowing absorption from both the
stomach and duodenal compartments. Notably, the addition of possible wash-in/wash-out,
respiratory metabolism, and extrahepatic metabolism (i.e., kidney GSH conjugation) was
insufficient to remove the long-standing discrepancy of PBPK models overpredicting TCE blood
levels from mouse inhalation exposures, suggesting another source of model or experimental
error is the cause. However, the availability of tissue concentration levels of TCE somewhat
ameliorates this limitation.
3-109
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In terms of TCA and TCOH, the overall mass balance and metabolic disposition to these
metabolites also appeared to be robust, as urinary excretion following dosing with TCE, TCOH,
and TCA could be modeled accurately (see Figure 3-9, panels K and Q). The residual GSDs for
the urinary excretions are small: 1.34 for TCA and 1.26 for total TCOH. In addition, the blood
and tissue concentrations were also accurately predicted (see Figure 3-9, panels H-J, L-P). All of
the residual GSDs were less than twofold, with those for TCA measurements <1.5-fold. This
improvement over the Hack et al. (2006) model was likely due in part to the addition of
nonurinary clearance (—ntracked" metabolism) of TCA and TCOH. Also, the addition of a liver
compartment for TCOH and TCOG, so that first-pass metabolism could be properly accounted
for, was essential for accurate simulation of the metabolite pharmacokinetics both from
intravenous (i.v.) dosing of TCOH and from exposure to TCE.
3.5.6.3.2. Rat model and data
Each panel of Figure 3-10 shows a scatter plot of the calibration data and a random
posterior prediction for each of the measured endpoint. The endpoint abbreviations are listed in
Table 3-41, as are the implied GSDs for the residual" errors, which include intrastudy
variability, interindividual variability, and measurement and model errors. The residual-error
GSDs are also shown as grey dashed or dotted lines in Figure 3-10. A summary evaluation of
the predictions of the rat model as compared to the data are provided in Tables 3-43 and 3-44,
with figures showing individual time-course data and predictions in Appendix A.
3-110
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ClnhPPM Data
CMixExh Data
1(T2 10"' 1 10' 10a
CArt Data
102
CBIdMix Data
10"2 1tr' 1 10' 102
CGut Data
10"' 1 101 10Z 103
CUv Data
Each panel shows results for a different measurement. The solid line represents
prediction = data, and the grey lines show prediction = data * GSDerr and data ^
GSDerr, where GSDerr is the lowest (dotted) and highest (dashed) median estimate
of the residual-error GSD shown in Table 3-41.
Figure 3-10. Comparison of rat data and PBPK model predictions from a
random posterior sample.
3-111
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1CT2 10"' 1 101 102
CMus Data
jji p^-j^m j™^^™^
1G~2 10*1 1 101 102 103
AExhpost Data
•T--rTrrrrfpI~Tn~rnTnr-~~T~TTTrni|~'^
10"' 1 10' 1Q2 103
CPlasTCA Data
(
it,*
10"1 1 10' 10s 1Q3
CBldTCA Data
CLtvTCA Data
AUrnTCA Data
10"'1 1 101 TO2 103
CTCOH Data
10-'
102
TotCTCOH Data
ABileTCOG Data
Each panel shows results for a different measurement. The solid line represents
predict!on=data, and the grey lines show prediction = data * GSDerr and data ^
GSDerr, where GSDerr is the lowest (dotted) and highest (dashed) median estimate
of the residual-error GSD shown in Table 3-41.
Figure 3-10 (continued). Comparison of rat data and PBPK model
predictions from a random posterior sample.
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* a -
10"4 -icr3 -icr2
AUrnNDCVC Data
AUrnTCOGTCOH Data
AUrnTCTotMoIe Data
Each panel shows results for a different measurement. The solid line represents
predict! on=data, and the grey lines show prediction = data * GSDerr and data +
GSDgrr, where GSDerr is the lowest (dotted) and highest (dashed) median estimate
of the residual-error GSD shown in Table 3-41.
Figure 3-10 (continued). Comparison of rat data and PBPK model
predictions from a random posterior sample.
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Table 3-43. Summary comparison of updated PBPK model predictions and
in vivo data used for —alibration" in rats
Study
Exposure(s)
Discussion
Bernauer et al.
(1996)
TCE inhalation
Posterior fits to these data were adequate, especially with the requirement that
all predictions for dose levels utilize the same PBPK model parameters.
Predictions of TCOG and TCA urinary excretion was relatively accurate,
though the time-course of TCA excretion seemed to proceed more slowly
with increasing dose, an aspect not captured in the model. Importantly, unlike
the Hack et al. (2006) results, the time-course of NAcDCVC excretion was
quite well simulated, with the excretion rate remaining nonnegligible at the
last time point (48 hrs). It is likely that the addition of the DCVG submodel
between TCE and DCVC, along with prior distributions that accurately
reflected the lack of reliable, independent (e.g., in vitro) data on bioactivation,
allowed for the better fit.
Dallas et al.
(1991)
TCE inhalation
These data, consisting of arterial blood and exhaled breath concentrations of
TCE, were accurately predicted by the model using both subject-specific and
population-sampled parameters. In the case of population predictions, most
of the data were within the 95% CI of the predictions.
Fisher et al.
(1989)
TCE inhalation
These data, consisting of closed-chamber TCE concentrations, were
accurately simulated by the model using both subject-specific and population-
sampled parameters. In the case of population predictions, most of the data
were within the 95% CI of the predictions.
Fisher et al.
(1991)
TCE inhalation
These data, consisting of TCE blood, and TCA blood and urine time-courses,
were accurately simulated by the model using both subject-specific and
population-sampled parameters. In the case of population predictions, most
of the data were within the 95% CI of the predictions.
Table 3-43. Summary comparison of updated PBPK model predictions and
in vivo data used for —alibration" in rats (continued)
Study
Exposure(s)
Discussion
Green and Prout
(1985)
TCE gavage (corn
oil)
TCA i.v.
TCA gavage
(aqueous)
For TCE treatment, these data, consisting of one time point each in urine for
TCA, TCA +TCOG, and TCOG, were accurately simulated by both subject-
specific and population predictions.
For TCA i.v. treatment, the single datum of urinary TCA+TCOG at 24 hrs
was at the lower 95% CI in the subject-specific simulations, but accurately
predicted with the population-sampled parameters, suggesting intrastudy
variability is adequately accounted for by population variability.
For TCA gavage treatment, the single datum of urinary TCA+TCOG at
24 hrs was accurately simulated by both subject-specific and population
predictions.
Hissink et al.
(2002)
TCE gavage (corn
oil)
TCE i.v.
These data, consisting of TCE blood, and TCA+TCOG urinary excretion
time-courses, were accurately simulated by the model using subject-specific
parameters. In the case of population predictions, TCA+TCOH urinary
excretion appeared to be somewhat underpredicted.
Kaneko et al.
f!994)
TCE inhalation
These data, consisting of TCE blood and TCA and TCOG urinary excretion
time-courses, were accurately predicted by the model using both subject-
specific and population-sampled parameters. In the case of population
predictions, TCA+TCOH urinary excretion appeared to be somewhat
underpredicted, However, all of the data were within the 95% CI of the
predictions.
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Table 3-43. Summary comparison of updated PBPK model predictions and
in vivo data used for —alibration" in rats (continued)
Study
Exposure(s)
Discussion
Keys et al.
(2003)
TCE inhalation,
gavage (aqueous),
i.a.
These data, consisting of TCE blood, gut, kidney, liver, muscle, and fat
concentration time-courses, were accurately predicted by the model using
both subject-specific and population-sampled parameters. In the case of
population predictions, most of the data were within the 95% CI of the
predictions.
Kimmerle and
Eben (1973b)
TCE inhalation
Some inaccuracies were noted in subject-specific predictions, particularly
with TCA and TCOG urinary excretion, TCE exhalation postexposure, and
TCE venous blood concentrations. In the case of TCA excretion, the rate was
underpredicted at the lowest dose (49 mg/kg) and overpredicted at 330 ppm.
In terms of TCOG urinary excretion, the rate was overpredicted at 175 ppm
and underpredicted at 330 ppm. Similarly for TCE exhaled postexposure,
there was some overprediction at 175 ppm and some underprediction at
300 ppm. Finally, venous blood concentrations were overpredicted at
3,000 ppm. However, for population predictions, most of the data were
within the 95% confidence region.
Larson and Bull
f!992b)
TCA gavage
(aqueous)
These data, consisting of TCA plasma time-courses, were accurately
predicted by the model using both subject-specific and population-sampled
parameters. In the case of population predictions, all of the data were within
the 95% CI of the predictions.
Larson and Bull
f!992a)
TCE gavage
(aqueous)
These data, consisting of TCE, TCA, and TCOH in blood, were accurately
predicted by the model using both subject-specific and population-sampled
parameters. In the case of population predictions, all of the data were within
the 95% CI of the predictions.
Lee et al.
(2000a: Lee et
al.. 2000b)
TCE i.v., p.v.
These data, consisting of TCE concentration time course in mixed arterial
and venous blood and liver, were predicted using both the subject specific
and population predictions. In both cases, most of the data were within the
95% CI of the predictions.
Merdink et al.
(1999)
TCOH i.v.
TCOH blood concentrations were accurately predicted using subject-specific
parameters. However, population-based parameters seemed to lead to some
underprediction, though most of the data were within the 95% CI of the
predictions.
Prout et al.
(1985)
TCE gavage (corn
oil)
Most of these data were accurately predicted using both subject-specific and
population-sampled parameters. However, at the highest two doses
(1,000 and 2,000 mg/kg), there were some discrepancies in the (very sparsely
collected) urinary excretion measurements. In particular, using subject-
specific parameters, TCA+TCOH urinary excretion was underpredicted at
1,000 mg/kg and overpredicted at 2,000 mg/kg. Using population-sampled
parameters, this excretion was underpredicted in both cases, though not
entirely outside of the 95% CI.
Simmons et al.
(2002)
TCE inhalation
Most of these data were accurately predicted using both subject-specific and
population-sampled parameters. In the open-chamber experiments, there was
some scatter in the data that did not seem to be accounted for in the model.
The closed-chamber data were accurately fit.
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Table 3-43. Summary comparison of updated PBPK model predictions and
in vivo data used for —alibration" in rats (continued)
Study
Exposure(s)
Discussion
Stenner et al.
(1997)
TCE
intraduodenal
TCOH i.v.
TCOH i.v., bile-
cannulated
These data, consisting of TCA and TCOH in blood and TCA and TCOG in
urine, were generally accurately predicted by the model using both subject-
specific and population-sampled parameters. However, using subject-
specific parameters, the amount of TCOG in urine was overpredicted for
100 TCOH mg/kg i.v. dosing, though total TCOH in blood was accurately
simulated. In addition, in bile-cannulated rats, the TCOG excretions at 5 and
20 mg/kg i.v. were underpredicted, while the amount at 100 mg/kg was
accurately predicted. On the other hand, in the case of population
predictions, all of the data were within the 95% CI of the predictions, and
mostly within the interquartile region, even for TCOG urinary excretion.
This suggests that intrastudy variability may be a source of the poor fit in
using the subject-specific parameters.
Templin et al.
1995b)
TCE oral
(aqueous)
These data, consisting of TCE, TCA, and TCOH in blood, were accurately
predicted by the model using both subject-specific and population-sampled
parameters. In the case of population predictions, all of the data were within
the 95% CI of the predictions.
Yu et al. (2000)
TCA i.v.
These data, consisting of TCA in blood, liver, plasma, and urine, were
generally accurately predicted by the model using both subject-specific and
population-sampled parameters. The only notable discrepancy was at the
highest dose of 50 mg/kg, in which the rate of urinary excretion from 0 to
6 hrs appeared to more rapid than the model predicted. However, all of the
data were within the 95% CI of the predictions based on population-sampled
parameters.
Table 3-44. Summary comparison of updated PBPK model predictions and
in vivo data used for —ut-of-sample" evaluation in rats
Study
Andersen et al.
(1987a)
Bruckner et al.
unpublished
Fisher et al. (1991)
Jakobson et al.
(1986)
Lee et al. (1996)
Lee et al. (2000a;
2000b)
Exposure(s)
TCE inhalation
TCE inhalation
TCE inhalation
TCE inhalation
TCE i.a., i.v., p.v.,
gavage
TCE gavage
Discussion
These closed-chamber data were well within the 95% CI of the predictions
based on population-sampled parameters.
These data on TCE in blood, liver, kidney, fat, muscle, gut, and venous
blood were generally accurately predicted based on population-sampled
parameters. The only notable exception was TCE in the kidney during the
exposure period at the 500 ppm level, which was somewhat underpredicted
(though levels postexposure were accurately predicted).
These data on TCE in blood were well within the 95% CI of the
predictions based on population-sampled parameters.
These data on TCE in arterial blood were well within the 95% CI of the
predictions based on population-sampled parameters.
Except at some very early time-points (<0.5 hr), these data on TCE in
blood were well within the 95% CI of the predictions based on population-
sampled parameters.
These data on TCE in blood were well within the 95% CI of the
predictions based on population-sampled parameters.
Similar to previous analyses (Hack et al., 2006), the TCE submodel for the rat appears to
be robust, accurately predicting blood and tissue concentrations (see Figure 3-10, panels A-K),
with residual-error GSDs generally less than twofold. The only exceptions are the predictions of
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venous blood from Kimmerle and Eben (1973b), which have residual-error GSDs greater than
fourfold, and the predictions of fat concentrations from Simmons et al. (2002): with residual-
error GSD of 2.7-fold. For Kimmerle and Eben (1973b), the inaccuracy was primarily at the
3,000-ppm exposure, which might reflect other factors related to the high exposure. For
Simmons et al. (2002), the high residual-error appears to reflect scatter due to intrastudy
variability. Unlike in the mouse, some data consisting of TCE blood and tissue concentrations
were used for —outof-sample evaluation" (sometimes loosely termed —vladation"). These data
were generally well simulated (see Table 3-44); most of the data were within the 95% CI of
posterior predictions. This provides additional confidence in the predictions for the parent
compound.
In terms of TCA and TCOH, as with the mouse, the overall mass balance and metabolic
disposition to these metabolites also appeared to be robust: urinary excretion following dosing
with TCE, TCOH, and TCA could be modeled accurately (see Figure 3-10 panels O, T, and U),
with the residual-errors also indicating good predictions in most cases. Residual-error for these
measurements was larger for Green and Prout (1985), Prout et al. (1985), and Stenner et al.
(1997), ranging from a GSD of 1.8 to 2.3, reflecting largely intrastudy variability. Residual-
errors for the other studies had GSDs of 1.1-1.5. This improvement over the Hack et al. (2006)
model was likely due in part to the addition of nonurinary clearance (—utracked" metabolism) of
TCA and TCOH. In addition, adding a liver compartment for TCOH and TCOG, so that first-
pass metabolism could be properly accounted for, was essential for accurate simulation of the
metabolite pharmacokinetics both from i.v. dosing of TCOH and from TCE exposure. Blood
and plasma concentrations of TCA and free or total TCOH were also fairly well simulated (see
Figure 3-10, panels L, M, P, Q, and S), with GSDs for the residual-error of 1.1-1.6. A bit more
discrepancy (residual-error GSD of 1.7) was evident with TCA liver concentrations (see
Figure 3-10, panel N). However, TCA liver concentrations were only available in one study (Yu
et al., 2000), and the data show a change in the ratio of liver to blood concentrations at the last
time point, which may be the source of the added residual-error. Predictions of biliary excretion
of TCOG in bile-cannulated rats (see Figure 3-10, panel R), from Green and Prout (1985), and
TCOG in blood (see Figure 3-10, panel S), from Stenner et al. (1997), were less accurate, with
residual-error GSDs >2. However, the biliary excretion data consisted of a single measurement,
and the amount of free TCOH in the same experiment from Stenner et al. (1997) was accurately
predicted.
In terms of total metabolism, as with the mouse, closed-chamber data (see Figure 3-10,
panel A) were fit accurately with the updated model (residual-error GSD of about 1.1). In
addition, the data on NAcDCVC urinary excretion was well predicted (see Figure 3-10, panel V),
with residual-error GSD of 1.18. In particular, the fact that excretion was still ongoing at the end
of the experiment was accurately predicted (see Figure 3-11, panels A and B). Thus, there is
greater confidence in the estimate of the flux through the GSH pathway than there was from the
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Hack et al. (2006) model. However, the overall flux is still estimated indirectly, and there
remains some ambiguity as to the relative contributions of respiratory wash-in/wash-out,
respiratory metabolism, extrahepatic metabolism, DCVC bioactivation vs. 7V-acetylation, and
oxidation in the liver producing something other than TCOH or TCA. Therefore, there remains a
large range of possible values for the flux through the GSH conjugation and other indirectly
estimated pathways that are nonetheless consistent with all of the available in vivo data. The use
of noninformative priors for the metabolism parameters for which there were no in vitro data
means that a fuller characterization of the uncertainty in these various metabolic pathways could
be achieved. Thus, the model should be reliable for estimating lower and upper bounds on
several of these pathways.
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Hack et al. (2006) model (ral)
is -
?
g 0
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0 °
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30 40 50
Time (h)
V
t) O
*3 ^H
X
OJ
I
CM
O
CM
O
00
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D
Updated model (human)
10
I
20
I
30
I
40
I
50
Time (h)
Data are from Bernauer et al. (1996) for (A and B) rats or (C and D) humans
exposed for 6 hour to 40 (o), 80 (A), or 160 (+) ppm in air (thick horizontal line
denotes the exposure period). Predictions from Hack et al. (2006) and the
corresponding data (A and C) are only for the 1,2 isomer, whereas those from the
updated model (B and D) are for both isomers combined. Parameter values used
for each prediction are a random sample from the subject-specific parameters
from the rat and human MCMC chains (the last iteration of the first chain was
used in each case). Note that in the Hack et al. (2006) model, each dose group
had different model parameters, whereas in the updated model, all dose groups are
required to have the same model parameters. See files linked to Appendix A for
comparisons with the full distribution of predictions.
Figure 3-11. Comparison of urinary excretion data for NAcDCVC and
predictions from the Hack et al. (2006) and the updated PBPK models.
3.5.6.3.3. Human model and data
Each panel of Figure 3-12 shows a scatter plot of the calibration data and a random
posterior prediction for each of the measured endpoint. The endpoint abbreviations are listed in
Table 3-41, as are the implied GSDs for the residual" errors, which include intrastudy
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variability, interindividual variability, and measurement and model errors. The residual-error
GSDs are also shown as grey dashed or dotted lines in Figure 3-12. Table 3-45-3-46 provide a
summary evaluation of the predictions of the model as compared to the human data, with figures
showing individual time-course data and predictions in Appendix A.
RetDose Data
10' 1CT
CPIasTCA Data
103
*+ '
ti<+
-v-f+
AUrnTCA collect Data
-Hv
CAivPPM Data
1D"3 10"2 10"'1 1 101 103 103
CBIdTCA Data
H
CTCOH Data
CVen Data
tcr 10-
0^1 1 1Qf 102 103
AUrnTCA Data
104
10"* 10"3 1Q~2 10"' 1 101
TotCTCOH Data
Each panel shows results for a different measurement. The solid line represents
prediction = data, and the grey lines show prediction = data * GSDerr and data ^
GSDgrr, where GSDerr is the lowest (dotted) and highest (dashed) median estimate
of the residual-error GSD shown in Table 3-41.
Figure 3-12. Comparison of human data and PBPK model predictions from
a random posterior sample.
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AUrnTCOGTCOH Data
AUrnTCOGTCOH collect Data
K«I 1—i—rrmrj i—r~T"
10"'" 10~3 1CT2
CDCVGmol Data
10'' 10 1
AUrnNDCVC Data
Each panel shows results for a different measurement. The solid line represents
prediction = data, and the grey lines show prediction = data * GSDerr and data +
GSDgrr, where GSDerr is the lowest (dotted) and highest (dashed) median estimate
of the residual-error GSD shown in Table 3-41.
Figure 3-12 (continued). Comparison of rat data and PBPK model
predictions from a random posterior sample.
Table 3-45. Summary comparison of updated PBPK model predictions and
in vivo data used for — alibration" in humans
Reference
Exposure(s)
Discussion
Bernauer et al.
(1996)
TCE inhalation
These data, consisting of TCA, TCOG and NAcDCVC excreted in urine,
were accurately predicted by the model using both individual-specific and
population-sampled parameters. The posterior NAcDCVC predictions were
an important improvement over the predictions of Hack et al. (2006X which
predicted much more rapid excretion than observed. The fit improvement is
probably a result of the addition of the DCVG submodel between TCE and
DCVC, along with the broader priors on DCVC excretion and bioactivation.
Interestingly, in terms of population predictions, the NAcDCVC excretion
data from this study were on the low end, though still within the 95% CI.
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Table 3-45. Summary comparison of updated PBPK model predictions and
in vivo data used for —alibration" in humans (continued)
Reference
Exposure(s)
Discussion
Chiu et al. (2007)
TCE inhalation
Overall, posterior predictions were quite accurate across most of the
individuals and exposure occasions. TCE alveolar breath concentrations
were well simulated for both individual-specific and population-generated
simulations, though there was substantial scatter (intraoccasion variability).
However, TCE blood concentrations were consistently overpredicted in most
of the experiments, both using individual-specific and population-generated
parameters. This was not unexpected, as Chiu et al. (2007) noted the TCE
blood measurements to be lower by about twofold relative to previously
published studies. As discussed in Chiu et al. (2007) wash-in/wash-out and
extrahepatic (including respiratory) metabolism were not expected to be able
to account for the difference, and indeed all of these processes were added to
the current model without substantially improving the discrepancy.
With respect to metabolite data, TCA and total TCOH in blood were
relatively accurately predicted. There was individual experimental variability
observed for both TCA and TCOH in blood at 6 hrs (end of exposure). The
population-generated simulations overpredicted TCA in blood, while they
were accurate in predicting blood TCOH. Predictions of free TCOH in blood
also showed overprediction for individual experiments, with variability at the
end of exposure timepoint. However, TCOH fits were improved for the
population-generated simulations. TCA and TCOG urinary excretion was
generally well simulated, with simulations slightly under- or overpredicting
the individual experimental data in some cases.
Fisher etal. (1998^
TCE inhalation
The majority of the predictions for these data were quite accurate.
Interestingly, in contrast to the predictions for Chiu et al. (2007). TCE blood
levels were somewhat underpredicted in a few cases, both from using
individual-specific and population-generated predictions. These two results
together suggest some unaccounted-for study-to-study variance, though
interindividual variability cannot be discounted as the data from Chiu et al.
(2007) were from individuals in the Netherlands and that from Fisher et al.
(1998) were from individuals in the United States. As reported by Fisher
et al. (1998). TCE in alveolar air was somewhat overpredicted in several
cases; however, the discrepancies seemed smaller than originally reported for
the Fisher et al. model.
Fisher et al.
1998) (continued)
TCE inhalation
(continued)
With respect to metabolite data, TCOH and TCA in blood and TCOG and
TCA in urine were generally well predicted, though data for some individuals
appeared to exhibit inter- and/or intraoccasion variability. For example, in
one case in which the same individual (female) was exposed to both 50 and
100 ppm, the TCOH blood data was overpredicted at the higher one exposure.
In addition, in one individual, initial individual-specific simulations for TCA
in urine were underpredicted but shifted to overpredictions towards the end of
the simulations. The population-generated results overpredicted TCA in urine
for the same individual. Given the results from Chiu et al. (2007).
interoccasion variability is likely to be the cause, though some dose-related
effect cannot be ruled out.
Finally, DCVG data was well predicted in light of the high variability in the
data and availability of only grouped data or data from multiple individuals
who cannot be matched to the appropriate TCE and oxidative metabolite data
set. In all cases, the basic shape (plateau and then sharp decline) and order of
magnitude of the time-course were well predicted, Furthermore, the range of
the data was well-captured by the 95% CI of the population-generated
predictions.
Kimmerle and
Eben (1973a)
TCE inhalation
These data were well fit by the model, using either individual-specific or
population-generated parameters.
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Table 3-45. Summary comparison of updated PBPK model predictions and
in vivo data used for —alibration" in humans (continued)
Reference
Monster et al.
(1976)
Muller et al.
(1974)
Paykoc et al.
(19451
Exposure(s)
TCE inhalation
TCA,
TCOH oral
TCA i.v.
Discussion
The data simulated in this case were exhaled alveolar TCE, TCE in venous
blood, TCA in blood, TCA in urine, and TCOG in urine. Both using
individual-specific and population-generated simulations, all fits are within
the 95% CI. The one exception was the retained dose for a male exposed to
65 ppm, which was outside the 95% CI for the population-generated results.
The data measured after oral TCA was timecourse TCA measured in plasma
and urine. Individual-specific predictions were accurate, but both data sets
were overpredicted in the population-generated simulations.
The data measured after oral TCOH were timecourse TCOH in blood, TCOG
in urine, TCA in plasma, and TCA in urine. Individual-specific predictions
were accurate, but the population-generated simulations overpredicted TCOH
in blood and TCOG in urine. The population-based TCA predictions were
accurate.
These results indicate that — unsual" parameter values were necessary in the
individual-specific simulations to give accurate predictions.
These data were well fit by the model, using either individual-specific or
population-generated parameters.
Table 3-46. Summary comparison of updated PBPK model predictions and
in vivo data used for —ut-of-sample" evaluation in humans
Reference
Bartonicek (1962)
Bloemen et al. (2001)
Fernandez et al. (1977)
Lapare et al. (1995)
Monster et al. (1979a)
Muller et al. (1975: 1974)
Sato et al. (1977)
Stewart et al. (1970)
Triebig et al. (1976)
Exposure(s)
TCE inhalation
TCE inhalation
TCE inhalation
TCE inhalation
TCE inhalation
TCE inhalation
TCE inhalation
TCE inhalation
TCE inhalation
Discussion
While these data were mostly within the 95% CI of the predictions,
they tended to be at the high end for all of the individuals in the
study.
These data were all well within the 95% CI of the predictions.
These data were all well within the 95% CI of the predictions.
These data were all well within the 95% CI of the predictions.
These data were all well within the 95% CI of the predictions.
Except for TCE in alveolar air, which was overpredicted during
exposure, these data were all well within the 95% CI of the
predictions.
These data were all well within the 95% CI of the predictions.
These data were all well within the 95% CI of the predictions.
Except for TCE in alveolar air, these data were all well within the
95% CI of the predictions.
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With respect to the TCE submodel, retained dose, blood, and exhaled air measurements
(see Figure 3-12, panels A-C) appeared more robust than previously found from the Hack et al.
(2006) model. TCE blood concentrations from most studies were well predicted, with residual-
error GSD in most studies of less than twofold. However, those from Chiu et al. (2007) were
consistently overpredicted (i.e., data <0.1 mg/L in Figure 3-12, panel C), with residual-error
GSD of almost threefold, and a few of those from Fisher et al. (1989) were consistently
underpredicted. Alveolar breath concentrations and retained dose of TCE were well predicted
(residual-error GSD <1.5-fold) from all studies except Fisher et al. (1998), which had a residual-
error GSD of 1.8-fold. However, the discrepancy in alveolar breath appeared smaller than that
originally reported by Fisher et al. (1998) for their PBPK model. In addition, the majority of the
—out)f-sample" evaluation data consisted of TCE in blood or breath, and were generally well
predicted (see Table 3-46), lending confidence to the model predictions for the parent compound.
In terms of TCA and TCOH, as with the mouse and rat, the overall mass balance and
metabolic disposition to these metabolites also appeared to be robust, as urinary excretion
following TCE exposure could be modeled accurately (see Figure 3-12, panels F, G, J, and K).
In most cases, the residual-error GSD was less than twofold. However, TCA urinary data from
Chiu et al. (2007) (panel G in Figure 3-12) indicated greater interoccasion variability, reflected in
the residual-error GSD of 2.8. In this study, the same individual exposed to the same
concentration on different occasions sometimes had substantial differences in urinary excretion.
In addition, many TCA urine measurements in this study were saturated, and had to be omitted,
and the fact that the remaining data were sparse and possibly censored may have contributed to
the greater intrastudy variability. Blood and plasma concentrations of TCA and free TCOH (see
Figure 3-12, panels D, E, and H) were fairly well simulated, with GSD for the residual-error of
1.1-1.4, though total TCOH in blood (see Figure 3-12, panel I) had slightly greater residual-error
with GSD of about 1.6. This partially reflects the —foarper" peak concentrations of total TCOH
in the Chiu et al. (2007) data relative to the model predictions. In addition, TCA and TCOH
blood and urine data were available from several studies for —outof-sample" evaluation and
were generally well predicted by the model (see Table 3-46), lending further confidence to the
model predictions for these metabolites.
In terms of total metabolism, no closed-chamber data exist in humans, but, as discussed
above, alveolar breath concentrations and retained dose (see Figure 3-12, panels A and B) were
generally well simulated, suggesting that total metabolism may be fairly robust. In addition, as
with the rat, the data on NAcDCVC urinary excretion was well predicted (see Figure 3-11,
Figure 3-12 panel M), with residual-error GSD of 1.12). In particular, the model accurately
predicted the fact that excretion was still ongoing at the end of the experiment (48 hours after the
end of exposure). Thus, there is greater confidence in the estimate of the flux through this part of
the GSH pathway than there was from the Hack et al. (2006) model, in which excretion was
completed within the first few hours after exposure (see Figure 3-11, panels C and D).
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If only urinary NAcDCVC data were available, as is the case for the rat, the overall GSH
conjugation flux would still be estimated indirectly, and there would remain some ambiguity as
to the relative contributions of respiratory wash-in/wash-out, respiratory metabolism,
extrahepatic metabolism, DCVC bioactivation vs. TV-acetylation, and oxidation in the liver
producing something other than TCOH or TCA. However, unlike in the rat, the blood DCVG
data, while highly variable, nonetheless provide substantial constraints (at least a strong lower
bound) on the flux of GSH conjugation, and is well fit by the model (see Figure 3-12, panel L,
and Figure 3-13). Importantly, the high residual-error GSD for blood DCVG reflects the fact
that only grouped or unmatched individual data were available, so in this case, the residual-error
includes interindividual variability, which is not included in the other residual-error estimates.
However, as discussed above in Section 3.3.3.2.1, there are uncertainties as to the accuracy of
analytical method used by Lash et al. (1999b) in the measurement of DCVG in blood. Because
these data are so determinative of the overall GSH conjugation flux, these analytical
uncertainties are important to consider in the overall evaluation of the PBPK model predictions
(see below, Section 3.5.7).
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o
a
o
in
o _
o _
o -
\
0
\
4
6
5
Time (h)
are mean concentrations for males (A) and females (o) reported in Lash et al.
(1999b) for humans exposed for 4 hours to 100 ppm TCE in air (thick horizontal
line denotes the exposure period). Data for oxidative metabolites from the same
individuals were reported in Fisher et al. (1998) but could not be matched with the
individual DCVG data (Lash 2007, personal communication). The vertical error
bars are SEs of the mean as reported in Lash et al. (1999b) (n = 8, so SD is
80.5-fold larger). Lines are PBPK model predictions for individual male (solid)
and female (dashed) subjects. Parameter values used for each prediction are a
random sample from the individual-specific parameters from the human MCMC
chains (the last iteration of the 1st chain was used). See files linked to Appendix
A for comparisons with the full distribution of predictions.
Figure 3-13. Comparison of DCVG concentrations in human blood and
predictions from the updated model.
For the other indirectly estimated pathways, there remain a large range of possible values
that are nonetheless consistent with all of the available in vivo data. The use of noninformative
priors for the metabolism parameters for which there were no in vitro data means that a fuller
characterization of the uncertainty in these various metabolic pathways could be achieved. Thus,
as with the rat, the model should be reliable for estimating lower and upper bounds on several of
these pathways.
3.5.6.4. Sensitivity Analysis With Respect to Calibration Data
To assess the informativeness of the calibration data to the parameters, local sensitivity
analysis is performed with respect to the calibration data points. For each scaling parameter, the
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central difference is used to estimate the partial derivatives by centering on the sample mean of
its estimated population mean, and then increasing and decreasing by 5%. The relative change in
the model output f(0) is used to estimate a local sensitivity coefficient (SC) as follows:
sc = 10 x {/(0+) -xe_)}/['/2 x (/(0+) +x0_)}]
Here,y(0) is one of the model predictions of the calibration data, 0± is the maximum
likelihood estimate (MLE) or baseline value of ± 5%. For log-transformed parameters, 0.05 was
added or subtracted from the baseline value, whereas for untransformed parameters, the baseline
value was multiplied by 1.05 or 0.95. The resulting values of SC are binned into five categories
according to their sensitivity coefficient: negligible (|SC| < 0.01) very low (0.01 < |SC| < 0.1),
low (0.1 < |SC| < 0.5), medium (0.5 < |SC| < 1.0), and high (|SC| > 1.0).
Note that local sensitivity analyses as typically performed in deterministic PBPK
modeling can only inform the —pmary" effects of parameter uncertainties (i.e., the direct change
on the quantity of interest due to change in a parameter). They cannot address the propagation
of uncertainties, such as those that can arise due to parameter correlations in the parameter fitting
process. Those can only be addressed in a global sensitivity analysis, which is left for future
research.
The results of local sensitivity analyses are shown in Figures 3-14-3-16. For each
parameter, the number of data points (out of the entire calibration set) that have sensitivity
coefficients in the various categories are shown graphically. As summarized in Table 3-47, most
of the parameters have at least some calibration data to which they are at least moderately
sensitive (|SC| > 0.5). Across species, the cardiac output (InQCC), ventilation-perfusion ratio
(InVPRC), blood-air partition coefficient (InPBC), VMAX for oxidation (InVMAxC), and VLivC
are consistently among the most sensitive parameters, with >10% of the calibration data
exhibiting |SC| > 0.5 to these parameters. Note that the reason the liver volume is sensitive is
that it is used to scale the capacity or clearance rate for oxidation.
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InQCC
InVPRC
QFatC
QGutC
QLivC
QSIwC
InDRespC "
QKidC H
FracPlasC
VFatC
VGutC
VLivC
VRapC "
VRespLumC
VRespEffC "
VKidC H
VBIdC
InPBC
InPFatC
InPGutC
InPLivC .
InPRapC "
InPRespC '
InPKidC H
InPSIw C
InPRBCPIasTCAC
•_ InPBodTCAC
0> InPLivTCAC
InkDssocC
E InBMaxkDC !
JO InPBodTCOHC
(5 InPLivTCOHC
0- InPBodTCOGC
InPLivTCOGC
InkTSD
InkAS
InkAD
InkASTCA
InVMaxC
InKMC
InFracOtherC
InFracTCAC
InVMaxDCVGC
InCIDCVGC
InVMaxKidDCVGC
InCIKidDCVGC .
InVMaxLungLivC
InKMCIara "
InFracLungSysC
InVMaxTCOHC
InKMTCOH
InVMaxGlucC
InKMGluc
InkMetTCOHC
InkUrnTCAC !
InkMetTCAC
InkBileC
InkEHRC
InkUrnTCOGC
Number of mouse calibration data points
200 400 600 800 1000
1200
111 111 11! I i HI 1111 i ill 11111111 lfH4+H+fH-_
- - -"1111111111111! 1111111 IB?:
::r:....::::.:.:.r ::.:.::•::...:"[ 11 IJ IJ 111! 111111JI11 Pffl,
1 rrr rpjj-|;
,. ,. _ , ,.:—,._:] m UK.,
" :] 111111 i 11 ill 11 IJ I mijflffil'
11111111 111 11111111111111111111111111 "
•1111111111111111111111111
:; ::. -..-•— iiiiiiiiiiiiiiiiMM;::::._._.
11111111111111111111113
11 111111111111111111111 ±t'
JiiiiiiMiii±tmi±t±t±i'
jiiiiiiiiiiiimiTifflSi1'
• -1 m 111 m 11111111 •
MiMMnmUMMtM
1111113;
- .•imiiiiiiiMtnr
.:: :„„;.....:.... I iTFtH';
-Illllllllllllllll.jlt-Wti-l
III III 11 III 1111 \' •
-r 111 III 1111! 111
inn
iniiiiiiiiiiiiiiitiimmiiii'H-
^_^___^-___i 1111111111 >
jiiiiinii HUM.
•"• : m 11 m m i m±an
IllimillllllH+HIB
, -11111-'
|SC|<0.01 - 0.01<|SC|<0.1 n0.1<|SC|<0.5 40.5<|SC|<1
Figure 3-14. Sensitivity analysis results: Number of mouse calibration data
points with SC in various categories for each scaling parameter.
3-128
-------
200
Number of rat calibration data points
400 600 800 1000
1200
Parameter
InQCC "
InVPRC '
QFatC "
QGutC '
QLivC "
QSIw C "
InDRespC
QKidC "
FracRasC
VFatC "
VGutC '
VLivC "
VRapC '
VRespLumC
VRespEffC "
VKidC "
VBIdC "
InPBC
InPFatC "
InPGutC '
InPLivC "
InPRapC "
InPRespC
InPKidC "
InPSIw C
InPRBCRasTCAC !
InPBodTCAC
InPLivTCAC "
InkDissocC
InBMaxkDC "
InPBodTCOHC "
InPLivTCOHC "
InPBodTCOGC "
InPLivTCOGC
InkTSD "
InkAS '
InkAD"
InkASTCA '
InVMaxC "
InKMC "
InFracOtherC
InFracTCAC "
InVMaxDCVGC "
InCIDCVGC "
InVMaxKidDCVGC '
InCIKidDCVGC "
InV Max Lung LivC "
InKMCIara |
InFracLungSysC
InVMaxTCOHC
InKMTCOH "
InV Max Glue C
InKMGluc "
InkMetTCOHC '
InkUrnTCAC "
InkMetTCAC '
InkBileC "
InkEHRC "
InkUrnTCOGC "
InkNATC "
InkKidBioactC
1 1 1 ! 1 1 1 1 1 1 1 ! 1 1 1 1 1 1 1 ! 1 1 1 1 1 1 1 ! 1 1 1 It+HIW+tt+tH-H+t-HB
. I i I 1 I 1 1 1444444444444444JJ4444_l
: I11I1111I1I11I11111I1I
- •""• "• -" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
— - • • -•- n 1 1 1 i i
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 i,i
1 1 1 H 1 i 1 1 1 1 1 i 1 i 1 1 1 1 1 i 1 1 1 1 1 1 H 1 i IM4M4-H4444444444444-B
^^^=^^^— I 1 i I i I I 1 I 1 i I i I I 1 I 1 i I 1 I I 1 I 1 i I i I I 1 I i i I
-------
Number of human calibration data points
500 1000 1500 2000 2500 3000 3500 4000 4500 5000
InQCC "
InVPRC '
QFatC "
QGutC '
QLivC "
QSIw C "
InDRespC
QKidC "
FracRasC
VFatC "
VGutC '
VLivC "
VRapC"
VRespLumC
VRespEffC "
VKidC "
VBIdC "
InPBC
InPFatC "
InPGutC '
InPLivC "
InPRapC "
InPRespC
InPKidC "
InPSIw C
InPRBCPIasTC !
InPBodTCAC
i- InPLivTCAC "
& InkDissocC "
0) InBMaxkDC "
E InPBodTCOHC "
P InPLivTCOHC "
(5 InPBodTCOGC "
Q. InPLivTCOGC
InPeffDCVG "
InkASTCA '
InkASTCOH "
InVMaxC '
InCIC "
InFracOtherC
InFracTCAC
InCIDCVGC "
InKMDCVGC "
InCIKidDCVGC "
InKMKidDCVGC '
InV Max Lung Liv
InKMUara "
InFracLungSys "
InCITCOHC "
InKMTCOH
InCIGIucC "
InKMGluc
InkMetTCOHC "
InkUrnTCAC '
InkMetTCAC "
InkBileC '
InkEHRC "
InkUrnTCOGC "
InkDCVGC "
InkNATC "
InkKidBioactC
Illllillilllli
S I 1 i 1 1 1 f I I 1 i 1 1 1 f I I f i 1 1 I 1 i I 1 I I f I 1
•-•-•• -•• i ( 1 1 1 1 1 IP n 1 1 n i n 1 1 1 1 1 1 P 1 1 1 1
illlllllilllll
.1 i
Hillll!
- - -
-.- --
i 1 1 1 1 1 1 1
"iimm
|SC|<0.01 0.01<|SC|<0.1 i 0
MMIItW-H+H-H+H-H+H
nnnniiiii'jLiiHi'i
itlfff
1 1 1 1 1 1 1 1 II 1 1 1 1 1 1 1 1 1 1 1 1 ti
I i 1 1 II
~—\\\
llilllllllillllllljllll!
r ^-iil ill Mill ill
iiitiiniiiimiiimiii
JIHjJIJ
liilllll
M I i 1 1 1 1 1 1 1 1 II 1 1 1 1 ! i 1 1 1 1
I
lllllllillllllllllllllll
llilllllllillll!
Ililitll
! 1
" '"" i n 1 1 1 1 1 1 1 1 n n
-____..-. . „_
n i f 1 1 1 1 f 1 1 1 n i "tTtTTTf
— liiiiiiniiiiii
Illllllllillllllllillill
•••II
"III
^f^^m
HI f H H If I ? !•
1 1 1 1 1 1 1 1 1 1 1 m
dJititttiJJ^JLll.
llllllllllllll
1 1 1 1 1 1 1 1 M+
" ' I II I i II 1 1 ! 1 1
1 1 1 CtttttttnB:
iiini'fwSw
" " * " " " i i 1 1 n i
- i [ |
1 1 1 1 1 1 1 1 1 1 :±i±
i l i 1 1
-•-.—Ml Milt
1 1 1 ' 1 1 1
1 1 ! ! I M I i 1 1 !«
Ell
if II 1 (444-44444-
yr;r-jIJI!II
1 1 1 1 1 n i i-e-m+
1 1 JjyJUJjJ4JUL
iiii!ii'!Ti!T±t
1 1 i I.LLLLLLJJJJ.
lilliMHIIIlt
IHIIIIIfillil
liKlMlIllIll
! 1 1 1 1 1 1 1 i 1 1 1H
=:- ~^: - 1 1
ill
~ 1 1 1 1 M 1 1 1 1 1 1 1
f 1 1 i j
TnTTtrllTTTT
i 1 1 1 1 i 1 1 1 1 1 1 1 It
1 44-H4444444444-
" "" "j I
'TtTnTTTtTfTT
Ill II
1 1 1 1 1 1 i i i i i in:
~ ::II!I!III
- — -nil u±
•t+i
1<|SC|<0.5 u 0.5<|SC|<1 »1<|SC|
Figure 3-16. Sensitivity analysis results: Number of human calibration data
points with SC in various categories for each scaling parameter.
3-130
-------
Table 3-47. Summary of scaling parameters ordered by fraction of
calibration data of moderate or high sensitivity
Mouse
Parameter"
InVMAxC
VLivC
InPBC
InQCC
InkAD
InPBodTCAC
InVPRC
InFracTCAC
InVMAxGlucC
InPFatC
InPLivTCAC
InkAS
VFatC
InKMGluc
InkMetTCAC
InkUrnTCAC
InKMC
InkUrnTCOGC
InVMAxLungLivC
InkTSD
QGutC
QFatC
InPLivC
InPLivTCOHC
InPKidC
InPLivTCOGC
InPRBCPlasTCAC
InVMAxTCOHC
InPBodTCOHC
InPSlwC
InBMaxkDC
InDRespC
InkBileC
FracPlasC
InPBodTCOGC
VGutC
InPGutC
InKMTCOH
InkMetTCOHC
InkEHRC
QKidC
VKidC
Fraction
with
|SC|>0.5
0.4405
0.428
0.3233
0.2454
0.1675
0.1642
0.1575
0.1323
0.1147
0.093
0.0896
0.0863
0.0762
0.0762
0.0762
0.0754
0.0653
0.0544
0.0511
0.0469
0.0452
0.0402
0.0402
0.0377
0.0352
0.0352
0.031
0.0235
0.0201
0.0134
0.0134
0.0109
0.0084
0.0059
0.005
0.0025
0.0025
0.0017
0.0017
0.0017
0.0008
0.0008
Rat
Parameter"
VLivC
InQCC
InVPRC
InVMAxC
InPBC
VFatC
QFatC
InPBodTCAC
InPFatC
InVMAxGlucC
QGutC
InkUrnTCAC
InPSlwC
InFracTCAC
InKMGluc
InkBileC
InPLivTCOGC
InPLivC
InkAD
InKMC
InVMAxTCOHC
InPKidC
InPGutC
InFracOtherC
InPLivTCAC
InBMaxkDC
InkMetTCAC
InVMAxLungLivC
InKMTCOH
InkAS
InPBodTCOHC
FracPlasC
InkTSD
VKidC
InVMAxKidDCVGC
InkNATC
InDRespC
QSlwC
InPLivTCOHC
InkASTCA
InkMetTCOHC
VGutC
InPRBCPlasTCAC
InkUrnTCOGC
Fraction
with
|SC|>0.5
0.4213
0.4182
0.4158
0.3984
0.2893
0.1455
0.1273
0.1162
0.1154
0.1083
0.0885
0.0696
0.0664
0.064
0.0625
0.0538
0.0514
0.0482
0.0474
0.0427
0.0427
0.0324
0.03
0.03
0.0292
0.0285
0.0213
0.0182
0.0182
0.0158
0.015
0.0126
0.0103
0.0095
0.0095
0.0095
0.0063
0.0055
0.0016
0.0016
0.0016
0.0008
0.0008
0.0008
Human
Parameter"
InQCC
InVPRC
InClTCOHC
QGutC
InClGlucC
InkUrnTCAC
FracPlasC
InPBodTCOHC
InVMAxC
InPBC
VLivC
InPBodTCAC
InBMaxkDC
VBldC
InkDCVGC
InPLivTCOGC
InClDCVGC
InkBileC
QFatC
InPSlwC
QSlwC
InKMTCOH
InPFatC
InCIC
InkUrnTCOGC
InPRBCPlasTCAC
InPLivTCAC
InkMetTCAC
InFracTCAC
InPBodTCOGC
VRapC
VKidC
InClKidDCVGC
InkNATC
InPRapC
InPLivTCOHC
InkMetTCOHC
InFracOtherC
VFatC
InkEHRC
InDRespC
InKMDCVGC
InkKidBioactC
Fraction
with
|SC|>0.5
0.4159
0.3777
0.2871
0.2137
0.186
0.1789
0.1553
0.1486
0.1358
0.1269
0.1225
0.12
0.0897
0.0586
0.0515
0.0446
0.0435
0.0422
0.0401
0.0372
0.0345
0.0305
0.0292
0.0288
0.0282
0.0147
0.0135
0.013
0.0103
0.0095
0.0063
0.0057
0.0057
0.0057
0.005
0.005
0.005
0.0046
0.0036
0.0036
0.0011
0.0011
0.0002
""Parameters not shown have no data with |SC| > 0.5.
3-131
-------
For scaling parameters for which all of the calibration data are negligibly sensitive
(|SC| < 0.01), it is important that they either have informative prior data or are unimportant for
dose-metric predictions. For mice, these parameters are the volumes of the respiratory lumen
and tissue (VRespLumC, VRespEffC), the partition coefficient for the respiratory tissue
(InPRespC), and the VMAX values for GSH conjugation in the liver and kidney. For the
respiratory tract parameters, there are prior data to identify the parameters. Moreover, none of
the dose-metric predictions are sensitive to these parameters (see Section 3.5.7.2, below). For
GSH conjugation, it should be noted that for the clearance in the liver and lung (VMAX/KM), some
data are available with sensitivity 0.01 < |SC| < 0.1. The data are not at all informative as to the
maximum capacity for GSH conjugation.
For rats, all of the scaling parameters have at least one calibration data point with
|SC| > 0.01. However, for the volumes of the respiratory lumen and tissue (VRespLumC,
VRespEffC), the partition coefficient for the respiratory tissue (InPRespC), and the VMAX values
for GSH conjugation in the liver, these consist of only one or two data points. As with mice,
there are prior data to help identify the respiratory tract parameters. Moreover, none of the dose-
metric predictions are sensitive to the respiratory tract parameters (see Section 3.5.7.2, below).
The data are not very informative as to maximum capacity for GSH conjugation in the liver.
However, there are some data that have low or moderate informativeness (0.1 < |SC| < 1) as to
the maximum capacity for GSH conjugation in the kidney, and clearance via GSH conjugation
(VMAX/KM) in the liver and kidney, which have much greater impact on the dose-metric
predictions than the maximum capacity in the liver (see Section 3.5.7.2, below).
For humans, all of the scaling parameters have at least one calibration data point with
|SC| > 0.01. However, for the volumes of the respiratory lumen and tissue (VRespLumC,
VRespEffC), the partition coefficient for the respiratory tissue (InPRespC), and the oral
absorption rate for TCA, these consist of only one or two data points. As with mice and rats,
there are prior data to help identify the respiratory tract parameters. Moreover, none of the dose-
metric predictions are sensitive to the respiratory or TCA oral absorption parameters (see
Section 3.5.7.2, below).
Therefore, the local sensitivity analysis with respect to calibration data confirms that
most of the scaling parameters are informed by at least some of the calibration data. In addition,
the parameters for which the calibration data have very little or negligible sensitivity are either
informed by prior data or have little impact on dose-metric predictions.
3.5.6.5. Summary Evaluation of Updated PBPK Model
Overall, the updated PBPK model, utilizing parameters consistent with the available
physiological and in vitro data from published literature, provides reasonable fits to an extremely
large database of in vivo pharmacokinetic data in mice, rats, and humans. Posterior parameter
distributions were obtained by MCMC sampling using a hierarchical Bayesian population
3-132
-------
statistical model and a large fraction of this in vivo database. Convergence of the MCMC
samples for model parameters was good for mice, and adequate for rats and humans. Evaluation
of posterior parameter distributions suggests reasonable results in light of prior expectations and
the nature of the available calibration data. In addition, in rats and humans, the model produced
predictions that are consistent with in vivo data from many studies not used for calibration
(insufficient studies were available in mice for such —oufcf sample" evaluation). Finally, the
local sensitivity analysis with respect to calibration data confirms that most of the scaling
parameters are informed by at least some of the calibration data, and those that were not either
were informed by prior data or would not have great impact on dose-metric predictions.
3.5.7. PBPK Model Dose-Metric Predictions
3.5.7.1. Characterization of Uncertainty and Variability
Since it is desirable to characterize the contributions from both uncertainty in population
parameters and variability within the population, the following procedure is adopted. First,
500 sets of population parameters (i.e., population mean and variance for each parameter) are
extracted from the posterior MCMC samples—these represent the uncertainty in the population
parameters. To minimize autocorrelation, they were obtained by —tinning" the chains to the
appropriate degree. From each of these sets of population parameters, 100 subject-specific
parameters were generated by Monte Carlo—each of these represents the population variability,
given ^particular set of population parameters. Thus, a total of 50,000 subjects, representing
100 (variability) each for 500 different populations (uncertainty), were generated.
Each set was run for a variety of generic exposure scenarios. The combined distribution
of all 50,000 individuals reflects both uncertainty and variability (i.e., the case in which one is
trying to predict the dosimetry for a single random subject). In addition, for each dose-metric,
the mean predicted internal dose was calculated from each of the 500 sets of 100 individuals,
resulting in a distribution for the uncertainty in the population mean. Comparing the combined
uncertainty and variability distribution with the uncertainty distribution in the population mean
gives a sense of how much of the overall variation is due to uncertainty vs. variability.
Figures 3-17-3-25 show the results of these simulations for a number of representative
dose-metrics across species continuously exposed via inhalation or orally. For display purposes,
dose-metrics have been scaled by total intake (resulting in a predicted —fnation" metabolized) or
exposure level (resulting in an internal dose per ppm for inhalation or per mg/kg-day for oral
exposures). In these figures, the thin error bars represent the 95% CI for overall uncertainty and
variability, and the thick error bars represent the 95% CI for the uncertainty in the population
mean. The interpretation of these figures is that if the thick error bars are much smaller (or
greater) than the thin error bars, then variability (or uncertainty) contributes the most to overall
uncertainty and variability.
3-133
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Fraction Metabolized
B
Fraction Metabolized
CO
CD
cp
CD
CN
0
p
CD
CO
CD
CD
CD
CN
CD
O
CD
0.1 1 10 100 1000
Continuous inhalation (ppm )
0.1
10
100 1000
Continuous oral ( mg/kg-d )
Bars and thin error bars represent the median estimate and 95% CI for a random
subject, and reflect combined uncertainty and variability. Circles and thick error
bars represent the median estimate and 95% CI for the population mean, and
reflect uncertainty only.
Figure 3-17. PBPK model predictions for the fraction of intake that is
metabolized under continuous inhalation (A) and oral (B) exposure
conditions in mice (white), rats (diagonal hashing), and humans (horizontal
hashing).
3-134
-------
A
Fraction Oxidized
B
Fraction Oxidized
CO
d
cp
CD
CN
cb
p
cb
00
CD
cp
CD
CN
CD
O
CD
0.1
10
100 1000
0.1
10
100 1000
Continuous inhalation (ppm )
Continuous oral ( mg/kg-d )
Bars and thin error bars represent the median estimate and 95% CI for a random
subject, and reflect combined uncertainty and variability. Circles and thick error
bars represent the median estimate and 95% CI for the population mean, and
reflect uncertainty only.
Figure 3-18. PBPK model predictions for the fraction of intake that is
metabolized by oxidation (in the liver and lung) under continuous inhalation
(A) and oral (B) exposure conditions in mice (white), rats (diagonal hashing),
and humans (horizontal hashing).
3-135
-------
o -
o -
o -
O -;
o -1
Fraction Conjugated
Fraction Conjugated
} :.__
MRH IVRH IVRH IVRH IVRH
r
10
-1
\
10'
\
10°
10
-1
10'
10"
10°
Continuous inhalation (ppm)
Continuous oral (mg/kg-d)
X-values are slightly offset for clarity. Open circles (connected by lines) and thin
error bars represent the median estimate and 95% CI for a random subject, and
reflect combined uncertainty and variability. Filled circles and thick error bars
represent the median estimate and 95% CI for the population mean, and reflect
uncertainty only.
Figure 3-19. PBPK model predictions for the fraction of intake that is
metabolized by GSH conjugation (in the liver and kidney) under continuous
inhalation (A) and oral (B) exposure conditions in mice (dotted line), rats
(dashed line), and humans (solid line).
3-136
-------
A Fraction bioactivated in kidney
o -
o -
o -
o -;
o -1
\
RH
RH
RH
RH
RH
o —
o —
o —
O -;
o —'
3 Fraction bioactivated in kidney
RH
RH
RH
RH
RH
10
10'
10°
10
-1
10'
10"
10°
Continuous inhalation (ppm)
Continuous oral (mg/kg-d)
X-values are slightly offset for clarity. Open circles (connected by lines) and thin
error bars represent the median estimate and 95% CI for a random subject, and
reflect combined uncertainty and variability. Filled circles and thick error bars
represent the median estimate and 95% CI for the population mean, and reflect
uncertainty only.
Figure 3-20. PBPK model predictions for the fraction of intake that is
bioactivated DCVC in the kidney under continuous inhalation (A) and oral
(B) exposure conditions in rats (dashed line) and humans (solid line).
3-137
-------
A
Fraction lung oxidation
o -=
o -=
o -=
o -=
o -
CD : MRH
o -*
IVRH IVRH IVRH IVRH
\
10'
\
1CT
\
10°
Fraction lung oxidation
o -=
o -=
o -=
o —
o —
CD : IVRH
o —'
10
IVRH IVRH MRH IVRH
\
\
10'
\
\
10°
Continuous inhalation (ppm)
Continuous oral (mg/kg-d)
X-values are slightly offset for clarity. Open circles (connected by lines) and thin
error bars represent the median estimate and 95% CI for a random subject, and
reflect combined uncertainty and variability. Filled circles and thick error bars
represent the median estimate and 95% CI for the population mean, and reflect
uncertainty only.
Figure 3-21. PBPK model predictions for fraction of intake that is oxidized
in the respiratory tract under continuous inhalation (A) and oral (B)
exposure conditions in mice (dotted line), rats (dashed line), and humans
(solid line).
3-138
-------
A Fraction 'other' liver oxidation
o -
o -
-------
AUCTCE in blood
_/\ per ppm
o -
E
Q.
Q.
8.
|
^
O I
o -1
B
O —
Q. T z
I
IVRH IVRH IVRH IVRH IVRH
I I I I I
1CT1 1 101 102 103
Continuous inhalation (ppm)
o —
o —•
AUCTCE in blood
per mg/kg-d
VRH IVRH IVRH IVRH MRH
r
\
\
\
10 1 10 10 10
Continuous oral (mg/kg-d)
X-values are slightly offset for clarity. Open circles (connected by lines) and thin
error bars represent the median estimate and 95% CI for a random subject, and
reflect combined uncertainty and variability. Filled circles and thick error bars
represent the median estimate and 95% CI for the population mean, and reflect
uncertainty only.
Figure 3-23. PBPK model predictions for the weekly AUC of TCE in venous
blood (mg-hour/L-week) per unit exposure (ppm or mg/kg-day) under
continuous inhalation (A) and oral (B) exposure conditions in mice (dotted
line), rats (dashed line), and humans (solid line).
3-140
-------
E
Q.
Q.
8.
|
^
O -=
o -=
O -
AUCTCOH in blood
per ppm
V
O -:
CN : IVRH
o -1
o —
o ^
Q. O -
o —
IVRH IVRH IVRH IVRH
\
\
AUCTCOH in blood
per mg/kg-d
J :
- IVRH IVRH IVRH IVRH MRH
I
\
\
\
10
10'
10°
10'
10°
Continuous inhalation (ppm)
Continuous oral (mg/kg-d)
X-values are slightly offset for clarity. Open circles (connected by lines) and thin
error bars represent the median estimate and 95% CI for a random subject, and
reflect combined uncertainty and variability. Filled circles and thick error bars
represent the median estimate and 95% CI for the population mean, and reflect
uncertainty only.
Figure 3-24 PBPK model predictions for the weekly AUC of TCOH in blood
(mg-hour/L-week) per unit exposure (ppm or mg/kg-day) under continuous
inhalation (A) and oral (B) exposure conditions in mice (dotted line), rats
(dashed line), and humans (solid line).
3-141
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ra
AUCTCA in liver
per ppm
O —
o —'
AUCTCA in liver
per mg/kg-d
IVRH IVRH IVRH IVRH MRH
r
\
\
\
10
10'
10°
10
10'
10°
Continuous inhalation (ppm)
Continuous oral (mg/kg-d)
X-values are slightly offset for clarity. Open circles (connected by lines) and thin
error bars represent the median estimate and 95% CI for a random subject, and
reflect combined uncertainty and variability. Filled circles and thick error bars
represent the median estimate and 95% CI for the population mean, and reflect
uncertainty only.
Figure 3-25 PBPK model predictions for the weekly AUC of TCA in the
liver (mg-hour/L-week) per unit exposure (ppm or mg/kg-day) under
continuous inhalation (A) and oral (B) exposure conditions in mice (dotted
line), rats (dashed line), and humans (solid line).
For application to human health risk assessment, the uncertainty in and variability among
rodent internal dose estimates both contribute to uncertainty in human risk estimates. Therefore,
it is appropriate to combine uncertainty and variability when applying rodent dose-metric
predictions to quantitative risk assessment. The median and 95% CI for each dose-metric at
some representative exposures in rodents are given in Tables 3-48 and 3-49, and the CI in these
tables includes both uncertainty in the population mean and variance as well as variability in the
population. On the other hand, for use in predicting human risk, it is often necessary to separate,
to the extent possible, interindividual variability from uncertainty, and this disaggregation is
summarized in Table 3-50.
3.5.7.2. Local Sensitivity Analysis With Respect to Dose-Metric Predictions
To assess the parameter sensitivity of dose-metric predictions, a local sensitivity analysis
is performed. The representative exposure scenarios in Tables 3-48-3-50 are used, but with
3-142
-------
metabolic flux dose-metrics converted to —fratron of intake" (i.e., amount metabolized through a
pathway divided by total dose). Each parameter is centered on the sample mean of its estimated
population mean, and then increased and decreased by 5%. The relative change in the model
output y(9) is used to estimate a local SC as follows:
sc = 10
Here,X6) is one of dose-metric predictions, 0± is the MLE or baseline value of ± 5%. For
log-transformed parameters, 0.05 was added or subtracted from the baseline value, whereas for
untransformed parameters, the baseline value was multiplied by 1.05 or 0.95.
Note that local sensitivity analyses as typically performed in deterministic PBPK
modeling can only inform the —pmary" effects of parameter uncertainties (i.e., the direct change
on the quantity of interest due to change in a parameter). They cannot address the propagation
of uncertainties through an analysis, such as those that can arise due to parameter correlations in
the parameter fitting process. Those can only be addressed in a global sensitivity analysis, which
is left for future research.
The results of local sensitivity analyses are shown in Figures 3-26-3-31. As expected,
each dose-metric is sensitive to a only a small fraction of the scaling parameters. Many of these
are well-specified a priori, either due to their being physiological parameters or partition
coefficients that can be measured in vitro. The remaining sensitive parameters are generally
related to metabolism or clearance.
3-143
-------
Table 3-48. Posterior predictions for representative internal doses: mouse"
Dose-metric
ABioactDCVCBW34
ABioactDCVCKid
AMetGSHBW34
AMetLivlBW34
AMetLivOtherBW34
AMetLivOtherLiv
AMetLngBW34
AMetLngResp
AUCCBld
AUCCTCOH
AUCLivTCA
TotMetabBW34
TotOxMetabBW34
TotTCAInBW
Posterior predictions for mouse dose-metrics: median (2.5%, 97.5%)
100 ppm, 7 hr/d, 5 d/wk
0.304 (0.000534, 12.4)
43.7 (0.0774, 1780)
0.684 (0.0307, 17.6)
170 (61.2, 403)
3.81 (0.372, 38.4)
196 (19, 2,070)
187 (7.75, 692)
638,000
(26,500,2,510,000)
96.9(45,211)
87.9 (9.9, 590)
1,880 (444, 7,190)
377 (140, 917)
375 (139, 916)
272 (88.9, 734)
600 ppm, 7 hr/d, 5 d/wk
2.35 (0.00603, 37)
336 (0.801, 5,240)
5.15(0.285,44.9)
878 (342, 2,030)
20 (1.86, 192)
1,030(96.5, 10,100)
263 (10.9, 2,240)
918,000
(36,800, 7,980,000)
822 (356, 2,040)
480(42.1,4,140)
5,070(1,310, 18,600)
1,260 (475, 3,480)
1,250(451,3,450)
729 (267, 1,950)
300 mg/kg-d, 5 d/wk
0.676 (0.00193, 18.4)
96.8 (0.281, 2,550)
1.66 (0.0718, 24.5)
400 (125, 610)
8.38(0.773,80.1)
437(39.5,4,180)
38.5 (3.49, 147)
134,000
(12,500,514,000)
110(6.95,411)
132 (14.4, 670)
2,260 (520, 8,750)
472 (165, 617)
465(161,616)
334 (106, 875)
1,000 mg/kg-d, 5 d/wk
2.81(0.0086,42.4)
393(1.23,6,170)
6.37 (0.567, 49.4)
874 (233, 1,960)
20 (1.55, 202)
1,020(82.1, 10,400)
127 (8.59, 484)
433,000
(30,200, 1,690,000)
592 (56, 1,910)
389 (34, 2,600)
4,660 (939, 18,900)
1,110(303,2,010)
1,100(294,2,010)
694(185, 1,910)
Units
mg/wk-kg3'4
mg/wk-kg tissue
mg/wk-kg3'4
mg/wk-kg374
mg/wk-kg3'4
mg/wk-kg tissue
mg/wk-kg3'4
mg/wk-kg tissue
mg-hr/L-wk
mg-hr/L-wk
mg-hr/L-wk
mg/wk-kg3'4
mg/wk-kg3'4
mg/wk-kg
"Mouse body weight is assumed to be 0.03 kg. Predictions are weekly averages over 10 weeks of the specified exposure protocol. CI reflects both uncertainties
in population parameters (mean, variance) as well as population variability.
5-144
-------
Table 3-49. Posterior predictions for representative internal doses: rata
Dose-metric
ABioactDCVCBW34
ABioactDCVCKid
AMetGSHBW34
AMetLivlBW34
AMetLivOtherBW34
AMetLivOtherLiv
AMetLngBW34
AMetLngResp
AUCCBld
AUCCTCOH
AUCLivTCA
TotMetabBW34
TotOxMetabBW34
TotTCAInBW
Posterior predictions for rat dose-metrics: median (2.5%,97.5%)
100 ppm, 7 hr/d,
5 d/wk
0.341 (0.0306, 2.71)
67.8(6.03,513)
0.331(0.0626,2.16)
176(81.1,344)
45.5 (2.52, 203)
1,870(92.1,8,670)
15 (0.529, 173)
41,900(1,460,496,000)
86.7 (39.2, 242)
83.6 (1.94, 1,560)
587 (53.7, 4,740)
206 (103, 414)
206 (103, 414)
31.7(3.92, 174)
600 ppm, 7 hr/d, 5 d/wk
2.3(0.175,22.6)
450 (35.4, 4,350)
2.27(0.315,19.3)
623 (271, 1,270)
160 (7.84, 749)
6,660(313,31,200)
24.5 (0.819, 227)
67,900 (2,350, 677,000)
1,160(349,2,450)
446 (6, 10,900)
2,030 (186, 13,400)
682 (288, 1,430)
677 (285, 1,430)
110(13.8,490)
300 mg/kg-d, 5 d/wk
2.15(0.17,20.2)
420(31.6,3,890)
2.13 (0.293, 16)
539 (176, 1,060)
134 (6.83, 659)
5,490 (280, 27,400)
15.1(0.527, 115)
40,800 (1,500, 325,000)
670 (47.8, 1,850)
304 (4.71, 7,590)
1,730(124, 11,800)
572 (199, 1,080)
568 (191, 1,080)
90.1 (10.4,417)
1,000 mg/kg-d, 5 d/wk
8.89(0.711,84.1)
1,720 (134, 15,800)
8.84(1.35,69.3)
951(273,2,780)
238(11.3, 1390)
9,900 (492, 59,600)
32.1(1.01,311)
85,700 (2,660, 877,000)
3,340 (828, 8,430)
685(8.14,32,500)
3,130 (200, 21,000)
1,030 (302, 2,920)
1,010 (286, 2,910)
164 (17.3, 800)
Units
mg/wk-kg3'4
mg/wk-kg tissue
mg/wk-kg3'4
mg/wk-kg374
mg/wk-kg3'4
mg/wk-kg tissue
mg/wk-kg3'4
mg/wk-kg tissue
mg-hr/L-wk
mg-hr/L-wk
mg-hr/L-wk
mg/wk-kg3'4
mg/wk-kg3'4
mg/wk-kg
aRat body weight is assumed to be 0.3 kg. Predictions are weekly averages over 10 weeks of the specified exposure protocol. CI reflects both uncertainties in
population parameters (mean, variance) as well as population variability.
5-145
-------
Table 3-50. Posterior predictions for representative internal doses: human3
Dose-metric
ABioactDCVCBW34
ABioactDCVCKid
AMetGSHBW34
AMetLivlBW34
AMetLivOtherBW34
AMetLivOtherLiv
AMetLngBW34
Posterior predictions for human dose-metrics:
2.5% population: median (2.5%, 97.5%)
50% population: median (2.5%, 97.5%)
97.5% population: median (2.5%, 97.5%)
Female
0.001 ppm continuous
0.000256 (6.97 x 10'5, 0.000872)
0.00203 (0.00087, 0.00408)
0.0119(0.00713,0.0177)
0.02 (0.00549, 0.0709)
0.16(0.0671,0.324)
0.95(0.56, 1.45)
0.000159 (4.38 x 10'5, 0.000539)
0.00126(0.000536,0.00253)
0.00736(0.00442,0.011)
0.00161 (0.000619,0.00303)
0.00637(0.00501,0.00799)
0.0157(0.0118,0.0206)
4.98 x 10'5 (8.59 x 10'6, 0.000222)
0.000671 (0.000134,0.00159)
0.00507 (0.00055, 0.00905)
0.000748 (0.000138, 0.00335)
0.0104(0.00225,0.0237)
0.0805(0.00871,0.147)
6.9 x 10-6(6.13 x 10'7,7.99x 10'5)
0.00122(0.000309,0.0032)
0.0123(0.00563,0.0197)
Male
0.001 ppm continuous
0.000254 (6.94 x 10'5, 0.000879)
0.00202(0.000859,0.00413)
0.012(0.00699,0.0182)
0.0207 (0.00558, 0.0743)
0.163(0.0679,0.342)
0.979(0.563, 1.51)
0.000157 (4.37 x 10'5, 0.00054)
0.00125(0.000528,0.00254)
0.00736(0.00434,0.0112)
0.00157(0.000608,0.00292)
0.00619(0.00484,0.00779)
0.0152(0.0115,0.02)
4.87 x 10'5 (8.33 x 10'6, 0.000214)
0.000652 (0.000129, 0.00153)
0.00491 (0.000531,0.00885)
0.00065(0.000119,0.00288)
0.00898(0.00193,0.0203)
0.0691 (0.00751,0.127)
7.25 x 10'6 (6.44 x 10'7, 8.39 x 10'5)
0.00127(0.000325,0.00329)
0.0124(0.00582,0.0199)
Female
0.001 mg/kg-d continuous
0.000197 (6.13 x 10'5, 0.000502)
0.00262(0.0012,0.00539)
0.021 (0.0118,0.0266)
0.0152(0.0048,0.0384)
0.207 (0.0957, 0.43)
1.68(0.956,2.26)
0.000121 (3.82 x 10'5, 0.000316)
0.00161 (0.000748,0.00331)
0.013(0.00725,0.0164)
0.00465(0.00169,0.0107)
0.0172(0.0153,0.0183)
0.0192(0.019,0.0193)
0.000143 (2.35 x 10'5, 0.000681)
0.00166 (0.00035, 0.00365)
0.00993(0.00109,0.0153)
0.00214(0.000354,0.00979)
0.0253 (0.00564, 0.0543)
0.157(0.0188,0.251)
7.54 x 10'8 (6.59 x 10'9, 7.85 x 10'7)
1.51 x I0'5(3.44x 10'6, 4.6 x 10'5)
0.000396 (0.000104, 0.00097)
Male
0.001 mg/kg-d continuous
0.0002 (6.24 x 10'5, 0.000505)
0.00271 (0.00125,0.00559)
0.022(0.0124,0.0277)
0.016 (0.00493, 0.0407)
0.22(0.102,0.459)
1.81(1.03,2.43)
0.000123 (3.82 x 10'5, 0.000323)
0.00167(0.000777,0.00343)
0.0136(0.00759,0.0171)
0.00498(0.00184,0.0112)
0.018(0.0161,0.0191)
0.02(0.0198,0.0201)
0.00015 (2.49 x 10'5, 0.000713)
0.00173(0.000365,0.00382)
0.0103(0.00113,0.0159)
0.00197(0.00033,0.00907)
0.0234 (0.00526, 0.0503)
0.146(0.0173,0.232)
7.05 x 10'8(6.1 x 10'9, 7.25 x 10'7)
1.39 x 10'5(3.21 x 10'6, 4.24 x 10'5)
0.000366 (9.54 x 10'5, 0.000906)
5-146
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Table 3-50. Posterior predictions for representative internal doses: human3 (continued)
Dose-metric
AMetLngResp
AUCCBld
AUCCTCOH
AUCLivTCA
TotMetabBW34
TotOxMetabBW34
Posterior predictions for human dose-metrics:
2.5% population: median (2.5%, 97.5%)
50% population: median (2.5%, 97.5%)
97.5% population: median (2.5%, 97.5%)
Female
0.001 ppm continuous
0.0144(0.00116,0.155)
2.44(0.613,6.71)
25.8(12.4,42.3)
0.00151(0.00122,0.00186)
0.00285(0.00252,0.00315)
0.00444 (0.00404, 0.00496)
0.00313(0.00135,0.00547)
0.0181 (0.0135,0.0241)
0.082(0.0586,0.118)
0.0152(0.00668,0.0284)
0.126(0.0784,0.194)
0.754(0.441, 1.38)
0.0049 (0.00383, 0.00595)
0.0107(0.00893,0.0129)
0.0246(0.0185,0.0326)
0.00273(0.00143,0.00422)
0.00871(0.0069,0.0111)
0.0224(0.0158,0.0309)
Male
0.001 ppm continuous
0.0146(0.00118,0.157)
2.44(0.621,6.65)
25.3(12.2,41.2)
0.00158(0.00127,0.00191)
0.00295 (0.00262, 0.00326)
0.00456(0.00416,0.00507)
0.00305(0.00134,0.00532)
0.0179(0.0133,0.0238)
0.0812(0.0585,0.117)
0.0137(0.00598,0.0258)
0.114(0.0704,0.177)
0.699(0.408, 1.3)
0.00482 (0.0038, 0.00585)
0.0105(0.00877,0.0127)
0.0244(0.0183,0.0324)
0.00269(0.00143,0.00415)
0.00857(0.00675,0.011)
0.0222(0.0155,0.0308)
Female
0.001 mg/kg-d continuous
0.00015 (1.27 x 10'5, 0.00153)
0.0313(0.00725,0.0963)
0.813(0.216,2.13)
4.33 x 10'5(3.3 x 10'5, 6.23 x 10'5)
0.000229 (0.000122, 0.000436)
0.00167 (0.000766, 0.00324)
0.00584 (0.00205, 0.0122)
0.0333(0.025,0.0423)
0.115(0.0872,0.163)
0.029(0.0116,0.0524)
0.227(0.138,0.343)
1.11(0.661,1.87)
0.0163(0.0136,0.0181)
0.0191(0.0188,0.0193)
0.0194(0.0194,0.0194)
0.0049(0.00183,0.0108)
0.0173(0.0154,0.0183)
0.0192(0.019,0.0193)
Male
0.001 mg/kg-d continuous
0.000134(1. 15 x 10'5, 0.00137)
0.0279 (0.00644, 0.086)
0.716(0.189, 1.9)
3.84x 10'5(2.89x 10'5, 5.61 x 10'5)
0.000204 (0.000109, 0.000391)
0.00153(0.000693,0.00303)
0.00615(0.00213,0.0127)
0.035 (0.0264, 0.0445)
0.122(0.0919,0.172)
0.0279(0.0114,0.0501)
0.219(0.133,0.33)
1.09(0.64, 1.88)
0.0173(0.0147,0.019)
0.0199(0.0196,0.0201)
0.0202 (0.0202, 0.0202)
0.00516(0.00194,0.0114)
0.018(0.0161,0.0191)
0.02(0.0198,0.0201)
5-147
-------
Table 3-50. Posterior predictions for representative internal doses: human3 (continued)
Dose-metric
TotTCAInBW
Posterior predictions for human dose-metrics:
2.5% population: median (2.5%, 97.5%)
50% population: median (2.5%, 97.5%)
97.5% population: median (2.5%, 97.5%)
Female
0.001 ppm continuous
0.000259 (0.000121, 0.000422)
0.00154(0.00114,0.00202)
0.00525 (0.00399, 0.00745)
Male
0.001 ppm continuous
0.000246 (0.000114, 0.000397)
0.00146(0.00109,0.00193)
0.00499(0.0038,0.0071)
Female
0.001 mg/kg-d continuous
0.000501 (0.000189,0.000882)
0.00286 (0.00222, 0.00357)
0.00659 (0.00579, 0.00724)
Male
0.001 mg/kg-d continuous
0.000506 (0.000192, 0.00089)
0.00289 (0.00222, 0.0036)
0.00662(0.00581,0.00726)
"Human body weight is assumed to be 70 kg for males, 60 kg for females. Predictions are weekly averages over 100 weeks of continuous exposure (dose-metric units same
as previous tables). Each row represents a different population percentile (2.5, 50, and 97.5%), and the CI in each entry reflects uncertainty in population parameters (mean,
variance).
5-148
-------
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FracOxMetab
FracMetGSH
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100 ppm (light bars) and 600 ppm (dark bars), 7 hours/day, 5 days/week inhalation exposures.
5-149
-------
InQCC
InVPRC
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5-150
-------
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5-151
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5-152
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5-153
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parameters with respect to dose-metrics following 0.001 mg/kg-day continuous oral exposures.
5-154
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3.5.7.3. Implications for the Population Pharmacokinetics of TCE
3.5.7.3.1. Results
The overall uncertainty and variability in key toxicokinetic predictions, as a function of
dose and species, is shown in Figures 3-17-3-25. As expected, TCE that is inhaled or ingested is
substantially metabolized in all species, predominantly by oxidation (see Figures 3-17-3-18). At
higher exposures, metabolism becomes saturated and the fraction metabolized declines. Mice,
on average, have a greater capacity to oxidize TCE than rats or humans, and this is reflected in
the predictions at the two highest levels for each route. The uncertainty in the predictions for the
population means for total and oxidative metabolism is relatively modest; therefore, the wide CI
for combined uncertainty and variability largely reflects intersubject variability. Of particular
note is the high variability in oxidative metabolism at low doses in humans, with the 95% CIs
spanning 0.1-0.7 for inhalation and 0.2-1.0 for ingestion.
Predictions of GSH conjugation and renal bioactivation of DCVC are highly uncertain in
rodents, spanning >1,000-fold in mice and 100-fold in rats (see Figures 3-19-3-20). In both
mice and rats, the uncertainty in the population mean virtually overlaps with the combined
uncertainty and variability. The uncertainty in mice reflects the lack of GSH-conjugate specific
data in that species, and is, therefore, based on overall mass balance only. The somewhat smaller
uncertainty in rats reflects the fact that, in addition to overall mass balance, urinary NAcDCVC
excretion data are available in that species. However, while the lower bound of GSH
conjugation is informed by NAcDCVC excretion data, the upper bound for GSH conjugation and
the amount of DCVC bioactivation are still indirectly estimated from data on other clearance
pathways. In humans, however, overall GSH conjugation is strongly constrained by the blood
concentrations of DCVG from Lash et al. (1999b), with 95% CIs on the population mean
spanning only about threefold. DCVC bioactivation is still indirectly estimated, derived from the
difference between overall GSH conjugation flux and NAcDCVC excretion data from Bernauer
et al. (1996). However, substantial variability is predicted (reflecting variability in the
measurements of Lash et al., (1999b), since the error bars for the population mean are
substantially smaller than those for overall uncertainty and variability. Of particular note is the
prediction of 1 or 2 orders of magnitude more GSH conjugation and DCVC bioactivation, on
average, in humans than in rats, although importantly, the 95% CIs for the predicted population
means do overlap. However, as discussed above in Section 3.3.3.2.1, there are uncertainties as to
the accuracy of analytical method used by Lash et al. (1999b) in the measurement of DCVG in
blood. Because these data are so influential, the analytical uncertainties contribute substantially
to the overall uncertainty in the estimates of the overall GSH conjugation flux, and may be
greater than the statistical uncertainties calculated using the model.
Predictions for respiratory tract oxidative metabolism were, as expected, greatest in mice,
followed by rats and then humans (see Figure 3-21). In addition, due to the —prepstemic" nature
of the respiratory tract metabolism model as well as the hepatic first-pass effect, substantially
3-155
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more metabolism was predicted from inhalation exposures as compared to oral exposures.
Interestingly, the population means appeared to be fairly well constrained despite the lack of
direct data, suggesting that overall mass balance is an important constraint for the presystemic
respiratory tract metabolism modeled here.
Some constraints were also placed on —oth^' hepatic oxidation (i.e., through a pathway
that does not result in chloral formation and subsequent formation of TCA and TCOH, see
Figure 3-22). The 95% CI for overall uncertainty and variability spanned about 100-fold, a large
fraction of that due to uncertainty in the population mean. Interestingly, a higher rate per kg
tissue was predicted for rats than for mice or humans, although importantly, the 95% CIs for the
population means overlap among all three species.
The AUC of TCE in blood (see Figure 3-23) showed the expected nonlinear behavior
with increasing dose, with the nonlinearity more pronounced with oral exposure, as would be
expected by hepatic first-pass. Notably, the predicted AUC of TCE in blood from inhalation
exposures corresponds closely with cross-species ppm-equivalence, as is assumed for Category 3
gases for which the blood:air partition coefficient in laboratory animals is greater than that in
humans (U.S. EPA, 1994b). For low oral exposures (<1 mg/kg-day), cross-species mg/kg-day
equivalence appears to be fairly accurate (within twofold), implying the usual assumption of
mg/kg3/4-day equivalence would be somewhat less accurate, at least for humans. Interestingly,
the AUC of TCOH in blood (see Figure 3-24) was relatively constant with dose, reflecting the
parallel saturation of both TCE oxidation and TCOH glucuronidation. In fact, in humans, the
mean AUC for TCOH in blood increases up to 100 ppm or 100 mg/kg-day, due to saturation of
TCOH glucuronidation, before decreasing at 1,000 ppm or 1,000 mg/kg-day, due to saturation of
TCE oxidation.
The predictions for the AUC for TCA in the liver showed some interesting features (see
Figure 3-25). The predictions for all three species with within an order of magnitude of each
other, with a relatively modest uncertainty in the population mean (reflecting the substantial
amount of data on TCA). The shape of the curves, however, differs substantially, with humans
showing saturation at much lower doses than rodents, especially for oral exposures. In fact, the
ratio between the liver TCA AUC and the rate of TCA production, although differing between
species, is relatively constant as a function of dose within species (not shown). Therefore, the
shape of the curves largely reflect saturation in the production of TCA from TCOH, not in the
oxidation of TCE itself, for which saturation is predicted at higher doses, particularly via the oral
route (see Figure 3-18). In addition, while for the same exposure (ppm or mg/kg-day TCE),
more TCA (on a mg/kg-day basis) is produced in mice relative to rats and humans, humans and
rats have longer TCA half-lives even though plasma protein binding of TCA is, on average,
greater.
3-156
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3.5.7.3.2. Discussion
This analysis substantially informs four of the major areas of pharmacokinetic
uncertainty previously identified in numerous reports (reviewed in Chiu et al., 2006b): GSH
conjugation pathway, respiratory tract metabolism, alternative pathways of TCE oxidation
including DC A formation, and the impact of plasma binding on TCA kinetics, particularly in the
liver. In addition, the analysis helps identify data that have the potential to further reduce the
uncertainties in TCE toxicokinetics and risk assessment.
With respect to the first, previous estimates of the degree of TCE GSH conjugation and
subsequent bioactivation of DCVC in humans were based on urinary excretion data alone
(Bernauer et al., 1996; Birner et al., 1993). For instance, Bloemen et al. (2001) concluded that
due to the low yield of identified urinary metabolites through this pathway (<0.05% as compared
to 20-30% in urinary metabolites of TCE oxidation), GSH conjugation of TCE is likely of minor
importance. However, as noted by Lash et al. (2000a; 2000b), urinary excretion is a poor
quantitative marker of flux through the GSH pathway because it only accounts for the portion
detoxified, and not the portion bioactivated (a limitation acknowledged by Bloemen et al., 2001).
A reexamination of the available in vitro data on GSH conjugation by Chiu et al. (2006b)
suggested that the difference in flux between TCE oxidation and GSH conjugation may not be as
large as suggested by urinary excretion data. For example, the formation rate of DCVG from
TCE in freshly isolated hepatocytes was similar in order of magnitude to the rate measured for
oxidative metabolites (Lash et al., 1999a: Lipscomb et al., 1998b). A closer examination of the
only other available human in vivo data on GSH conjugation, the DCVG blood levels reported in
Lash et al. (1999b), also suggests a substantially greater flux through this pathway than inferred
from urinary data. In particular, the peak DCVG blood levels reported in this study were
comparable on a molar basis to peak blood levels of TCOH, the major oxidative metabolite, in
the same subjects, as previously reported by Fisher et al. (1998). A lower bound estimate of the
GSH conjugation flux can be derived as follows. The reported mean peak blood DCVG
concentrations of 46 uM in males exposed to 100 ppm TCE for 4 hours (Lash et al., 1999b),
multiplied by a typical blood volume of 5 L (ICRP, 2003), yields a peak amount of DCVG in
blood of 0.23 mmoles. In comparison, the retained dose from 100 ppm exposure for 4 hours is
4.4 mmol, assuming retention of about 50% (Monster et al., 1976) and minute-volume of
9 L/minute (ICRP, 2003). Thus, in these subjects, about 5% of the retained dose is present in
blood as DCVG at the time of peak blood concentration. This is a strong lower bound on the
total fraction of retained TCE undergoing GSH conjugation because DCVG clearance is ongoing
at the time of peak concentration, and DCVG may be distributed to tissues other than blood. It
should be reiterated that only grouped DCVG blood data were available for PBPK model-based
analysis; however, this should only result in an underestimation of the degree of variation in
GSH conjugation. Finally, this hypothesis of a significant flux through the human GSH
conjugation pathway is consistent with the limited available total recovery data in humans in
3-157
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which only 60-70% of the TCE dose is recovered as TCE in breath and excreted urinary
metabolites (reviewed in Chiu et al., 2007).
Thus, there is already substantial qualitative and semi-quantitative evidence to suggest a
substantially greater flux through the GSH conjugation pathway than previously estimated based
on urinary excretion data alone. The scientific utility of applying a combination of PBPK
modeling and Bayesian statistical methods to this question comes from being able to
systematically integrate these different types of data—in vitro and in vivo, direct (blood DCVG)
and indirect (total recovery, urinary excretion)—and quantitatively assess their consistency and
implications. For example, the in vitro data discussed above on GSH conjugation were used for
developing prior distributions for GSH conjugation rates, and were not used in previous PBPK
models for TCE. Then, both the direct and indirect in vivo data were used to the extent possible
either in the Bayesian calibration or model evaluation steps.
However, this evidence—both qualitative and quantitative—is highly dependent on the
reliability of the human DCVG measurements, both in vitro and in vivo, from Lash et al. (1999a:
1999b). In vitro, Green et al. (1997a) reported much lower rates of DCVG formation in humans
using a different analytical method. Similarly, the rates of in vitro DCVG formation in rats have
uneven consistency among studies. In male rat liver cytosol, Green et al. (1997a) reported a rate
of 0.54 pmol/minute-mg, consistent with the <2 pmol/minute-mg reported by Dekant et al.
(1990), but much less than the 121 pmol/minute-mg reported by Lash et al. (1999a). However,
in microsomes, Green et al. (1997a) reported no enzymatic formation, whereas Dekant et al.
(1990) reported a higher rate (i.e., 2 pmol/minute-mg) and Lash et al. (1999a) reported a much
higher rate (i.e., 171 pmol/minute-mg). Differing results in humans may be attributable to true
interindividual variation (especially since GSTs are known to be polymorphic). However, this
may be less plausible for rats, suggesting that significant uncertainties remain in the quantitative
estimation of GSH conjugation flux.
Several other aspects of the predictions related to GSH conjugation of TCE are worthy of
note. Predictions for rats and mice remain more uncertain due to their having less direct
toxicokinetic data, but are better constrained by total recovery studies. For instance, the total
recovery of 60-70% of dose in exhaled breath and oxidative metabolites in human studies is
substantially less than the >90% reported in rodent studies (also noted by Goeptar et al., 1995).
In addition, it has been suggested that "saturation" of the oxidative pathway for volatiles in
general, and TCE in particular, may lead to marked increases in flux through the GSH
conjugation pathway (Slikker et al., 2004a, b; Goeptar et al., 1995), but the PBPK model predicts
only a modest, at most -twofold, change in flux. This is because there is evidence that both
pathways are saturable in the liver for this substrate at similar exposures and because GSH
conjugation also occurs in the kidney. Therefore, the available data are not consistent with
toxicokinetics alone causing substantially nonlinearites in TCE kidney toxicity or cancer, or in
any other effects associated with GSH conjugation of TCE.
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Finally, the present analysis suggests a number of areas where additional data can further
reduce uncertainty in and better characterize the TCE GSH conjugation pathway. The Bayesian
analysis predicts a relatively low distribution volume for DCVG in humans, a hypothesis that
could be tested experimentally. In addition, in vivo measurements of DCVG in blood via a
different, validated analytical method, in humans with known exposures to TCE, would be
highly influential in either corroborating the DCVG blood levels reported in Lash et al. (1999b)
or providing evidence that those reported DCVG blood levels are too high due to analytical
issues. Moreover, it would be useful in such studies to be able to match individuals with respect
to toxicokinetic data on oxidative and GSH conjugation metabolites so as to better characterize
variability. A consistent picture as to which GST isozymes are involved in TCE GSH
conjugation, along with data on variability in isozyme polymorphisms and activity levels, can
further inform the extent of human variability. In rodents, more direct data on GSH metabolites,
such as reliably-determined DCVG blood concentrations, preferably coupled with simultaneous
data on oxidative metabolites, would greatly enhance the assessment of GSH conjugation flux in
laboratory animals. Given the large apparent variability in humans, data on interstrain variability
in rodents may also be useful.
With respect to oxidative metabolism, as expected, the liver is the major site of oxidative
metabolism in all three species, especially after oral exposure, where >85% of total metabolism
is oxidation in the liver in all three species. However, after inhalation exposure, the model
predicts a greater proportion of metabolism via the respiratory tract than previous models for
TCE. This is primarily because previous models for TCE respiratory tract metabolism (Hack et
al., 2006; Clewell et al., 2000) were essentially flow-limited—i.e., the amount of respiratory tract
metabolism (particularly in mice) was determined primarily by the (relatively small) blood flow
to the tracheobronchial region. However, the respiratory tract structure used in the present model
is more biologically plausible, is more consistent with that of other volatile organics metabolized
in the respiratory tract (e.g., styrene), and leads to a substantially better fit to closed-chamber
data in mice.
Consistent with the qualitative suggestions from in vitro data, the analysis here predicts
that mice have a greater rate of respiratory tract oxidative metabolism as compared to rats and
humans. However, the predicted difference of about 50-fold on average between mice and
humans is not as great as the 600-fold suggested by previous reports (NRC, 2006; Green, 2000;
Green etal., 1997b). The suggested factor of 600-fold was based on multiplying the Green et al.
(1997b) data on TCE oxidation in lung microsomes from rats vs. mice (23-fold lower) by a
factor for the total CYP content of human lung compared to rat lung (27-fold lower) (incorrectly
cited as being from Raunio et al., 1998; Wheeler and Guenthner, 1990). However, because of
the isozyme-specificity of TCE oxidation, and the differing proportions of different isozymes
across species, total CYP content may not be the best measure of interspecies differences in TCE
respiratory tract oxidative metabolism. Wheeler et al. (1992) reported that CYP2E1 content of
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human lung microsomes is about 10-fold lower than that of human liver microsomes. Given that
Green et al. (1997b) report that TCE oxidation by human liver microsomes is about threefold
lower than that in mouse lung microsomes, this suggests that the mouse-to-human comparison
TCE oxidation in lung microsomes would be about 30-fold. Moreover, the predicted amount of
metabolism corresponds to about the detection limit reported by Green et al. (1997b) in their
experiments with human lung microsomes, suggesting overall consistency in the various results.
Therefore, the 50-fold factor predicted by our analysis is biologically plausible given the
available in vitro data. More direct in vivo measures of respiratory tract metabolism would be
especially beneficial to reduce its uncertainty as well as better characterize its human variability.
TCA dosimetry is another uncertainty that was addressed in this analysis. In particular,
the predicted interspecies differences in liver TCA AUC are modest, with a range of about
10-fold across species, due to the combined effects of interspecies differences in the yield of
TCA from TCE, plasma protein binding, and elimination half-life. This result is in contrast to
previous analyses that did not include TCA protein binding (Clewell et al., 2000; Fisher, 2000),
which predicted significantly more than an order of magnitude difference in TCA AUC across
species. In addition, in order to be consistent with available data, the model requires some
metabolism or other clearance of TCA in addition to urinary excretion. That urinary excretion
does not represent 100% of TCA clearance is evident empirically, as urinary recovery after TCA
dosing is not complete even in rodents (Yu et al., 2000; Abbas et al., 1997). Additional
investigation into possible mechanisms, including metabolism to DCA or enterohepatic
recirculation with fecal excretion, would be beneficial to provide a stronger biological basis for
this empirical finding.
With respect to -tmtracked" oxidative metabolism, this pathway appears to be a relatively
small contribution to total oxidative metabolism. While it is tempting to use this pathway as a
surrogate for DCA production through from the TCE epoxide (Cai and Guengerich, 1999), one
should be reminded that DCA may be formed through multiple pathways (see Section 3.3).
Therefore, this pathway at best represents a lower bound on DCA production. In addition, better
quantitative markers of oxidative metabolism through the TCE epoxide pathway (e.g.,
dichloroacetyl lysine protein adducts, as reported in [e.g., dichloroacetyl lysine protein adducts,
as reported in Forkert et al. (2006)] are needed in order to more confidently characterize its flux.
In a situation such as TCE in which there is large database of studies coupled with
complex toxicokinetics, the Bayesian approach provides a systematic method of simultaneously
estimating model parameters and characterizing their uncertainty and variability. While such an
approach is not necessarily needed for all applications, such as route-to-route extrapolation (Chiu
and White, 2006), as discussed in Barton et al. (2007), characterization of uncertainty and
variability is increasingly recognized as important for risk assessment while representing a
continuing challenge for both PBPK modelers and users. If there is sufficient reason to
characterize uncertainty and variability in a highly transparent and objective manner, there is no
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reason why our approach could not be applied to other chemicals. However, such an endeavor is
clearly not trivial, though the high level of effort for TCE is partially due to the complexity of its
metabolism and the extent of its toxicokinetic database.
It is notable that, with experience, the methodology for the Bayesian approach to PBPK
modeling of TCE has evolved significantly from that of Bois (2000b, a), to Hack et al. (2006), to
the present analysis. Part of this evolution has been a more refined specification of the problem
being addressed, showing the importance of—prfolem formulation" in risk assessment
applications of PBPK modeling. The particular hierarchical population model for each species
was specified based on the intended use of the model predictions, so that relevant data can be
selected for analysis (e.g., excluding most grouped human data in favor of individual human
data) and data can be appropriately grouped (e.g., in rodent data, grouping by sex and strain
within a particular study). Thus, the predictions from the population model in rodents are the
—aveage" for a particular —16't of rodents of a particular species, strain, and sex. This is in
contrast to the Hack et al. (2006) model, in which each dose group was treated as a separate
subject. As discussed above, this previous population model structure led to the unlikely result
that different dose groups within a closed-chamber study had significantly different VMAX values.
In humans, however, interindividual variability is of interest, and furthermore, substantial
individual data are available in humans. Hack et al. (2006) mixed individual- and group-level
data, depending on the availability from the published study, but this approach likely
underestimates population variability due to group means being treated as individuals. In
addition, in some studies, the same individual was exposed more than once, and in Hack et al.
(2006), these were treated as different -^idivi duals." In this case, actual interindividual
variability may be either over- or underestimated, depending on the degree of interoccasion
variability. While it is technically feasible to include interoccasion variability, it would have
added substantially to the computational burden and reduced parameter identifiability. In
addition, a primary interest for this risk assessment is chronic exposure, so the predictions from
the population model in humans are the —laerage" across different occasions for a particular
individual (adult).
The second aspect of this evolution is the drive towards increased objectivity and
transparency. For instance, available information, or the lack thereof, is formally codified and
explicit either in prior distributions or in the data used to generate posterior distributions, and not
both. Methods at minimizing subjectivity (and hence improving reproducibility) in parameter
estimation include: (1) clear separation between the in vitro or physiologic data used to develop
prior distributions and the in vivo data used to generate posterior distributions; (2) use of
noninformative distributions, first updated using a probabilistic model of interspecies-scaling
that allows for prediction error, for parameters lacking in prior information; and (3) use of a
more comprehensive database of physiologic data, in vitro measurements, and in vivo data for
parameter calibration or for out-of-sample evaluation (-v-alidation"). These measures increase
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the confidence that the approach employed also provides adequate characterization of the
uncertainty in metabolic pathways for which available data was sparse or relatively indirect, such
as GSH conjugation in rodents and respiratory tract metabolism. Moreover, this approach yields
more confident insights into what additional data can reduce these uncertainties than approaches
that rely on more subjective methods.
3.5.7.4. Key Limitations and Potential Implications of Violating Key Assumptions
Like all analyses, this one has a number of limitations and opportunities for refinement,
both biological and statistical. Of course, the modeling results are highly dependent on the
assumed PBPK model structure. However, most of the elements of the model structure are well
established for volatile, lipophilic chemicals such as TCE, and, thus, these assumptions are
unlikely to introduce much bias or inaccuracy. In terms of the statistical model, a key
assumption is the choice of prior and population distributions—particularly the choice of
unimodal distributions for population variability. While reasonable as a first approximation,
especially without data to suggest otherwise, this assumption may introduce inaccuracies in the
predictions of population variability. For example, if there were an underlying bimodal
distribution, then fitting using a unimodal population distribution would lead to a high estimate
for the variance, and potentially overestimate the degree of population variability. In some cases
in the human model where larger population variance distributions are estimated, this may be the
underlying cause. However, only in the case of GSH conjugation in humans do the larger
estimates of population variability impact the dose-metric predictions used in the dose-response
assessment, so the impact of this assumption is limited for this assessment.
In addition, certain sources of variability, such as between-animal variability in rodents
and between-occasion variability in humans were not included in the hierarchical model, but
were aggregated with other sources of variability in a "residual" error term. Based on the
posterior predictions, it does not appear that this assumption has introduced significant bias in
the estimates because the residuals between predictions and data do not overall appear
systematically high or low. However, this could be verified by addressing between-animal
variability in rodents [requiring a more rigorous treatment of aggregated data, e.g., Chiu and Bois
(2007)] and incorporation of interoccasion variability in humans (e.g., Bernillon and Bois, 2000).
Some key potential refinements are as follows. First would be the inclusion of a CH
submodel, so that pharmacokinetic data, such as that recently published by Merdink et al. (2008),
could be incorporated. In addition, the current analysis is still dependent on a model structure
substantially informed by deterministic analyses that test alternative model structures (Evans et
al., 2009), as probabilistic methods for discrimination or selection among complex, nonlinear
models such as that for TCE toxicokinetics have not yet been widely accepted. Therefore,
additional refinement of the respiratory tract model may be possible, though more direct in vivo
data would likely be necessary to strongly discriminating among models. In terms of validation,
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application of more sophisticated methods such as cross-validation, may be useful in further
assessing the robustness of the modeling. Finally, additional model changes that may be of
utility to risk assessment, such as development of models for different lifestages (including
childhood and pregnancy), would likely require additional in vivo or in vitro data, particularly as
to metabolism, to ensure model identifiability.
3.5.7.5. Overall Evaluation of PBPK Model-Based Internal Dose Predictions
The utility of the PBPK model developed here for making predictions of internal dose
can be evaluated based on four different components: (1) the degree to which the simulations
have converged to the true posterior distribution; (2) the degree of overall uncertainty and
variability; (3) for humans, the degree of uncertainty in the population; and (4) the degree to
which the model predictions are consistent with in vivo data that are informative to a particular
dose-metric. Table 3-51 summarizes these considerations for each dose-metric prediction. Note
that this evaluation does not consider in any way the extent to which a dose-metric may be the
appropriate choice for a particular toxic endpoint.
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Table 3-51. Degree of variance in dose-metric predictions due to incomplete convergence (columns 2-4),
combined uncertainty and population variability (columns 5-7), uncertainty in particular human population
percentiles (columns 8-10), model fits to in vivo data (column 11); the GSD is a —fol-change" from the central
tendency
Dose-metric
abbreviation
ABioactDCVCBWS
4, ABioactDCVCKid
AMetGSHBW34
AMetLivlBW34
AMetLivOtherBW34
, AMetLivOtherLiv
AMetLngBW34,
AMetLngResp
AUCCBld
AUCCTCOH
AUCLivTCA
TotMetabBW34
TotOxMetabBW34
TotTCAInBW
Convergence: R for generic
scenarios
Mouse
-
<1.011
<1.000
<1.004
<1.001
<1.001
<1.001
<1.000
<1.001
<1.001
<1.002
Rat
<1.016
<1.024
<1.003
<1.151
<1.003
<1.004
<1.029
<1.005
<1.004
<1.003
<1.002
Human
<1.015
<1.015
<1.004
<1.012
<1.002
<1.005
<1.002
<1.002
<1.004
<1.004
<1.001
GSD for combined
uncertainty and variability
Mouse
-
<9.09
<2.02
<3.65
<4.65
<3.04
<3.35
<2.29
<1.92
<1.94
<1.96
Rat
<3.92
<3.28
<1.84
<3.36
<4.91
<3.16
<8.78
<3.18
<1.82
<1.85
<2.69
Human
<3.77
<3.73
<1.97
<3.97
<10.4
<3.32
<5.84
<2.90
<1.81
<1.96
<2.30
GSD for uncertainty in human
population percentiles
1-5%
<2.08
<2.08
<1.82
<2.63
<4.02
<1.20
<1.73
<1.65
<1.13
<1.77
<1.68
25-75%
<1.64
<1.64
<1.16
<1.92
<2.34
<1.43
<1.20
<1.30
<1.12
<1.15
<1.19
95-99%
<1.30
<1.29
<1.16
<2.05
<1.83
<1.49
<1.23
<1.40
<1.18
<1.20
<1.19
Comments regarding model fits to
in vivo data
Good fits to urinary NAcDCVC and
blood DCVG.
Good fits to urinary NAcDCVC and
blood DCVG.
Good fits to oxidative metabolites.
No direct in vivo data.
No direct in vivo data, but good fits to
closed-chamber.
Generally good fits, but poor fit to a few
mouse and human studies.
Good fits across all three species.
Good fits to rodent data.
Good fits to closed-chamber.
Good fits to closed-chamber and
oxidative metabolites.
Good fits to TCA data.
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Overall, the least uncertain dose-metrics are the fluxes of total metabolism
(TotMetabBW34), total oxidative metabolism (TotOxMetabBW34), and hepatic oxidation
(AMetLivlBW34). These all have excellent posterior convergence (R diagnostic <1.01),
relatively low uncertainty and variability (GSD <2), and relatively low uncertainty in human
population variability (GSD for population percentiles <2). In addition, the PBPK model
predictions compare well with the available in vivo pharmacokinetic data.
Predictions for TCE in blood (AUCCBld) are somewhat more uncertain. Although
convergence was excellent across species (R < 1.01), overall uncertainty and variability was
about threefold. In humans, the uncertainty in human population variability was relatively low
(GSD for population percentiles <1.5). TCE blood level predictions were somewhat high in
comparison to the Chiu et al. (2007) study at 1 ppm, though the predictions were better for most
of the other studies at higher exposure levels. In mice, TCE blood levels were somewhat
overpredicted in open-chamber inhalation studies. In both mice and rats, there were some cases
in which fits were inconsistent across dose groups if the same parameters were used across dose
groups, indicating unaccounted-for dose-related effects or intrastudy variability. However, in
both rats and humans, TCE blood (humans and rats) and tissue (rats only) concentrations from
studies not used for calibration (i.e., saved for -eut-of-sample" evaluation/—vitiation") were
well simulated, adding confidence to the parent compound dose-metric predictions.
For the TCA dose-metric predictions (TotTCAInBW, AUCLivTCA) convergence in all
three species was excellent (R < 1.01). Overall uncertainty and variability was intermediate
between dose-metrics for metabolism and that for TCE in blood, with GSDs of about two to
threefold. Uncertainty in human population percentiles was relatively low (GSD of 1.2-1.7).
While liver TCA levels were generally well fit, the data was relatively sparse. Plasma and blood
TCA levels were generally well fit, though in mice, there were again some cases in which fits
were inconsistent across dose groups if the same parameters were used across dose groups,
indicating unaccounted-for dose-related effects or intrastudy variability. In humans, the accurate
predictions for, TCA blood and urine concentrations from studies used for "out of sample"
evaluation lends further confidence to dose-metrics involving TCA.
The evaluation of TCOH in blood followed a similar pattern. Convergence in all three
species was good, though the rat model had slightly worse convergence (R ~ 1.03) than the
mouse and humans (R < 1.01). In mice, overall uncertainty and variability was slightly more
than for TCE in blood. There was much higher overall uncertainty and variability in the rat
predictions (GSD of almost 9), which likely reflects true interstudy variability. The population-
generated predictions for TCOH and TCOG in blood and urine were quite wide, with some in
vivo data at both the upper and lower ends of the range of predictions. In humans, the overall
uncertainty and variability was intermediate between mice and rats (GSD = 5.8). As with the
rats, this likely reflects true population heterogeneity, as the uncertainty in human population
percentiles was relatively low (GSD of around 1.2-1.7-fold). For all three species, fits to in vivo
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data are generally good. In mice, however, there were again some cases in which fits were
inconsistent across dose groups if the same parameters were used across dose groups, indicating
unaccounted-for, dose-related effects or intrastudy variability. In humans, the accurate
predictions for TCOH blood and urine concentrations from studies used for -out of sample"
evaluation lends further confidence to those dose-metrics involving TCOH.
GSH metabolism dose-metrics (ABioactDCVCBW34, ABioactDCVCKid,
AMetGSHBW34) had the greatest overall uncertainty in mice but was fairly well characterized
in rats and humans. In mice, there were no in vivo data informing this pathway except for the
indirect constraint of overall mass balance. So although convergence was adequate (R < 1.02),
the uncertainty/variability was very large, with a GSD of ninefold for the overall flux (the
amount of bioactivation was not characterized because there are no data constraining
downstream GSH pathways). For rats, there were additional constraints from (well-fit) urinary
NAcDCVC data, which reduced the overall uncertainty and variability substantially (GSD less
than fourfold). In humans, in addition to urinary NAcDCVC data, DCVG blood concentration
data was available, though only at the group level. These data, both of which were well fit, in
addition to the greater amount of in vitro metabolism data, allowed for the flux through the GSH
pathway and the rate of DCVC bioactivation to be fairly well constrained, with overall
uncertainty and variability having GSD less than fourfold, and uncertainty in population
percentiles no more than about twofold. However, these predictions may need to be interpreted
with caution, given potential analytical issues with quantifying DCVG either in vitro or in vivo
(see Section 3.3.3.2). Thus, the substantial inconsistencies across studies and methods in the
quantification of DCVG following TCE exposure suggest lower confidence in the accuracy of
these predictions.
The final two dose-metrics, respiratory metabolism (AMetLngBW34, AMetLngResp)
and —dter" oxidative metabolism (AMetLivOtherBW34, AMetLivOtherLiv), also lacked direct
in vivo data and were predicted largely on the basis of mass balance and physiological
constraints. Respiratory metabolism had good convergence (R < 1.01), helped by the availability
of closed-chamber data in rodents. In rats and mice, overall uncertainty and variability was
rather uncertain (GSD of 4~5-fold), but the overall uncertainty and variability was much greater
in humans, with a GSD of about 10-fold. This largely reflects the significant variability across
individuals as well as substantial uncertainty in the low population percentiles (GSD of fourfold).
However, the middle (i.e., —typtial" individuals) and upper percentiles (i.e., the individuals at
highest risk) are fairly well constrained with a GSD of around twofold. For the —ther" oxidative
metabolism dose-metric, convergence was good in mice and humans (R < 1.02), but less than
ideal in rats (R ~ 1.15). In rodents, the overall uncertainty and variability were moderate, with a
GSD around 3.5-fold, slightly higher than that for TCE in blood. The overall uncertainty and
variability in this metric in humans had a GSD of about fourfold, slightly higher than for GSH
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conjugation metrics. However, uncertainty in the middle and upper population percentiles had
GSDs of only about twofold, similar to that for respiratory metabolism.
Overall, as shown in Table 3-51, the updated PBPK model appears to be most reliable for
the fluxes of total, oxidative, and hepatic oxidative metabolism. In addition, dose-metrics related
to blood levels of TCE and oxidative metabolites, TCOH and TCA, had only modest uncertainty.
In the case of TCE in blood, for some data sets, model predictions overpredicted the in vivo data,
and, in the case of TCOH in rats, substantial interstudy variability was evident. For GSH
metabolism, dose-metric predictions for rats and humans had only slightly greater uncertainty
than the TCE and metabolism metrics. Predictions for mice were much more uncertain,
reflecting the lack of GSD-specific in vivo data. Finally, for —dter" oxidative metabolism and
respiratory oxidative metabolism, predictions also had somewhat more uncertainty than the TCE
and metabolism metrics, though uncertainty in middle and upper human population percentiles
was modest.
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4. HAZARD CHARACTERIZATION
This section presents the hazard characterization of TCE health effects. Because of the
number of studies and their relevance to multiple endpoints, the evaluation of epidemiologic
studies of cancer and TCE is summarized in Section 4.1 (endpoint-specific results are presented
in subsequent sections). Genotoxicity data are discussed in Section 4.2. Due to the large number
of endpoints and studies in the toxicity database, subsequent sections (see Sections 4.3-4.10) are
organized by tissue/organ system. Each section is further organized by noncancer and cancer
endpoints, discussing data from human epidemiologic and laboratory experimental studies. In
cases where there is adequate information, the role of metabolism in toxicity, comparisons of
toxicity between TCE and its metabolites, and carcinogenic mode of action are also discussed.
Finally, Section 4.11 summarizes the overall hazard characterization and the weight of evidence
for noncancer and carcinogenic effects.
4.1. EPIDEMIOLOGIC STUDIES ON CANCER AND TCE—METHODOLOGICAL
OVERVIEW
This brief overview of the epidemiologic studies on cancer and TCE below provides
background to the discussion contained in Sections 4.4-4.10. Over 50 epidemiologic studies on
cancer and TCE exposure (see Tables 4-1 through 4-3) were examined to assess their ability to
inform weight-of-evidence evaluation (i.e., to inform the cancer hazard from TCE exposure)
according to 15 standards of study design (see Table 4-4), conduct, and analysis. The analysis of
epidemiologic studies on cancer and TCE serves to document essential design features, exposure
assessment approaches, statistical analyses, and potential sources of confounding and bias. This
analysis, furthermore, supports the discussion of site-specific cancer observations in
Sections 4.4-4.9. In those sections, study findings are presented with an assessment and
discussion of their observations according to a study's weight of evidence and the potential for
alternative explanations, including bias and confounding. Tables containing observed findings
for site-specific cancers are also found in Sections 4.4-4.9. Full details of the weight-of
evidence-review to identify a cancer hazard and study selections for meta-analysis may be found
in Appendix B.
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Table 4-1. Description of epidemiologic cohort and proportionate mortality ratio (PMR) studies assessing
cancer and TCE exposure
Reference
Description
Study group (TV)
comparison group (TV)
Exposure assessment and other information
Aircraft and aerospace workers
Radican et al.
2008): Blair
etal. (1998)
Civilian aircraft-maintenance
workers with at least 1 yr in 1952-
1956 at Hill Air Force Base, Utah.
VS to 1990 (Blairetal.. 1998) or
2000 (Radicanetal.. 2008): cancer
incidence 1973-1990 (Blairetal..
1998)
14,457 (7,204 ever exposed to
TCE).
Incidence (Blairetal.. 1998) and
mortality rates (Radican et al.. 2008:
Blair etal.. 1998) of nonchemical
exposed subjects.
Most subjects (n = 10,718) with potential exposure to 1-25 solvents.
Cumulative TCE assigned to individual subjects using JEM.
Exposure-response patterns assessed using cumulative exposure,
continuous or intermittent exposures, and peak exposure. TCE
replaced in 1968 with 1,1,1-trichloroethane and was discontinued in
1978 in vapor degreasing activities. Median TCE exposures were
about 10 ppm for rag and bucket; 100-200 ppm for vapor
degreasing. Poisson regression analyses controlled for age, calendar
time, sex (Blair etal.. 1998). or Cox proportional hazard model for
age and race.
Krishnadasan
et al. (2007)
Nested case-control study within a
cohort of 7,618 workers employed
between 1950 and 1992, or who had
started employment before 1980 at
Boeing/Rockwell/
Rocketdyne [SSFL, the UCLA
cohort of Morgenstern (1997)1.
Cancer incidence 1988-1999.
326 prostate cancer cases,
1,805 controls.
Response rate:
cases, 69%; controls, 60%.
JEM for TCE, hydrazine, PAHs, benzene, and mineral oil constructed
from company records, walk-through, or interviews. Lifestyle factors
obtained from living subjects through mail and telephone surveys.
Conditional logistic regression controlled for cohort, age at diagnosis,
physical activity, SES, and other occupational exposure (benzene,
PAHs, mineral oil, hydrazine).
4-2
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Table 4-1. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure (continued)
Reference
Description
Study group (N)
comparison group (N)
Exposure assessment and other information
Ritz et al.
(1999): Zhao
et al. (2005)
Aerospace workers with >2 yrs of
employment at Rockwell/
Rocketdyne (now Boeing) and who
worked at SSFL, Ventura,
California, from 1950 to 1993 [the
UCLA cohort of (Morgenstern et al..
1997)1. Cancer mortality as of
December 31,2001. Cancer
incidence 1988-2000 for subjects
alive as of 1988.
6,044 (2,689 with high cumulative
exposure to TCE). Mortality rates of
subjects in lowest TCE exposure
category.
5,049 (2,227 with high cumulative
exposure to TCE). Incidence rates of
subjects in lowest TCE exposure
category.
JEM for TCE, hydrazine, PAHs, mineral oil, and benzene. IH
ranked each job title ranked for presumptive TCE exposure as high
(3), medium (2), low (1), or no (0) exposure for three time periods
(1951-1969, 1970-1979, 1980-1989). Cumulative TCE score: low
(<3), medium (>3-12), high (>12) assigned to individual subjects
using JEM. Cox proportional hazard, controlled for time, since
1st employment, SES, age at diagnosis, and hydrazine exposure.
Boice et al.
(2006b)
Aerospace workers with >6 mo
employment at Rockwell/
Rocketdyne (SSFL and nearby
facilities) from 1948 to 1999 [IEI
cohort (IEI. 2005)1. VS to 1999.
41,351, 1,642 male hourly test stand
mechanics (1,111 with potential TCE
exposure).
Mortality rates of U.S. population and
California population. Internal
referent groups including male hourly
nonadministrative Rocketdyne
workers; male hourly,
nonadministrative SSFL workers; and
test stand mechanics with no potential
exposure to TCE.
Potential TCE exposure assigned to test stands workers only whose
tasks included the cleaning or flushing of rocket engines (engine
flush) (n = 639) or for general utility cleaning (n = 472); potential
for exposure to large quantities of TCE was much greater during
engine flush than when TCE used as a utility solvent. JEM for TCE
and hydrazine without semiquantitative intensity estimates.
Exposure to other solvents not evaluated due to low potential for
confounding (few exposed, low exposure intensity, or not
carcinogenic). Exposure metrics included employment duration,
employment decade, year worked with potential TCE exposure, and
year worked with potential TCE exposure via engine cleaning,
weighted by number of tests. Lifetable (SMR); Cox proportional
hazard controlling for birth year, hire year, and hydrazine exposure.
Boice et al.
(1999)
Aircraft-manufacturing workers
with at least 1 yr >1960 at Lockheed
Martin (Burbank, California). VS to
1996.
77,965 (2,267 with potential routine
TCE exposures and 3,016 with
routine or intermittent TCE
exposure).
Mortality rates of U.S. population
(routine TCE exposed subjects) and
nonexposed internal referents (routine
and intermittent TCE exposed
subjects).
12% with potential routine mixed solvent exposure and 30% with
route or intermittent solvent exposure. JEM for potential TCE
exposure on (1) routine basis or (2) intermittent or routine basis
without semiquantitative intensity estimate. Exposure-response
patterns assessed by any exposure or duration of exposure and
internal control group. Vapor degreasing with TCE before 1966 and
perchloroethylene, afterwards. Lifetable analyses; Poisson
regression analysis adjusting for birth date, starting employment
date, finishing employment date, sex, and race.
4-3
-------
Table 4-1. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure (continued)
Reference
Morgan et al.
(1998)
Costa et al.
(1989)
Garabrant
etal.
(1988)
Description
Aerospace workers with >6 mo
1950-1985 at Hughes (Tucson,
Arizona). VSto 1993.
Aircraft manufacturing workers
employed 1954-1981 at plant in
Italy. VSto 1981.
Aircraft manufacturing workers
>4 yrs employment and who had
worked at least 1 d at San Diego,
California, plant 1958-1982. VS to
1982.
Study group (TV)
comparison group (TV)
20,508 (4,733 with TCE exposures).
Mortality rates of U.S. population for
overall TCE exposure; mortality rates
of all-other cohort subjects (internal
referents).
8,626 subjects
Mortality rates of the Italian
population.
14,067
Mortality rates of U.S. population.
Exposure assessment and other information
TCE exposure intensity assigned using JEM. Exposure-response
patterns assessed using cumulative exposure (low vs. high) and job
with highest TCE exposure rating (peak, medium/high exposure vs.
no/low exposure). — I^h exposure" job classification defined as
>50 ppm. Vapor degreasing with TCE 1952-1977, but limited IH
data <1975. Limited IH data before 1975 and medium/low rankings
likely misclassified given temporal changes in exposure intensity not
fully considered (NRC. 2006).
No exposure assessment to TCE and job titles grouped into one of
four categories: blue- and white-collar workers, technical staff, and
administrative clerks. Lifetable (SMR).
TCE exposure assessment for 70 of 14,067 subjects; 14 cases of
esophageal cancer and 56 matched controls. For these 70 subjects,
company work records identified 37% with job title with potential
TCE exposure without quantitative estimates. Lifetable (SMR).
Cohorts identified from biological monitoring (U-TCA)
Hansen et al.
(2001)
Anttila et al.
(1995)
Workers biological monitored using
U-TCA and air-TCE, 1947-1989.
Cancer incidence from 1964 to
1996.
Workers biological monitored using
U-TCA, 1965-1982. VS 1965-
1991 and cancer incidence 1967-
1992.
803 total.
Cancer incidence rates of the Danish
population.
3,974 total (3,089 with U-TCA
measurements).
Mortality and cancer incidence rates
of the Finnish population.
712 with U-TCA, 89 with air-TCE measurement records, two with
records of both types. U-TCA from 1947 to 1989; air TCE
measurements from 1974. Historic median exposures estimated
from the U-TCA concentrations were: 9 ppm for 1947-1964, 5 ppm
for 1965-1973, 4 ppm for 1974-1979, and 0.7 ppm for 1980-1989.
Air TCE measurements from 1974 onward were 19 ppm (mean) and
5 ppm (median). Overall, median TCE exposure to cohort as
extrapolated from air TCE and U-TCA measurements was
4 ppm (arithmetic mean, 12 ppm). Exposure metrics: year 1st
employed, employment duration, mean exposure, cumulative
exposure. Exposure metrics: employment duration, average TCE
intensity, cumulative TCE, period 1st employment. Lifetable
analysis (SIR).
Median U-TCA, 63 umol/L for females and 48 umol/L for males;
mean U-TCA was 100 umol/L. Average 2.5 U-TCA measurements
per individual. Using the Ikeda et al. (1972) relationship for TCE
exposure to U-TCA, TCE exposures were roughly 4 ppm
(median) and 6 ppm (mean). Exposure metrics: year since
1st measurement. Lifetable analysis (SMR, SIR).
4-4
-------
Table 4-1. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure (continued)
Reference
Description
Study group (TV)
comparison group (TV)
Exposure assessment and other information
Axelson et al.
(1994)
Workers biological monitored using
U-TCA, 1955-1975. VS to 1986
and cancer incidence 1958-1987.
1,421 males.
Mortality and cancer incidence rates
of Swedish male population.
Biological monitoring for U-TCA from 1955 and 1975. Roughly
% of cohort had U-TCA concentrations equivalent to <20 ppm
TCE. Exposure metrics: duration exposure, mean U-TCA.
Lifetable analysis (SMR, SIR).
Other cohorts
Clapp and
Hoffman
(2008)
Deaths between 1969 and 2001
among employees >5 yrs
employment duration at an IBM
facility (Endicott, New York).
360 deaths.
Proportion of deaths among New
York residents during 1979-1998.
No exposure assessment to TCE. PMR analysis.
Sung et al.
(2008: 2007)
Female workers 1st employed 1973-
1997 at an electronics (RCA)
manufacturing factory (Taoyuan,
Taiwan). Cancer incidence 1979-
2001 (Sung etal.. 2007). Childhood
leukemia 1979-2001 among first
born of female subjects in Sung
et al. (2008).
63,982 females and 40,647 females
with 1st live born offspring.
Cancer incidence rates of Taiwan
population (Sung etal.. 2007).
Childhood leukemia incidence rates
of first born live births of Taiwan
population (Sung etal.. 2008).
Chang et al.
(2003:2005)
Male and female workers employed
1978-1997 at electronics factory as
studied by Sung et al. (2007). VS
from 1985 to 1997 and cancer
incidence 1979 to 1997.
86,868 total.
Incidence (Chang et al.. 2005)
mortality (Chang et al.. 2003) rates
Taiwan population.
No exposure assessment. Chlorinated solvents including TCE and
perchloroethylene found in soil and groundwater at factory site.
Company records indicated TCE not used 1975-1991 and
perchloroethylene 1975-1991 and perchloroethylene after 1981. No
information for other time periods. Exposure-response using
employment duration. Lifetable analysis (SMR, SIR) (Sung et al..
2007: Chang etal.. 2005: Chang etal.. 2003) or Poisson regression
adjusting for maternal age, education, sex, and birth year (Sung et
al.. 2008).
ATSDR
(2004a)
Workers 1952-1980 at the View-
Master factory (Beaverton, Oregon).
616 deaths 1989-2001.
Proportion of deaths between 1989
and 2001 in Oregon population.
No exposure information on individual subjects. TCE and other
VOCs detected in well water at the time of the plant closure in 1998
were TCE, 1,220-1,670 ug/L; 1,1-DCE, up to 33 ug/L; and,
perchloroethylene up to 56 ug/L. PMR analysis.
Raaschou-
Nielsen et al.
(2003)
Blue-collar workers employed
>1968 at 347 Danish TCE-using
companies. Cancer incidence
through 1997.
40,049 total (14,360 with presumably
higher level exposure to TCE).
Cancer incidence rates of the Danish
population.
Employers had documented TCE usage. Blue-collar vs. white-collar
workers and companies with <200 workers were variables identified
as increasing the likelihood for TCE exposure. Subjects from iron
and metal, electronics, painting, printing, chemical, and dry cleaning
industries. Median exposures to TCE were 40-60 ppm for the
year before 1970,10-20 ppm for 1970-1979, and approximately
4 ppm for 1980-1989. Exposure metrics: employment duration,
year 1st employed, and # employees in company. Lifetable (SIR).
4-5
-------
Table 4-1. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure (continued)
Reference
Description
Study group (TV)
comparison group (TV)
Exposure assessment and other information
Male uranium-processing plant
workers >3 mo employment 1951-
1972 at DOE facility (Fernald,
Ohio). VS 1951-1989, cancer.
3,814 white males monitored for
radiation (2,971 with potential TCE
xposure).
Mortality rates of the U.S.
population; non-TCE exposed
internal controls for TCE exposure-
response analyses.
JEM for TCE, cutting fluids, kerosene, and radiation generated by
employees and industrial hygienists. Subjects assigned potential
TCE according to intensity: light (2,792 subjects), moderate
(179 subjects), heavy (no subjects). Lifetable (SMR) and conditional
logistic regression adjusted for pay status, date first hire, radiation.
Henschler
etal. (1995)
Male workers >1 yr 1956-1975 at
cardboard factory (Arnsberg region,
Germany). VS to 1992.
169 exposed; 190 unexposed.
Mortality rates from German
Democratic Republic (broad
categories) or RCC incidence rates
from Danish population, German
Democratic, or non-TCE exposed
subjects.
Walk-through surveys and employee interviews used to identify
work areas with TCE exposure. TCE exposure assigned to renal
cancer cases using workman's compensation files. Lifetable (SMR,
SIR) or Mantel-Haenszel.
Greenland
etal. (1994)
Cancer deaths, 1969-1984, among
pensioned workers employed <1984
at GE transformer manufacturing
plant (Pittsfield, Massachusetts),
and who had job history record;
controls were noncancer deaths
among pensioned workers.
512 cases, 1,202 controls.
Response rate:
cases, 69%; controls, 60%.
IH assessment from interviews and position descriptions. TCE
(no/any exposure) assigned to individual subjects using JEM.
Logistic regression.
Sinks et al.
f!992)
Workers employed 1957-1980 at a
paperboard container manufacturing
and printing plant (Newnan,
Georgia). VS to 1988. Kidney and
bladder cancer incidence through
1990.
2,050 total.
Mortality rates of the U.S. population,
bladder and kidney cancer incidence
rates from the Atlanta-SEER registry
for the years 1973-1977.
No exposure assessment to TCE; analyses of all plant employees
including white- and blue-collar employees. Assignment of work
department in case-control study based upon work history; Material
Safety Data Sheets identified chemical usage by department.
Lifetable (SMR, SIR) or conditional logistic regression adjusted for
hire date and age at hire, and using 5- and 10-yr lagged employment
duration.
Blair et al.
(1989)
Workers employed 1942-1970 in
U.S. Coast. VSto 1980.
3,781 males of whom 1,767 were
marine inspectors (48%).
Mortality rates of the U.S. population.
Mortality rates of marine inspectors
also compared to that of
noninspectors.
No exposure assessment to TCE. Marine inspectors worked in
confined spaces and had exposure potential to multiple chemicals.
TCE was identified as one of 10 potential chemical exposures.
Lifetable (SMR) and directly adjusted RRs.
4-6
-------
Table 4-1. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure (continued)
Reference
Description
Study group (TV)
comparison group (TV)
Exposure assessment and other information
Shannon et al.
(1988)
Workers employed >6 mo at GE
lamp manufacturing plant, 1960-
1975. Cancer incidence from 1964
to 1982.
1,870 males and females, 249 (13%)
in coiling and wire-drawing area.
Cancer incidence rates from Ontario
Cancer Registry.
No exposure assessment to TCE. Workers in CWD had potential
exposure to many chemicals including metals and solvents. A
195 5-dated engineering instruction sheet identified TCE used as
degreasing solvent in CWD. Lifetable (SMR).
Shindell and
Ulrich (1985)
Workers employed >3 mo at a TCE
manufacturing plant 1957-1983.
VSto 1983.
2,646 males and females.
Mortality rates of the U.S. population.
No exposure assessment to TCE; job titles categorized as either
white- or blue-collar. Lifetable analysis (SMR).
Wilcosky
et al. (1984)
Respiratory, stomach, prostate,
lymphosarcoma, and lymphatic
leukemia cancer deaths 1964-1972
among 6,678 active and retired
production workers at a rubber plant
(Akron, Ohio); controls were a
20% age-stratified random sample
of the cohort.
183 cases (101 respiratory,
33 prostate, 30 stomach,
9 lymphosarcoma, and 10 lymphatic
leukemia cancer deaths).
JEM without quantitative intensity estimates for 20 exposures
including TCE. Exposure metric: ever held job with potential TCE
exposure.
CWD = coiling and wire drawing; DOE = U.S. Department of Energy; GE = General Electric; IBM = International Business Machines Corporation;
IEI = International Epidemiology Institute; IH = industrial hygienist; JEM = job-exposure matrix; PAH = polycyclic aromatic hydrocarbon; RCC = renal cell
carcinoma; RR = relative risk; SES = socioeconomic status; SIR = standardized incidence ratio; SMR = standardized mortality ratio; SSFL = Santa Susanna Field
Laboratory; U-TCA = urinary TCA; UCLA = University of California, Los Angeles; VS = vital status
4-7
-------
Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Bladder
Pesch et al.
(2QQQa)
Siemiatycki
et al. (19941:
(1991)
Histologically confirmed urothelial
cancer (bladder, ureter, renal
pelvis) cases from German
hospitals (five regions) in 1991-
1995; controls randomly selected
from residency registries matched
on region, sex, and age.
Male bladder cancer cases, age 35-
75 yrs, diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
1,035 cases.
4,298 controls.
Cases, 84%; controls, 71%.
484 cases.
533 population controls;
740 other cancer controls.
Cases, 78%; controls, 72%.
Occupational history using job title or self-reported exposure. JEM and
ITEM to assign exposure potential to metals and solvents (chlorinated
solvents, TCE, perchloroethylene). Lifetime exposure to TCE exposure
examined as 30th, 60th, and 90th percentiles (medium, high, and substantial) of
exposed control exposure index. Duration used to examine occupational title
and job task duties and defined as 30th, 60th, and 90th percentiles (medium,
long, and very long) of exposed control durations.
Logistic regression with covariates for age, study center, and smoking.
JEM to assign 294 exposures including TCE on semiquantitative scales
categorized as any or substantial exposure. Other exposure metrics included
exposure duration in occupation or job title.
Logistic regression adjusted for age, ethnic origin, SES status, smoking,
coffee consumption, and respondent status (occupation or job title) or
Mantel-Haenszel stratified on age, income, index for cigarette smoking,
coffee consumption, and respondent status (TCE).
Brain
DeRoos et al.
(2001): Olshan
et al. (1999)
Neuroblastoma cases in children of
<19 yrs selected from Children's
Cancer Group and Pediatric
Oncology Group with diagnosis in
1992-1994; population controls
(random digit dialing) matched to
control on birth date.
504 cases.
504 controls.
Cases, 73%; controls, 74%.
Telephone interview with parent using questionnaire to assess parental
occupation and serf-reported exposure history and judgment-based attribution
of exposure to chemical classes (halogenated solvents) and specific solvents
(TCE). Exposure metric was any potential exposure.
Logistic regression with covariate for child's age and material race, age, and
education.
4-8
-------
Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Heineman et al.
1994)
White, male cases, age >30 yrs,
identified from death certificates in
1978-1981; controls identified
from death certificates and
matched for age, year of death and
study area.
300 cases.
386 controls.
Cases, 74%; controls, 63%.
In-person interview with next-of-kin; questionnaire assessing lifetime
occupational history using job title and JEM of Gomez et al. (1994).
Cumulative exposure metric (low, medium, or high) based on weighted
probability and duration.
Logistic regression with covariates for age and study area.
Colon and rectum
Goldberg et al.
(2001);
Siemiatycki
(1991)
Male colon cancer cases, 35-
75 yrs, from 16 large Montreal-
area hospitals in 1979-1985 and
histologically confirmed; controls
identified concurrently at 18 other
cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
497 cases.
533 population controls and
740 cancer controls.
Cases, 82%; controls, 72%.
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); potential
TCE exposure defined as any or substantial exposure.
Logistic regression adjusted for age, ethnic origin, birthplace, education,
income, parent's occupation, smoking, alcohol consumption, tea consumption,
respondent status, heating source SES status, smoking, coffee consumption,
and respondent status (occupation, some chemical agents) or Mantel-Haenszel
stratified on age, income, index for cigarette smoking, coffee consumption,
and respondent status (TCE).
Dumas et al.
(2000):
Simeiatycki
(1991)
Male rectal cancer cases, age 35-
75 yrs, diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
292 cases.
533 population controls and
740 other cancer controls.
Cases, 78%; controls, 72%.
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); potential
TCE exposure defined as any or substantial exposure.
Logistic regression adjusted for age, education, respondent status, cigarette
smoking, beer consumption and BMI (TCE) or Mantel-Haenszel stratified on
age, income, index for cigarette smoking, coffee consumption, ethnic origin,
and beer consumption (TCE).
Fredriksson
et al. (1989)
Colon cancer cases aged 30-75 yrs
identified through the Swedish
Cancer Registry among patients
diagnosed in 1980-1983;
population-based controls were
frequency-matched on age and sex
and were randomly selected from a
population register.
329 cases.
658 controls.
Not available.
Mailed questionnaire assessing occupational history with telephone interview
follow-up. Serf-reported exposure to TCE defined as any exposure.
Mantel-Haenszel stratified on age, sex, and physical activity.
4-9
-------
Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Esophagus
Parent et al.
(2000b);
Siemiatycki
(1991)
Male esophageal cancer cases, 35-
75 yrs, diagnosed in 19 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
292 cases.
533 population controls;
740 subjects with other
cancers.
Cases, 78%; controls, 72%.
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); potential
TCE exposure defined as any or substantial exposure.
Logistic regression adjusted for age, education, respondent status, cigarette
smoking, beer consumption and BMI (solvents) or Mantel-Haenszel stratified
on age, income, index for cigarette smoking, coffee consumption, ethnic
origin, and beer consumption (TCE).
Lymphoma
Purdue et al.
(2011)
Cases aged 20-74 with
histologically-confirmed NHL
(B-cell diffuse and follicular,
T-cell, lymphoreticular) without
HIV in 1998-2000 and identified
from four SEER areas (Los
Angeles County and Detroit
metropolitan area, random sample;
Seattle_Puget Sound and Iowa, all
consecutive cases); population
controls aged 20-74 with no
previous diagnosis of HIV
infection or NHL, identified
through (1) if >65 yrs of age,
random digit dialing, or (2) if
>65 yrs, identified from Medicare
eligibility files and stratified on
geographic area, age, and race.
1,321 cases.
1,057 controls.
Cases, 76%; controls, 78%.
In-person interview using questionnaire or computer-assisted personal
interview questionnaire specific for jobs held for >1 yr since the age of 16 yrs,
hobbies, and medical and family history. For occupational history, 32 job- or
industry-specific interview modules asked for detailed information on
individual jobs and focused on solvents exposure, including TCE, assessment
by expert industrial hygienist blinded to case and control status by levels of
probability, frequency, and intensity. Exposure metric of overall exposure,
average weekly exposure, year exposed, average exposure intensity, and
cumulative exposure.
Logistic regression adjusted for sex, age, race, education and SEER site.
4-10
-------
Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Gold et al.
(2011)
Cases aged 35-74 with
histologically-confirmed multiple
myeloma in 2000-2002 and
identified from Seer areas (Detroit,
Seattle-Puget Sound); population
controls.
181 cases.
481 controls.
Cases, 71%; controls, 52%.
In-person interview using computer-assisted personal interview questionnaire
for jobs held >1 yr since 1941 (cases) or 1946 (controls) and since age 18 yrs.
For occupational history, 20 occupations, job- or industry-specific interview
modules asked for detailed information on individual jobs held at least 2 yrs
and focused on solvents exposure, including TCE, assessment by expert
industrial hygienist blinded to case and control status by levels of probability,
duration, and cumulative exposure.
Logistic regression adjusted for sex, age, race, education, and SEER site.
Cocco et al.
(2010)
Histologically or cytologically
confirmed cases aged >17 yrs with
lymphoma (B-cell, T-cell, CLL,
multiple myeloma, Hodgkin) in
1998-2004 and residents of
referral areas from seven European
countries (Czech Republic,
Finland, France, Germany, Ireland,
Italy, and Spain); hospital
(four participating countries) or
population controls (all others);
controls from: (1) Germany and
Italy selected by random digit
dialing from general population
and matched (individually in
German and group-based in Italy)
to cases by sex, age, and residence
area and (2) for all other
countries, matched hospital
controls with diagnoses other than
cancer, infectious diseases, and
immundeficient diseases
(individually in Czech Republic
group-based in all other countries).
2,348 cases.
2,462 controls.
Cases, 88%; controls,
81% hospital and
52% population.
In-person interviews using same structured questionnaire translated to the
local language for information on sociodemographic factors, lifestyle, health
history, and all full-time job held >1 yr. Assessment by industrial hygienists
in each participating center to 43 agents, including TCE, by confidence,
exposure intensity, and exposure frequency. Exposure metric of overall TCE
exposure and cumulative TCE exposure for subjects assessed with high
degree of confidence.
Logistic regression adjusted for age, gender, education, and study center.
4-11
-------
Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
German
centers:
Seidler et al.
(2007): Mester
et al. (2006):
Becker et al.
(2004)
NHL and Hodgkin lymphoma
cases aged 18-80 yrs identified
through all hospitals and
ambulatory physicians in six
regions of Germany between 1998
and 2003; population controls
were identified from population
registers and matched on age, sex,
and region.
710 cases.
710 controls.
Cases, 87%; controls, 44%.
In-person interview using questionnaire assessing personal characteristics,
lifestyle, medical history, UV light exposure, and occupational history of all
jobs held for >1 yr. Exposure of a prior interest were assessed using job task-
specific supplementary questionnaires. JEM used to assign cumulative
quantitative TCE exposure metric, categorized according to the distribution
among the control persons (50th and 90th percentile of the exposed controls).
Conditional logistic regression adjusted for age, sex, region, smoking, and
alcohol consumption.
Wang et al.
(2009)
Cases among females aged 21 and
84 yrs with NHL in 1996-2000
and identified from Connecticut
Cancer Registry; population-based
female controls: (1) if <65 yrs of
age, having Connecticut address
stratified by 5-yr age groups
identified from random digit
dialing or (2) >65 yrs of age, by
random selection from Centers for
Medicare and Medicaid Service
files.
601 cases.
717 controls.
Cases, 72%; controls, 69%
(<65 yrs), 47% (>65 yrs)
In-person interview using questionnaire assessment specific for jobs held for
>1 yr. Intensity and probability of exposure to broad category of organic
solvents and to individual solvents, including TCE, estimated using JEM
(Dosemeci et al.. 1994; Gomez etal.. 1994) and assigned blinded. Exposure
metric of any exposure, exposure intensity (low, medium/high), and exposure
probability (low, medium/high). Logistic regression adjusted for age, family
history of hematopoietic cancer, alcohol consumption, and race.
Costantini et al.
(2008); Miligi
et al. (2006)
Cases aged 20-74 with NHL,
including CLL, all forms of
leukemia, or MM in 1991-1993
and identified through surveys of
hospital and pathology
departments in study areas and in
specialized hematology centers in
eight areas in Italy; population-
based controls stratified by 5-yr
age groups and by sex selected
through random sampling of
demographic or of National Health
Service files.
1,428 NHL + CLL, 586
Leukemia, 263, MM.
1,278 controls (leukemia
analysis).
1,100 controls (MM
analysis).
Cases, 83%; controls, 73%.
In-person interview primarily at interviewee's home (not blinded) using
questionnaire assessing specific jobs, extra occupational exposure to solvents
and pesticides, residential history, and medical history. Occupational
exposure assessed by job-specific or industry-specific questionnaires. JEM
used to assign TCE exposure and assessed using intensity (two categories)
and exposure duration (two categories). All NHL diagnoses and 20% sample
of all cases confirmed by panel of three pathologists.
Logistic regression with covariates for sex, age, region, and education.
Logistic regression for specific NHL included an additional covariate for
smoking.
4-12
-------
Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Persson and
Fredriksson
1999):
combined
analysis of
NHL cases in
Persson et al.
1993: 1989)
Histologically confirmed cases of
B-cell NHL, age 20-79 yrs,
identified in two hospitals in
Sweden: Oreboro in 1964-1986
(Persson etaL 1989) and in
Linkoping between 1975 and 1984
(PerssonetaL 1993): controls
NHL cases, 199.
479 controls.
Cases, 96% (Oreboro),
90% (Linkoping);
controls, not reported.
Mailed questionnaire to assess self reported occupational exposures to TCE
and other solvents.
Unadjusted Mantel-Haenszel %2.
were identified from previous
studies and were randomly
selected from population registers.
Nordstrom
etal. (1998)
Histologically-confirmed cases in
males of hairy-cell leukemia
reported to Swedish Cancer
Registry in 1987-1992 (includes
one case latter identified with an
incorrect diagnosis date);
population-based controls
identified from the National
Population Registry and matched
(1:4 ratio) to cases for age and
county.
Ill cases.
400 controls.
Cases, 91%; controls, 83%.
Mailed questionnaire to assess serf reported working history, specific
exposure, and leisure time activities.
Univariate analysis for chemical-specific exposures (any TCE exposure).
Fritschi and
Siemiatycki
(1996b):
Siemiatycki
(1991)
Male NHL cases, age 35-75 yrs,
diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
215 cases.
533 population controls
(Group 1) and
1,900 subjects with other
cancers (Group 2).
Cases, 83%; controls, 71%.
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales).
Exposure metric defined as any or substantial exposure.
Logistic regression adjusted for age, proxy status, income, and ethnicity
(solvents) or Mantel-Haenszel stratified by age, BMI, and cigarette smoking
(TCE).
4-13
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Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Histologically-confirmed cases of
NHL in males, age 25-85 yrs,
admitted to Swedish (Umea)
hospital between 1974 and 1978;
living controls (1:2 ratio) from the
National Population Register,
matched to living cases on sex,
age, and place of residence;
deceased controls from the
National Registry for Causes of
Death, matched (1:2 ratio) to dead
cases on sex, age, place of
residence, and year of death.
105 cases.
335 controls.
Response rate not available.
Self-administered questionnaire assessing self-reported solvent exposure;
phone follow-up with subject, if necessary.
Unadjusted Mantel-Haenszel %2.
Persson et al.
(1993: 1989)
Histologically confirmed cases of
Hodgkin lymphoma, age 20-
80 yrs, identified in two hospitals
in Sweden: Oreboro in 1964-1986
(Persson etal.. 1989) and in
Linkoping between 1975 and 1984
(Perssonetal.. 1993); controls
randomly selected from population
registers.
54 cases (1989 study);
3 leases (1993 study).
275 controls (1989 study);
204 controls (1993 study).
Response rate not available.
Mailed questionnaire to assess self reported occupational exposures to TCE
and other solvents.
Logistic regression with adjustment for age and other exposure; unadjusted
Mantel-Haenszel %2.
Childhood leukemia
Shu et al.
(2004: 1999)
Childhood leukemia cases,
<15 yrs, diagnosed between 1989
and 1993 by a Children's Cancer
Group member or affiliated
institute; population controls
(random digit dialing), matched for
age, race, and telephone area code
and exchange.
1,842 cases.
1,986 controls.
Cases, 92%; controls, 77%.
Telephone interview with mothers, and whenever available, fathers, using
questionnaire to assess occupation using job-industry title and self-reported
exposure history. Questionnaire included questions specific for solvent,
degreaser, or cleaning agent exposures.
Logistic regression with adjustment for maternal or paternal education, race,
and family income. Analyses of paternal exposure also included age and sex
of the index child.
4-14
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Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Costas et al.
(2002): MDPH
(1997c,b)
Childhood leukemia (<19 yrs age)
diagnosed in 1969-1989 and who
were resident of Woburn,
Massachusetts; controls randomly
selected from Woburn public
school records, matched for age.
19 cases.
37 controls.
Cases, 91%; controls, not
available.
Questionnaire administered to parents separately assessing demographic and
lifestyle characteristics, medical history information, environmental and
occupational exposure, and use of public drinking water in the home.
Hydraulic mixing model used to infer delivery of TCE and other solvents
water to residence.
Logistic regression with composite covariate, a weighted variable of
individual covariates.
McKinney
etal. (1991)
Incident childhood leukemia and
NHL cases, 1974-1988, ages not
identified, from three geographical
areas in England; controls
randomly selected from children of
residents in the three areas and
matched for sex and birth health
district.
109 cases.
206 controls.
Cases, 72%; controls, 77%.
In-person interview with questionnaire with mother to assess maternal
occupational exposure history, and with father and mother, as surrogate, to
assess paternal occupational exposure history. No information provided in
paper whether interviewer was blinded as to case and control status.
Matched pair design using logistic regression for univariate and multivariate
analysis.
Lowengart
et al. (1987)
Childhood leukemia cases aged
<10 yrs and identified from the
Los Angeles (California) Cancer
Surveillance Program in 1980-
1984; controls selected from
random digit dialing or from
friends of cases and matched on
age, sex, and race.
123 cases.
123 controls.
Cases, 79%; controls,
not available.
Telephone interview with questionnaire to assess parental occupational and
self-reported exposure history.
Matched (discordant) pair analysis.
Melanoma
Fritschi and
Siemiatycki
(1996b);
Siemiatycki
(1991)
Male melanoma cases, age 35-
75 yrs, diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
103 cases.
533 population controls and
533 other cancer controls.
Cases, 78%; controls, 72%.
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); potential
TCE exposure defined as any or substantial exposure.
Logistic regression adjusted for age, education, and ethnic origin (TCE) or
Mantel-Haenszel stratified on age, income, index for cigarette smoking, and
ethnic origin (TCE).
4-15
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Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Pancreas
Kernan et al.
(1999)
Pancreatic cancer deaths from
1984 to 1993 in 24 U.S. states;
age-, sex-, race-, and state-matched
noncancer deaths, excluding other
pancreatic diseases and
pancreatitis, controls.
63,097 cases.
252,386 population controls.
Response rates not
identified.
Exposure surrogate assigned for 1 1 1 chlorinated hydrocarbons, including
TCE, and two broad chemical categories using usual occupation on death
certificate and job-exposure-matrix of Gomez et al. (1994).
Race and sex-specific mortality ORs from logistic regression analysis
adjusted for age, marital status, metropolitan area, and residential status.
Prostate
Aronson et al.
(1996):
Siemiatycki
(1991)
Male prostate cancer cases, age
35-75 yrs, diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
449 cases.
533 population controls
(Group 1) and
other cancer cases from
same study (Group 2).
Cases, 81%; controls, 72%.
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales).
Logistic regression adjusted for age, ethnic origin, SES status, Quetlet, and
respondent status (occupation) or Mantel-Haenszel stratified on age, income,
index for cigarette smoking, ethnic origin, and respondent status (TCE).
Renal cell
Moore et al.
(2010)
Cases aged 20-74 yrs from
four European countries (Russia,
Romania, Poland, Czech Republic)
with histologically confirmed
kidney cancer in 1999-2003;
hospital controls with diagnoses
unrelated to smoking or
genitourinary disorders in 1998-
2003 and frequency matched by
sex, age, and study center.
1,097 cases (825 RCCs).
1,184 controls.
Cases, 90-99%; controls,
90.3-96%.
In-person interview using questionnaire for information on lifestyle habits,
smoking, anthropometric measures, personal and family medical history, and
occupational history. Specialized job-specific questionnaire for specific jobs
or industries of interest focused on solvents exposure, including TCE, with
exposure assignment by expert blinded to case and control status by
frequency, intensity, and confidence of TCE exposure. Exposure metric of
overall exposure, duration (total hr, yr), and cumulative exposure.
Logistic regression adjusted for sex, age, and study center. BMI,
hypertension, smoking, residence location also included in initial models but
did not alter ORs by >10%.
4-16
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Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Charbotel et al.
(2009: 2006)
Cases from Arve Valley region in
France identified from local
urologists files and from area
teaching hospitals; age- and sex-
matched controls chosen from file
of same urologist as who treated
case or recruited among the
patients of the case's general
practitioner.
87 cases.
316 controls.
Cases, 74%; controls, 78%.
Telephone interview with case or control, or, if deceased, with next-of-kin
(22% cases, 2% controls). Questionnaire assessing occupational history,
particularly, employment in the screw cutting jobs, and medical history.
Semiquantitative TCE exposure assigned to subjects using a task/TCE-
Exposure Matrix designed using information obtained from questionnaires
and routine atmospheric monitoring of workshops or biological monitoring
(U-TCA) of workers carried out since the 1960s. Cumulative exposure,
cumulative exposure with peaks, and TWA.
Conditional logistic regression with covariates for tobacco smoking and BMI.
Briining et al.
(2003)
Histologically-confirmed cases
1992-2000 from German hospitals
(Arnsberg); hospital controls
(urology department) serving area,
and local geriatric department, for
older controls, matched by sex and
age.
134 cases.
401 controls.
Cases, 83%; controls, not
available.
In-person interviews with case or next-of-kin; questionnaire assessing
occupational history using job title. Exposure metrics included longest job
held, JEM of Pannett et al. (1985) to assign cumulative exposure to TCE and
perchloroethylene, and exposure duration.
Logistic regression with covariates for age, sex, and smoking.
Pesch et al.
(2000b)
Histologically-confirmed cases
from German hospitals
(five regions) in 1991-1995;
controls randomly selected from
residency registries matched on
region, sex, and age.
935 cases.
4,298 controls.
Cases, 88%; controls, 71%.
In-person interview with case or next-of-kin; questionnaire assessing
occupational history using job title (JEM approach), self-reported exposure, or
job task (JTEM approach) to assign TCE and other exposures.
Logistic regression with covariates for age, study center, and smoking.
Parent et al.
(2000a);
Siemiatycki
(1991)
Male RCC cases, age 35-75 yrs,
diagnosed in 16 large Montreal-
area hospitals in 1979-1985 and
histologically confirmed; controls
identified concurrently at 18 other
cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
142 cases.
533 population controls
(Group 1) and
other cancer controls
(excluding lung and bladder
cancers) (Group 2).
Cases, 82%; controls, 71%.
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (about 300 exposures on semiquantitative scales);
TCE defined as any or substantial exposure.
Mantel-Haenszel stratified by age, BMI, and cigarette smoking (TCE) or
logistic regression adjusted for respondent status, age, smoking, and BMI
(occupation, job title).
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Table 4-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (TV)
comparison group (TV)
response rates
Exposure assessment and other information
Dosemeci et al.
1999)
Histologically-confirmed cases,
1988-1990, white males and
females, 20-85 yrs, from
Minnesota Cancer Registry;
controls stratified for age and sex
using random digit dialing, 21-
64 yrs, or from HCFA records, 64-
85 yrs.
438 cases.
687 controls.
Cases, 87%; controls, 86%.
In-person interviews with case or next-of-kin; questionnaire assessing
occupational history of TCE using job title and JEM of Gomez et al. (1994).
Exposure metric was any TCE exposure.
Logistic regression with covariates for age, smoking, hypertension, and BMI.
Vamvakas et al.
(1998)
Cases who underwent
nephrectomy in 1987-1992 in a
hospital in Arnsberg region of
Germany; controls selected
accident wards from nearby
hospital in 1992.
58 cases.
84 controls.
Cases, 83%; controls, 75%.
In-person interview with case or next-of-kin; questionnaire assessing
occupational history using job title or self-reported exposure to assign TCE
and perchloroethylene exposure.
Logistic regression with covariates for age, smoking, BMI, hypertension, and
diuretic intake.
Multiple or other sites
Lee et al.
(2003)
Liver, lung, stomach, colorectal
cancer deaths in males and females
between 1966 and 1997 from
two villages in Taiwan; controls
were cardiovascular and cerebral-
vascular disease deaths from same
underlying area as cases.
53 liver,
39 stomach,
26 colorectal,
41 lung cancer cases.
286 controls.
Response rate not reported.
Residence as recorded on death certificate.
Mantel-Haenszel stratified by age, sex, and time period.
Siemiatycki
(1991)
Male cancer cases, 1979-1985,
35-75 yrs, diagnosed in
16 Montreal-area hospitals,
histologically confirmed; cancer
controls identified concurrently;
age-matched, population-based
controls identified from electoral
lists and random digit dialing.
857 lung and 117 pancreatic
cancer cases.
533 population controls
(Group 1) and other cancer
cases from same study
(Group 2).
Cases, 79% (lung), 71%
(pancreas); controls, 72%.
In-person interviews (direct or proxy) with segments on work histories (job
titles and self-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); TCE
defined as any or substantial exposure.
Mantel-Haenszel stratified on age, income, index for cigarette smoking, ethnic
origin, and respondent status (lung cancer) and age, income, index for
cigarette smoking, and respondent status (pancreatic cancer).
Bolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
BMI = body mass index; CLL = chronic lymphocytic leukemia; HCFA = Health Care Financing Administration; ITEM = job-task exposure matrix; MM =
multiple myeloma; NCI = National Cancer Institute; NHL = non-Hodgkin lymphoma; OR = odds ratio; UV = ultra-violet
4-18
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Table 4-3. Geographic-based studies assessing cancer and TCE exposure
Reference
Description
Analysis approach
Exposure assessment
Broome County, New York studies
ATSDR
(2006a),
(2008b)
Total, 22 site-specific, and
childhood cancer incidence from
1980 to 2001 among residents in
two areas in Endicott, New York.
SIR among all subjects (ATSDR. 2006a) or
among white subjects only (ATSDR. 2008b) with
expected numbers of cancers derived using age-
specific cancer incidence rates for New York
State, excluding New York City. Limited
assessment of smoking and occupation using
medical and other records in lung and kidney
cancer subjects (ATSDR. 2008b).
Two study areas, Eastern and Western study areas,
identified based on potential for soil vapor intrusion
exposures as defined by the extent of likely soil vapor
contamination. Contour lines of modeled VOC soil
vapor contamination levels based on exposure model
using GIS mapping and soil vapor sampling results
taken in 2003. The study areas were defined by 2000
Census block boundaries to conform to model predicted
areas of soil vapor contamination. TCE was the most
commonly found contaminant in indoor air in Eastern
study area at levels ranging from 0.18 to 140 ug/m3,
with tetrachloroethylene, cis-l,2-dichloroethene, 1,1,1-
trichloroethane, 1,1 -DCE, 1,1-dichloroethane, and
Freon 113 detected at lower levels. Perchloroethylene
was most common contaminant in indoor air in Western
study area with other VOCs detected at lower levels.
Maricopa County, Arizona studies
Aickin et al.
(1992): Aickin
(2QQ4)
Cancer deaths, including leukemia,
1966-1986, and childhood (<19 yrs
old) leukemia incident cases (1965-
1986), Maricopa County, Arizona.
Standardized mortality rate ratio from Poisson
regression modeling. Childhood leukemia
incidence data evaluated using Bayes methods and
Poisson regression modeling.
Location of residency in Maricopa County, Arizona, at
the time of death as surrogate for exposure. Some
analyses examined residency in West Central Phoenix
and cancer. Exposure information is limited to TCE
concentration in two drinking water wells in 1982.
Pima County, Arizona studies
ADHS (1995.
1990)
Cancer incidence in children
(<19 yrs old) and testicular cancer in
1970-1986 and 1987-1991, Pima
County, Arizona.
Standardized incidence RR from Poisson
regression modeling using method of Aickin et al.
(Aickin et al.. 1992). Analysis compares
incidence in Tucson Airport Area to rate for rest
of Pima County.
Location of residency in Pima, County, Arizona, at the
time of diagnosis or death as surrogate for exposure.
Exposure information is limited to monitoring since
1981 and include VOCs in soil gas samples (TCE,
perchloroethylene, 1,1-DCE, 1,1,1-trichloroaceticacid);
PCBs in soil samples, and TCE in municipal water
supply wells.
4-19
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Table 4-3. Geographic-based studies assessing cancer and TCE exposure (continued)
Reference
Description
Analysis approach
Exposure assessment
Other
Coyle et al.
(2005)
Incident breast cancer cases among
men and women, 1995-2000,
reported to Texas Cancer Registry.
Correlation study using rank order statistics of
mean average annual breast cancer rate among
women and men and atmospheric release of
12 hazardous air pollutants.
Reporting to EPA Toxic Release Inventory the number
of pounds released for 12 hazardous air pollutants,
(carbon tetrachloride, formaldehyde, methylene
chloride, styrene, tetrachloroethylene, TCE, arsenic,
cadmium, chromium, cobalt, copper, and nickel).
Morgan and
Cassady
(2002)
Incident cancer cases, 1988-1989,
among residents of 13 census tracts
in Redlands area, San Bernardino
County, California.
SIR for all cancer sites and 16 site-specific
cancers; expected numbers using incidence rates
of site-specific cancer of a four-county region
between 1988 and 1992.
TCE and perchlorate detected in some county wells; no
information on location of wells to residents,
distribution of contaminated water, or TCE exposure
potential to individual residents in studied census tracts.
Vartiainen
et al. (1993)
Total cancer and site-specific cancer
cases (lymphoma sites and liver)
from 1953 to 1991 in two Finnish
municipalities.
SIR with expected number of cancers and site-
specific cancers derived from incidence of the
Finnish population.
Monitoring data from 1992 indicated presence of TCE,
tetrachloroethylene and 1,1,1,-trichloroethane in
drinking water supplies in largest towns in
municipalities. Residence in town used to infer
exposure to TCE.
Cohn et al.
(1994b);
Fagliano et al.
(1990)
Incident leukemia and NHL cases,
1979-1987, from 75 municipalities
and identified from the New Jersey
State Cancer Registry. Histological
type classified using WHO scheme
and the classification of NIH
Working Formulation Group for
grading NHL.
Logistic regression modeling adjusted for age.
Monitoring data from 1984 to 1985 on TCE,
trihalomethanes, and VOCs concentrations in public
water supplies, and historical monitoring data
conducted in 1978-1984.
Incident bladder cancer cases and
deaths, 1978-1985, among residents
of nine northwestern Illinois
counties.
SIR and SMR by county of residence and zip
code; expected numbers of bladder cancers using
age-race-sex specific incidence rates from SEER
or bladder cancer mortality rates of the U.S.
population from 1978 to 1985.
Exposure data are lacking for the study population with
the exception of noting one of two zip code areas with
observed elevated bladder cancer rates also had
groundwater supplies contaminated with TCE,
perchloroethylene, and other solvents.
Isacson et al.
(1985)
Incident bladder, breast, prostate,
colon, lung and rectal cancer cases
reported to Iowa cancer registry
between 1969 and 1981.
Age-adjusted site-specific cancer incidence in
Iowa towns with populations of 1,000-10,000 and
who were serviced by a public drinking water
supply.
Monitoring data of drinking water at treatment plant in
each Iowa municipality with populations of 1,000-
10,000 used to infer TCE and other VOC
concentrations in finished drinking water supplies.
GIS = geographic information system; NIH = National Institutes of Health; PCB = polychlorinated biphenyl; SEER = Surveillance, Epidemiology, and End
Results; WHO = World Health Organization
4-20
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Table 4-4. Standards of epidemiologic study design and analysis use for identifying cancer hazard and TCE
exposure
Category A: STUDY DESIGN
Clear articulation of study objectives or hypothesis. The ideal is a clearly stated hypothesis or study objectives and the study is designed to achieve the
identified objectives.
Selection and characterization in cohort studies of exposure and control groups and of cases and controls (case-control studies) is adequate. The ideal is
for selection of cohort and referents from the same underlying population and differences between these groups to be due to TCE exposure or level of TCE
exposure and not to physiological, health status, or lifestyle factors. Controls or referents are assumed to lack or to have background exposure to TCE. These
factors may lead to a downward bias including one of which is known as —hearty worker bias," often introduced in analyses when mortality or incidence rates
from a large population such as the U.S. population are used to derive expected numbers of events. The ideal in case-control studies is cases and controls are
derived from the same population and are representative of all cases and controls in that population. Any differences between controls and cases are due to
exposure to TCE itself and not to confounding factors related to both TCE exposure and disease. Additionally, the ideal is for controls to be free of any disease
related to TCE exposure. In this latter case, potential bias is toward the null hypothesis.
Category B: ENDPOINT MEASURED
Levels of health outcome assessed. Three levels of health outcomes are considered in assessing the human health risks associated with exposure to TCE:
biomarkers of effects and susceptibility, morbidity, and mortality. Both morbidity, as enumerated by incidence, and mortality, as identified from death
certificates, are useful indicators in risk assessment for hazard identification. The ideal is for accurate and predictive indicator of disease. Incidence rates are
generally considered to provide an accurate indication of disease in a population and cancer incidence is generally enumerated with a high degree of accuracy in
cancer registries. Death certifications are readily available and have complete national coverage but diagnostic accuracy is reduced and can vary by specific
diagnosis. Furthermore, diagnostic inaccuracies can contribute to death certificates as a poor surrogate for disease incidence. Incidence, when obtained from
population-based cancer registries, is preferred for identifying cancer hazards.
Changes in diagnostic coding systems for lymphoma, particularly NHL. Classification of lymphomas today is based on morphologic, immunophenotypic,
genotypic, and clinical features using the WHO classification, introduced in 2001, and incorporation of WHO terminology into International Classification of
Disease (ICD)-0-3. ICD Versions 7 and earlier had rubrics for general types of lymphatic and hematopoietic cancer, but no categories for distinguishing specific
types of cancers, such as acute leukemia. Epidemiologic studies based on causes of deaths as coded using these older ICD classifications typically grouped
together lymphatic neoplasms instead of examining individual types of cancer or specific cell types. Before the use of immunophenotyping, these grouping of
ambiguous diseases such as NHL and Hodgkin lymphoma may be have misclassified. With the introduction of ICD-10 in 1990, lymphatic tumors coding,
starting in 1994 with the introduction of the Revised European-American Lymphoma classification, the basis of the current WHO classification, was more
similar to that presently used. Misclassification of specific types of cancer, if unrelated to exposure, would have attenuated estimate of RR and reduced
statistical power to detect associations. When the outcome was mortality, rather than incidence, misclassification would be greater because of the errors in the
coding of underlying causes of death on death certificates (IOM. 2003). Older studies that combined all lymphatic and hematopoietic neoplasms must be
interpreted with care.
4-21
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Table 4-4. Standards of epidemiologic study design and analysis use for identifying cancer hazard and TCE
exposure (continued)
Category C: TCE-EXPOSURE CRITERIA
Adequate characterization of exposure. The ideal is for TCE exposure potential known for each subject and quantitative assessment (job-exposure-matrix
approach) of TCE exposure assessment for each subject as a function of job title, year exposed, duration, and intensity. The assessment approach is accurate for
assigning TCE intensity (TCE concentration or a TWA) to individual study subjects and estimates of TCE intensity are validated using monitoring data from the
time period. For the purpose of this report, the objective for cohort and case-controls studies is to differentiate TCE-exposed subjects from subjects with little or
no TCE exposure. A variety of dose-metrics may be used to quantify or classify exposures for an epidemiologic study. They include precise summaries of
quantitative exposure, concentrations of biomarkers, cumulative exposure, and simple qualitative assessments of whether exposure occurred (yes or no). Each
method has implicit assumptions and potential problems that may lead to misclassification. Studies in which it was unclear that the study population was
actually exposed to TCE are excluded from analysis.
Category D: FOLLOW-UP (COHORT)
Loss to follow-up. The ideal is complete follow-up of all subjects; however, this is not achievable in practice, but it seems reasonable to expect loss to follow-up
not to exceed 10%. The bias from loss to follow-up is indeterminate. Random loss may have less effect than if subjects who are not followed have some
significant characteristics in common.
Follow-up period allows full latency period for over 50% of the cohort. The ideal to follow all study subjects until death. Short of the ideal, a sufficient follow-
up period to allow for cancer induction period or latency over 15 or 20 yrs is desired for a large percentage of cohort subjects.
Category E: INTERVIEW TYPE (CASE-CONTROL)
Interview approach. The ideal interviewing technique is face-to-face by trained interviewers with >90% of interviews with cases and control subjects
conduced face-to-face. The effect on the quality of information from other types of data collection is unclear, but telephone interviews and mail-in
questionnaires probably increase the rate of misclassification of subject information. The bias is toward the null hypothesis if the proportion of interview by
type is the same for case and control, and of indeterminate direction otherwise.
Blinded interviewer. The ideal is for the interviewer to be unaware whether the subject is among the cases or controls and the subject to be unaware of the
purpose and intended use of the information collected. Although desirable for case-control studies, blinding is usually not possible to fully accomplish because
subject responses during the interview provide clues as to subject status. The potential for bias from face-to-face interviews is probably less than with mail-in
interviews. Some studies have assigned exposure status in a blinded manner using a JEM and information collected in the unblinded interview. The potential
for bias in this situation is probably less with this approach than for nonblinded assignment of exposure status.
4-22
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Table 4-4. Standards of epidemiologic study design and analysis use for identifying cancer hazard and TCE
exposure (continued)
Category F: PROXY RESPONDENTS
Proxy respondents. The ideal is for data to be supplied by the subject because the subject generally would be expected to be the most reliable source; <10% of
either total cases or total controls for case-control studies. A subject may be either deceased or too ill to participate, however, making the use of proxy responses
unavoidable if those subjects are to be included in the study. The direction and magnitude of bias from use of proxies is unclear, and may be inconsistent across
studies.
Category G: SAMPLE SIZE
The ideal is for the sample size is large enough to provide sufficient statistical power to ensure that any elevation of effect in the exposure group, if present,
would be found, and to ensure that the confidence bounds placed on RR estimates can be well characterized.
Category H: ANALYSIS ISSUES
Control for potentially confounding factors of importance in analysis. The ideal in cohort studies is to derive expected numbers of cases based on age-sex-
and time-specific cancer rates in the referent population and in case-control studies by matching on age and sex in the design and then adjusting for age in the
analysis of data. Age and sex are likely correlated with exposure and are also risk factors for cancer development. Similarly, other factors such as cigarette
smoking and alcohol consumption are risk factors for several site-specific cancers reported as associative with TCE exposure. To be a confounder of TCE,
exposure to the other factor must be correlated, and the association of the factor with the site-specific cancer must be causal. The expected effect from
controlling for confounders is to move the estimated RR estimate closer to the true value.
Statistical methods are appropriate. The ideal is that conclusions are drawn from the application of statistical methods that are appropriate to the problem and
accurately interpreted.
Evaluation of exposure-response. The ideal is an examination of a linear exposure-response as assessed with a quantitative exposure metric such as
cumulative exposure. Some studies, absent quantitative exposure metrics, examine exposure response relationships using a semiquantitative exposure metric or
by duration of exposure. A positive dose-response relationship is usually more convincing of an association as causal than a simple excess of disease using TCE
dose-metric. However, a number of reasons have been identified for a lack of linear exposure-response finding and the failure to find such a relationship mean
little from an etiological viewpoint.
Documentation of results. The ideal is for analysis observations to be completely and clearly documented and discussed in the published paper, or provided in
supplementary materials accompanying publication.
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Twenty-four of the studies identified in a systematic review were selected for inclusion in
the meta-analysis through use of the following meta-analysis inclusion criteria: (1) cohort
or0020case-control designs; (2) evaluation of incidence or mortality; (3) adequate selection in
cohort studies of exposure and control groups and of cases and controls in case-control studies;
(4) TCE exposure potential inferred to each subject and quantitative assessment of TCE exposure
assessment for each subject by reference to industrial hygiene records indicating a high
probability of TCE use, individual biomarkers, job-exposure matrices (JEMs), water distribution
models, or obtained from subjects using questionnaire (case-control studies); and (5) relative risk
(RR) estimates for kidney cancer, liver cancer, or non-Hodgkin lymphoma (NHL) adjusted, at
minimum, for possible confounding of age, sex, and race (see Table 4-5). This evaluation is
summarized below, separately for cohort and case-control studies. Appendix C contains a full
discussion of the meta-analysis, its analytical methodology, including sensitivity analyses, and
findings. The meta-analysis focuses on kidney cancer, liver cancer, and NHL, as most studies
reported RRs for these sites. Fewer numbers of studies reported RRs for other site-specific
cancers and TCE exposure and examination of these site-specific cancers and TCE exposure
using meta-analysis was not attempted.
Table 4-5. Summary of criteria for meta-analysis study selection
Decision
outcome
Studies
Primary reason(s)
Studies recommended for meta-analysis:
Axelson et al. (1994): Greenland et al. (1994):
Hardell et al. (1994): Siemiatycki (1991):
Anttila et al. (1995): Morgan et al. (1998):
Nordstrom et al. (1998): Boice et al. (1999):
Boice et al. (2006b): Dosemeci et al. (1999):
Persson and Fredriksson (1999): Pesch et al.
(2000b): Hansen et al. (2001): Briining et al.
(2003): Raaschou-Nielsen et al. (2003): Zhao
et al. (2005): Miligi et al. (2006): Charbotel
et al. (2006): Blair et al. (1998): its follow-up
Radican et al. (2008): Wang et al. (2009):
Cocco et al. (2010): Moore et al. (2010):
Purdue etal. (2011)
Analytical study designs of cohort or case-control;
evaluation of incidence or mortality; adequate selection
in cohort studies of exposure and control groups and of
cases and controls in case-control studies; TCE
exposure potential inferred to each subject and
quantitative assessment of TCE exposure assessment
for each subject by reference to industrial hygiene
records indicating a high probability of TCE use,
individual biomarkers, JEMs, water distribution
models, or obtained from subjects using questionnaire
(case-control studies); RR estimates for kidney cancer,
liver cancer, or NHL adjusted, at minimum, for
possible confounding of relevant risk factors (e.g., age,
sex, and race).
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Table 4-5. Summary of criteria for meta-analysis study selection (continued)
Decision
outcome
Studies
Primary reason(s)
Studies not recommended for meta-analysis:
Clapp and Hoffman (2008): ATSDR (2004a:
Cohnetal.. 1994b)
Garabrant et al. (1988): Isacson et al.(1985);
Shindell and Ulrich (1985): Wilcosky et al.
(1984): Shannon et al. (1988): Blair et al.
(1989): Costa etal. (1989): (ADHS. 1995.
1990); Mallin (1990); Aickin et al. (1992);
Sinks et al. (1992); Vartiainen et al. (1993);
Morgan and Cassady (2002); Lee et al. (2003);
Aickin (2004); Chans et al. (2005; Chans et al..
2003); Covle et al. (2005); ATSDR (2006a);
ATSDR (2008b); Sung et al. (2008; 2007)
Lowengart et al. (1987); Fredriksson et al.
(1989); McKinney et al. (1991); Heineman
et al. (1994); Siemiatycki et al. (1994);
Aronson et al. (1996); Fritschi and Siemiatycki
(1996b); Dumas et al. (2000); Kernan et al.
(1999); Shu et al. (2004; 1999); Parent et al.
(2000b); Pesch et al. (2000a); DeRoos et al.
(2001); Goldberg et al. (2001); Costas et al.
(2002); Krishnadasan et al. (2007) Costantini
et al. (2008); Gold et al. (2011)
Ritz (1999a)
Henschler et al. (1995)
Weakness with respect to analytical study design (i.e.,
geographic-based, ecological or PMR design).
TCE exposure potential not assigned to individual
subjects using JEM, individual biomarkers, water
distribution models, or industrial hygiene data from
other process indicating a high probability of TCE use
(cohort studies).
Cancer incidence or mortality reported for cancers
other than kidney, liver, or NHL.
Subjects monitored for radiation exposure with
likelihood for potential confounding; cancer mortality
and TCE exposure not reported for kidney cancer and
all hemato- and lymphopoietic cancer reported as
broad category.
Incomplete identification of cohort and index kidney
cancer cases included in case series.
The cohort studies (Clapp and Hoffman. 2008: Radican et al.. 2008: Sung et al.. 2008:
Krishnadasan et al.. 2007: Sung et al.. 2007: Boice et al.. 2006b: Chang et al.. 2005: Zhao et al..
2005: ATSDR. 2004a: Chang etal.. 2003: Raaschou-Nielsen et al.. 2003: Hansen etal.. 2001:
Boice etal.. 1999: Ritz. 1999a: Blair etal.. 1998: Morgan et al.. 1998: Anttila et al.. 1995:
Henschler et al.. 1995: Axel son et al.. 1994: Greenland et al.. 1994: Sinks etal.. 1992: Blair et
al.. 1989: Costa et al.. 1989: Garabrant et al.. 1988: Shannon et al.. 1988: Shindell and Ulrich.
1985: Wilcosky et al., 1984) (see Table 4-1), with data on the incidence or morality of site-
specific cancer in relation to TCE exposure, range in size 803 (Hansen et al., 2001) to 86,868
(Chang et al., 2005: Chang et al., 2003), and were conducted in Denmark, Sweden, Finland,
Germany, Taiwan, and the United States (see Table 4-1). Three case-control studies nested
within cohorts (Krishnadasan et al., 2007: Greenland et al., 1994: Wilcosky et al., 1984) are
considered as cohort studies because the summary risk estimate from a nested case-control study,
the odds ratio (OR), was estimated from incidence density sampling. This is considered an
unbiased estimate of the hazard ratio, similar to a RR estimate from a cohort study, if, as is the
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case for these studies, controls are selected from the same source population as the cases, the
sampling rate is independent of exposure status, and the selection probability is proportional to
time-at-risk (IOM, 2003). Cohort and nested case-control study designs are analytical
epidemiologic studies and are generally relied on for identifying a causal association between
human exposure and adverse health effects (U.S. EPA, 2005b).
While all of these cohort studies are considered in the overall weight of evidence, 11 of
them met all meta-analysis inclusion criteria: the cohorts of Blair et al. (1998) and its follow-up
by Radican et al. (2008): Morgan et al. (1998). Boice et al. (Boice et al.. 2006b: 1999). and Zhao
et al.(2005), of aerospace workers or aircraft mechanics; and Axelson et al. (1994), Anttila et al.
(1995), Hansen et al. (2001), and Raaschou-Nielsen et al. (2003) of Nordic workers in multiple
industries with TCE exposure; and Greenland et al. (1994) of electrical manufacturing workers.
Subjects or cases and controls in these studies are considered to sufficiently represent the
underlying population, and the bias associated with selection of referent populations is
considered minimal. The exposure-assessment approaches included detailed JEM,
biomonitoring data, or use of industrial hygiene data on TCE exposure patterns and factors that
affect such exposure, with high probability of TCE exposure potential to individual subjects.
The statistical analyses methods were appropriate and well documented, the measured endpoint
was an accurate indicator of disease, and the follow-up was sufficient for cancer latency. These
studies are also considered as strong studies for identifying kidney, liver, and NHL cancer
hazard. The remaining cohort studies less satisfactorily meet identified criteria or standards of
epidemiologic design and analysis, having deficiencies in multiple criteria (Clapp and Hoffman,
2008: Sung et al., 2008: Sung et al., 2007: Chang et al., 2005: AT SDR, 2004a: Chang et al.,
2003: Ritz, 1999a: Henschler et al., 1995: Sinks etal., 1992: Costa etal., 1989: Garabrant et al.,
1988: Shindell andUlrich, 1985: Wilcosky et al., 1984). Krishnandansen et al. (2007), who
reported on prostate cancer, met four of the five meta-analysis inclusion criteria except that for
reporting an RR estimate cancer of the kidney, liver, or NHL, the site-specific cancers examined
using meta-analysis.
The case-control studies on TCE exposure are of several site-specific cancers, including
bladder (Pesch et al., 2000a: Siemiatvcki et al., 1994: Siemiatvcki, 1991): brain (De Roos et al.,
2001; Heineman et al., 1994); childhood lymphoma or leukemia (Shu et al., 2004; Costas et al.,
2002: Shu etal., 1999: McKinnev et al., 1991: Lowengart et al., 1987): colon cancer (Goldberg
etal., 2001: Siemiatvcki, 1991): esophageal cancer (Parent et al., 2000b: Siemiatvcki, 1991):
liver cancer (Lee et al., 2003); lung cancer (Siemiatycki, 1991); adult lymphoma or leukemia
(Hardell etal., 1994) [NHL, Hodgkin lymphoma]; (Fritschi and Siemiatycki, 1996a; Siemiatycki,
1991) [NHL]; (Nordstrom et al., 1998) [hairy cell leukemia]; (Persson and Fredrikson, 1999)
[NHL]; (Miligi et al., 2006) [NHL and chronic lymphocytic leukemia (CLL)]; (Seidler et al.,
2007) [NHL, Hodgkin lymphoma and subjects included in (Cocco et al., 2010; Costantini etal.,
2008) [leukemia types, CLL included with NHL] (Wang et al., 2009: Miligi et al., 2006) [NHL];
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(Cocco et al., 2010) [B-cell including CLL and multiple myeloma, T-cell, and Hodgkin
lymphomas]; (Purdue et al., 2011) [NHL]; Gold et al. (2011) [multiple myeloma]); melanoma
(Fritschi and Siemiatycki, 1996b; Siemiatycki, 1991); rectal cancer (Dumas et al., 2000;
Siemiatycki, 1991); renal cell carcinoma (RCC), a form of kidney cancer (Moore et al., 2010;
Charbotel et al.. 2006: Briming et al.. 2003: Parent et al.. 2000a: Pesch et al.. 2000b: Dosemeci et
al., 1999; Vamvakas et al., 1998; Siemiatycki, 1991); pancreatic cancer (Siemiatycki, 1991); and
prostate cancer (Aronson et al., 1996; Siemiatycki, 1991) (see Table 4-2). No case-control
studies of reproductive cancers (breast or cervix) and TCE exposure were found in the peer-
reviewed literature.
While all of these case-control studies are considered in the overall weight of evidence,
13 of them met the meta-analysis inclusion criteria identified in Section B.2.9 (Purdue et al.,
2011: Cocco etal.. 2010: Moore etal.. 2010: Wang et al.. 2009: Charbotel et al.. 2006: Miligi et
al.. 2006: Briming etal.. 2003: Pesch et al.. 2000b: Dosemeci etal.. 1999: Persson and
Fredrikson, 1999: Nordstrom et al.. 1998: Hardell et al.. 1994: Siemiatvcki, 1991). They were of
analytical study design, cases and controls were considered to represent underlying populations
and selected with minimal potential for bias; exposure assessment approaches included
assignment of TCE exposure potential to individual subjects using information obtained from
face-to-face, mailed, or telephone interviews; analyses methods were appropriate, well-
documented, included adjustment for potential confounding exposures, with RR estimates and
associated CIs reported for kidney cancer, liver cancer or NHL.
These studies were also considered, to varying degrees, as strong studies for weight-of
evidence characterization of hazard. Both Briming et al. (2003) and Charbotel et al. (2006) had a
priori hypotheses for examining RCC and TCE exposure. Strengths of both studies are in their
examination of populations with potential for high exposure intensity and in areas with high
frequency of TCE usage and their assessment of TCE potential. An important feature of the
exposure assessment approach of Charbotel et al. (2006) is their use of a large number of studies
on biological monitoring of workers in the screw-cutting industry, a predominant industry with
documented TCE exposures, as support. Charbotel et al. (2006) is preferred to Charbotel et al.
(2009), who examined kidney cancer risk and TCE exposure at the existing French occupational
exposure limit for cases and controls from their earlier publication (Charbotel et al., 2009); the
earlier publication contained more extensive analyses and included exposure-response analyses
using several exposure metrics and multiple exposure categories. Other studies were either large
multiple-center studies (Purdue et al., 2011; Cocco et al., 2010; Moore et al., 2010; Wang et al.,
2009; Miligi et al., 2006; Pesch et al., 2000b) or reporting from one location of a larger
international study (Seidler et al., 2007; Dosemeci etal., 1999). Cocco et al. (2010) includes
subjects in Seidler et al. (2007) and is preferred because of the larger number of subjects from
four other European countries. In contrast to Briming et al. (2003) and Charbotel et al. (2006),
two studies conducted in geographical areas with widespread TCE usage and potential for
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exposure to higher intensity; in these other studies, a lower exposure prevalence to TCE is found
[any TCE exposure: 15% of cases (Dosemeci et al.. 1999): 6% of cases (Miligi et al.. 2006): 13%
of cases (Wang et al., 2009): 4% of cases (Cocco et al., 2010)1, and most subjects were identified
as exposed to TCE probably had minimal contact (3% of cases with moderate/high TCE
exposure (Miligi et al., 2006): 2% of cases with high intensity, but of low probability of TCE
exposure (Wang et al., 2009). This pattern of lower exposure prevalence and intensity is
common to community-based, population case-control studies (Teschke et al., 2002).
Fourteen case-control studies did not meet specific meta-analysis inclusion criterion
(Gold etal.. 2011: Shu et al.. 2004: Lee et al.. 2003: Costas et al.. 2002: Goldberg et al.. 2001:
Dumas et al.. 2000: Parent et al.. 2000b: Pesch et al., 2000a: Kernanetal., 1999: Shu et al..
1999: Vamvakas et al., 1998: Aronson et al., 1996: Fritschi and Siemiatycki, 1996b: Siemiatycki
et al., 1994). Twelve studies reported RR estimates for site-specific cancers other than kidney,
liver, and NHL (Gold et al.. 2011: Shu et al.. 2004: Costas et al.. 2002: Goldberg etal.. 2001:
Dumas et al.. 2000: Parent et al.. 2000b: Pesch et al., 2000a: KernanetaL. 1999: Shu et al..
1999: Aronson et al., 1996: Fritschi and Siemiatycki, 1996b: Siemiatycki et al., 1994).
Vamvakas et al. (1998) has been the subject of considerable controversy (Cherrie etal., 2001:
Mandel, 2001: Green and Lash, 1999: McLaughlin and Blot, 1997: Bloemen and Tomenson,
1995: Swaen, 1995) with questions raised on the potential for selection bias related to the study's
controls. This study was deficient in the criterion for adequacy of case and control selection.
Briining et al. (2003), a study from the same region as Vamvakas et al. (1998), is considered a
stronger study for identifying cancer hazard since it addresses many of the deficiencies of
Vamvakas et al. (1998) Lee et al. (2003), in their study of hepatocellular cancer, assigns one
level of exposure to all subjects in a geographic area, an inherent measurement error and
misclassification bias because not all subjects are exposed uniformly. Additionally, statistical
analyses in this study did not control for hepatitis viral infection, a known risk factor for
hepatocellular cancer and of high prevalence in the study area.
The geographic-based studies (ATSDR, 2008b, 2006a; Aickin, 2004: Morgan and
Cassadv, 2002: APRS, 1995: Cohnetal., 1994b: Vartiainen et al., 1993: Aickin etal., 1992:
ADHS, 1990: Mallin, 1990: Isacson et al., 1985) with data on cancer incidence are correlation
studies to examine cancer outcomes of residents in communities with TCE and other chemicals
detected in groundwater wells or in municipal drinking water supplies (see Table 4-3). These
studies did not meet all five meta-analysis inclusion criteria. The geographic-base studies are not
of analytical designs such as cohort and case-control designs. Another deficiency in all studies is
their low level of detail to individual subjects for TCE. One level of exposure to all subjects in a
geographic area is assigned without consideration of water distribution networks, which may
influence TCE concentrations delivered to a home, or a subject's ingestion rate to estimate TCE
exposure to individual study subjects. Some inherent measurement error and misclassification
bias is likely in these studies because not all subjects are exposed uniformly. Additionally, in
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contrast to case-control studies, the geographic-based studies, including the Agency for Toxic
Substances and Disease Registry (ATSDR, 2008b), had limited accounting for other potential
risk factors. These studies are of low sensitivity for weight-of evidence characterization of
hazard compared to other cohort and case-control studies.
4.2. GENETIC TOXICITY
This section discusses the genotoxic potential of TCE and its metabolites. A summary is
provided at the end of each section for TCE or its metabolite for their mutagenic potential in
addition to an overall synthesis summary at the end of the genotoxicity section. The liver and
kidney are subjects of study for the genotoxic potential of TCE and its metabolites, and are
discussed more in-depth in Sections 4.4.3, 4.4.7, 4.5.6.2.7, 4.5.7, E.2.3, and E.2.4.
The application of genotoxicity data to predict potential carcinogenicity is based on the
principle that genetic alterations are found in all cancers. Genotoxicity is the ability of chemicals
to alter the genetic material in a manner that permits changes to be transmitted during cell
division. Although most tests for mutagenicity detect changes in DNA or chromosomes, some
specific modifications of the epigenome including proteins associated with DNA or RNA, can
also cause transmissible changes. Changes that occur due to the modifications in the epigenome
are discussed in endpoint-specific Sections 4.3-4.9 as well as Sections E.3.1-E.3.4.
Genetic alterations can occur through a variety of mechanisms including gene mutations,
insertions, deletions, translocations, or amplification; evidence of mutagenesis provides
mechanistic support for the inference of potential for carcinogenicity in humans.
Evaluation of genotoxicity data entails a weight-of-evidence approach that includes
consideration of the various types of genetic damage that can occur. In acknowledging that
genotoxicity tests are by design complementary evaluations of different mechanisms of
genotoxicity, a recent International Programme on Chemical Safety (IPCS) publication
(Eastmond et al., 2009) notes that —raltiple negative results may not be sufficient to remove
concern for mutagenicity raised by a clear positive result in a single mutagenicity assay." These
considerations inform the present approach. In addition, consistent with U.S. EPA's Guidelines
on Carcinogenic Risk Assessment and Supplemental Guidance for Assessing Susceptibility from
Early-Life Exposure to Carcinogens (U.S. EPA, 2005c, b), the approach does not address
relative potency (e.g., among TCE metabolites, or of such metabolites with other known
genotoxic carcinogens) per se, nor does it consider quantitative issues related to the probable
production of these metabolites in vivo. Instead, the analysis of genetic toxicity data presented
here focuses on the identification of a genotoxic hazard of these metabolites; a quantitative
analysis of TCE metabolism to reactive intermediates, via PBPK modeling, is presented in
Section 3.5.
TCE and its known metabolites, TCA, DC A, CH, TCOH, DCVC, and DCVG, have been
studied to varying degrees for their genotoxic potential. The following section summarizes
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available data on genotoxicity for both TCE and its metabolites for each potential genotoxic
endpoints, when available, in different organisms.
4.2.1. TCE
4.2.1.1. DNA Binding Studies
Covalent binding of TCE to DNA and protein in cell-free systems has been studied by
several investigators. Incubation of [14C]-TCE with salmon sperm DNA in the presence of
microsomal preparations from B6C3Fi mice resulted in dose-related covalent binding of TCE to
DNA. The binding was enhanced when the microsomes were taken from mice pretreated with
phenobarbital, which induces CYP enzymes, suggesting that the binding may be related to an
oxidative metabolite, or when l,2-epoxy-3,3,3-trichloropropane, an inhibitor of epoxide
hydrolase, was added to the incubations (Banerjee and Van Duuren, 1978). In addition, covalent
binding of [14C]-TCE with microsomal proteins was detected after incubation with microsomal
preparations from mouse lung, liver, stomach, and kidney, and rat liver (Banerjee and Van
Duuren, 1978). Furthermore, incubation of [14C]-TCE with calf thymus DNA in the presence of
hepatic microsomes from phenobarbital-pretreated rats yielded significant covalent binding
(DiRenzo et al.. 1982).
A number of studies have also examined the role of TCE metabolism in covalent binding
to DNA and proteins. Miller and Guengerich (1983) used liver microsomes from control, b-
naphthoflavone- and phenobarbital-induced B6C3Fi mice, Osborne-Mendel rats, and human
liver microsomes. Significant covalent binding of TCE metabolites to calf thymus DNA and
proteins was observed in all experiments. Phenobarbital treatment increased the formation of
chloral and TCE oxide formation, DNA, and protein adducts. In contrast, b-naphthoflavone
treatment did not induce the formation of any microsomal metabolite, suggesting that the forms
of CYP induced by phenobarbital are primarily involved in TCE metabolism while the
b-naphthoflavone-inducible forms of CYP have only a minor role in TCE metabolism. TCE
metabolism (based on TCE-epoxide and DNA-adduct formation) was 2.5-3-fold higher in mouse
than in rat microsomes due to differences in rates and clearance of metabolism (discussed in
Section 3.3.3.1). The levels of DNA and protein adducts formed in human liver microsomal
system approximated those observed in liver microsomes prepared from untreated rats. It was
also shown that whole hepatocytes of both untreated mice and phenobarbital-induced rats and
mice could activate TCE into metabolites able to covalently bind extracellular DNA. A study by
Cai and Guengerich (200la) postulates that TCE oxide (an intermediate in the oxidative
metabolism of TCE in rat and mouse liver microsomes) is responsible for the covalent binding of
TCE with protein, and to a lesser extent, DNA. Mass spectrometry was used to analyze the
reaction of TCE oxide (synthesized by m-chloroperbenzoic acid treatment of TCE) with
nucleosides, oligonucleotides, and protein to understand the transient nature of the inhibition of
enzymes in the context of adduct formation. Protein amino acid adducts were observed during
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the reaction of TCE oxide with the model peptides. The majority of these adducts were unstable
under physiological conditions. Results using other peptides also indicate that adducts formed
from the reaction of TCE oxide with macromolecules and their biological effects are likely to be
relatively short-lived.
Studies have been conducted using in vitro and in vivo systems to understand the DNA
and protein binding capacity of TCE. In a study in male mice, after repeated i.p. injections of
[14C]-TCE, radioactivity was detected in the DNA and RNA of all organs studied (kidney, liver,
lung, spleen, pancreas, brain, and testis) (Bergman, 1983). However, in vivo labeling was shown
to be due to metabolic incorporation of Cl fragments, particularly in guanine and adenine, rather
than to DNA-adduct formation. In another study (Stottetal., 1982), following i.p. injection of
[14C]-TCE in male Sprague-Dawley rats (10-100 mg/kg) and B6C3Fi mice (10-250 mg/kg),
high liver protein labeling was observed while very low DNA labeling was detected. Stott et al.
(1982) also observed very low levels of DNA binding (0.62 ± 0.43 alkylation/106 nucleotides) in
mice administered 1,200 mg/kg of TCE. In addition, a dose-dependent binding of TCE to
hepatic DNA and protein at low doses in mice was demonstrated by Kautiainen et al. (1997). In
their dose-response study (doses between 2 |ig/kg and 200 mg/kg body weight), the highest level
of protein binding (2.4 ng/g protein) was observed 1 hour after the treatment followed by a rapid
decline, indicating pronounced instability of the adducts and/or rapid turnover of liver proteins.
Highest binding of DNA (120 pg/g DNA) was found between 24 and 72 hours following
treatment. Dose-response curves were linear for both protein and DNA binding. In this study,
the data suggest that TCE does bind to DNA and proteins in a dose-dependent fashion; however,
the type and structure of adducts were not determined.
Mazzullo et al. (1992) reported that TCE was covalently bound in vivo to DNA, RNA,
and proteins of rat and mouse organs 22 hours after i.p. injection. Labeling of proteins from
various organs of both species was higher than that of DNA. Bioactivation of TCE to its
intermediates using various microsomal fractions was dependent on CYP enzyme induction and
the capacity of these intermediates to bind to DNA. It appeared that mouse lung microsomes
were more efficient in forming the intermediates than rat lung microsomes, although no other
species specific differences were found (Mazzullo et al., 1992). This also supports the results
described by Miller and Guengerich (1983). The authors suggest some binding ability of TCE to
interact covalently with DNA (Mazzullo et al., 1992).
In summary, studies report that TCE exposure in vivo can lead to binding to nucleic acids
and proteins, and some authors have suggested that such binding is likely due to conversion to
one or more reactive metabolites.
4.2.1.2. Bacterial Systems—Gene Mutations
Gene mutation studies (Ames assay) in various Salmonella typhimurium strains of
bacteria exposed to TCE both in the presence and absence of stabilizing agent have been
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conducted by different laboratories (McGregor et al., 1989; Mortelmans et al., 1986; Shimada et
al.. 1985: Crebelli et al.. 1982: Baden etal.. 1979: Waskell. 1978: Henschler et al.. 1977:
Simmon etal., 1977) (see Table 4-6). It should be noted that these studies have tested TCE
samples of different purities using various experimental protocols. In all in vitro assays,
volatilization is a concern when TCE is directly administered.
Waskell (1978) studied the mutagenicity of several anesthetics and their metabolites.
Included in their study was TCE (and its metabolites) using the Ames assay. The study was
conducted both in the presence and absence of a metabolic activation system, S9, and caution
was exercised to perform the experiment under proper conditions (incubation of reaction mixture
in sealed dessicator vials). This study was performed in both TA98 and TA100 S. typhimurium
strains at a dose range of 0.5-10% between 4 and 48 hours. No change in revertant colonies was
observed in any of the doses or time courses tested. No information either on the presence or
absence of stabilizers in TCE obtained commercially nor its effect on cytotoxicity was provided
in the study.
In other studies, highly purified, epoxide-free TCE samples were not mutagenic in
experiments with and without exogenous metabolic activation by S9 in S. typhimurium strain
TA100 using the plate incorporation assay (Henschler et al., 1977). Furthermore, no mutagenic
activity was found in several other strains including TA1535, TA1537, TA97, TA98, and TA100
using the preincubation protocol (Mortelmans et al., 1986). Simmon et al. (1977) observed a less
than twofold but reproducible and dose-related increase in his + revertants in plates inoculated
with S. typhimurium TA100 and exposed to a purified, epoxide-free TCE sample. The authors
observed no mutagenic response in strain TA1535 with S9 mix and in either TA1535 or TA100
without rat or mouse liver S9. Similar results were obtained by Baden et al. (1979), Bartsch
et al. (1979), and Crebelli et al. (1982). In all of these studies, purified, epoxide-free TCE
samples induced slight but reproducible and dose-related increases in his + revertants in
S. typhimurium TA100 only in the presence of S9. No mutagenic activity was detected without
exogenous metabolic activation or when liver S9 from naive rats, mice, and hamsters (Crebelli et
al., 1982) was used for activation. Therefore, a number of these studies showed positive results
in TA100 with metabolic activation, but not in other strains or without metabolic activation.
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Table 4-6. TCE genotoxicity: bacterial assays
Test system/endpoint
S. typhimurium (TA100)
S. typhimurium (TA1535, TA100)
S. typhimurium (TA98, TA100)
S. typhimurium (TA100, TA1535)
S. typhimurium (TA100)
S. typhimurium (TA1535, TA100)
S. typhimurium (TA98, TA100,
TA1535, TA1537, TA97)
S. typhimurium (TA98, TA100,
TA1535)
S. typhimurium
S. typhimurium (YG7108)
Escherichia coli (K12)
Doses tested
0.1-10 \\L (epoxide-free)
1-2.5% (epoxide-free)
0.5-10%
1-3% (epoxide-free)
5-20% (v/v)
0.33-1.33% (epoxide-free)
1-5% (higher and lower
purity)
10-1,000 uL/plate
<10,000 ug/plate
(unstabilized)
<10,000 ug/plate (oxirane-
stabilized)
<10,000 ug/plate
(epoxybutane stabilized)
<10,000 ug/plate
(epichlorohydrin stabilized)
1.000-3.000 ug/plate
0.9 mM (analytical grade)
With activation
-
+ (TA100)
-(TA1535)
-
+ (TA100)
± (TA1535)
+
- (higher purity)
+ (lower purity)
-
-
+
Not determined
Not determined
Not determined
+
Without
activation
-
-
-
-
-
-
Not determined
+
+
+
+
—
Comments
Plate incorporation assay
The study was conducted in
sealed dessicator vials
Negative under normal
conditions, but twofold
increase in mutations in a
preincubation assay
Extensive cytotoxicity
Preincubation protocol
Vapor assay
Vapor assay
Preincubation assay
Vapor assay
Microcolony assay /revertants
Revertants at arg56 but not
nadl!3 or other loci
References
Henschler et al. (1977)
Simmon et al. (1977)
Waskell (1978)
Baden et al. (1979)
Bartsch et al. (1979)
Crebelli et al. (1982)
Shimada et al. (1985)
Mortelmans et al.
(1986)
McGregor et al. (1989)
McGregor et al. (1989)
McGregor et al. (1989)
McGregor et al. (1989)
Emmert et al. (2006)
Greim et al. (1975)
4-33
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Shimada et al. (1985) tested a low-stabilized, highly purified TCE sample in an Ames
reversion test, modified to use vapor exposure, in S. typhimurium TA1535 and TA100. No
mutagenic activity was observed—either in the presence or absence of S9 mix. However, at the
same concentrations (1, 2.5, and 5%), a sample of lower purity, containing undefined stabilizers,
was directly mutagenic in TA100 (>5-fold) and TA1535 (>38-fold) at 5% concentration
regardless of the presence of S9. It should be noted that the doses used in this study resulted in
extensive killing of bacterial population, particularly at 5% concentration; >95% toxicity was
observed.
A series of studies evaluating TCE (with and without stabilizers) were conducted by
McGregor et al. (1989). The authors tested high-purity and oxirane-stabilized TCE samples for
their mutagenic potential in S. typhimurium strains TA1535, TA98, and TA100. Preincubation
protocol was used to test stabilized TCE (up to 10,000 jig/plate). Mutagenic response was not
observed either in the presence or absence of metabolic activation. When TCE was tested in a
vapor delivery system without the oxirane stabilizers, the authors did not observe any mutagenic
activity. However, TA1535 and TA100 produced a mutagenic response both in the presence and
absence of S9 when exposed to TCE containing 0.5-0.6% 1,2-epoxybutane. Furthermore,
exposure to epichlorohydrin also increased the frequency of mutants.
Emmert et al. (2006) used a CYP2E1-competent bacterial strain (S. typhimurium
containing YG7108pin3ERbs plasmid) in their experiments. TCE was among several other
compounds investigated and was tested at concentrations of 1,000-3,000 jig/plate. TCE induced
toxicity and microcolonies > 1,000 jig per plate. A study on Escherichia coll K12 strain was
conducted by Greim et al. (1975) using analytical-grade TCE samples. Revertants were scored
at two loci: argS6, sensitive to base-pair substitution and nadus, reverted by frameshift mutagens.
In addition, forward mutations to 5-methyltryptophan resistance and galactose fermentation were
selected. Approximately twofold increase in arg + colonies was observed. No change in other
sites was observed. No definitive conclusion can be drawn from this study due to lack of
information on reproducibility and dose-response.
In addition to the above studies, the ability of TCE to induce gene mutations in bacterial
strains has been reviewed and summarized by several authors (Clewell and Andersen, 2004;
Moore and Harrington-Brock. 2000: Douglas et al.. 1999: Fahrigetal.. 1995: Crebelli and
Carere, 1989). In summary, TCE, in its pure form as a parent compound, is unlikely to induce
point mutations in most bacterial strains. It is possible that some mutations observed in response
to exposure to technical-grade TCE may be contributed by the contaminants/impurities such as
1,2-epoxybutane and epichlorohydrin, which are known bacterial mutagens. However, several
studies of TCE reported low, but positive responses in the TA100 strain in the presence of S9
metabolic activation, even when genotoxic stabilizers were not present.
4-34
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4.2.1.3. Fungal and Yeast Systems—Gene Mutations, Conversions, and
Recombination
Gene mutations, conversions, and recombinations have been studied to identify the effect
of TCE in fungi and yeast systems (see Table 4-7).
Crebelli et al. (1985) studied the mutagenicity of TCE in Aspergillus nidulans both for
gene mutations and mitotic segregation. No increase in mutation frequency was observed when
A. nidulans was plated on selective medium and then exposed to TCE vapors. A small but
statistically significant increase in mutations was observed when conidia of cultures were grown
in the presence of TCE vapors and then plated on selective media. Since TCE required actively
growing cells to exerts its genotoxic activity and previous studies (Bignami et al., 1980) have
shown activity in the induction ofmethGJ suppressors by TCOH and CH, it is possible that
endogenous metabolic conversion of TCE into TCOH or CH may have been responsible for the
positive response.
To understand the CYP mediated genotoxic activity of TCE, Callen et al. (1980)
conducted a study in two yeast strains (D7 and D4) CYP. The D7 strain in it log-phase had a
CYP concentration up to 5 times higher than a similar cell suspension of D4 strain. Two
different concentrations (15 and 22 mM) at two different time points (1 and 4 hours) were
studied. A significant increase in frequencies of mitotic gene conversion and recombination was
observed at 15 mM concentrations at 1-hour exposure period in the D7 strain; however, the
22 mM concentration was highly cytotoxic (only 0.3% of the total number of colonies survived).
No changes were seen in D4 strain, suggesting that metabolic activation via CYP played an
important role in both genotoxicity and cytotoxicity. However, marginal or no genotoxic activity
was observed when incubation of cells and test compounds were continued for 4 hours in either
strain, possibly because of increased cytotoxicity, or a destruction of the metabolic system.
Koch et al. (1988) studied the genotoxic effects of chlorinated ethylenes including TCE
in various yeast Saccharomyces cerevisiae strains. Strain D7 was tested (11.1, 16.6, and
22.2 mM TCE) in stationary-phase cells without S9, stationary-phase cells with S9, and
logarithmic-phase cells using different concentrations. No significant change in mitotic gene
conversion or reverse mutation was observed in either the absence or presence of S9. In
addition, there was a considerable increase in the induction of mitotic aneuploidy in strain
D61.M, though no statistical analysis was performed.
4-35
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Table 4-7. TCE genotoxicity: fungal and yeast systems
Test system/endpoint
Doses tested
With activation
Without
activation
Comments
References
Gene conversions
Saccharomyces cerevisiae D7 and
D4
S. cerevisiae D7
Schizosaccharomyces pombe
S. cerevisiae D7
A. nidulans
15 and 22 mM; 1 and 4 hrs
11.1, 16.6, and 22.2 mM
0.2-200 mM (-pre" and
technical-grade)
Not determined
"
+
No data
+ at 1 hr, D7
strain;
- at 4 hrs, both
D7andD4
"
-
+
Gene conversion;
CYP content fivefold greater
in D7 strain;
high cytotoxicity at 22 mM
Both stationary and log
phase/production of
phototropic colonies
Forward mutation, different
experiments with different
doses and time
Forward mutation
Callen et al. (1980)
Koch et al. (1988)
Rossi et al. (1983)
Bronzetti et al. (1980)
Crebelli et al. (1985)
Recombination
S. cerevisiae
S. cerevisiae D7 and D4
A. nidulans
15 and 22 mM; 1 and 4 hrs
+
Not determined
Not determined
-
+
+
Gene conversion
Gene cross over
Bronzetti et al. (1980)
Callen etal. (1980)
Crebelli et al. (1985)
Mitotic aneuploidy
S. cerevisiae D6 1 .M
5.5, 11.1, and 16.6 mM
+
+
Loss of dominant color
homolog
Koch et al. (1988)
4-36
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Rossi et al. (1983) studied the effect of TCE on yeast species Schizosaccharomyces
pombe both using in vitro and host-mediated mutagenicity studies and the effect of two
stabilizers, epichlorohydrin and 1,2-epoxybutane, that are contained in technical-grade TCE.
The main goal of this study was to evaluate the genotoxic activity of TCE samples of different
purity and determine whether the effect was due to the additives present in the TCE or TCE
itself. Forward mutations at five loci (ade 1, 3, 4, 5, 9) of the adenine pathway in the yeast,
strain PI was evaluated. The stationary-phase cells were exposed to 25 mM concentration of
TCE for 2, 4, and 8 hours in the presence and absence of S9. No change in mutation frequency
was observed either in pure- or technical-grade samples either in the presence or absence of S9 at
any of the time-points tested. Interestingly, this suggests that the stabilizers used in technical-
grade TCE are not genotoxic in yeast. In a follow-up experiment, the same authors studied the
effect of different concentrations (0.22, 2.2, and 22.0 mM) in a host-mediated assay using liver
microsome preparations obtained from untreated mice, from phenobarbital- and naphthoflavone-
pretreated mice and rats, which also suggested that stabilizers were not genotoxic in yeast. This
experiment is described in more detail in Section 4.2.1.4.1.
Furthermore, TCE was tested for its ability to induce both point mutation and mitotic
gene conversion in diploid strain of yeast S. cerevisiae (strain D7) both with and without a
mammalian microsomal activation system. In a suspension test with D7, TCE was active only
with microsomal activation (Bronzetti et al., 1980).
These studies are consistent with those of bacterial systems in indicating that pure TCE as
a parent compound is not likely to cause mutations, gene conversions, or recombinations in
fungal or yeast systems. In addition, the data suggest that contaminants used as stabilizers in
technical-grade TCE are not genotoxic in these systems, and that the observed genotoxic activity
in these systems is predominantly mediated by TCE metabolites.
4.2.1.4. Mammalian Systems Including Human Studies
4.2.1.4.1. Gene mutations (bacterial, fungal, or yeast with a mammalian host)
Very few studies have been conducted to identify the effect of TCE, particularly on gene
(point) mutations using mammalian systems (see Table 4-8). An overall summary of different
endpoints using mammalian systems will be provided at the end of this section. In order to
assess the potential mutagenicity of TCE and its possible contaminants, Rossi et al. (1983)
performed genotoxicity tests using two different host-mediated assays with pure- and technical-
grade TCE. Male mice were administered with one dose of 2 g/kg of pure or technical-grade
TCE by gavage. Following the dosing, for the i.p. host-mediated assay, yeast cell suspensions
(2 x 109 cells/mL) were inoculated into the peritoneal cavity of the animals. Following
16 hours, animals were sacrificed and yeast cells were recovered to detect the induction of
forward mutations at five loci (ade 1, 2, 4, 5, 9} of the adenine pathway. A second host-mediated
assay was performed by exposing the animals to 2 g/kg of pure or technical-grade TCE and
4-37
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inoculating the cells into the blood system. Yeast cells were recovered from livers following
4 hours of exposure. Forward mutations in the five loci (ade 1,2,4,5,9) were not observed in
host-mediated assay either with pure or technical-grade TCE. Genotoxic activity was not
detected when the mutagenic epoxide stabilizers were tested for mutagenicity independently or
in combination. To confirm the sensitivity of the assay, the authors tested a positive control,
7V-nitroso-dimethyl-nitrosamine (1 mg/kg), and found a mutation frequency of >20 times the
spontaneous level. The authors suggested that the negative result could have been due to an
inadequate incubation time of the sample with the yeast cells.
Male and female transgenic lac Z mice were exposed by inhalation to an actual concentrations of
0, 203, 1,153, and 3,141 ppm TCE, 6 hours/day for 12 days (Douglas et al.. 1999). Following
14 and 60 days of last exposure, animals were sacrificed and the mutation frequencies were
determined in various organs such as bone marrow, kidney, spleen, liver, lung, and testicular
germ cells. No statistically significant increases in base-changes or small-deletions were
observed at any of the doses tested in male or female lung, liver, bone marrow, spleen, and
kidney, or in male testicular germ cells when the animals were sampled 60 days after exposure.
In addition, statistically significantly increased gene mutations were not observed in the lungs at
14 days after the end of exposure (Douglas et al., 1999). The authors acknowledged that lacZ
bacteriophage transgenic assay does not detect large deletions. The authors also acknowledged
that their hypothesis does not readily explain the increases in small deletions and base-change
mutations found in the von Hippel-Lindau (VHL) tumor suppressor gene in RCCs of the
TCE-exposed population. DCA, a TCE metabolite has been shown to increase lad mutations in
transgenic mouse liver, however, only after 60-weeks-of-exposure to high concentration
(> 1,000 ppm) in drinking water (Leavitt et al., 1997). DCA induced relatively small increase in
lac I mutations when the animals were exposed for 60 weeks, a significantly longer duration than
the TCE exposure in the Douglas et al. (1999) study (<2 weeks). Because a relatively small
fraction of TCE is metabolized to DCA (see Section 3.3), the mutagenic effect of DCA is
unlikely to have been detected in the experiments in Douglas et al. (1999). GSH conjugation,
which leads to the production of genotoxic metabolites (see Section 4.2.5), constitutes a
relatively small (and relatively uncertain) portion of TCE metabolism in mice, with little data on
the extent of renal DCVC bioactivation vs. detoxification in mice (see Sections 3.3 and 3.5). In
addition, statistically significantly increased kidney tumors have not been reported in mice with
TCE treatment, and the increased incidence of kidney tumors in rats, while considered
biologically significant, are quite low and not always statistically significant (see Section 4.4).
Therefore, although Douglas et al. (1999) did not detect increased mutations in the kidney, these
results are not highly informative as to the role of mutagenicity in TCE-induced kidney tumors,
given the uncertainties in the production in genotoxic GSH conjugation metabolites in mice and
the low carcinogenic potency of TCE for kidney tumors in rodents relative to what is detectable
in experimental bioassays.
4-38
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Table 4-8. TCE genotoxicity: mammalian systems—gene mutations and chromosome aberrations
Test system/endpoint
Doses tested
With
activation
Without
activation
Comments
References
Gene mutations (forward mutations)
Schizosaccharomyces pombe
2 g/kg, 4 and 16 hrs
Not determined
-
Host-mediated: i.v. and i.p.
injections of yeast cells
Rossi et al. (1983)
Gene mutations (mutations frequency)
lac Z transgenic mice
0,203, 1,153, or 3, 141 ppm
No base
changes or
small deletions
No base
changes or
small deletions
Lung, liver, bone marrow,
spleen, kidney, testicular
germ cells used
Douglas et al. (1999)
Chromosomal aberrations"
Chinese hamster ovary
C57BL/6J mice
Sprague-Dawley rats
745-14,900 ug/mL
499-14,900 ug/mL
5, 50, 500, or 5,000 ppm (6 hrs)
5, 50, 500, or 5,000 ppm (6 hrs,
single and 4-d exposure)
Not determined
-
-
-
Not determined
Not applicable
Not applicable
8-14 hrs
2 hrs exposure
Splenocytes
Peripheral blood lymphocytes
Galloway et al. (1987)
Galloway et al. (1987)
Kligerman et al. (1994)
Kligerman et al. (1994)
alt should be noted that results of most chromosomal aberration assays report the combined incidence of multiple effects, including chromatid breaks,
isochromatid or chromosome breaks, chromatid exchanges, dicentric chromosomes, ring chromosomes, and other aberrations.
4-39
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4.2.1.4.2. VHL gene mutations
Studies have been conducted to determine the role of VHL gene mutations in RCC, with
and without TCE exposure, and are summarized here. Most of these studies are epidemiologic,
comparing VHL mutation frequencies of TCE-exposed to nonexposed cases from RCC case-
control studies, or to background mutation rates among other RCC case series (described in
Section 4.4.3). Inactivation of the VHL gene through mutations, loss of heterozygosity, and
imprinting has been observed in about 70% of renal clear cell carcinomas (Alimov et al., 2000;
Kenck et al., 1996). Recent studies have also examined the role of other genes or pathways in
RCC subtypes, including c-Myc activation and vascular endothelial growth factor (VEGF)
(Toma et al.. 2008: Furge et al.. 2007).
Several studies have examined the role of VHL gene inactivation in RCC, including a
recent study that measured not only mutations but also promoter hypermethylation (Nickerson et
al., 2008). This study focused on kidney cancer regardless of cause, and found that 91% of cc-
RCC exhibited alterations of the VHL gene, suggesting a role for VHL mutations as an early
event in clear cell-RCC. A recent analysis of current epidemiological studies of renal cell cancer
suggests VHL gene alterations as a marker of clear cell-RCC, but that limitations of previous
studies may make the results difficult to interpret (Chow and Devesa, 2008). Conflicting results
have been reported in epidemiological studies of VHL mutations in TCE-exposed cases and are
described in detail in Section 4.2.7. Both Briining et al. (1997b) and Brauch et al. (2004: 1999)
associated increased VHL mutation frequency in TCE-exposed RCC cases. The two other
available studies of Schraml et al. (1999) and Charbotel et al. (2007), because of their limitations
and lower mutation detection rate in the case of Charbotel et al. (2007) neither add nor detract to
the conclusions from the earlier studies. Additional discussion of these data is provided in
Section 4.4.3.
Limited animal studies have examined the role of TCE and VHL mutations, although
Mally et al. (2006) have recently conducted both in vitro and in vivo studies using the Eker rat
model (see Section 4.4.6.1.1). The Eker rat model (Tsc-2^) is at increased risk for the
development of spontaneous RCC and as such, has been used to understand the mechanisms of
renal carcinogenesis (Stemmer et al., 2007: Wolf et al., 2000). One study has demonstrated
similar pathway activation in Eker rats as that seen in humans with VHL mutations leading to
RCC, suggesting that Tsc-2 inactivation is analogous to inactivation of VHL in human RCC (Liu
et al., 2003). In Mally et al. (2006), male rats carrying the Eker mutation were exposed to TCE
(0, 100, 250, 500, or 1,000 mg/kg body weight by gavage, 5 days/week) for 13 weeks to
determine the renal effects (additional data from this study on in vitro DCVC exposure are
discussed below, Section 4.2.5). A significant increase in labeling index in kidney tubule cells
was observed; however, no enhancement of preneoplastic lesions or tumor incidence was found
in Eker rat kidneys compared to controls. In addition, no VHL gene mutations in exons 1-3 were
4-40
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detected in tumors obtained from either control or TCE-exposed Eker rats. Although no other
published studies have directly examined VHL mutations following exposure to TCE,
two studies performed mutational analysis of archived formalin-fixed paraffin embedded tissues
from renal carcinomas from previous rat studies. These carcinomas were induced by the
genotoxic carcinogens potassium bromate (Shiao et al., 2002) or 7V-nitrosodimethylamine (Shiao
et al., 1998). Limited mutations in the VHL gene were observed in all samples, but, in both
studies, these were found only in the clear cell renal carcinomas. Limitations of these
two studies include the small number of total samples analyzed, as well as potential technical
issues with DNA extraction from archival samples (see Section 4.4.6.6.1). However, analyses of
VHL mutations in rats may not be informative as to the potential genotoxicity of TCE in humans
because the VHL gene may not be the target for nephrocarcinogenesis in rats to the extent that it
appears to be in humans.
4.2.1.4.3. Chromosomal aberrations
A few studies were conducted to investigate the ability of TCE to induce chromosomal
aberrations in mammalian systems (see Table 4-8). Galloway et al. (1987) studied the effect of
TCE on chromosome aberrations in Chinese hamster ovary cells. When the cells were exposed
to TCE (499-14,900 |ig/mL) for 2 hours with metabolic activation, S9, no chromosomal
aberrations were observed. Furthermore, without metabolic activation, no changes in
chromosomal aberrations were found when the cells were exposed to TCE concentrations of
745-14,900 |ig/mL for 8-14 hours. It should be noted that in this study, liquid incubation
method was used and the experiment was part of a larger study to understand the genotoxic
potential of 108 chemicals.
Three inhalation studies in mice and rats examined if TCE could induce cytogenetic
damage (Kligerman et al., 1994). In the first two studies, CD rats or C57B1/6 mice, were
exposed to 0-, 5-, 500-, or 5,000-ppm TCE for 6 hours. Peripheral blood lymphocytes in rats and
splenocytes in mice were analyzed for induction of chromosomal aberrations, sister chromatid
exchanges (SCEs), and micronucleus formation. The results of micronucleus and SCEs will be
discussed in the next sections (see Sections 4.2.1.4.4 and 4.2.1.4.5). No significant increase in
chromosomal aberrations was observed in binucleated peripheral blood lymphocytes. In the
third study, the authors exposed the same strain of rats for 6 hours/day over 4 consecutive days.
No statistically significant concentration-related increases in chromosomal aberrations were
observed. The limited results of the above studies have not reported TCE to cause chromosomal
aberrations either in in vitro or in vivo mammalian systems.
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4.2.1.4.4. Micronucleus induction
The appearance of micronuclei is another endpoint that can demonstrate the genotoxic
effect of a chemical. Several studies have been conducted to identify if TCE can cause
micronucleus formation (see Table 4-9).
Wang et al. (2001) investigated micronucleus formation by TCE administered as a vapor
in Chinese hamster ovary-Kl cells in vitro. Cells were grown in culture media with an inner
Petri dish containing TCE that would evaporate into the media containing cells. The
concentration of TCE in cultured medium was determined by gas chromatography. The actual
concentration of TCE ranged from 0.8 and 1.4 ppm after a 24-hour treatment. A significant
dose-dependent increase in micronuclei formation was observed. A dose-dependent decrease in
cell growth and cell number was also observed. The authors did not test if the micronuclei
formed were due to direct damage to the DNA or spindle formation.
Robbiano et al. (2004) conducted an in vitro study on DNA damage and micronuclei
formation in rat and human kidney cells exposed to six carcinogenic chemicals including TCE.
The authors examined for the ability of TCE to induce DNA fragmentation and formation of
micronuclei in primary cultures of rat and human kidney cells derived from kidney cancer
patients with 1-4 mM TCE concentrations. A significant dose-dependent increase in the
frequency of micronuclei was obtained in primary kidney cells from both male rats and human of
both genders. The authors acknowledged that the significance of the results should be
considered in light of the limitations, including: (1) examination of TCE on cells from only three
rats; (2) considerable variation in the frequency of DNA lesions induced in the cells; and (3) the
possibility that kidney cells derived from kidney cancer patients may be more sensitive to
DNA-damaging activity due to a more marked expression of enzymes involved in the metabolic
activation of kidney procarcinogens and suppression of DNA repair processes. Nevertheless,
this study is important and provides information of the possible genotoxic effects of TCE.
In the same study, Robbiano et al. (2004) administered rats a single oral dose of TCE
(3,591 mg/kg) corresponding to !/2 LD50, which had been pre-exposed to folic acid for 48 hours
and the rats were euthanized 48 hours later following exposure to TCE. The frequency of
binucleated cells was taken as an index of kidney cell proliferation. A statistically significant
increase in the average frequency of micronucleus was observed.
Hu et al. (2008) studied the effect of TCE on micronuclei frequencies using human
hepatoma HepG2 cells. The cells were exposed to 0.5, 1, 2, and 4 mM TCE for 24 hours. TCE
caused a significant increase in micronuclei frequencies at all concentrations tested. It is
important to note that similar concentrations were used in Robbiano et al. (2004).
4-42
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Table 4-9. TCE genotoxicity: mammalian systems—micronucleus, sister chromatic exchanges
Test system/endpoint
Doses tested
With
activation
Without
activation
Comments
References
Micronucleus
Human hepatoma HepG2 cells
Primary cultures of human and rat kidney
cells
Sprague-Dawley rats
Chinese hamster ovary -Kl cells
Male CD-I mice
C56BL/6J mice
Sprague-Dawley rats
0.5-4 mM, 24 hrs
1.0, 2.0, or 4.0 mM
3,591 mg/kg
0.8-1. 4 ppm
457 mg/kg
5, 50, 500, or
5,000 ppm
5, 50, 500, or
5,000 ppm
Not applicable
Not applicable
+
+
-
+
+
+
-
+
Not applicable
Not applicable
Not applicable
Dose-dependent significant
increase
Dose-dependent significant
increase
Bone marrow, correlated with
TCOH in urine
Splenocytes
Dose dependent; peripheral
blood lymphocytes
Hu et al. (2008)
Robbiano et al. (2004)
Robbiano et al. (2004)
Wang et al. (2001)
Hrelia et al. (1994)
Kligerman et al. (1994)
Kligerman et al. (1994)
SCEs
Chinese hamster ovary
Human lymphocytes
Sprague-Dawley rats
Peripheral blood lymphocytes from humans
occupationally exposed
C57BL/6J mice
0.17%
17.9-700 ug/mL
49.7-14,900 ug/mL
178 ug/mL
5, 50, 500, or
5,000 ppm
Occupational
exposure
5, 50, 500, or
5,000 ppm
-
Not determined
+
Not determined
-
-
—
Not determined
+
Not determined
+
Not applicable
Not applicable
Not applicable
1 hr (vapor)
25 hrs (liquid)
2 hrs
Peripheral blood lymphocytes
Splenocytes
White et al. (1979)
Galloway et al. (1987)
Galloway et al. (1987)
Gu et al. O981a; 1981b)
Kligerman et al. (1994)
Nagaya et al. (1989a)
Kligerman et al. (1994)
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As described in the chromosomal aberration section (see Section 4.2.1.4.3), inhalation
studies were performed using male C57BL/6 mice and CD rats (Kligerman et al., 1994) to
determine if TCE could induce micronuclei. In the first and second study, rats or mice
respectively, were exposed to 0-, 5-, 500-, or 5,000 ppm TCE for 6 hours. Peripheral blood
lymphocytes in rats and splenocytes in mice were cultured and analyzed for induction of
micronuclei formation. Bone marrow polychromatic erythrocytes (PCEs) were also analyzed for
micronuclei. TCE caused a statistically significant increase in micronuclei formation at all
concentrations in rat bone marrow PCEs but not in mice. The authors note that TCE was
significantly cytotoxic at the highest concentration tested as determined by significant
concentration-related decrease in the ratio of PCEs/normochromatic erythrocytes. In the
third study, to confirm the results of the first study, the authors exposed rats to one dose of
5,000 ppm for 6 hours. A statistical increase in bone marrow micronuclei-PCEs was observed
confirming the results of the first study.
Hrelia et al. (1994) treated male CD-I mice with TCE (457 mg/kg body weight; i.p.) for
30 hours. Bone marrow cells were harvested for determination of micronuclei frequencies in
PCEs. An increase in micronuclei frequency at 30 hours after treatment was observed. Linear
regression analysis showed that micronuclei frequency induced by TCE correlated with TCOH
concentrations in urine, a marker of TCE oxidative metabolism (Hrelia et al., 1994).
In summary, based on the results of the above studies, TCE is capable of inducing
micronuclei in different in vitro and in vivo systems tested. Specific methods were not used that
could definitively identify the mechanism of micronuclei formation. These are important
findings that indicate TCE has genotoxic potential as measured by the micronucleus formation.
4.2.1.4.5. SCEs
Studies have been conducted to understand the ability of TCE to induce SCEs both in
vitro and in vivo systems (see Table 4-9). White et al. (1979) evaluated the possible induction
of SCE in Chinese hamster ovary cells using a vapor exposure procedure by exposing the cells
to TCE (0.17%) for 1 hour in the presence of S9 metabolic activation. No change in SCE
frequencies were observed between the control and the treatment group. However, in another
study by Galloway et al. (1987) a dose-related increase in SCE frequency in repeated
experiments both with and without metabolic activation was observed. It should be noted that in
this study, liquid incubation was used, and the exposure times were 25 hours without metabolic
activation at a concentration between 17.9 and 700 |ig/mL and 2 hours in the presence of S9 at a
concentration of 49.7-14,900 |ig/mL. Due to the difference in the dose, length of exposure, and
treatment protocol (vapor exposure vs. liquid incubation), no direct comparison can be made. It
should also be noted that inadequacy of dose selection and the absence of positive control in the
White et al. (1979) makes it difficult to interpret the study. In another study (Gu etal., 1981a), a
small but positive response was observed in assays with peripheral lymphocytes.
4-44
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No statistically significant increase in SCEs was found when male C57B1/6 mice or
CD rats were exposed to TCE at concentrations of 5, 500, or 5,000 ppm for 6 hours (Kligerman
et al., 1994). Furthermore, in another study by Nagaya et al. (1989b), lymphocytes of
TCE-exposed workers (n = 22) and matched controls (n = 22) were analyzed for SCEs. The
workers had constantly used TCE in their jobs, although the exact exposure was not provided.
The duration of their employment ranged from 0.7 to 34 years, averaging about 10 years. It
should be noted that there were both smokers and nonsmokers among the exposed population. If
a subject had not smoked for at least 2 years before the samples were taken, then they were
considered as nonsmokers. There were eight nonsmokers in the group. If they were classified as
smokers, then they smoked between 10 and 50 cigarettes per day. No significant increase in
mean SCE frequencies were found in exposed population compared to controls, though the study
is relatively small.
In summary, induction of SCEs has been reported in several, though not all, paradigms of
TCE exposure, consistent with the structural damage to DNA/chromosomes indicated by excess
micronuclei formation.
4.2.1.4.6. Unscheduled DNA synthesis (UDS)
In vitro studies are briefly described here, with additional discussion of effects related to
TCE-induced UDS in the context of the liver in Section E.2.4.1. Perocco and Prodi (1981)
studied UDS in human lymphocytes cultured in vitro (see Table 4-10). Three doses of TCE (2.5,
5.0, and 10 |iL/mL) were used as final concentrations with and without S9. The results indicate
that there was an increase in UDS only in the presence of S9, and in addition, the increase was
maximal at the TCE concentration of 5 |iL/mL. Three chlorinated ethane and ethylene solvent
products were examined for their genotoxicity in hepatocyte primary culture DNA repair assays
using vapor-phase exposures. Rat hepatocytes primary cultures were initiated and exposed to
low-stabilized or standard stabilized TCE (0.1-2.5%) for 3 or 18 hours. UDS or DNA repair was
not observed using either low or standard stabilized TCE, even at vapor phase doses up to those
that produced extensive cell killing after 3 or 18 hour exposure (Shimada et al., 1985). Costa and
Ivanetich (1984) examined the ability of TCE to induce UDS hepatocytes isolated from
phenobarbital treated rats. The UDS was assessed only at the highest concentration that is
tolerated by the hepatocytes (2.8 mM TCE).
These results indicate that TCE stimulated UDS in isolated rodent hepatocytes, and,
importantly, in human lymphocytes in vitro.
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Table 4-10. TCE genotoxicity: mammalian systems—UDS, DNA strand breaks/protein crosslinks, and cell
transformation
Test system/endpoint
Doses tested
With
activation
Without
activation
Comments
References
UDS
Rat primary hepatocytes
Human lymphocytes
Phenobarbital-induced rat hepatocytes
2.5, 5, or 10 uL/mL
2.8 mM
Not determined
±
Not determined
-
"
+
Increase was only in certain
doses and maximum at
5 uL/mL concentration
Shimada et al. (1985)
Perocco and Prodi (1981)
Costa and Ivanetich (1984)
DNA strand breaks/protein crosslinks
Primary rat kidney cells
Primary cultures of human kidney cells
Sprague-Dawley rats
Sprague-Dawley rats
0.5, 1.0, 2.0, or
4.0 mM
1.0, 2.0, or 4.0 mM
3,591 mg/kg
500, 1,000, and
2,000 ppm
Not applicable
Not determined
+
-
+
+
Not applicable
Not applicable
Dose-dependent significant
increase
Dose-dependent significant
increase
Single oral administration
Comet assay
Robbiano et al. (2004)
Robbiano et al. (2004)
Robbiano et al. (2004)
Clay (2008)
Cell transformation
BALB/c 3T3 mouse cells
Rat embryo cells
Syrian hamster embryo cells
4, 20, 100, or
250 ug/mL
5, 10, or 25 ug/mL
Not applicable
Not applicable
Not applicable
+
+
-
Weakly positive compared to
other halogenated compounds
tested in the same experiment
Tu et al. (1985)
Price et al. (1978)
Amacher and Zelljadt
(1983)
4-46
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4.2.1.4.7. DNA strand breaks
DNA damage in response to TCE exposure was studied using comet assay in human
hepatoma HepG2 cells (Hu et al., 2008; see Table 4-10). The cells were exposed to 0.5, 1,2, and
4 mM for 24 hours. TCE increased the DNA migration in a significant dose-dependent manner
at all tested concentrations suggesting TCE caused DNA strand breaks and chromosome damage.
TCE (4-10 mmol/kg body weight) were given to male mice by i.p. injection. The
induction of single-strand breaks (SSBs) in DNA of liver, kidney, and lung was studied by the
DNA unwinding technique. There was a linear increase in the level of SSBs in kidney and liver
DNA but not in lung DNA 1 hour after administration (Walles, 1986).
Robbiano et al. (2004) conducted an in vitro study on DNA damage in rat and human
kidney cells exposed to six carcinogenic chemicals, including TCE, in the comet assay. The
authors examined the ability of TCE to induce DNA fragmentation in primary cultures of rat and
human kidney cells with 1-4 mM TCE concentrations. TCE was dissolved in ethanol with a
maximum concentration of 0.3% and the rat cultures were exposed to 20 hours. Primary human
kidney cells were isolated from fragments of kidney discarded during the course of surgery for
carcinoma of both male and female donors with an average age of 64.2 years and were also
exposed to 20 hours. Significant dose-dependent increases in the ratio of treated/control tail
length (average 4-7 jiM compared to control) was observed as measured by comet assay in
primary kidney cells from both male rats and human of both genders.
Clay et al. (2008) studied the DNA damage inducing capacity of TCE using the comet
assay in rat kidney proximal tubules. Rats were exposed by inhalation to a range of TCE
concentrations (500, 1,000, or 2,000 ppm) for 6 hours/day for 5 days. TCE did not induce DNA
damage (as measured by tail length and percentage tail DNA and tail movement) in rat kidney
proximal tubules in any of the doses tested possibly due to study limitations (small number of
animals tested [n = 5] and limited exposure time [6 hours/day for only 5 days]). These results
are in contrast to the findings of Robbiano et al. (2004), which showed DNA damage and
increased micronuclei in the rat kidney 20 hours following a single dose (3,591 mg/kg body
weight) of TCE. The DNA damage reported by comet assay is consistent with results for other
markers of chromosomal damage or DNA structural damage such as excess micronuclei
formation and SCE induced by TCE exposure.
4.2.1.4.8. DNA damage related to oxidative stress, polymorphisms
A detailed description of studies related to lipid peroxidation of TCE is presented in
conjunction with discussion of liver toxicity (see Section 4.5, E.2.4.3, and E.3). A recent study
reported on genetic polymorphism in solvent exposed population (Kumar et al., 2009). Normal
(n = 220) and solvent-exposed (n = 97) populations were genotyped for CYP1A1, GSTM1,
GSTT1 and GSTP1 polymorphisms. No exposure related differences were observed. In
addition, the authors also examined TCE-exposed lymphocytes for the presence of chromosomal
4-47
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aberrations and micronucleus at concentration of 2, 4 or 6 mM TCE. No significant changes in
any of the parameters were observed.
4.2.1.4.9. Cell transformation
In vitro cell transformation using BALB/C-3T3 cells was conducted using TCE with
concentrations varying from 0 to 250 |ig/mL in liquid phase exposed for 72 hours (see
Table 4-10). The cytotoxicity of TCE at the concentration tested in the transformation assay was
determined by counting cells from duplicate plates of each test conditions at the end of the
treatment period. A dose-dependent increase in Type III foci was observed, although no
statistical analysis was conducted (Tu etal., 1985). In another study by Amacher and Zelljadt
(1983), Syrian hamster embryo cells were exposed to 5, 10, or 25 |ig/mL of TCE. In this
experiment, two different serums (horse serum and fetal bovine serum) were also tested to
understand the importance of serum quality in the transformation assay. A preliminary toxicity
assay was performed to select dose levels that had 50-90% cell survival. One week after dosing,
the cell colonies were fixed and counted for variability determination and examination of
individual colonies for the evidence of morphological transformation. No significant change in
morphological cell transformation was obtained. Furthermore, no significant changes were seen
in transformed colonies when tested in different serum. However, these studies are of limited
use for determining the genotoxic potential of TCE because they did not examine the foci for
mutations, for instance in oncogenes or tumor suppressor genes.
4.2.1.5. Summary
Evidence from a number of different analyses and a number of different laboratories
using a fairly complete array of endpoints suggests that TCE, following metabolism, has the
potential to be genotoxic. A series of carefully controlled studies evaluating TCE itself (without
mutagenic stabilizers and without metabolic activation) found it to be incapable of inducing gene
mutations in most standard mutation bacterial assays (Mortelmans et al., 1986; Shimada et al.,
1985: Crebelli et al., 1982: Baden etal., 1979: Bartsch et al., 1979: Waskell, 1978: Henschler et
al., 1977: Simmon etal., 1977). Therefore, it appears that it is unlikely that TCE is a direct-
acting mutagen, though TCE has shown potential to affect DNA and chromosomal structure.
Low, but positive, responses were observed in the TA100 strain in the presence of S9 metabolic
activation, even when genotoxic stabilizers were not present, suggesting that metabolites of TCE
are genotoxic. TCE is also positive in some but not all fungal and yeast systems (Koch et al.,
1988: Crebelli et al., 1985: Rossi etal., 1983: Callenetal., 1980). Data from human
epidemiological studies support the possible mutagenic effect of TCE leading to VHL gene
damage and subsequent occurrence of RCC. Association of increased VHL mutation frequency
in TCE-exposed RCC cases has been observed (Brauch et al., 2004: Brauch etal., 1999: B riming
etal., 1997b).
4-48
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TCE can lead to binding to nucleic acids and proteins (Kautiainen et al., 1997; Mazzullo
etal.. 1992: Bergman. 1983: Miller and Guengerich. 1983: DiRenzo et al.. 1982). and such
binding appears to be due to conversion to one or more reactive metabolites. For instance,
increased binding was observed in samples bioactivated with mouse and rat microsomal fractions
(Mazzullo et al., 1992: Miller and Guengerich, 1983: DiRenzo et al., 1982: Banerjee and Van
Duuren, 1978). DNA binding is consistent with the ability to induce DNA and chromosomal
perturbations. Several studies report the induction of micronuclei in vitro and in vivo from TCE
exposure (Hu et al.. 2008: Robbiano et al.. 2004: Wang et al.. 2001: Hreliaetal.. 1994:
Kligerman et al., 1994). Reports of SCE induction in some studies are consistent with DNA
effects, but require further study (Kligerman et al., 1994: Nagayaet al., 1989b: Guet al., 1981a:
Gu et al.. 1981b: White et al.. 1979).
Overall, evidence from a number of different analyses and a number of different
laboratories using various genetic endpoints indicates that TCE has a potential to induce damage
to the structure of the chromosome in a number of targets, but has a more limited ability to
induce mutation in bacterial systems.
Below, the genotoxicity data for TCE metabolites, TCA, DCA, TCOH, CH, DCVC, and
DCVG, are briefly reviewed. The contributions of these data are twofold. First, to the extent
that these metabolites may be formed in the in vitro and in vivo test systems for TCE, they
provide insight into what agent or agents may contribute to the limited activity observed with
TCE in these genotoxicity assays. Second, because the in vitro systems do not necessarily fully
recapitulate in vivo metabolism, the genotoxicity of the known in vivo metabolites themselves
provide data as to whether one may expect genotoxicity to contribute to the toxicity of TCE
following in vivo exposure.
4.2.2. TCA
The TCE metabolite, TCA, has been studied using a variety of genotoxicity assay for its
genotoxic potential (see International Agency for Research on Cancer [IARC, 2004] for
additional information). Evaluation of in vitro studies of TCA must consider toxicity and
acidification of medium resulting in precipitation of proteins, as TCA is commonly used as a
reagent to precipitate proteins.
4.2.2.1. Bacterial Systems—Gene Mutations
TCA has been evaluated in a number of in vitro test systems including the bacterial
assays (Ames) using different S. typhimurium strains such as TA98, TA100, TA104, TA1535,
and RSJ100 (see Table 4-11). The majority of these studies did not report positive findings for
genotoxicity ((1976) (1980) (1983) (Kargalioglu et al.. 2002: Nelson et al.. 2001: DeMarini et
al.. 1994: Rapsonetal.. 1980: Waskell. 1978). Waskell (1978) studied the effect of TCA
(0.45 mg/plate) on bacterial strains TA98 and TA100 both in the presence and absence of S9.
4-49
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The author did not find any revertants at the maximum nontoxic dose tested. Following
exposure to TCA, Rapson et al. (1980) reported no change in mutagenic activity in strain TA100
in the absence of S9. DeMarini et al. (1994) performed different studies to evaluate the
genotoxicity of TCA, including the Microscreen prophage-induction assay (TCA concentrations
0-10 mg/mL) and use of the S. typhimurium TA100 strain using bag vaporization technique
(TCA concentrations 0-100 ppm), neither of which yielded positive results. Nelson et al. (2001)
reported no positive findings with TCA using a S. typhimurium microsuspension bioassay (S.
typhimurium strain TA104) following incubation of TCA for various lengths of time, with or
without rat cecal microbiota. Similarly, no activity was observed in a study conducted by
Kargalioglu et al. (2002) where S. typhimurium strains TA98, TA100, and RSJ100 were exposed
to TCA (0.1-100 mM) either in the presence or absence of S9 (Kargalioglu et al.. 2002).
Table 4-11. Genotoxicity of TCA—bacterial systems
Test system/endpoint
A, Prophage induction, E. coli WP2s
SOS chromotest, E. coli PQ37
S. typhimurium TA1535, 1536, 1537,
1538, reverse mutation
S. typhimurium TA100, 98, reverse
mutation
S. typhimurium TA100, 1535, reverse
mutation
S. typhimurium TA1537, 1538, 98, reverse
mutation
S. typhimurium TA100, reverse mutation
S. typhimurium TA100, 98, reverse
mutation
S. typhimurium TA100, reverse mutation
S. typhimurium TA100, reverse mutation,
liquid medium
S. typhimurium TA104, reverse mutation,
microsuspension
S. typhimurium TA100, RSJ100, reverse
mutation
S. typhimurium TA98, reverse mutation
S. typhimurium TA1535, SOS DNA repair
Doses
(LED or HID)a
10,000
10,000
20 ug/plate
450 ug/plate
4,000 ug/plate
2,000 ug/plate
520 ug/plate
5,000 ug/plate
600 ppm
1,750
250 ug/plate
16,300
13,100
Results
With
activation
-
-
NT
—
—
—
NT
—
-
+
—
—
—
+
Without
activation
-
-
—
—
—
—
-
—
-
+
—
—
—
-
Reference
DeMarini et al. (1994)
Ciller et al. (1997)
Shirasu et al. (1976)
Waskell (1978)
Nestmann et al. (1980)
Nestmann et al. (1980)
Rapson et al. (1980)
Moriya et al. (1983)
DeMarini et al. (1994)
Ciller et al. (1997)
Nelson et al. (2001)
Kargalioglu et al.
(2002)
Kargalioglu et al.
(2002)
Ono et al. (1991)
aLED = lowest effective dose; HID = highest ineffective dose; doses are in ug/mL for in vitro tests unless specified.
+ = positive; - = negative; NT = not tested
Source: Table adapted from IARC monograph (2004b) and modified/updated for newer references.
4-50
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TCA was also negative in other bacterial systems. The SOS chromotest (which measures
DNA damage and induction of the SOS repair system) in E. coll PQ37, ± S9 (Giller et al.. 1997)
evaluated the genotoxic activity of TCA ranging from 10 to 10,000 |ig/mL and did not find any
response. Similarly, TCA was not genotoxic in the Microscreen prophage-induction assay in
E. coli with TCA concentrations ranging from 0 to 10,000 |ig/mL, with and without S9
activation (DeMarini et al., 1994).
However, TCA induced a small increase in SOS DNA repair (an inducible error-prone
repair system) in S. typhimurium strain TA1535 in the presence of S9 (Ono et al., 1991).
Furthermore, Giller et al. (1997) reported that TCA demonstrated genotoxic activity in an Ames
fluctuation test in S. typhimurium TA100 in the absence of S9 at noncytotoxic concentrations
ranging from 1,750 to 2,250 |ig/mL. The addition of S9 decreased the genotoxic response, with
effects observed at 3,000-7,500 |ig/mL. Cytotoxic concentrations in the Ames fluctuation assay
were 2,500 and 10,000 |ig/mL without and with microsomal activation, respectively.
4.2.2.2. Mammalian Systems
4.2.2.2.1. Gene mutations
The mutagenicity of TCA has also been tested in cultured mammalian cells (see Table 4-12).
Harrington-Brock et al. (1998) examined the potential of TCA to induce mutations in
L5178Y/TK ± -3.7.2C mouse lymphoma cells. In this study, mouse lymphoma cells were
incubated in culture medium treated with TCA concentrations up to 2,150 |ig/mL in the presence
of S9 metabolic activation and up to 3,400 |ig/mL in the absence of S9 mixture. In the presence
of S9, a doubling of mutant frequency was seen at concentrations of >2,250 |ig/mL, including
several concentrations with survival >10%. In the absence of S9, TCA increased the mutant
frequency by twofold or greater only at concentrations of >2,000 |ig/mL. These results were
obtained at <11% survival rates. The authors noted that the mutants included both large- and
small-colony mutants. The small-colony mutants are indicative of chromosomal damage. It
should be noted that no rigorous statistical evaluation was conducted on these data. Cytotoxic
and genotoxic effects of TCA were tested in a microplate-based cytotoxicity test and a HGPRT
gene mutation assay using Chinese hamster ovary Kl cells, respectively (Zhang et al., 2010).
TCA was the least cytotoxic when compared to six other haloacetic acids. TCA, at
concentrations of 0, 200, 1,000, 5,000 and 10,000 jiM, induced a visible increase in mutant
frequency but did not show any statistically significant increase at any of the doses tested.
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Table 4-12. TCA Genotoxicity—mammalian systems (both in vitro and in vivo)
Test system/endpoint
Gene mutation, mouse lymphoma L5178Y/TK ± cells, in vitro
Gene mutation, Chinese hamster ovary cells in vitro, HGPRT gene mutation
assay
DNA strand breaks, B6C3FJ mouse and F344 rat hepatocytes, in vitro
DNA strand breaks, human CCRF-CEM lymphoblastic cells, in vitro
DNA damage, Chinese hamster ovary cells, in vitro, comet assay
DNA strand breaks, B6C3F! mouse liver, in vivo
DNA strand breaks, B6C3F! mouse liver, in vivo
DNA strand breaks, B6C3F! mouse liver, in vivo
DNA strand breaks, B6C3F! mouse liver and epithelial cells from stomach and
duodenum, in vivo
DNA strand breaks, male B6C3Fi mice, in vivo
Micronucleus formation, Swiss mice, in vivo
Micronucleus formation, female C57BL/6JfBL10/Alpk mouse bone-marrow
erythrocytes, in vivo
Micronucleus formation, male C57BL/6JfBL10/Alpk mouse bone-marrow
erythrocytes, in vivo
Micronucleus formation, Pleurodeles waltl newt larvae peripheral erythrocytes,
in vivo
Chromosomal aberrations, Swiss mouse bone-marrow cells in vivo
Chromosomal aberrations, chicken Gallus domesticus bone marrow, in vivo
Doses
(LED or HID)a
3,000
10,000 uM
1,630
1,630
3mM
1.0, oral, x 1
500, oral, x 1
500, oral,
10 repeats
1,630, oral, x 1
500 (neutralized)
125, i.p., x 2
1,300, i.p., x 2
1,080, i.p., x 2
80
125, i.p., x 1
100, i.p., x 5
500, oral, x 1
200, i.p., x l
Results
With
activation
(+)
NT
NT
NT
NT
Without
activation
?
-
-
-
-
+
+
-
-
-
+
-
-
+
+
+
+
+
Reference
Harrington-Brock et al. (1998)
Zhang et al. (2010)
Chang et al. (1992)
Chang et al. (1992)
Plewa et al. (2002)
Nelson and Bull (1988)
Nelson et al. (1989)
Nelson et al. (1989)
Chang et al. (1992)
Styles et al. (1991)
Bhunya and Behera (1987)
Mackay et al. (1995)
Mackay et al. (1995)
Ciller et al. (1997)
Bhunya and Behera (1987)
Bhunya and Behera (1987)
Bhunya and Behera (1987)
Bhunya and Jena (1996)
4-52
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Table 4-12. TCA Genotoxicity—mammalian systems (both in vitro and in vivo) (continued)
Test system/endpoint
Chromosomal aberrations, human lymphocytes, in vitro
Sperm morphology, Swiss mice, in vivo
Doses
(LED or HID)a
5,000,
(neutralized)
125, i.p., x 5
Results'1
With
activation
-
Without
activation
+
Reference
Mackay et al. (1995)
Bhunya and Behera (1987)
"LED = lowest effective dose; HID = highest ineffective dose; doses are in ug/mL for in vitro tests; mg/kg for in vivo tests unless specified.
+ = positive; (+) = weakly positive; - = negative; NT = not tested; ? = inconclusive
Source: Table adapted from IARC monograph (2004b) and modified/updated for newer references.
4-53
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4.2.2.2.2. Chromosomal aberrations
Mackay et al. (1995) investigated the ability of TCA to induce chromosomal damage in
an in vitro chromosomal aberration assay using cultured human cells. The authors treated the
cells with TCA as free acid, both in the presence and absence of metabolic activation. TCA
induced chromosomal damage in cultured human peripheral lymphocytes at concentrations
(2,000 and 3,500 |ig/mL) that significantly reduced the pH of the medium. However, exposure
of cells to neutralized TCA did not have any effect even at a cytotoxic concentration of
5,000 jig/mL. It is possible that the reduced pH was responsible for the TCA-induced
clastogenicity in this study. To further evaluate the role of pH changes in the induction of
chromosome damage, the authors isolated liver-cell nuclei from B6C3Fi mice and suspended in
a buffer at various pH levels. The cells were stained with chromatin-reactive (fluorescein
isothiocyanate) and DNA-reactive (propidium iodide) fluorescent dyes. A decrease in chromatin
staining intensity was observed with the decrease in pH, suggesting that pH changes,
independent of TCA exposure, can alter chromatin conformation. It was concluded by the
authors that TCA-induced pH changes are likely to be responsible for the chromosomal damage
induced by un-neutralized TCA. In another in vitro study, Plewa et al. (2002) evaluated the
induction of DNA strand breaks induced by TCA (1-25 mM) in Chinese hamster ovary cells and
did not observe any genotoxicity.
4.2.2.2.3. Micronucleus
Relative genotoxicity of TCA was tested in a mouse in vivo system (see Table 4-12)
using three different cytogenetic assay (bone marrow chromosomal aberrations, micronucleus,
and sperm-head abnormalities) (Bhunya and Behera, 1987) and for chromosomal aberrations in
chicken (Bhunya and Jena, 1996). TCA induced a variety of anomalies including micronucleus
in the bone marrow of mice and chicken. A small increase in the frequency of micronucleated
erythrocytes at 80 |ig/mL in a newt (Pleurodeles waltl larvae) micronucleus test was observed in
response to TCA exposure (Giller et al., 1997). Mackay et al. (1995) investigated the ability of
TCA to induce chromosomal DNA damage in the in vivo bone-marrow micronucleus assay in
mice. C57BL mice were given TCA intraperitoneally at doses of 0, 337, 675, or 1,080 mg/kg-
day for males and 0, 405, 810, or 1,300 mg/kg-day for females for two consecutive days, and
bone-marrow samples were collected 6 and 24 hours after the last dose. The administered doses
represented 25, 50, and 80% of the median lethal dose, respectively. No treatment-related
increase in micronucleated PCEs was observed.
4.2.2.2.4. Other DNA damage studies
DNA unwinding assays have been used as indicators of SSBs and are discussed in detail
in Section E.2.3. Studies were conducted on the ability of TCA to induce SSBs (see Table 4-12)
4-54
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(Chang et al.. 1992: Styles et al.. 1991: Nelson et al.. 1989: Nelson and Bull 1988). Nelson and
Bull (1988) evaluated the ability of TCA and other compounds to induce single-strand DNA
breaks in vivo in Sprague-Dawley rats and B6C3Fi mice. Single oral doses were administered to
three groups of three animals, with an additional group as a vehicle control. Animals were
sacrificed after 4 hours, and 10% liver suspensions were analyzed for single-strand DNA breaks
by the alkaline unwinding assay. Dose-dependent increases in single-strand DNA breaks were
induced in both rats and mice, with mice being more susceptible than rats. The lowest dose of
TCA that produced significant SSBs was 0.6 mmol/kg (98 mg/kg) in rats but 0.006 mmol/kg
(0.98 mg/kg) in mice.
However, in a follow-up study, Nelson et al. (1989) male B6C3Fi mice were treated with
500 mg/kg TCA, and SSBs in whole liver homogenate were examined, and no significant
differences from controls were reported. Moreover, in the experiments in the same study with
DCA, increased SSBs were reported, but with no dose-response between 10 and 500 mg/kg,
raising concerns about the reliability of the DNA unwinding assay used in these studies. For
further details, see Section E.2.3. In an additional follow-up experiment with a similar
experimental paradigm, Styles et al. (1991) tested TCA for its ability to induce strand breaks in
male B6C3Fi mice in the presence and absence of liver growth induction. The test animals were
given one, two, or three daily doses of neutralized TCA (500 mg/kg) by gavage and killed 1 hour
after the final dose. Additional mice were given a single 500-mg/kg gavage dose and sacrificed
24 hours after treatment. Liver nuclei DNA were isolated, and the induction of SSBs was
evaluated using the alkaline unwinding assay. Exposure to TCA did not induce strand breaks
under the conditions tested in this assay. In a study by Chang et al. (1992), administration of
single oral doses of TCA (1-10 mmol/kg) to B6C3Fi mice did not induce DNA strand breaks in
a dose-related manner as determined by the alkaline unwinding assay. No genotoxic activity
(evidence for strand breakage) was detected in F344 rats administered by gavage up to
5 mmol/kg (817 mg/kg).
In summary, although Nelson and Bull (1988) report effects on DNA unwinding for TCE
and its metabolites with DCA having the highest activity and TCA the lowest, Nelson et al.
(1989), using the same assay, reported no effect for TCA and the same effect at 10 and
500 mg/kg for DCA in mice. Moreover, Styles et al. (1991) did not find a positive result for
TCA using the same paradigm as Nelson and Bull (1988) and Nelson et al. (1989). Furthermore,
Chang et al. (1992) also did not find increased SSBs for TCA exposure in rats, (see
Section E.2.4.3).
4.2.2.3. Summary
In summary, TCA has been studied using a variety of genotoxicity assays, including the
recommended battery. No mutagenicity was reported in S. typhimurium strains in the presence
or absence of metabolic activation or in an alternative protocol using a closed system, except in
4-55
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one study on strain TA100 using a modified protocol in liquid medium. This is largely
consistent with the results from TCE, which was negative in most bacterial systems except some
studies with the TA100 strain. Mutagenicity in mouse lymphoma cells was only induced at
cytotoxic concentrations. Measures of DNA-repair responses in bacterial systems have been
inconclusive, with induction of DNA repair reported in S. typhimurium but not in E. coll.
TCA-induced clastogenicity may be secondary to pH changes and not a direct effect of TCA.
4.2.3. DCA
DCA is another metabolite of TCE that has been studied using a variety of genotoxicity
assay for its genotoxic potential (see Tables 4-13 and 4-14; see IARC (2004b) for additional
information).
4.2.3.1. Bacterial and Fungal Systems—Gene Mutations
Studies were conducted to evaluate mutagenicity of DCA in different S. typhimurium and
E. coli strains (Kargalioglu et al.. 2002: Nelson etal.. 2001: Gilleretal.. 1997: Foxetal.. 1996a:
Fox et al.. 1996b: DeMarini et al.. 1994: Herbert et al.. 1980: Waskell. 1978). DCA was
mutagenic in three strains of S. typhimurium: strain TA100 in three of five studies, strain RSJ100
in a single study, and strain TA98 in two of three studies. DCA failed to induce point mutations
in other strains of S. typhimurium (TA104, TA1535, TA1537, and TA1538) or in E. coli strain
WP2uvrA. In one study, DCA caused a weak induction of SOS repair in E. coli strain PQ37
(Gilleretal.. 1997).
DeMarini et al. (1994), in the same study as described in the TCA section of this section,
also studied DCA as one of their compounds for analysis. In the prophage-induction assay using
E. coli, DCA, in the presence of S9, was genotoxic producing 6.6-7.2 plaque-forming units
(PFU)/mM and slightly less than threefold increase in PFU/plate in the absence of S9. In the
second set of studies, which involved the evaluation of DCA at concentrations of 0-600 ppm for
mutagenicity in S. typhimurium TA100 strain, DCA was mutagenic both in the presence and
absence of S9, producing 3-5 times increases in the revertants/plate compared to the
background. The lowest effective concentrations for DCA without S9 were 100 and 50 ppm in
the presence of S9. In the third and most important study, mutation spectra of DCA were
determined at the base-substitution allele hisG46 of S. typhimurium TA100. DCA-induced
revertants were chosen for further molecular analysis at concentrations that produced mutant
yields that were two- to fivefold greater than the background. The mutation spectra of DCA
were significantly different from the background mutation spectrum. Thus, despite the modest
increase in the mutant yields (3-5 times) produced by DCA, the mutation spectra confirm that
DCA is mutagenic. DCA primarily induced GC-AT transitions.
4-56
-------
Table 4-13. Genotoxicity of DCA (bacterial systems)
Test system/endpoint
A, Prophage induction, E. coli WP2s
SOS chromotest, E. coli PQ37
S. typhimurium, DNA repair-deficient strains TS24, TA2322, TA1950
S. typhimurium TA100, TA1535, TA1537, TA1538, reverse mutation
S. typhimurium TA100, reverse mutation
S. typhimurium TA100,TA1535, TA1537, TA98, reverse mutation
S. typhimurium TA100, reverse mutation, liquid medium
S. typhimurium RSJ100, reverse mutation
S. typhimurium TA104, reverse mutation, microsuspension
S. typhimurium TA98, reverse mutation
S. typhimurium TA100, reverse mutation
E. coli WP2uvrA, reverse mutation
Doses
(LED or HID)a
2,500
500
31,000
50
5,000
100
1,935
150 ug/plate
10 ug/plate
5,160
1,935
5,000
Results'1
With
activation
+
-
-
-
+
-
+
-
-
(+)
-
+
-
Without
activation
-
(+)
-
-
+
-
+
+
-
-
+
+
-
Reference
DeMarini et al. (1994)
Ciller et al. (19971
Waskell (1978)
Herbert et al. (1980)
DeMarini et al. (1994)
Foxetal. (1996b)
Ciller et al. (1997)
Kargalioglu et al. (2002)
Nelson et al. (2001)
Herbert et al. (1980)
Kargalioglu et al. (2002)
Kargalioglu et al. (2002)
Foxetal., Q996b)
aLED = lowest effective dose; HID = highest ineffective dose; doses are in ug/mL for in vitro tests unless specified.
+ = positive; (+) = weakly positive; - = negative
Source: Table adapted from IARC monograph (2004b) and modified/updated for newer references.
4-57
-------
Table 4-14. Genotoxicity of DCA—mammalian systems
Test system/endpoint
Gene mutation, mouse lymphoma cell line L5178Y/TK ± in vitro
Gene mutation, mouse lymphoma cell line L5178Y/TK ± -3.7.2C in vitro
Gene mutation, Chinese hamster ovary cells in vitro, HGPRT gene mutation
assay
DNA strand breaks and alkali-labile damage, Chinese hamster ovary cells in
vitro (single-cell gel electrophoresis assay)
DNA strand breaks, B6C3F! mouse hepatocytes in vitro
DNA strand breaks, F344 rat hepatocytes in vitro
Micronucleus formation, mouse lymphoma L5178Y/TK ± -3.7.2C cell line in
vitro
Chromosomal aberrations, Chinese hamster ovary in vitro
Chromosomal aberrations, mouse lymphoma L5178Y/Tk ± -3.7.2C cell line
in vitro
Aneuploidy, mouse lymphoma L5 178Y/Tk ± -3 .7.2C cell line in vitro
DNA strand breaks, human CCRF-CEM lymphoblastoid cells in vitro
DNA strand breaks, male B6C3F! mouse liver in vivo
DNA strand breaks, male B6C3F! mouse liver in vivo
DNA strand breaks, male B6C3F! mouse liver in vivo
DNA strand breaks, male B6C3F! mouse splenocytes in vivo
DNA strand breaks, male B6C3F! mouse epithelial cells from stomach and
duodenum in vivo
DNA strand breaks, male B6C3F! mouse liver in vivo
DNA strand breaks, alkali-labile sites, cross linking, male B6C3FJ mouse
blood leukocytes in vivo (single-cell gel electrophoresis assay)
Doses
(LED or HID)a
5,000
400
1,000 uM
3,225 ug/mL
2,580
1,290
800
5,000
600
800
1,290
13, oral, x 1
10, oral, x 1
1,290, oral, x 1
1,290, oral, x 1
1,290, oral, x 1
5,000, dw, x 7-14 d
3,500, dw, x 28 d
Results
With
activation
-
NT
NT
NT
NT
NT
NT
-
NT
NT
NT
Without
activation
-
+
+
-
-
-
-
-
+
-
-
+
+
-
-
-
-
+
Reference
Foxetal., (1996b)
Harrington-Brock et al.
(1998)
Zhang et al. (2010)
Plewa et al. (2002)
Chang et al. (1992)
Chang et al. (1992)
Harrington-Brock et al.
(1998)
Foxetal., (1996b)
Harrington-Brock et al.
(1998)
Harrington-Brock et al.
(1998)
Chang et al. (1992)
Nelson and Bull (1988)
Nelson et al. (1989)
Chang et al. (1992)
Chang et al. (1992)
Chang et al. (1992)
Chang et al. (1992)
Fuscoe et al. (1996)
4-58
-------
Table 4-14. Genotoxicity of DCA—mammalian systems (continued)
Test system/endpoint
DNA strand breaks, male Sprague-Dawley rat liver in vivo
DNA strand breaks, male F344 rat liver in vivo
DNA strand breaks, male F344 rat liver in vivo
Gene mutation, lacl transgenic male B6C3F! mouse liver assay in vivo
Micronucleus formation, male B6C3F! mouse peripheral erythrocytes in vivo
Micronucleus formation, male B6C3F! mouse peripheral erythrocytes in vivo
Micronucleus formation, male B6C3FJ mouse peripheral erythrocytes in vivo
Micronucleus formation, male and female Crl:CD (Sprague-Dawley) BR rat
bone-marrow erythrocytes in vivo
Micronucleus formation, Pleurodeles waltl newt larvae peripheral erythrocytes
in vivo
Doses
(LED or HID)a
30, oral, x 1
645, oral, x 1
2,000, dw, x 30 wks
1,000, dw, x 60 wks
3,500, dw, x 9 d
3,500, dw, x 28 d
3,500, dw, x 10 wks
1,100, i.v., x 3
80 d
Results
With Without
activation activation
+
-
-
+
+
-
+
-
-
Reference
Nelson and Bull (1988)
Chang et al. (1992)
Chang et al. (1992)
Leavitt et al. (1997)
Fuscoe et al. (1996)
Fuscoe et al. (1996)
Fuscoe et al. (1996)
Foxetal.. (1996b)
Ciller etal. (1997)
aLED = lowest effective dose; HID = highest ineffective dose; doses are in ug/mL for in vitro tests; mg/kg for in vivo tests unless specified; dw = drinking-water
(in mg/L).
+ = positive; - = negative; NT = not tested
Source: Table adapted from IARC monograph (2004b) and modified/updated for newer references.
4-59
-------
Kargalioglu et al. (2002) analyzed the cytotoxicity and mutagenicity of the drinking
water disinfection byproducts including DC A in S. typhimurium strains TA98, TA100, and
RSJ100 ± S9. DCA was mutagenic in this test although the response was low when compared to
other disinfection byproducts tested in strain TA100. This study was also summarized in a
review by Plewa et al. (2002). Nelson et al. (2001) investigated the mutagenicity of DCA using
a S. typhimurium microsuspension bioassay following incubation of DCA for various lengths of
time, with or without rat cecal microbiota. No mutagenic activity was detected for DCA with
S. typhimurium strain TA104.
Although limited data, it appears that DCA has mutagenic activity in the S. typhimurium
strains, particularly TA100.
4.2.3.2. Mammalian Systems
4.2.3.2.1. Gene mutations
The mutagenicity of DCA has been tested in mammalian systems, particularly, mouse
lymphoma cell lines in vitro (Harrington-Brock et al., 1998; Fox et al., 1996b): and lacl
transgenic mice in vivo (Leavitt et al., 1997). Harrington-Brock et al. (1998) evaluated DCA for
it mutagenic activity in L5178Y/TK ± (-) 3.7.2C mouse lymphoma cells. A dose-related
increase in mutation (and cytotoxic) frequency was observed at concentrations between 100 and
800 |ig/mL. Most mutagenic activity of DCA at the Tk locus was due to the production of small-
colony Tk mutants (indicating chromosomal mutations). Different pH levels were tested in
induction of mutant frequencies and it was determined that the mutagenic effect observed was
due to the chemical and not pH effects.
Mutation frequencies were studied in male transgenic B6C3Fi mice harboring the
bacterial lad gene administered DCA at either 1.0 or 3.5 g/L in drinking water (Leavitt et al.,
1997). No significant difference in mutant frequency was observed after 4 or 10 weeks of
treatment in both of the doses tested as compared to control. However, at 60 weeks, mice treated
with 1.0 g/L DCA showed a slight increase (1.3-fold) in the mutant frequency over the control,
but mice treated with 3.5 g/L DCA had a 2.3-fold increase in the mutant frequency. Mutational
spectra analysis revealed that -33% had G:C-A:T transitions and 21% had G:C-T:A
transversions and this mutation spectra was different than that was seen in the untreated animals,
indicating that the mutations were likely induced by the DCA treatment. The authors conclude
that these results are consistent with the previous observation that the proportion of mutations at
T:A sites in codon 61 of the H-ras gene was increased in DCA-induced liver tumors in B6C3Fi
mice (Leavitt et al.. 1997).
Zhang et al. (2010) tested the cytotoxic and genotoxic effects of DCA in a microplate-
based cytotoxicity test and HGPRT gene mutation assay using Chinese hamster ovary Kl cells,
respectively. The concentrations at which these tests were conducted were 0, 200, 1,000,
5,000 and 10,000 jiM. Two parameters were used to indicate chronic cytotoxicity: the lowest
4-60
-------
cytotoxic concentration and the percent Cl/2 value. The lowest cytotoxic concentration for DC A
was 2.87 x 10"3M. Statistically significant increase in HGPRT mutant frequency was observed at
concentrations >1,000 jiM.
4.2.3.2.2. Chromosomal aberrations and micronucleus
Harrington-Brock et al. (1998) evaluated DCA for its potential to induce chromosomal
aberrations in DCA-treated (0, 600, and 800 |ig/mL) mouse lymphoma cells. A clearly positive
induction of aberrations was observed at both concentrations tested. No significant increase in
micronucleus was observed in DCA-treated (0, 600, and 800 jig/mL) mouse lymphoma cells
(Harrington-Brock et al., 1998). However, no chromosomal aberrations were found in Chinese
hamster ovary cells exposed to DCA (Fox et al., 1996b)
Fuscoe et al. (1996) investigated in vivo genotoxic potential of DCA in bone marrow and
blood leukocytes using the peripheral-blood-erythrocyte micronucleus assay (to detect
chromosome breakage and/or malsegregation) and the alkaline single cell gel electrophoresis
(comet) assay, respectively. Mice were exposed to DCA in drinking water, available ad libitum,
for up to 31 weeks. A statistically significant dose-related increase in the frequency of
micronucleated PCEs was observed following subchronic exposure to DCA for 9 days.
Similarly, a significant increased was also observed when exposed for >10 weeks particularly at
the highest dose of DCA tested (3.5 g/L). DNA cross-linking was observed in blood leukocytes
in mice exposed to 3.5 g/L DCA for 28 days. These data provide evidence that DCA may have
some potential to induce chromosome damage when animals were exposed to concentrations
similar to those used in the rodent bioassay.
4.2.3.2.3. Other DNA damage studies
Nelson and Bull (1988) and Nelson et al. (1989) have been described in
Sections 4.2.2.2.4 and E.2.3, with positive results for DNA unwinding for DCA, though Nelson
et al. (1989) reported the same response at 10 and 500 mg/kg in mice, raising concerns about the
reliability of the assay in these studies. Chang et al. (1992) conducted both in vitro and in vivo
studies to determine the ability of DCA to cause DNA damage. Primary rat (F344) hepatocytes
and primary mouse hepatocytes treated with DCA for 4 hours did not in induce DNA SSBs as
detected by alkaline DNA unwinding assay. No DNA strand breaks were observed in human
CCRF-CEM lymphoblastoid cells in vitro exposed to DCA. Similarly, analysis of the DNA
SSBs in mice killed 1 hour after a single dose of 1, 5, or 10 mM/kg DCA did not cause DNA
damage. None of the F344 rats killed 4 hours after a single gavage treatment (1-10 mM/kg)
produced any detectable DNA damage.
4-61
-------
4.2.3.3. Summary
In summary, DCA has been studied using a variety but limited number of genotoxicity
assays. Within the available data, DCA has been demonstrated to be mutagenic in the
S. typhimurium assay, particularly in strain TA100, the in vitro mouse lymphoma assay and in
vivo cytogenetic and gene mutation assays. DCA can cause DNA strand breaks in mouse and rat
liver cells following in vivo administration by gavage.
4.2.4. CH
CH has been evaluated for its genotoxic potential using a variety of genotoxicity assays
(see Tables 4-15, 4-16, and 4-17). These data are particularly important because it is known that
a large flux of TCE metabolism leads to CH as an intermediate, so a comparison of their
genotoxicity profiles is likely to be highly informative.
4.2.4.1. DNA Binding Studies
Limited analysis has been performed examining DNA binding potential of CH (Von
Tungeln et al.. 2002: Metal.. 1995: Keller and Hd'A. 1988). Keller and Heck (1988) conducted
both in vitro and in vivo experiments using B6C3Fi mouse strain. The mice were pretreated
with 1,500 mg/kg TCE for 10 days and then given 800 mg/kg [14C] chloral. No detectable
covalent binding of [14C] to DNA in the liver was observed. Another study with in vivo
exposures to nonradioactive CH at a concentration of 1,000 and 2,000 nmol in mice B6C3Fi
demonstrated an increase in malondialdehyde-derived and 8-oxo-2'-deoxyguanosine adducts in
liver DNA (Von Tungeln et al., 2002). Ni et al. (1995) observed malondialdehyde adducts in
calf thymus DNA when exposed to CH and microsomes from male B6C3Fi mouse liver.
Keller and Heck (1988) investigated the potential of chloral to form DNA-protein cross-
links in rat liver nuclei using concentrations 25, 100, or 250 mM. No statistically significant
increase in DNA-protein cross-links was observed. DNA and RNA isolated from the
[14C] chloral-treated nuclei did not have any detectable [14C] bound. However, the proteins from
choral-treated nuclei did have a concentration-related binding of [14C].
4-62
-------
Table 4-15. CH genotoxicity: bacterial, yeast, and fungal systems
Test system/endpoint
SOS chromotest, Escherichia coli PQ37
S. typhimurium TA100, TA1535, TA98, reverse mutation
S. typhimurium TA100, TA1537, TA1538, TA98, reverse mutation
S. typhimurium TA100, reverse mutation
S. typhimurium TA100, reverse mutation
S. typhimurium TA100, reverse mutation, liquid medium
S. typhimurium TA100, TA104, reverse mutation
S. typhimurium TA104, reverse mutation
S. typhimurium TA1535, reverse mutation
S. typhimurium TA1535, TA1537 reverse mutation
S. typhimurium TA1535, reverse mutation
S. typhimurium TA98, reverse mutation
S. typhimurium TA98, reverse mutation
A.nidulans, diploid strain 35X17, mitotic cross-overs
A. nidulans, diploid strain 30, mitotic cross-overs
A. nidulans, diploid strain NH, mitotic cross-overs
A. nidulans, diploid strain PI, mitotic cross-overs
A. nidulans, diploid strain 35X17, nondisjunctions
A. nidulans, diploid strain 30, aneuploidy
A. nidulans, haploid conidia, aneuploidy, polyploidy
A. nidulans, diploid strain NH, nondisjunctions
Doses
(LED or HID)a
10,000
10,000
1,000
5,000 ug/plate
2,000 ug/plate
300
1,000 ug/plate
1,000 ug/plate
1,850
6,667
10,000
7,500
10,000 ug/plate
1,650
6,600
1,000
990
825
825
1,650
450
Results
With
activation
-
-
+
-
+
+
+
+
-
-
-
-
-
Not tested
Not tested
Not tested
Not tested
Not tested
Not tested
Not tested
Not tested
Without
activation
-
-
+
-
+
-
+
+
-
-
-
-
+
-
-
-
-
+
+
+
+
Reference
Ciller et al. (1995)
Waskell (1978)
Haworth et al. (1983)
Leuschner and Leuschner (1991)
Ni et al. (1994)
Ciller et al. (1995)
Beland (1999)
Ni et al. (1994)
Leuschner and Leuschner (1991)
Haworth et al. (1983)
Beland (1999)
Haworth et al. (1983)
Beland (1999)
Crebelli et al. (1985)
Kafer (1986)
Kappas (1989)
Crebelli et al. (1991)
Crebelli et al. (1985)
Kafer (1986)
Kafer (1986)
Kappas (1989)
4-63
-------
Table 4-15. CH genotoxicity: bacterial, yeast, and fungal systems (continued)
Test system/endpoint
A. nidulans, diploid strain PI, nondisjunctions
A. nidulans, haploid strain 35, hyperploidy
S. cerevisiae, meiotic recombination
S. cerevisiae, disomy in meiosis
S. cerevisiae, disomy in meiosis
S. cerevisiae, D61.M, mitotic chr. malsegregation
Drosophila melanogaster, somatic mutation wing spot test
Drosophila melanogaster, induction of sex-linked lethal mutation
Drosophila melanogaster, induction of sex-linked lethal mutation
Doses
(LED or HID)a
660
2,640
3,300
2,500
3,300
1,000
825
37.2 feed
67.5 inj
Results'1
With
activation
Not tested
Not tested
Not tested
Not tested
Not tested
Not tested
Without
activation
+
+
Inconclusive
+
+
+
+
Inconclusive
-
Reference
Crebelli et al. (19911
Crebelli et al. (1991)
Sora and Agostini Carbone
(1987)
Sora and Agostini Carbone
(1987)
Sora and Agostini Carbone
(1987)
Albertini, (1990)
Zordan et al. (1994)
Beland (1999)
Beland (1999)
aLED = lowest effective dose; HID = highest ineffective dose; doses are in ug/mL for in vitro tests; inj = injection.
+ = positive; - = negative
Source: Table adapted from IARC monograph (2004b) and modified/updated for newer references.
4-64
-------
Table 4-16. CH genotoxicity: mammalian systems—all genetic endpoints, in vitro
Test system/endpoint
DNA-protein cross-links, rat nuclei in vitro
DNA SSBs, rat primary hepatocytes in vitro
Gene mutation, mouse lymphoma L5178Y/TK ±, in vitro
SCEs, Chinese hamster ovary cells, in vitro
Micronucleus formation (kinetochore-positive), Chinese hamster Cl cells, in vitro
Micronucleus formation (kinetochore-negative), Chinese hamster Cl cells, in vitro
Micronucleus formation (kinetochore-positive), Chinese hamster LUC2 cells, in vitro
Micronucleus formation (kinetochore-positive), Chinese hamster LUC2 cells, in vitro
Micronucleus formation, Chinese hamster V79 cells, in vitro
Micronucleus formation, mouse lymphoma L5 178Y/TK ±, in vitro
Micronucleus formation, mouse lymphoma L5 178Y/TK ±, in vitro
Chromosomal aberrations, Chinese Hamster CHED cells, in vitro
Chromosomal aberrations, Chinese Hamster ovary cells, in vitro
Chromosomal aberrations, mouse lymphoma L5178Y/TK ± cells line, in vitro
Aneuploidy, Chinese hamster CHED cells, in vitro
Aneuploidy, primary Chinese hamster embryonic cells, in vitro
Aneuploidy, Chinese hamster LUC2p4 cells, in vitro
Aneuploidy, mouse lymphoma L5178Y/TK±, in vitro
Tetraploidy and endoredupliation, Chinese hamster LUC2p4cells, in vitro
Cell transformation, Syrian hamster embryo cells (24-hr treatment)
Cell transformation, Syrian hamster dermal cell line (24 -hr treatment)
DNA SSBs, human lymphoblastoid cells, in vitro
Doses
(LED or
HID)a
41,250
1,650
1,000
100
165
250
400
400
316
1,300
500
20
1,000
1,250
10
250
250
1,300
500
350
50
1,650
Results
With
activation
NT
NT
+
NT
NT
NT
NT
NT
NT
NT
NT
+
NT
NT
NT
NT
NT
NT
NT
NT
NT
Without
activation
-
-
(+)
+
+
-
+
+
+
-
+
+
+
(+)
+
+
+
-
+
+
+
-
Reference
Keller and Heck (1988)
Chang et al. (1992)
Harrington-Brock et al. (1998)
Beland (1999)
Degrassi and Tanzarella (1988)
Degrassi and Tanzarella (1988)
Parry et al., (1990)
Lynch and Parry (1993)
Seelbach et al. (1993)
Harrington-Brock et al. (1998)
Nesslany and Marzin (1999)
Furnus et al. (1990)
Beland (1999)
Harrington-Brock et al. (1998)
Furnus et al. (1990)
Natarajan et al. (1993)
Warretal. (1993)
Harrington-Brock et al. (1998)
Warretal. (1993)
Gibson et al. (1995)
Parry et al. (1996)
Chang et al. (1992)
4-65
-------
Table 4-16. CH genotoxicity: mammalian systems—all genetic endpoints, in vitro (continued)
Test system/endpoint
Gene mutation, tk and hprt locus, human lymphoblastoid
SCEs, human lymphocytes, in vitro
Micronucleus formation, human lymphocytes, in vitro
Micronucleus formation, human lymphoblastoid AHH-1 cell line, in vitro
Micronucleus formation, human lymphoblastoid maximum contaminant level-5 cell
line, in vitro
Micronucleus formation (kinetochore-positive), human diploid LEO fibroblasts, in vitro
Aneuploidy (double Y induction), human lymphocytes, in vitro
Aneuploidy (hyperdiploidy and hypodiploidy), human lymphocytes in vitro
Polyploidy, human lymphocytes, in vitro
C-Mitosis, human lymphocytes, in vitro
Doses
(LED or
HID)a
1,000
54
100
100
500
120
250
50
137
75
Results
With
activation
NT
NT
-
NT
NT
NT
NT
NT
NT
NT
Without
activation
+
(+)
+
+
-
+
+
+
+
+
Reference
Beland (1999)
Gu et al. (1981a)
Van Hummelen and Kirsch-
Volders (1992)
Parry et al. (1996)
Parry et al. (1996)
Bonatti et al. (1992)
Vagnarelli et al. (1990)
Sbrana et al. (1993)
Sbrana et al. (1993)
Sbrana et al. (1993)
aLED = lowest effective dose; HID = highest ineffective dose; doses are in ug/mL for in vitro tests.
+ = positive; (+) = weakly positive in an inadequate study; - = negative; NT = not tested
Source: Table adapted from IARC monograph (2004b) and modified/updated for newer references.
4-66
-------
Table 4-17. CH genotoxicity: mammalian systems—all genetic damage, in vivo
Test system/endpoint
DNA SSBs, male Sprague-Dawley rat liver
DNA SSBs, male F344 rat liver
DNA SSBs, male B6C3FJ mouse liver
DNA SSBs, male B6C3FJ mouse liver
Micronucleus formation, male and female NMRI mice, bone-marrow erythrocytes
Micronucleus formation, BALB/c mouse spermatids
Micronucleus formation, male BALB/c mouse bone-marrow erythrocytes and early
spermatids
Micronucleus formation, male BALB/c mouse bone-marrow erythrocytes
Micronucleus formation, male Fl mouse bone-marrow erythrocytes
Micronucleus formation, C57B 1 mouse spermatids
Micronucleus formation, male Swiss CD-I mouse bone-marrow erythrocytes
Micronucleus formation, B6C3FJ mouse spermatids after spermatogonial stem-cell treatment
Micronucleus formation, B6C3FJ mouse spermatids after meiotic cell treatment
Micronucleus formation, male Fl, BALB/c mouse peripheral-blood erythrocytes
Micronucleus formation, male B6C3FJ mouse bone-marrow erythrocytes
Micronucleus formation, infants, peripheral lymphocytes
Chromosomal aberrations, male and female Fl mouse bone marrow cells
Chromosomal aberrations, male and female Sprague-Dawley rat bone-marrow cells
Chromosomal aberrations, BALB/c mouse spermatogonia treated
Chromosomal aberrations, Fl mouse secondary spermatocytes
Chromosomal aberrations, male Swiss CD-I mouse bone-marrow erythrocytes
Chromosomal aberrations, ICR mouse oocytes
Doses
(LED or HID)a
300, oral
1,650, oral
100, oral
825, oral
500, i.p.
83, i.p.
83, i.p.
200, i.p.
400, i.p.
41, i.p.
200, i.p.
165, i.p.
413, i.p.
200, i.p.
500, i.p., x 3
50, oral
600, i.p.
1,000, oral
83, i.p.
82.7, i.p.
400, i.p.
600, i.p.
Results
+
-
+
-
-
-
+
+
-
+
+
+
-
-
+
+
-
-
-
+
-
-
Reference
Nelson and Bull (1988)
Chang et al. (1992)
Nelson and Bull (1988)
Chang et al. (1992)
Leuschner and Leuschner (1991)
Russo and Levis (1992b)
Russo and Levis (1992a)
Russo et al. (1992)
Leopardi et al. (1993)
Allen etal., 1994
Marrazzini et al., (1994)
Nutley et al. (1996)
Nutley et al. (1996)
Grawe et al. (1997)
Beland (1999)
Ikbal et al. (2004)
Xu and Alder (1990)
Leuschner and Leuschner (1991)
Russo and Levis, (1992a)
Russo et al. (1984)
Marrazzini et al. (1994)
Mailhes et al. (1993)
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Table 4-17. CH genotoxicity: mammalian systems—all genetic damage, in vivo (continued)
Test system/endpoint
Micronucleus formation, infants, peripheral lymphocytes
Polyploidy, male and female Fl, mouse bone-marrow cells
Aneuploidy Fl mouse secondary spermatocytes
Aneuploidy, male Fl mouse secondary spermatocytes
Hyperploidy, male Swiss CD-I mouse bone-marrow erythrocytes
Doses
(LED or HID)a
50, oral
600, i.p.
200, i.p.
400, i.p.
200, i.p.
Results
+
-
+
-
+
Reference
Ikbal et al. (20041
Xu and Alder (1990)
Miller and Adler (1992)
Leopardi et al. (1993)
Marrazzini et al. (1994)
aLED = lowest effective dose; HID = highest ineffective dose; doses are in mg/kg body weight for in vivo tests.
+ = positive; - = negative
Source: Table adapted from IARC monograph (2004b) and modified/updated for newer references.
4-68
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4.2.4.2. Bacterial and Fungal Systems—Gene Mutations
CH induced gene mutations in S. typhimurium TA100 and TA104 strains, but not in most
other strains assayed. Four of six studies of CH exposure in S. typhimurium TA100 and two of
two studies in S. typhimurium TA104 were positive for revertants (Beland, 1999; Giller et al.,
1995: Metal.. 1994: Haworth et al.. 1983). Waskell (1978) studied the effect of CH along with
TCE and its other metabolites. CH was tested at different doses (1.0-13 mg/plate) in different
S. typhimurium strains (TA98, TA100, TA1535) for gene mutations using Ames assay. No
revertant colonies were observed in strains TA98 or TA1535 both in the presence and absence of
S9 mix. Similar results were obtained by Leuschner and Leuschner (1991). However, in
TA100, a dose-dependent statistically significant increase in revertant colonies was obtained
both in the presence and absence of S9. It should be noted that CH that was purchased from
Sigma was recrystallized 1-6 times from chloroform and the authors describe this as crude CH.
However, this positive result is consistent with other studies in this strain as noted above.
Furthermore, Giller et al. (1995) studied CH genotoxicity in three short-term tests. Chloral-
induced mutations in strain TA100 of S. typhimurium (fluctuation test). Similar results were
obtained by Haworth et al. (1983). These are consistent with several studies of TCE, in which
low, but positive responses were observed in the TA100 strain in the presence of S9 metabolic
activation, even when genotoxic stabilizers were not present.
A significant increase in mitotic segregation was observed in A. nidulam when exposed
to 5 and 10 mM CH (Crebelli et al., 1985). Studies of mitotic crossing-over in A. nidulans have
been negative, while these same studies were positive for aneuploidy (Crebelli et al., 1991:
Kappas. 1989: Kafer, 1986: Crebelli et al.. 1985).
Two studies were conducted in S. cerevisiae to understand the chromosomal
malsegregation as a result of exposure to CH (Albertini, 1990: Sora and Agostini Carbone,
1987). CH (1-25 mM) was dissolved in sporulation medium and the frequencies of various
meiotic events such as recombination and disomy were analyzed. CH inhibited sporulation as a
function of dose and increased diploid and disomic clones. CH was also tested for mitotic
chromosome malsegregation using S. cerevisiae D61.M (Albertini, 1990). The tester strain was
exposed to a dose range of 1-8 mg/mL. An increase in the frequency of chromosomal
malsegregation was observed as a result of exposure to CH.
Limited analysis of CH mutagenicity has been performed in Drosophila (Beland, 1999:
Zordan et al., 1994). Of these two studies, CH was positive in the somatic mutation wing spot
test (Zordan etal., 1994) and equivocal in the induction of sex-linked lethal mutation when in
feed but negative when exposed via injection (Beland, 1999).
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4.2.4.3. Mammalian Systems
4.2.4.3.1. Gene mutations
Harrington-Brock et al. (1998) noted that CH-induced concentration related cytotoxicity
in TK± mouse lymphoma cell lines without S9 activation. A nonstatistical increase in mutant
frequency was observed in cells treated with CH. The mutants were primarily small colony TK
mutants, indicating that most CH-induced mutants resulted from chromosomal mutations rather
than point mutations. It should be noted that in most concentrations tested (350-1,600 ug/mL),
cytotoxicity was observed. Percentage cell survival ranged from 96 to 4%.
4.2.4.3.2. Micronucleus
Micronuclei induction following exposure to CH is positive in most test systems in both
in vitro and in vivo assays, although some negative tests also exist (Harrington-Brock et al.,
1998: Allen etal.. 1994: Marrazzini etal.. 1994) (Ikbal et al.. 2004: Beland, 1999: Nesslany and
Marzin, 1999: Graweetal., 1997: Nutlevetal., 1996: Parry etal.. 1996: Gilleretal.. 1995:
Leopard! et al.. 1993: Lynch and Parry. 1993: Seelbach et al.. 1993: Bonatti etal.. 1992: Russo
andLevis, 1992b, a; Russo etal., 1992: Van Hummel en and Kirsch-Volders, 1992: Leuschner
and Leuschner, 1991: Degrassi and Tanzarella, 1988). Some studies have attempted to make
inferences regarding aneuploidy induction or clastogenicity as an effect of CH. Aneuploidy
results from defects in chromosome segregation during mitosis and is a common cytogenetic
feature of cancer cells (see Section E.3.1.5).
Ciller et al. (1995) studied CH genotoxicity in three short-term tests. CH caused a
significant increase in the frequency of micrenucleated erythrocytes following in vivo exposure
of the amphibian, Pleurodeles waltl, newt larvae.
CH induced aneuploidy in vitro in multiple Chinese hamster cell lines (Natarajan etal.,
1993: Warretal., 1993: Furnus et al.. 1990) and human lymphocytes (Sbrana et al., 1993:
Vagnarelli etal., 1990) but not in mouse lymphoma cells (Harrington-Brock et al., (1998). In
vivo studies performed in various mouse strains led to increased aneuploidy in spermatocytes
(Miller and Adi er, 1992: Liang and Pacchierotti, 1988: Russo etal., 1984), but not oocytes
(Mailhes et al., (1993)) or bone marrow cells (Leopardi et al., 1993: Xu and Adler, 1990).
The potential of CH to induce aneuploidy in mammalian germ cells has been of particular
interest since Russo et al. (1984) first demonstrated that CH treatment of male mice results in
significant increase in frequencies of hyperploidy in metaphase II cells. This hyperploidy was
thought to have arisen from chromosomal nondisjunction in premeiotic/meiotic cell division and
may be a consequence of CH interfering with spindle formation (reviewed by Russo et al.
(1984)1 and Liang and Brinkley (1985)1). CH also causes meiotic delay, which may be
associated with aneuploidy (Miller and Adler, 1992). CH has been shown to induce micronuclei
but not structural chromosomal aberrations in mouse bone-marrow cells. Micronuclei induced
by nonclastogenic agents are generally believed to represent intact chromosomes that failed to
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segregate into either daughter-cell nucleus at cell division (Russo et al., 1992; Xu and Adler,
1990). Furthermore, CH-induced micronuclei in mouse bone-marrow cells (Russo et al., 1992)
and in cultured mammalian cells (Bonatti etal., 1992; Degrassi and Tanzarella, 1988) have
shown to be predominantly kinetochore-positive in composition upon analysis with
immunofluorescent methods. The presence of a kinetochore in a micronucleus is considered
evidence that the micronucleus contains a whole chromosome lost at cell division (Eastmond and
Tucker. 1989: Degrassi and Tanzarella. 1988: Hennigetal.. 1988). Therefore, both TCE and CH
appear to increase the frequency of micronuclei.
Allen et al. (1994) treated male C57B1/6J mice were given a single i.p. injection of 0, 41,
83, or 165 mg/kg CH. Spermatids were harvested at 22 hours, and 11, 13.5, and 49 days
following exposure (Allen et al., 1994). Harvested spermatids were processed to identify both
kinetochore-positive micronucleus (aneugen) and kinetochore-negative micronucleus
(clastogen). All CH doses administered 49 days prior to cell harvest were associated with
significantly increased frequencies of kinetochore-negative micronuclei in spermatids, however;
dose dependence was not observed. This study is in contrast with other studies (Bonatti et al.,
1992: Degrassi and Tanzarella, 1988), which demonstrated predominantly kinetochore-positive
micronucleus.
The ability of CH to induce aneuploidy and polyploidy was tested in human lymphocyte
cultures established from blood samples obtained from two healthy nonsmoking donors (Sbrana
et al., 1993). Cells were exposed for 72 and 96 hours at doses between 50 and 250 jig/mL. No
increase in percentage hyperdiploid, tetraploid, or endoreduplicated cells were observed when
cells were exposed to 72 hours at any doses tested. However, at 96 hours of exposure,
significant increase in hyperdiploid was observed at one dose (150 ug/mL) and was not dose
dependent. Significant increase in tetraploid was observed at a dose of 137 mg/mL, but again,
no dose dependence was observed.
Ikbal et al. (2004) assessed the genotoxic effects in cultured peripheral blood
lymphocytes of 18 infants (age range of 31-55 days) before and after administration of a single
dose of CH (50 mg/kg of body weight) for sedation before a hearing test for micronucleus
frequency. A significant increase in micronuclei frequency was observed after administration of
CH.
4.2.4.3.3. Chromosomal aberrations
Several studies have included chromosomal aberration analysis in both in vitro and in
vivo systems exposed to CH and have had positive results in vitro—although not all studies had
statistically significant increases (Harrington-Brock et al., (1998): (Beland, 1999: Furnus et al.,
1990).
Analysis of CH treated mouse lymphoma cell lines for chromosomal aberrations resulted
in a nonsignificant increase in chromosomal aberrations (Harrington-Brock et al., (1998).
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However, it should be noted that the concentrations tested (1,250 and 1,300 jig/mL) were
cytotoxic (with a cell survival of 11 and 7%, respectively). Chinese hamster embryo cells were
also exposed to 0.001, 0.002, and 0.003% CH for 1.5 hours (Furnus et al.. 1990). A
nonstatistically significant increase in frequency of chromosomal aberrations was observed only
0.002 and 0.003% concentrations, with the increase not dose-dependent. In this study, it should
be noted that the cells were only exposed for 1.5 hours to CH and cells were allowed to grow for
48 hours (two cell cycles) to obtain similar mitotic index before analyzing for chromosomal
aberrations. No information on cytotoxicity was provided except that higher doses decreased the
frequency of mitotic cells at the time of fixation.
In vivo chromosome aberration studies have mostly reported negative or null results
(Mailhes et al., 1993; Russo and Levis, 1992b, a; Leuschner and Leuschner, 1991; Xu and Adler,
1990; Liang and Pacchierotti, 1988) with the exception of one study (Russo et al., 1984) in an Fl
cross of mouse strain between C57Bl/Cne x C3H/Cne.
4.2.4.3.4. SCEs
SCEs were assessed by Ikbal et al. (2004) in cultured peripheral blood lymphocytes of
18 infants (age range of 31-55 days) before and after administration of a single dose of CH
(50 mg/kg of body weight) for sedation before a hearing test. The authors report a significant
increase in the mean number of SCEs, from before administration (7.03 ± 0.18 SCEs/cell) and
after administration (7.90 ± 0.19 SCEs/cell), with each of the 18 individuals showing an increase
with treatment. Micronuclei were also significantly increased. SCEs were also assessed by Gu
et al. (1981a) in human lymphocytes exposed in vitro with inconclusive results, although positive
results were observed by Beland (1999) in Chinese hamster ovary cells exposed in vitro with and
without an exogenous metabolic system.
4.2.4.3.5. Cell transformation
CH was positive in the two studies designed to measure cellular transformation (Parry et
al., 1996; Gibson et al., 1995). Both studies exposed Syrian hamster cells (embryo and dermal)
to CH and induced cellular transformation.
4.2.4.4. Summary
CH has been reported to induce micronuclei formation, aneuploidy, and mutations in
multiple in vitro systems and in vivo. In vivo studies have limited results to an increased
micronuclei formation mainly in mouse spermatocytes. CH was positive in some in vitro
genotoxicity assays that detected point mutations, micronuclei induction, chromosomal
aberrations, and/or aneuploidy. The in vivo data exhibited mixed results (Allen et al., 1994)
(Leuschner and Beuscher. 1998: Nutlevetal., 1996: Adler. 1993: Mailhes et al.. 1993: Russo et
al., 1992: Xu and Adler, 1990). Most of the positive studies showed that CH induces
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aneuploidy. Based on the existing array of data, CH has the potential to be genotoxic,
particularly when aneuploidy is considered in the weight of evidence for genotoxic potential.
Some have suggested that CH may act through a mechanism of spindle poisoning and resulting
in numerical changes in the chromosomes, but some data also suggest induction of chromosomal
aberrations. These results are consistent with TCE, albeit there are more limited data on TCE for
these genotoxic endpoints.
4.2.5. DCVCandDCVG
DCVC and DCVG have been studied for their genotoxic potential; however, since there
is a limited number of studies to evaluate them based on each endpoint, particularly in
mammalian systems, the following section has been combined to include all of the available
studies for different endpoints of genotoxicity. Study details can be found in Table 4-18.
DCVC and DCVG, cysteine intermediates of TCE formed by the GST pathway, are
capable of inducing point mutations as evidenced by the fact that they are positive in the Ames
assay. Dekant et al. (1986c) demonstrated mutagenicity of DCVC in S. typhimurium strains
(TA100, TA2638, and TA98) using the Ames assay in the absence of S9. The effects were
decreased with the addition of a beta-lyase inhibitor aminooxyacetic acid, suggesting that
bioactivation by this enzyme plays a role in genotoxicity. Vamvakas et al. (1987) tested
NAcDCVC for mutagenicity following addition of rat kidney cytosol and found genotoxic
activity. Furthermore, Vamvakas (1988b), in another experiment, investigated the mutagenicity
of DCVG and DCVC in S. typhimurium strain TA2638, using kidney subcellular fractions for
metabolic activation and AOAA (a beta-lyase inhibitor) to inhibit genotoxicity. DCVG and
DCVC both exhibited direct-acting mutagenicity, with kidney mitochondria, cytosol, or
microsomes enhancing the effects for both compounds and AOAA diminishing, but not
abolishing the effects. Importantly, addition of liver subcellular fractions did not enhance the
mutagenicity of DCVG, consistent with in situ metabolism playing a significant role in the
genotoxicity of these compounds in the kidney.
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Table 4-18. TCE GSH conjugation metabolites genotoxicity
Test system/endpoint
Doses tested
With
activation
Without
activation
Comments
References
Gene mutations (Ames test)
S. typhimurium, TA100, 2638, 98
S. typhimurium, TA2638
0. 1-0.5 nmol
50-300 nmol
ND
+
+
+
DCVC was mutagenic in all three strains of
S. typhimurium without the addition of
mammalian subcellular fractions.
Increase in number of revertants in DCVC
alone at low doses; further increase in
revertants was observed in the presence of
microsomal fractions. Toxicity as indicated
by decreased revertants per plate were seen at
higher doses.
Dekant et al.,
(1986c)
Vamvakas et al.
(1988b)
Mutation analysis
In vitro — rat kidney epithelial cells, LOH
in Tsc gene
In vitro — rat kidney epithelial cells, VHL
gene (exons 1-3)
10 uM
10 uM
NA
NA
-
"
Only 1/9 transformed cells showed LOH.
No mutations in VHL gene. Note: VHL is not
a target gene in rodent models of chemical-
induced or spontaneous renal carcinogenesis.
Mally et al. (2006)
Mally et al. (2006)
UDS
Porcine kidney tubular epithelial cell line
(LLC-PK1)
Syrian hamster embryo fibroblasts
2.5 uM-5, 10, 15,
24 hrs; 2.5-100 uM
NA
NA
+
+
Dose-dependent in UDS up to 24 hrs tested at
2.5 uM. Also, there was a dose-dependent
increase at lower concentrations. Higher
concentrations were cytotoxic as determined
by LDH release from the cells.
Increase in UDS in treatment groups.
Vamvakas et al.
(1989)
Vamvakas et al.
(1988a)
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Table 4-18. TCE GSH conjugation metabolites genotoxicity (continued)
Test system/endpoint
Doses tested
With
activation
Without
activation
Comments
References
DNA strand breaks
Male rabbit renal tissue (perfused kidneys
and proximal tubules)
Primary kidney cells from both male rats
and human
In vivo — male Sprague-Dawley rats
exposed to TCE or DCVC — comet assay
0-100 mg/kg or
10 uM to 10 mM
1-4 mM; 20 hrs
exposure
TCE: 500-
2,000 ppm,
inhalation, 6 hrs/d,
5d
DCVC: 1 or
10 mg/kg, single oral
dose for 16 hrs
ND
NA
+ (DCVC)
- (TCE)
+
+
NA
Dose-dependent increase in strand breaks in
both i.v. and i.p. injections (i.v. injections
were done only for 10 and 20 mg/kg) were
observed. Perfusion of rabbit kidney (45-min
exposure) and proximal tubules (30-min
exposure) experiment resulted in a dose-
dependent difference in the amount of SSBs.
Statistically significant increase in all doses
(1, 2, or 4 mM) both in rats and human cells.
No significant increase in tail length in any of
the TCE exposed groups. In 1, 2 hrs
exposure — 1 or 10 mg to DCVC — resulted in
significant increase with no dose-response,
but not at 16 hrs. In 2, ND for 1 mg,
significant increase at 10 mg.
Jaffe et.al. (1985)
Robbiano et al.
(2004)
Clay (2008)
Micronucleus
Syrian hamster embryo fibroblasts
Primary kidney cells from both male rats
and human
Male Sprague-Dawley rats; proximal
tubule cells (in vivo)
1-4 mM; 20 hrs
exposure
4 mM/kg TCE
exposure, single dose
NA
NA
NA
-
+
+
No micronucleus formation.
Statistically significant increase in all doses
(1, 2, and 4 mM) both in rat and human cells.
Statistically significant increase in the
average frequency of micronucleated kidney
cells was observed.
Vamvakas et al.
(1988a)
Robbiano et al.
(2004)
Robbiano et al.
(1998)
Cell transformation
Kidney tubular epithelial cell line (LLC-
PK1)
Rat kidney epithelial cells (in vitro)
lor 5 uM; 7 wks
10 uM; 24 hrs
exposure, 7 wks post
incubation
NA
NA
+
+
Induced morphological cell transformation at
both concentrations tested. Furthermore,
cells maintained both biochemical and
morphological alterations remained stable for
30 passages.
Cell transformation was higher than control;
however, cell survival percentage ranged
from 39 to 64%, indicating cytotoxicity.
Vamvakas et al.
(1996)
Mally et al. (2006)
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Table 4-18. TCE GSH conjugation metabolites genotoxicity (continued)
Test system/endpoint
Doses tested
With
activation
Without
activation
Comments
References
Gene expression
Kidney tubular epithelial cell line (LLC-
PK1)
Kidney tubular epithelial cell line (LLC-
PK1)
1 or 5 uM clones, 30,
60, or 90 min
NA
NA
+
+
Increased c-Fos expression in 1 and 5 uM
exposed clones at three different times tested.
Expression of c-Fos and c-Myc increased in a
time-dependent manner.
Vamvakas et al.
(1996)
Vamvakas et al.
(1993)
LDH = lactate dehydrogenase; ND = not determined; NA = not applicable
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While additional data are not available on DCVG or NAcDCVC, the genotoxicity of
DCVC is further supported by the predominantly positive results in other available in vitro and
in vivo assays. Jaffe et al. (1985) reported DNA strand breaks due to DCVC administered in
vivo, in isolated perfused kidneys, and in isolated proximal tubules of albino male rabbits.
Vamvakas et al. (1989) reported dose-dependent increases in UDS in LLC-PK1 cell clones at
concentrations without evidence of cytotoxicity. In addition, Vamvakas et al. (1996) reported
that 7-week DCVC exposure to LLC-PK1 cell clones at noncytotoxic concentrations induces
morphological and biochemical de-differentiation that persists for at least 30 passages after
removal of the compound. This study also reported increased expression of the proto-oncogene
c-Fos in the cells in this system. In a Syrian hamster embryo fibroblast system, DCVC did not
induce micronuclei, but demonstrated a UDS response (Vamvakas et al., 1988a).
Two more recent studies are discussed in more detail. Mally et al. (2006) isolated
primary rat kidney epithelial cells from Tsc-2Ek/+ (Eker) rats, and reported increased
transformation when exposed to 10 jiM DCVC, similar to that of the genotoxic renal carcinogens
TV-methyl-TV'-nitro-7V-nitrosoguanidine (Horesovsky et al., 1994). The frequency was variable
but consistently higher than background. No loss-of-heterozygosity (LOH) of the Tsc-2 gene
was reported either in these DCVC transformants or in renal tumors (which were not increased in
incidence) from TCE-treated Eker rats, which Mally et al. (2006) suggested support a
nongenotoxic mechanism because a substantial fraction of spontaneous renal tumors in Eker rats
showed LOH at this locus (Yeung et al., 1995; Kubo et al., 1994) and because LOH was
exhibited both in vitro and in vivo with 2,3,4-tris(glutathion-S-yl)-hydroquinone treatment in
Eker rats (Yoon et al., 2001). However, 2,3,4-tris(glutathion-S-yl)-hydroquinone is not
genotoxic in standard mutagenicity assays (Yoon et al., 2001), and Kubo et al. (1994) also
reported that none of renal tumors induced by the genotoxic carcinogen, TV-ethyl-TV-nitrosourea,
showed LOH. Therefore, the lack of LOH at the Tsc-2 locus induced by DCVC in vitro, or TCE
in vivo, reported by Mally et al. (2006) is actually more similar to the response from the
genotoxic carcinogen TV-ethyl-TV-nitrosourea than the nongenotoxic carcinogen 2,3,4-tris-
(glutathion-S-yl)-hydroquinone. Therefore, these data do not substantially contradict the body of
evidence on DCVC genotoxicity.
Finally, Clay (2008) evaluated the genotoxicity of DCVC in vivo using the comet assay
to assess DNA breakage in the proximal tubules of rat kidneys. Rats were exposed orally to a
single dose of DCVC (1 or 10 mg/kg). The animals were sacrificed either 2 or 16 hours after
dosing and samples were prepared for detecting the DNA damage. DCVC (1 and 10 mg/kg)
induced no significant DNA damage in rat kidney proximal tubules at the 16-hour sampling time
or after 1 mg/kg DCVC at the 2-hour sampling time. While Clay et al. (2008) concluded that
these data were insufficient to indicate a positive response in this assay, the study did report a
statistically significant increase in percentage tail DNA 2 hours after treatment with 10 mg/kg
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DCVC, despite the small number of animals at each dose (n = 5) and sampling time. Therefore,
these data do not substantially contradict the body of evidence on DCVC genotoxicity.
Overall, DCVC, and to a lesser degree DCVG and NAcDCVC, have demonstrated
genotoxicity based on consistent results in a number of available studies. While some recent
studies (Clay, 2008; Mally et al., 2006) have reported a lack of positive responses in some in
vivo measures of genotoxicity with DCVC treatment, due to a number of limitations discussed
above, these studies do not substantially contradict the body of evidence on DCVC genotoxicity.
It is known that these metabolites are formed in vivo following TCE exposure, specifically in the
kidney, so they have the potential to contribute to the genotoxicity of TCE, especially in that
tissue. Moreover, DCVC and DCVG genotoxic responses were enhanced when metabolic
activation using kidney subcellular fractions was used (Vamvakas et al., 1988b). Finally, the
lack of similar responses in in vitro genotoxicity assays with TCE, even with metabolic
activation, is likely the result of the small yield (if any) of DCVC under in vitro conditions, since
in vivo, DCVC is likely formed predominantly in situ in the kidney while S9 fractions are
typically derived from the liver. This hypothesis could be tested in experiments in which TCE is
incubated with subcellular fractions from the kidney, or from both the kidney and the liver (for
enhanced GSH conjugation).
4.2.6. TCOH
Limited studies are available on the effect of TCOH on genotoxicity (see Table 4-19).
TCOH is negative in the S. typhimurium assay using the TA100 strain (DeMarini et al., 1994;
Bignami et al., 1980; Waskell, 1978). A study by Beland (1999) using S. typhimurium strain
TA104 did not induce reverse mutations without exogenous metabolic activation; however, it did
increase mutant frequency in the presence of exogenous metabolic activation at a dose
>2,500 jig/plate. TCOH has not been evaluated in the other recommended screening assays.
Therefore, the database is limited for the determination of TCOH genotoxicity.
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Table 4-19. Genotoxicity of TCOH
Test system/endpoint
S. typhimurium TA100, 98, reverse
mutation
S. typhimurium TA100, reverse
mutation
S. typhimurium TA104, reverse
mutation
S. typhimurium TA100, 1535
reverse mutation
SCEs
Doses
(LED or HID)a
7,500 ug/plate
0.5 ug/cm3 vapor
2,500 ug/plate
NA
NA
Results
With
activation
-
-
+
-
NA
Without
activation
-
-
-
-
+
Reference
Waskell (1978)
DeMarini et al. (1994)
Beland (1999)
Bignami et al. (1980)
Gu et al. (1981b)
"LED = lowest effective dose; HID = highest ineffective dose.
+ = positive; - = negative; NA = doses not available, results based on the abstract
4.2.7. Synthesis and Overall Summary
TCE and its metabolites (TCA, DCA, CH, DCVC, DCVG, and TCOH) have been
evaluated to varying degrees for their genotoxic activity in several in vitro systems such as
bacteria, yeast, and mammalian cells, as well as in in vivo systems.
There are several challenges in interpreting the genotoxicity results obtained from TCE
exposure. For example, some studies in bacteria should be interpreted with caution if conducted
using technical-grade TCE since it may contain known bacterial mutagens in trace amounts as
stabilizers (e.g., 1,2-epoxybutane and epichlorohydrin). Because of the volatile nature of TCE,
there could be false negative results if proper precautions are not taken to limit evaporation, such
as the use of a closed, sealed system. The adequacy of the enzyme-mediated activation of TCE
in vitro tests is another consideration. For example, it is not clear if standard S9 fractions can
adequately recapitulate the complex in vivo metabolism of TCE to reactive intermediates, which
in some cases entails multiple sequential steps involving multiple enzyme systems (e.g., CYP,
GST, etc.) and interorgan processing (as is described in more detail in Section 3.3). In addition,
the relative potency of the metabolites in vitro may not necessarily inform their relative
contribution to the overall mechanistic effects of the parent compound, TCE. Furthermore,
although different assays provided data relevant to different types of genotoxic endpoints, not all
effects that are relevant for carcinogenesis are encompassed. The standard battery of prokaryotic
as well as mammalian genotoxicity test protocols typically specify the inclusion of significantly
cytotoxic concentrations of the test compound.
With respect to potency, several TCE studies have been conducted along with numerous
other chlorinated compounds and the results interpreted as a comparison of the group of
compounds tested (relative potency). However, for the purposes of hazard characterization, such
comparisons are not informative—particularly if they are not necessarily correlated with in vivo
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carcinogenic potency. Also, differentiating the effects of TCE with respect to its potency can be
influenced by many factors such as the type of cells, their differing metabolic capacities,
sensitivity of the assay, need for greater concentration to show any effect, interpretation of data
when the effects are marginal, and gradation of severity of effects.
Also, type of samples used, methodology used for the isolation of genetic material, and
duration of exposure can particularly influence the results of several studies. This is particularly
true for human epidemiological studies. For example, while some studies use tissues obtained
directly from the patients, others use formalin fixed tissues sections to isolate DNA for mutation
detection. Type of fixing solution, fixation time, and period of storage of the tissue blocks often
affect the quality of DNA. Formic acid contained in the formalin solution or picric acid
contained in Bouin's solution is known to degrade nucleic acids resulting in either low yield or
poor quality of DNA. In addition, during collection of tumor tissues, contamination of
neighboring normal tissue can easily occur if proper care is not exercised. This could lead to the
=dJution effect' of the results (i.e., because of the presence of some normal tissue) frequency of
mutations detected in the tumor tissue can be lower than expected. Due to some of these
technical difficulties in obtaining proper material (DNA) for the detection of mutation, the results
of these studies should be interpreted cautiously.
The following synthesis, summary, and conclusions focus on the available studies that
may provide some insight into the potential genotoxicity of TCE considering the above
challenges when interpreting the mutagenicity data for TCE.
Overall, evidence from a number of different analyses and a number of different
laboratories using a fairly complete array of endpoints suggests that TCE, following metabolism,
has the potential to be genotoxic. TCE has a limited ability to induce mutation in bacterial
systems, but greater evidence of potential to bind or to induce damage in the structure of DNA or
the chromosome in a number of targets. A series of carefully controlled studies evaluating TCE
itself (without mutagenic stabilizers and without metabolic activation) found it to be incapable of
inducing gene mutations in most standard mutation bacterial assays (Mortelmans et al., 1986;
Shimada et al.. 1985: Crebelli et al.. 1982: Baden etal.. 1979: Bartsch et al.. 1979: Waskell.
1978: Henschler et al., 1977: Simmon etal., 1977). Therefore, it appears that it is unlikely that
TCE is a direct-acting mutagen, though TCE has shown potential to affect DNA and
chromosomal structure. TCE is also positive in some, but not all, fungal and yeast systems
(Koch et al.. 1988: Crebelli et al.. 1985: Rossi et al.. 1983: Callen et al.. 1980). Data from
human epidemiological studies support the possible mutagenic effect of TCE leading to VHL
gene damage and subsequent occurrence of RCC. Association of increased VHL mutation
frequency in TCE-exposed RCC cases has been observed (Brauch et al., 2004: Brauch et al.,
1999: Bruning et al.. 1997b).
TCE can lead to binding to nucleic acids and proteins (Kautiainen et al., 1997: Mazzullo
etal.. 1992: Bergman. 1983: Miller and Guengerich, 1983: DiRenzo et al.. 1982). and such
4-80
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binding appears to be due to conversion to one or more reactive metabolites. For instance,
increased binding was observed in samples bioactivated with mouse and rat microsomal fractions
(Mazzullo et al., 1992; Miller and Guengerich, 1983; DiRenzo et al., 1982; Banerjee and Van
Duuren, 1978). DNA binding is consistent with the ability to induce DNA and chromosomal
perturbations. Several studies report the induction of micronuclei in vitro and in vivo from TCE
exposure (Hu et al.. 2008: Robbiano et al.. 2004: Wang et al.. 2001: Hreliaetal.. 1994:
Kligerman et al., 1994). Reports of SCE induction in some studies are consistent with DNA
effects, but require further study (Kligerman et al., 1994: Nagayaet al., 1989b: Guet al., 1981a:
Gu et al.. 1981b: White et al.. 1979).
TCA, an oxidative metabolite of TCE, exhibits little, if any genotoxic activity in vitro.
TCA did not induce mutations in S. typhimurium strains in the absence of metabolic activation or
in an alternative protocol using a closed system (Kargalioglu et al., 2002: Nelson et al., 2001:
Gilleretal.. 1997: DeMarini et al.. 1994: Rapsonetal.. 1980: Waskell. 1978). but a mutagenic
response was induced in TA100 in the Ames fluctuation test (Giller et al., 1997). However, in
vitro experiments with TCA should be interpreted with caution if steps have not been taken to
neutralize pH changes caused by the compound (Mackay et al., 1995). Measures of DNA-repair
responses in bacterial systems have shown induction of DNA repair reported in S. typhimurium
but not in E. coli. Mutagenicity in mouse lymphoma cells was only induced at cytotoxic
concentrations (Harrington-Brock et al., 1998). TCA was positive in some genotoxicity studies
in vivo mouse, newt, and chick test systems (Giller et al., 1997: Bhunya and Jena, 1996: Birner
et al., 1994: Bhunya and Behera, 1987). DNA unwinding assays have either shown TCA to be
much less potent than DCA (Nelson and Bull, 1988) or negative (Styles etal., 1991: Nelson et
al., 1989). Due to limitations in the genotoxicity database, the possible contribution of TCA to
TCE genotoxicity is unclear.
DCA, a chloroacid metabolite of TCE, has also been studied using different types of
genotoxicity assays. Although limited studies are conducted for different genetic endpoints,
DCA has been demonstrated to be mutagenic in the S. typhimurium assays, in vitro (Kargalioglu
et al., 2002: Plewa et al., 2002: DeMarini etal., 1994) in some strains, mouse lymphoma assay,
(Harrington-Brock et al., 1998) in vivo cytogenetic tests (Leavitt et al., 1997: Fuscoe et al.,
1996), the micronucleus induction test, the Big Blue mouse system, and other tests (Harrington-
Brock etal., 1998: Leavitt et al., 1997: Fuscoe etal., 1996: DeMarini et al., 1994: Chang et al.,
1992: Nelson etal., 1989: Nelson and Bull, 1988: Bignami et al., 1980). DCA can cause DNA
strand breaks in mouse and rat liver cells following in vivo exposure in mice and rats (Fuscoe et
al., 1996). Because of uncertainties as to the extent of DCA formed from TCE exposure,
inferences as to the possible contribution from DCA genotoxicity to TCE toxicity are difficult to
make.
CH is mutagenic in the standard battery of screening assays. Effects include positive
results in bacterial mutation tests for point mutations and in the mouse lymphoma assay for
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mutagenicity at the Tk locus (Haworth et al., 1983). In vitro tests showed that CH also induced
micronuclei and aneuploidy in human peripheral blood lymphocytes and Chinese hamster
pulmonary cell lines. Micronuclei were also induced in Chinese hamster embryonic fibroblasts.
Several studies demonstrate that CH induces aneuploidy (loss or gain of whole chromosomes) in
both mitotic and meiotic cells, including yeast (Gualandi, 1987; Sora and Agostini Carbone,
1987; Kafer, 1986; Singh and Sinha, 1979, 1976), cultured mammalian somatic cells (Degrassi
and Tanzarella, 1988), and spermatocytes of mice (Liang and Pacchierotti, 1988; Russo et al.,
1984). CH was negative for sex-linked recessive lethal mutations in Drosophila (Yoon et al.,
1985). It induces SSBs in hepatic DNA of mice and rats (Nelson and Bull, 1988) and mitotic
gene conversion in yeast (Bronzetti et al., 1984). Schatten and Chakrabarti (1998) showed that
CH affects centrosome structure, which results in the inability to reform normal microtubule
formations and causes abnormal fertilization and mitosis of sea urchin embryos. Based on the
existing array of data, CH has the potential to be genotoxic, particularly when aneuploidy is
considered in the weight of evidence for genotoxic potential. CH appears to act through a
mechanism of spindle poisoning, resulting in numerical changes in the chromosomes. These
results are consistent with TCE, albeit there are limited data on TCE for these genotoxic
endpoints.
DCVC, and to a lesser degree DCVG, has demonstrated bacterial mutagenicity based on
consistent results in a number of available studies (Vamvakas et al., 1988b: Vamvakas et al.,
1987; Dekantetal., 1986c). DCVC has demonstrated a strong, direct-acting mutagenicity both
with and without the presence of mammalian activation enzymes. It is known that these
metabolites are formed in vivo following TCE exposure, so they have the potential to contribute
to the genotoxicity of TCE. The lack of similar response in bacterial assays with TCE is likely
the result of the small yield (if any) of DCVC under in vitro conditions, since in vivo, DCVC is
likely formed predominantly in situ in the kidney (S9 fractions are typically derived from the
liver). DCVC and DCVG have not been evaluated extensively in other genotoxicity assays, but
the available in vitro and in vivo data are predominantly positive. For instance, several studies
have reported that DCVC can induce primary DNA damage in mammalian cells in vitro and in
vivo (Clay, 2008; Vamvakas et al., 1989; Jaffe etal., 1985). Long-term exposure to
DCVC-induced de-differentiation of cells (Vamvakas et al., 1996). It has been shown to induce
expression of the protooncogene c-Fos (Vamvakas et al., 1996) and cause cell transformation in
rat kidney cells (Mally et al., 2006). In LLC-PK1 cell clones, DCVC was reported in induce
UDS, but not micronuclei (Vamvakas et al., 1988a). Finally, DCVC-induced transformation in
kidney epithelial cells isolated from Eker rats carrying the heterozygous Tsc-2 mutations (Mally
et al., 2006). Moreover, the lack of LOH at the Tsc-2 locus observed in exposed cells does not
constitute negative evidence of DCVC genotoxicity, as none of renal tumors induced in Eker rats
by the genotoxic carcinogen 7V-ethyl-7V-nitrosourea showed LOH (Kubo etal., 1994).
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In support of the importance of metabolism, there is some concordance between effects
observed from TCE and those from several metabolites. For instance, both TCE and CH have
been shown to induce micronuclei in mammalian systems, but chromosomal aberrations have
been more consistently observed with CH than with TCE. The role of TCA in TCE genotoxicity
is less clear, as there is less concordance between the results from these two compounds.
Finally, several other TCE metabolites show at least some genotoxic activity, with the strongest
data from DC A, DCVG, and DCVC. While quantitatively smaller in terms of flux as compared
to TCA and TCOH (for which there is almost no genotoxicity data), these metabolites may still
be lexicologically important.
Thus, uncertainties with regard to the characterization of TCE genotoxicity remain,
particularly because not all TCE metabolites have been sufficiently tested in the standard
genotoxicity screening battery to derive a comprehensive conclusion. However, the metabolites
that have been tested, particularly DCVC, have predominantly resulted in positive data, although
to a lesser extent in DCVG and NAcDCVC, This supports the conclusion that these compounds
are genotoxic, particularly in the kidney, where in situ metabolism produces and/or bioactivates
these TCE metabolites.
4.3. CENTRAL NERVOUS SYSTEM (CNS) TOXICITY
TCE exposure results in CNS effects in both humans and animals that can result from
acute, subchronic, or chronic exposure. There are studies indicating that TCE exposure results in
CNS tumors and this discussion can be found in Section 4.9. The studies discussed in this
section focus on the most critical neurological effects that were extracted from the
neurotoxicological literature. Although there are several studies and reports that have evaluated
TCE as an anesthetic, those studies were not included in this section because of the high
exposure levels in comparison to the selected critical neurological effects described below. The
critical neurological effects are nerve conduction changes, sensory effects, cognitive deficits,
changes in psychomotor function, and changes in mood and sleep behaviors. The selection
criteria that were used to determine study importance included study design and validity,
pervasiveness of neurological effect, and for animal studies, the relevance of these reported
outcomes in humans. More detailed information on human and animal neurological studies with
TCE can be found in Appendix D.
4.3.1. Alterations in Nerve Conduction
4.3.1.1. Trigeminal Nerve Function: Human Studies
A number of human studies have been conducted that examined the effects of
occupational or drinking water exposures to TCE on trigeminal nerve function (see Table 4-20).
Many studies reported that humans exposed to TCE present trigeminal nerve function
abnormalities as measured by blink reflex and masseter reflex test measurements (Kilburn,
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2002a. b: Kilburn and Warshaw. 1993a: Feldman et al.. 1992: Feldman et al.. 1988). The blink
and masseter reflexes are mediated primarily by the trigeminal nerve and changes in
measurement suggest impairment in nerve conduction. Other studies measured the trigeminal
somatosensory evoked potential (TSEP) following stimulation of the trigeminal nerve and
reported statistically significantly delayed response on evoked potentials among exposed subjects
compared to nonexposed individuals (Mhiri et al., 2004; Barret et al., 1987; Barret et al., 1984;
Barret etal., 1982). Two studies that also measured trigeminal nerve function did not find any
effect (Rasmussen et al., 1993a: El Ghawabi et al., 1973), but the methods were not provided in
either study (Rasmussen et al., 1993a: El Ghawabi et al., 1973) or an appropriate control group
was not included (Rasmussen et al., 1993a). These studies and results are described below and
summarized in detail in Table 4-20.
Table 4-20. Summary of human trigeminal nerve and nerve conduction
velocity studies
Reference"
Subjects
Exposure
Effect
Barret et al.
(1982)
11 workers with chronic
TCE exposure.
Controls: 20 unexposed
subjects.
Presence of TCE and TCA found
through urinalysis. Atmospheric
TCE concentrations and duration
of exposure not reported in paper.
Following stimulation of the
trigeminal nerve, significantly higher
voltage stimuli was required to obtain
a normal response and there was a
significant increase in latency for
response and decreased response
amplitude.
Barret et al.
(19841
188 factory workers.
No unexposed controls;
lowest exposure group
used as comparison.
>150 ppm; n = 54 < 150 ppm;
n=134.
7 hrs/d for 7 yrs.
Trigeminal nerve and optic nerve
impairment, asthenia and dizziness
were significantly increased with
exposure.
Barret et al.
1987)
104 degreaser machine
operators.
Controls: 52 unexposed
subjects
Mean age 41.6 yrs.
Mean duration, 8.2 yrs, average
daily exposure 7 hrs/d.
Average TCOH range = 162-
245 mg/g creatinine.
Average TCA range = 93-
131 mg/g creatinine.
Evoked trigeminal responses were
measured following stimulation of the
nerve and revealed increased latency
to respond, amplitude or both and
correlated with length of exposure
(p < 0.01) and with age (p < 0.05), but
not concentration.
El-Ghawabi
etal. (1973)
30 money printing shop
workers.
Controls: 20 nonexposed
males.
10 workers exposed to
inks not containing TCE.
Mean TCE air concentrations
ranged from 41 to 163 ppm.
Exposure durations:
<1 yr: n = 3
1 yr: n = 1
2 yrs: n = 2
3 yrs: n= 11
4 yrs: n = 4
>5 yrs: n = 9
No effect on trigeminal nerve function
was noted.
Feldman et al.
(1988)
21 Woburn,
Massachusetts residents.
27 controls.
TCE maximum reported
concentration in well water was
267 ppb; other solvents also
present.
Exposure duration 1-12 yrs.
Measurement of the blink reflex as
mediated by the trigeminal nerve
resulted in significant increases in the
latency of reflex components
(p< 0.001).
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Table 4-20. Summary of human trigeminal nerve and nerve conduction
velocity studies (continued)
Reference"
Subjects
Exposure
Effect
Feldman et al.
(1992)
18 workers.
30 controls.
TCE exposure categories of
—extensie," "occasional," and
—chemcal other than TCE."
—Extensie" = chronically
exposed (>1 yr) to TCE for
5 d/wk and >50% workday.
—Occpational" = chronically
exposed to TCE for 1-3 d/wk and
>50% workday.
The blink reflex as mediated by the
trigeminal was measured. The
-extensie" group revealed latencies
>3 SDs above the nonexposed group
mean on blink reflex components.
Kilburn and
Warshaw
(1993a)
160 residents living in
Southwest Tucson with
TCE, other solvents, and
chromium in
groundwater.
Control: 113 histology
technicians from a
previous study (Kilburn
and Warshaw. 1992b:
Kilburn etal. 1987).
>500 ppb of TCE in well water
before 1981 and 25-100 ppb
afterwards.
Duration ranged from 1 to 25 yrs.
Significant impairments in sway speed
with eyes open and closed and blink
reflex latency (R-l), which suggests
trigeminal nerve impairment.
Kilburn
(2002b.
2002a)
236 residents near a
microchip plant in
Phoenix, Arizona.
Controls: 161 regional
referents from
Wickenburg, Arizona and
67 referents in
northeastern Phoenix.
0.2-10,000 ppb of TCE, O.2-
260,000 ppb TCA, O.2-
6,900 ppb 1,1-DCE, 0.2-
1,600 ppb 1,2-DCE, O.2-
23,000 ppb perchloroethylene,
O.02-330 ppb vinyl chloride in
well water.
Exposure duration 2-37 yrs.
Trigeminal nerve impairment as
measured by the blink reflex test; both
right and left blink reflex latencies
(R-l) were prolonged. Exposed group
mean 14.2 + 2.1 ms (right) or
13.9 + 2.1 ms (left) vs. referent group
mean of 13.4 + 2.1 ms (right) or
13.5 + 2.1 ms (left),/? = 0.0001 (right)
and 0.008 (left).
Mhiri et al.
(2004)
23 phosphate industry
workers.
Controls: 23 unexposed
workers.
Exposure ranged from 50 to
150 ppm, for 6 hrs/d for at least
2 yrs.
Mean urinary TCOH and TCA
levels were 79.3 ± 42 and 32.6 ±
22 mg/g creatinine.
TSEPs were recorded. Increase in
the TSEP latency was observed in
15/23 (65%) workers.
Rasmussen
etal. (1993a)
96 Danish metal
degreasers.
Age range: 19-68.
No unexposed controls;
low exposure group used
as comparison.
Average exposure duration:
7.1 yrs); range of full-time
degreasing: 1 mo to 36 yrs.
Exposure to TCE or to CFC113:
(1) Low exposure: n = 19,
average full-time exposure 0.5 yr.
(2) Medium exposure: n = 36,
average full-time exposure
2.1 yrs.
(3) High exposure: n = 41,
average full-time exposure 11 yrs.
TCA in high exposure group =
7.7 mg/L (maximum =
26.1mg/L).
No statistically significant trend on
trigeminal nerve function, although
some individuals had abnormal
function.
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Table 4-20. Summary of human trigeminal nerve and nerve conduction
velocity studies (continued)
Reference"
Subjects
Exposure
Effect
Ruijten et al.
(1991)
31 male printing
workers. Mean age
44 yrs; mean duration
16 yrs.
Controls: 28 unexposed;
mean age 45 yrs.
Mean cumulative
exposure = 704 ppm x yr
(SD 583, range: 160-2,150 ppm
yr.
Mean, 17 ppm at time of study;
historic TCE levels from 1976 to
1981, mean of 35 ppm.
Mean duration of 16 yrs.
Measurement of trigeminal nerve
function by using the blink reflex
resulted in no abnormal findings.
Increased latency in the masseter
reflex is indicative of trigeminal nerve
impairment.
Triebig et al.
(1982)
24 workers (20 males,
4 females) occupationally
exposed—ages 17-56.
Controls: 144 individuals
to establish normal nerve
conduction parameters.
Matched group:
24 unexposed workers
(20 males, 4 females).
Exposure duration of 1-258 mo
(mean 83 mo). Air exposures
were between 5 and 70 ppm.
No statistically significant difference
in nerve conduction velocities between
the exposed and unexposed groups.
Triebig et al.
(1983)
66 workers
occupationally exposed.
Control: 66 workers not
exposed to solvents.
Subjects were exposed to a
mixture of solvents, including
TCE.
Exposure-response relationship
observed between length of solvent
exposure and statistically significant
reduction in mean sensory ulnar nerve
conduction velocities.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
Integrity of the trigeminal nerve is commonly measured using blink and masseter
reflexes. Five studies (Kilburn, 2002b, a; Kilburn and Warshaw, 1993a: Feldman et al., 1992;
Feldman et al., 1988; Barret etal., 1984) reported a significant increase in the latency to respond
to the stimuli generating the reflex. The latency increases in the blink reflex ranged from 0.4 ms
(Kilburn, 2002b, a) to up to 3.44 ms (Feldman et al., 1988). The population groups in these
studies were exposed by inhalation occupationally (Barret et al., 1984) and through drinking
water environmentally (Kilburn, 2002b, a; Kilburn and Warshaw, 1993a: Feldman et al., 1988).
Feldman et al. (1992) demonstrated persistence in the increased latency of the blink reflex
response. In one subject, exposure to TCE (levels not reported by authors) occurred through a
degreasing accident (high and acute exposure), and increased latency response times persisted
20 years after the accident. Another two subjects, evaluated at 9 months and 1 month following
a high occupational exposure (exposure not reported by authors), also had higher blink reflex
latencies with an average increase of 2.8 ms over the average response time in the control group
used in the study. Although one study (Ruijten et al., 1991) did not find these increases in male
printing workers exposed to TCE, this study did find a statistically significant average increase
of 0.32 ms (p < 0.05) in the latency response time in TCE-exposed workers on the masseter
reflex test, another test commonly used to measure the integrity of the trigeminal nerve.
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Three studies (Mhiri et al.. 2004: Barret etal.. 1987: Barret etal.. 1982). adopting TSEPs
to measure trigeminal nerve function, found significant abnormalities in these evoked potentials.
These studies were conducted on volunteers who were occupationally exposed to TCE through
metal degreasing operations (Barret et al., 1987: Barret et al., 1982) or through cleaning tanks in
the phosphate industry (Mhiri et al., 2004). Barret et al. (1982) reported that in 8/11 workers, an
increased voltage ranging from a 25 to a 45 volt increase was needed to generate a normal TSEP
and two of workers had an increased TSEP latency. Three out of 11 workers had increases in
TSEP amplitudes. In a later study, Barret et al. (1987) also reported abnormal TSEPs (increased
latency and/or increased amplitude) in 38% of the degreasers who were evaluated. The
individuals with abnormal TSEPs were significantly older (45 vs. 40.1 years;/? < 0.05) and were
exposed to TCE longer (9.9 vs. 5.6 years;/? < 0.01). Mhiri et al. (2004) was the only study to
evaluate individual components of the TSEP and noted significant increases in latencies for all
TSEP potentials (Nl, PI, N2, P2, N3;/> < 0.01) and significant decreases in TSEP amplitude
(Pl,p < 0.02; N2,p < 0.05). A significant positive correlation was demonstrated between
exposure duration and increased TSEP latency (p < 0.02).
Two studies reported no statistically significant effect of TCE exposure on trigeminal
nerve function (Rasmussen et al., 1993a; El Ghawabi et al., 1973). El-Ghawabi et al. (1973)
conducted a study on 30 money printing shop workers occupationally exposed to TCE.
Trigeminal nerve involvement was not detected, but the authors did not include the experimental
methods that were used to measure trigeminal nerve involvement and did not provide any data as
to how this assessment was made. Rasmussen et al. (1993a) conducted a historical cohort study
on 99 metal degreasers, 70 exposed to TCE and 29 to the fluorocarbon, CFC113. It was reported
that 1/21 people (5%) in the low exposure group, 2/37 (5%) in the medium exposure group, and
4/41 (10%) in the high-exposure group experienced abnormalities in trigeminal nerve sensory
function, with a linear trend test/?-value of 0.42. The mean urinary TCA concentration was
reported for the high-exposure group only and was 7.7 mg/L (maximum concentration,
26.1 mg/L). The trigeminal nerve function findings of high-exposure group subjects were
compared to that of the low-exposure group since this study did not include an unexposed or
non-TCE exposed group, and decreased the sensitivity of the study.
4.3.1.2. Nerve Conduction Velocity—Human Studies
Two occupational studies assessed ulnar and median nerve function using tests of
conduction latencies (Triebig et al., 1983; Triebig et al., 1982) (see Table 4-20). The ulnar nerve
and median nerves are major nerves located in the arm and forearm. Triebig (1982) studied
24 healthy workers (20 males, 4 females) exposed to TCE occupationally (5-70 ppm) at three
different plants and did not find statistically significant differences in ulnar or median nerve
conduction velocities between exposed and unexposed subjects. This study measured exposure
data, but exposures/responses were not reported by dose levels. The Triebig (1983) study is
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similar in design to the previous study (Triebig et al., 1982), but with a larger number of
subjects. In this study, a dose-response relationship was observed between lengths of exposure
to mixed solvents that included TCE (at unknown concentration). A statistically significant
reduction in nerve conduction velocities was observed for the medium- and long-term exposure
groups for the sensory ulnar nerve as was a statistically significant reduction in mean nerve
conduction velocity observed between exposed and control subjects.
4.3.1.3. Trigeminal Nerve Function: Laboratory Animal Studies
There is little evidence that TCE disrupts trigeminal nerve function in animal studies.
Two studies demonstrated that TCE produces morphological changes in the trigeminal nerve at a
dose of 2,500 mg/kg-day for 10 weeks (Barret et al.. 1992: Barret etal.. 1991). However,
dichloroacetylene, a degradation product formed during the volatilization of TCE, was found to
produce more severe morphological changes in the trigeminal nerve and at a lower dose of
17 mg/kg-day (Barret et al.. 1992: Barret etal.. 1991). Only one study (Albee et al.. 2006)
evaluated the effects of TCE on trigeminal nerve function; a subchronic inhalation exposure did
not result in any significant functional changes. A summary of these studies is provided in
Table 4-21.
Table 4-21. Summary of animal trigeminal nerve studies
Reference"
Barret et al.
(1991)
Barret
etal.
(1992)
Albee et al.
(1997)
Albee et al.
(2006)
Exposure route
Direct gastric
administration
Direct gastric
administration
Inhalation
Inhalation
Species/strain/
sex/number
Rat, Sprague-
Dawley, female,
7/group
Rat, Sprague-
Dawley, female,
7/group
Rat, F344, male, 6
Rat, F344, male
and female,
10/sex/group
Dose level/
exposure
duration
0, 2.5 g/kg, acute
administration.
17 mg/kg
dichloroacetylene .
0, 2.5 g/kg;
one dose/d,
5 d/wk, 10 wks.
17 mg/kg
dichloroacetylene
0 or 300 ppm
dichloro-
acetylene, 2.25 hrs
0, 250, 800, or
2,500 ppm
NOAEL;
LOAELb
LOAEL:
2.5 g/kg
LOAEL:
2.5 g/kg
LOAEL:
300 ppm
dichloro-
acetylene
NOAEL:
2,500 ppm
Effects
Morphometric analysis was
used for analyzing the
trigeminal nerve. Increases in
external and internal fiber
diameter as well as myelin
thickness were observed in the
trigeminal nerve after TCE
treatment.
Trigeminal nerve analyzed
using morphometric analysis.
Increased internode length
and fiber diameter in class A
fibers of the trigeminal nerve
observed with TCE
treatment. Changes in fatty
acid composition also noted.
Dichloroacetylene (TCE
byproduct) exposure impaired
the TSEP up to 4 d
postexposure.
No effect on TSEPs was noted
at any exposure level.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
bNOAEL = no-observed-adverse-effect level, LOAEL = lowest-observed-adverse-effect-level.
4-S
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Barret et al. (1992; 1991) conducted two studies evaluating the effects of both TCE and
dichloroacetylene on trigeminal nerve fiber diameter and internodal length as well as several
markers for fiber myelination. Female Sprague-Dawley rats (n = 7/group) were dosed with
2,500 mg/kg TCE or 17 mg/kg-day dichloroacetylene by gavage for 5 days/week for 10 weeks.
TCE-dosed animals only exhibited changes in the smaller Class A fibers where internode length
increased marginally (<2%) and fiber diameter increased by 6%. Conversely, dichloroacetylene-
treated rats exhibited significant and more robust decreases in internode length and fiber
diameter in both fiber classes A (decreased 8%) and B (decreased 4%).
Albee et al. (2006) evaluated the effects of a subchronic inhalation TCE exposure in F344
rats (10/sex/group). Rats were exposed to 0, 250, 800, and 2,500 ppm TCE for 6 hours/day,
5 days/week for 13 weeks. TCE exposures were adequate to produce permanent auditory
impairment even though TSEPs were unaffected. While TCE appears to be negative in
disrupting the trigeminal nerve, the TCE breakdown product, dichloroacetylene, does impair
trigeminal nerve function. Albee et al. (1997) showed that a single inhalation exposure of rats to
300-ppm dichloroacetylene, for 2.25 hours, disrupted trigeminal nerve evoked potentials for at
least 4 days post exposure.
4.3.1.4. Discussion and Conclusions: TCE-Induced Trigeminal Nerve Impairment
Epidemiologic studies of exposure to TCE found impairment of trigeminal nerve
function, assessed by the blink reflex test or the TSEP, in humans exposed occupationally by
inhalation or environmentally by ingestion (see Table 4-20). Mean inhalational exposures
inferred from biological monitoring or from a range of atmospheric monitoring in occupational
studies was approximately 50-<150 ppm TCE exposure. Residence location is the exposure
surrogate in geographical-based studies of contaminated water supplies with several solvents.
Well water contaminant concentrations of TCE ranged from <0.2 to 10,000 ppb and do not
provide an estimate of TCE concentrations in drinking water to studied individuals.
Two occupational studies, each including >100 subjects, reported statistically significant dose-
response trends based on ambient TCE concentrations, duration of exposure, and/or urinary
concentrations of the TCE metabolite TCA (Barret et al.. 1987: Barret etal.. 1984).
Three geographical-based studies of environmental exposures to TCE via contaminated drinking
water are further suggestive of trigeminal nerve function decrements; however, these studies are
more limited than occupational studies due to questions of subject selection. Both exposed
subjects, who were litigants, and control subjects may not be representative of exposed (Kilburn,
2002b, a; Kilburn and Warshaw, 1993a): referents in Kilburn and Warshaw (1993a), were
histology technicians and subjects in a previous study of formaldehyde and other solvent
exposures and neurobehavioral effects (Kilburn and Warshaw, 1992b: Kilburn et al., 1987).
Results were mixed in a number of smaller studies. Two of these studies reported changes in
4-89
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trigeminal nerve response (Mhiri et al., 2004; Barret et al., 1982), including evidence of a
correlation with duration of exposure and increased latency in one study (Mhiri etal., 2004).
Ruijten et al. (1991) reported no significant change in the blink reflex, but did report an increase
in the latency of the masseter reflex, which also may reflect effects on the trigeminal nerve.
Two other studies reported no observed effect on trigeminal nerve impairment, but the authors
failed to provide assessment of trigeminal nerve function (Rasmussen et al., 1993a: El Ghawabi
etal., 1973) or there was not a control (nonexposed) group included in the study (Rasmussen et
al., 1993a). Therefore, because of limitations in statistical power, the possibility of exposure
misclassification, and possible differences in measurement methods, these studies are not judged
to provide substantial evidence against a causal relationship between TCE exposure and
trigeminal nerve impairment. Overall, the weight of evidence supports a relationship between
TCE exposure and trigeminal nerve dysfunction in humans.
Impairment of trigeminal nerve function is observed in studies of laboratory animal
studies. Although one subchronic animal study demonstrated no significant impairment of
trigeminal nerve function following TCE exposure up to 2,500 ppm (no observed-adverse-effect
level [NOAEL]) (Albee et al., 2006), morphological analysis of the nerve revealed changes in its
structure (Barret et al., 1992; Barret et al., 1991). However, the dose at which an effect was
observed by Barret et al. (1992; 1991) was high (2,500 mg/kg-day—lowest-observed-adverse-
effect level [LOAEL]) compared to any reasonable occupational or environmental setting,
although no lower doses were used. The acute or subchronic duration of these studies, as
compared to the much longer exposure duration in many of the human studies, may also
contribute to the apparent disparity between the epidemiologic and (limited) laboratory animal
data.
The subchronic study of Barret et al. (1992) and the acute exposure study of Albee et al.
(Albee et al., 1997) also demonstrated that dichloroacetylene, a (ex vivo) TCE degradation
product, also induces trigeminal nerve impairment, at much lower doses than TCE. It is possible
that under some conditions, co-exposure to dichloroacetylene from TCE degradation may
contribute to the changes observed to be associated with TCE exposure in human studies, and
this issue is discussed further below in Section 4.3.10.
Overall evidence from numerous epidemiologic studies supports a conclusion that TCE
exposure induces trigeminal nerve impairment in humans. Laboratory animal studies provide
limited additional support, and do not provide strong contradictory evidence. Persistence of
these effects after cessation of exposure cannot be determined since exposure was ongoing in the
available human and laboratory animal studies.
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4.3.2. Auditory Effects
4.3.2.1. Auditory Function: Human Studies
The TCE Subregistry from the National Exposure Registry developed by the ATSDR was
the subject of three studies (ATSDR. 2002: Burg and Gist 1999: Burgetal.. 1995). A
fourth study (Rasmussen et al., 1993a) of degreasing workers exposed to either TCE or CFC113
also indirectly evaluated auditory function. These studies are discussed below and presented in
detail in Table 4-22.
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Table 4-22. Summary of human auditory function studies
Reference
Subjects
Exposure
Effect
116 children, under
10 yrs of age, residing
near six Superfund sites.
Further study of children
in Burg etal. (1999:
1995).
Control: 182 children.
TCE and other solvents in
groundwater supplies.
Exposures were modeled
using tap water TCE
concentrations and GIS for
spatial interpolation, and
LaGrange for temporal
interpolation to estimate
exposures from gestation to
1990 across the area of
subject residences.
Control = 0 ppb; low
exposure group = 0-
<23 ppb-yr; and high
exposure group = >23 ppb-
yr.
Auditory screening revealed increased
incidence of abnormal middle ear
function in exposed groups as indicated
from acoustic reflex test. Adjusted ORs
for right ear ipsilateral acoustic reflects
control, OR: 1.0, low exposure group,
OR: 5.1, p < 0.05; high exposure group,
OR: 7.2, p < 0.05. ORs adjusted for
age, sex, medical history, and other
chemical contaminants. No significant
decrements reported in the pure tone and
typanometry screening.
Burg et al. (1995)
FromanNHISTCE
subregistry of 4,281
(4,041 living and
240 deceased) residents.
Environmentally exposed
to TCE and other solvents
via well water in Indiana,
Illinois, and Michigan.
Increase in serf-reported hearing
impairments for children <9 yrs.
Burg et al.
3,915 white registrants.
Mean age 34 yrs (SD =
19.9 yrs).
Cumulative TCE exposure
subgroups: <50 ppb,
n = 2,867; 50-500 ppb,
n = 870; 500-5,000 ppb,
n = 190; >5,000 ppb,
n=35.
Exposure duration
subgroups: <2, 2-5, 5-10,
and > 10 yrs.
A statistically significant
association (adjusted for age and sex)
between duration of exposure and serf-
reported hearing impairment
was found.
Rasmussen et al.
1993c)
96 Danish metal
degreasers. Age range:
19-68 yrs.
No unexposed controls;
low exposed group is
referent.
Average exposure duration:
7.1 yrs range of full-time
degreasing: 1 mo to 36 yrs.
Exposure to TCE or and
CFC113.
(1) Low exposure: n = 19,
average full-time exposure
0.5 yr.
(2) Medium exposure:
n = 36, average full-time
exposure 2.1 yrs.
(3) High exposure: n = 41,
average full-time exposure
11 yrs. MeanU-TCAin
high exposure group =
7.7 mg/L (maximum =
26.1mg/L).
Auditory impairments noted through
several neurological tests.
Significant relationship of exposure was
found with acoustic-motor function
(p < 0.001), Paced Auditory Serial
Addition Test (p < 0.001), and Rey
Auditory Verbal Learning Test
(p< 0.001).
NHIS = National Health Interview Survey
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Burg et al. (1999: 1995) reviewed the effects of TCE on 4,281 individuals (TCE
Subregistry) residentially exposed to this solvent for more than 30 consecutive days. Face-to-
face interviews were conducted with the TCE subregistry population and self-reported hearing
loss was evaluated based on personal assessment through the interview (no clinical evaluation
was conducted). TCE registrants who were <9 years old had a statistically significant increase in
hearing impairment as reported by the subjects. The RR in this age group for hearing
impairments was 2.13 (95% CI: 1.12-4.06), which decreased to 1.12 (95% CI: 0.52-2.24) for
the 10-17-year-old age group and 0.32 (95% CI: 0.10-1.02) for all older age groups. A
statistically significant association (when adjusted for age and sex) was found between duration
of exposure (in these studies, this was length of residency) and reported hearing impairment.
The ORs were 2.32 (95% CI: 1.18-4.56) for subjects exposed to TCE >2-<5 years, 1.17
(95% CI: 0.55-2.49) for exposure >5-<10 years, and 2.46 (95% CI: 1.30-5.02) for exposure
durations >10 years.
AT SDR (2002) conducted a follow-up study to the TCE subregistry findings (Burg and
Gist 1999: Burgetal., 1995) and focused on the subregistry children located in Elkhart, Indiana,
Rockford, Illinois, and Battle Creek, Michigan using clinical tests for oral motor, speech, and
hearing function. Exposures were modeled using tap water TCE concentrations and geographic
information system (GIS) for spatial interpolation, and LaGrange for temporal interpolation to
estimate exposures from gestation to 1990 across the area of subject residences. Modeled data
were used to estimate lifetime exposures (ppb-years) to TCE in residential wells. The median
TCE exposure for the children was estimated from drinking water as 23 ppb/year of exposure
(ranging from 0 to 702 ppb/year). Approximately 20% (17-21%, depending on ipsilateral or
contralateral test reflex) of the children in the TCE subregistry and 5-7% in the control group
exhibited an abnormal acoustic reflex (involuntary muscle contraction that measures movement
of the stapedius muscle in the middle ear following a noise stimulus), which was statistically
significant (p = 0.003). Abnormalities in this reflex could be an early indicator of more serious
hearing impairments. No significant decrements were reported in the pure tone and typanometry
screening.
Rasmussen et al. (1993c) used a psychometric test to measure potential auditory effects
of TCE exposure in an occupational study. Results from 96 workers exposed to TCE and other
solvents were presented in this study. Details of the exposure groups and exposure levels are
provided in Table 4-22. The acoustic motor function test was used for evaluation of auditory
function. Significant decrements (p < 0.05) in acoustic motor function performance scores
(average decrement of 2.5 points on a 10-point scale) were reported for TCE exposure.
4.3.2.2. Auditory Function: Laboratory Animal Studies
The ability of TCE to permanently disrupt auditory function and produce abnormalities in
inner ear histopathology has been demonstrated in several studies using a variety of test methods.
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Two different laboratories have identified NOAELs following inhalation exposure for auditory
function of 1,600 ppm for 12 hours/day for 13 weeks in Long-Evans rats (n = 6-10) (Rebert et
al., 1991) and 1,500 ppm for 18 hours/day, 5 days/week for 3 weeks in Wistar-derived rats
(n = 12) (Jaspers et al., 1993). The LOAELs identified in these and similar studies are 2,500-
4,000 ppm TCE for periods of exposure ranging from 4 hours/day for 5 days to 12 hours/day for
13 weeks (e.g., Albee et al.. 2006: Boves et al.. 2000: Muijseretal.. 2000: Fechter et al.. 1998:
Crofton and Zhao. 1997: Rebert et al.. 1995: Crofton et al.. 1994: Rebert et al.. 1993). Rebert
et al. (1993) estimated acute blood TCE levels associated with permanent hearing impairment at
125 ug/mL by methods that probably underestimated blood TCE values (rats were anaesthetized
using 60% carbon dioxide [CCh]). A summary of these studies is presented in Table 4-23.
Table 4-23. Summary of animal auditory function studies
Reference"
Rebert et al.
(1991)
Rebert et al.
(1993)
Rebert et al.
(1995)
Crofton et al.
(1994)
Exposure
route
Inhalation
Inhalation
Inhalation
Inhalation
Species/strain/
sex/number
Rat, Long-Evans,
male, 10/group
Rat, F344, male, 4-
5/group
Rat, Long-Evans,
male, 9/group
Rat, Long-Evans,
male, 9/group
Rat, Long-Evans,
male, 7-8/group
Dose level/
exposure
duration
Long-Evans: 0,
1,600, and
3,200 ppm;
12 hrs/d, 12 wks
F344: 0, 2,000,
and 3,200 ppm;
12 hrs/d, 3 wks
0, 2,500, 3,000,
and 3,500 ppm;
8 hrs/d, 5 d
0 and 2,800 ppm;
8 hrs/d, 5 d
0 and 3,500 ppm
TCE; 8 hrs/d, 5 d
NOAEL;
LOAEL3
Long-
Evans:
NOAEL:
1,600 ppm;
LOAEL:
3,200 ppm
F344:
LOAEL:
2,000 ppm
NOAEL:
2,500 ppm;
LOAEL:
3,000 ppm.
LOAEL:
2,800 ppm
LOAEL:
3,500 ppm
Effects
BAERs were measured.
Significant decreases in BAER
amplitude and an increase in
latency of appearance of the
initial peak (PI).
BAERs were measured 1-2 wks
postexposure to assess auditory
function. Significant decreases in
BAERs were noted with TCE
exposure.
BAER measured 2-14 ds
postexposure at a 16 kHz tone.
Hearing loss ranged from 55 to
85 dB.
BAER measured and auditory
thresholds determined 5-8 wks
postexposure. Selective
impairment of auditory function
for mid-frequency tones (8 and
16 kHz).
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Table 4-23. Summary of animal auditory function studies (continued)
Reference"
Crofton and
Zhou
(1997);
Boyes et al.
(2000)
Fechter et al.
(1998)
Jaspers et al.
(1993)
Muijser et al.
(2000)
Albee et al.
(2006)
Exposure
route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Species/strain/
sex/number
Rat, Long-Evans,
male, 8-10/group
Rat, Long-Evans,
male, 8-10/group
Rat, Long-Evans,
male, 8-10/group
Rat, Long-Evans,
male, 9-12/group
Rat, Long-Evans,
male, 12/group
Rat, Wistar
derived WAG-
Rii/MBL, male,
12/group
Rat, Wistar
derived WAG-
Rii/MBL, male, 8
Rat, F344, male
and female,
10/sex/group
Dose level/
exposure
duration
0, 800, 1,600,
2,400, and
3,200 ppm;
6 hrs/d, 5 d/wk,
13wks
0, 1,600, 2,400,
and 3,200 ppm;
6 hrs/d, 5 d
0, 800, 1,600,
2,400, and
3,200 ppm;
6 hrs/d, 5 d/wk,
4 wks
0, 4,000, 6,000,
and 8,000 ppm;
6 hrs
0 and 4,000 ppm;
6 hrs/d, 5 d
0, 1,500, and
3,000 ppm;
18 hrs/d, 5 d/wk,
3 wks
Oand
3,000 ppm;
18 hrs/d, 5 d/wk,
3 wks
0, 250, 800, and
2,500 ppm;
6 hrs/d, 5 d/wk,
13 wks
NOAEL;
LOAEL
NOAEL:
1,600 ppm;
LOAEL:
2,400 ppm
NOAEL:
2,400 ppm;
LOAEL:
3,200 ppm
NOAEL:
2,400 ppm;
LOAEL:
3,200 ppm
NOAEL:
6,000 ppm;
LOAEL:
8,000 ppm
LOAEL:
4,000 ppm
NOAEL:
1,500 ppm
LOAEL:
3,000 ppm
NOAEL:
800 ppm;
LOAEL:
2,500 ppm
Effects
Auditory thresholds as
measured by BAERs for the
16 kHz tone increased with
TCE exposure. Measured 3-
5 wks post exposure.
Cochlear function measured 5-
7 wks after exposure. Loss of
spiral ganglion cells noted.
Auditory function was
significantly decreased 3 wks
postexposure, as measured by
compound action potentials and
reflex modification.
Auditory function assessed
repeatedly 1-5 wks postexposure
for 5, 20, and 35 kHz tones; no
effect at 5 or 35 kHz; decreased
auditory sensitivity at 20 kHz,
3, 000 ppm.
Auditory sensitivity decreased
with TCE exposure at 4, 8, 16, and
20 kHz tones. White noise
potentiated the decrease in
auditory sensitivity.
Mild frequency specific hearing
deficits; focal loss of cochlear
hair cells.
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Table 4-23. Summary of animal auditory function studies (continued)
Reference"
Yamamura
et al. (1983)
Exposure
route
Inhalation
Species/strain/
sex/number
Guinea Pig, albino
Hartley, male, 7-
10/group
Dose level/
exposure
duration
0, 6,000, 12,000,
and 17,000 ppm;
4 hrs/d, 5 d
NOAEL;
LOAEL
NOAEL:
17,000 ppm
Effects
No change in auditory sensitivity
at any exposure level as measured
by cochlear action potentials and
microphonics. Study was
conducted in guinea pig and
species is less sensitive to
auditory toxicity than rats.
Studies were also not conducted
in a sound-isolation chamber and
effects may be impacted by
background noise.
Bolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
BAER = brainstem auditory-evoked potential
Reflex modification was used in several studies to evaluate the auditory function in TCE-
exposed animals (Boyes et al., 2000; Muijser et al., 2000; Fechter et al., 1998; Crofton and Zhao,
1997: Crofton et al.. 1994: Crofton and Zhao. 1993: Jaspers et al.. 1993: Yamamura et al.. 1983).
These studies collectively demonstrate significant decreases in auditory function at midfrequency
tones (8-20 kHz tones) for TCE exposures >1,500 ppm after acute, short-term, and chronic
durations. Only one study (Yamamura et al., 1983) did not demonstrate impairment in auditory
function from TCE exposures as high as 17,000 ppm for 4 hours/day over 5 days. This was the
only study to evaluate auditory function in guinea pigs, whereas the other studies used various
strains of rats. Despite the negative finding in Yamamura et al. (1983), auditory testing was not
performed in an audiometric sound attenuating chamber and extraneous noise could have
influenced the outcome. It is also important to note that the guinea pig has been reported to be
far less sensitive than the rat to the effects of ototoxic aromatic hydrocarbons such as toluene.
Crofton and Zhao (1997) also presented a benchmark dose (BMD) for which the
calculated dose of TCE would yield a 15 dB loss in auditory threshold. This benchmark
response (BMR) was selected because a 15 dB threshold shift represents a significant loss in
threshold sensitivity for humans. The benchmark concentrations for a 15 dB threshold shift are
5,223 ppm for 1 day, 2,108 ppm for 5 days, 1,418 ppm for 20 days and 1,707 ppm for 65 days of
exposure. While more sensitive test methods might be used and other definitions of a benchmark
effect chosen with a strong rationale, these data provide useful guidance for exposure
concentrations that yield hearing loss in rats.
Brainstem auditory-evoked responses (BAERs) were also measured in several studies
(Albee et al., 2006: Rebertetal., 1995: Rebertetal., 1993: Rebertetal., 1991) following at
exposures of 3-13 weeks. Rebert et al. (1991) measured BAERs in male Long-Evans rats
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(n = 10) and F344 rats (n = 4-5) following stimulation with 4, 8, and 16 kHz sounds. The Long-
Evans rats were exposed to 0, 1,600, or 3,200 ppm TCE, 12 hours/day for 12 weeks and the F344
rats were exposed to 0, 2,000, or 3,200 ppm TCE, 12 hours/day for 3 weeks. BAER amplitudes
were significantly decreased at all frequencies for F344 rats exposed to 2,000 and 3,000 ppm
TCE and for Long-Evans rats exposed to 3,200 ppm TCE. These data identify a LOAEL at
2,000 ppm for the F344 rats and a NOAEL at 1,600 ppm for the Long-Evans rats. In subsequent
studies, Rebert et al. (1995; 1993) again demonstrated that TCE significantly decreases BAER
amplitudes and also significantly increases the latency of appearance. Similar results were
obtained by Albee et al. (2006) for male and female F344 rats exposed to TCE for 13 weeks.
The NOAEL for this study was 800 ppm based on ototoxicity at 2,500 ppm.
Notable physiological changes were also reported in a few auditory studies. Histological
data from cochleas in Long-Evans rats exposed to 4,000 ppm TCE indicated that there was a loss
in spiral ganglion cells (Fechter et al., 1998). Similarly, there was an observed loss in hair cells
in the upper basal turn of the cochlea in F344 rats exposed to 2,500 ppm TCE (Albee et al.,
2006).
4.3.2.3. Summary and Conclusion of Auditory Effects
Human and animal studies indicated that TCE produces decrements in auditory function.
In the human epidemiological studies (ATSDR, 2002: Burg and Gist 1999: Burgetal., 1995:
Rasmussen et al., 1993d), it is suggested that auditory impairments result from both an inhalation
and oral TCE exposure. A LOAEL of approximately 23 ppb-years TCE (extrapolated from
<23 ppb-years group in ATSDR (2002) from oral intake is noted for auditory effects in children.
The only occupational study where auditory effects were seen reported mean urinary
trichloroacetic acid (U-TCA) concentration, a nonspecific metabolite of TCE, of 7.7 mg/L for the
high cumulative exposure group only (Rasmussen et al., 1993d). A NOAEL or a LOAEL for
auditory changes resulting from inhalational exposure to TCE cannot be interpolated from
average U-TCA concentration of subjects in the high-exposure group because of a lack of
detailed information on long-term exposure levels and duration (Rasmussen et al., 1993d).
Two studies (Burg and Gist, 1999: Burg etal., 1995) evaluated self-reported hearing effects in
people included in the TCE subregistry comprised of people residing near Superfund sites in
Indiana, Illinois, and Michigan. In Burg et al. (1995), interviews were conducted with the TCE-
exposed population and it was found that children aged <9 years old had statistically significant
hearing impairments in comparison to nonexposed children. This significant increase in hearing
impairment was not observed in any other age group that was included in this epidemiological
analysis. This lack of effect in other age groups may suggest association with another exposure
other than drinking water; however, it may also suggest that children may be more susceptible
than adults. In a follow-up analysis, Burg et al. (1999) adjusted the statistical analysis of the
original data (Burg et al., 1995) for age and sex. When these adjustments were made, a
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statistically significant association was reported self-reported for auditory impairment and
duration of residence. These epidemiological studies provided only limited information given
their use of an indirect exposure metric of residence location, no auditory testing of this studied
population, and self-reporting of effects. ATSDR (2002) further tested the findings in the Burg
studies (Burg and Gist 1999; Burg etal., 1995) by contacting the children who were classified as
having hearing impairments in the earlier study and conducting several follow-up auditory tests.
Significant abnormalities were reported for the children in the acoustic reflex test, which
suggested effects to the lower brainstem auditory pathway with the large effect measure, the OR,
was reported for the high-cumulative-exposure group. Strength of analyses was its adjustment
for potential confounding effects of age, sex, medical history, and other chemical contaminants
in drinking water supplies. The ATSDR findings were important in that the results supported
Burg et al. (1999; 1995). Rasmussen et al. (1993c) also evaluated auditory function in metal
workers with inhalation exposure to either TCE or CFC113. Results from tasks, including an
auditory element, suggested that these workers may have some auditory impairment. However,
the tasks did not directly measure auditory function.
Animals studies strongly indicated that TCE produces deficits in hearing and provides
biological context to the epidemiological study observations. Although there is a strong
association between TCE and ototoxicity in the animal studies, most of the effects began to occur
at higher inhalation exposures. NOAELs for ototoxicity ranged from 800 to 1,600 ppm for
exposure durations of at least 12 weeks (Albee et al., 2006; Boyes et al., 2000; Crofton and Zhao,
1997; Rebertet al., 1991). Inhalation exposure to TCE was the route of administration in all of
the animal studies. These studies either used reflex modification audiometry (Muijser et al.,
2000; Crofton and Zhao, 1997: Crofton et al., 1994: Jaspers et al., 1993) procedures or measured
BAERs (Rebertetal., 1995: Rebertetal., 1993: Rebertetal., 1991) to evaluate hearing in rats.
Collectively, the animal database demonstrates that TCE produces ototoxicity at midfrequency
tones (4-24 kHz), and no changes in auditory function were observed at either the low (<4 kHz)
or high (>24 kHz) frequency tones. Additionally, deficits in auditory effects were found to
persist for at least 7 weeks after the cessation of TCE exposure (Boyes et al., 2000: Fechter et al.,
1998: Crofton and Zhao, 1997: Jaspers et al., 1993: Rebertetal., 1991). Decreased amplitude
and latency were noted in the BAERs (Rebertetal., 1995: Rebertetal., 1993: Rebert et al.,
1991), suggesting that TCE exposure affects central auditory processes. Decrements in auditory
function following reflex modification audiometry (Muijser et al., 2000: Crofton and Zhao,
1997: Crofton et al., 1994: Jaspers et al., 1993) combined with changes observed in cochlear
histopathology (Albee et al., 2006: Fechter et al., 1998) suggest that ototoxicity is occurring at
the level of the cochlea and/or brainstem.
Changes in auditory function are noteworthy considering that TCE exposure is also
associated with immunotoxicity and inflammatory-based diseases (discussed in Section 4.6).
Autoimmune sensorineural hearing loss is a rare condition, sometimes seen with systemic
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autoimmune diseases (Bovo et al., 2006; Ruckenstein, 2004). The potential role of
immunotoxicity in the observed auditory impairment seen with TCE is an area that requires
additional research.
4.3.3. Vestibular Function
4.3.3.1. Vestibular Function: Human Studies
The earliest reports of neurological effects resulting from TCE exposures focused on
subjective vestibular system symptoms, such as headaches, dizziness, and nausea. These
symptoms are subjective and self-reported. However, there is little doubt that these effects can
be caused by exposures to TCE, as they have been reported extensively in the literature, resulting
from occupational exposures (Liuetal., 1988; Rasmussen and Sabroe, 1986; Smith, 1970;
Grandjean et al., 1955), environmental exposures (Hirsch et al., 1996), and chamber studies
(Smith, 1970: Stewart et al., 1970).
Kylin et al. (1967) exposed 12 volunteers to 1,000 ppm (5,500 mg/m3) TCE for 2 hours
in a 1.5 x 2 x 2 chamber. Volunteers served as their own controls since 7 of the 12 were
pretested prior to exposure and the remaining 5 were post-tested days after exposure. Subjects
were tested for optokinetic nystagmus, which was recorded by electronystagmography, that is,
—he potential difference produced by eye movements between electrodes placed in lateral angles
between the eyes." Venous blood was also taken from the volunteers to measure blood TCE
levels during the vestibular task. The authors concluded that there was an overall reduction in
the limit (fusion limit") to reach optokinetic nystagmus when individuals were exposed to TCE.
Reduction of the —fusionihiit" persisted for up to 2 hours after the TCE exposure was stopped
and the blood TCE concentration was 0.2 mg/100 mL.
4.3.3.2. Vestibular Function: Laboratory Animal Data
The effect of TCE on vestibular function was evaluated by either: (1) promoting
nystagmus (vestibular system dysfunction) and comparing the level of effort required to achieve
nystagmus in the presence and absence of TCE or (2) using an elevated beam apparatus and
measuring the balance. Overall, it was found that TCE disrupts vestibular function as presented
below and summarized in Table 4-24.
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Table 4-24. Summary of vestibular system studies
Reference
Exposure route
Species/strain/
sex/number
Dose level/
exposure duration
NOAEL;
LOAEL
Effects
Vestibular system studies — humans
Kylin et al.
(1967)
Inhalation
Humans, male
and female, 12
1,000 ppm; 2 hrs
LOAEL:
1,000 ppm
Reduction in potential to
reach nystagmus following
TCE exposure.
Vestibular system studies — animals
Tham et al.
(1972)
Tham et al.
(1984)
Niklasson
et al. (1993)
Umezu
et al. (1997)
i.v.
i.v.
Inhalation
i.p.
Rabbit, strain
unknown, sex
unspecified, 19
Rat, Sprague-
Dawley, female,
11
Rat, strain
unknown, male
and female, 28
Mouse, ICR,
male, 116
1-5 mg/kg/min
80 ug/kg/min
0, 2,700, 4,200,
6,000, or 7,200 ppm;
Ihr
0, 250, 500, or
1,000 mg/kg, single
dose and evaluated
30 mins
po stadministration
LOAEL:
2,700 ppm
NOAEL:
250 mg/kg
LOAEL:
500 mg/kg
Positional nystagmus
developed once blood levels
reached 30 ppm.
Excitatory effects on the
vestibule-oculomotor
reflex. Threshold effect at
blood (TCE) of 120 ppm or
0.9mM/L.
Increased ability to produce
nystagmus.
Decreased equilibrium and
coordination as measured by
the Bridge test (staying time
on an elevated balance
beam).
Niklasson et al. (1993) showed acute impairment of vestibular function in male- and
female-pigmented rats during acute inhalation exposure to TCE (2,700-7,200 ppm) and to
trichloroethane (500-2,000 ppm). Both of these agents were able to promote nystagmus during
optokinetic stimulation in a dose-related manner. While there were no tests performed to assess
persistence of these effects, Tham et al. (1984; 1979) did find complete recovery of vestibular
function in rabbits (n = 19) and female Sprague-Dawley rats (n = 11) within minutes of
terminating a direct arterial infusion with TCE solution.
The finding that TCE can yield transient abnormalities in vestibular function is not
unique. Similar impairments have also been shown for toluene, styrene, and trichloroethane
(Niklasson et al., 1993) and for a broad range of aromatic hydrocarbons (Tham et al., 1984). The
concentration of TCE in blood at which effects were observed for TCE (0.9 mM/L) was quite
close to that observed for most of these other vestibulo-active solvents.
4.3.3.3. Summary and Conclusions for the Vestibular Function Studies
Studies of TCE exposure in both humans and animals reported abnormalities in vestibular
function. Headaches, dizziness, nausea, and motor incoordination, among other subjective
symptoms, are reported in occupational epidemiological studies of TCE exposure (Hirsch et al.,
1996: LiuetaL 1988: Rasmussen and Sabroe. 1986: Smith. 1970: Stewart etal.. 1970:
4-100
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Grandjean et al., 1955). One human exposure study (Kylin et al., 1967) found that vestibular
function was affected following an acute exposure to 1,000-ppm TCE (LOAEL). Individuals
had a decreased threshold to reach nystagmus than when exposed to TCE than to air. Animal
studies also evaluated the threshold to reach nystagmus and reported that TCE decreased the
threshold to produce nystagmus in rats (LOAEL: 2,700 ppm) (Niklasson et al., 1993; Tham et
al.. 1984) and rabbits (Thametal.. 1984).
4.3.4. Visual Effects
4.3.4.1. Visual Effects: Human Studies
Visual impairment in humans has been demonstrated following exposures through
groundwater (Reif etal., 2003; Kilburn, 2002b, a), from occupational exposure through
inhalation (Rasmussen et al., 1993c: Troster and Ruff, 1990), and from a controlled inhalation
exposure study (Vernon and Ferguson, 1969). Visual functions such as color discrimination and
visuospatial learning tasks are impaired in TCE-exposed individuals. Additionally, an acute
exposure can impair visual depth perception. Details of the studies are provided below and
summarized in Table 4-25.
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Table 4-25. Summary of human visual function studies
Reference
Subjects
Exposure
Effect
236 residents near a
microchip plant in
Phoenix, Arizona.
Controls: 67 local
referents from Phoenix,
Arizona and 161 regional
referents from
Wickenburg, Arizona.
TCE, TCA, 1,1-DCE, 1,2-DCE,
perchloroethylene, and vinyl
chloride detected in well water up to
260,000 ppm; TCE concentrations in
well water were 0.2-10,000 ppb.
Exposure duration 2-37 yrs.
Color discrimination errors were
increased among residents compared
to regional referents (p< 0.01). No
adjustment for possible confounding
factors.
Reifetal.
(2003)
143 residents of the
Rocky Mountain Arsenal
community of Denver.
Referent group at lowest
concentration (<5 ppb).
Exposure modeling of TCE
concentrations in groundwater and
distribution system to estimate mean
TCE concentration by census block
of residence. High exposure group
>15 ppb.
Medium exposure group >5-
<15 ppb.
Low exposure referent group
<5 ppb.
Contrast sensitivity test
in performances (C and D) was
marginally statistically significant
(p = 0.06 and 0.07, respectively).
No significant effects reported for
the Benton visual retention test.
Significant decrements (p = 0.02)
were reported in the Benton visual
retention test when stratified with
alcohol consumption.
Rasmussen
etal. (1993c)
96 Danish metal
degreasers. Age range:
19-68; no unexposed
controls; low exposure
group was referent.
Average exposure duration: 7.1 yrs;
range of full-time degreasing: 1 mo
to 36 yrs. Exposure to TCE or
CFC113:
(1) Low exposure: n = 19, average
full-time exposure 0.5 yr.
(2) Medium exposure: n = 36,
average full-time exposure 2.1 yrs.
(3) high exposure: n = 41, average
full-time exposure 11 yrs. TCA in
high exposure group = 7.7 mg/L
(maximum = 26.1 mg/L).
Statistically significant relationship
of exposure was found with the
Visual Gestalts learning and
retention test (cognitive test)
indicating deficits in visual
performance.
Troster and
Ruff (1990)
Two occupationally
TCE-exposed workers.
Controls: two groups of
n = 30 matched controls;
(all age and education
matched).
Exposure concentration unknown.
Exposure duration, 3-8 mo.
Both workers experienced impaired
visuospatial learning.
Vernon and
Ferguson
(1969)
8 male volunteers age
range 21-30; serf
controls.
0, 100, 300, and 1,000 ppm of TCE
for 2 hrs.
Statistically significant effects on
visual depth perception as measured
by the Howard-Dolman test.
NOAEL: 300 ppm; LOAEL:
1,000 ppm. No significant changes
in any of the other visual test
measurements.
Geographical-based studies utilized color discrimination and contrast sensitivity tests to
determine the effect of TCE exposure on vision. In these studies, it was reported that TCE
exposure significantly increased color discrimination errors (Kilburn, 2002b, a) or that decreased
contrast sensitivity tests approached statistical significance after adjustments for several possible
confounders (p = 0.06 or 0.07) (Reif et al., 2003). Exposure in Kilburn (2002b, a) is poorly
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characterized, and for both studies, TCE is one of several contaminants in drinking water
supplies; neither study provided an estimate of an individual's exposure to TCE.
Rasmussen et al. (1993c) evaluated visual function in 96 metal workers, working in
degreasing at various factories and with exposure to TCE or CFC113. Visual function was tested
through the visual gestalts test (visual perception) and a visual recall test. In the visual gestalts
test, the number of total errors significantly increased from the low-exposure group (3.4 errors)
to the high-exposure group (6.5 errors; p = 0.01). No significant changes were observed in the
visual recall task. Troster and Ruff (1990) presented case studies conducted on two
occupationally exposed workers to TCE. Both patients presented with a visual-spatial task and
neither could complete the task within the number of trials allowed, suggesting visual function
deficits as a measure of impaired visuospatial learning.
In a chamber exposure study (Vernon and Ferguson, 1969), eight male volunteers (ages
21-30) were exposed to 0, 100, 300, and 1,000-ppm TCE for 2 hours. Each individual was
exposed to all TCE concentrations and a span of at least 3 days was given between exposures.
When the individuals were exposed to 1,000-ppm TCE (5,500 mg/m3), significant abnormalities
were noted in depth perception as measured by the Howard-Dolman test (p < 0.01). There were
no effects on the flicker fusion frequency test (threshold frequency at which the individual sees a
flicker as a single beam of light) or on the form perception illusion test (volunteers presented
with an illusion diagram).
4.3.4.2. Visual Effects: Laboratory Animal Data
Changes in visual function have been demonstrated in animal studies during acute (Boyes
et al., 2005b; Boyes et al., 2003) and subchronic exposure (Blain et al., 1994; Rebert et al.,
1991). In these studies, the effect of TCE on visual evoked responses to patterns (Boyes et al.,
2005b: Bovesetal., 2003: Rebert etal., 1991) or a flash stimulus (Blain etal., 1994: Rebert et
al., 1991) were evaluated. Overall, the studies demonstrated that exposure to TCE results in
significant changes in the visual evoked response, which is reversible once TCE exposure is
stopped. Details of the studies are provided below and are summarized in Table 4-26.
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Table 4-26. Summary of animal visual system studies
Reference
Rebert et al.
(1991)
Boyes et al.
(2003)
Boyes et al.
(2005a)
Blain et al.
(1994)
Exposure route
Inhalation
Inhalation
Inhalation
Inhalation
Species/strain/
sex/number
Rat, Long-Evans,
male, 10/group
Rat, Long-Evans,
male, 9- 10/group
Rat, Long-Evans,
male, 8- 10/group
Rabbit, New
Zealand albino,
male, 6-8/group
Dose level/
exposure duration
0, 1,600, and
3,200 ppm; 12 hrs/d,
12wks
0 ppm, 4 hrs;
1,000 ppm, 4 hrs;
2,000 ppm, 2 hrs;
3, 000 ppm, 1.3 hrs
4,000 ppm, 1 hr
0 ppm, 4 hrs;
500 ppm, 4 hrs;
1,000 ppm, 4 hrs;
2,000 ppm, 2 hrs;
3,000 ppm, 1.3 hrs
4,000 ppm, 1 hr;
5,000 ppm, 0.8 hr
0, 350, and 700 ppm;
4 hrs/d, 4 d/wk,
12 wks
NOAEL;
LOAEL
NOAEL:
1,600 ppm
LOAEL:
1,000 ppm,
4 hrs
LOAEL:
500 ppm,
4 hrs
LOAEL:
350 ppm
Effects
Significant amplitude
decreases in pattern reversal
evoked potentials (N1P1
amplitude) at 6, 9, and
12 wks.
Visual function significantly
affected as measured by
decreased amplitude (F2) in
Fourier-transformed visual
evoked potentials. Peak
brain TCE concentration
correlated with dose-
response.
Visual function significantly
affected as measured by
decreased amplitude (F2) in
Fourier-transformed visual
evoked potentials. Peak
brain TCE concentration
correlated with dose-
response.
Significant effects noted in
visual function as
measured by ERG and
OPs immediately after
exposure. No differences
in ERG or OP
measurements were noted
at 6 wks post-TCE
exposure.
Bolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
ERG = electroretinogram, OP = oscillatory potential
Boyes et al. (2005a; 2003) exposed adult, male Long-Evans rats to TCE in a head-only
exposure chamber while pattern onset/offset visual evoked potentials (VEPs) were recorded.
Exposure conditions were designed to provide concentration x time products of 0 ppm/hours
(0 ppm for 4 hours) or 4,000 ppm/hours (see Table 4-26 for more details). VEP amplitudes were
depressed by TCE exposure during the course of TCE exposure. The degree of VEP depression
showed a high correlation with the estimated brain TCE concentration for all levels of
atmospheric TCE exposure.
In a subchronic exposure study, Rebert et al. (1991) exposed male Long-Evans rats to
1,600 or 3,200 ppm TCE, for 12 weeks, 12 hours/day. No significant changes in flash evoked
potential measurements were reported following this exposure paradigm. Decreases in pattern
reversal VEPs (N1P1 amplitude) reached statistical significance following 6, 9, and 12 weeks of
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exposure. The drop in response amplitude ranged from approximately 20% after 8 weeks to
nearly 50% at week 14, but recovered completely within 1 week postexposure.
This transient effect of TCE on the peripheral visual system has also been reported by
Blain (1994) in which New Zealand albino rabbits were exposed by inhalation to 350 and
700 ppm TCE 4 hours/day, 4 days/week for 12 weeks. Electroretinograms (ERG) and oscillatory
potentials (OPs) were recorded weekly under mesopic conditions. Recordings from the 350- and
700-ppm exposed groups showed a significant increase in the amplitude of the a- and b-waves
(ERG). The amplitude of the OPs was significantly decreased at 350 ppm (57%) and increased
at 700 ppm (117%). These electroretinal changes returned to pre-exposure conditions within
6 weeks after the inhalation stopped.
4.3.4.3. Summary and Conclusion of Visual Effects
Changes in visual function are reported in human studies. Although central visual function
was not evaluated in the human studies (such as ERGs, evoked potential measurements), clinical
tests indicated deficits in color discrimination (Kilburn, 2002b, a) visual depth perception (Vernon
and Ferguson, 1969), and contrast sensitivity (Reif etal., 2003). These changes in visual function
were observed following both an acute exposure (Vernon and Ferguson, 1969) and residence in
areas with groundwater contamination with TCE and other chemicals (Reif et al., 2003; Kilburn,
2002b, a). The exposure assessment approach of Reif et al., (2003) who adopted exposure
modeling and information on water distribution patterns, is considered superior to that of Kilburn
(Kilburn, 2002b, a), who used residence location as a surrogate for exposure. In the one acute
inhalation study (Vernon and Ferguson, 1969), aNOAEL of 300 ppm and a LOAEL of 1,000 ppm
for 2 hours was reported for visual effects. A NOAEL is not available from the drinking water
studies since well water TCE concentration is a poor surrogate for an individual's TCE ingestion
(Kilburn, 2002b, a) and there was limited statistical analysis comparing the high-exposure group to
the low-exposure group (Reif et al., 2003).
Animal studies have also demonstrated changes in visual function. All of the studies
evaluated central visual function by measuring changes in evoked potential response following a
visual stimulus that was presented to the animal. Two acute exposure inhalation studies (Boyes et
al., 2005a: Boyes et al., 2003) exposed Long-Evans rats to TCE based on a concentration x time
schedule (Haber's law) and reported decreases in VEP amplitude. All of the exposures from these
two studies resulted in decreased visual function with a LOAEL of 500 ppm for 4 hours. Another
important finding that was noted is the selection of the appropriate dose-metric for visual function
changes following an acute exposure. Boyes et al. (2005a; 2003) found that among other potential
dose-metrics, brain TCE concentration was best correlated with changes in visual function as
measured by evoked potentials under acute exposure conditions. Two subchronic exposure studies
(Blain etal., 1994; Rebert etal., 1991) demonstrated visual function changes as measured by
pattern reversal evoked potentials (Rebert et al., 1991) or ERGs/OPs (Blain etal., 1994). Unlike
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the other three visual function studies conducted with rats, Blain et al. (1994) demonstrated these
changes in rabbits. Significant changes in ERGs and OPs were noted following a 12-week
exposure at 350 ppm (LOAEL) in rabbits (Blain etal., 1994), and in rats exposed to 3,200-ppm
TCE for 12 weeks, there were significant decreases in pattern reversal evoked potentials, but no
effect was noted in the 1,600-ppm exposure group (Rebert et al., 1991). Both subchronic studies
examined visual function following an exposure-free period of either 2 weeks (Rebert et al., 1991)
or 6 weeks (Blain etal., 1994) and found that visual function returned to pre-exposure levels and
the changes are reversible.
4.3.5. Cognitive Function
4.3.5.1. Cognitive Effects: Human Studies
Effects of TCE on learning and memory have been evaluated in populations
environmentally exposed to TCE through well water, in workers occupationally exposed through
inhalation and under controlled exposure scenarios. Details of the studies are provided in
Table 4-27 and discussed briefly below. In the geographical-based studies (Kilburn, 2002b, a;
Kilburn and Warshaw, 1993a) cognitive function was impaired in both studies and was evaluated
by testing verbal recall and digit span memory among other measures. In Arizona residents
involved in a lawsuit (Kilburn and Warshaw, 1993a), significant impairments in all
three cognitive measures were reported; verbal recall (p = 0.001), visual recall (p = 0.03), and
digit span test (p = 0.07), although a question exists whether the referent group was comparable
to exposed subjects and the study had a lack of consideration of possible confounding exposures
in statistical analyses. Significant decreases in verbal recall ability was also reported in another
environmental exposure study where 236 residents near a microchip plant with TCE
concentration in well water ranging from 0.2 to 10,000 ppb (Kilburn, 2002b, a).
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Table 4-27. Summary of human cognition effect studies
Reference
Subjects
Exposure
Effect
Kilburn and
Warshaw
(1993a)
170 residents living in
Southwest Tucson with
TCE, other solvents, and
chromium in
groundwater.
Control: 68 residential
referents matched to
subjects from two
previous studies of waste
oil and oil refinery
exposures.
>500 ppb of TCE in well water
before 1981 and 25-100 ppb
afterwards.
Exposure duration ranged from
1 to 25 yrs.
Decreased performance in the digit span
memory test and story recall ability.
Kilburn (2002b,
a)
236 residents near a
microchip plant.
Controls: 67 local
referents from Phoenix,
Arizona and
161 regional referents
from Wickenburg,
Arizona.
<0.2-10,000 ppb TCE, O.2-
260,000 ppb TCA, O.2-
6,900 ppb 1,1-DCE, O.2-
1,600 1,2-DCE, O.2-
23,000 ppb perchloroethylene,
O.02-330 ppb vinyl chloride
in well water.
Exposure duration 2 to 37 yrs.
Cognitive effects decreased as measured
by lower scores on Culture Fair 2A,
vocabulary, grooved pegboard
(dominant hand), trail making test, and
verbal recall (i.e., memory).
Rasmussen
(1993c. d)
96 Danish metal
degreasers. Age range:
19-68; no external
controls.
Average exposure duration:
7.1 yrs); range of full-time
degreasing: 1 mo to 36 yrs.
1) Low exposure: n = 19,
average full-time exposure
0.5 yr.
2) Medium exposure: n = 36,
average full-time exposure
2.1 yrs.
3) High exposure: n = 41,
average full-time exposure
11 yrs. TCA in high exposure
group = 7.7 mg/L
(maximum = 26.1 mg/L).
Cognitive impairment (psycho-organic
syndrome) prevalent in exposed
individuals. The incidence of this
syndrome was 10.5% for low exposure,
39.5% for medium exposure, and 63.4%
for high exposure. Age is a confounder.
Dose-response with 9 of 15 tests;
Controlling for confounds, significant
relationship of exposure was found with
acoustic-motor function (p < 0.001),
Paced Auditory Serial Addition Test
(p < 0.001), Rey Auditory Verbal-
Learning Test (p < 0.001), vocabulary
(p < 0.001), and visual gestalts
(p < 0.001); significant age effects. Age
is a confounder.
Troster and Ruff
1990)
Two occupationally
TCE-exposed workers.
Controls: two groups of
n = 30 matched controls;
(all age and education
matched.
Exposure concentration
unknown; exposure duration,
3-8 mo.
Both TCE cases exhibited significant
deficits in verbal recall and visuospatial
learning.
Controlled exposure
study four females, three
males.
Controls: four females,
three males.
0, 100 ppm (550 mg/m3),
6 hrs/d, 5 d.
There was no correlation seen between
exposed and unexposed subjects for any
measured psychological test results. No
methods description was provided.
Triebig (1977c)
Seven men and one
woman occupationally
exposed with an age
range from 23 to 38 yrs.
No control group.
50 ppm (260 mg/m').
Exposure duration not
reported.
The psychological tests showed no
statistically significant difference in the
results before or after the exposure-free
time period. No methods description
was provided.
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Table 4-27. Summary of human cognition effect studies (continued)
Reference
Salvini et al.
(1971)
Gamberale et al.
(1976)
Stewart et al.
(1970)
Chalupa (1960)
Subjects
Controlled exposure
study six students, male.
Self used as control.
15 healthy men aged 20-
3 1 yrs old.
Controls: Within
Subjects (15 self-
controls).
130 (108 males,
22 females).
Controls: 63 unexposed
men.
Case study — 6 subjects.
Average age 38.
Exposure
TCE concentration was
1 10 ppm for 4-hr intervals,
twice per d. 0 ppm control
exposure for all as self controls
0 mg/m3, 540 mg/m3 (97 ppm),
1,080 mg/m3 (194 ppm),
70min.
TCA metabolite levels in urine
were measured: 60.8% had
levels up to 20 mg/L, and
82.1% had levels up to
60 mg/L.
No exposure data were
reported.
Effect
Statistically significant results were
observed for perception tests learning
(p < 0.001) and CRT learning
(p
-------
4.3.5.2. Cognitive Effects: Laboratory Animal Studies
Many reports have demonstrated significant differences in performance of learning tasks
such as the speed to complete the task. However, there is little evidence that learning and
memory function are themselves impaired by exposure. There are also limited data that suggest
alterations in the hippocampus of laboratory animals exposed to TCE. Given the important role
that this structure plays in memory formation, such data may be relevant to the question of
whether TCE impairs memory. The studies are briefly discussed below and details are provided
in Table 4-28.
Table 4-28. Summary of animal cognition effect studies
Reference"
Kjellstrand
et al. (19801
Isaacson
et al. (1990)
Kishi et al.
(1993)
Umezu et al.
(1997)
Oshiro et al.
(2004)
Exposure
route
Inhalation
Oral,
drinking
water
Inhalation
i.p.
Inhalation
Species/strain/
sex/number
Gerbil, Mongolian,
males and females,
12/sex/dose
Rat, Sprague-
Dawley, male
weanlings, 12/dose
Rats, Wistar, male,
number not specified
Mouse, ICR, male,
six exposed to all
treatments (repeated
exposure)
Rat, Long-Evans,
male, 24
Dose level/
exposure duration
0, 320 ppm; 9 mo,
continuous (24 hrs/d)
except 1-2 hrs/wk for
cage cleaning
(1) 0 mg/kg-d, 8 wks.
(2) 5.5 mg-d (47 mg/kg-
db), 4 wks + 0 mg/kg-d, 4
wks.
(3) 5.5 mg/d, 4 wks
(47 mg/kg-d) + 0 mg/kg-
d, 2 wks + 8.5 mg/d (24
mg/kg-d), 2 wks
0, 250,500, 1,000, 2,000,
and 4,000 ppm, 4 hrs
0, 125, 250, 500, and
1,000 mg/kg, single dose
and evaluated 30 min
po stadministration
0, 1,600, and 2,400 ppm;
6 hrs/d, 5 d/wk, 4 wks
NOAEL;
LOAEL
NOAEL:
320 ppm
NOAEL:
5.5 mg/d,
4 wks — spatial
learning
LOAEL:
5.5 mg/d —
hippocampal
demyelination
LOAEL:
250 ppm
NOAEL:
500 mg/kg
LOAEL:
1,000 mg/kg
NOAEL:
2,400 ppm
Effects
No significant effect
on spatial memory
(radial arm maze).
Decreased latency to
find platform in the
Morris water maze
(Group #
3); Hippocampal
demyelination
observed in all TCE-
treated groups.
Decreased lever
presses and avoidance
responses in a shock
avoidance task.
Decreased response
rate in an operant
response — condition
avoidance task.
No change in reaction
time in signal
detection task and
when challenged with
amphetamine, no
change in response
from control.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
bmg/kg-day conversion estimated from average male Sprague-Dawley rat body weight from ages 21-49 days
(118 g) for the 5.5 mg dosing period and ages 63-78 d (354 g) for the 8.5 mg dosing period.
Two studies (Umezu et al., 1997; Kulig, 1987) reported decreased performance in
operant-conditioning cognitive tasks for rodents. Kishi et al. (1993) acutely exposed Wistar rats
to TCE at concentrations of 250, 500, 1,000, 2,000, and 4,000 ppm for 4 hours. Rats exposed to
>250 ppm TCE showed a significant decrease both in the total number of lever presses and in
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avoidance responses compared with controls. The rats did not recover their pre-exposure
performance until about 2 hours after exposure. Likewise, Umezu et al. (1997) reported a
depressed rate of operant responding in male ICR strain mice (n = 6, exposed to all TCE doses,
see Table 4-28) in a conditioned avoidance task that reached significance with i.p. injections of
1,000 mg/kg. Increased responding during the signaled avoidance period at lower doses
(250 and 500 mg/kg) suggests an impairment in ability to inhibit responding or failure to attend
to the signal.
Although cognitive impairments are noted, two additional studies indicate no change in
cognition with continuous TCE exposure or improvements in cognitive tasks. No decrements in
cognitive function as measured by the radial arm maze were observed in Mongolian gerbils
exposed continuously by inhalation to 320 ppm TCE for 9 months (Kjellstrand et al., 1980).
Improved performance was noted in a Morris swim test for weanling rats orally dosed with
5.5 mg/day for 4 weeks followed by 2 weeks of no exposure and an additional 2 weeks of
8.5 mg/day (Isaacson et al., 1990). This improved performance occurred despite a loss in
hippocampal myelination.
4.3.5.3. Summary and Conclusions of Cognitive Function Studies
Human environmental and occupational exposure studies suggest impairments in
cognitive function. Kilburn and Warshaw (1993a) and Kilburn (2002b, a) reported memory
deficits in individuals, although a question exists whether the referent group was comparable to
exposed subjects and these studies lack of consideration of possible confounding exposures in
statistical analyses. Significant impairments were found in visual and verbal recall and with the
digit span test. Similarly, in occupational exposure studies (Rasmussen et al., 1993c, d; Troster
and Ruff 1990), short-term memory tests indicated that immediate memory and learning were
impaired in the absence of an effect on digit span performance. In controlled exposure and/or
chamber studies, two studies did not report any cognitive impairment (Gamberale et al., 1976;
Stewart et al., 1970) and one study (Salvini etal., 1971) reported significant impairments in
learning memory and complex choice reaction tasks. All of the controlled exposure studies were
acute and/or short-term exposure studies and the sensitivity of test procedures is unknown due to
the lack of methodologic information provided in the reports. Despite identified study
deficiencies, these studies collectively suggest cognitive function impairment.
The animal studies measured cognitive function through spatial memory and operant
responding tasks. In the two studies where spatial memory was evaluated, there was either no
effect at 320 ppm TCE (Kjellstrand et al., 1980) or improved cognitive performance in weanling
rats at a dose of 5.5 mg/day for 4 weeks (Isaacson et al., 1990). Improved cognitive performance
was observed in weanling rats (Isaacson et al., 1990) and could be due to continuing
neurodevelopment as well as compensation from other possible areas in the brain since there was
a significant loss in hippocampal myelination. Significant decreases in operant responding
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(avoidance/punished responding) during TCE exposure were reported in two studies (Umezu et
al., 1997; Kishi et al., 1993). When TCE exposure was discontinued, operant responding return
to control levels; it is unclear if the significant effects are due to decreased motor function or
decreased cognitive ability.
4.3.6. Psychomotor Effects
There is considerable evidence in the literature for both animals and humans on
psychomotor testing, although human and laboratory animal studies utilize very different
measures of motor behavior. Generally, the human literature employs a wide variety of
psychomotor tasks and assesses error rates and reaction time (RT) in the performance of the task.
The laboratory animal data, by contrast, tend to include unlearned naturalistic behaviors such as
locomotor activity, gait changes, and foot splay to assess neuromuscular ability.
4.3.6.1. Psychomotor Effects: Human Studies
The effects of TCE exposure on psychomotor response have been studied primarily as a
change in RT with studies on motor dyscoordination resulting from TCE exposure providing
subjective reporting.
4.3.6.1.1. Reaction time
Several studies have evaluated the effects of TCE on RT using simple and CRT tasks
(simple reaction time [SRT] and CRT tasks). The studies are presented below and summarized
in more detail in Table 4-29.
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Table 4-29. Summary of human CRT studies
Reference
Subjects
Exposure
Effect
Kilburn
2QQ2b,a)
236 residents near a microchip
plant in Phoenix, Arizona.
Controls:
161 regional referents from
Wickenburg, Arizona.
67 referents from Phoenix,
Arizona not residing near a
plant.
0.2-10,000 ppb of TCE,
chronic exposure.
SRT and CRT were increased in the
exposed group (p < 0.05).
Kilburn and
Warshaw
(1993a)
160 residents living in
Southwest Tucson with TCE and
other solvents in groundwater.
Control: 68 residential referents
matched to subjects from
two previous studies of waste oil
and oil refinery exposures.
>500ppbofTCEin
well water before 1981
and 25-100 ppb
afterwards.
Exposure duration 1 to
25 yrs.
Mean SRT was 67 milliseconds (msec)
longer than the referent group
p< 0.0001).
CRT of the exposed subjects was
between 93 and 100 msec longer in
three different trials (p < 0.0001)
compared to referents.
Reifetal.
2003)
143 residents of the Rocky
Mountain Arsenal community of
Denver.
Referent group at lowest
concentration (<5 ppb).
High exposure group
>15 ppb.
Medium exposure
group >5-<15 ppb.
Low exposure referent
group <5 ppb.
Significant increase in RT as measured
by the SRT test (p < 0.04) in only
among subjects who reported alcohol
use (defined as having at least one drink
per mo).
Kilburn and
Thornton
(1996)
Group A: Registered voters from
Arizona and Louisiana with no
exposure to TCE: n = 264, aged
18-83. Group B volunteers
from California n = 29 (17 males
and 12 females). Group C:
exposed to TCE and other
chemicals for >5 yrs n = 217.
No exposure or
groundwater analyses
reported.
Significant increase in SRT and CRT in
exposed group compared to the
unexposed populations.
Gamberale
et al. (1976)
15 healthy men aged 20-31 yrs
old.
Controls: Within subjects
(15 serf-controls).
0, 540 mg/nr1 (97 ppm),
1,080 mg/m3 (194
ppm), 70 min.
No change in CRT or SRT. Increase in
time required to perform the
RT-Addition Test (task for adding
numbers) (p < 0.05).
Gun et al.
(1978)
Four female workers from one
plant exposed to TCE and four
female workers from another
plant exposed to TCE +
nonhalogenated hydrocarbon
solvent.
Control: (n = 8) four unexposed
female workers from each plant.
3-419 ppm, duration
not specified.
TCE-only exposure increased RT in
comparison to controls. In
TCE + solvent group, ambient TCE was
lower and mean RT shortened in
Session 2, then rose subsequently to be
greater than at the start.
Increases in RT were observed in environmental exposure studies by Kilburn (2002b, a),
Kilburn and Warshaw (1993a), and Kilburn and Thornton (1996) as well as in an occupational
exposure study by Gun et al. (1978). All populations except that of Gun et al. (1978) were
exposed through groundwater contaminated as the result of environmental spills; the exposure
duration was for at least 1 year and exposure levels ranged from 0.2 to 10,000 ppb for the
three studies. Kilburn and Warshaw (1993a) reported that SRT significantly increased from
4-112
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281 ± 55 msec to 348 ± 96 msec in individuals (p < 0.0001). CRT of the exposed subjects was
93 msec longer (p < 0.0001) than referents. Kilburn and Thornton (1996) evaluated SRT and
CRT function and also found similar increases in RT. The average SRT and CRT for the
combined control groups were 276 and 532 msec, respectively. These RTs increased in the TCE
exposure group where the average SRT was 334 msec and CRT was 619 msec. Similarly,
Kilburn (2002b, a) compared RTs between 236 TCE-exposed persons and the 161 unexposed
regional controls. SRTs significantly increased from 283 ± 63 msec in controls to 334 ±
118 msec in TCE-exposed individuals (p < 0.0001). Similarly, CRTs also increased from
510 ± 87 to 619 ± 153 msec with exposure to TCE (p < 0.0001).
No effect on SRT was reported in a geographical-based study by Reif et al. (2003). SRTs
were 301 msec for the lowest exposure group and 316 msec for the highest exposure group
(p = 0.42). When the SRT data were analyzed for individuals who consumed at least on
alcoholic drink per month (n = 80), a significant increase (18%, p < 0.04) in SRT times was
observed between the lowest exposure and the highest exposure groups. In TCE-exposed
individuals who did not consume alcohol (n = 55), SRTs decreased from 321 msec in the lowest
exposed group to 296 msec in the highest exposed group, but this effect was not statistically
significantly different. A controlled exposure (chamber study) of 15 healthy men aged 20-
31 years old, were exposed to 0, 540, and 1,080 mg/m3 TCE for 70 minutes or served as his own
control, reported no statistically significant differences with the SRT or CRT tasks. However, in
the RT-addition test, the level of performance varied between the different exposure conditions
(F(2.24) = 4.35; p < 0.05) and between successive measurement occasions (F(2.24) = 19.25;
p< 0.001).
4.3.6.1.2. Muscular dyscoordination
Three studies examined motor dyscoordination effects from TCE exposure using
subjective and self-reported individual assessment. Rasmussen et al. (1993a) presented findings
on muscular dyscoordination for 96 metal degreasers exposed to either TCE or CFC113. A
statistically significant increasing trend of dyscoordination with TCE exposure was observed
(p = 0.01) in multivariate regression analyses, which adjusted for the effects of age, neurological
disease, arteriosclerotic disease, and alcohol abuse. Furthermore, a greater number of abnormal
coordination tests were observed in the higher-exposure group compared to the low-exposure
group (p = 0.003).
Gash et al. (2008) reported fine motor hand movement times in subjects who had filed
workman compensation claims were significantly slower (p < 0.0001) than age-matched
nonexposed controls. Exposures were based on self-reported information, and no information on
the control group was presented. Troster and Ruff (1990) reported a case study conducted on
two occupationally exposed workers to TCE. Mild deficits in motor speed were reported for
both cases. In the first case, manual dexterity was impaired in a male exposed to TCE (unknown
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concentration) for 8 months. In the second case study where a female was exposed to TCE (low
concentration; exact level not specified) for 3 months, there was weakness in the quadriceps
muscle as evaluated in a neurological exam and a decreased sensation to touch on one hand.
Both Gash et al. (2008) and Troster and Ruff (1990) provide very limited information given their
deficiencies related to lack of exposure data, self-reported information, and limited reporting of
referents and statistical analysis.
4.3.6.2. Psychomotor Effects: Laboratory Animal Data
Several animal studies have demonstrated that TCE exposure produces changes in
psychomotor function. At high doses (>2,000 mg/kg), TCE causes mice to lose their righting
reflex when the compound is injected i.p. (Shih et al., 2001; Umezu et al., 1997). At lower
exposures (inhalation and oral), TCE produces alterations in neurobehavioral measures including
locomotor activity, gait, operant responding, and reactivity. The studies are described in
Sections 4.3.6.2.1-4.3.6.2.3 and summarized in Tables 4-30 and 4-31.
Table 4-30. Summary of animal psychomotor function and RT studies
Reference"
Savolainen
et al. (19771
Kishi et al.
(1993)
Kulig et al.
(1987J
Moser et al.
(1995)
Bushnell
(1997)
Shih et al.
(2001)
Exposure
route
Inhalation
Inhalation
Inhalation
Oral
Inhalation
i.p.
Species/strain/
sex/number
Rat, Sprague-
Dawley, male,
10
Rats, Wistar,
male, number
not specified
Rat, Wistar,
male, 8/dose
Rat, F344,
female, 8/dose
Rat, Long-
Evans, male, 12
Mouse, MF1,
male, 6
Dose level/
exposure duration
0 and 200 ppm;
6 hrs/d, 4 d
0, 250,500, 1,000,
2,000, and
4,000 ppm, 4 hrs
0, 500, 1,000, and
1,500 ppm;
16 hrs/d, 5 d/wk,
18wks
0, 150, 500, 1,500,
and 5,000 mg/kg,
one dose
0, 50, 150, 500, and
1,500 mg/kg-d,
14 d
0, 400, 800, 1,200,
1,600, 2,000, or
2,400 ppm, 1 hr/test
d, 4 consecutive test
d, 2 wks
0 and 5,000 mg/kg,
acute
NOAEL; LOAEL
LOAEL: 200 ppm
LOAEL: 250 ppm
NOAEL: 1,500 ppm
NOAEL: 500 mg/kg
LOAEL:
1,500 mg/kg
NOAEL:
150 mg/kg-d
LOAEL:
500 mg/kg-d
NOAEL: 800 ppm
LOAEL: 1,200 ppm
LOAEL: 5,000
mg/kg
Effects
Increased frequency of
preening, rearing, and
ambulation. Increased
preening time.
Decreased lever presses
and increased responding
when lever press coupled
with a 10-s electric shock
(decreased avoidance
response).
No change in spontaneous
activity, grip strength, or
hindlimb movement.
Decreased motor
activity; Neuro-muscular
and sensorimotor
impairment.
Increased rearing
activity and decreased
forelimb grip strength.
Decreased sensitivity and
increased response time in
the signal detection task.
Impairment of righting
reflex.
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Table 4-30. Summary of animal psychomotor function and RT studies
(continued)
Reference"
Umezu et al.
(1997)
Bushnell and
Oshiro
(2000)
Nunes et al.
(2001)
Moser et al.
(2003)
Albee et al.
(2006)
Exposure
route
i.p.
Inhalation
Oral
Oral
Inhalation
Species/strain/
sex/number
Mouse, ICR,
male, 10/group
Mouse, ICR,
male, 6-
10/group
Rat, Long-
Evans, male, 32
Rat, Sprague-
Dawley, male,
10/group
Rat, F344,
female, 10/group
Rat, F344, male
and female,
10/sex/group
Dose level/
exposure duration
0, 2,000, 4,000, and
5,000 mg/kg— loss
of righting reflex
measure
0, 62.5, 125, 250,
500, and
1,000 mg/kg, single
dose and evaluated
30 min
postadministration
0, 2,000, 2,400 and
ppm; 70 min/d, 9 d
(land
2,000 mg/kg-d, 7 d
0, 40, 200, 800, and
1,200 mg/kg-d,
10 d
0, 250, 800, and
2,500 ppm; 6 hrs/d,
5 d/wk, 13 wks
NOAEL; LOAEL
LOAEL:
2,000 mg/kg— loss of
righting reflex
NOAEL: 500 mg/kg
LOAEL:
1,000 mg/kg—
operant behavior
NOAEL: 125 mg/kg
LOAEL:
250 mg/kg—
punished responding
LOAEL: 2,000 ppm
LOAEL:
2,000 mg/kg-d
NOAEL: 2,500 ppm
Effects
Loss of righting reflex.
Decreased responses
(lever presses) in an
operant response task for
food reward.
Increased responding
when lever press coupled
with a 20-V electric shock
(punished responding).
Decreased performance on
the signal detection task.
Increased response time
and decreased response
rate.
Increased foot splay. No
change in any other FOB
parameter (e.g.,
piloerection, activity,
reactivity to handling).
Decreased motor activity;
Decreased sensitivity to
tail pinch; Increased
abnormality in gait;
Decreased grip strength;
Adverse changes in several
FOB parameters.
No change in any FOB
measured parameter.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
FOB = functional observational battery
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Table 4-31. Summary of animal locomotor activity studies
Reference"
Wolff and
Siegmund
(1978)
Kulig et al.
(1987)
Moser et al.
(1995)
Waseem et al.
(2001)
Moser et al.
(2003)
Exposure
route
i.p.
Inhalation
Oral
Oral
Inhalation
Oral
Species/strain/
sex/number
Mouse, AB,
male, 18
Rat, Wistar,
male, 8/dose
Rat, F344,
female, 8/dose
Rat, Wistar,
male, 8/group
Rat, Wistar,
male, 8/group
Rat, F344,
female,
10/group
Dose level/
exposure duration
0 and 182 mg/kg,
tested 30 min after
injection
0, 500, 1,000, and
1,500 ppm;
16 hrs/d, 5 d/wk,
18wks
0, 150, 500, 1,500,
and 5,000 mg/kg,
one dose
0, 50, 150, 500, and
1,500 mg/kg-d, 14 d
0, 350, 700, and
1,400 ppm in
drinking water for
90 d
0 and 376 ppm for
up to 180 d;
4 hrs/d, 5 d/wk
0, 40, 200, 800, and
1,200 mg/kg-d, 10 d
NOAEL;
LOAEL
LOAEL:
182 mg/kg
NOAEL:
500 ppm
LOAEL:
1,000 ppm
NOAEL:
500 mg/kg
LOAEL:
1,500 mg/kg
NOAEL:
150 mg/kg-d
LOAEL:
500 mg/kg-d
NOAEL:
1,400 ppm
LOAEL: 376
ppm
Effects
Decreased spontaneous motor
activity.
No change in spontaneous
activity, grip strength, or
hindlimb movement.
Increased latency time in the
2-choice visual discrimination
task (cognitive disruption
and/or motor activity related
effect).
Decreased motor activity;
neuro-muscular and
sensorimotor impairment.
Increased rearing activity.
No significant effect on
spontaneous locomotor activity.
Changes in locomotor activity
and vary by timepoint when
measured over the 180-d
period.
Decreased motor activity;
Decreased sensitivity; Increased
abnormality in gait; Adverse
changes in several FOB
parameters.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
4.3.6.2.1. Loss of righting reflex
Umezu et al. (1997) studied disruption of the righting reflex following acute injection
(i.p.) of 2,000, 4,000, and 5,000 mg/kg TCE in male ICR mice. TCE disrupted the righting
reflex at doses of 2,000 mg/kg and higher. At 2,000 mg/kg, loss of righting reflex (LORR) was
observed in only 2/10 animals injected. At 4,000 mg/kg, 9/10 animals experienced LORR and
100% of the animals experienced LORR at 5,000 mg/kg.
Shih et al. (2001) reported impaired righting reflexes at exposure doses of 5,000 mg/kg
(i.p.) in male MF1 mice. Mice pretreated with dimethyl sulfoxide or disulfuram (CYP2E1
inhibitor) delayed LORR in a dose related manner. By contrast, the alcohol dehydrogenase
inhibitor, 4-metylpyradine, did not delay LORR that resulted from 5,000 mg/kg TCE. These
4-116
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data suggest that the anesthetic properties of TCE involve its oxidation via CYP2E1 to an active
metabolite.
4.3.6.2.2. Activity, sensory-motor and neuromuscular function
Changes in sensory-motor and neuromuscular activity were reported in three studies
(Moseretal.. 2003: Moser et al.. 1995: Kishietal.. 1993). Kishi et al. (1993) exposed male
Wistar rats to 250, 500, 1,000, 2,000, and 4,000 ppm TCE for 4 hours. Rats exposed to 250 ppm
TCE showed a significant decrease both in the total number of lever presses and in avoidance
responses at 140 minutes of exposure compared with controls. Moser et al. (1995) evaluated the
effects of acute and short-term (14 day) administration of TCE in adult female F344 rats (n = 8-
10/dose) on activity level, neuromuscular function, and sensorimotor function as part of a larger
functional observational battery (FOB) testing. The NOAEL levels identified by the authors are
500 mg/kg (10% of the limit dose) for the acute treatment and 150 mg/kg (3% of the limit dose)
for the 14-day study. In the acute study, TCE produced the most significant effects in motor
activity (activity domain), gait (neuromuscular domain), and click response (sensorimotor
domain). In the 14-day study, only the activity domain (rearing) and neuromuscular domain
(forelimb grip strength) were significantly different (p < 0.05) from control animals. In a
separate 10-day study (Moser et al., 2003), TCE administration significantly (p < 0.05) reduced
motor activity, tail pinch responsiveness, reactivity to handling, hind limb grip strength and body
weight. Significant increases (p < 0.05) in piloerection, gait scores, lethality, body weight loss,
and lacrimation were also reported in comparison to controls.
There are also two negative studies that used adequate numbers of subjects in their
experimental design but used lower doses than did Moser et al. (2003). Albee et al. (2006)
exposed male and female F344 rats (n = 10/sex) to TCE by inhalation at exposure doses of 250,
800, and 2,500 ppm, for 6 hours/day, 5 days/week, for 13 weeks. The FOB was performed
monthly, although it is not certain how much time elapsed from the end of exposure until the
FOB test was conducted. No treatment-related differences in grip strength or landing foot splay
were demonstrated in this study. Kulig et al. (1987) also failed to show significant effects of
TCE inhalation exposure on markers of motor behavior. Wistar rats (n = 8) exposed to 500,
1,000, and 1,500 ppm, for 16 hours/day, 5 days/week, for 18 weeks failed to show changes in
spontaneous activity, grip strength, or coordinated hind limb movement. Measurements were
made every 3 weeks during the exposure period and occurred between 45 and 180 minutes
following the previous TCE inhalation exposure.
4.3.6.2.3. Locomotor activity
The data, with regard to locomotor activity, are inconsistent. Several studies showed that
TCE exposure can decrease locomotor activity including Wolff and Siegmund (1978) where AB
mice (n = 18) were treated acutely with a dose of 182 mg/kg, i.p. at one of four time points
4-117
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during a 24-hour day. Moser et al. (2003; 1995) reported reduced locomotor activity in female
F344 rats (n = 8-10) gavaged with TCE over an acute (LOAEL = 5,000 mg/kg TCE) or subacute
period (LOAEL = 500 mg/kg, but no effect at 5,000 mg/kg). In the Moser et al. (2003) study, it
appears that 200 mg/kg TCE yielded a significant reduction in locomotor activity and that the
degree of impairment at this dose represented a maximal effect on this measure. That is, higher
doses of TCE appear to have produced equivalent or slightly less of an effect on this behavior.
While this study identifies a LOAEL of 200 mg/kg TCE by gavage over a 10-day period, this is a
much lower dose effect than that reported in Moser et al. (1995). Both studies (Moser et al.,
2003; Moser et al., 1995) demonstrate a depression in motor activity that occurs acutely
following TCE administration. Kulig et al. (1987) demonstrated that rats had increased response
latency to a two choice visual discrimination following 1,000- and 1,500-ppm TCE exposures for
18 weeks. However, no significant changes in grip strength, hindlimb movement, or any other
motor activity measurements were noted.
There are also a few studies (Waseem et al., 2001; Fredriksson et al., 1993) generally
conducted using lower exposure doses that failed to demonstrate impairment of motor activity or
ability following TCE exposure. Waseem et al. (2001) failed to demonstrate changes in
locomotor activity in male Wistar rats (n = 8) dosed with TCE (350, 700, and 1,400 ppm) in
drinking water for 90 days. Wistar rats (n = 8) exposed to 500, 1,000, and 1,500 ppm for
16 hours/day, 5 days/week, for 18 weeks failed to show changes in spontaneous activity. No
changes in locomotor activity were observed for 17-day-old male NMRI mice that were dosed
postnatally with 50 or 290 mg/kg-day from day 10 to 16 (Fredriksson et al., 1993). However,
rearing activity was significantly decreased in the NMRI mice at day 60.
4.3.6.3. Summary and Conclusions for Psychomotor Effects
In human studies, psychomotor effects such as RT and muscular dyscoordination have
been examined following TCE exposure. In the RT studies, statistically significant increases in
CRT and SRT were reported in the Kilburn studies (2002b, a; 1996: 1993 a). All of these studies
were geographically based and it was suggested that the results were used for litigation and the
differences between exposed and referent groups on other factors influencing reaction speed time
may introduce a bias to the findings. Additionally, in these studies exposure to TCE and other
chemicals occurred through drinking water for at least 1 year and TCE concentrations in well
water ranged from 0.2 to 10,000 ppb. Reif et al. (2003) whose exposure assessment approach
included exposure modeling of water distribution system to estimate TCE concentrations in tap
water at census track of residence found that residents with drinking water containing TCE (up to
>15 ppb—the highest level not specified) and other chemicals did not significantly increase
CRTs or SRTs. Inhalation studies also demonstrated increased RTs. An acute exposure
chamber study (Gamberale et al., 1976) tested for CRT, SRT, and RT-addition following a 70-
minute exposure to TCE. A concentration-dependent significant decrease in performance was
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observed with the RT-addition test and not for CRT or SRT tasks. An occupational exposure
study on eight female workers exposed to TCE (Gun et al., 1978) also reported increased RT in
the females exposed to TCE-only. Muscular dyscoordination for humans following TCE
exposure has been reported in a few studies as a subjective observation. The studies indicated
that exposure resulted in decreased motor speed and dexterity (Rasmussen et al., 1993a: Troster
and Ruff 1990) and self-reported faster asymptomatic fine motor hand movements (Gash et al.,
2008).
Animal studies evaluated psychomotor function by examining locomotor activity, operant
responding, changes in gait, loss of righting reflex, and general motor behavior (see Tables 4-30
and 4-31 for references). Overall, the studies demonstrated that TCE causes loss of righting
reflex at injection doses of >2,000 mg/kg (Shih et al., 2001; Umezu et al., 1997). Regarding
general psychomotor testing, significant decreases in lever presses and avoidance were observed
at inhalation exposures as low as 250 ppm for 4 hours (LOAEL; Kishi et al., 1993). Following
subchronic inhalation exposures, no significant changes in psychomotor activity were noted at up
to 2,500 ppm for 13 weeks (Albee et al.. 2006) or at 1,500 ppm for 18 weeks (Kulig, 1987). In
the oral administration studies (Moser et al., 2003; Moser et al., 1995), psychomotor effects were
evaluated using an FOB. More psychomotor domains were significantly affected by TCE
treatment in the acute study in comparison to the 14-day study, but a lower NOAEL (150 mg/kg-
day) was reported for the 14-day study in comparison to the acute study (500 mg/kg: Moser et
al., 1995). Upon closer examination of the data, a biphasic effect in one measure of the FOB
(rearing) was resulting in the lower NOAEL for the 14-day study and doses that were higher and
lower than the NOAEL did not produce a statistically significant increase in the number of rears.
Therefore, it can be surmised that acute exposure to TCE results in significant changes in
psychomotor function. However, there may be some tolerance to these psychomotor changes in
increased exposure duration to TCE as evidenced by the results noted in the short-term and
subchronic exposure studies.
4.3.7. Mood Effects and Sleep Disorders
4.3.7.1. Effects on Mood: Human Studies
Reports of mood disturbance (depression, anxiety) resulting from TCE exposure are
numerous in the human literature. These symptoms are subjective and difficult to quantify.
Studies by Gash et al. (2008), Kilburn and Warshaw (1993a), Kilburn (2002b, a),
McCunney et al. (1988), Mitchell et al. (1969), Rasmussen and Sabroe (1986), and Troster and
Ruff (1990) reported mood disturbances in humans. Reif et al. (2003) and Triebig et al. (1976)
reported no effect on mood following TCE exposures.
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4.3.7.2. Effects on Mood: Laboratory Animal Findings
It is difficult to obtain comparable data of emotionality in laboratory studies. However,
Moser et al. (2003) and Albee et al. (2006) both report increases in handling reactivity among
rats exposed to TCE. In the Moser study, female F344 rats received TCE by gavage for periods
of 10 days at doses of 0, 40, 200, 800, and 1,200 while Albee et al. (2006) exposed F344 rats to
TCE by inhalation at exposure doses of 250, 800, and 2,500 ppm for 6 hours/day, 5 days/week,
for 13 weeks. These studies are summarized and described in Table 4-32.
Table 4-32. Summary of animal mood effect and sleep disorder studies
Reference"
Exposure
route
Species/strain/
sex/number
Dose level/
exposure duration
NOAEL; LOAEL
Effects
Mood effects
Albee et al.
(2006)
Moser et al.
(2003)
Inhalation
Oral
Rat, F344, male
and female,
10/sex/group
Rat, F344,
female, 10/group
0, 250, 800, and
2,500 ppm; 6 hrs/d,
5 d/wk, 13 wks
0, 40, 200, 800, and
1,200 mg/kg-d, 10 d
NOAEL: 800 ppm
-
Increased handling
reactivity.
Decreased handling
reactivity score.
Sleep disorder
Arito et al.
(1994)
Inhalation
Rat, Wistar,
male, 5/group
0,50, 100, and
300 ppm; 8 hrs/d,
5 d/wk, 6 wks
LOAEL: 50 ppm
Significant changes in
sleep cycle as measured
through EEC changes;
significant decreases in
wakefulness.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
4.3.7.3. Sleep Disturbances
Arito et al. (1994) exposed male Wistar rats to 50, 100, and 300 ppm TCE for 8 hour/day,
5 days/week, for 6 weeks and measured electroencephalographic (EEG) responses (see
Table 4-32). EEG responses were used as a measure to determine the number of awake
(wakefulness hours) and sleep hours. Exposure to all of the TCE levels significantly decreased
amount of time spent in wakefulness during the exposure period. Some carry over was observed
in the 22 hours post exposure period, with significant decreases in wakefulness seen at 100 ppm
TCE. Significant changes in wakefulness-sleep elicited by the long-term exposure appeared at
lower exposure levels. These data seem to identify a low dose effect of TCE and established a
LOAEL of 50 ppm for sleep changes.
4.3.8. Developmental Neurotoxicity
4.3.8.1. Human Studies
In humans, CNS birth defects were observed in a few studies (ATSDR, 2001; Bove,
1996; Bove etal., 1995; Lagakos et al., 1986). Postnatally, observed adverse effects in humans
include delayed newborn reflexes following exposure to TCE during childbirth (Beppu, 1968),
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impaired learning or memory (White et al., 1997; Bernad et al., 1987, abstract): aggressive
behavior (Bernad et al., 1987, abstract): hearing impairment (Burg and Gist 1999): speech
impairment (Burg and Gist 1999: White etal.. 1997): encephalopathy (White etal.. 1997):
impaired executive and motor function (White et al., 1997): attention deficit (White et al., 1997:
Bernad et al., 1987, abstract): and autism spectrum disorder (ASD) (Windham et al., 2006). The
human developmental neurotoxicity studies are discussed in more detail in Section 4.8.3.1.2.1,
and summarized in Table 4-33.
Table 4-33. Summary of human developmental neurotoxicity associated with
TCE exposures
Finding
CNS defects, neural tube defects
Delayed newborn reflexes
Impaired learning or memory
Aggressive behavior
Hearing impairment
Speech impairment
Encephalopathy
Impaired executive function
Impaired motor function
Attention deficit
ASD
Species
Human
Human
Human
Human
Human
Human
Human
Human
Human
Human
Human
Human
References
ATSDR (2001)
Bove (1996); Bove et al. (1995)
Lagakos et al. (1986)
Beppu (1968)
Bernad et al. (1987. abstract)
White et al. (1997)
Bernad et al., (1987. abstract)
Burg and Gist (1999)
Burg and Gist (1999)
White et al. (1997)
White et al. (1997)
White et al. (1997)
White et al. (1997)
White et al. (1997)
Bernad et al. (1987. abstract)
Windham et al. (2006)
4.3.8.2. Animal Studies
There are a few studies demonstrating developmental neurotoxicity following TCE
exposure (range of exposures) to experimental animals. These studies collectively suggest that
developmental neurotoxicity result from TCE exposure; however, some types of effects such as
learning and memory measures have not been evaluated. Most of the studies demonstrate either
spontaneous motor activity changes (Taylor et al., 1985) or neurochemical changes such as
decreased glucose uptake and changes in the specific gravity of the cortex and cerebellum
(Isaacson and Taylor, 1989: Noland-Gerbec et al., 1986: Westergren et al., 1984). In addition, in
most of these studies, there is no assessment of the exposure to TCE or metabolites in the
pups/offspring. Details of the studies are presented below and summarized in Table 4-34.
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Table 4-34. Summary of mammalian in vivo developmental neurotoxicity
studies—oral exposures
Reference"
Fredriksson
et al. (1993)
George et al.
(1986)
Isaacson and
Taylor (1989)
Noland-Gerbec
et al. (1986)
Taylor et al.
(1985)
Blossom et al.
(2008)
Species/strain/
sex/number
Mouse, NMRI, male
pups, 12 pups from
3-4 different
litters/group
Rat, F334, male and
female,
20 pairs/treatment
group,
40 controls/sex
Rat, Sprague-
Dawley, females, six
dams/group
Rat, Sprague-
Dawley, females, 9-
1 1 dams/group
Rat, Sprague-Dawley,
females, no.
dams/group not
reported
Mouse, MRL +/+,
dams and both sexes
offspring, eight
litters/group; three-
eight pups/group
Dose level/
exposure duration
0, 50, or 290 mg/kg-d
PNDs 10-16
0, 0.15, 0.30, or 0.60%
microencapsulated TCE
in diet.
Breeders exposed 1 wk
premating, then for
13 wks; pregnant females
throughout pregnancy
(i.e., 18 wks total).
0, 312, or 625 mg/L
(0,4.0,or8.1mg/d)c.
Dams (and pups) exposed
from 14 d prior to mating
until end of lactation.
0,3 12 mg/L.
Average total intake of
dams: 825 mg TCE over
61 d.
Dams (and pups) exposed
from 14 d prior to mating
until end of lactation.
0,312, 625, and
1,250 mg/L in drinking
water.
Dams (and pups) exposed
from 14 d prior to mating
until end of lactation.
Drinking water, from GD 0
to PND 42; 0 or
0.1 mg/mL; maternal
dose = 25.7 mg/kg-d;
offspring PNDs 24-42
dose = 31.0 mg/kg-d.
NOAEL;
LOAELb
LOAEL:
50 mg/kg-d
LOAEL:
0.15%
LOAEL:
312 mg/L
LOAEL:
3 12 mg/L
LOAEL:
3 12 mg/L
LOAEL:
3 1 mg/kg-d
for offspring
Effects
Rearing activity significant J,
at both dose levels on PND 60.
Open field testing in pups: a
significant dose-related trend
toward | time required for
male and female pups to cross
the first grid in the test
device.
Significant J, myelinated fibers
in the stratum lacunosum-
moleculare of pups. Reduction
in myelin in the CA1 region of
the hippocampus.
Significant J, uptake of
[3H]-2-DG in whole brains
and cerebella (no effect in
hippocampus) of exposed
pups at 7, 11, and 16 d, but
returned to control levels by
21 d.
Exploratory behavior
significant | in 60- and 90-d-
old male rats at all treatment
levels.
Locomotor activity (measured
through the wheel-running
tasks) was higher in rats from
dams exposed to 1,250 mg/L
TCE.
Righting reflex, bar holding,
and negative geotaxis were not
impaired. Significant
association between impaired
nest quality and TCE exposure.
Lower GSH levels and
GSH:GSSG ratios with TCE
exposure.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
bLOAELs are based upon reported study findings.
°Dose conversions provided by study author(s).
GSSG = oxidized GSH
Taylor et al. (1985) administered TCE to female Sprague-Dawley rats in their drinking
water from 14 days before breeding throughout gestation and until pups were weaned at 21 days.
Measured TCE concentrations in the dams were 312-646, 625-1,102, and 1,250-1,991 mg/L in
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the low-, mid-, and high-dose groups as measured from the drinking water. Pups were evaluated
for exploratory activity at 28, 60, or 90 days. No significant differences were noted between
control and treated pups at 28 days. At 60 days, all TCE-treated animals had significantly
increased exploratory activity in comparison to age-matched controls, but only the high-dose
group had increased activity at 90 days. A significant increase in spontaneous motor activity (as
measured by a wheel-running task) was noted in only the high dose TCE (1,250-1,991 mg/L)
group during the onset of the darkness period. This study demonstrated that both spontaneous
and open field activities are significantly affected by developmental TCE exposure.
Spontaneous behavioral changes were also investigated in another study by Fredriksson
et al. (1993). Male and female NMRI pups (mice) were orally administered 50 or 290 mg/kg-
day for 7 days starting at PND 10. Spontaneous motor activity was investigated in male mice at
ages 17 and 60 days. TCE-treated animals tested at day 17 did not demonstrate changes in any
spontaneous activity measurements in comparison to control animals. Both doses of TCE
(50 and 290 mg/kg-day) significantly decreased rearing in 60-day-old male mice.
Westergren et al. (1984) examined the brain specific gravity of litters from mice exposed
to TCE. NMRI mice (male and female) were exposed to 150 ppm TCE (806.1 mg/m3) for
30 days prior to mating. Exposure in males continued until the end of mating and females were
exposed until the litters were born. Brains were removed from the offspring at either PNDs 1,
10, 20-22, or 29-31. At PNDs 1 and 10, significant decreases were noted in the specific gravity
of the cortex. Significant decreases in the specific gravity of the cerebellum were observed at
PND 10 (decrease from 1.0429 ± 0.00046 to 1.0405 ± 0.00030) and 20-22 (decrease from
1.0496 ± 0.00014 to 1.0487 ± 0.00060). Cerebellum measurements were not reported for
PND 29-31 animals. Neurobehavioral assessments were not conducted in this study.
Additionally, decreased brain specific gravity is suggestive of either decreased brain weight or
increased brain volume (probably from edema) or a combination of the two factors and is highly
suggestive of an adverse neurological effect. The effects of TCE on the cortical specific gravity
were not persistent since cortices from PNDs 29-31 animals did not exhibit any significant
changes. It is unclear if the effects on the cerebellum were persistent since results were not
reported for the PND 29-31 animals. However, the magnitude of the change in the specific
gravity of the cerebellum is decreased from PNDs 10 to 20-22, suggesting that the effect may be
reversible given a longer recovery period from TCE.
The effect of TCE on glucose uptake in the brain was evaluated in rat pups exposed to
TCE during gestation and through weaning. The primary source of energy utilized in the CNS is
glucose. Changes in glucose uptake in the brain are a good indicator for neuronal activity
modification. Noland-Grebec et al. (1986) administered 312 mg/L TCE through drinking water
to female Sprague-Dawley rats from 2 weeks before breeding and up until pups reached 21 days
of age. To measure glucose uptake, 2-deoxyglucose was administered intraperitoneally to male
pups at either PND 7, 11, 16, or 21. Significant decreases in glucose uptake were noted in whole
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brain and cerebellum at all PNDs tested. Significant decreases in glucose uptake were also
observed in the hippocampus except for animals tested at PND 21. The observed decrease in
glucose uptake suggests decreased neuronal activity.
Female Sprague-Dawley rats (70 days old) were administered TCE in drinking water at a
level of either 4.0 or 8.1 mg/day for 14 days prior to mating and continuing up through lactation
(Isaacson and Taylor, 1989). Only the male pups were evaluated in the studies. At PND 21,
brains were removed from the pups, sectioned, and stained to evaluate the changes in myelin.
There was a significant decrease (40% decrease) in myelinated fibers in the CA1 region of the
hippocampus of the male pups. This effect appeared to be limited to the CA1 region of the
hippocampus since other areas such as the optic tract, fornix, and cerebral peduncles did not have
decreases in myelinated fibers.
Neurological changes were found in pups exposed to TCE in a study conducted by the
National Toxicology Program (NTP) in F344 rats (George et al., 1986). TCE was administered
to rats at dietary levels of 0, 0.15, 0.30, or 0.60%. No intake calculations were presented for the
rat study and therefore, a dose rate is unavailable for this study. Open field testing revealed a
significant (p < 0.05) dose-related trend toward an increase in the time required for male and
female Fl weanling pups (PND 21) to cross the first grid in the testing device, suggesting an
effect on the ability to react to a novel environment.
Blossom et al. (2008) treated male and female MRL +/+ mice with 0 or 0.1 mg/mL TCE
in the drinking water. Treatment was initiated at the time of mating, and continued in the
females (8/group) throughout gestation and lactation. Behavioral testing consisted of righting
reflex on PNDs 6, 8, and 10; bar-holding ability on PNDs 15 and 17; and negative geotaxis on
PNDs 15 and 17. Nest building was assessed and scored on PND 35, the ability of the mice to
detect and distinguish social odors was examined with an olfactory habituation/dishabituation
method at PND 29, and a resident intruder test was performed at PND 40 to evaluate social
behaviors. Righting reflex, bar holding, and negative geotaxis were not impaired by treatment.
There was a significant association between impaired nest quality and TCE exposure in tests of
nest-building behavior; however, TCE exposure did not have an effect on the ability of the mice
to detect social and nonsocial odors using habituation and dishabituation methods. Resident
intruder testing identified significantly more aggressive activities (i.e., wrestling and biting) in
TCE-exposed juvenile male mice as compared to controls, and the cerebellar tissue from the
male TCE-treated mice had significantly lower GSH levels and GSH:oxidized GSH
(GSH:GSSG) ratios, indicating increased oxidative stress and impaired thiol status, which have
been previously reported to be associated with aggressive behaviors (Franco et al., 2006).
Histopathological examination of the brain did not identify alterations indicative of neuronal
damage or inflammation.
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4.3.8.3. Summary and Conclusions for the Developmental Neurotoxicity Studies
Gestational exposure to TCE in humans has resulted in several developmental
abnormalities. These changes include neuroanatomical changes such as neural tube defects
(ATSDR, 2001: Bove, 1996: Boveetal.. 1995: Lagakos et al.. 1986) and encephalopathy (White
et al., 1997). Clinical neurological changes such as impaired cognition (White etal., 1997:
Bernad et al., 1987), aggressive behavior (Bernad et al., 1987), and speech and hearing
impairment (Burg and Gist, 1999: White et al., 1997) are also observed when TCE exposure
occurs in utero.
In animal studies, anatomical and clinical developmental neurotoxicity is also observed.
Following inhalation exposures of 150 ppm to mice during mating and gestation, the specific
gravity of offspring brains was significantly decreased at postnatal time points through the age of
weaning; this effect did not persist to 1 month of age (Westergren et al., 1984). In studies
reported by Taylor et al. (1985), Isaacson and Taylor (1989), and Noland-Gerbec et al. (1986),
312 mg/L exposures in drinking water that were initiated 2 weeks prior to mating and continued
to the end of lactation resulted in: (1) significant increase in exploratory behavior at PNDs 60
and 90; (2) reductions in myelination in the CA1 hippocampal region of offspring at weaning;
and (3) significantly decreased uptake of 2-deoxyglucose in the rat brain at PND 21. Gestational
exposures to mice (Fredriksson et al., 1993) resulted in significantly decreased rearing activity
on PND 60, and dietary exposures during the course of a continuous breeding study in rats
(George et al., 1986) found a significant trend toward increased time to cross the first grid in
open field testing. In a study by Blossom et al. (2008), male mice exposed gestationally to TCE
exhibited lower GSH levels and lower GSH:GSSG ratios, which are also observed in mice that
have more aggressive behaviors (Franco et al., 2006).
4.3.9. Mechanistic Studies of TCE Neurotoxicity
4.3.9.1. Dopamine Neuron Disruption
There are very recent laboratory animal findings resulting from short-term TCE
exposures that demonstrate vulnerability of dopamine neurons in the brain to this chlorinated
hydrocarbon. The key limitation of these laboratory animal studies is that only one dosing
regimen was included in each study. Moreover, there has been no systematic body of data to
show that other chlorinated hydrocarbons such as tetrachloroethylene or aromatic solvents
similarly target this cell type. Confidence in the limited data regarding dopamine neuron death
and in vivo TCE exposure would be greatly enhanced by identifying a dose-response
relationship. If indeed TCE can target dopamine neurons, it would be anticipated that human
exposure to this agent would result in elevated rates of parkinsonism. There are no systematic
studies of this potential relationship in humans, although one limited report attempted to address
this possibility. Difficulties in subject recruitment into that study limit the weight that can be
given to the results.
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Endogenously formed chlorinated tetrahydro-beta-carbolines (TaClo) have been
suggested to contribute to the development of Parkinson-like symptoms (Kochen etal., 2003;
Riederer et al., 2002; Bringmann et al., 1995; Bringmann et al., 1992). TaClo can be formed
endogenously from metabolites of TCE such as trichloroacetaldehyde. TaClo has been
characterized as a potent neurotoxicant to the dopaminergic system. Some research groups have
hypothesized that Parkinson-like symptoms resulting from TCE exposure may occur through the
formation of TaClo, but not enough evidence is available to determine if this mechanism occurs.
4.3.9.1.1. Dopamine neuron disruption: human studies
There are no human studies that present evidence that TCE exposure results in dopamine
neuron disruption. Nagaya et al. (1990) examined serum dopamine p-hydroxylase activity
without differences observed in mean activities between control and exposed subjects. In the
study, 84 male workers exposed to TCE were compared to 83 male age-matched controls. The
workers had constantly used TCE in their jobs and their length of employment ranged from
0.1 to 34 years.
4.3.9.1.2. Dopamine neuron disruption: animal studies
There are limited data from mice and rats that suggest the potential for TCE to disrupt
dopamine neurons in the basal ganglia (see Table 4-35). Gash et al. (2008) showed that TCE
administered by gavage in F344 rats (n = 9) at an exposure level of 1,000 mg/kg-day,
5 days/week, for 6 weeks yielded degeneration of dopamine neurons in the substantia nigra and
alterations in dopamine turnover as reflected in a shift in dopamine metabolite to parent
compound ratios. Guehl et al. (1999) reported similar findings in OF1 mice (n = 10) that were
injected i.p. with 400 mg/kg-day TCE 5 days/week for 4 weeks. Each of these studies evaluated
only a single dose level of TCE, so establishing a dose-response relationship is not possible.
Consequently, these data are of limited utility in risk assessment because they do not establish the
potency of TCE to damage dopamine neurons. They are important, however, in identifying a
potential permanent impairment that might occur following TCE exposure at relatively high
exposure doses. They also identify a potential mechanism by which TCE could produce CNS
injury.
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Table 4-35. Summary of animal dopamine neuronal studies
Reference"
Guehl et al.
(1999)
Gash et al.
(2008)
Exposure route
i.p.
administration
Gavage
Species/strain/
sex/number
Mouse, OF1, male,
10
Rat, F344, male,
9/group
Dose level/
exposure
duration
Oand
400 mg/kg-d;
5 d/wk, 4 wks
Oand
1,000 mg/kg-d;
5 d/wk, 6 wks
NOAEL;
LOAEL
LOAEL:
400 mg/kg-d
LOAEL:
1,000 mg/kg-d
Effects
Significant dopaminergic
neuronal death in substantia
nigra.
Degeneration of dopamine-
containing neurons in
substantia nigra.
Change in dopamine
metabolism.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
4.3.9.1.3. Summary and conclusions of dopamine neuron studies
Only two animal studies have reported changes in dopamine neuron effects from TCE
exposure (Gash et al., 2008; Guehl et al., 1999). Both studies demonstrated toxicity to
dopaminergic neurons in the substantia nigra in rats or mice. LOAELs of 400 mg/kg-day (mice:
Guehl et al., 1999) and 1,000 mg/kg-day (rats: Gash et al., 2008) were reported for this effect.
Dopaminergic neuronal degeneration following TCE exposure has not been studied in humans.
However, there were no changes in serum dopamine p-hydroxylase activity in TCE-exposed or
control individuals (Nagaya et al., 1990). Loss of dopaminergic neurons in the substantia nigra
also occurs in patients with Parkinson's disease and the substantia nigra is an important region in
helping to control movements. As a result, loss of dopaminergic neurons in the substantia nigra
may be one of the potential mechanisms involved in the clinical psychomotor effects that is
observed following TCE exposure.
4.3.9.2. Neurochemical and Molecular Changes
There are limited data obtained only from laboratory animals that TCE exposure may
have consequences on GABAergic (gamma-amino butyric acid [GABA]) and glutamatergic
neurons (Shih et al., 2001; see Table 4-36; Briving et al., 1986). However, the data obtained are
limited with respect to brain region examined, persistence of effect, and whether there might be
functional consequences to these changes. The data of Briving et al. (1986) demonstrating
changes in cerebellar high affinity uptake for GABA and glutamate following chronic, low-level
(50 and 150 ppm) TCE exposure do not appear to be reflected in the only other brain region
evaluated (hippocampus). However, glutamate levels were increased in the hippocampus. The
data of Shih et al. (2001) are indirect in that it shows an altered response to GABAergic
antagonist drugs in mice treated by acute injection with 250, 500, 1,000, and 2,000 mg/kg TCE.
However, these data do show some dose dependency with significant findings observed with
TCE exposure as low as 250 mg/kg.
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Table 4-36. Summary of neurophysiological, neurochemical, and
neuropathological effects with TCE exposure
Reference"
Exposure
route
Species/strain/
sex/number
Dose level/
exposure duration
NOAEL; LOAEL
Effects
Neurophysiological studies
Shih et al.
(2001)
Ohta et al.
(2001)
i.p.
i.p.
Mouse, MF1,
male, 6/group
Mouse, ddY,
male, 5/group
0, 250 500, 1,000, or
2,000 mg/kg, 15 min;
followed by tail
infusion of PTZ
(5 mg/mL), picrotoxin
(0.8 mg/mL),
bicuculline
(0.06 mg/mL),
strychnine
(0.05 mg/mL), 4-AP
(2 mg/mL), or NMDA
(8 mg/mL)
0, 300, or
1,000 mg/kg,
sacrificed 24 hrs after
injection
-
LOAEL: 300 mg/kg
Increased threshold for
seizure appearance
with TCE pretreatment
for all convulsants.
Effects strongest on the
GABAA antagonists,
PTZ, picrotoxin, and
bicuculline, suggesting
GAB AA receptor
involvement. NMDA
and glycine Re
involvement also
suggested.
Decreased response
(long-term potentiation
response) to tetanic
stimulation in the
hippocampus.
Neurochemical studies
Briving et al.
(1986)
Subramoniam
et al. (1989)
Inhalation
Oral
Gerbils,
Mongolian, male
and female,
6/group
Rat, Wistar,
female
0, 50, or 150 ppm,
continuous, 24 hrs/d,
12 mo
0 or 1,000 mg/kg, 2 or
20 hrs.
0 or 1,000 mg/kg-d,
5 d/wk, 1 yr
NOAEL: 50 ppm;
LOAEL: 150 ppm
for glutamate levels
in hippocampus.
NOAEL: 150 ppm
for glutamate and
GABA uptake in
hippocampus.
LOAEL: 50 ppm for
glutamate and
GABA uptake in
cerebellar vermis.
-
Increased glutamate
levels in the
hippocampus.
Increased glutamate
and GABA uptake in
the cerebellar vermis.
PI and PIP decreased
by 24 and 17% at 2 hrs.
PIP and PIP2 increased
by 22 and 3 8% at
20 hrs.
PI, PIP, and PIP2
reduced by 52, 23, and
45% in 1 yr study.
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Table 4-36. Summary of neurophysiological, neurochemical, and
neuropathological effects with TCE exposure (continued)
Reference"
Haglid et al.
(1981)
Exposure
route
Inhalation
Species/strain/
sex/number
Gerbil,
Mongolian, male
and female, 6-
7/group
Dose level/
exposure duration
0, 60, or 320 ppm,
24 hrs/d, 7 d/wk, 3 mo
NOAEL; LOAEL
LOAEL: 60 ppm,
brain protein
changes.
NOAEL: 60 ppm;
LOAEL: 320 ppm,
brain DNA changes
Effects
(1) Decreases in total
brain soluble protein
whereas increase in
S 100 protein.
(2) Elevated DNA in
cerebellar vermis and
sensory motor cortex.
Neuropathological studies
Kjellstrand
et al. (1987)
Isaacson and
Taylor (1989)
Inhalation
Oral
Mouse,
NMRI, male
Rat, Sprague-
Dawley,
female
Rat, Sprague-
Dawley,
females,
six dams/
group
0, 150, or 300 ppm,
24 hrs/d, 4 or 24 d
0, 300 ppm, 24 hrs/d,
4 or 24 d
0,312, or 625 mg/L.
(0,4.0, or 8.1 mg/d);
dams (and pups)
exposed from 14 d
prior to mating until
end of lactation
LOAEL: 150 ppm,
4 and 24 d
NOAEL: 300 ppm,
4d.
LOAEL: 300 ppm,
24 d.
LOAEL: 3 12 mg/L
Sciatic nerve
regeneration was
inhibited in both mice
and rats.
Significant
I myelinated fibers in
the stratum lacunosum-
moleculare of pups.
Reduction in myelin in
the hippocampus.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
NMDA = N-nitrosodimethylamine; PI = phosphatidyl inositol; PIP = phosphatidyl inositol-4-phosphate;
PIP2 = phosphatidylinositol-4,5-bisphosphate; PTZ = pentylenetetrazole
The development and physiology of the hippocampus have also been evaluated in
two different studies (Ohtaet al., 2001; Isaacson and Taylor, 1989). Isaacson and Taylor (1989)
found a 40% decrease in myelinated fibers from hippocampi dissected from neonatal Sprague-
Dawley rats (n = 2-3) that were exposed to TCE (4 and 8.1 mg/day) in utero and during the
preweaning period. Ohta et al. (2001) injected male ddY mice with 300 mg/kg TCE and found a
significant reduction in response to titanic stimuli in excised hippocampal slices. Both of these
studies demonstrated that there is some interaction with TCE and the hippocampal area in the
brain.
Impairment of sciatic nerve regeneration was demonstrated in mice and rats exposed to
TCE (Kjellstrand et al., 1987). Under heavy anesthesia, the sciatic nerve of the animals was
artificially crushed to create a lesion. Prior to the lesion, some animals were pre-exposed to TCE
for 20 days and then for an additional 4 days after the lesion. Another set of animals was only
exposed to TCE for 4 days following the sciatic nerve lesion. For mice, regeneration of the
sciatic nerve in comparison to air-exposed animals was 20 and 33% shorter in groups exposed to
150 and 300 ppm TCE for 4 days, respectively. This effect did not significantly increase in mice
pre-exposed to TCE for 20 days, and the regeneration was 30% shorter in the 150-ppm group
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and 22% shorter in the 300-ppm group. Comparatively, a 10% reduction in sciatic nerve
regeneration length was observed in rats exposed to TCE for 20 days prior to the lesion plus the
4 days after the sciatic nerve lesion.
There are also a few in vitro studies (summarized in Table 4-37) that have demonstrated
that TCE exposure alters the function of inhibitory ion channels such as GABAA and glycine
receptors (Beckstead et al., 2000; Krasowski and Harrison, 2000), and serotonin receptors
(Lopreato et al., 2003). Krasowski and Harrison (2000) and Beckstead et al. (2000) were able to
demonstrate that human GABAA and glycine receptors could be potentiated by TCE when a
receptor agonist was coapplied. Krasowski and Harrison (2000) conducted an additional
experiment in order to determine if TCE was interacting with the receptor or perturbating the
cellular membrane (bilipid layer). Specific amino acids on the GABAA and glycine receptors
were mutated and in the presence of a receptor agonist (GAB A for GABAA and glycine for
glycine receptors) and in these mutated receptors, TCE-mediated potentiation was significantly
decreased or abolished, suggesting that there was an interaction between TCE and these
receptors. Lopreato et al. (2003) conducted a similar study with the 5HT3A serotonin receptor
and found that when TCE was coapplied with serotonin, there was a potentiation in receptor
response. Additionally, TCE has been demonstrated to alter the function of voltage sensitive
calcium channels (VSCCs) by inhibiting the calcium mediated-current at a holding potential of
-70 mV and shifting the activation of the channels to a more hyperpolarizing potential (Shafer et
al.. 2005).
Table 4-37. Summary of in vitro ion channel effects with TCE exposure
Reference
Cellular
system
Neuronal channel/
receptor
Concentrations
Effects
In vitro studies
Shafer et al.
(2005)
Beckstead
et al. (2000)
Lopreato et al.
(2003)
Krasowski and
Harrison
(2000)
PC12 cells
Xenopus
oocytes
Xenopus
oocytes
Human
embryonic
kidney 293 cells
vscc
Human
recombinant:
glycine receptor al,
GABAA receptors,
alpl, alp2y2L
Human recombinant
serotonin 3 A
receptor
Human recombinant
Glycine receptor al,
GABAA receptors
a2pl
0, 500, 1,000,
1,500, or
2,000 uM
0 or 390 uM
0 or 390 uM
Not provided
Shift of VSCC activation to a more
hyperpolarizing potential.
Inhibition of VSCCs at a holding
potential of -70 mV.
50% potentiation of the GABAA
receptors; 100% potentiation of the
glycine receptor.
Potentiation of serotonin receptor
function.
Potentiation of glycine receptor function
with an EC50 of 0.65 ± 0.05 mlVf .
Potentiation of GABAA receptor function
with an EC50 of 0.85 ± 0.2 mM.
aEC50 = concentration of the chemical at which 50% of the maximal effect is produced
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4.3.10. Potential Mechanisms for TCE-Mediated Neurotoxicity
The mechanisms of TCE neurotoxicity have not been established despite a significant
level of research on the outcomes of TCE exposure. Results from several mechanistic studies
can be used to help elucidate the mechanism(s) involved in TCE-mediated neurological effects.
The disruption of the trigeminal nerve appears to be a highly idiosyncratic outcome of
TCE exposure. There are limited data to suggest that it might entail a demyelination
phenomenon, but similar demyelination does not appear to occur in other nerve tracts. In this
regard, then, TCE is unlike a variety of hydrocarbons that have more global demyelinating
action. There are some data from CNS data that focus on shifts in lipid profiles as well as data
showing loss of myelinated fibers in the hippocampus. However, the changes in lipid profiles
are both quite small and, also, inconsistent. And the limited data from hippocampus are not
sufficient to conclude that TCE has significant demyelinating effects in this key brain region.
Indeed, the bulk of the evidence from studies of learning and memory function (which would be
tied to hippocampal function) suggests no clear impairments due to TCE.
Some researchers (Albee et al., 2006; Albee et al., 1997; Laureno, 1993; Barret et al.,
1992; Barret etal., 1991; Laureno, 1988) have indicated that changes in trigeminal nerve
function may be due to dichloroacetylene, which is formed under nonbiological conditions of
high alkalinity or temperature during volatilization of TCE. In experimental settings, trigeminal
nerve function (Albee et al., 1997) and trigeminal nerve morphology (Barret et al., 1992; Barret
et al., 1991) were found to be more altered following a low exposure to dichloroacetylene in
comparison to the higher TCE exposure. Barret et al. (1992: 1991) also demonstrated that TCE
administration results in morphological changes in the trigeminal nerve. Thus, dichloroacetylene
may contribute to trigeminal nerve impairment may be plausible following an inhalation
exposure under conditions favoring its formation. Examples of such conditions include passing
through a carbon dioxide scrubber containing alkaline materials, application to remove a wax
coating from a concrete-lined stone floor, or mixture with alkaline solutions or caustic (Bingham
etal., 2001; Greim etal., 1984; Saunders, 1967). However, dichloroacetylene exposures have
not been identified or measured in human epidemiologic studies with TCE exposure, and thus,
do not appear to be common to occupational or residential settings (Lash and Green, 1993).
Moreover, changes in trigeminal nerve function have also been consistently reported in humans
exposed to TCE following an oral exposure (Kilburn, 2002b, a), across many human studies of
occupational and drinking water exposures under conditions with highly varying potentials for
dichloroacetylene formation individuals (Feldman et al., 1988; Barret et al., 1987; Barret et al.,
1984; Barret et al., 1982). As a result, the mechanism(s) for trigeminal nerve function
impairment following TCE exposure is unknown (Campo et al., 2007; Mhiri et al., 2004;
Kilburn. 2002a: Kilburn and Warshaw, 1993a: Ruiiten et al.. 1991). The varying
dichloroacetylene exposure potential across these studies suggests TCE exposure, which is
common to all of them, as the most likely etiologic agent for the observed effects.
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The clearest consequences of TCE are permanent impairment of hearing in animal
models and disruption of trigeminal nerve function in humans with animal models showing
comparable changes following administration of a TCE metabolite. With regard to hearing loss,
the effect of TCE has much in common with the effects of several aromatic hydrocarbons
including ethylbenzene, toluene, and/?-xylene. Many studies have attempted to determine how
these solvents damage the cochlea. Of the hypotheses that have been advanced, there is little
evidence to suggest oxidative stress, changes in membrane fluidity, or impairment of central
efferent nerves whose endings innervate receptor cells in the cochlea. Rather, for reasons that
are still uncertain, these solvents seem to preferentially target supporting cells in the cochlea
whose death then alters key structural elements of the cochlea resulting ultimately in hair cell
displacement and death. Recently, potential modes of action resulting in ototoxicity have been
speculated to be due to blockade of neuronal nicotinic receptors present on the auditory cells
(Campo et al., 2007) and potentially changes in calcium transmission (Maguin et al., 2009)ui
from toluene exposure. Although these findings were reported following an acute toluene
exposure, it is speculated that this mechanism may be a viable mechanism for TCE-mediated
ototoxicity.
A few studies have tried to relate TCE exposure with selective impairments of dopamine
neurons. Two studies (Gash et al., 2008; Guehl etal., 1999) demonstrated dopaminergic
neuronal death and/or degeneration following an acute TCE administration. However, the only
human TCE exposure study examining dopamine neuronal activity found no changes in serum
dopamine p-hydroxylase activity in comparison to nonexposed individuals (Nagaya et al., 1990).
It is thought that TaClo, which can be formed from TCE metabolites such as
trichloroacetaldehyde, may be the potent neurotoxicant that selectively targets the dopaminergic
system. More studies are needed to confirm the dopamine neuronal function disruption and if
this disruption is mediated through TaClo.
There is good evidence that TCE and certain metabolites such as choral hydrate have
CNS depressant properties and may account for some of the behavioral effects (such as
vestibular effects, psychomotor activity changes, central visual changes, sleep and mood
changes) that have been observed with TCE. Specifically, in vitro studies have demonstrated
that TCE exposure results in changes in neuronal receptor function for the GABAA, glycine, and
serotonin receptors (Lopreato et al., 2003; Beckstead et al., 2000; Krasowski and Harrison,
2000). All of these inhibitory receptors that are present in the CNS are potentiated when a
receptor-specific agonist and TCE are applied. These results are similar to other anesthetics and
suggest that some of the behavioral functions are mediated by modifications in ion channel
function. However, it is quite uncertain whether there are persistent consequences to such high
dose TCE exposure. Additionally, with respect to the GABAergic system, acute administration
of TCE increased the seizure threshold appearance and this effect was the strongest with
convulsants that were GABA receptor antagonists (Shih et al., 2001). Therefore, this result
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suggests that TCE interacts with the GABA receptor; this was also verified in vitro (Beckstead et
al., 2000; Krasowski and Harrison, 2000).
TCE exposure has also been linked to decreased sensitivity to titanic stimulation in the
hippocampus (Ohta et al., 2001) as well as to a significant reduction in myelin in the
hippocampus in a developmental exposure study (Isaacson et al., 1990). These effects are
notable since the hippocampus is highly involved in memory and learning functions. Changes in
the hippocampal physiology may correlate with the cognitive changes that were reported
following TCE exposure.
4.3.11. Overall Summary and Conclusions—Weight of Evidence
Both human and animal studies have associated TCE exposure with effects on several
neurological domains. The strongest neurological evidence of hazard in humans is for changes
in trigeminal nerve function or morphology and impairment of vestibular function. Fewer and
more limited evidence exists in humans on delayed motor function and changes in auditory,
visual, and cognitive function or performance. Acute and subchronic animal studies show
morphological changes in the trigeminal nerve, disruption of the peripheral auditory system
leading to permanent function impairments and histopathology, changes in visual evoked
responses to patterns or flash stimulus, and neurochemical and molecular changes. Additional
acute studies reported structural or functional changes in hippocampus, such as decreased
myelination or decreased excitability of hippocampal CA1 neurons, although the relationship of
these effects to overall cognitive function is not established. Some evidence exists for motor-
related changes in rats/mice exposed acutely/subchronically to TCE, but these effects have not
been reported consistently across all studies.
Epidemiologic evidence supports a relationship between TCE exposure and trigeminal
nerve function changes, with multiple studies in different populations reporting abnormalities in
trigeminal nerve function in association with TCE exposure (Mhiri et al., 2004; Kilburn, 2002b,
a; Kilburn and Warshaw. 1993a: Feldman et al.. 1992: Ruiiten et al.. 1991: Feldman et al.. 1988:
Barret etal.. 1987: Barret etal.. 1984: Barret etal.. 1982). Of these, two well-conducted
occupational cohort studies, each including >100 TCE-exposed workers without apparent
confounding from multiple solvent exposures, additionally reported statistically significant dose-
response trends based on ambient TCE concentrations, duration of exposure, and/or urinary
concentrations of the TCE metabolite TCA (Barret et al., 1987: Barret etal., 1984). Limited
additional support is provided by a positive relationship between prevalence of abnormal
trigeminal nerve or sensory function and cumulative exposure to TCE (most subjects) or
CFC113 (<25% of subjects) (Rasmussen et al., 1993a). Test for linear trend in this study was
not statistically significant and may reflect exposure misclassification since some subjects
included in this study did not have TCE exposure. The lack of association between TCE
exposure and overall nerve function in three small studies (ulnar and medial: Triebig et al., 1983:
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Triebig et al., 1982; trigeminal: El Ghawabi et al., 1973) does not provide substantial evidence
against a causal relationship between TCE exposure and trigeminal nerve impairment because of
limitations in statistical power, the possibility of exposure misclassification, and differences in
measurement methods. Laboratory animal studies have also shown TCE-induced changes in the
trigeminal nerve. Although one study reported no significant changes in TSEP in rats exposed to
TCE for 13 weeks (Albee et al., 2006), there is evidence of morphological changes in the
trigeminal nerve following short-term exposures in rats (Barret et al., 1992; Barret et al., 1991).
Human chamber, occupational, geographic-based/drinking water, and laboratory animal
studies clearly established TCE exposure causes transient impairment of vestibular function.
Subjective symptoms such as headaches, dizziness, and nausea resulting from occupational (Liu
et al., 1988; Rasmussen and Sabroe, 1986; Smith, 1970; Grand]ean et al., 1955), environmental
(Hirsch et al., 1996), or chamber exposures (Smith, 1970; Stewart et al., 1970) have been
reported extensively. A few laboratory animal studies have investigated vestibular function,
either by promoting nystagmus or by evaluating balance (Umezu et al., 1997; Niklasson et al.,
1993: Thametal., 1984: Thametal., 1979).
In addition, mood disturbances have been reported in a number of studies, although these
effects also tend to be subjective and difficult to quantify (Gash et al., 2008: Kilburn, 2002b, a;
Kilburn and Warshaw, 1993a: Troster and Ruff, 1990: McCunney, 1988: Rasmussen and Sabroe,
1986: Mitchell and Parsons-Smith, 1969), and a few studies have reported no effects from TCE
on mood (Reif et al., 2003: Triebig et al., 1977a: Triebig et al., 1976). Few comparable mood
studies are available in laboratory animals, although both Moser et al. (2003) and Albee et al.
(2006) reported increases in handling reactivity among rats exposed to TCE. Finally, a
significantly increased number of sleep hours was reported by Arito et al. (1994) in rats exposed
via inhalation to 50-300 ppm TCE for 8 hours/day for 6 weeks.
Four epidemiologic studies of chronic exposure to TCE observed disruption of auditory
function. One large occupational cohort study showed a statistically significant difference in
auditory function with cumulative exposure to TCE or CFC113 as compared to control groups
after adjustment for possible confounders, as well as a positive relationship between auditory
function and increasing cumulative exposure (Rasmussen et al., 1993c). Of the three studies
based on populations from ATSDR's TCE Subregistry from the National Exposure Registry,
more limited than Rasmussen et al. (1993c) due to inferior exposure assessment, Burg et al.
(1995) and Burg and Gist (1999) reported a higher prevalence of self-reported hearing
impairments. The third study reported that auditory screening revealed abnormal middle ear
function in children <10-years-old, although a dose-response relationship could not be
established and other tests did not reveal differences in auditory function (ATSDR, 2002).
Further evidence for these effects is provided by numerous laboratory animal studies
demonstrating that high-dose subacute and subchronic TCE exposures in rats disrupt the auditory
system, leading to permanent functional impairments and histopathology.
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Studies in humans exposed under a variety of conditions, both acutely and chronically,
report impaired visual functions such as color discrimination, visuospatial learning tasks, and
visual depth perception in subjects with TCE exposure. Abnormalities in visual depth perception
were observed with a high acute exposure to TCE under controlled conditions (Vernon and
Ferguson, 1969). Studies of lower TCE exposure concentrations also observed visuofunction
effects. One occupational study (Rasmussen et al., 1993c) reported a statistically significant
positive relationship between cumulative exposure to TCE or CFC113 and visual gestalts
learning and retention among Danish degreasers. Two studies of populations living in a
community with drinking water containing TCE and other solvents further suggested changes in
visual function (Reif etal., 2003; Kilburn, 2002b, a). These studies used more direct measures
of visual function as compared to Rasmussen et al. (1993c), but their exposure assessment is
more limited because TCE exposure is not assigned to individual subjects (Kilburn, 2002b, a) or
because there are questions regarding control selection (Kilburn, 2002b, a) and exposure to
several solvents (Reif etal.. 2003: Kilburn. 2002b, a).
Additional evidence of effects of TCE exposure on visual function is provided by a
number of laboratory animal studies demonstrating that acute or subchronic TCE exposure
causes changes in visual evoked responses to patterns or flash stimulus (Boyes et al., 2005a:
Boyes etal., 2003; Blain et al., 1994). Animal studies have also reported that the degree of some
effects is correlated with simultaneous brain TCE concentrations (Boyes et al., 2005a: Boyes et
al., 2003) and that, after a recovery period, visual effects return to control levels (Blain et al.,
1994; Rebertet al., 1991). Overall, the human and laboratory animal data together suggest that
TCE exposure can cause impairment of visual function, and some animal studies suggest that
some of these effects may be reversible with termination of exposure.
Studies of human subjects exposed to TCE either acutely in chamber studies or
chronically in occupational settings have observed deficits in cognition. Five chamber studies
reported statistically significant deficits in cognitive performance measures or outcome measures
suggestive of cognitive effects (Triebig et al., 1977a: Triebig et al., 1977b: Gamberale et al.,
1976; Triebig et al., 1976: Stewart et al., 1970). Danish degreasers with high cumulative
exposure to TCE or CFC113 had a high risk (OR: 13.7, 95% CI: 2.0-92.0) for psychoorganic
syndrome characterized by cognitive impairment, personality changes, and reduced motivation,
vigilance, and initiative compared to workers with low cumulative exposure. Studies of
populations living in a community with contaminated groundwater also reported cognitive
impairments (Kilburn, 2002b, a; Kilburn and Warshaw, 1993a), although these studies carry less
weight in the analysis because TCE exposure is not assigned to individual subjects and their
methodological design is weaker.
Laboratory studies provide some additional evidence for the potential for TCE to affect
cognition, though the predominant effect reported has been a change in the time needed to
complete a task, rather than impairment of actual learning and memory function (Umezu et al..
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1997; Kishi et al., 1993; Kulig, 1987). In addition, in laboratory animals, it can be difficult to
distinguish cognitive changes from motor-related changes. However, several studies have
reported structural or functional changes in the hippocampus, such as decreased myelination
(Isaacson et al., 1990; Isaacson and Taylor, 1989) or decreased excitability of hippocampal CA1
neurons (Ohta et al., 2001), although the relationship of these effects to overall cognitive
function has not been established.
Two studies of TCE exposure, one chamber study of acute exposure duration and
one occupational study of chronic duration, reported changes in psychomotor responses. The
chamber study of Gamberale et al. (1976) reported a dose-related decrease in performance in a
CRT test in healthy volunteers exposed to 100 and 200 ppm TCE for 70 minutes as compared to
the same subjects without exposure. Rasmussen et al. (1993a) reported a statistically significant
association with cumulative exposure to TCE or CFC113 and dyscoordination trend among
Danish degreasers. Observations in a third study (Gunetal., 1978) are difficult to judge given
the author's lack of statistical treatment of data. In addition, Gash et al. (2008) reported that
14/30 TCE-exposed workers exhibited significantly slower fine motor hand movements as
measured through a movement analysis panel test. Studies of population living in communities
with TCE and other solvents detected in groundwater supplies reported significant delays in
SRTs and CRTs in individuals exposed to TCE in contaminated groundwater as compared to
referent groups (Kilburn, 2002b, a; Kilburn and Thornton, 1996; Kilburn and Warshaw, 1993a).
Observations in these studies are more uncertain given questions of the representativeness of the
referent population, lack of exposure assessment to individual study subjects, and inability to
control for possible confounders including alcohol consumption and motivation. Finally, in a
presentation of two case reports, decrements in motor skills as measured by the grooved
pegboard and finger tapping tests were observed (Troster and Ruff, 1990).
Laboratory animal studies of acute or subchronic exposure to TCE observed psychomotor
effects, such as loss of righting reflex (Shih etal., 2001; Umezu et al., 1997) and decrements in
activity, sensory-motor function, and neuromuscular function (Moser et al., 2003; Moser et al.,
1995; Kishi et al., 1993). However, two studies also noted an absence of significant changes in
some measures of psychomotor function (Albee et al., 2006; Kulig, 1987). In addition, less
consistent results have been reported with respect to locomotor activity in rodents. Some studies
have reported increased locomotor activity after an acute i.p. dosage (Wolff and Siegmund,
1978) or decreased activity after acute- or short-term gavage dosing (Moser et al., 1995, 2003).
No change in activity was observed following exposure through drinking water (Waseem et al.,
2001), inhalation (Kulig, 1987), or orally during the neurodevelopment period (Fredriksson et
al., 1993).
Several neurochemical and molecular changes have been reported in laboratory
investigations of TCE toxicity. Kjellstrand et al. (1987) reported inhibition of sciatic nerve
regeneration in mice and rats exposed continuously to 150 ppm TCE via inhalation for 24 days.
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Two studies reported changes in GAB Aergic and glutamatergic neurons in terms of GAB A or
glutamate uptake (Briving etal., 1986) or response to GABAergic antagonistic drugs (Shih et al.,
2001) as a result of TCE exposure, with the Briving et al. (1986) study conducted at 50 ppm for
12 months. Although the functional consequences of these changes is unclear, Tham et al.
(1984; 1979) described central vestibular system impairments as a result of TCE exposure that
may be related to altered GABAergic function. In addition, several in vitro studies have
demonstrated that TCE exposure alters the function of inhibitory ion channels such as receptors
for GABAA glycine, and serotonin (Lopreato et al., 2003; Beckstead et al., 2000; Krasowski and
Harrison, 2000) or of voltage-sensitive calcium channels (Shafer et al., 2005).
4.4. KIDNEY TOXICITY AND CANCER
4.4.1. Human Studies of Kidney
4.4.1.1. Nonspecific Markers of Nephrotoxicity
Investigations of nephrotoxicity in human populations show that workers highly exposed
to TCE exhibit evidence of damage to the proximal tubule (NRC, 2006). The magnitude of
exposure needed to produce kidney damage is not clear. Several kidney early biological effect
markers, or biomarkers, are examined in these studies, as are less sensitive clinical kidney
outcomes such as glomerular filtration rate and end-stage disease. Observation of elevated
excretion of urinary proteins in the four studies of TCE exposure (Bolt et al., 2004; Green et al.,
2004; Briming et al., 1999a: Briming et al., 1999b) indicates the occurrence of a toxic insult
among TCE-exposed subjects compared to unexposed controls. Two studies are of subjects with
previously diagnosed kidney cancer (Bolt et al., 2004; B riming et al., 1999a), with limited
interpretation of whether effects are associated with exposure or to the disease process. Subjects
in Briining et al. (1999b) and Green et al. (2004) were disease-free. Urinary proteins are
considered nonspecific markers of nephrotoxicity and include al-microglobulin, albumin, and
TV-acetyl-p-D-glucosaminidase (NAG; Lybarger et al., 1999; Price etal., 1999; Price et al.,
1996). Four studies measure al-microglobulin with elevated excretion observed in the German
studies (Bolt et al., 2004: Briining et al., 1999a: Briining et al., 1999b) but not Green et al.
(2004). However, Green et al. (2004) found statistically significant group mean differences in
NAG, another nonspecific marker of tubular toxicity, in disease-free subjects. Observations in
Green et al. (2004) provide evidence of tubular damage among workers exposed to TCE at
32 ppm (mean) (range, 0.5-252 ppm). Elevated excretion of NAG as a nonspecific marker of
tubular damage has also been observed with acute TCE poisoning (Carried et al., 2007). These
and other studies relevant to evaluating TCE nephrotoxicity are discussed in more detail below.
Biological monitoring of persons who previously experienced —hjh" exposures to TCE
(100-500 ppm) in the workplace show altered kidney function evidenced by urinary excretion of
proteins suggestive of renal tubule damage. Similar results were observed in the only study
available of subjects with TCE exposure at current occupational limits (NRC, 2006). Table 4-38
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provides details and results from these studies. Briining et al. (1999a) reported that a statistically
significantly higher prevalence of elevated proteinuria suggestive of severe tubular damage
(n = 24, 58.5%, p< 0.01) and an elevated excretion of al-microglobin, another urinary
biomarker of renal tubular function, were observed in 41 RCC cases with prior TCE exposure
and with pending workman's compensation claims compared with the nonexposed renal cell
cancer patients (n = 14, 28%) and to hospitalized surgical patients (n = 2, 2%). Statistical
analyses did not adjust for differences in median systolic and diastolic blood pressure that
appeared higher in exposed RCC cases compared to nonexposed controls. Similarly, severe
tubular proteinuria is seen in 14/39 workers (35%) exposed to TCE in the electrical department,
fitters shop, and through general degreasing operations of felts and sieves in a cardboard
manufacturing factory compared to no subjects of 46 nonexposed males office and
administrative workers from the same factory (p < 0.01) (Briining et al., 1999b). Furthermore,
slight tubular proteinuria was seen in 20% of exposed workers and 2% of nonexposed workers
(Briining et al., 1999b). Exposed subjects also had statistically significantly elevated levels of
al-microglobulin compared to unexposed controls. Subjects with tubular damage, as indicated
by urinary protein patterns, had higher GST-alpha concentrations than nonexposed subjects
(p < 0.001). Both sex and use of spot or 24-hour urine samples were shown to influence
al-microglobulin (Andersson et al., 2008): however, these factors are not considered to greatly
influence observations given that only males were subjects and al-microglobulin levels in spot
urine sample were adjusted for creatinine concentration.
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Table 4-38. Summary of human kidney toxicity studies
Subjects
Effect
Exposure
Reference
206 subjects—
104 male workers exposed to
TCE; 102 male controls
(source not identified)
Increased (32-microglobulin and total
protein in spot urine specimen.
p2-micro globulin:
Exposed, 129.0 ± 113.3 mg/g
creatinine.
Controls, 113.6 ± 110.6 mg/g
creatinine.
Total protein:
Exposed, 83.4 ± 113.2 mg/g
creatinine.
Controls, 54.0 ± 18.6 mg/g creatinine.
TCE exposure was through
degreasing activities in metal
parts factory or semiconductor
industry.
U-TTCs: Exposed, 83.4 mg/g
Creatinine (range, 2-
66.2 mg/g creatinine.
Controls, ND.
8.4 ± 7.9yrs mean
employment duration.
Nagaya et al.
1989a)
29 metal workers
NAG in morning urine specimen,
0.17 ± 0.11 U/mmol creatinine.
Breathing zone monitoring,
3 ppm (median) and 5 ppm
(mean).
Selden et al.
(1993)
191 subjects—
41 RCC cases pending cases
involving compensation with
TCE exposure;
50 unexposed RCC cases
from the same area as TCE-
exposed cases;
100 nondiseased control and
hospitalized surgical patients
Increased urinary proteins patterns,
al-microglobulin, and total protein in
spot urine specimen.
Slight/severe tubular damage:
TCE RCC cases, 93%.
Nonexposed RCC cases, 46%.
Surgical controls, 11%.
p<0.0l.
al-microglobulin (mg/g creatinine):
Exposed RCC cases, 24.6 ± [SD] 13.9
Unexposed RCC cases, 11.3 ± [SD]
9.8.
Surgical controls, 5.5 ± [SD] 6.8.
All exposed RCC cases
exposed to Jiigh" and -very
high" TCE intensity.
18 yrs mean exposure
duration.
Briining et al.
1999a)
85 male workers employed
in cardboard manufacturing
factory (39 TCE exposed,
46) nonexposed office and
administrative controls)
Increased urinary protein patterns and
excretion of proteins in spot urine
specimen.
Slight/severe tubular damage:
TCE exposed, 67%
Nonexposed, RCC cases, 9%
p< 0.001.
al-microglobulin (mg/g creatinine):
Exposed, 16.2 ± [SD] 10.3
Unexposed, 7.8 ± [SD] 6.9
p< 0.001.
GST-alpha (ug/g creatinine):
Exposed 6.0 ± [SD] 3.3
Unexposed, 2.0 ± [SD] 0.57
p< 0.001.
No group differences in total protein
or GST-pi.
—Hihf' TCE exposure to
workers in the fitters shop and
electrical department.
—Vej high" TCE exposure to
workers through general
degreasing operations in
carton machinery section.
Briining et al.
1999b)
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Table 4-38. Summary of human kidney toxicity studies (continued)
Subjects
Effect
Exposure
Reference
99 RCC cases and
298 hospital controls (from
Briining et al. [(2003)1 and
alive at the time of interview)
Increased excretion of al-micro-
globulin in spot urine specimen.
Proportion of subjects with al-micro-
globulin<5.0 mg/L:
Exposed cases, 15%
Unexposed cases, 51%
Exposed controls, 55%
Unexposed controls, 55%
p < 0.05, prevalence of exposed cases
compared to prevalences of either
exposed controls or unexposed
controls.
Mean al-microglobulin:
Exposed cases, 18.1 mg/L
Unexposed cases, <5.0 mg/L
p<0.05.
All exposed RCC cases
exposed to _high" and -very
high" TCE intensity.
Bolt et al.
2004)
124 subjects (70 workers
currently exposed to TCE
and 54 hospital and
administrative staff controls)
Analysis of urinary proteins in spot
urine sample obtained 4 d after
exposure.
Increased excretion of albumin, NAG,
and formate in spot urine specimen.
Albumin (mg/g creatinine):3
Exposed, 9.71 ± [SD] 11.6
Unexposed, 5.50 ± [SD] 4.27
p<0.05.
Total NAG (U/g creatinine):
Exposed, 5.27 ±[SD] 3.78
Unexposed, 2.41 ± [SD] 1.91
p<0.0l.
Format (mg/g creatinine):
Exposed, 9.45 ± [SD] 4.78
Unexposed, 5.55 ±[SD] 3.00
p<0.0l.
No group mean differences in
GST-alpha, retinol binding protein,
al-microglobulin, p2-microglobulin,
total protein, and methylmalonic acid.
Mean U-TCA of exposed
workers was 64 ± [SD] 102
(Range, 1-505).
MeanU-TCOH of exposed
workers was 122 ± [SD] 119
(Range, 1-639).
Mean TCE concentration to
exposed subjects was
estimated as 32 ppm (range,
0.5-252 ppm) and was
estimated by applying the
German occupational
exposure limit (maximale
arbeitsplatz konzentration,
MAK) standard to U-TCA
and assuming that the linear
relationship holds for
exposures >100 ppm.
86% of subjects with exposure
to <50 ppm TCE.
Green et al.
(2004)
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Table 4-38. Summary of human kidney toxicity studies (continued)
Subjects
101 cases or deaths from
ESDR among male and
female subjects in Hill Air
Force Base aircraft
maintenance worker cohort
of Blair etal. (1998)
269 cases of IgA
nephropathy or membranous
nephropathy
glomerulonephritis followed
5 yrs (mean) for progression
toESRD
Effect
TCE exposure:
Cox Proportional Hazard Analysis:
Ever exposed to TCE,b
1.86(1.02,3.39).
Logistic regression:13
No chemical exposure (referent
group): 1.0
<5unit-yr, 1.73 (0.86,3.48)
5-25 unit-yr, 1.65 (0.82, 3.35)
>25 unit-yr, 1.65 (0.82, 3.35)
Monotonic trend test,;? > 0.05.
Indirect low-intermittent TCE
exposure, 2.47(1.17, 5.19)
Indirect peak/infrequent TCE
exposure 3. 55 (1.25, 10.74)
Direct TCE exposure, -^iot
statistically significant" but hazard
ratio and CIs were not presented in
paper.
TCE exposure:
Cox Proportional Hazard Analysis:
Ever exposed to TCE,b
2.5 (0.9, 6.5)
High exposure level to TCE,b
2.7(0.7, 10.1).
Exposure
Cumulative TCE exposure
(intensity x duration)
identified using three
categories, <5 unit-yr, 5-
25 unit-yr, >25 unit-yr per
JEM of Stewart et al. (1991).
Exposure to TCE assigned
using job title and JEM; two
dose surrogates, ever exposed
and high exposure level.
Reference
Radican et al.
(2006)
Jacob et al.
(2007)
aFor a urine sample, 10-17 mg of albumin per g of creatinine is considered to be suspected albuminuria in males
(15-25 in females) (de Jong and Brenner. 2004).
bHazard ratio and 95% CI.
ESRD = end-stage renal disease; ND = not detectable
Bolt et al. (2004) measured al-microglobulin excretion in living subjects from the RCC
case-control study by Briining et al. (2003). Some subjects in this study were highly exposed.
Of the 134 with renal cell cancer, 19 reported past exposures that led to narcotic effects and 18 of
the 401 controls experienced similar effects (OR: 3.71, 95% CI: 1.80-7.54) (Briining et al..
2003). Bolt et al. (2004) found that al-microglobulin excretion increased in exposed renal
cancer patients compared with nonexposed patients controls. A lower proportion of exposed
cancer patients had normal al-microglobulin excretion, <5 mg/L, the detection level for the
assay and the level considered by these investigators as associated with no clinical or subclinical
tubule damage, and a higher proportion of high values, defined as >45 mg/L, compared to cases
who did not report TCE occupational exposure and to nonexposed controls (p < 0.05). Exposed
cases, additionally, had statistically significantly higher median concentrations of al-micro-
globulin compared to unexposed cases in creatinine-unadjusted spot urine specimens (p < 0.05).
Reduced clearance of creatinine attributable to renal cancer does not explain the lower
4-141
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percentage of normal values among exposed cases given findings of similar prevalence of
normal excretion among unexposed renal cell cases and controls.
In their study of 70 current employees (58 males, 12 females) of an electronic factory
with TCE exposure and 54 (50 males, 4 females) age-matched subjects drawn from hospital or
administrative staff, Green et al. (2004) found that urinary excretion of albumin, total NAG and
formate were increased in the exposed group compared with the unexposed group.4 No
differences between exposed and unexposed subjects were observed in other urinary proteins,
including al-microglobulin, p2-microglobulin, and GST-alpha. Green et al. (2004) stated that
NAG is not an indicator of nephropathy, or kidney damage, but rather is an indicator of
functional change in the kidney. Green et al. (2004) further concluded that increased urinary
albumin or NAG was not related to TCE exposure; analyses to examine the exposure-response
relationship found neither NAG nor albumin concentration correlated to U-TCA or employment
duration (years). The National Research Council (NRC, 2006) did not consider U-TCA as
sufficiently reliable to use as a quantitative measure of TCE exposure, concluding that the data
reported by Green et al. (2004) were inadequate to establish exposure-response information
because the relationship between U-TCA and ambient TCE intensity is highly variable and
nonlinear, and conclusions about the absence of association between TCE and nephrotoxicity
cannot be made based on U-TCA. Moreover, use of employment duration does not consider
exposure intensity differences between subjects with the same employment duration, and bias
introduced through misclassification of exposure may explain the Green et al. (2004) findings.
Selden et al. (1993), in their study of 29 metal workers (no controls), reported a
correlation between NAG and U-TCA (r = 0.48,/> < 0.01) but not with other exposure metrics of
recent or long-term exposure. Personal monitoring of worker breath indicated median and mean
TWA TCE exposures of 3 and 5 ppm, respectively. Individual NAG concentrations were within
normal reference values. Rasmussen et al. (1993b) also reported a positive relationship
(p = 0.05) between increasing urinary NAG concentration (adjusted for creatinine clearance) and
increasing duration in their study of 95 metal degreasers (no controls) exposed to either TCE
(70 subjects) or CFC113 (25 subjects). Multivariate regression analyses that adjusted for age
were suggestive of an association between NAG and exposure duration (p = 0.011). Mean
urinary NAG concentration was higher among subjects with annual exposure of >30 hours/week,
defined as peak exposure, compared to subjects with annual exposure of <30 hours/week (72.4 ±
44.1 compared to 45.9 ± 30.0 ug/g creatinine, p< 0.01).
Nagaya et al. (1989a) did not observe statistically significant group differences in urinary
p2-microglobulin and total protein in spot urine specimens of male degreasers and their controls,
nor were these proteins correlated with urinary total trichloro-compounds (U-TTCs). The paper
4Elevation of NAG in urine is a sign of proteinuria, and proteinuria is both a sign and a cause of kidney malfunction
(Zandi-Nejad et al.. 2004). For a urine sample, 10-17 mg of albumin per g of creatinine is considered to be
suspected albuminuria in males (15-25 in females) (de Jong and Brenner. 2004).
4-142
-------
lacks details on subject selection, whether urine collection was at the start of work week or after
sufficient exposure, and presentation ofp-values and correlation coefficients. The presentation
of urinary protein concentrations stratified by broad age groups is less statistically powerful than
examination of this confounder using logistic regression. Furthermore, although valid for
pharmacokinetic studies, examination of renal function using U-TTC as a surrogate for TCE
exposure is uncertain, as discussed above for Green et al. (2004).
4.4.1.2. End-Stage Renal Disease (ESRD)
ESRD is associated with hydrocarbon or organic solvent exposures in two studies
examining this endpoint (Jacob et al., 2007; Radican et al., 2006). Table 4-38 provides details
and results from Radican et al. (2006) and Jacob et al. (2007). Radican et al. (2006) assessed
ESRD in a cohort of aircraft maintenance workers at Hill Air Force Base (Blair et al., 1998) with
strong exposure assessment to TCE (NRC, 2006) and reported a twofold risk with overall TCE
exposure and ESRD (1.86, 95% CI: 1.02, 3.39). A second study, the GN-PROGRESS
retrospective cohort study, observed a twofold elevated risk for progression of
glomerulonephritis to ESRD from TCE (overall exposure: 2.5, 95% CI: 0.9-6.5; high-level TCE
exposure: 2.7, 95% CI: 0.7, 10.1) (Jacob et al., 2007). Statistical power was more limited in
Jacob et al. (2007) because of its smaller number of exposed cases, 21 with overall exposure,
compared to 56 exposed cases in Radican et al. (2006). Other occupational studies do not
examine ESRD specifically, instead reporting RRs associated with deaths due to nephritis and
nephrosis (Boice et al., 2006b: ATSDR, 2004a: Boiceet al., 1999), all genitourinary system
deaths (Ritz, 1999a: Costa et al., 1989; Garabrant et al., 1988), or providing no information on
renal disease mortality in the published paper (Chang et al., 2003; Blair et al., 1998; Morgan et
al., 1998).
4.4.2. Human Studies of Kidney Cancer
Cancer of the kidney and renal pelvis is the 6th leading cause of cancer in the
United States with an estimated 54,390 (33,130 men and 21,260 women) newly diagnosed cases
and 13,010 deaths (Jemal et al., 2008; Ries et al., 2008). Age-adjusted incidence rates based on
cases diagnosed in 2001-2005 from 17 Surveillance, Epidemiology, and End Results (SEER)
geographic areas are 18.3 per 100,000 for men and 9.2 per 100,000 for women. Age-adjusted
mortality rates are much lower; 6.0 per 100,000 for men and 2.7 for women.
Cohort, case-control, and geographical studies have examined TCE and kidney cancer,
defined either as cancer of kidney and renal pelvis in cohort and geographic-based studies or as
RCC, the most common type of kidney cancer, in case-control studies. Appendix C identifies
these studies' design and exposure assessment characteristics. Observations in these studies are
presented below in Table 4-39. Rate ratios for incidence studies in Table 4-39 are, generally,
larger than for mortality studies.
4-143
-------
Table 4-39. Summary of human studies on TCE exposure and kidney cancer
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Cohort and PMR studies — incidence
Aerospace workers (Rocketdyne)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
Not reported
1.00a
1.87 (0.56, 6.20)
4.90(1.23, 19.6)
p = 0.023
6
6
4
TCE, 20 yr exposure lagb
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
1.00a
1.19(0.22,6.40)
7.40(0.47, 116)
p = 0.120
6
7
o
J
All employees at electronics factory (Taiwan)
Males
Females
Females
1.06 (0.45, 2.08)c
1.09(0.56, 1.91)c
1.10(0.62, 1.82)c
8
12
15
Danish blue-collar worker with TCE exposure
Any exposure, all subjects
Any exposure, males
Any exposure, females
1.2 (0.98, 1.46)
1.2 (0.97, 1.48)
1.2(0.55,2.11)
103
93
10
Exposure lag time
20yrs
1.3 (0.86, 1.88)
28
Employment duration
5yrs
0.8 (0.5, 1.4)
1.2 (0.8, 1.7)
1.6(1.1,2.3)
16
28
32
Subcohort with higher exposure
Any TCE exposure
1.4(1.0, 1.8)
53
Employment duration
l-4.9yrs
>5yrs
1.1 (0.7, I. if
1.7(1.1, 2.4)d
23
30
Zhao et al. (2005)
Chang et al. (2005)
Sung et al. (2008)
Raaschou-Nielsen
et al. (2003)
4-144
-------
Table 4-39. Summary of human studies on TCE exposure and kidney
cancer (continued)
Exposure group
Biologically monitored Danish workers
Any TCE exposure, males
Any TCE exposure, females
Cumulative exposure (Ikeda)
<17 ppm-yr
>17 ppm-yr
Mean concentration (Ikeda)
<4ppm
4+ppm
Employment duration
<6.25 yrs
>6.25
RR (95% CI)
1.1(0.3,2.8)
0.9 (0.2, 2.6)
2.4 (0.03, 14)
Not reported
Not reported
Not reported
Number of
observable
events
4
3
1
Aircraft maintenance workers from Hill Air Force Base
TCE subcohort
Not reported
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
1.4 (0.4, 4.7)
1.3(0.3,4.7)
0.4(0.1,2.3
9
5
2
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
3.6 (0.5, 25.6)
0
0
2
Biologically -monitored Finnish workers
All subjects
0.87 (0.32, 1.89)
6
Mean air-TCE (Ikeda extrapolation)
<6ppm
6+ppm
Not reported
Not reported
Cardboard manufacturing workers in Arnsberg, Germany
Exposed workers
7.97 (2.59, 8.59)e
5
Biologically -monitored Swedish workers
Any TCE exposure, males
Any TCE exposure, females
1.16(0.42,2.52)
Not reported
6
Reference
Hansen et al. (2001)
Blair et al. (1998)
Anttila et al. (1995)
Henschler et al.
(1995)
Axelson et al. (1994)
4-145
-------
Table 4-39. Summary of human studies on TCE exposure and kidney
cancer (continued)
Exposure group
RR (95% CI)
Number of
observable
events
Cardboard manufacturing workers, Atlanta area, Georgia
All subjects
All departments
Finishing department
3.7(1.4,8.1)
oo (3.0, oo)f
16.6(1.7, 453. l)f
6
5
3
Cohort and PMR studies — mortality
Computer manufacturing workers (IBM), New York
Males
Females
Aerospace workers (Rocketdyne)
Any TCE (utility/eng flush)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
1.64 (0.45, 4.21)g
2.22 (0.89, 4.57)
Not reported
1.00a
1.43(0.49,4.16)
2.13(0.50,8.32)
p = 0.3l
4
0
7
7
7
3
TCE, 20 yr exposure lagb
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
1.00a
1.69 (0.29, 9.70)
1.82 (0.09, 38.6)
p = 0.635
10
6
1
View-Master employees
Males
Females
2.76 (0.34, 9.96)8
6.21 (2.68, 12.23)g
2
8
United States Uranium-processing workers (Fernald)
Any TCE exposure
Light TCE exposure, 2-10 yrs duration
Light TCE exposure, >10 yrs duration
Mod TCE exposure, >2 yrs duration
Not reported
1.94 (0.59, 6.44)
0.76(0.14,400.0)
5
2
0
Reference
Sinks et al. (1992)
Clapp and Hoffman
(2008)
Boice et al. (2006b)
Zhao et al. (2005)
ATSDR (2QQ4a)
Ritz (1999a) (as
reported in NRC,
2006)
4-146
-------
Table 4-39. Summary of human studies on TCE exposure and kidney
cancer (continued)
Exposure group
RR (95% CI)
Number of
observable
events
Aerospace workers (Lockheed)
Routine exposure
Routine-Intermittent3
0.99 (0.40, 2.04)
Not presented
7
11
Duration of exposure
Oyr
5yrs
1.0
0.97 (0.37, 2.50)
0.19(0.02, 1.42)
0.69(0.22,2.12)
22
6
1
4
p for trend
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)e
High intensity (>50 ppm)e
1.32 (0.57, 2.60)
0.47 (0.01, 2.62)
1.78 (0.72, 3.66)
8
1
7
TCE subcohort (Cox analysis)
Never exposed
Ever exposed
1.00a
1.14(0.51, 2.58)h
24
8
Peak
No/Low
Med/Hi
1.00a
1.89 (0.85, 4.23)h
24
8
Cumulative
Referent
Low
High
1.00a
0.31(0.04, 2.36)h
1.59(0.68, 3.71)h
24
1
7
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
1.6(0.5, 5. l)a
15
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
2.0 (0.5, 7.6)
0.4(0.1,4.0)
1.2(0.3,4.8)
8
1
4
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
Males, cumulative exposure
0
1.0a
9.8 (0.6, 157)
3.5 (0.2, 56.4)
1.18(0.47,2.94)'
1.24(0.41,3.71)'
l.O1
0
1
1
18
16
Reference
Boice et al. (1999)
Morgan et al. (1998)
Blair et al. (1998)
Radican et al. (2008)
4-147
-------
Table 4-39. Summary of human studies on TCE exposure and kidney
cancer (continued)
Exposure group
RR (95% CI)
Number of
observable
events
Aircraft maintenance workers (Hill Air Force Base, Utah) (continued)
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.87 (0.59, 5.97)1
0.31(0.03,2.75)'
1.16(0.31,4.32)'
0.93(0.15,5.76)'
1.0a
2.86 (0.27, 29.85)'
0.97(0.10,9.50)'
10
1
5
2
0
1
1
Cardboard manufacturing workers in Arnsberg, Germany
TCE exposed workers
Unexposed workers
Deaths reported to among GE pension fund (Pittsfield,
Massachusetts)
3.28(0.40, 11.84)
(0.00, 5.00)
0.99 (0.30, 3.32)f
2
0
12
Cardboard manufacturing workers, Atlanta area, Georgia
1.4 (0.0, 7.7)
1
U.S. Coast Guard employees
Marine inspectors
Noninspectors
1.06(0.22,3.10)
1.03(0.21,3.01)
o
J
o
J
Aircraft manufacturing plant employees (Italy)
All subjects
Aircraft manufacturing plant employees (San Diego,
California)
All subjects
Not reported
0.93 (0.48, 1.64)
12
Case-control studies
Population of four countries in central and eastern Europe
Any TCE exposure
Any TCE exposure (High confidence exposure)
1.63 (1.04, 2.54)
2.05(1.13,3.73)
48
29
Cumulative TCE exposure
< 1.5 8 ppm-yr
>1.58 ppm-yr
p for trend
Average intensity
O.076 ppm
>0.076 ppm
p for trend
1.19(0.61,2.35)
2.02(1.14,3.59)'
p = 0.02
1.38(0.81,2.35)
2.34(1.05,5.21)
p = 0.02
17
31
31
17
Reference
Blair et al. (1998)
Henschler et al.
(1995)
Greenland et al.
(1994)
Sinks et al. (1992)
Blair et al. (1989)
Costa et al. (1989)
Garabrant et al.
(1988)
Moore et al. (2010)
4-148
-------
Table 4-39. Summary of human studies on TCE exposure and kidney
cancer (continued)
Exposure group
RR (95% CI)
Number of
observable
events
Population of Arve Valley, France
Any TCE exposure
Any TCE exposure (High confidence exposure)
1.64 (0.95, 2.84)
1.88 (0.89, 3.98)
37
16
Cumulative TCE exposure
Referent/nonexposed
Low, 62.4 ppm-yi^
Medium, 253.2 ppm-y^
High, 925 ppm-yr1"
Test for trend
1.00a
1.62 (0.75, 3.47)
1.15(0.47,2.77)
2.16(1.02,4.60)'
p = 0.04
49
12
9
16
Cumulative TCE exposure + peak
Referent/nonexposed
Low/medium, no peaks
Low/medium + peaks
High, no peaks
High + peaks
1.00a
1.35 (0.69, 2.63)
1.61 (0.36. 7.30)
1.76 (0.65, 4.73)
2.73 (1.06, 7.07)1
49
18
3
8
8
Cumulative TCE exposure, 10-yr lag
Referent/nonexposed
Low/medium, no peaks
Low/medium + peaks
High, no peaks
High + peaks
1.00a
1.44 (0.69, 2.80)
1.38 (0.32, 6.02)
1.50(0.53,4.21)
3.15(1.19,8.38)
49
19
3
7
8
TWA TCE exposure111
Referent/nonexposed
Any TCE without cutting fluid
Any cutting fluid without TCE
<50 ppm TCE + cutting fluid
50 + ppm TCE + cutting fluid
1.00a
1.62 (0.76, 3.44)
2.39(0.52, 11.03)
1.14(0.49,2,66)
2.70(1.02,7.17)
46
15
3
12
10
Population of Arnsberg Region, Germany
Longest job held— TCE/PERC
(CAREX)
Self-assessed exposure to TCE
1.80(1.01,3.20)
2.47 (1.36, 4.49)
117
25
Duration of serf -assessed TCE exposure
0
<10 yrs
10-20 yrs
>20 yrs
1.00a
3.78(1.54,9.28)
1.80 (0.67, 4.79)
2.69 (0.84, 8.66)
109
11
7
8
Reference
Charbotel et al.
(2009: 2007: 2006)
Bruning et al. (2003)
4-149
-------
Table 4-39. Summary of human studies on TCE exposure and kidney
cancer (continued)
Exposure group
RR (95% CI)
Number of
observable
events
Population in five German Regions
Any TCE exposure
Males
Females
Not reported
Not reported
Not reported
TCE exposure (ITEM)
Males
Medium
High
Substantial
1.3(1.0, 1.8)
1.1(0.8, 1.5)
1.3(0.8,2.1)
68
59
22
Females
Medium
High
Substantial
1.3 (0.7, 2.6)
0.8 (0.4, 1.9)
1.8 (0.6, 5.0)
11
7
5
Population of Minnesota
Ever exposed to TCE, NCI JEM
Males
Females
Males + Females
1.04 (0.6, 1.7)
1.96(1.0,4.0)
1.30(0.9, 1.9)
33
22
55
Population of Arnsberg Region, Germany
Self-assessed exposure to TCE
10.80 (3.36, 34.75)
19
Population of Montreal, Canada
Any TCE exposure
Substantial TCE exposure
0.8 (0.4, 2.0)n
0.8 (0.2, 2.6)n
4
2
Geographic-based studies
Residents in two study areas in Endicott, New York
Residents of 13 census tracts inRedlands, California
1.90(1.06,3.13)
0.80(0.54, 1.12)°
15
54
Finnish residents
Residents of Hausjarvi
Residents of Huttula
Not reported
Not reported
Reference
Peschetal.,(2QQQb)
Dosemeci et al.
(1999)
Vamvakas et al.
(1998)
Siemiatycki et al.
(1991)
ATSDR (2006a)
(2008b)
Morgan and Cassady
(2002)
Vartiainen et al.
(1993)
Internal referents, workers not exposed to TCE.
bRRs for TCE exposure after adjustment for 1st employment, SES status, age at event, and all other carcinogens,
including hydrazine.
°Chang et al. (2005)—urinary organs combined.
dSIRforRCC.
eHenschler et al. (1995) Expected number of incident cases calculated using incidence rates from the Danish Cancer
Registry.
fOR from nested case-control analysis.
gPMR.
4-150
-------
Table 4-39. Summary of human studies on TCE exposure and kidney cancer
(continued)
hRisk ratio from Cox Proportional Hazard Analysis, stratified by age, sex and decade (EHS. 1997).
'In Radican et al. (2008). kidney cancer defined as RCC (ICDA 8 code 189.0) and estimated RRs from Cox
proportional hazard models were adjusted for age and sex.
JThe OR, adjusted for age, sex, and center, for subjects with high-confidence exposure assessment with cumulative
exposure, >1.58 ppm-yr, was 2.23 (95% CI: 1.07, 4.64) and^-value for trend = 0.02.
kMean cumulative exposure score in Charbotel et al. (2006) (personal communication from Barbara Charbotel,
University of Lyon, to Cheryl Scott, U.S. EPA, 11 April 2008).
'in Charbotel et al. (2006) analyses adjusted for age, sex, smoking, and BMI. The OR, adjusted for age, sex,
smoking, BMI, and exposure to cutting fluids and other petroleum oils, for high cumulative TCE exposure was
1.96 (95% CI: 0.71, 5.37) and for high cumulative + peak TCE exposure was 2.63 (95% CI: 0.79, 8.83). The OR
for, considering only job periods with high confidence TCE exposure assessment, adjusted for age, sex, smoking,
and BMI, for high cumulative dose plus peaks was 3.80 (95% CI: 1.27. 11.40).
mThe exposure surrogate is calculated for one occupational period only and is not the average exposure
concentration over the entire employment period.
"90% CI.
°99% CI.
PERC = perchloroethylene
Additionally, a large body of evidence exists on kidney cancer risk and either job or
industry titles where TCE usage has been documented. TCE has been used as a degreasing
solvent in a number of jobs, task, and industries, some of which include metal, electronic, paper
and printing, leather manufacturing, and aerospace/aircraft manufacturing or maintenance
industries and job title of degreaser, metal workers, electrical worker, and machinist (Purdue et
al., 2011; IARC, 1995b). NRC (2006) identifies characteristics for kidney cancer case-control
studies that assess job title or occupation in their Table 3-8. RRs and 95% CIs reported in these
studies are found in Table 4-40.
4-151
-------
Table 4-40. Summary of case-control studies on kidney cancer and
occupation or job title
Case ascertainment area/exposure group
RR
(95% CI)
No. exposed
cases
Swedish Cancer Registry Cases
Machine/electronics industry
Shop and construction metal work
Machine assembly
Metal plating work
Shop and construction metal work
1.30 (1.08, 1.55)a [M]
1.75 (1.04, 2.76a [F]
1.19(1.00, 1.40)a[M]
1.62 (0.94, 2.59)a [M]
2.70 (0.73, 6.92)a [M]
1.66 (0.71, 3.26)a [F]
120
18
143
4
8
Arve Valley, France
Metal industry
Metal workers, job title
Metal industry, screw-cutting workshops
Machinery, electrical and transportation
equipment manufacture
1.02 (0.59, 1.76)
1.00 (0.56, 1.77)
1.39 (0.75, 2.58)
1.19(0.61,2.33)
28
25
22
15
Iowa Cancer Registry Cases
Assemblers
>10 yr employment
2.5 (0.8, 7.6)
4.2(1.2, 15.3)
5
4
Arnsberg Region, Germany
Iron/steel
Occupations with contact to metals
Longest job held
Metal greasing/degreasing
1.15(0.29,4.54)
1.53 (0.97, 2.43)
1.14(0.66, 1.96)
5.57 (2.33, 13.32)
3
46
24
15
Degreasing agents
Low exposure
High exposure
2.11(0.86,5.18)
1.01 (0.40, 2.54)
9
7
Bologna, Italy
Metal workers
Printers
Solvents
2.21 (0.99, 5.37)
1.55(0.17, 13.46)
0.79(0.31, 1.98) [M]
1.47(0.12, 17.46) [F]
37
7
17
3
Montreal, Canada
Metal fabricating and machining industry
Metal processors
Printing and publishing industry
Printers
Aircraft mechanics
1.0 (0.6, 1.8) 14
1.2 (0.4, 3.4) 4
1.1(0.4,3.0) 4
3.0(1.2,7.5) 6
2.8(1.0,8.4) 4
Reference
Wilson et al. (2008)
Charbotel et al.
(2006)
Zhang et al. (2004)
Briining et al. (2003)
Mattioli et al. (2002)
Parent et al. (2000a)
4-152
-------
Table 4-40. Summary of case-control studies on kidney cancer and occupation
or job title (continued)
Case ascertainment area/exposure group
RR
(95% CI)
No. exposed
cases
Five Regions in Germany
Electrical and electronic equipment
assembler
Printers
Metal cleaning/degreasing, job task
3.2 (1.0, 10.3) [M]
2.7 (1.3, 5.8) [F]
3.5(1.1, 11.2) [M]
2.1(0.4, 11.7)[F]
1.3 (0.7, 2.3) [M]
1.5 (0.3, 7.7) [F]
5
11
5
2
15
2
New Zealand Cancer Registry
Toolmakers and blacksmiths
Printers
1.48 (0.72, 3.03)
0.67 (0.25, 1.83)
No information
Minnesota Cancer Surveillance System
Iron or steel
1.6(1.2,2.2)
8
Rhein-Neckar-Odenwald Area, Germany
Metal
Industry
Occupation
1.63 (1.07, 2.48)
1.38(0.89,2.12)
71
Electronic
Industry
Occupation
Chlorinated solvents
Metal and metal compounds
0.51 (0.26, 1.01)
0.57 (0.25, 1.33)
2.52(1.23,5.16)
1.47 (0.94, 2.30)
14
9
27
62
Danish Cancer Registry
Iron and steel
Solvents
1.4 (0.8, 2.4) [M]
1.0(0.1, 3.2) [F]
1.5 (0.9, 2.4) [M]
6.4 (1.8, 23) [F]
31
1
50
16
France
Machine fitters, assemblers, and precision
instrument makers
0.7 (0.3, 1.9)
16
Reference
Pesch et al. (2000b)
Delahunt et al.
(1995)
Mandel et al. (1995)
Schlehofer et al.
(1995)
Mellemgaard et al.
(1994)
Auperin et al. (1994)
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Table 4-40. Summary of case-control studies on kidney cancer and occupation
or job title (continued)
Case ascertainment area/exposure group
RR
(95% CI)
No. exposed
cases
New South Wales, Australia
Iron and steel
Printing or graphics
Machinist or tool maker
Solvents
1.18(0.75, 1.85)b
2.39 (1.26, 4.52)c
1.18(0.87, 2.08)b
0.82(0.32, 2.1 l)d
1.15(0.72, 1.86)b
1.83 (0.92, 3.61)c
1.54(1. 11, 2.14)b
1.40 (0.82, 2.40)c
52
19
29
6
48
16
109
24
Finnish Cancer Registry
Iron and metalware work
Machinists
Paper and pulp; printing/publishing
Nonchlorinated solvents
1.87 (0.94, 3.76)
2.33(0.83,6.51)
2.20 (1.02, 4.72) [M]
5.95 (1.21, 29.2) [F]
3.46 (0.91, 13.2) [M]
22
10
18
7
9
West Midlands U.K. Cancer Registry
Organic solvents
Ever exposed
Intermediate exposure
1.30(0.31,8.50)
1.54(0.69,4.10)
3
3
Montreal, Canada
Organic solvents
Degreasing solvents
1.68 (0.83, 2.22)
3.42 (0.92, 12.66)
33
10
Oklahoma
Metal degreasing
Machining
Painter, paint manufacture
1.7 (0.7, 3.8) [M]
1.7 (0.7, 4.3) [M]
1.3 (0.7, 2.6) [M]
19
13
22
Missouri Cancer Registry
Machinists
2.2 (0.5, 10.3)
o
J
Danish Cancer Registry
Iron and metal, blacksmith
Painter, paint manufacture
1.4 (0.7, 2.9)d
1.8 (0.7, 4.6)
17
10
Reference
McCredie and
Stewart (1993)
Partanen et al. (1991)
Harrington et al.
(1989)
Sharpe et al. (1989)
Asal et al. (1988a:
1988b)
Brownson (1988)
Jensen et al. (1988)
"Renal pelvis, Wilson et al. (2008).
bRCC, McCredie and Stewart (1993).
°Renal pelvis, McCredie and Stewart (1993).
dRenal pelvis and ureter, Jensen et al. (1988).
4.4.2.1. Studies of Job Titles and Occupations with Historical TCE Usage
Elevated risks are observed in many of the cohort or case-control studies between kidney
cancer and industries or job titles with historical use of TCE (Wilson et al., 2008; Charbotel et
al.. 2006: Zhang et al.. 2004: Briining et al.. 2003: Mattioli et al.. 2002: Parent et al.. 2000a:
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Pesch et al.. 2000b: Mandeletal.. 1995: Schlehofer et al.. 1995: McCredie and Stewart. 1993:
Partanen etal., 1991). Overall, these studies, although indicating association with metal work
exposures and kidney cancer, are insensitive for identifying a TCE hazard. The use of job title or
industry as a surrogate for exposure to a chemical is subject to substantial misclassification that
will attenuate rate ratios due to exposure variation and differences among individuals with the
same job title. Several small case-control studies (Parent et al., 2000a: Vamvakas et al., 1998:
Auperin et al.. 1994: Harrington et al.. 1989: Sharpeetal.. 1989: Jensen etal.. 1988) have
insufficient statistical power to detect modest associations due to their small size and potential
exposure misclassification (NRC, 2006). For these reasons, statistical variation in the risk
estimate is large and observation of statistically significantly elevated risks associated with metal
work in many of these studies is noteworthy. Some studies also examined broad chemical
grouping such as degreasing solvents or chlorinated solvents. Observations in studies that
assessed degreasing agents or chlorinated solvents reported statistically significant elevated
kidney cancer risk (Bruning et al., 2003: Pesch et al.. 2000b: Schlehofer et al.. 1995:
Mellemgaard et al.. 1994: McCredie and Stewart. 1993: Harrington et al.. 1989: Asal et al..
1988a: Asal etal., 1988b). Observations of association with degreasing agents, together with job
title or occupations where TCE has been used historically, provide a signal and suggest an
etiologic agent common to degreasing activities.
4.4.2.2. Cohort and Case-Controls Studies of TCE Exposure
Cohort and case-controls studies that include JEMs for assigning TCE exposure potential
to individual study subjects show associations with kidney cancer, specifically RCC, and TCE
exposure. Support for this conclusion derives from findings of increased risks in cohort studies
(Zhao et al., 2005: Raaschou-Nielsen et al., 2003: Henschler et al., 1995) and in case-control
studies from the Arnsberg region of Germany (Bruning et al., 2003: Pesch et al., 2000b:
Vamvakas et al., 1998), the Arve Valley region in France (Charbotel et al., 2009: Charbotel et
al.. 2006). the United States (Dosemeci etal.. 1999: Sinks etal.. 1992). and the four central and
eastern Europe countries of Czech Republic, Poland, Romania, and Russia (Moore et al., 2010).
A consideration of a study's statistical power and exposure assessment approach is
necessary to interpret observations in Table 4-39. Most cohort studies are underpowered to
detect a doubling of kidney cancer risks including the essentially null studies by Greenland et al.
(1994). Axelson et al. (1994 [incidence]). Anttila et al. (1995 [incidence]). Blair et al. (1998
[incidence and mortality]), Morgan et al. (1998), Boice et al. (1999), and Hansen et al. (2001).
Only the exposure duration-response analysis of Raaschou-Nielsen et al. (2003) had over
80% statistical power to detect a doubling of kidney cancer risk (NRC, 2006), and they observed
a statistically significant association between kidney cancer and >5-year employment duration.
Rate ratios estimated in the mortality cohort studies of kidney cancer (e.g., Boice et al., 2006b:
Ritz, 1999a: Blair etal., 1998: Morgan etal., 1998: Axelson et al., 1994: Greenland et al., 1994:
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Sinks et al., 1992; Garabrant et al., 1988) are likely underestimated to some extent because their
reliance on death certificates and increased potential of nondifferential misclassification of
outcome in these studies, although the magnitude is difficult to predict (NRC, 2006). Cohort or
PMR studies with more uncertain exposure assessment approaches, e.g., studies of all subjects
working at a factory (Clapp and Hoffman. 2008: Sung et al.. 2007: Chang et al.. 2005: ATSDR,
2004a: Chang etal.. 2003: Costa etal.. 1989: Garabrant et al.. 1988). do not show association but
are quite limited given their lack of attribution of higher or lower exposure potentials; risks are
likely diluted due to their inclusion of no or low exposed subjects.
Two studies were carried out in geographic areas with a high frequency and a high degree
of TCE exposure and were designed with a priori hypotheses to test for the effects of TCE
exposure on renal cell cancer risk (Charbotel et al., 2009: Charbotel et al., 2006: Briming et al.,
2003) and a third study carried out in four central and eastern European countries with high RCC
rates unexplained by established risk factors (Moore etal., 2010: Ferlay et al., 2008). For these
reasons, their observations have important bearing to the epidemiologic evidence evaluation.
These studies found a twofold elevated risk with any TCE exposure after adjustment for several
possible confounding factors including smoking (2.47, 95% CI: 1.36, 4.49) for self-assessed
exposure to TCE (Bruning et al., 2003): any confidence job with high cumulative TCE exposure,
925 ppm-years (2.16, 95% CI: 1.02, 4.60) with a positive and statistically significant trend test,
p = 0.04, high confidence jobs with high cumulative TCE exposure (3.34, 95% CI: 1.27, 8.74)
(Charbotel et al., 2006): high confidence assessment of high TCE cumulative exposure
>1.58 ppm-years (2.23, 95% CI: 1.07, 4.64) with a positive and statistically significant trend test,
p = 0.02 (Moore etal., 2010). Furthermore, RCC risk in Charbotel et al. (2005) increased to over
threefold (95% CI: 1.19, 8.38) in statistical analyses, which considered a 10-year exposure lag
period. An exposure lag period is often adopted in analysis of cancer epidemiology to reduce
exposure measurement biases (Salvan et al., 1995). Most exposed cases in this study were
exposed to TCE below any current occupational standard (26 of 37 cases [70%]) had held a job
with a highest TWA (<50 ppm) (Charbotel et al., 2009). A subsequent analysis of Charbotel
et al. (2009) using an exposure surrogate defined as the highest TWA for any job held, an
inferior surrogate given that TCE exposures in other jobs were not considered, reported an
almost threefold elevated risk (2.80, 95% CI: 1.12, 7.03) adjusted for age, sex, body mass index
(BMI), and smoking with exposure to TCE in any job to >50-ppm TWA (Charbotel et al., 2009).
Considering all jobs, Moore et al. (2010) reported a risk of 2.34 (95% CI: 1.05, 5.21) for average
TCE intensity (>0.76 ppm), an exposure metric similar to a TWA exposure category. Zhao et al.
(2005) compared 2,689 TCE-exposed workers at a California aerospace company to nonexposed
workers from the same company as the internal referent population, and found a monotonic
increase in incidence of kidney cancer by increasing cumulative TCE exposure. In addition, a
fivefold increased incidence was associated with high cumulative TCE exposure. This
relationship for high cumulative TCE exposure, lagged 20 years, was accentuated with
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adjustment for other occupational exposures (RR = 7.40, 95% CI: 0.47, 116), although the CIs
were increased. An increased CI with adjustments is not unusual in occupational studies, as
exposure is usually highly correlated with them, so that adjustments often inflate SE without
removing any bias (NRC, 2006). Observed risks were lower for kidney cancer mortality and
because of reliance on cause of death on death certificates are likely underestimated because of
nondifferential misclassification of outcome (Percy et al., 1981). Boice et al. (2006b), another
study of 1,111 workers with potential TCE exposure at this company and which overlaps with
Zhao et al. (2005), found a twofold increase in kidney cancer mortality (standardized mortality
ratio [SMR] = 2.22, 95% CI: 0.89, 4.57). This study examined mortality in a cohort whose
definition date differs slightly from Zhao et al. (2005), working between 1948 and 1999 with
vital status as of 1999 (Boice et al., 2006b) compared to working between 1950 and 1993 with
follow-up for mortality as of 2001 (Zhao et al., 2005), and used a qualitative approach for TCE
exposure assessment. Boice et al. (2006b) is a study of fewer subjects identified with potential
TCE exposure, of fewer kidney cancer deaths [7 deaths; 10 incident cases, 10 deaths in Zhao
et al. (2005)], of subjects with more recent exposures, and with a inferior exposure assessment
approach compared to Zhao et al. (2005): a finding of a twofold mortality increase (95% CI:
0.89, 4.57) is noteworthy given the insensitivities.
Zhao et al. (2005), Charbotel et al. (2006), and Moore et al. (2010), furthermore, are three
of the few studies to conduct a detailed assessment of exposure that allowed for the development
of a JEM that provided rank-ordered levels of exposure to TCE and other chemicals. NRC
(2006) discussed the inclusion of rank-ordered exposure levels is a strength increasing precision
and accuracy of exposure information compared to more inferior exposure assessment
approaches in some other studies such as duration of exposure or a grouping of all exposed
subjects.
The finding in Raaschou-Nielsen et al. (2003) of an elevated RCC risk with longer
employment duration is noteworthy given this study's use of a relatively insensitive exposure
assessment approach. One strength of this study is the presentation of incidence ratios for a
subcohort of higher exposed subjects, those with at least a 1-year duration of employment and
first employment before 1980, as a sensitivity analysis for assessing the effect of possible
exposure misclassification bias. RCC risk was higher in this subcohort compared to the larger
cohort and indicated some potential for misclassification bias in the grouped analysis. For both
the cohort and subcohort analyses, risk appeared to increase with increasing employment
duration, although formal statistical tests for trend are not presented in the published paper.
4.4.2.2.1. Discussion of controversies on studies in the Arnsberg region of Germany
Two previous studies of workers in this region, a case-control study of Vamvakas et al.
(1998) and Henschler et al. (1995), a study prompted by a kidney cancer case cluster, observed
strong associations between kidney cancer and TCE exposure. A fuller discussion of the studies
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from the Arnsberg region and their contribution to the overall weight of evidence on cancer
hazard is warranted in this evaluation given the considerable controversy (Cherrie etal., 2001;
Mandel, 2001; Green and Lash, 1999; McLaughlin and Blot 1997; Bloemen and Tomenson,
1995; Swaen, 1995) surrounding Henschler et al. (1995) and Vamvakas et al. (1998).
Criticisms of Henschler et al. (1995) and Vamvakas et al. (1998) relate, in part, to
possible selection biases that would lead to inflating observed associations and limited inferences
of risk to the target population. Specifically, these include: (1) the inclusion of kidney cancer
cases first identified from a cluster and the omission of subjects lost to follow-up from Henschler
et al. (1995): (2) use of a Danish population as referent, which may introduce bias due to
differences in coding cause of death and background cancer rate differences (Henschler et al.,
1995): (3) follow-up of some subjects outside the stated follow-up period (Henschler et al.,
1995): (4) differences between hospitals in the identification of cases and controls in Vamvakas
et al. (1998): (5) lack of temporality between case and control interviews (Vamvakas et al.,
1998): (6) lack of blinded interviews (Vamvakas et al., 1998): (7) age differences in Vamvakas
et al. (1998) cases and controls that may lead to a different TCE exposure potential; (8) inherent
deficiencies in Vamvakas et al. (1998) as reflected by its inability to identify other known kidney
cancer risk factors; and (9) exposure uncertainty, particularly unclear intensity of TCE exposure.
Overall, NRC (2006) noted that some of the points above may have contributed to an
underestimation of the true exposure distribution of the target population (points 5, 6, and 7),
other points would underestimate risk (points 3), and that these effects could not have explained
the entire excess risk observed in these studies (points 1, 2, and 4). The NRC (2006) furthermore
disagreed with the exposure uncertainty criticism (point 9), and concluded TCE exposures,
although of unknown intensity, were substantial and, clearly showed graded differences on
several scales in Vamvakas et al. (1998) consistent with this study's semi quantitative exposure
assessment.
B riming et al. (2003) was carried out in a broader region in southern Germany, which
included the Arnsberg region and a different set of cases and control identified from a later time
period than Vamvakas et al. (1998). The TCE exposure range in this study was similar to that in
Vamvakas et al. (1998), although at a lower exposure prevalence because of the larger and more
heterogeneous ascertainment area for cases and controls. For —eversposed" to TCE,
Briining et al. (2003) observed a risk ratio of 2.47 (95% CI: 1.36, 4.49) and a fourfold increase in
risk (95% CI: 1.80, 7.54) among subjects with any occurrence of narcotic symptom and a sixfold
increase in risk (95% CI: 1.46, 23.99) for subjects who had daily occurrences of narcotic
symptoms; risks which are lower than observed in Vamvakas et al. (1998). The lower rate ratio
in Briining et al. (2003) might indicate bias in the Vamvakas et al. (1998) study or statistical
variation between studies related to the broader base population included in Briining et al.
(2003).
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Observational studies such as epidemiologic studies are subject to biases and
confounding, which can be minimized but never completely eliminated through a study's design
and statistical analysis methods. While Briining et al. (2003) overcome many of the deficiencies
of Henschler et al. (1995) and Vamvakas et al. (1998), nonetheless, possible biases and
measurement errors could be introduced through their use of prevalent cases and residual
noncases, use of controls from surgical and geriatric clinics, nonblinding of interviewers, a
2-year difference between cases and controls in median age, use, or proxy or next-of-kin
interviews, and self-reported occupational history.
The impact of any one of the above points could either inflate or depress observed
associations. Biases related to a longer period for case compared to control ascertainment could
go in either direction. Next-of-kin interviewers for deceased cases, all controls being alive at the
time of interview, would be expected to underestimate risk if exposures were not fully reported
and thus, misclassified. On the other hand, the control subjects who were enrolled when the
interviews were conducted might not represent the true exposure distribution of the target
population through time and would lead to overestimate of risk. Selection of controls from
clinics is not expected to greatly influence observed associations since these clinics specialized
in the type of care they provided (NRC, 2006). Briining et al. (2003) is not the only kidney case-
control study where interviewers were not blinded; in fact, only the study of Charbotel et al.
(2006) included blinding of interviewers. Blinding of interviewers is preferred to reduce
possible bias. The Briining et al. (2003) study's use of frequency matching using 5-year age
groupings is common in epidemiologic studies and any biases introduced by age difference
between cases and controls is expected to be minimal because the median age difference was
3 years.
Despite these issues, the three studies of the Arnsberg region, with very high apparent
exposure and different base populations showed a significant elevation of risk and all have
bearing on kidney cancer hazard evaluations. The emphasis provided by each study for
identifying a kidney cancer hazard depends on its strengths and weaknesses. Briining et al.
(2003) overcomes many of the deficiencies in Henschler et al. (1995) and Vamvakas et al.
(1998). The finding of a statistically significantly approximately threefold elevated OR with
occupational TCE exposure in Briining et al. (2003) strengthens the signal previously reported by
Henschler et al. (1995) and Vamvakas et al. (1998). A previous study of cardboard workers in
the United States (Sinks etal., 1992), a study like Henschler et al. (1995), which was prompted
by a reported cancer cluster, had observed association with kidney cancer incidence, particularly
with work in the finishing department where TCE use was documented. Henschler et al. (1995),
Vamvakas et al. (1998), and Sinks et al. (1992) are less likely to provide a precise estimate of the
magnitude of the association given greater uncertainty in these studies compared to Briining
et al. (2003). For this reason, Briining et al. (2003) is preferred for meta-analysis treatment since
it is considered to better reflect risk in the target population than the two other studies. Another
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study (Charbotel et al., 2006) of similar exposure conditions of a different base population and of
different case and control ascertainment methods as the Arnsberg region studies has become
available since the Arnsberg studies. This study shows a statistically significant elevation of risk
and high cumulative TCE exposure in addition to a positive trend with rank-order exposure
levels. Charbotel et al. (2006) added evidence to observations from earlier studies on high TCE
exposures in Southern Germany and suggested that peak exposure may add to risk associated
with cumulative TCE exposure.
4.4.2.3. Examination of Possible Confounding Factors
Examination of potential confounding factors is an important consideration in the
evaluation of observations in the epidemiologic studies on TCE and kidney cancer. A known
risk factor for kidney cancer is cigarette smoking. Obesity, diabetes, hypertension and
antihypertensive medications, and analgesics are linked to kidney cancer, but causality has not
been established (McLaughlin et al., 2006; Moore et al., 2005). On the other hand, fruit and
vegetable consumption is considered protective of kidney cancer risk (McLaughlin et al., 2006).
Studies by Asal et al. Q988a: 1988b). Partanen et al. (1991). McCredie and Stewart (1993).
Auperin et al. (1994). Chow et al. (1994). Mellemgaard et al. (1994). Mandel et al. (1995).
Vamvakas et al. (1998). Dosemeci et al. (1999). Pesch et al. (2000b). Briining et al. (2003). and
Charbotel et al. (2006) controlled for smoking, and all studies except Pesch et al. (2000b)
controlled for BMI. Moore et al. (2010) examined, but did not find, smoking or BMI as potential
confounders because statistical examination of cigarette smoking and BMI altered risk estimates
for the association between TCE exposure and kidney cancer by <10%. Vamvakas et al. (1998)
and Dosemeci et al. (1999) controlled for hypertension and/or diuretic intake in the statistical
analysis. Because it is unlikely that exposure to TCE is associated with smoking, BMI,
hypertension, or diuretic intake, these possible confounders do not significantly affect the
estimates of risk (NRC. 2006).
Direct examination of possible confounders is less common in cohort studies than in
case-control studies where information is obtained from study subjects or their proxies. Use of
internal controls, such as for Zhao et al. (2005), in general, minimizes effects of potential
confounding due to smoking or socioeconomic (SES) status since exposed and referent subjects
are drawn from the same target population. Information on possible confounding due to BMI
(obesity) and to diabetes is lacking in cohort studies; however, any uncertainties are likely small
given the generally healthy nature of an employed population and its favorable access to medical
care.
The effect of smoking as a possible confounder may be assessed indirectly through:
(1) examination of risk ratios for other smoking-related sites; (2) examination of the expected
contribution by smoking to cancer risks; and (3) examination of lung cancer in nine TCE cohort
studies in which there is a high likelihood of TCE exposure in individual study subjects (and
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which met, to a sufficient degree, the standards of epidemiologic design and analysis in a
systematic review using meta-analysis methods). Some information on smoking-related lung
and kidney cancer risks may be obtained from IARC (2004a) for indirectly evaluating the
expected magnitude by smoking on kidney cancer risks in TCE cohort studies. Five cohort
studies of cigarette smoking reported risk estimates for both lung and kidney cancers with an
observed ratio of lung:kidney cancer risks of 3.5-10.6 for active smokers, who will have higher
smoking-related risks than former smokers (see Table 4-41). The nine cohort studies (Radican et
al.. 2008: Zhao et al.. 2005: Raaschou-Nielsen et al.. 2003: Hansenet al.. 2001: Boice et al..
1999: Morgan et al.. 1998: Anttila et al.. 1995: Axel son et al.. 1994: Greenland et al.. 1994)
present lung cancer risks and reported risks for overall TCE exposure ranging from 0.69 (95%
CI: 0.31, 1.30) by Axelson et al. (1994) to 1.4 (95% CI: 1.32, 1.52) by Raaschou-Nielsen et al.
(2003) (see Table 4-81). Smoking was more prevalent in the Raaschou-Nielsen et al. (2003)
cohort than the background population as suggested by the elevated risks for lung and other
smoking-related sites. If smoking fully contributes to the observed 40% excess lung cancer risk
in this study and based on observations in the five smoking cohorts, the expected contribution by
smoking to RCC risk is estimated as 1-6% and far smaller than the 20 and 40% excess in RCC
risk in the cohort and subcohort. The use of internal referents who are unexposed subjects drawn
from the occupational settings as TCE exposed subjects in three studies reduces any confounding
related to smoking as referents (Radican et al., 2008: Zhao et al., 2005: Morgan et al., 1998). In
the other cohort studies lacking direct adjustment for smoking and internal referents, difference
in cigarette smoking between exposed and referent subjects is not sufficient to fully explain
observed excess kidney cancer risks associated with TCE, particularly high TCE exposure. Lung
cancer risk estimates are lower than or equal to kidney cancer risk estimates and inconsistent
with observations in the five smoking cohorts (Hansen et al., 2001: Boice et al., 1999: Axelson et
al.. 1994).
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Table 4-41. Summary of lung and kidney cancer risks in active smokers
Cohort
MRFIT (USA)
1975-1985, men
British Doctor's Study (United
Kingdom)
1957-1 991, men
U.S. Veterans Study (United States)
1954-1980, men
Swedish Census Study (Sweden)
1963-1989, women
Cancer Prevention Study II (United
States)
1982-1986, women
RR
Lung
6.7
14.9a
11.6
4.7
12.4
Kidney
1.9
1.4"
1.5
1.1
1.4
Ratio
lung; kidney
3.5
10.6
7.7
4.3
9.1
Reference
Kuller et al. (1991)
Doll etal. (1994)
McLaughlin et al. (1995)
Nordlund et al. (1999. 1997)
Garfmkel and Stellman (1988):
Heath et al. (1997)
""Relative mortality rate compared to nonsmokers.
Source: from I ARC (2004a)
Meta-analysis methods were adopted, additionally, as a tool for examining risk estimates
from the nine cohort studies in which there is a high likelihood of TCE exposure in individual
study subjects (e.g., based on JEMs or biomarker monitoring) and which met, to a sufficient
degree, the standards of epidemiologic design and analysis in a systematic review reporting lung
cancer to assess the presence of potential systematic error related to confounding from smoking.
Significant heterogeneity was observed across the nine studies of overall exposure (I2 = 90%)
and for six of the nine studies with highest exposure groups (/ = 80%). Although the
appropriateness of conducting any meta-analysis without attempting to explain the heterogeneity
is arguable, the summary estimate from the primary random effects meta-analysis of the nine
studies was 0.96 (95% CI: 0.76, 1.21) for overall TCE exposure, and 0.96 (95% CI: 0.72, 1.27)
for the highest group exposure reported by six studies. These observations suggest potential
confounding by smoking of kidney cancer summary risk estimates can be reasonably excluded in
cohort studies of TCE exposure.
Mineral oils such as cutting fluids or hydrazine common to some job titles with potential
TCE exposures (such as machinists, metal workers, and test stand mechanics) were included as
covariates in statistical analyses of Zhao et al. (2005), Boice et al. (2006b) and Charbotel et al.
(2009; 2006) or evaluated as a single exposure for cases and controls in Moore et al. (Karami et
al., 2011; 2010). A TCE effect on kidney cancer incidence was still evident, although effect
estimates were often imprecise due to lowered statistical power (Charbotel et al., 2009:
Charbotel et al., 2006: Zhao et al., 2005). Observed associations were similar in analyses
including chemical co-exposures in both Zhao et al. (2005) and Charbotel et al. (2009: 2006)
compared to chemical co-exposure unadjusted risks. The association or OR between high TCE
score and kidney cancer incidence in Zhao et al. (2005) was 7.71 (95% CI: 0.65, 91.4) after
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adjustment for other carcinogens including hydrazine and cutting oils, compared to analyses
unadjusted for chemical co-exposures (4.90, 95% CI: 1.23, 19.6).
In Charbotel et al. (2006), exposure to TCE was strongly associated with exposure to
cutting fluids and petroleum oils (22 of the 37 TCE-exposed cases were exposed to both).
Statistical modeling of all factors significant at 10% threshold showed the OR for cutting fluids
to be almost equal to one, whereas the OR for the highest level of TCE exposure was close to
two (Charbotel et al., 2006). Moreover, when exposure to cutting oils was divided into
three levels, a decrease in OR with level of exposure was found. In conditional logistic
regression adjusted for cutting oil exposure, the OR for RCC and TCE was similar to ORs
unadjusted for cutting fluid exposures (high cumulative TCE exposure: 1.96 [95% CI: 0.71-
5.37] compared to 2.16 [95% CI: 1.02-4.60]; high cumulative and peak: 2.63 [95% CI: 0.79-
8.83] compared to 2.73 [95% CI: 1.06-7.07] (Charbotel et al.. 2006). Charbotel et al. (2009)
further examined TCE exposure defined as the highest TWA in any job held, inferior to
cumulative exposure given its lack of consideration of TCE exposure potential in other jobs,
either as exposure to TCE alone, cutting fluids alone, or to both after adjusting for smoking,
BMI, age, sex, and exposure to other oils (TCE alone: 1.62 [95% CI: 0.75, 3.44]); cutting fluids
alone: 2.39 (95% CI: 0.52, 11.03); TCE >50-ppm TWA + cutting fluids: 2.70 (95% CI: 1.02,
7.17). There were few cases exposed to cutting fluids alone (n = 3) or to TCE alone (n = 15), all
of whom had TCE exposure (in the highest exposed job held) of <35 ppm TWA, and the
subgroup analyses were of limited statistical power. A finding of higher risk for both cutting oil
and TCE exposure >50 ppm compared to cutting oil alone supports a TCE effect for kidney
cancer. Adjustment for cutting oil exposures, furthermore, did not greatly affect the magnitude
of TCE effect measures in the many analyses presented by Charbotel et al. (2009; 2006)
suggesting cutting fluid exposure as not greatly confounding TCE effect measures. Two other
kidney case-control studies of TCE exposure examined the effect of cutting oil as a single
occupational exposure on kidney cancer risk (Karami etal., 2011; Briming et al., 2003).
Although Briining et al. (2003) reported an OR of 2.11 (95% CI: 0.66, 6.70) for self-reported
cutting oil exposure and kidney cancer, cutting oil exposure did not appear highly correlated with
TCE exposure as only 5 cases reported exposure to cutting oils compared to 25 cases reporting
TCE exposure. Karami et al. (2011), who examined mineral oil or cutting fluid exposure among
cases and controls in Moore et al. (2010), reported an OR of 0.8 (95% CI: 0.6, 1.1) and 1.1 (95%
CI: 0.8, 1.4), for cutting oil mists or other mineral oil mists respectively, and provides evidence
that the reported association with TCE exposure in Moore et al. (2010) is not likely confounded
by cutting or mineral oil exposures. Moreover, cutting oils and mineral oils have not been
associated with kidney cancer in other cohort or case-control studies (Mirer, 2010; NIOSH,
1998), which provide additional support for potential confounding by cutting oils as of minimal
concern.
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Boice et al. (2006b) was unable to directly examine hydrazine exposure on TCE effect
measures because of a lack of model convergence in statistical analyses. Three of seven
TCE-exposed kidney cancer cases were identified with hydrazine exposure of <1.5 years and the
absence of exposure to the other four cases suggested confounding related to hydrazine was
unlikely to greatly modify observed association between TCE and kidney cancer.
4.4.2.4. Susceptible Populations—Kidney Cancer and TCE Exposure
Two studies of kidney cancer cases from the Arnsberg region in Germany and the study
of kidney cancer cases from three Central and Eastern European countries have examined the
influence of polymorphisms of the GST metabolic pathway on RCC risk and TCE exposure
(Moore etal.. 2010: Wiesenhutter et al.. 2007: Briining et al.. 1997a). In their study of
45 TCE-exposed male and female RCC cases pending legal compensation and 48 unmatched
male TCE-exposed controls, Briining et al. (1997a) observed a higher prevalence of exposed
cases homozygous and heterozygous for GSTM1 positive, 60%, than the prevalence for this
genotype among exposed controls, 35%. The frequency of GSTM1 positive was lower among
this control series than the frequency found in other European population studies, 50% (Briming
et al., 1997a). The prevalence of the GSTT1 positive genotype was 93% among exposed cases
and 77% among exposed controls. The prevalence of GSTT1 positive genotype in the European
population is 75% (Briining et al., 1997a).
Wiesenhutter et al. (2007) compares the frequency of genetic polymorphism among
subjects from the renal cancer case-control study of Briining et al. (2003) and to the frequencies
of genetic polymorphisms in the areas of Dormund and Lutherstadt Wittenberg, Germany.
Wiesenhutter et al. (2007) identified the genetic frequencies of GSTM1 and GSTT1 phenotypes
for 98 of the original 134 cases (73%) and 324 of the 401 controls (81%). The prevalence of
GSTM1 positive genotype was 48% among all RCC cases, 40% among TCE-exposed cases, and
52% among all controls. The prevalence of GSTT1 positive genotypes was 81% among all cases
and 81% among all controls. The prevalence of GSTT1 positive genotypes reported in this paper
for all TCE-exposed cases was 20%. Wiesenhutter et al. (2007) noted background frequencies in
the German population in the expanded control group were 50% for GSTM1 positive and 81%
for GSTT1 positive genotypes. The observations are limited as the paper is sparsely reported
and numbers of exposed (n = 4) and unexposed (n = 15) GSTT1 positive cases do not sum to the
79 cases with the GSTT1 positive genotype identified in the table's first row.
Moore et al. (2010) presents associations between TCE exposure and RCC risk stratified
by GSTT genotype and for single nucleotide polymorphisms (SNPs) of the renal cysteine
conjugate p-lyase gene. Genotyping was available for 925 of the 1,097 cases and 1,192 of the
1,476 controls. The percentage of cases and controls genotyped did not significantly differ
among TCE-exposed and unexposed subjects nor was the active GSTT1 genotype association
with kidney cancer risk (0.94, 95% CI: 0.75, 1.19). However, adopting statistical analysis
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examining TCE exposure and kidney cancer that stratified on GSTT1 polymorphism as null
(deleted allele) or active (>1 intact allele), Moore et al. (2010) reported significant associations
for GSTT1 active genotype and no association was suggested for subjects with GSTT1 null
genotype. The risk estimate for the association for TCE exposure and kidney cancer among
subjects with an active GSTT1 genotype was 1.88 (95% CI: 1.06, 3.33), with higher risk
estimates for long exposure duration, cumulative exposure, and average exposure intensity
(>13.5 years, 2.13 [95% CI: 1.04, 4.39]; >1.58 ppm-years, 2.59 [95% CI: 1.25, 5.35]; >0.076
ppm, 2.77 [95% CI: 1.01, 7.58]) and a positive trend with increasing exposure duration,
cumulative exposure or average intensity categories (p < 0.03) (Moore et al., 2010). The
associations between TCE exposure and kidney cancer was stronger for subjects with a
functionally active GSTT1 than those for all subjects (both genotypes combined) (see Table 4-
39). Moore et al. (2010) tested but did not find statistical interaction between GSTT1 genotype
and TCE exposure (p > 0.17). Moore et al. (2010) also examined the effect of polymorphisms of
the cysteine conjugate p-lyase gene on TCE risk and reported interaction between TCE exposure
and four minor alleles (SNPs rs2293968, rs2280841, rs2259043, and rs941960) (p < 0.05).
Associations with TCE exposure and kidney cancer were threefold higher compared to
unexposed subjects with these SNPs.
Observations in Briining et al. (1997a) and Wiesenhiitter et al. (2007) must be interpreted
cautiously. Few details were provided in these studies on selection criteria and not all subjects
from the Briining et al. (2003) case-control study were included. For GSTM1 positive, the
higher prevalence among exposed cases in Briining et al. (1997a) compared Wiesenhiitter et al.
(2007) and the lower prevalence among controls compared to background frequency in the
European population may reflect possible selection biases. On the other hand, the broader base
population included in Briining et al. (2003) may explain the observed lower frequency of
GSTM1 positive cases in Wiesenhiitter et al. (2007). Moreover, Wiesenhiitter et al. (2007) does
not report genotype frequencies for controls by exposure status and this information is essential
to an examination of whether RCC risk and TCE exposure may be modified by polymorphism
status. The statistical analyses in both studies was a simple comparison of exposure prevalence
between cases and controls and did not include analyses that stratified on exposure status. An
examination of exposure prevalence is limited as Moore et al. (2010), too, reported TCE
exposure prevalence as similar between exposed cases and controls. Associations between TCE
exposure and kidney cancer for GSTT1 active genotype, however, were reported in stratified
analyses. The more rigorous study design and statistical methods in Moore et al. (2010) affords
more weight to their reported observations than for Briining et al. (1997a) and Wiesenhiitter et al.
(2007). Moore et al. (2010) provides evidence of greater susceptibility to TCE exposure and
kidney cancer among subjects with a functionally active GSTT polymorphism, particularly
among those with certain alleles in single nucleotide polymorphisms of the cysteine conjugation
P-lyase gene region.
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Of the three larger (in terms of number of cases) studies that did provide results
separately by sex, Dosemeci et al. (1999) suggested that there may be a sex difference for TCE
exposure and RCC (OR: 1.04, [95% CI: 0.6, 1.7]) in males and 1.96 (95% CI: 1.0, 4.0 in
females), while Raaschou-Nielsen et al. (2003) report the same standardized incidence ratio
(SIR = 1.2) for both sexes and crude ORs calculated from data from the Pesch et al. (2000b)
study (provided in a personal communication from Beate Pesch, Forschungsinstitut fur
Arbeitsmedizin, to Cheryl Scott, EPA, 21 February 2008) are 1.28 for males and 1.23 for
females. Whether the Dosemeci et al. (1999) observations are due to susceptibility differences or
to exposure differences between males and females cannot be evaluated. Blair et al. (1998) and
Hansen et al. (2001) also present some results by sex, but these two studies have too few cases to
be informative about a sex difference for kidney cancer.
4.4.2.5. Meta-Analysis for Kidney Cancer
Meta-analysis (detailed methodology in Appendix C) was adopted as a tool for
examining the body of epidemiologic evidence on kidney cancer and TCE exposure and to
identify possible sources of heterogeneity. The meta-analyses of the overall effect of TCE
exposure on kidney cancer suggest a small, statistically significant increase in risk that was
stronger in a meta-analysis of the highest exposure group. There was no observable
heterogeneity for any of the meta-analyses of the 15 studies and no indication of publication bias.
Thus, these findings of increased risks of kidney cancer associated with TCE exposure are
robust.
The meta-analysis of kidney cancer examines 15 cohort and case-control studies
identified through a systematic review and evaluation of the epidemiologic literature on TCE
exposure (Moore etal.. 2010: Charbotel et al.. 2006: Zhao et al.. 2005: Briming et al.. 2003:
Raaschou-Nielsen et al.. 2003: Hansen etal.. 2001: Pesch et al.. 2000b: Boiceetal.. 1999:
Dosemeci etal., 1999: Blair etal., 1998: Morgan et al., 1998: Anttila et al., 1995: Axelson et al.,
1994: Greenland et al., 1994: Siemiatycki, 1991). Details of the systematic review and meta-
analysis of the TCE studies are fully discussed in Appendix B and C.
The summary relative risk (RRm) estimate from the primary random effects meta-
analysis of the 15 studies was 1.27 (95% CI: 1.13, 1.43). The analysis was dominated by
two (contributing almost 70% of the weight) or three (almost 80% of the weight) large studies
(Raaschou-Nielsen et al., 2003: Pesch et al., 2000b: Dosemeci et al., 1999). Figure 4-1 arrays
individual studies by their weight. No single study was overly influential; removal of individual
studies resulted in RRm estimates that were all statistically significant (p < 0.005) and ranged
from 1.24 (with the removal of Briining et al., (2003)) to 1.30 (with the removal of Raaschou-
Nielsen et al., (2003)). Similarly, the overall RRm estimate was not highly sensitive to alternate
RR estimate selections nor was publication bias apparent. There was no apparent heterogeneity
across the 15 studies, i.e., the random effects model and the fixed effect model gave the same
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results (phetero = 0.67; /= 0%). Nonetheless, subgroup analyses were done examining the cohort
and case-control studies separately with the random effects model; the resulting RRm estimates
were 1.16 (95% CI: 0.96, 1.40) for the cohort studies and 1.48 (1.15, 1.91) for the case-control
studies. There was no heterogeneity in the cohort subgroup (p = 0.998; / =0%). There was
heterogeneity in the case-control subgroup, but it was not statistically significant (p = 0.14) and
the / value of 41% suggests that the extent of the heterogeneity in this subgroup was low-to-
moderate.
Ten studies reported risks for higher exposure groups (Moore et al., 2010; Charbotel et
al.. 2006: Zhao et al.. 2005: Briming et al.. 2003: Raaschou-Nielsen et al.. 2003: Pesch et al..
2000b: Boiceetal.. 1999: Dosemeci et al.. 1999: Morgan et al.. 1998: Siemiatycki. 1991).
Different exposure metrics were used in the various studies, and the purpose of combining
results across the different highest exposure groups was not to estimate an RRm associated with
some level of exposure. Instead, the focus on the highest exposure category was meant to result
in an estimate less affected by exposure misclassification. In other words, it is more likely to
represent a greater differential TCE exposure compared to people in the referent group than the
exposure differential for the overall (typically any vs. none) exposure comparison. Thus, if TCE
exposure increases the risk of kidney cancer, the effects should be more apparent in the highest
exposure groups.
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Study
Anttila (1995)
Axe (son (1994)
_. . .Mrnfv
DDIO@ c iyyy,!
Greenland (1 S'94)
Hansen (2001) '
\ A * 1 CtClQ\ '
Raaschou-Nielsen (2003)
. nnnn
Had ic -an (zUUo)
^nao (fcUUD)
Bruning (2003) '
fh k 4 i "">nrti3s
LnarDote! (^.uud)
Dosemeci (1999)
Moore (2010)
Peseh (2000) .
Siemiafyeki (1991)
OVERALL
0.1
TCE Exposure and Kidney Cancer
Relative Ri* and 05% CI RR
•1 ' ' 0.87
1 1.18
i . . n QQ
j .™~ ™^™^™^™ U.S,'«?
1 U.
-------
The RRm estimate from the random effects meta-analysis of the studies with results
presented for higher exposure groups was 1.64 (95% CI: 1.31, 2.04), higher than the RRm from
the overall kidney cancer meta-analysis. As with the overall analyses, the meta-analyses of the
highest-exposure groups were dominated by Pesch et al. (2000b) and Raaschou-Nielsen et al.
(2003), which provided about 60% of the weight. Axelson et al. (1994), Anttila et al. (1995),
and Hansen et al. (2001) do not report risk ratios for kidney cancer by higher exposure and a
sensitivity analysis was carried out to address reporting bias. The RRm estimate from the
primary random effects meta-analysis with null RR estimates (i.e., RR = 1.0) included for
Axelson et al. (1994), Anttila et al. (1995), and Hansen et al. (2001) to address reporting bias
associated with ever exposed was 1.58 (95% CI: 1.28, 1.96). Figure 4-2 arrays individual studies
by their weight. The inclusion of these three additional studies contributed <7% of the total
weight. No single study was overly influential; removal of individual studies resulted in RRm
estimates that were all statistically significant (p < 0.005) and that ranged from 1.52 [with the
removal of Raaschou-Nielsen et al., (2003)] to 1.64 [with the removal of Pesch et al. (2000b)].
Similarly, the RRm estimate was not highly sensitive to alternate RR estimate selections (all with
p < 0.0005) and other than a negligible amount of heterogeneity observed in the sensitivity
analysis with the Pesch JEM alternate (/ = 0.64%), there was no observable heterogeneity across
the studies for any of the meta-analyses conducted with the highest-exposure groups, including
those in which RR values for Anttila, Axelson, and Hansen were assumed (/ = 0%), For Pesch,
the job-task exposure matrix (ITEM) approach is preferred because it seemed to be a more
comprehensive and discriminating approach, taking actual job tasks into account, rather than just
larger job categories. No subgroup analyses (e.g., cohort vs. case-control studies) were done
with the highest exposure group results.
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TCE Exposure and Kidney Cancer - highest exposure groups
Study Relative Risk and 95% Cl RR
1 1
Raaschou-Nielsen (2003)
Charbotel(2006)
Moore (2010)
Pesch (2000)
OVERALL
1.11
— EH — 1.40
h^H 1.58
I
0.1 1 10
LCL
0.22
0.68
1.10
0.35
0.47
0.84
1.27
1.07
0.90
0.20
0.25
0.14
0.32
1.28
UCL
2.12
3.71
2.40
3.46
116.0
8.66
8.74
4.64
2.10
3.40
4.00
7.10
3.10
1.96
With assumed null RR estimates for Antilla, Axelson, and Hansen (see Appendix C text). Random effects model; fixed effect
model same. The summary estimate is in the bottom row, represented by the diamond. Symbol sizes reflect relative weights
of the studies.
Figure 4-2. Meta-analysis of kidney cancer and TCE exposure—highest exposure groups.
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NRC (2006) deliberations on TCE commented on two prominent evaluations of the then-
current TCE epidemiologic literature using meta-analysis techniques, Wartenberg et al. (2000)
and Kelsh et al. (2005), submitted by Exponent-Health Sciences to NRC during their
deliberations and who updated their analysis by including subsequently published studies of
Boice et al. (2006b) and Charbotel et al. (2006) but not Radican et al. (2008). and presented
summary relative risk (RRm) estimates for cohort and (Kelsh et al., 2005)case-control studies,
separately, and combined (Kelsh et al., 2010). Wartenberg et al. (2000) reported an RRm of 1.7
(95% CI: 1.1, 2.7) for kidney cancer incidence in the TCE subcohorts (Blair et al., 1998; Anttila
et al., 1995; Henschler et al., 1995; Axelson et al., 1994). For kidney cancer mortality in TCE
subcohorts (Boice et al.. 1999: Ritz, 1999a: Blair etal.. 1998: Morgan et al.. 1998: Henschler et
al.. 1995). Wartenberg et al. (2000) reported an RRm of 1.2 (95% CI: 0.8, 1.7). Kelsh et al.
(2010) examined a slightly different grouping of cohort studies as did Wartenberg et al. (2000),
presenting an RRm estimate for kidney cancer incidence and mortality combined. The RRm for
kidney cancer in Group I cohort studies (Raaschou-Nielsen et al., 2003: Hansen et al., 2001:
Boice etal.. 1999: Blair etal.. 1998: Morgan et al.. 1998: Anttila et al.. 1995: Axelson et al..
1994) was 1.34 (95% CI: 1.07-1.67) with no evidence of heterogeneity and, in Group II cohort
studies, studies lacking documented TCE exposure (Chang et al., 2003: Henschler et al., 1995:
Sinks etal.. 1992: Selden and Ahlborg. 1991: Blair etal.. 1989: Costa etal.. 1989: Garabrant et
al.. 1988). was 1.58 (95% CI: 0.75, 3.32) with evidence of heterogeneity. Removing both
Henschler et al. (1995) and Sinks et al. (1992), considered by Kelsh et al. (2010) as outliers,
eliminated observed heterogeneity and the summary risk estimate was 0.88 (95% CI: 0.8, 1.33).
Kelsh et al. (2010), also, presented separately a RRm for renal cancer case-control studies and
TCE. For case-control studies (Charbotel etal., 2005: B riming etal., 2003: Pesch et al., 2000b:
Dosemeci etal., 1999: Vamvakas et al., 1998: Greenland et al., 1994: Siemiatvcki, 1991), the
RRm for RCC was 1.57 (95% CI: 1.06, 2.30) with evidence of heterogeneity, and 1.33 (95% CI:
1.02, 1.73) with no evidence of heterogeneity in a sensitivity analysis removing Vamvakas et al.
(1998), a study Kelsh et al. (2010) considered as an outlier. Last, Kelsh et al. (2010) presented
three RRm estimates for renal cell cancer Groups I and II cohort and case-control studies
combined: 1.30 (95% CI: 1.04, 1.61) with evidence of heterogeneity and included 23 studies
with kidney cancer risk estimates for all subjects, those with documented TCE exposure and
those unexposed to TCE, and Ritz (1999a) in Group I studies; 1.42 (95% CI 1.13, 1.77) with
evidence of heterogeneity and included 23 studies, with TCE subcohort kidney cancer risk
estimates replacing the total cohort estimate for Group I studies; and 1.24 (95% CI; 1.06, 1.45)
with no evidence of heterogeneity and included 20 studies, counting TCE subcohort kidney
cancer risk estimates in Group I studies and removing the three studies Kelsh et al. (2010)
considered as outliers.
The present analysis was conducted according to NRC (2006) suggestions for
transparency, systematic review criteria, and examination of both cohort and case-control
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studies. EPA's meta-analysis has several advantages to previous ones of TCE exposure and
cancer. The selection criteria adopted in this meta-analysis were intended to identify informative
studies for the evaluation of TCE exposure and cancer, studies with reduced systematic errors.
Neither Henschler et al. (1995) nor Vamvakas et al. (1998), two studies with incomplete cohort
identification or potential selection bias of study controls, met our inclusion criteria, and their
inclusion in other meta-analysis may have contributed to the observed heterogeneity in kidney
cancer RRm (Kelsh et al., 2010). Studies with background or low TCE exposure potential also
did not meet another selection criterion as our analysis focused on TCE exposure potential
inferred to each subject by reference to industrial hygiene records, individual biomarkers, JEMs,
water distribution models, or questionnaire responses that likely had fewer biases associated with
exposure misclassification, although this bias would not have been completely minimized.
Inclusion of studies of lower exposure potential in meta-analyses can have important
implications for identifying a cancer hazard (Vlaanderen et al., 2011; Zhang et al., 2009;
Steinmaus et al., 2008). The present analysis includes the recently published studies of
Charbotel et al. (2006), Moore et al. (2010), and updated mortality of the Blair et al. (1998)
cohort by Radican et al. (2008). As discussed above, the summary estimate from the primary
random effects meta-analysis of the 15 studies was 1.27 (95% CI: 1.13, 1.43). Additionally,
EPA examined kidney cancer risk for higher exposure group. The RRm estimate from the
random effects meta-analysis of the studies with results presented for higher exposure groups
was 1.64 (95% CI: 1.31, 2.04), higher than the RRm from the overall kidney cancer meta-
analysis, and 1.58 (95% CI: 1.28, 1.96) in the meta-analysis with null RR estimates (i.e.,
RR = 1.0) to address possible reporting bias for three studies.
4.4.3. Human Studies of Somatic Mutation of VHL Gene
Studies have been conducted to identify mutations in the VHL gene in RCC patients, with
and without TCE exposures (Wells et al., 2009: Toma et al., 2008: Charbotel et al., 2007: Furge
et al., 2007: Brauch et al., 1999: Schraml et al., 1999: Kenck et al., 1996). Inactivation of the
VHL gene through mutations, LOH, and imprinting has been observed in about 70% of sporadic
renal clear cell carcinomas, the most common RCC subtype (Kenck etal., 1996). Other genes or
pathways, including c-Myc activation and VEGF, have also been examined as to their role in
various RCC subtypes (Toma et al., 2008: Furge et al., 2007). Furge et al. (2007) reported that
there are molecularly distinct forms of RCC and possibly molecular differences between clear-
cell RCC subtypes. This study was performed using tissues obtained from paraffin blocks.
These results are supported by a more recent study that examined the genetic abnormalities of
clear cell RCC using frozen tissues from 22 clear cell-RCC patients and paired normal tissues
(Toma et al., 2008). This study found that 20 (91%) of the 22 cases had LOH on chromosome
3p (harboring the VHL gene). Alterations in copy number were also found on chromosome 9
(32% of cases), chromosome arm 14q (36% of cases), chromosome arm 5q (45% of cases), and
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chromosome 7 (32% of cases), suggesting roles for multiple genetic changes in RCC, and is also
supported by genomes-wide single-nucleotide polymorphism analysis (Toma et al., 2008).
Several papers link mutation of the VHL gene in RCC patients to TCE exposure. These
reports are based on comparisons of VHL mutation frequencies in TCE-exposed cases from RCC
case-control studies or from comparison to background mutation rates among RCC case series
(see Table 4-42). Briining et al. (1997b) first reported a high somatic mutation frequency
(100%) in a series of 23 RCC cases with medium to high intensity TCE exposure as determined
by an abnormal single stand conformation polymorphism (SSCP) pattern, with most variations
found in exon two. Only four samples were sequenced at the time of publication and showed
mutations in exon one, two and three (see Table 4-42). Some of the cases in this study were
from the case-control study of Vamvakas et al. (1998) (see Section 4.4.3 and Appendix B).
Brauch et al. (2004; 1999) analyzed renal cancer cell tissues for mutations of the VHL
gene and reported increased occurrence of mutations in patients exposed to high concentrations
of TCE. In the first study (Brauch et al., 1999), an employer's liability or worker's
compensation registry was used to identify 44 RCC cases, 18 of whom were also included in
Briining et al. (1997b). Brauch et al. (1999) found multiple mutations in 42% of the exposed
patients who experienced any mutation and 57% showed loss of heterozygosity. A hot spot
mutation of cytosine to thymine at nucleotide 454 (C454T) was found in 39% of samples that
had a VHL mutation and was not found in renal cell cancers from nonexposed patients or in
lymphocyte DNA from either exposed or nonexposed cases or controls. As discussed above,
little information was given on how subjects were selected and whether there was blinding of
from the RCC case-control study of Vamvakas et al. (1998). Brauch et al. (2004) compared age
at diagnosis and histopathologic parameters of tumors as well as somatic mutation characteristics
in the VHL tumor suppressor gene between the TCE-exposed and non-TCE-exposed RCC patient
groups (TCE-exposed from their previous 1999 publication to the non TCE-exposed cases newly
sequenced in this study). RCC did not differ with respect to histopathologic characteristics in
either patient group. Comparing results from TCE-exposed and nonexposed patients revealed
clear differences with respect to: (1) frequency of somatic VHL mutations; (2) incidence of
C454T transition; and (3) incidence of multiple mutations. The C454T hot spot mutation at
codon 81 was exclusively detected in tumors from TCE-exposed patients, as were multiple
mutations. Also, the incidence of VHL mutations in the TCE-exposed group was at least twofold
higher than in the nonexposed group. Overall, these finding support the view that the effect of
TCE is not limited to clonal expansion of cells mutated spontaneously or by some other agent.
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Table 4-42. Summary of human studies on somatic mutations of the VHL genea
TCE exposure
status
Number of
subjects/
number with
mutations (%)
RCC subtype
Tissue type
analyzed
Assay
Number of
mutations
Type of mutation
Missense
Nonmissense0
Briining et al.
(1997b)
Exposed
23/23 (100%)
Unknown
Paraffin
SSCPb, sequencing13
23
1
o
J
Branch et al. (1999)
Exposed
44/33 (75%)
Unexposed
73/42 (58%)
Unknown
Paraffin, fresh
(lymphocyte)
SSCP, sequencing,
restriction enzyme
digestion
50
27
23
42
NA
NA
Schraml et al. (1999)
Exposed
9/3 (33%)
Clear cell 9 (75%)
Papillary 2 (18%)
Oncocytomas 1 (8%)
Unexposed
113/38
(34%)
Unknown
Paraffin
CGH, sequencing
4
1
o
J
50
Unknown
Unknown
Brauch et al. (2004)
Exposed
17/14 (82%)
Unexposed
21/2 (10%)
Clear cell 37 (%)
Oncocytic adenoma 1 (%)
Bilateral metachronous
1 (%)
Paraffin
Sequencing
24
17
7
2
2
0
Charbotel et al. (2007)
Exposed
25/2 (9%)
Unexposed
23/2 (8%)
Clear cell 5 1(75%)
Papillary 10 (10-15%)
Chromophobe 4 (5%)
Oncocytomas 4 (5%)
Paraffin, frozen tissues,
Bouin's fixative
Sequencing
2
1
1
2
1
1
"Adapted from NRC (2006) with addition of Schraml et al. (1999) and Charbotel et al. (2007).
bBy SSCP. Four (4) sequences confirmed by comparative genomic hybridization.
Includes insertions, frameshifts, and deletions.
CGH = comparative genomic hybridization
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Brauch et al. (2004) were not able to analyze all RCCs from the Vamvakas study
(Vamvakas et al., 1998), in part because samples were no longer available. Using the data
described by Brauch et al. (2004) (VHL mutation found in 15 exposed and 2 nonexposed
individuals, and VHL mutation not found in 2 exposed and 19 unexposed individuals), the
calculated OR is 71.3. The lower bound of the OR including the excluded RCCs is derived from
the assumption that all 20 cases that were excluded were exposed but did not have mutations in
VHL (VHL mutations were found in 15 exposed and 2 unexposed individuals and VHL was not
found in 22 exposed and 18 unexposed individuals), leading to an OR of 6.5 that remains
statistically significant.
Charbotel et al. (2007) examines somatic mutations in the three VHL coding exons in
RCC cases from their case-control study (Charbotel et al., 2006). Of the 87 RCCs in the case-
control study, tissue specimens were available for 69 cases (79%) of which 48 were clear cell-
RCC. VHL sequencing was carried out for only the clear cell-RCC cases, 66% of the 73 clear
cell-RCC cases in Charbotel et al. (2006). Of the 48 clear cell-RCC cases available for VHL
sequencing, 15 subjects were identified with TCE exposure (31%), an exposure prevalence lower
than 43% observed in the case-control study. Partial to full sequencing of the VHL gene was
carried out using polymerase chain reaction (PCR) amplification and VHL mutation pattern
recognition software of Beroud et al. (1998). Full sequencing of the VHL gene was possible for
only 26 RCC cases (36% of all RCC cases). Single point mutations were identified in four cases
(8% prevalence): two unexposed cases, a G > C mutation in exon 2 splice site and a G > A in
exon 1; one case identified with low/medium exposure, T > C mutation in exon 2, and, one case
identified with high TCE exposure, T > C in exon 3. It should be noted that the two cases with
T > C mutations were smokers unlike the cases with G > A or G > C mutations. The prevalence
of somatic VHL mutation in this study is quite low compared to that observed in other RCC case
series from this region; around 50% (Gallou et al., 2001; Bailly et al., 1995). To address possible
bias from misclassification of TCE exposure, Charbotel et al. (2006) examined renal cancer risk
for jobs associated with a high level of confidence for TCE exposure. As would be expected if
bias was a result of misclassification, they observed a stronger association between higher
confidence TCE exposure and RCC, suggesting that some degree of misclassification bias is
associated with their broader exposure assessment approach. Charbotel et al. (2007) do not
present findings on VHL mutations for those subjects with higher level of confidence TCE
exposure assignment.
Schraml et al. (1999) did not observe statistically significant differences in DNA
sequence or mutation type in a series of 12 RCCs from subjects exposed to solvents including
varying TCE intensity and a parallel series of 113 clear cell carcinomas from non-TCE exposed
patients. Only nine of the RCC were clear cell-RCC and were sequenced for mutations. VHL
mutations were observed in clear cell tumors only; 4 mutations in 3 TCE-exposed subjects
compared to 50 mutations in tumors of 38 nonexposed cases. Details as to exposure conditions
4-175
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are limited to a statement that subjects had been exposed to high doses of solvents, potential for
mixed solvent exposures, and that exposure included a range of TCE concentrations. Limitations
of this study include having a wider range of TCE exposure intensities as compared to the studies
described above (Brauch et al., 1999; Briming et al., 1997b), which focused on patients exposed
to higher levels of TCE, and the limited number of TCE-exposed subjects analyzed, being the
smallest of all available studies on RCC, TCE, and VHL mutation. For these reasons, Schraml
et al. (1999) is quite limited for examining the question of VHL mutations and TCE exposure.
Szymahska et al. (2010) examined somatic mutations in three VHL coding exons in
359 RCC cases, 334 with clear-cell carcinomas, from the case-control study of Moore et al.
(2010) as part of a pilot examination of mutation in three other genes, TP53, EGFR, and KRAS.
The prevalence of VHL mutations was high in the RCC series, 72% of the tumors carried at least
one function mutation, Although occupational exposures were not examined and data were not
presented, Szymahska et al. (2010) reported that VHL mutations were not associated with TCE
exposure.
A number of additional methodological issues need to be considered in interpreting these
studies. Isolation of DNA for mutation detection has been performed using various tissue
preparations, including frozen tissues, formalin fixed tissues and tissue sections fixed in Bouin's
solution. Ideally, studies would be performed using fresh or freshly frozen tissue samples to
limit technical issues with the DNA extraction. When derived from other sources, the quality
and quantity of the DNA isolated can vary, as the formic acid contained in the formalin solution,
fixation time and period of storage of the tissue blocks often affect the quality of DNA. Picric
acid contained in Bouin's solution is also known to degrade nucleic acids resulting in either low
yield or poor quality of DNA. In addition, during collection of tumor tissues, contamination of
neighboring normal tissue can easily occur if proper care is not exercised. This could lead to the
=dJution effect' of the results—i.e., because of the presence of some normal tissue, frequency of
mutations detected in the tumor tissue can be lower than expected. These technical difficulties
are discussed in these papers, and should be considered when interpreting the results.
Additionally, selection bias is possible given tissue specimens were not available for all RCC
cases in Vamvakas et al. (1998) or in Charbotel et al. (2006). Some uncertainty associated with
misclassification bias is possible given the lack of TCE exposure information to individual
subjects in Schraml et al. (1999) and in Charbotel et al. (2007) from their use of broader
exposure assessment approach compared to that associated with the higher confident exposure
assignment approach. A recent study by Nickerson et al. (2008) addresses many of these
concerns by utilizing more sensitive methods to look at both the genetic and epigenetic issues
related to VHL inactivation. This study was performed on DNA from frozen tissue samples and
used a more sensitive technique for analysis for mutations (endonuclease scanning) as well as
analyzing for methylation changes that may lead to inactivation of the VHL gene. This method
of analysis was validated on tissue samples with known mutations. Of the 205 clear cell-RCC
4-176
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samples analyzed, 169 showed mutations in the VHL gene (82.4%). Of those 36 without
mutation, 11 were hypermethylated in the promoter region, which will also lead to inactivation
of the VHL gene. Therefore, this study showed inactivating alterations in the VHL gene (either
by mutation or hypermethylation) in 91% tumor samples analyzed.
The limited animal studies examining the role of VHL mutation following exposure to
chemicals including TCE are described below in Section 4.4.6.1.1. Conclusions as to the role of
VHL mutation in TCE-induced kidney cancer, taking into account both human and experimental
data, are presented below in Section 4.4.7.
4.4.4. Kidney Noncancer Toxicity in Laboratory Animals
Acute, subchronic, and chronic exposures to TCE cause toxicity to the renal tubules in
rats and mice of both sexes, via both inhalation (see Table 4-43) and oral (see Table 4-44)
exposures. Nephrotoxicity from acute exposures to TCE has only been reported at relatively
high doses, although histopathological changes have not been investigated in these experiments.
Information about specific location of lesions is presented where available. TCE exposure for
13-weeks (corn oil gavage) led to increased nephrotoxicity but no significant increases in
preneoplastic or neoplastic lesions as compared to controls (Mally et al., 2006). Chronic
nephropathy was also observed in both sexes of Osborne-Mendel rats following exposure to TCE
(549 and 1,097 mg/kg-day, 78 week). Chakrabarti and Tuchweber (1988) found that TCE
administered to male F344 rats by i.p. injection (723-2,890 mg/kg) or by inhalation (1,000-
2,000 ppm for 6 hours) produced elevated urinary NAG, GOT, glucose excretion, blood urea
nitrogen (BUN), and high molecular weight protein excretion, characteristic signs of proximal
tubular, and possibly glomerular injury, as soon as 24 hours postexposure. In the i.p. injection
experiments, inflammation was observed, although some inflammation is expected due to the
route of exposure, and nephrotoxicity effects were only statistically significantly elevated at the
highest dose (2,890 mg/kg). In the inhalation experiments, the majority of the effects were
statistically significant at both 1,000 and 2,000 ppm. Similarly, at these exposures, renal cortical
slice uptake of /7-aminohippurate was inhibited, indicating reduced proximal tubular function.
Cojocel et al. (1989) found similar effects in mice administered TCE by i.p. injection (120-
1,000 mg/kg) at 6 hours postexposure, such as the dose-dependent increase in plasma BUN
concentrations and decrease in/>-aminohippurate accumulation in renal cortical slices. In
addition, malondialdehyde (MDA) and ethane production were increased, indicating lipid
peroxidation.
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Table 4-43. Inhalation studies of kidney noncancer toxicity in laboratory animals
Reference"
Chakrabarti and
Tuchweber (1988)
Green et al. (1998)
Kjellstrand et al.
(1983a)
Maltoni et al.
(1986)
Mensing et al.
(2002)
Woolhiser et al.
(2006)
Animals (sex)
F344 rats (M)
F344 rats (M)
NMRI mice (M
andF)
Sprague-Dawley
rats, (M and F)
B6C3Fj mice (M
andF)
Long-Evans rats
(M)
Sprague-Dawley
rats (F)
Exposure route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Dose/exposure concentration
0-20,00 ppm, 6 hrs
0, 250, and 500 ppm, 6 hrs/d for 1, 7,
15, 21, 28 d
0-3.600 ppm, variable time periods
of 1-24 hrs/d, for 30 or 120 d
0, 100, 300, and 600 ppm, 7 hrs/d,
5 d/wk, 104 wks exposure, observed
for lifespan
0-500 ppm, 6 hrs/5 d/wk, 6 mo
0, 100, 300, and 1,000 ppm, 6 hrs/d,
5 d/wk, 4 wks
Exposed
6/group
3-5/group
10-20/group
116-141/group
5/group
16/group
Kidney effect(s) discussed in Section 4.4.4
Increased signs of proximal tubular damage.
Increased formic acid excretion; plasma and
urinary markers of nephrotoxicity
unchanged.
Increased kidney weight.
Meganucleocytosis in male rats (Details in
Table 4-49).
Increased signs of nephrotoxicity.
Increased kidney weight.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
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Table 4-44. Oral and i.p. studies of kidney noncancer toxicity in laboratory animals
Reference"
Chakrabarti and
Tuchweber (1988)
Cojocel et al.
(1989)
Green et al.
(1997a)
Green et al. (2003)
Mally et al. (2006)
Maltoni et al.
(1986)
NCI (1976)
NTP (1988)
NTP (1990)
Peden-Adams
et al. (2008)
Animals (sex)
F344 rats (M)
NMRI mice (M)
F344 rats (M)
B6C3FJ mice (M)
F344 rats (M)
Eker rat (M)
Sprague-Dawley
rats (M and F)
Osborne-Mendel
rats (M and F)
B6C3Fj mice (M
andF)
ACI, August,
Marshall, and
Osborne-Mendel
rats (M and F)
F344 rats (M and
F)
B6C3Fi mice (M
andF)
MRL mice (M and
F)
Exposure route
i.p. injection
i.p. injection
(sesame oil)
Gavage (corn oil)
Drinking water
Gavage (corn oil)
Gavage (olive oil)
Gavage (corn oil)
Gavage (corn oil)
Gavage (corn oil)
Drinking water
Dose/exposure concentration
0-2,890 mg/kg-d
0-1,000 mg/kg
0, 500, and 2,000 mg/kg-d, 1 or 10 d
0-54.3 mg/kg-d, 52 wks
0-1,000 mg/kg-d body weight,
5 d/wk, 13 wks
0, 50, and 250 mg/kg-d 4-5 d/wk,
52 wks
0-2,339 mg/kg-d, variable doses,
5 d/wk, 78 wks
0, 500, and 1,000 mg/kg-d, 5 d/wk,
103 wks
Rats: 0-2,000 mg/kg-d, Mice: 0-
6,000 mg/kg-d, 5d/wk, 13 wks
0; 1,400; and 14,000 ppb; lifetime
Exposed
6/group
4/group
5 or
10/group
60/group
10/group
30/group
50/group
50/group
10/group
6/group
Kidney effect(s) discussed in Section 4.4.4
Increased signs of proximal tubular damage.
Increased signs of nephrotoxicity.
Increases in biochemical markers of kidney
damage.
Increased kidney weights and tubular
degeneration.
Increased nephrotoxicity.
Megakaryocytosis in male rats (details in
Table 4.47).
Toxic nephrosis in all exposed animals
(details in Table 4.46).
Cytomegaly and toxic nephropathy observed
in all exposed rats (details in Table 4-48).
Cytomegaly and karyomegaly of renal
tubular epithelium in mice and rats (details
in Table 4-45).
Increased kidney weight in male mice.
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
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Kidney weight increases have been observed following inhalation exposure to TCE in
both mice (Kjellstrand et al. (1983a) and rats (Woolhiser et al., 2006) and following lifetime
drinking water exposure in a genetically-prone murine model (Peden-Adams et al., 2008).
Kjellstrand et al. (1983a) demonstrated an increase in kidney weights in both male (20%
compared to control) and female (10% compared to control) mice following intermittent and
continuous TCE whole-body inhalation exposure (up to 120 days). This increase was significant
in males as low as 75 ppm exposure and in females starting at 150 ppm. The latter inhalation
study, an unpublished report by Woolhiser et al. (2006), was designed to examine
immunotoxicity of TCE but also contains information regarding kidney weight increases in
female Sprague-Dawley rats exposed to 0-, 100-, 300-, and 1,000-ppm TCE for 6 hours/day,
5 days/week, for 4 weeks. Relative kidney weights were significantly elevated (17.4% relative
to controls) at 1,000 ppm. However, the small number of animals and the variation in initial
animal weight limit the ability of this study to determine statistically significant increases. The
Peden-Adams et al. (2008) study was designed to assess the effects of TCE exposure in a
genetically-prone murine lupus model. Although the study did not demonstrate an increase in
the development of autoimmune disease markers (for details, see Section 4.6.2), changes in body
weight and organ weights in males were observed. Following lifetime exposure to TCE
(14.000 ppb) in drinking water, males exhibited a decreasing trend in body mass of 12% from
controls (female body weights not altered). Spleen, thymic, and kidney mass in females were not
altered following exposure to TCE, while an 18% increase in kidney mass was observed in the
high-dose treatment group (14.000 ppb) in males.
Similarly, overt signs of subchronic nephrotoxicity, such as changes in blood or urinary
biomarkers, are also primarily a high-dose phenomenon, although histopathological changes are
evident at lower exposures. Green et al. (1997a) reported administration of 2,000 mg/kg-day
TCE by corn oil gavage for 42 days in F344 rats caused increases of around twofold of control
results in urinary markers of nephrotoxicity such as urine volume and protein (both 1.8 x), NAG
(1.6 x)5 glucose (2.2 x) and alkaline phosphatase (ALP; 2.0 x), similar to the results of the acute
study of Chakrabarti and Tuchweber (1988), above. No morphological changes were observed
in kidneys from any animals (Green etal., 1997a). At lower dose levels, Green et al. (1998)
reported that plasma and urinary markers of nephrotoxicity were unchanged. In particular, after
1-28 day exposures to 250 or 500 ppm TCE for 6 hours/day, there were no statistically
significant differences in plasma levels of BUN or in urinary levels of creatinine, protein, ALP,
NAG, or GGT. However, increased urinary excretion of formic acid, accompanied by changes
in urinary pH and increased ammonia, was found at these exposures. Interestingly, at the same
exposure level of 500 ppm (6 hours/day, 5 days/week, for 6 months), Mensing et al. (2002)
reported elevated excretion of low molecular weight proteins and NAG, biomarkers of
nephrotoxicity, but after the longer exposure duration of 6 months.
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Numerous studies have reported histological changes from TCE exposure for subchronic
and chronic durations (Mensing et al.. 2002: NTP. 1990: Maltoni et al.. 1988: NTP. 1988:
Maltoni etal., 1986). As summarized in Table 4-45, in 13-week studies in F344 rats and
B6C3Fi mice, NTP (1990) reported relatively mild cytomegaly and karyomegaly of the renal
tubular epithelial cells at the doses 1,000-6,000 mg/kg-day (at the other doses, tissues were not
examined). The NTP report noted that —these real effects were so minimal that they were
diagnosed only during a reevaluation of the tissues... prompted by the production of definite
renal toxicity in the 2-year study." In the 6-month, 500-ppm inhalation exposure experiments of
Mensing et al. (2002), some histological changes were noted in the glomeruli and tubuli of
exposed rats, but they provided no detailed descriptions beyond the statement that —pevascular,
interstitial infections and glomerulonephritis could well be detected in kidneys of exposed rats."
Table 4-45. Summary of renal toxicity and tumor findings in gavage studies
ofTCEbyNTP(1990)a
Sex
Dose (mg/kg)b
Cytomegaly and karyomegaly
incidence (severity0)
Adenoma
(overall;
terminal)
Adenocarcinoma
(overall; terminal)
1/d, 5 d/wk, 13-wk study, F344/N rats
Male
Female
0, 125, 250, 500, 100
2,000
0, 62.5, 125, 250, 500
1,000
Tissues not evaluated
8/9 (minimal/mild)
Tissues not evaluated
5/10 (equivocal/minimal)
None reported
1/d, 5 d/wk, 13-wk study, B6C3FJ mice
Male
Female
0, 375, 750, 1,500
3,000
6,000
0, 375, 750, 1,500
3,000
6,000
Tissues not evaluated
7/10d (mild/moderate)
e
Tissues not evaluated
9/10 (mild/moderate)
1/10 (mild/moderate)
None reported
1/d, 5 d/wk, 103-wk study, F344/N rats
Male
Female
0
500
1,000
0
500
1,000
0% (0)
98% (2.8)
98% (3.1)
0% (0)
100% (1.9)
100% (2.7)
0/48; 0/33
2/49; 0/20
0/49; 0/16
0/50; 0/37
0/49; 0/33
0/48; 0/26
0/48; 0/33
0/49; 0/20
3/49; 3/16'
0/50; 0/37
0/49; 0/33
1/48; 1/26
1/d, 5 d/wk, 103-wk study, B6C3FJ mice
Male
Female
0
1,000
0
1,000
0% (0)
90% (1.5)
0% (0)
98% (1.8)
1/49; 1/33
0/50; 0/16
0/48; 0/32
0/49; 0/23
0/49; 0/33
1/50; 0/16
0/48; 0/32
0/49; 0/23
"Study carried forward for consideration in dose-response assessment (see Chapter 5).
bCorn oil vehicle.
'Numerical scores reflect the average grade of the lesion in each group (1, slight; 2, moderate; 3, well marked; and 4,
severe).
dObserved in four mice that died after 7-13 weeks and in three that survived the study.
eAll mice died during the first week.
fp = 0.028.
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After 1-2 years of chronic TCE exposure by gavage flSTTP. 1990. 1988: NCL 1976) or
inhalation (Maltoni et al.. 1988: Maltoni et al.. 1986) (see Tables 4-45 to 4-49), both the
incidence and severity of these effects increases, with mice and rats exhibiting lesions in the
tubular epithelial cells of the inner renal cortex that are characterized by cytomegaly,
karyomegaly, and toxic nephrosis. As with the studies at shorter duration, these chronic studies
reported cytomegaly and karyomegaly of tubular cells. NTP (1990) specified the area of damage
as the pars recta, located in the corticomedullary region. It is important to note that these effects
are distinct from the chronic nephropathy and inflammation observed in control mice and rats
(Lash et al.. 2000b: Maltoni et al.. 1988: Maltoni et al.. 1986: NCL 1976).
Table 4-46. Summary of renal toxicity and tumor findings in gavage studies
ofTCEbyNCI(1976)a
Sex
Dose (mg/kg)b
Toxic nephrosis
(overall; terminal)
Adenoma or adenocarcinoma
(overall; terminal)0
1/d, 5 d/wk, 2-yr study, Osborne-Mendel rats
Males
Females
0
549
1,097
0
549
1,097
0/20; 0/2
46/50; 7/7
46/50; 3/3
0/20; 0/8
39/48; 12/12
48/50; 13/13
0/20; 0/2
l/50;d 0/7
0/50; 0/3
0/20; 0/8
0/48; 0/12
0/50; 0/13
1/d, 5 d/wk, 2-yr study, B6C3F! mice
Males
Females
0
1,169
2,339
0
869
1,739
0/20; 0/8
48/50; 35/35
45/50; 20/20
0/20; 0/17
46/50; 40/40
46/47;f 39/39
0/20; 0/8
0/50; 0/35
l/50;e 1/20
0/20; 0/17
0/50; 0/40
0/47; 0/39
"Study carried forward for consideration in dose-response assessment (see Chapter 5).
bTreatment period was 48 weeks for rats, 66 weeks for mice. Doses were changed several times during the study
based on monitoring of body weight changes and survival. Dose listed here is the TWA dose over the days on
which animals received a dose.
°A few malignant mixed tumors and hamartomas of the kidney were observed in control and low-dose male rats, but
are not counted here.
dTubular adenocarcinoma.
Tubular adenoma.
fOne mouse was reported with -^lephrosis," but not —nphrosis toxic," and so was not counted here.
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Table 4-47. Summary of renal toxicity findings in gavage studies of TCE by
Maltoni et al. (1988; 1986)
Sex
Dose (mg/kg)a
Megalonucleocytosisb (overall;
corrected0)
1/d, 4-5 d/wk, 52-wk exposure, observed for lifespan, Sprague-Dawley rats
Males
Females
0
50
250
0
50
250
0/20; 0/22
0/30; 0/24
14/30; 14/21
0/30; 0/30
0/30; 0/29
0/30; 0/26
aOlive oil vehicle.
bRenal tubuli megalonucleocytosis is the same as cytomegaly and karyomegaly of renal tubuli cells (Maltoni et al..
1988; Maltoni etal. 1986).
'Denominator for —orrected" incidences is the number of animals alive at the time of the first kidney lesion in this
experiment (39 weeks).
Table 4-48. Summary of renal toxicity and tumor incidence in gavage studies
ofTCEbyNTP(1988)a
Sex
Dose (mg/kg)b
Cytomegaly
Toxic
nephropathy
Adenoma
(overall; terminal)
Adenocarcinoma
(overall; terminal)
1/d, 5 d/wk, 2-yr study, ACI rats
Male
Female
0
500
1,000
0
500
1,000
0/50
40/49
48/49
0/48
43/47
42/43
0/50
18/49
18/49
0/48
21/47
19/43
0/50; 0/38
0/49; 0/19
0/49; 0/11
0/48; 0/34
2/47; 1/20
0/43; 0/19
0/50; 0/38
1/49; 0/19
0/49; 0/11
0/48; 0/34
1/47; 1/20
1/43; 0/19
1/d, 5 d/wk, 2-yr study, August rats
Male
Female
0
500
1,000
0
500
1,000
0/50
46/50
46/49
0/49
46/48
50/50
0/50
10/50
31/49
0/49
8/48
29/50
0/50; 0/21
1/50; 0/13
1/49; 1/16
1/49; 1/23
2/48; 1/26
0/50; 0/25
0/50; 0/21
1/50; 1/13
0/49; 0/16
0/49; 0/23
2/48; 2/26
0/50; 0/25
1/d, 5 d/wk, 2-yr study, Marshall rats
Male
Female
0
500
1,000
0
500
1,000
0/49
48/50
47/47
0/50
46/48
43/44
0/49
18/50
23/47
0/50
30/48
30/44
0/49; 0/26
1/50; 0/12
0/47; 0/6
1/50; 0/30
1/48; 1/12
0/44; 0/10
0/49; 0/26
0/50; 0/12
1/47; 0/6
0/50; 0/30
1/48; 0/12
1/44; 1/10
1/d, 5 d/wk, 2-yr study, Osborne-Mendel rats
Male
Female
0
500
1,000
0
500
1,000
0/50
48/50
49/50
0/50
48/50
49/49
0/50
39/50
35/50
0/50
30/50
39/49
0/50; 0/22
6/50; 5/17
1/50; 1/15
0/50; 0/20
0/50; 0/11
1/49; 0/7
0/50; 0/22
0/50; 0/17
1/50; 0/15
0/50; 0/20
0/50; 0/11
0/49; 0/7
"Study carried forward for consideration in dose-response assessment (see Chapter 5).
bCorn oil vehicle.
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Table 4-49. Summary of renal toxicity and tumor findings in inhalation
studies of TCE by Maltoni et al. (1988; 1986)"
Sex
Concentration
(ppm)
Meganucleocytosisb
(overall; corrected)
Adenoma
(overall; corrected)
Adenocarcinoma
(overall; corrected)
7 hrs/d, 5 d/wk, 2-yr exposure, observed for lifespan, Sprague-Dawley rats0
Male
Female
0
100
300
600
0
100
300
600
0/135; 0/122
0/130; 0/121
22/130; 22/1 16
101/130; 101/124
0/145; 0/141
0/130; 0/128
0/130; 0/127
0/130; 0/127
0/135; 0/122
1/130; 1/121
0/130; 0/1 16
1/130; 1/124
0/145; 0/141
1/130; 1/128
0/130; 0/127
0/130; 0/127
0/135; 0/122
0/130; 0/121
0/130; 0/1 16
4/130; 4/124
0/145; 0/141
0/130; 0/128
0/130; 0/127
1/130; 1/127
7 hrs/d, 5 d/wk, 78-wk exposure, observed for lifespan, B6C3F! miced
Male
Female
0
100
300
600
0
100
300
600
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
0/90
1/90
0/90
0/90
1/90
0/90
0/90
0/90
aStudy carried forward for consideration in dose-response assessment (see Chapter 5); three inhalation experiments
in this study found no renal megalonucleocytosis, adenomas, or adenocarcinomas: BT302 (8-week exposure to 0,
100, or 600 ppm in Sprague-Dawley rats); BT303 (8-week exposure to 0, 100, or 600 ppm in Swiss mice); and
BT305 (78-week exposure to 0, 100, 300, or 600 ppm in Swiss mice).
bRenal tubuli meganucleocytosis is the same as cytomegaly and karyomegaly of renal tubuli cells (Maltoni etal..
1988; Maltoni etal. 1986).
'Combined incidences from experiments BT304 and BT304bis. Corrected incidences reflect number of rats alive at
47 weeks, when the first renal tubular megalonucleocytosis in these experiments appeared.
dFemale incidences are from experiment BT306, while male incidences are from experiment BT306bis, which was
added to the study because of high, early mortality due to aggressiveness and fighting in males in experiment
BT306. Corrected incidences not show, because only the renal adenocarcinomas appeared at 107 weeks in the male
and 136 weeks in the female, when the most of the mice were already deceased.
These effects of TCE on the kidney appear to be progressive. Maltoni et al. (1988; 1986)
noted that the incidence and degree of renal toxicity increased with increased exposure time and
increased time from the start of treatment. As mentioned above, signs of toxicity were present in
the 13 week study (NTP. 1988). and NTP (1990) noted cytomegaly at 26 weeks. NTP (1990)
noted that as —exposujtime increased, affected tubular cells continued to enlarge and additional
tubules and tubular cells were affected," with toxicity extending to the cortical area as kidneys
became more extensively damaged. NTP (1990, 1988) noted additional lesions that increased in
frequency and severity with longer exposure, such as dilation of tubules and loss of tubular cells
lining the basement membrane (—striped appearance" (NTP, 1988) or flattening of these cells
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(NTP, 1990)). NTP (1990) also commented on the intratubular material and noted that the
tubules were empty or —coiained wisps of eosinophilic material."
With gavage exposure, these lesions were present in both mice and rats of both sexes, but
were on average more severe in rats than in mice, and in male rats than in female rats (NTP,
1990). Thus, it appears that male rats are most sensitive to these effects, followed by female rats
and then mice. This is consistent with the experiments of Maltoni et al. (1988; 1986), which
only reported these effects in male rats. The limited response in female rats or mice of either sex
in these experiments may be related to dose or strain. The lowest chronic gavage doses in the
National Cancer Institute (NCI, 1976) and NTP (1990, 1988) F344 rat experiments was
500 mg/kg-day, and in all of these cases, at least 80% (and frequently 100%) of the animals
showed cytomegaly or related toxicity. By comparison, the highest gavage dose in the Maltoni
et al. (1988; 1986) experiments (250 mg/kg-day) showed lower incidences of renal cytomegaly
and karyomegaly in male Sprague-Dawley rats (47 and 67%, overall and corrected incidences)
and none in female rats. The B6C3Fi mouse strain was used in the NCI (1976), NTP (1990), and
Maltoni et al. (1988: 1986) studies (see Tables 4-45-4-49). While the two gavage studies (NTP,
1990: NCI, 1976) were consistent, reporting at least 90% incidence of cytomegaly and
karyomegaly at all studied doses, whether dose accounts for the lack of kidney effects in Maltoni
et al. (1988: 1986) requires comparing inhalation and gavage dosing. Such comparisons depend
substantially on the internal dose-metric, so conclusions as to whether dose can explain
differences across studies cannot be addressed without dose-response analysis using PBPK
modeling. Some minor differences were found in the multistrain NTP study (1988), but the high
rate of response makes distinguishing among them difficult. Soffritti (personal communication
with JC Caldwell, February 14, 2006) did note that the colony from which the rats in Maltoni
et al. (1988: 1986) experiments were derived had historically low incidences of chronic
progressive nephropathy and renal cancer.
4.4.5. Kidney Cancer in Laboratory Animals
4.4.5.1. Inhalation Studies of TCE
A limited number of inhalation studies examined the carcinogenicity of TCE, with no
statistically-significantly increases in kidney tumor incidence reported in mice or hamsters
(Maltoni et al., 1988: 1986: Fukudaetal., 1983: Henschler et al., 1980). The cancer bioassay by
Maltoni et al. (1988: 1986) reported no statistically significant increase in kidney tumors in mice
or hamsters, but renal adenocarcinomas were found in male (4/130) and female (1/130) rats at
the high dose (600 ppm) after 2 years of exposure and observation at natural death. In males,
these tumors seemed to have originated in the tubular cells, and were reported to have never been
observed in over 50,000 Sprague-Dawley rats (untreated, vehicle-treated, or treated with
different chemicals) examined in previous experiments in the same laboratory (Maltoni et al.,
1986). The renal adenocarcinoma in the female rat was cortical and reported to be similar to that
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seen infrequently in historical controls. This study also demonstrated the appearance of
increased cytokaryomegaly or megalonucleocytosis in the tubular cells, a lesion that was
significantly and dose-dependently increased in male rats only (see Table 4-49). Maltoni et al.
(1986) noted that some considerations supported either the hypothesis that these were precursor
lesions of renal adenocarcinomas cancer or the hypothesis that these are not precursors but rather
the morphological expression of TCE-induced regressive changes. The inhalation studies by
Fukuda et al. (1983) in Sprague-Dawley rats and female ICR mice reported one clear cell
carcinoma in rats exposed to the highest concentration (450 ppm) but saw no increase in kidney
tumors in mice. This result was not statistically significant (see Table 4-50) and no details are
given about the specific location of the tumors. One negative study (Henschler et al., 1980)
tested NMRI mice, Wistar rats, and Syrian hamsters of both sexes (60 animals per strain), and
observed no significant increase in renal tubule tumors any of the species tested. Benign
adenomas were observed in male mice and rats, a single adenocarcinoma was reported in male
rats at the highest dose, and no renal adenocarcinomas were reported in females of either species
(see Table 4-50). RCCs appear to be very rare in Wistar rats, with historical control rates
reported to be about 0.4% in males and 0.2% in females (Poteracki and Walsh, 1998), so these
data are very limited in power to detect small increases in their incidence.
Table 4-50. Summary of renal tumor findings in inhalation studies of TCE
by Henschler et al. (1980)" and Fukuda et al. (1983)b
Sex
Concentration (ppm)
Adenomas
Adenocarcinomas
6 hrs/d, 5 d/wk, 18-mo exposure, 30-mo observation, Han:NMRI mice (Henschler et al., 1980)
Males
Females
0
100
500
0
100
500
4/30
1/29
1/29
0/29
0/30
0/28
1/30
0/30
0/30
0/29
0/30
0/28
6 hrs/d, 5 d/wk, 18-mo exposure, 36-mo observation, Han: WIST rats (Henschler et al., 1980)
Males
Females
0
100
500
0
100
500
2/29
1/30
2/30
0/28
0/30
1/30
0/29
0/30
1/30
0/28
0/30
0/30
7 hrs/d, 5 d/wk, 2-yr study, Crj:CD (Sprague-Dawley) rats (Fukuda et al.. 1983)
Females
0
50
150
450
0/50
0/50
0/47
0/51
0/50
0/50
0/47
1/50
aHenschler et al. (1980) observed no renal tumors in control or exposed Syrian hamsters.
bFukuda et al. (1983) observed no renal tumors in control or exposed Crj:CD-l (ICR) mice.
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4.4.5.2. Gavage and Drinking Water Studies of TCE
Several chronic gavage studies exposing multiple strains of rats and mice to 0-
3,000 mg/kg TCE for at least 52 weeks have been conducted (see Tables 4-45 to 4-48, 4-51)
(Maltoni et al.. 1988: Maltoni et al.. 1986. NCI, 1976: NTP, 1988. 1990: Henschler et al., 1984:
Van Duuren et al., 1979). Van Duuren et al. (1979) examined TCE and 14 other halogenated
compounds for carcinogenicity in both sexes of Swiss mice. While no excess tumors were
observed, the dose rate (0.5 mg once per week, or an average dose rate of approximately
2.4 mg/kg-day for a 30 g mouse) is about 400-fold lower than that in the other gavage studies.
Inadequate design and reporting of this study limit the ability to use the results as an indicator of
TCE carcinogenicity. In the NCI (1976) study, the results for Osborne-Mendel rats were
considered by the authors to be inconclusive due to significant early mortality. Two male rats
demonstrated kidney lesions (dilated renal pelvis and dark red renal medulla), but in rats of both
sexes, no increase was seen in primary tumor induction over that observed in controls. While
both sexes of B6C3Fi mice showed a compound-related increase in nephropathy, no increase in
tumors over controls was observed. The NCI study (1976) used technical-grade TCE that
contained two known carcinogenic compounds as stabilizers (epichlorohydrin and 1,2-
epoxybutane). However, a subsequent study by Henschler et al. (1984) in mice reported no
significant differences in systemic tumorigenesis between pure, industrial, and stabilized TCE,
suggesting that concentrations of these stabilizers are too low to be the cause of tumors. A later
gavage study by NTP (1988), using TCE stabilized with diisopropylamine, observed an
increased incidence of renal tumors in all four strains of rats (ACI, August, Marshall, and
Osborne-Mendel). All animals exposed for up to 2 years (rats and mice) had non-neoplastic
kidney lesions (tubular cell cytomegaly), even if they did not later develop kidney cancer (see
Table 4-48). This study was also considered inadequate by the authors because of chemically
induced toxicity, reduced survival, and incomplete documentation of experimental data. The
final NTP study (1990) in male and female F344 rats and B6C3Fi mice used epichlorohydrin-
free TCE. Only in the highest-dose group (1,000 mg/kg) of male F344 rats was renal carcinoma
statistically significant increased. The results for detecting a carcinogenic response in rats were
considered by the authors to be equivocal because both groups receiving TCE showed
significantly reduced survival compared to vehicle controls and because of a high rate (e.g., 20%
of the animals in the high-dose group) of death by gavage error. However, historical control
incidences at NTP of kidney tumors in F344 rats is very low,5 lending biological significance to
their occurrence in this study, despite the study's limitations. Cytomegaly and karyomegaly
were also increased, particularly in male rats. The toxic nephropathy (specific location in kidney
5NTP (1990) reported a historical control incidence of 0.4% in males. The NTP web site reports historical control
rates of renal carcinomas for rats dosed via corn oil gavage on the NIH-07 diet (used before 1995, when the TCE
studies were conducted) to be 0.5% (2/400) for males and 0% (0/400) for females
(http://ntp-server.niehs.nih.gov/ntp/research/database_searches/historical_controls/path/r_gavco.txt). In addition,
the two occurences in males came from the same study, with all other studies reporting 0/50 carcinomas.
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not stated) observed in both rats and mice and contributed to the poor survival rate (see Table 4-
45). As discussed previously, this toxic nephropathy was clearly distinguishable from the
spontaneous chronic progression nephropathy commonly observed in aged rats.
Table 4-51. Summary of renal tumor findings in gavage studies of TCE by
Henschler et al. (1984)" and Van Duuren et al. (1979)b
Sex
(TCE dose)
Control or TCE exposed (stabilizers if
present)
Adenomas
Adenocarcinomas
5 d/wk, 18-mo exposure, 24-mo observation, Swiss mice (Henschler et al., 1984)
Males
(2.4g/kg body
weight)
Females
(1.8g/kgbody
weight)
Control (none)
TCE (triethanolamine)
TCE (industrial)
TCE (epichlorohydrin (0.8%))
TCE (1,2-epoxybutane (0.8%))
TCE (both epichlorohydrin (0.25%) and 1,2-
epoxybutane (0.25%))
Control (none)
TCE (triethanolamine)
TCE (industrial)
TCE (epichlorohydrin (0.8%))
TCE (1,2-epoxybutane (0.8%))
TCE (both epichlorohydrin (0.25%) and 1,2-
epoxybutane (0.25%))
1/50
1/50
0/50
0/50
2/50
0/50
0/50
4/50
0/50
0/50
0/50
0/50
1/50
1/50
0/50
0/50
2/50
0/50
1/50
0/50
0/50
0/50
0/50
0/50
1 d/wk, 89-wk exposure, Swiss rats (Van Duuren et al.. 1979)
Males
(0.5mg)
Females (0.5mg)
Control
TCE (unknown)
Control
TCE(unknown)
0/30
0/30
0/30
0/30
0/30
0/30
0/30
0/30
aHenschler et al. (1984). Due to poor condition of the animals resulting from the nonspecific toxicity of high doses
of TCE and/or the additives, gavage was stopped for all groups during weeks 35-40, 65, and 69-78, and all doses
were reduced by a factor of 2 from the 40th week on.
bVan Duuren et al. (1979) observed no renal tumors in control or exposed Swiss mice.
4.4.5.3. Conclusions: Kidney Cancer in Laboratory Animals
Chronic TCE carcinogenicity bioassays have shown evidence of neoplastic lesions in the
kidney in rats (mainly in males, with less evidence in females), treated via inhalation and gavage.
As discussed above, individual studies have a number of limitations and have shown limited
increases in kidney tumors. However, given the rarity of these tumors as assessed by historical
controls and the repeatability of this result, these are considered biologically significant.
4.4.6. Role of Metabolism in TCE Kidney Toxicity
It is generally thought that one or more TCE metabolites rather than the parent compound
are the active moieties for TCE nephrotoxicity. As reviewed in Section 3.3, oxidation by CYPs,
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of which CYP2EI is thought to be the most active isoform, results in the production of CH, TCA,
DCA, and TCOH. The GSH conjugation pathway produces metabolites such as DCVG, DCVC,
dichlorovinylthiol, and NAcDCVC, although, as discussed in Section 3.3.3.2, the quantitative
estimates of the amount systemically produced following TCE exposure remains uncertain.
Because several of the steps for generating these reactive metabolites occur in the kidney, the
GSH conjugation pathway has been thought to be responsible for producing the active moiety or
moieties of TCE nephrotoxicity. A comparison of TCE's nephrotoxic effects with the effects of
TCE metabolites, both in vivo and in vitro, thus provides a basis for assessing the relative roles
of different metabolites. While most of the available data have been on metabolites from GSH
conjugation, such as DCVC, limited information is also available on the major oxidative
metabolites, TCOH and TCA.
4.4.6.1. In Vivo Studies of the Kidney Toxicity of TCE Metabolites
Studies of kidney toxicity of TCE metabolites discussed in this section are shown in
Table 4-52.
4.4.6.1.1. Role of GSH conjugation metabolites of TCE
In numerous studies, DCVC has been shown to be acutely nephrotoxic in rats and mice.
Mice receiving a single dose of 1 mg/kg DCVC (the lowest dose tested in this species) exhibited
karyolytic proximal tubular cells in the outer stripe of the outer medulla, with some sloughing of
cells into the lumen and moderate desquamation of the tubular epithelium (Eyre et al., 1995a).
Higher doses in mice were associated with more severe histological changes similar to those
induced by TCE, such as desquamation and necrosis of the tubular epithelium (Vaidya et al.,
2003a, b; Darnerud et al., 1989; Terracini and Parker, 1965). In rats, no histological changes in
the kidney were reported after single doses of 1, 5, and 10 mg/kg DCVC (Green et al., 1997a:
Eyre et al., 1995b, a), but cellular debris in the tubular lumen was reported at 25 mg/kg (Eyre et
al., 1995a) and slight degeneration and necrosis were seen at 50 mg/kg (Green etal., 1997a).
Green et al. (1997a) reported no histological changes were noted in rats after 10 doses of 0.1-
5.0 mg/kg DCVC (although increases in urinary protein and GGT were found), but some
karyomegaly was noted in mice after 10 daily doses of 1 mg/kg. Therefore, mice appear more
sensitive than rats to the nephrotoxic effects of acute exposure to DCVC, although the number of
animals used at each dose in these studies was limited (10 or less). Although the data are not
sufficient to assess the relatively sensitivity of other species, it is clear that multiple species,
including rabbits, guinea pigs, cats, and dogs, are responsive to DCVC's acute nephrotoxic
effects (Kreici et al., 1991: Wolfgang et al.. 1989a: Jaffeetal., 1984: Terracini and Parker.
1965).
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Table 4-52. Laboratory animal studies of kidney noncancer toxicity of TCE metabolites
Reference
Dow and Green
(2000)
Jaffe et al. (19841
Mather et al.
(1990)
Terracini and
Parker (1965)
Animals (sex)
F344 rats (M)
Swiss-Webster
mice (M)
Sprague-Dawley
rats (M)
Wistar rats
(Gender not
specified)
Grey mice
(Gender not
specified)
Exposure route
Drinking water
Drinking water
Drinking water
Drinking water
Dose/exposure concentration
0, 0.5, 1 g/L TCOH, 12 wks
0-22 mg/kg-d DCVC, 37 wks
0-355 mg/kg-dTCA, 90 d
0,0.01% DCVC, 12 wks
Exposed
3/group
5/group
10/group
3 5/group
Kidney effect(s) discussed in Section 4.4.4
Increased formic acid in urine.
Cytomegaly and tubular degeneration.
Increased kidney weight.
Necrosis of tubular epithelium in mice and rats.
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Very few studies are available at longer durations. Terracini and Parker (1965) gave
DCVC in drinking water to rats at a concentration of 0.01% for 12 weeks (approximately
10 mg/kg-day), and reported consistent pathological and histological changes in the kidney. The
progression of these effects was as follows: (1) during the first few days, completely necrotic
tubules, with isolated pyknotic cells being shed into the lumen; (2) after 1 week, dilated tubules
in the inner part of the cortex, lined with flat epithelial cells that showed thick basal membranes,
some with big hyperchromatic nuclei; and (3) in the following weeks, increased prominence of
tubular cells exhibiting karyomegaly, seen in almost all animals, less pronounced tubular
dilation, and cytomegaly in the same cells showing karyomegaly. In addition, increased mitotic
activity was reported the first few days, but was not evident for the rest of the experiment.
Terracini and Parker (1965) also reported the results of a small experiment (13 male and
5 female rats) given the same concentration of DCVC in drinking water for 46 weeks, and
observed for 87 weeks. They noted renal tubular cells exhibiting karyomegaly and cytomegaly
consistently throughout the experiment. Moreover, a further group of eight female rats given
DCVC in drinking water at a concentration of 0.001% (approximately 1 mg/kg-day) also
exhibited similar, though less severe, changes in the renal tubules. In mice, Jaffe et al. (1984)
gave DCVC in drinking water at concentrations of 0.001, 0.005, and 0.01% (estimated daily
doses of 1-2, 7-13, and 17-22 mg/kg-day), and reported similar effects in all dose groups,
including cytomegaly, nuclear hyperchromatism, and multiple nucleoli, particularly in the pars
recta section of the kidney. Thus, effects were noted in both mice and rats under chronic
exposures at doses as low as 1-2 mg/kg-day (the lowest dose tested). Therefore, while limited,
the available data do not suggest differences between mice and rats to the nephrotoxic effects of
DCVC under chronic exposure conditions, in contrast to the greater sensitivity of mice to acute
and subchronic DCVC-induced nephrotoxicity.
Importantly, as summarized in Table 4-53, the histological changes and their location in
these subchronic and chronic experiments with DCVC are quite similar to those reported in
chronic studies of TCE, described above, particularly the prominence of karyomegaly and
cytomegaly in the pars recta section of the kidney. Moreover, the morphological changes in the
tubular cells, such as flattening and dilation, are quite similar. Similar pathology is not observed
with the oxidative metabolites alone (see Section 4.4.6.1.2).
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Table 4-53. Summary of histological changes in renal proximal tubular cells induced by chronic exposure to
TCE, DCVC, and TCOH
Effects
Karyomegaly
Cytomegaly
Cell necrosis/
hyperplasia
Morphology/
content of
tubules
TCE
Enlarged, hyperchromatic nuclei, irregular to oblong in
shape. Vesicular nuclei containing prominent nucleoli.
Epithelial cells were large, elongated, and flattened.
Stratified epithelium that partially or completely filled the
tubular lumens. Cells in mitosis were variable in number
or absent. Cells had abundant eosinophilic or basophilic
cytoplasm.
Some tubules enlarged/dilated to the extent that they were
difficult to identify. Portions of basement membrane had
a stripped appearance. Tubules were empty or contained
— wispsf eosinophilic material."
DCVC
Enlarged, hyperchromatic nuclei with and
multiple nucleoli. Nuclear pyknosis and
karyorrhexis.
Epithelial cells were large, elongated, and
flattened cells.
Thinning of tubular epithelium, frank tubular
necrosis, re-epitheliation. Tubular atrophy,
interstitial fibrosis and destruction of renal
parenchyma. More basophilic and finely
vacuolated.
Tubular dilation, denuded tubules. Thick
basal membrane. Focal areas of dysplasia,
intraluminal casts.
TCOH
None reported.
No report of enlarged cells.
No flattening or loss of epithelium
reported. Increased tubular cell
basophilia, followed by increased
cellular eosinophilia, tubular cell
vacuolation.
No tubular dilation reported.
Intratubular cast formation.
Sources: NCI (1976): NTP (1990. 1988): Maltoni et al. (1988: Maltoni et al.. 1986): Terracini and Parker (1965): Jaffe et al. (1985): Green et al. (2003).
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Additionally, it is important to consider whether sufficient DCVC may be formed from
TCE exposure to account for TCE nephrotoxicity. While direct pharmacokinetic measurements,
such as the excretion of NAcDCVC, have been used to argue that insufficient DCVC would be
formed to be the active moiety for nephrotoxicity (Green et al., 1997a), as discussed in
Chapter 3, urinary NAcDCVC is a poor marker of the flux through the GSH conjugation
pathway because of the many other possible fates of metabolites in that pathway. In another
approach, Eyre et al. (Eyre etal., 1995b), using acid-labile adducts as a common internal
dosimeter between TCE and DCVC, reported that a single TCE dose of 400 mg/kg in rats
(similar to the lowest daily doses in the NCI and NTP rat bioassays) and 1,000 mg/kg (similar to
the lowest daily doses in the NCI and NTP mouse bioassays) corresponded to a single equivalent
DCVC dose of 6 and 1 mg/kg-day in rats and mice, respectively. These equivalent doses of
DCVC are greater or equal to those in which nephrotoxicity has been reported in these species
under chronic conditions. Therefore, assuming that this dose correspondence is accurate under
chronic conditions, sufficient DCVC would be formed from TCE exposure to explain the
observed histological changes in the renal tubules. Nevertheless, direct estimates of how much
DCVC is formed after TCE exposure are lacking.
The Eker rat model (Tsc-2^) is at increased risk for the development of spontaneous RCC
and as such, has been used to understand the mechanisms of renal carcinogenesis (Stemmer et
al., 2007; Wolf et al., 2000). One study has demonstrated similar pathway activation in Eker rats
as that seen in humans with VHL mutations leading to RCC, suggesting that Tsc-2 inactivation is
analogous to inactivation of VHL in human RCC (Liu et al., 2003). Although the Eker rat model
is a useful tool for analyzing the progression of renal carcinogenesis, it has some limitations in
analysis of specific genetic changes, particularly given the potential for different genetic changes
depending on type of exposure and tumor. The results of short-term assays to genotoxic
carcinogens in the Eker rat model (Stemmer et al., 2007; Morton et al., 2002) reported limited
preneoplastic and neoplastic lesions, which may be related to the increased background rate of
renal carcinomas in this animal model.
Recently, Mally et al. (2006) exposed male rats carrying the Eker mutation to TCE (0-
1,000 mg/kg body weight) by corn oil gavage and demonstrated no increase in renal
preneoplastic lesions or tumors. Primary Eker rat kidney cells exposed to DCVC in this study
did induce an increase in transformants in vitro but no DCVC-induced VHL or Tsc-2 mutations
were observed. In vivo exposure to TCE (5 days/week for 13 weeks), decreased body weight
gain and increased urinary excretion at the two highest TCE concentrations analyzed (500 and
1,000 mg/kg body weight) but did not change standard nephrotoxicity markers (GOT, creatinine,
and urinary protein). Renal tubular epithelial cellular proliferation as measured by BrdU
incorporation was demonstrated at the three highest concentrations of TCE (250, 500 and
1,000 mg/kg-day). A minority of these cells also showed karyomegaly at the two higher TCE
concentrations. Although renal cortical tumors were demonstrated in all TCE exposed groups,
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these were not significantly different from controls (13 weeks). These studies were
complemented with in vitro studies of DCVC (10-50 uM) in rat kidney epithelial (RKE) cells
examining proliferation at 8, 24, and 72 hours and cellular transformation at 6-7 weeks.
Treatment of RKE cells from susceptible rats with DCVC gave rise to morphologically
transformed colonies consistently higher than background (Mally et al., 2006). Analyzing 10 of
the renal tumors from the TCE-exposed rats and 9 of the DCVC transformants from these studies
for alterations to the VHL gene that might lead to inactivation found no alterations to VHL gene
expression or mutations.
One paper has linked the VHL gene to chemical-induced carcinogenesis. Shiao et al.
(1998) demonstrated VHL gene somatic mutations in 7V-nitrosodimethylamine-induced rat kidney
cancers that were of the clear cell type. The clear cell phenotype is rare in rat kidney cancers,
but it was only the clear cell cancers that showed VHL somatic mutation (three of eight tumors
analyzed). This provided an additional link between VHL inactivation and clear cell kidney
cancer. However, this study examined archived formalin-fixed, paraffin-embedded tissues from
previous experiments. As described previously (see Section 4.4.3), DNA extraction from this
type of preparation creates some technical issues. Similarly, archived formalin-fixed, paraffin-
embedded tissues from rats exposed to potassium bromide were analyzed in a later study by
Shiao et al. (2002). This later study examined the VHL gene mutations following exposure to
potassium bromide, a rat renal carcinogen known to induce clear cell renal tumors. Clear cell
renal tumors are the most common form of human renal epithelial neoplasms, but are extremely
rare in animals. Although F344 rats exposed to potassium bromide in this study did develop
renal clear cell carcinomas, only two of nine carried the same C to T mutation at the core region
of the Spl transcription-factor binding motif in the VHL promoter region, and one of
four untreated animals had a C to T mutation outside the conserved core region. Mutation in the
VHL coding region was only detected in one tumor, so although the tumors developed following
exposure to potassium bromide were morphologically similar to those found in humans, no
similarities were found in the genetic changes.
Elfarra et al. (1984) found that both DCVG and DCVC administered to male F344 rats by
i.p. injections in isotonic saline resulted in elevations in BUN and urinary glucose excretion.
Furthermore, inhibition of renal GGT activity with acivicin-protected rats from DCVG-induced
nephrotoxicity. In addition, both the p-lyase inhibitor, AOAA, and the renal organic anion
transport inhibitor, probenecid, provided protection from DCVC, demonstrating a requirement
for metabolism of DCVG to the cysteine conjugate by the action of renal GGT and dipeptidase,
uptake into the renal cell by the organic anion transporter, and subsequent activation by the
P-lyase. This conclusion was supported further by showing that the methyl analog of DCVC,
which cannot undergo a p-elimination reaction due to the presence of the methyl group, was not
nephrotoxic.
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Korrapati et al. (2005) built upon a series of investigations of hetero- (by mercuric
chloride [HgCb]) and homo-(by DCVC, 15 mg/kg) protection against a lethal dose of DCVC
(75 mg/kg). Priming, or preconditioning, with pre-exposure to either HgCl2 or DCVC of male
Swiss-Webster mice was said to augment and sustain cell division and tissue repair, hence
protecting against the subsequent lethal DCVC dose (Vaidya et al., 2003b, a; 2003c). Korrapati
et al. (2005) showed that a lethal dose of DCVC downregulates phosphorylation of endogenous
retinoblastoma protein (pRb), which is considered critical in renal proximal tubular and
mesangial cells for the passage of cells from Gl to S-phase, thereby leading to a block of renal
tubule repair. Priming, in contrast, upregulated P-pRB which was sustained even after the
administration of a lethal dose of DCVC, thereby stimulating S-phase DNA synthesis, which was
concluded to result in tissue repair and recovery from acute renal failure and death. These
studies are more informative about the mechanism of autoprotection than on the mechanism of
initial injury caused by DCVC. In addition, the priming injury (not innocuous, as it caused 25-
50% necrosis and elevated BUN) may have influenced the toxicokinetics of the second DCVC
injection.
4.4.6.1.2. Role of oxidative metabolites of TCE
Some investigators (Green et al., 2003; Dow and Green, 2000; Green etal., 1998) have
proposed that TCE nephrotoxicity is related to formic acid formation. They demonstrated that
exposure to either TCOH or TCA causes increased formation and urinary excretion of formic
acid (Green etal., 1998). The formic acid does not come from TCE. Rather, TCE (or a
metabolite) has been proposed to cause a functional depletion of vitamin 812, which is required
for the methionine salvage pathway of folate metabolism. Vitamin Bi2 depletion results in folate
depletion. Folate is a cofactor in one-carbon metabolism and depletion of folate allows formic
acid to accumulate, and then to be excreted in the urine (Dow and Green, 2000).
TCE (1 and 5 g/L), TCA (0.25, 0.5, and 1 g/L), and TCOH (0.5 and 1.0 g/L) exposure in
male Fischer rats substantially increased excretion of formic acid in urine, an effect suggested as
a possible explanation for TCE-induced renal toxicity in rats (Green et al., 1998). Green et al.
(2003) reported tubular toxicity as a result of chronic (1 year) exposure to TCOH (0, 0.5, and
1.0 g/L). Although TCOH causes tubular degeneration in a similar region of the kidney as TCE,
there are several dissimilarities between the characteristics of nephrotoxicity between the
two compounds, as summarized in Table 4-53. In particular, Green et al. (1998) did not observe
TCOH causing karyomegaly and cytomegaly. These effects were seen as early as 13 weeks after
the commencement of TCE exposure (NTP, 1990), with 300 ppm inhalation exposures to TCE
(Maltoni etal., 1988; Maltoni etal., 1986), as well as at very low chronic exposures to DCVC
(Jaffe et al., 1984; Terracini and Parker, 1965). In addition, Green et al. (2003) reported neither
flattening nor loss of the tubular epithelium nor hyperplasia, but suggested that the increased
early basophilia was due to newly divided cells, and therefore, represented tubular regeneration
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in response to damage. Furthermore, they noted that such changes were seen with the
spontaneous damage that occurs in aging rats. However, several of the chronic studies of TCE
noted that the TCE-induced damage observed was distinct from the spontaneous nephropathy
observed in rats. A recent in vitro study of rat hepatocytes and primary human renal proximal
tubule cells from two donors measured formic acid production following exposure to CH (0.3-3
mM, 3-10 days) (Lock et al., 2007). This study observed increased formic acid production at
day 10 in both human renal proximal tubule cell strains, but a similar level of formic acid was
measured when CH was added to media alone. The results of this study are limited by the use of
only two primary human cell strains, but suggest that exposure to CH does not lead to significant
increases in formic acid production in vivo.
Interestingly, it appears that the amount of formic acid excreted reaches a plateau at a
relatively low dose. Green et al. (2003) added folic acid to the drinking water of the group of
rats receiving the lower dose of TCOH (18.3 mg/kg-day) in order to modulate the excretion of
formic acid in that dose group, and retain the dose-response in formic acid excretion relative to
the higher-dose group (54.3 mg/kg-day). These doses of TCOH are much lower than what
would be expected to be formed in vivo at chronic gavage doses. For instance, after a single
500-mg/kg dose of TCE (the lower daily dose in the NTP rat chronic bioassays), Green and
Prout (1985) reported excretion of about 41% of the TCE gavage dose in urine as TCOH or
TCOG in 24 hours. Thus, using the measure of additional excretion after 24 hours and the
TCOH converted to TCA as a lower bound as to the amount of TCOH formed by a single
500 mg/kg dose of TCE, the amount of TCOH would be about 205 mg/kg, almost fourfold
greater than the high dose in the Green et al. (2003) study. By contrast, these TCOH doses are
somewhat smaller than those expected from the inhalation exposures of TCE. For instance, after
6-hour exposures to 100 and 500 ppm TCE (similar to the daily inhalation exposures in Maltoni
et al. (1988; 1986), male rats excreted 1.5 and 4.4 mg of TCOH over 48 hours, corresponding to
5 and 15 mg/kg for a rat weighing 0.3 kg (Kaneko et al., 1994). The higher equivalent TCOH
dose is similar to the lower TCOH dose used in Green et al. (2003), so it is notable that while
Maltoni et al. (1988; Maltoni et al., 1986) reported a substantial incidence of cytomegaly and
karyomegaly after TCE exposure (300 and 600 ppm), none was reported in Green et al. (2003).
TCOH alone does not appear sufficient to explain the range of renal effects observed
after TCE exposure, particularly cytomegaly, karyomegaly, and flattening and dilation of the
tubular epithelium. However, given the studies described above, it is reasonable to conclude that
TCOH may contribute to the nephrotoxicity of TCE, possibly due to excess formic acid
production, because: (1) there are some similarities between the effects observed with TCE and
TCOH and (2) the dose at which effects with TCOH are observed overlap with the approximate
equivalent TCOH dose from TCE exposure in the chronic studies.
Dow and Green (2000) noted that TCA also induced formic acid accumulation in rats,
and suggested that TCA may therefore, contribute to TCE-induced nephrotoxicity. However,
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TCA has not been reported to cause any similar histologic changes in the kidney. Mather et al.
(1990) reported an increase of kidney-weight to body-weight ratio in rats after 90 days of
exposure to TCA in drinking water at 5,000 ppm (5 g/L) but reported no histopathologic changes
in the kidney. DeAngelo et al. (1997) reported no effects of TCA on kidney weight or
histopathology in rats in a 2-year cancer bioassay. Dow and Green (2000) administered TCA at
quite high doses (1 and 5 g/L in drinking water), greater than the subsequent experiments of
Green et al. (2003) with TCOH (0.5 and 1 g/L in drinking water), and reported similar amounts
of formic acid produced (about 20 mg/day for each compound). However, cytotoxicity or
karyomegaly did not appear to be analyzed. Furthermore, much more TCOH is formed from
TCE exposure than TCA. Therefore, if TCA contributes substantially to the nephrotoxicity of
TCE, its contribution would be substantially less than that of TCOH. Lock et al. (2007) also
measured formic acid production in human renal proximal tubule cells exposed to 0.3-3 mM CH
for 10 days CH. This study measured metabolism of CH to TCOH and TCA as well as formic
acid production and subsequent cytotoxicity. Increased formic acid was not observed in this
study, and limited cytotoxicity was observed. However, this study was performed in human
renal proximal tubular cells from only two donors, and there is potential for large interindividual
variability in response, particularly with CYP enzymes.
In order to determine the ability of various chlorinated hydrocarbons to induce
peroxisomal enzymes, Goldsworthy and Popp (1987) exposed male F344 rats and male B6C3Fi
mice to TCE (1,000 mg/kg body weight) and TCA (500 mg/kg body weight) by corn oil gavage
for 10 consecutive days. Peroxisomal activation was measured by palmitoyl coenzyme A (CoA)
oxidase activity levels. TCE led to increased peroxisomal activation in the kidneys of both rats
(300% of control) and mice (625% of control), while TCA led to an increase only in mice (280%
of control). A study by Zanelli et al. (1996) exposed Sprague-Dawley rats to TCA for 4 days and
measured both renal and hepatic peroxisomal and CYP enzyme activities. TCA-treated rats had
increased activity in CYP 4A subfamily enzymes and peroxisomal palmitoyl-CoA oxidase. Both
of these acute studies focused on enzyme activities and did not further analyze resulting
histopathology.
4.4.6.2. In Vitro Studies of Kidney Toxicity of TCE and Metabolites
Generally, it is believed that TCE metabolites are responsible for the bulk of kidney
toxicity observed following exposure. In particular, studies have demonstrated a role for DCVG
and DCVC in kidney toxicity, though, as discussed in Section 3.3.3.2, the precise metabolic yield
of these metabolites following TCE exposure remains uncertain. The work by Lash and
colleagues (Cummings and Lash, 2000; Cummings et al., 2000a: Cummings et al., 2000b: Lash
et al., 2000b) examined the effect of TCE and its metabolites in vitro. TCE and DCVC are toxic
to primary cultures of rat proximal and distal tubular cells (Cummings et al., 2000c), while the
TCE metabolites, DCVG and DCVC, have been demonstrated to be cytotoxic to rat and rabbit
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kidney cells in vitro (Lash etal.. 200Ib: Lash et al.. 2000b: Groves etal.. 1993: Wolfgang et al..
1989b: Hassall etal., 1983). GSH-related enzyme activities were well maintained in the cells,
whereas CYP activities were not. The enzyme activity response to DCVC was greater than the
response to TCE; however, the proximal and distal tubule cells had similar responses even
though the proximal tubule is the target in vivo. The authors attributed this to the fact that the
proximal tubule is exposed before the distal tubule in vivo and to possible differences in uptake
transporters. They did not address the extent to which transporters were maintained in the
cultured cells.
In further studies, Lash et al. (200 Ib) assessed the toxicity of TCE and its metabolites,
DCVC and DCVG, using in vitro techniques as compared to in vivo studies. Experiments using
isolated cells were performed only with tissues from F344 rats, and lactate dehydrogenase (LDH)
release was used as the measure of cellular toxicity. The effects were greater in males. DCVC
and TCE had similar effects, but DCVG exhibited increased efficacy compared with TCE and
DCVC.
In vitro mitochondrial toxicity was assessed in renal cells from both F344 rats and
B6C3Fi mice following exposure to both DCVC and DCVG (Lash et al.. 2001 b). Renal
mitochondria from male rats and mice responded similarly; a greater effect was seen in cells
from the female mice. These studies show DCVC to be slightly more toxic than TCE and
DCVG, but species differences are not consistent with the effects observed in long-term
bioassays. This suggests that in vitro data should be used with caution in risk assessment, being
mindful that in vitro experiments do not account for in vivo pharmacokinetic and metabolic
processes.
In LLC-PK1 cells, DCVC causes loss of mitochondrial membrane potential,
mitochondrial swelling, release of cytochrome c, caspase activation, and apoptosis (Chen et al.,
2001). Thus, DCVC is toxic to mitochondria, resulting in either apoptosis or necrosis.
DCVC-induced apoptosis also has been reported in primary cultures of human proximal tubule
cells (Lash etal.. 200la).
DCVC was further studied in human renal proximal tubule cells for alterations in gene
expression patterns related to proposed modes of action in nephrotoxicity (Lock et al., 2006). In
cells exposed to subtoxic levels of DCVC to better mimic workplace exposures, the expression
of genes involved with apoptosis (caspase 8, FADD-like regulator) was increased at the higher
dose (1 uM) but not at the lower dose (0.1 uM) of DCVC exposure. Genes related to oxidative
stress response (SOD, NF-KB, p53, c-Jun) were altered at both subtoxic doses, with genes
generally upregulated at 0.1 uM DCVC being downregulated at 1 uM DCVC. The results of this
study support the need for further study, and highlight the involvement of multiple pathways and
variability of response based on different concentrations.
Lash et al. (2007) examined the effect of modulation of renal metabolism on toxicity of
TCE in isolated rat cells and microsomes from kidney and liver. Following exposure to
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modulating chemicals, LDH was measured as a marker of cytotoxicity, and the presence of
specific metabolites was documented (DCVG, TCA, TCOH, and CH). Inhibition of the CYP
stimulated an increase of GSH conjugation of TCE and increased cytotoxicity in kidney cells.
This modulation of CYP had a greater effect on TCE-induced cytotoxicity in liver cells than in
kidney cells. Increases in GSH concentrations in the kidney cells led to increased cytotoxicity
following exposure to TCE. Depletion of GSH in hepatocytes exposed to TCE, however, led to
an increase in hepatic cytotoxicity. The results of this study highlight the role of different
bioactivation pathways needed in both the kidney and the liver, with the kidney effects being
more affected by the GSH conjugation pathways metabolic products.
In addition to the higher susceptibility of male rats to TCE-induced nephrocarcino-
genicity and nephrotoxicity, isolated renal cortical cells from male F344 rats are more
susceptible to acute cytotoxicity from TCE than cells from female rats. TCE caused a modest
increase in LDH release from male rat kidney cells but had no significant effect on LDH release
from female rat kidney cells. These results on male susceptibility to TCE agree with the in vivo
data.
4.4.6.3. Conclusions as to the Active Agents of TCE-induced Nephrotoxicity
In summary, the TCE metabolites, DCVC, TCOH, and TCA, have all been proposed as
possible contributors to the nephrotoxicity of TCE. Both in vivo and in vitro data strongly
support the conclusion that DCVC and related GSH conjugation metabolites are the active agents
of TCE-induced nephrotoxicity. Of these, DCVC induces effects in renal tissues, both in vivo
and in vitro, that are most similar to those of TCE, and formed in sufficient amounts after TCE
exposure to account for those effects. A role for formic acid due to TCOH or TCA formation
from TCE cannot be ruled out, as it is known that substantial TCOH and TCA are formed from
TCE exposure, that formic acid is produced from all three compounds, and that TCOH exposure
leads to toxicity in the renal tubules. However, the characteristics of TCOH-induced
nephrotoxicity do not account for the range of effects observed after TCE exposure, while those
of DCVC-induced nephrotoxicity do. Also, TCOH does not induce the same pathology as TCE
or DCVC. TCA has also been demonstrated to induce peroxisomal proliferation in the kidney
(Goldsworthy and Popp, 1987), but this has not been associated with kidney cancer. Therefore,
although TCOH and possibly TCA may contribute to TCE-induced nephrotoxicity, their
contribution is likely to be small compared to that of DCVC. However, as discussed in
Section 3.3.3.2, the precise metabolic yield of these DCVC following TCE exposure remains
uncertain.
4.4.7. Mode(s) of Action for Kidney Carcinogenicity
This section will discuss the evidentiary support for several hypothesized modes of action
for kidney carcinogenicity, including mutagenicity, cytotoxicity and regenerative proliferation,
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peroxisome proliferation, a2|i-related nephropathy, and formic acid-related nephropathy,
following the framework outlined in the Cancer Guidelines (U.S. EPA, 2005b).6 The data and
conclusions for the modes of action with the greatest experimental support are summarized in
Table 4-54.
4.4.7.1. Hypothesized Mode of Action: Mutagenicity
One hypothesis is that a mutagenic mode of action is operative in TCE-induced renal
carcinogenesis. According to this hypothesis, the key events leading to TCE-induced kidney
tumor formation constitute the following: TCE GSH conjugation metabolites (e.g., DCVG,
DCVC, NAcDCVC, and/or other reactive metabolites derived from subsequent beta-lyase, flavin
monooxygenases [FMO], or CYP metabolism) derived from the GSH-conjugation pathway, after
being either produced in situ in or delivered systemically to the kidney, cause direct alterations to
DNA (e.g., mutation, DNA damage, and/or micronuclei induction). Mutagenicity is a well-
established cause of carcinogenicity.
Toxicokinetic data are consistent with these genotoxic metabolites either being delivered
to or produced in the kidney. As discussed in Section 3, following in vivo exposure to TCE, the
metabolites DCVG, DCVC, and NAcDCVC have all been detected in the blood, kidney, or urine
of rats, and DCVG in blood and NAcDCVC in urine have been detected in humans (2006; Lash
et al., 1999b: Bernauer et al., 1996; Birner et al., 1993). In addition, in vitro data have shown
DCVG formation from TCE in cellular and subcellular fractions from the liver, from which it
would be delivered to the kidney via systemic circulation, and from the kidney (see Tables 3-23-
3-24, and references therein). Furthermore, in vitro data in both humans and rodents support the
conclusion that DCVC is primarily formed from DCVG in the kidney itself, with subsequent in
situ transformation to NAcDCVC by TV-Acetyl transferase or to reactive metabolites by beta-
lyase, FMO, or CYPs (see Sections 3.3.3.2.2 to 3.3.3.2.5). Therefore, it is highly likely that both
human and rodent kidneys are exposed to these TCE metabolites.
6As recently reviewed (Guvton et al.. 2008X the approach to evaluating mode of action information described in
EPA's Cancer Guidelines (2005b) considers the issue of human relevance of a hypothesized mode of action in the
context of hazard evaluation. This excludes, for example, consideration of toxicokinetic differences across species;
specifically, the Cancer Guidelines state, —He toxicokinetic processes that lead to formation or distribution of the
active agent to the target tissue are considered in estimating dose but are not part of the mode of action." In
addition, information suggesting quantitative differences in the occurrence of a key event between test species and
humans are noted for consideration in the dose-response assessment, but is not considered in human relevance
determination. In keeping with these principles, a formal analysis of the dose-response of key events in the
hypothesized modes of action is not presented unless it would aid in the overall weight of evidence analysis for
carcinogenicity, as presented in Section 4.1.
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Table 4-54. Summary of major mode-of-action conclusions for TCE kidney carcinogenesis
Hypothesized MOA Experimental support
postulated key events
Human
relevance
Weight-of-evidence conclusion
Mutagenicity
GSH conjugation-derived
metabolites produced in
situ or delivered
systemically to kidney.
Mutagenicity induced by
GSH-derived metabolites
advances acquisition of
the multiple critical traits
contributing to
carcinogenesis.
Overall Conclusion
Evidence that TCE or TCE metabolites induces key events:
• Multiple in vitro and in vivo studies demonstrate GSH conjugation of TCE,
and availability to the kidney (see Section 3.3.3).
• Uncertainties are quantitative (precise amount of flux), not qualitative.
Necessity of key events for carcinogenesis:
• Active GSTT1 alleles are associated with higher kidney cancer risk in
humans following TCE exposure as compared to null genotypes (Moore et
al. 2010).
Evidence that TCE or TCE metabolites induces key events:
• GSH conjugation derived metabolites (DCVG, DCVC, NAcDCVC)
demonstrated to be genotoxic in most in vitro assays in which they have
been tested, including Ames test (see Section 4.2.1.4.1).
• Kidney-specific genotoxicity in rats and rabbits after in vivo administration
of TCE or DCVC. Not seen in mice, but may be due to species differences
in metabolism and in sensitivity to renal carcinogenesis.
Necessity of key events for carcinogenesis:
• Inconsistent results with respect to VHL mutation status, with some studies
providing suggestive evidence of a TCE-induced kidney tumor genotype;
no data regarding other specific mutations.
Sufficiency of MOA for carcinogenesis:
• Mutagenicity is assumed to cause cancer, as a sufficient cause.
Yes:
demonstrated
in humans in
vivo and in
human cells in
vitro.
Yes: no basis
for discounting
in vitro or in
vivo
genotoxicity
results.
Yes: well
established.
Highly likely that both human and
rodent kidneys are exposed to the
GSH-conjugation derived
metabolites.
Predominance of positive
genotoxicity data consistent with
GSH-conjugation derived
metabolites causing mutations in
the kidney.
Data are sufficient to conclude that
a mutagenic MOA is operative in
TCE-induced kidney tumors.
(Section 4.4.7.1).
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Table 4-54. Summary of major mode-of-action conclusions for TCE kidney carcinogenesis (continued)
Hypothesized MO A
postulated key events
Experimental support
Human
relevance
Weight-of-evidence conclusion
Cytotoxicity and regenerative proliferation
GSH conjugation-derived
metabolites produced in
situ or delivered
systemically to kidney.
Evidence that TCE or TCE metabolites induces key events:
• Multiple in vitro and in vivo studies demonstrate GSH conjugation of TCE,
and availability to the kidney (see Section 3.3.3).
• Uncertainties are quantitative (precise amount of flux), not qualitative.
Necessity of key events for carcinogenesis:
• Active GSTT1 alleles are associated with higher kidney cancer risk in
humans following TCE exposure as compared to null genotypes (Moore et
al. 2010).
Yes:
demonstrated
in humans in
vivo and in
human cells in
vitro.
Highly likely that both human and
rodent kidneys are exposed to the
GSH-conjugation derived
metabolites.
• Cytotoxicity.
• Compensatory cell
proliferation.
• Clonal expansion of
initiated cells.
Evidence that TCE or TCE metabolites induces key events:
• Multiple human and laboratory animal studies demonstrating TCE to be
nephrotoxic, including chronic studies (see Sections 4.4.1 and 4.4.4).
• Multiple laboratory animal studies and in vitro studies in rat and human
kidney cells demonstrating DCVC to be nephrotoxic (see
Sections 4.4.6.1.1 and 4.4.6.2).
• Some evidence that TCOH is nephrotoxic, but histological changes caused
by TCE more similar to those caused by DCVC (see Section 4.4.6.1 and
4.4.6.3).
• Increased DNA synthesis as measured by BrdU in Eker rats.
• No increase in preneoplastic or neoplastic lesions in Eker rats exposed to
TCE for 13 wk, but no data on longer durations or from other rat strains
sensitive to TCE renal carcinogenesis.
Necessity of key events for carcinogenesis:
• No TCE-specific studies to establish the necessity of TCE-induced
proliferation resulting from nephrotoxicity to clonal expansion and cancer.
Yes:
demonstrated
human
nephrotoxicity
of TCE in vivo
and DCVC in
vitro.
TCE is nephrotoxic in
humans, and DCVC is likely
the predominant moiety
responsible.
TCE increases cell
proliferation.
Data linking TCE-induced
proliferation to clonal
expansion are lacking.
Overall Conclusion
Sufficiency of MO A for carcinogenesis:
• Maximal levels of Cytotoxicity are reached at doses below which the
incidence of tumors is elevated, suggesting Cytotoxicity is not sufficient for
carcinogenesis.
• While Cytotoxicity and regenerative cell proliferation occur and are
assumed to contribute to carcinogenesis, a more plausible MOA may
involve combination of Cytotoxicity with mutagenicity.
Yes: well
established.
Data are consistent with
hypothesis that Cytotoxicity and
regenerative proliferation
contribute to TCE-induced kidney
tumors, but data linking TCE-
induced proliferation to clonal
expansion are lacking. (Section
4.4.7.2)
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Table 4-54. Summary of major mode-of-action conclusions for TCE kidney carcinogenesis (continued)
Hypothesized MO A
postulated key events
Experimental support
Human
relevance
Weight-of-evidence conclusion
Peroxisome proliferation activated receptor alpha activation
• TCE oxidative
metabolites (e.g.,
TCA), after being
produced in the liver,
activate PPARa in
the kidney.
• Alterations in cell
proliferation and
apoptosis.
• Clonal expansion of
initiated cells.
Overall
Evidence that TCE or TCE metabolites induces key events:
• Increased PCO activity (marker for PPARa activation) observed in rats and
mice treated with TCE or TCA.
• No increases in kidney /body weight ratios (potential marker for changes in
cell proliferation/apoptosis) due to oxidative metabolites.
• No data on altered cell proliferation/apoptosis or clonal expansion in the
kidney due to PPARa activation.
Necessity of key events for carcinogenesis:
• No PCE-specific studies. No data from other chemicals on PPARa
involvement in kidney tumors.
Sufficiency of MO A for carcinogenesis:
• Inadequate data to support a role for PPARa activation in renal
carcinogenesis, in general, or for TCE specifically.
Yes. Humans
produce
oxidative
metabolites of
TCE, PPARa is
present in the
human kidney.
N/A
Highly likely that PPARa is
activated in the kidney, but little
evidence for other key events.
Little evidence that PPARa
activation contributes to renal
carcinogenesis.
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Table 4-54. Summary of major mode-of-action conclusions for TCE kidney carcinogenesis (continued)
Hypothesized MO A
postulated key events
Experimental support
Human
relevance
Weight-of-evidence conclusion
a2ji-Globulin-related nephropathy
• TCE oxidative
metabolites (e.g.,
TCOH), cause
hyaline droplet
accumulation and an
increase in a2ju-
globulin, resulting in
nephrotoxicity.
• Subsequent
cytotoxicity and
necrosis.
• Sustained
regenerative tubule
cell proliferation.
• Development of
intralumenal
granular casts from
sloughed cellular
debris associated
with tubule dilatation
and papillary
mineralization.
• Foci of tubule
hyperplasia in the
convoluted proximal
tubules.
• Renal tubule tumors.
Overall
Evidence that TCE or TCE metabolites induces key events:
• TCOH caused hyaline droplet accumulation and an increase in o2u-
globulin, but at levels insufficient to account for the observed nephropathy.
• TCE is associated with small increases in kidney cancer in female rats (not
consistent with o2u-globulin hypothesis).
• TCE is associated with kidney cancer in humans (not consistent with o2u-
globulin hypothesis).
Necessity of key events for carcinogenesis:
• Inadequate support that it is necessary for TCE-induced renal
carcinogenesis.
Sufficiency of MO A for carcinogenesis:
• Inadequate data to support a role in TCE-induced renal carcinogenesis.
No.
No.
Unlikely that o2u-globulin is the
major cause of TCE-induced
nephrotoxicity.
Little evidence that increases in
o2u-globulin contribute to TCE-
induced renal carcinogenesis.
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Table 4-54. Summary of major mode-of-action conclusions for TCE kidney carcinogenesis (continued)
Hypothesized MO A
postulated key events
Experimental support
Human
relevance
Weight-of-evidence conclusion
Formic acid-related nephrotoxicity
• TCE oxidative
metabolites (e.g.,
TCA or TCOH), after
being produced in
the liver, lead to
increased formation
and urinary
excretion of formic
acid, which causes
cytotoxicity in the
kidney.
• Compensatory cell
proliferation.
• Clonal expansion of
initiated.
Overall
Evidence that TCE or TCE metabolites induces key events:
• TCOH causes histological changes in the kidney, along with increased
formic acid.
• TCOH-induced kidney effects do not account for most of the kidney
effects observed after TCE exposure (not consistent with formic acid
hypothesis).
• No data as to oxidative metabolites causing regenerative proliferation, or
other key events in the kidney.
Necessity of key events for carcinogenesis:
• Inadequate data to support the necessity of formic acid formation in renal
carcinogenesis.
Sufficiency of MO A for carcinogenesis:
• Inadequate data supporting a sufficient role for formic acid in renal
carcinogenesis, whether generally or for TCE specifically.
Yes.
N/A
Unlikely that formic acid is a
major contributor to TCE-induced
nephrotoxicity.
Unlikely that formic acid
formation and its sequelae
contribute to TCE-induced renal
carcinogenesis.
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4.4.7.1.1. Experimental support for the hypothesized mode of action
Evidence for the hypothesized mode of action for TCE includes: (1) the formation of
GSH-conjugation pathway metabolites in the kidney demonstrated in TCE toxicokinetics studies
and (2) the genotoxicity of these GSH-conjugation pathway metabolites demonstrated in most
existing in vitro and in vivo assays of gene mutations (i.e., Ames test) and in assays of
unscheduled DNA synthesis, DNA strand breaks, and micronuclei using both "standard" systems
and renal cells/tissues.7 Additional relevant data come from analyses of VHL mutations in
human kidney tumors and studies using the Eker rat model. These lines of evidence are
elaborated below.
Toxicokinetic data are consistent with these genotoxic metabolites either being delivered
to or produced in the kidney. As discussed in Chapter 3, following in vivo exposure to TCE, the
metabolites DCVG, DCVC, and NAcDCVC have all been detected in the blood, kidney, or urine
of rats, and DCVG in blood and NAcDCVC in urine have been detected in humans (2006; Lash
et al., 1999b: Bernauer et al., 1996; Birner et al., 1993). In addition, in vitro data have shown
DCVG formation from TCE in cellular and subcellular fractions from the liver, from which it
would be delivered to the kidney via systemic circulation, and from the kidney (see Tables 3-23-
3-24, and references therein). Furthermore, in vitro data in both humans and rodents support the
conclusion that DCVC is primarily formed from DCVG in the kidney itself, with subsequent in
situ transformation to NAcDCVC by TV-Acetyl transferase or to reactive metabolites by beta-
lyase, FMO, or CYPs (see Sections 3.3.3.2.2 to 3.3.3.2.5). Therefore, it is highly likely that both
human and rodent kidneys are exposed to these TCE metabolites.
As discussed in Section 4.2.5, DCVG, DCVC, and NAcDCVC have been demonstrated
to be genotoxic in most available in vitro assays.8 In particular, DCVC was mutagenic in the
Ames test in three of the tested strains of S. typhimurium (TA100, TA2638, TA98) (Vamvakas et
7The EPA Cancer Guidelines (2005b, e) note reliance on —equation of in vivo or in vitro short-term testing results
for genetic endpoints" and evidence that —he carcinogen or a metabolite is DNA-reactive and/or has the ability to
bind to DNA"as part of this weight of evidence supporting a mutagenic mode of action. While evidence from
hypothesis-testing experiments that mutation is an early step in the carcinogenic process is considered if available, it
is not required for determination of a mutagenic mode of action; rather, reliance on short-term genotoxicity tests is
emphasized. Thus, such tests are the focus of this analysis, which also includes an analysis of other available data
from humans and animals. In keeping with these principles, a formal analysis of the temporal concordance of key
events in the hypothesized modes of action is not presented unless it would aid in the overall weight of evidence
analysis for carcinogenicity, as presented in Section 4.11.
Evaluation of genotoxicity data entails a weight of evidence approach that includes consideration of the various
types of genetic damage that can occur. In acknowledging that genotoxicity tests are by design complementary
evaluations of different mechanisms of genotoxicity, a recent IPCS publication (Eastmond et al.. 2009) notes that
-^nultiple negative eresults may not be sufficient to remove concern for mutagenicity raised by a clear positive result
in a single mutagenicity assay." These considerations inform the present approach. In addition, consistent with
EPA's Cancer Guidelines (2005b, e), the approach does not address relative potency (e.g., among TCE metabolites,
or of such metabolites with other known genotoxic carcinogens) per se, nor does it consider quantitative issues
related to the probable production of these metabolites in vivo. Instead, the analysis of genetic toxicity data
presented in Section 4.2 and summarized here focuses on the identification of a genotoxic hazard of these
metabolites; a quantitative analysis of TCE metabolism to reactive intermediates, via PBPK modeling, is presented
in Section 3.5.
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al., 1988b: Dekantetal., 1986c) and caused dose-dependent increases in UDS in the two
available assays: porcine kidney tubular epithelial cell line (Vamvakas et al., 1996) and Syrian
hamster embryo fibroblasts (Vamvakas et al., 1988a). DCVC has also been shown to induce
DNA strand breaks in both available studies (Robbiano et al., 2004; Jaffe et al., 1985), and
induce micronucleus formation in primary kidney cells from rats and humans (Robbiano et al.,
2004) but not in Syrian hamster embryo fibroblasts (Vamvakas et al., 1988a). Only one study
each is available for DCVG and 7V-AcDCVC, but notably, both were positive in the Ames test
(1988b: Vamvakas et al., 1987). Although the number of test systems was limited, these results
are consistent.
These in vitro results are further supported by studies reporting kidney-specific
genotoxicity after in vivo administration of TCE or DCVC. In particular, Robbiano et al. (1998)
reported increased numbers of micronucleated cells in the rat kidney following oral TCE
exposure. Oral exposure to DCVC in both rabbits (Jaffe et al., 1985) and rats (Clay, 2008)
increased DNA strand breaks in the kidney. However, in one inhalation exposure study in rats,
TCE did not increase DNA breakage in the rat kidney, possibly due to study limitations (limited
exposure time [6 hours/day for only 5 days] and small number of animals exposed [n = 5]; Clay,
(2008). One study of TCE exposure in the Eker rat, a rat model heterozygous for the tumor
suppressor gene Tsc-2, reported no significant increase in kidney tumors as compared to controls
(Mally et al., 2006). Inactivation of Tsc-2 in this rat model is associated with spontaneous RCC
with activation of pathways similar to that of VHL inactivation in humans (Liu et al., 2003).
TCE exposure for 13 weeks (corn oil gavage) led to increased nephrotoxicity but no significant
increases in preneoplastic or neoplastic lesions as compared to controls (Mally et al., 2006). This
lack of increased incidence of neoplastic or preneoplastic lesions reported by Mally et al. (2006)
in the tumor-prone Eker rat is similar to lack of significant short-term response exhibited by
other genotoxic carcinogens in the Eker rat (Stemmer et al., 2007; Morton et al., 2002) and may
be related to the increased background rate of renal carcinomas in this animal model. Mally
et al. (2006) also exposed primary kidney epithelial cells from the Eker rat to DCVC in vitro and
demonstrated increased transformation similar to that of other renal carcinogens (Horesovsky et
al., 1994).
As discussed in Section 4.2.1.4.1, although Douglas et al. (1999) did not detect increased
mutations in the kidney of 7acZ transgenic mice exposed to TCE for 12 days, these results are not
highly informative as to the role of mutagenicity in TCE-induced kidney tumors, given the
uncertainties in the production in genotoxic GSH conjugation metabolites in mice and the low
carcinogenic potency of TCE for kidney tumors in rodents relative to what is detectable in
experimental bioassays. Limited, mostly in vitro, toxicokinetic data do not suggest that mice
have less GSH conjugation or subsequent renal metabolism/bioactivation (see Section 3.3.3.2.7),
but quantitatively, the uncertainties in the flux through these pathways remain significant (see
Section 3.5). In additional, similar to other genotoxic renal carcinogens analyzed by NTP, there
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is limited evidence of mouse kidney tumors following TCE exposure. However, given the
already low incidences of kidney tumors observed in rats, a relatively small difference in potency
in mice would be undetectable in available chronic bioassays. Notably, of seven chemicals
categorized as direct-acting genotoxic carcinogens that induced rat renal tumors in NTP studies,
only two also led to renal tumors in the mouse (tris[2,3-dibromopropyl]phosphate and
ochratoxin A) (Reznik et al., 1979; Kanisawa and Suzuki, 1978), so the lack of detectable
response in mouse bioassays does not preclude a genotoxic mode of action.
VHL inactivation (via mechanisms such as deletion, silencing, or mutation) observed in
human renal clear cell carcinomas is the basis of a hereditary syndrome of kidney cancer
predisposition and is hypothesized to be an early and causative event in this disease (e.g., 2008).
Therefore, specific actions of TCE metabolites that produce or select for mutations of the VHL
suppressor gene could lead to kidney tumorigenesis. Several studies have compared VHL
mutation frequencies in cases with TCE exposures with those from control or background
populations. Briining et al. (1997b) and Brauch et al. (2004; 1999) reported differences between
TCE-exposed and nonexposed RCC patients in the frequency of somatic VHL mutations, the
incidence of a hot spot mutation of cytosine to thymine at nucleotide 454, and the incidence of
multiple mutations. These data suggest that kidney tumor genotype data in the form of a specific
mutation pattern may potentially serve to discriminate TCE-induced tumors from other types of
kidney tumors in humans. If validated, this would also suggest that TCE-induced kidney tumors
are dissimilar from those occurring in unexposed individuals. Thus, while not confirming a
mutation mode of action, these data suggest that TCE-induced tumors may be distinct from those
induced spontaneously in humans. However, it has not been examined whether a possible
linkage exists between VHL loss or silencing and mutagenic TCE metabolites.
By contrast, Schraml et al. (1999) and Charbotel et al. (2007) reported that TCE-exposed
RCC patients did not have significantly higher incidences of VHL mutations compared to
nonexposed patients. However, details as to the exposure conditions were lacking in Schraml
et al. (1999). In addition, the sample preparation methodology employed by Charbotel et al.
(2007) and others (Brauch et al., 1999; Briining et al., 1997b) often results in poor quality and/or
low quantity DNA, leading to study limitations (<100% of samples were able to be analyzed).
Therefore, further investigations are necessary to either confirm or contradict the validity of the
genetic biomarkers for TCE-related renal tumors reported by Briining et al. (1997b) and Brauch
et al. (2004: 1999).
In addition, while exposure to mutagens is certainly associated with cancer induction (as
discussed with respect to the liver in Appendix E, Sections E.3.1 and E.3.2), examination of end-
stage tumor phenotype or genotype has limitations concerning determination of early key events.
The mutations that are observed with the progression of neoplasia are associated with increased
genetic instability and an increase in mutation rate. Further, inactivation of the VHL gene also
occurs through other mechanisms in addition to point mutations, such as loss of heterozygosity
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or hypermethylation (Nickerson et al., 2008; Kenck et al., 1996) not addressed in these studies.
Recent studies examining the role of other genes or pathways suggest roles for multiple genes in
RCC development (Toma et al., 2008; Furge et al., 2007). Therefore, the inconsistent results
with respect to VHL mutation status do not constitute negative evidence for a mutational mode of
action and the positive studies are suggestive of a TCE-induced kidney tumor genotype.
In sum, the predominance of positive genotoxicity data in the database of available
studies of TCE metabolites derived from GSH conjugation (in particular the evidence of kidney-
specific genotoxicity following in vivo exposure to TCE or DCVC), coupled with the
toxicokinetic data consistent with the in situ formation of these GSH-conjugation metabolites of
TCE in the kidney, is consistent with the hypothesis that a mutagenic mode of action is operative
in TCE-induced kidney tumors. Mutagenicity is a well-established cause of carcinogenicity.
Available data on the VHL gene in humans add biological plausibility to these conclusions.
Quantitatively, however, as discussed in Section 3.3.3.2, the precise metabolic yield of the GSH
conjugation metabolites following TCE exposure remains uncertain.
4.4.7.2. Hypothesized Mode of Action: Cytotoxicity and Regenerative Proliferation
Another hypothesis is that TCE acts by a cytotoxicity mode of action in TCE-induced
renal carcinogenesis. According to this hypothesis, the key events leading to TCE-induced
kidney tumor formation comprise the following: the TCE GSH-conjugation metabolite, DCVC,
after being either produced in situ in or delivered systemically to the kidney, causes cytotoxicity,
leading to compensatory cellular proliferation and subsequently increased mutations and clonal
expansion of initiated cells.
4.4.7.2.1. Experimental support for the hypothesized mode of action
Evidence for the hypothesized mode of action consist primarily of (1) the demonstration
of nephrotoxicity following TCE exposure at current occupational limits in human studies and
chronic TCE exposure in animal studies; (2) the relatively high potential of the TCE metabolite
DCVC to cause nephrotoxicity; and (3) toxicokinetic data demonstrating that DCVC is formed in
the kidney following TCE exposure. Data on nephrotoxicity of TCE and DCVC are discussed in
more detail below, while the toxicokinetic data were summarized previously in the discussion of
mutagenicity. Thus, the data are consistent with the hypothesized mode of action, and therefore,
do not rule out a contribution from cytotoxicity and regenerative proliferation to TCE-induced
kidney carcinogenesis. However, there is a lack of experimental data supporting a causal link
between TCE nephrotoxicity combined with sustained cellular proliferation and TCE-induced
nephrocarcinogenicity.
There is substantial evidence that TCE is nephrotoxic in humans and laboratory animals
and that its metabolite, DCVC, is nephrotoxic in laboratory animals. Epidemiological studies
have consistently demonstrated increased excretion of nephrotoxicity markers (NAG, protein,
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albumin) at occupational (Green et al., 2004) and higher (Bolt et al., 2004; Briming et al., 1999a:
1999b) levels of TCE exposure. However, direct evidence of tubular toxicity, particularly in
RCC cases, is not available. These studies are supported by the results of multiple laboratory
animal studies. Chronic bioassays have reported very high (nearly 100%) incidences of
nephrotoxicity of the proximal tubule in rats (NTP, 1990. 1988) and mice (NTP, 1990: NCL
1976) at the highest doses tested. In vivo studies examining the effect of TCE exposure on
nephrotoxicity showed increased proximal tubule damage following i.p. injection and inhalation
of TCE in rats (Chakrabarti and Tuchweber, 1988) and i.p. injection in mice (Cojocel et al.,
1989). Studies examining DCVC exposure in rats (Elfarra et al., 1986; Terracini and Parker,
1965) and mice (Darnerud et al., 1989; Jaffe et al., 1984) have also shown increases in kidney
toxicity. The greater potency for kidney cytotoxicity for DCVC compared to TCE was shown by
in vitro studies (Lash et al., 1986, 1995; Stevens et al., 1986). These studies also further
confirmed the higher susceptibility of male rats or mice to DCVC-induced cytotoxicity.
Cytokaryomegaly (an effect specific to TCE and not part of the chronic progressive nephropathy
or the pathology that occurs in aging rat kidneys) was observed in the majority of rodent studies
and may or may not progress to carcinogenesis. Finally, as discussed extensively in
Section 4.4.6.1, a detailed comparison of the histological changes in the kidney caused by TCE
and its metabolites supports the conclusion that DCVC is the predominant moiety responsible for
TCE-induced nephrotoxicity.
Because it is known that not all cytotoxins are carcinogens (i.e., cytotoxicity is not a
specific predictor of carcinogenicity), additional experimental support is required to causally link
nephrotoxicity to nephrocarcinogenicity. For chemicals that bind to a2|i-globulin, a mode of
action involving cell necrosis followed by subsequent regenerative proliferation has been
hypothesized to cause kidney tumors in the absence of genotoxicity (Short, 1993). However, for
other chemicals, toxicity and increased cell proliferation have been observed in the kidney
without inducing tumors even after chronic exposure (Tennant et al., 1991). Similarly, in the
liver, partial hepatectomy leading to regenerative hyperplasia does not by itself lead to increased
hepatocarcinogenicity, and requires administration of a mutagen to exhibit enhanced
carcinogenic effects. By analogy, a biologically plausible mode of action may involve a
combination of mutagenicity and cytotoxicity, with mutagenicity increasing the rate of mutation
and regenerative proliferation induced by cytotoxicity enhancing the selection, survival, or clonal
expansion of mutated cells.
For TCE and kidney cancer, clearly, cytotoxicity occurs at doses below those causing
carcinogenicity, as the incidence of nephrotoxicity in chronic bioassays is an order of magnitude
higher than that of renal tumors. Thus, these data are consistent with cytotoxicity being a
precursor to carcinogenicity (i.e., if the opposite were the case—carcinogenicity without
cytoxicity—then the hypothesis would be falsified). While chronic nephrotoxicity was reported
in the same bioassays showing increased kidney tumor incidences, the use of such data to inform
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mode of action is indirect and associative, and do not offer a test of the hypothesis (Short, 1993).
Nephrotoxicity is observed in both mice and rats, in some cases with nearly 100% incidence in
all dose groups, but kidney tumors are only observed at low incidences in rats at the highest
tested doses (NTP, 1990; NCI, 1976). Therefore, nephrotoxicity alone appears to be insufficient,
or at least not rate-limiting, for rodent renal carcinogenesis, since maximal levels of toxicity are
reached before the onset of tumors. Furthermore, there are multiple mechanisms by which TCE
has been hypothesized to induce cytotoxicity, including oxidative stress, disturbances in calcium
ion homeostasis, mitochondrial dysfunction, and protein alkylation (Lash et al., 2000a). Some of
these effects may therefore, have ancillary consequences related to tumor induction which are
independent of cytotoxicity per se. Therefore, data currently cannot distinguish as to whether
cytoxicity is causally related to tumorigenesis or merely associated by virtue of being a marker
for a different, key causal event.
Under the hypothesized mode of action, cytotoxicity leads to the induction of repair
processes and compensatory proliferation that could lead to an increased production or clonal
expansion of cells previously initiated by mutations occurred spontaneously, from co-exposures,
or from TCE or its metabolites. Data on compensatory cellular proliferation and the subsequent
hypothesized key events in the kidney are few, with no data from rat strains used in chronic
bioassays. In rats carrying the Eker mutation, Mally et al. (2006) reported increased DNA
synthesis as measured by BrdU incorporation in animals exposed to the high dose of TCE
(1,000 mg/kg-day) for 13 weeks, but there was no evidence of clonal expansion or tumorigenesis
in the form of increased preneoplastic or neoplastic lesions as compared to controls. Therefore,
in both rodent and human studies of TCE, data demonstrating a causal link between
compensatory proliferation and the induction of kidney tumors are lacking.
In sum, the predominance of positive nephrotoxicity data in the database of available
studies of TCE metabolites derived from GSH conjugation (in particular the evidence of kidney-
specific cytotoxicity following in vivo exposure to TCE or DCVC), coupled with the
toxicokinetic data consistent with the in situ formation of these GSH-conjugation metabolites of
TCE in the kidney, is consistent with the hypothesis that a mode of action involving cytotoxicity
and regenerative proliferation contributes to TCE-induced kidney tumors, either independently
or in combination with a mutagenic mode of action. However, nephrotoxicity is not in itself
predictive of tumorigenesis, and experimental data supporting for a causal link between TCE
nephrotoxicity combined with sustained cellular proliferation and TCE-induced
nephrocarcinogenicity are lacking. A more biologically plausible mode of action may involve a
combination of mutagenicity and cytotoxicity, with mutagenicity increasing the rate of mutation
and regenerative proliferation enhancing the selection, survival or clonal expansion of mutated
cells. However, this hypothesis has yet to be tested experimentally.
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4.4.7.3. Additional Hypothesized Modes of Action with Limited Evidence or
Inadequate Experimental Support
Along with metabolites derived from GSH conjugation of TCE, oxidative metabolites are
also present and could induce toxicity in the kidney. After TCE exposure, the oxidative
metabolite and peroxisome proliferator, TCA, is present in the kidney and excreted in the urine
as a biomarker of exposure. Hypotheses have also been generated regarding the roles of
a2|i-globulin or formic acid in nephrotoxicity induced by TCE oxidative metabolites TCA or
TCOH. However, the available data are limited or inadequate for supporting these hypothesized
modes of action.
4.4.7.3.1. Peroxisome proliferation
Although not as well studied as the effects of GSH metabolites in the kidney, there is
evidence that oxidative metabolites affect the kidney after TCE exposure. Both TCA and DCA
are peroxisome proliferator activated receptor alpha (PPARa) agonists although most activity has
been associated with TCA production after TCE exposure. Exposure to TCE has been found to
induce peroxisome proliferation not only in the liver, but also in the kidney. Peroxisome
proliferation in the kidney has been evaluated by only one study of TCE (Goldsworthy and Popp,
1987), using increases in cyanide-insensitive palmitoyl-CoA oxidation (PCO) activity as a
marker. Increases in renal PCO activity were observed in rats (3.0-fold) and mice (3.6-fold)
treated with TCE at 1,000 mg/kg-day for 10 days, with smaller increases in both species from
TCA treatment at 500 mg/kg-day for 10 days. However, no significant increases in kidney/body
weight ratios were observed in either species. There was no relationship between induction of
renal peroxisome proliferation and renal tumors (i.e., a similar extent of peroxisome
proliferation-associated enzyme activity occurred in species with and without TCE-induced renal
tumors). However, the increased peroxisomal enzyme activities due to TCE exposure are
indicative of oxidative metabolites being present and affecting the kidney. Such metabolites
have been associated with other tumor types, especially liver, and whether co-exposures to
oxidative metabolites and GSH metabolites contribute to kidney tumorigenicity has not been
examined.
4.4.7.3.2. a2ji-Globulin-related nephropathy
Induction of a2|i-globulin nephropathy by TCE has been investigated by Goldsworthy
et al. (1988), who reported that TCE did not induce increases in this urinary protein, nor did it
stimulate cellular proliferation in rats. In addition, whereas kidney tumors associated with
a2|i-globulin nephropathy are specific to the male rat, as discussed above, nephrotoxicity is
observed in both rats and mice and kidney tumor incidence is elevated (though not always
statistically significant) in both male and female rats. TCOH was recently reported to cause
hyaline droplet accumulation and an increase in a2|i-globulin, but these levels were insufficient
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to account for the observed nephropathy as compared to other exposures (Green et al., 2003).
Therefore, it is unlikely that a2|i-globulin nephropathy contributes significantly to TCE-induced
renal carcinogenesis.
4.4.7.3.3. Formic acid-related nephrotoxicity
Another mode-of-action hypothesis proposes that TCE nephrotoxicity is mediated by
increased formation and urinary excretion of formic acid mediated by the oxidative metabolites
TCA or TCOH (Green et al.. 2003: Dow and Green. 2000: 1998). The subsequent hypothesized
key events are the same as those for DCVC-induced cytotoxicity, discussed above (see
Section 4.4.7.2). As discussed extensively in Section 4.4.6.1.2, these oxidative metabolites do
not appear sufficient to explain the range of renal effects observed after TCE exposure,
particularly cytomegaly, karyomegaly, and flattening and dilation of the tubular epithelium.
Although TCOH and possibly TCA may contribute to the nephrotoxicity of TCE, perhaps due to
excess formic acid production, these metabolites do not show the same range of cytotoxic effects
observed following TCE exposure (see Table 4-53). Therefore, without specific evidence
linking the specific nephrotoxic effects caused by TCOH or TCA to carcinogenesis, and in light
of the substantial evidence that DCVC itself can adequately account for the nephrotoxic effects
of TCE, the weight of evidence supports a conclusion that cytotoxicity mediated by increased
formic acid production induced by oxidative metabolites TCOH and possibly TCA is not
responsible for the majority of the TCE-induced cytotoxicity in the kidneys, and therefore, would
not be the major contributor to the other hypothesized key events in this mode of action, such as
subsequent regenerative proliferation.
4.4.7.4. Conclusions About the Hypothesized Modes of Action
4.4.7.4.1. Is the hypothesized mode of action sufficiently supported in the test animals
4.4.7.4.1.1. Mutagenicity
The predominance of positive genotoxicity data in the database of available studies of
TCE metabolites derived from GSH conjugation (in particular the evidence of kidney-specific
genotoxicity following in vivo exposure to TCE or DCVC), coupled with the toxicokinetic data
consistent with the in situ formation of these GSH-conjugation metabolites of TCE in the kidney,
supports the conclusion that a mutagenic mode of action is operative in TCE-induced kidney
tumors.
4.4.7.4.1.2. Cytotoxicity
As reviewed above, in vivo and in vitro studies have shown a consistent nephrotoxic
response to TCE and its metabolites in proximal tubule cells from male rats. Therefore, it has
been proposed that cytotoxicity seen in this region of the kidney is a precursor to carcinogenicity.
Available data are consistent with the hypothesis that a mode of action involving cytotoxicity
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and regenerative proliferation contributes to TCE-induced kidney tumors, either independently
or in combination with a mutagenic mode of action. However, it has not been determined
whether tubular toxicity is a necessary precursor of carcinogenesis, and there is a lack of
experimental support for causal links, such as compensatory cellular proliferation or clonal
expansion of initiated cells, between nephrotoxicity and kidney tumors induced by TCE.
Nephrotoxicity alone appears to be insufficient, or at least not rate-limiting, for rodent renal
carcinogenesis, since maximal levels of toxicity are reached before the onset of tumors. A more
biologically plausible mode of action may involve a combination of mutagenicity and
cytotoxicity, with mutagenicity increasing the rate of mutation and regenerative proliferation
enhancing the survival or clonal expansion of mutated cells. However, this hypothesis has yet to
be tested experimentally.
4.4.7.4.1.3. Additional hypothese
The kidney is also exposed to oxidative metabolites that have been shown to be
carcinogenic in other target organs. TCA is excreted in kidney after its metabolism from TCE
and also can cause peroxisome proliferation in the kidney, but there are inadequate data to define
a mode of action for kidney tumor induction based on peroxisome proliferation. TCE induced
little or no a2|i-globulin and hyaline droplet accumulation to account for the observed
nephropathy, so available data do not support this hypothesized mode of action. The production
of formic acid following exposure to TCE and its oxidative metabolites TCOH and TCA may
also contribute to nephrotoxicity; however, the available data indicate that TCOH and TCA are
minor contributors to TCE-induced nephrotoxicity, and therefore, do not support this
hypothesized mode of action. Because these additional mode-of-action hypotheses are either
inadequately defined or are not supported by the available data, they are not considered further in
the conclusions below.
4.4.7.4.2. Is the hypothesized mode of action relevant to humans
4.4.7.4.2.1. Mutagenicity
The evidence discussed above demonstrates that TCE GSH-conjugation metabolites are
mutagens in microbial as well as test animal species. Therefore, the presumption that they would
be mutagenic in humans. Available data on the VHL gene in humans add biological plausibility
to this hypothesis. The few available data from human studies concerning the mutagenicity of
TCE and its metabolites suggest consistency with this mode of action, but are not sufficiently
conclusive to provide direct supporting evidence for a mutagenic mode of action. Therefore, this
mode of action is considered relevant to humans.
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4.4.7.4.2.2. Cytotoxicity
Although data are inadequate to determine that the mode of action is operative, none of
the available data suggest that this mode of action is biologically precluded in humans.
Furthermore, both animal and human studies suggest that TCE causes nephrotoxicity at
exposures that also induce renal cancer, constituting positive evidence of the human relevance of
this hypothesized mode of action.
4.4.7.4.3. Which populations or lifestages can be particularly susceptible to the
hypothesized mode of action
4.4.7.4.3.1. Mutagenicity
The mutagenic mode of action is considered relevant to all populations and lifestages.
According to EPA's Cancer Guidelines (U.S. EPA, 2005b) and Supplemental Guidance (U.S.
EPA, 2005e), there may be increased susceptibility to early-life exposures for carcinogens with a
mutagenic mode of action. Therefore, because the weight of evidence supports a mutagenic
mode of action for TCE carcinogenicity and in the absence of chemical-specific data to evaluate
differences in susceptibility, early-life susceptibility should be assumed and the age-dependent
adjustment factors (ADAFs) should be applied, in accordance with the Supplemental Guidance.
In addition, because the mode of action begins with GSH-conjugation metabolites being
delivered systemically or produced in situ in the kidney, toxicokinetic differences (i.e., increased
production or bioactivation of these metabolites) may render some individuals more susceptible
to this mode of action. However, as discussed in Section 3.3.3.2, quantitative estimates of the
amount of GSH conjugation following TCE exposure remain uncertain. Toxicokinetic-based
susceptibility is discussed further in Section 4.10.
In rat chronic bioassays, TCE-treated males have higher incidence of kidney tumors than
similarly treated females. However, the basis for this sex difference is unknown, and whether it
is indicative of a sex difference in human susceptibility to TCE-induced kidney tumors is
likewise unknown. The epidemiologic studies generally do not show sex differences in kidney
cancer risk. Lacking exposure-response information, it is not known if the sex-difference in one
RCC case-control study (Dosemeci et al., 1999) may reflect exposure differences or
susceptibility differences.
4.4.7.4.3.2. Cytotoxicity
Populations that may be more susceptible based on the toxicokinetics of the production of
GSH conjugation metabolites and the sex differences observed in rat chronic bioassays are the
same as for a mutagenic mode of action. No data are available as to whether other factors may
lead to different populations or lifestages being more susceptible to a cytotoxic mode of action
for TCE-induced kidney tumors. For instance, it is not known how the hypothesized key events
in this mode of action interact with known risk factors for human RCC.
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The weight of evidence sufficiently supports a mutagenic mode of action for TCE in the
kidney, based on supporting data that GSH-metabolites are genotoxic and produced in sufficient
quantities in the kidney to lead to tumorigenesis. Cytotoxicity and regenerative proliferation
were considered as an alternate mode of action; however, there are inadequate data to support a
causal association between cytotoxicity and kidney tumors. Further, hypothesized modes of
action relating to peroxisomal proliferation, a2u-globulin nephropathy and formic acid-related
nephrotoxicity were considered and rejected due to limited evidence and/or inadequate
experimental support.
4.4.8. Summary: TCE Kidney Toxicity, Carcinogenicity, and Mode of Action
Human studies have shown increased levels of proximal tubule damage in workers
exposed to high levels of TCE (NRC, 2006). These studies analyzed workers exposed to TCE
alone or in mixtures and reported increases in various urinary biomarkers of kidney toxicity or
ESRD (p2-microglublin, total protein, NAG, al-microglobulin) (Jacob et al., 2007; Radican et
al.. 2006: Bolt et al.. 2004: Green et al.. 2004: Bruning et al.. 1999a: 1999b: Seldenetal.. 1993:
Nagayaetal.. 1989a). Laboratory animal studies examining TCE exposure provide additional
support, as multiple studies by both gavage and inhalation exposure show that TCE causes renal
toxicity in the form of cytomegaly and karyomegaly of the renal tubules in male and female rats
and mice. By gavage, incidences of these effects under chronic bioassay conditions approach
100%, with male rats appearing to be more sensitive than either female rats or mice of either sex
based on the severity of effects. Under chronic inhalation exposures, only male rats exhibited
these effects. Further studies with TCE metabolites have demonstrated a potential role for
DCVC, TCOH, and TCA in TCE-induced nephrotoxicity. Of these, DCVC induces the renal
effects that are most like TCE, and it is formed in sufficient amounts following TCE exposure to
account for these effects.
Kidney cancer risk from TCE exposure has been studied related to TCE exposure in
cohort, case-control, and geographical studies. These studies have examined TCE in mixed
exposures as well as alone. Elevated risks are observed in many of the cohort and case-control
studies examining kidney cancer incidence in industries or job titles with historical use of TCE
(see Table 4-39 and 4-40), particularly among subjects ever exposed to TCE (Moore et al.,
2010: Briining et al.. 2003: Raaschou-Nielsen et al.. 2003: Dosemeci etal.. 1999) or subjects
with TCE surrogate for high exposure (Moore etal., 2010: Charbotel et al., 2006: Zhao et al.,
2005: Briining etal.. 2003: Raaschou-Nielsen et al., 2003). Greater susceptibility to TCE
exposure and kidney cancer is observed among subjects with a functionally active GSTT
polymorphism, particularly among those with certain alleles in single nucleotide polymorphisms
of the cysteine conjugation p-lyase gene region (Moore et al., 2010). Although there are some
controversies related to deficiencies of the epidemiological studies (Vamvakas et al., 1998:
Henschler et al., 1995), many of these are overcome in later studies (Moore et al., 2010:
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Charbotel et al., 2006; Briming et al., 2003). A meta-analysis of the overall effect of TCE
exposure on kidney cancer, additionally, suggests a small, statistically significant increase in risk
(RRm = 1.27 95% CI: 1.13, 1.43) with an RRm estimate in the higher exposure group of 1.58,
(95% CI: 1.28, 1.96), robust in sensitivity to alternatives and lacking observed statistical
heterogeneity among 17 studies meeting explicitly-defined inclusion criteria.
In vivo laboratory animal studies to date suggest a small increase in renal tubule tumors
in male rats and, to a lesser extent, in female rats, with no increases seen in mice or hamsters.
These results are based on limited studies of both oral and inhalation routes, some of which were
deemed insufficient to determine carcinogenicity based on various experimental issues.
However, because of the rarity of kidney tumors in rodents, the repeatability of this finding
across strains and studies supports their biological significance despite the limitations of
individual studies and relatively small increases in reported tumor incidence.
Some, but not all, human studies have suggested a role for VHL mutations in
TCE-induced kidney cancer (Charbotel et al.. 2007: 2004: Brauchetal.. 1999: Schraml et al..
1999: B riming et al., 1997b). Certain aspects of these studies may explain some of these
discrepant results. The majority of these studies have examined paraffmized tissue that may lead
to technical difficulties in analysis, as paraffin extractions yield small quantities of often low-
quality DNA. The chemicals used in the extraction process itself may also interfere with
enzymes required for further analysis (PCR, sequencing). Although these studies do not clearly
show mutations in all TCE-exposed individuals, or in fact in all kidney tumors examined, this
does not take into account other possible means of VHL inactivation, including silencing or loss,
and other potential targets of TCE mutagenesis were not systematically examined. A recent
study by Nickerson et al. (2008) analyzed both somatic mutation and promoter hypermethylation
of the VHL gene in clear cell-RCC frozen tissue samples using more sensitive methods. The
results of this study support the hypothesis that VHL alterations are an early event in clear cell
RCC carcinogenesis, but these alterations may not be gene mutations. No experimental animal
studies have been performed examining VHL inactivation following exposure to TCE, although
one in vitro study examined VHL mutation status following exposure to the TCE-metabolite
DCVC (Mally et al., 2006). This study found no mutations following DCVC exposure, although
this does not rule out a role for DCVC in VHL inactivation by some other method or VHL
alterations caused by other TCE metabolites.
Although not encompassing all of the actions of TCE and its metabolites that may be
involved in the formation and progression of neoplasia, available evidence supports the
conclusion that a mutagenic mode of action mediated by the TCE GSH-conjugation metabolites
(predominantly DCVC) is operative in TCE-induced kidney cancer. This conclusion is based on
substantial evidence that these metabolites are genotoxic and are delivered to or produced in the
kidney, including evidence of kidney-specific genotoxicity following in vivo exposure to TCE or
DCVC. Cytotoxicity caused by DCVC leading to compensatory cellular proliferation is also a
4-217
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potential mode of action in renal carcinogenesis. A combination of mutagenicity and
cytotoxicity, with mutagenicity increasing the rate of mutation and regenerative proliferation
enhancing the survival or clonal expansion of mutated cells, while biologically plausible, has yet
to be tested experimentally. The additional mode-of-action hypotheses of peroxisome
proliferation, accumulation of a2u-globulin, and cytotoxicity mediated by TCE-induced excess
formic acid production are not supported by the available data.
4.5. LIVER TOXICITY AND CANCER
4.5.1. Liver Noncancer Toxicity in Humans
The complex of chronic liver disease is a spectrum of effects and comprises nonalcoholic
fatty liver disease (nonalcoholic steatohepatitis) and cirrhosis, more rare anomalies ones such as
autoimmune hepatitis, primary biliary cirrhosis, and primary sclerosing cholangitis, and
hepatocellular and cholangiocarcinoma (intrahepatic bile duct cancer) (Juran and Lazaridis,
2006). Chronic liver disease and cirrhosis, excluding neoplasia, is the 12th leading cause of death
in the United States in 2005 with 27,530 deaths (Rung et al.. 2008) with a morality rate of
9.0 per 100,000 denial et al.. 2008).
Eight studies reported on liver outcomes and TCE exposure and are identified in
Table 4-55. Three studies are suggestive of effects on liver function tests in metal degreasers
occupationally exposed to TCE (Xu et al., 2009; Nagayaetal., 1993; Rasmussen et al., 1993b).
Nagaya et al. (1993) in their study of 148 degreasers in metal parts factories, semiconductor
factors, or other factories, observed total mean serum cholesterol concentration and mean serum
high density lipoprotein-cholesterol (HDL-C) concentrations to increase with increasing TCE
exposure, as defined by U-TTC), although a statistically significant linear trend was not found.
Nagaya et al. (1993) estimated that TCE exposures were 1 ppm in the low-exposure group,
6 ppm in the moderate-exposure group, and 210 ppm in the high-exposure group. No association
was noted between serum liver function tests and U-TTC, a finding not surprising given that
individuals with a history of hepatobiliary disease were excluded from this study. Nagaya et al.
(1993) follows 13 workers with higher U-TTC concentrations over a 2-year period; serum
HDL-C and two hepatic function enzymes, GOT and aspartate aminotransferase (AST)
concentrations were highest during periods of high level exposure, as indicated from U-TTC
concentrations. Similarly, in a study of 95 degreasers, 70 exposed to TCE and 25 exposed to
CFC113 (Rasmussen et al., 1993b), mean serum GOT concentration for subjects with the highest
TCE exposure duration was above normal reference values and was about threefold higher
compared to the lowest exposure group. Rasmussen et al. (1993b) estimated mean urinary TCE
concentration in the highest exposure group as 7.7 mg/L with past exposures estimated as
equivalent to 40-60 mg/L. Multivariate regression analysis showed a small statistically
nonsignificant association due to age and a larger effect due to alcohol abuse that reduced and
changed direction of a TCE exposure affect. The inclusion of CFC113-exposed subjects
4-218
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introduces a downward bias since liver toxicity is not associated with CFC113 exposure (U.S.
EPA, 2008b) and would underestimate any possible TCE effect. Xu et al. (2009) reported
symptoms and liver function tests of 21 metal degreasers with severe hypersensitivity dermatitis
(see last paragraph in this section for discussion of other liver effects in hypersensitivity
dermatitis cases). TCE concentration of agent used to clean metal parts ranged from 10.2 to
63.5% with workplace ambient monitoring TWA TCE concentrations of 18-683 mg/m3 (3-
127 ppm). Exposure was further documented by urinary TCA levels in 14 of 21 cases above the
recommended occupation level of 50 mg/L. The prevalence of elevated liver enzymes among
these subjects was 90% (19 cases) for alanine aminotransferase (ALT), 86% (18 cases) for AST,
and 76% (16 cases) for total bilirubin (Xu et al., 2009). Two studies provide evidence of plasma
or serum bile acids changes among TCE-exposed degreasers. Neghab et al. (1997) in a small
prevalence study of 10 healthy workers (5 unexposed controls and 5 exposed) observed
statistically significantly elevated total serum bile acids, particularly deoxycholic acid and the
subtotal of free bile acids, among TCE subjects at postexposure compared to their pre-exposure
concentrations and serum bile acid levels correlated well with TCE exposure (r = 0.94). Total
serum bile acid concentration did not change in control subjects between pre- and postexposure,
nor did enzyme markers of liver function in either unexposed or exposed subjects differ between
pre- and postexposure periods. However, the statistical power of this study is quite limited and
the prevalence design does not include subjects who may have left employment because of
possible liver problems. The paper provides minimal details of subject selection and workplace
exposure conditions, except that pre-exposure testing was carried out on the 1st work day of the
week (pre-exposure), repeated sampling after 2 days (postexposure), and a postexposure 8-hour
TWA TCE concentration of 9 ppm for exposed subjects; no exposure information is provided for
control subjects. Driscoll et al. (1992) in a study of 22 subjects (6 unexposed and 16 exposed)
employed at a factory manufacturing small appliances reported statistically significant group
differences in logistic regression analyses controlling for age and alcohol consumption in mean
fasting plasma bile acid concentrations. Other indicators of liver function such as plasma
enzyme levels were statistically significant different between exposed and unexposed subjects.
Laboratory samples were obtained at the start of subject's work shift. Exposure data are not
available on the 22 subjects and assignment of exposed and unexposed was based on work
duties. Limited personal monitoring from other nonparticipating workers at this facility
indicated TCE exposure as low, <5 ppm, with occasional peaks over 250 ppm, although details
are lacking whether these data represent exposures of study subjects.
4-219
-------
Table 4-55. Summary of human liver toxicity studies
Subjects
148 male metal degreasers in
metal parts, semiconductor and
other factories
95 workers (70 TCE exposed, 25
CFC1 13 exposed) selected from a
cohort of 240 workers at 72
factors engaged in metal
degreasing with chlorinated
solvents
2 1 metal degreasers with severe
hypersensitivity dermatitis
Five healthy workers engaged in
decreasing activities in steel
industry and five healthy workers
from clerical section of same
company
22 workers at a factory
manufacturing small appliances
4,489 males and female residents
from 15 Superfund site and
identified from AT SDR TCE
Exposure Subregistry
Case reports from eight countries
of individuals with idiosyncratic
generalized skin disorders
Deaths in California between
1 979 and 1 98 1 due to cirrhosis
Effect
Serum liver function enzyme
(HDL-C, AST, and GOT)
concentrations did not
correlated with TCE exposure
assesses in a prevalence study
but did correlate with TCE
concentration over a 2-yr
follow-up period
Increased serum GOT
concentration with increasing
cumulative exposure
High prevalence of serum liver
function enzymes above normal
levels: ALT, 19 or 21 cases;
AST, 18 of 21 cases, andT-Bili,
16 of 21 cases
Total serum bile acid
concentration increased
between pre- and postexposure
(2-d period)
Increased in several bile acids
Liver problems diagnosed with
past yr
Hepatitis in 46-94% of cases;
other liver effects includes
hepatomegaly and elevated liver
function enzymes; and in rare
cases, acute liver failure
SMRof211 (95% CI: 136,287)
for white male sheet metal
workers and SMR = 174 (95%
CI: 150-197) for metal workers
Exposure
U-TTC levels obtained from spot
urine sample obtained during working
hrs used to assign exposure category
included the following:
High: 209 ± 99 mg/g Cr
Medium: 35 ± 27 mg/g Cr
Low: 5 ± 2 mg/g Cr
Note: this study does not include an
unexposed referent group
4 groups (cumulative number of yr
exposed over a working life):
I: 0.6 (0-0.99)
II: 1.9(1-2.8)
III: 4.4 (2.9-6.7)
IV: 14.4 (6.8-35.6)
TWA mean ambient TCE
concentration occupational setting of
cases, 18 mg/m3-683 mg/m3
14 of 21 cases with U-TCE above
recommended occupational level of
50mg/L
8-hr TWA mean personal air: 8.9 ±
3.2 ppm postexposure
Regular exposure to <5 ppm TCE;
peak exposure for two workers to
>250 ppm
Residency in community with
Superfund site identified with TCE
and other chemicals
If reported, TCE, from <50 mg/m3 to
>4,000 mg/m ; symptoms developed
within 2-5 wks of initial exposure,
with some intervals up to 3 mo
Occupational title on death certificate
Reference
Nagaya et al.
(1993)
Rasmussen
et al. (1993b)
Xu et al. (2009)
Neghab et al.
(1997)
Driscoll et al.
(1992)
Davis et al.
(2005)
Kamijima et al.
(2007)
Leigh and
Jiang (1993)
Davis et al. (2005) in their analysis of subjects from the TCE subregistry of ATSDR's
National Exposure Registry examined the prevalence of subjects reporting liver problems
(defined as seeking treatment for the problem from a physician within the past year) using rates
for the equivalent health condition from the National Health Interview Survey (a nationwide
multipurpose health survey conducted by the National Center for Health Statistics, Centers for
Disease Control and Prevention). The TCE subregistry is a cohort of exposed persons from
15 sites in 5 states. The shortest time interval from inclusion in the exposure registry and last
follow-up was 5 years for one site and 10 years for seven sites. Excess in past-year liver
4-220
-------
disorders relative to the general population persisted for much of the lifetime of follow-up.
SMRs for liver problems were 3rd follow-up, SMR = 2.23 (99% CI: 1.13, 3.92); 4th follow-up,
SMR = 3.25 (99% CI: 1.82, 5.32); and 5th follow-up, SMR = 2.82 (99% CI: 1.46, 4.89).
Examination by TCE exposure, duration, or cumulative exposure to multiple organic solvents did
not show exposure-response patterns. Overall, these observations are suggestive of liver
disorders as associated with potential TCE exposure, but whether TCE caused these conditions is
not possible to determine given the study's limitations. These limitations include a potential for
misclassification bias, the direction of which could dampen observations in a negative direction,
and lack of adjustment in statistical analyses for alcohol consumption, which could bias
observations in a positive direction.
Evaluation in epidemiologic studies of risk factors for cirrhosis other than alcohol
consumption and Hepatitis A, B, and C is quite limited. NRC (2006) cited a case report of
cirrhosis developing in an individual exposed occupationally to TCE for 5 years from a hot-
process degreaser and to 1,1,1-trichloroethane for 3 months thereafter (Thiele et al., 1982). One
cohort study on cirrhosis deaths in California between 1979 and 1981 and occupational risk
factors as assessed using job title observed elevated risks with occupational titles of sheet metal
workers and metalworkers and cirrhosis among white males who comprised the majority of
deaths (Leigh and Jiang, 1993). This analysis lacks information on alcohol patterns by
occupational title in addition to specific chemical exposures. Few deaths attributable to cirrhosis
are reported for nonwhite male and for both white and nonwhite female metalworkers with
analyses examining these individuals limited by low statistical power. Some, but not all, TCE
mortality studies report risk ratios for cirrhosis (see Table 4-56). A statistically significant
deficit in cirrhosis mortality was observed in three studies (Boice et al., 2006b; Boice et al.,
1999; Morgan et al., 1998) and with risk ratios including a risk of 1.0 in the remaining studies
(ATSDR, 2004a: Ritz, 1999a: Blair etal.. 1989. 1998: Garabrant et al.. 1988). These results do
not rule out an effect of TCE on liver cirrhosis since disease misclassification may partly explain
observations. Available studies are based on death certificates where a high degree of
underreporting, up to 50%, is known to occur (Blake et al., 1988).
4-221
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Table 4-56. Selected results from epidemiologic studies of TCE exposure and
cirrhosis
Study
population
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Cohort and PMR-mortality
Aerospace workers (Rocketdyne)
Any TCE (utility/eng flush)
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
0.39(0.16,0.80)
Not reported
7
Boice et al. (2006b)
Zhao et al. (2005)
View-master workers
Males
Females
0.76(0.16,2.22)
1.51(0.72,2.78)
3
10
ATSDR (2003b)
Electronic workers (Taiwan)
Primary liver, males
Primary liver, females
Not reported
Not reported
Chang etal. (2005;
2003)
Uranium-processing workers
Any TCE exposure
Light TCE exposure, >2 yrs duration
Mod TCE exposure, >2 yrs duration
0.91 (0.63, 1.28)
Not reported
Not reported
33
Ritz ( 1999a)
Aerospace workers (Lockheed)
TCE routine exposure
TCE routine-intermittent
0.61(0.39,0.91)
Not reported
23
13
Boice et al. (1999)
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)
High intensity (>50 ppm)
0.55 (0.30, 0.93)
0.95 (0.43, 1.80)
0.32(0.10,0.74)
14
9
5
Morgan et al. (2000,
1998)
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
1.1 (0.6, 1.9)a
44
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
0.6 (0.2, 1.3)
0.8 (0.3, 1.9)
1.2(0.6,2.4)
10
9
17
Blair et al. (1998)
4-222
-------
Table 4-56. Selected results from epidemiologic studies of TCE exposure and
cirrhosis (continued)
Study
population
Aircraft
maintenance
workers
(continued)
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Deaths reported to GE pension fund (Pittsfield,
Massachusetts)
1.0a
2.4(1.4, 13.7)
1.8 (0.2, 15.0)
0.6(0.1,4.8)
1.04 (0.56, 1.93)a'b
0.87 (0.43, 1.73)
1.0a'b
0.56 (0.23, 1.40)
1.07 (0.45, 2.53)
1.06 (0.48, 2.38)
1.79 (0.54, 5.93)
1.00a
3.30(0.88, 12.41)
2.20 (0.26, 18.89)
0.59(0.97,5.10)
Not reported
6
1
1
37
31
8
10
13
6
4
1
1
U.S. Coast Guard employees
Marine inspectors
Noninspectors
1.36(0.79,2.17)
0.53 (0.23, 1.05)
17
8
Aircraft manufacturing plant employees (Italy)
All subjects
Not reported
Aircraft manufacturing plant employees (San Diego, California)
All subjects
0.86(0.67, 1.11)
63
Blair etal. (1998)
(continued)
Radican et al. (2008)
Greenland et al. (1994)
Blair et al. (1989)
Costa et al. (1989)
Garabrant et al. (1988)
aReferent group are subjects from the same plant or company, or internal referents.
^Numbers of cirrhosis deaths in Radican et al. (2008) are fewer than Blair et al. (1989) because Radican et al. (2008)
excluded cirrhosis deaths due to alcohol.
A number of case reports exist of liver toxicity including hepatitis accompanying
immune-related generalized skin diseases described as a variation of erythema multiforme,
Stevens-Johnson syndrome, toxic epiderma necrolysis patients, and hypersensitivity syndrome
(Section 4.6.1.2 describes these disorders and evidence on TCE) (Kamijima et al., 2007).
Kamijima et al. (2007) reported hepatitis was seen in 92-94% of cases presenting with an
immune-related generalized skin diseases of variation of erythema multiforme, Stevens-Johnson
syndrome, and toxic epiderma necrolysis patients, but the estimates within the hypersensitivity
syndrome group were more variable (46-94%). Many cases developed with a short time after
initial exposure and presented with jaundice, hepatomegaly or hepatosplenomegaly, in addition,
4-223
-------
to hepatitis. Hepatitis development was of a nonviral etiology, as antibody liters for Hepatitis A,
B, and C viruses were not detectable, and not associated with alcohol consumption (Kamijima et
al., 2007; Huang et al., 2002). Liver failure was moreover a leading cause of death among these
subjects. Kamijima et al. (2007) noted the similarities between specific skin manifestations and
accompanying hepatic toxicity and case presentations of TCE-related generalized skin diseases
and conditions that have been linked to specific medications (e.g., carbamezepine, allupurinol,
antibacterial sulfonamides), possibly in conjunction with reactivation of specific latent viruses.
However, neither cytomegalovirus nor Epstein-Barr viruses are implicated in the few reports that
did include examination of viral antibodies.
4.5.2. Liver Cancer in Humans
Primary hepatocellular carcinoma (HCC) and cholangiocarcinoma (intrahepatic and
extrahepatic bile ducts) are the most common primary hepatic neoplasms (Blechacz and Gores,
2008; El-Serag, 2007). Primary HCC is the 5th most common of cancer deaths in males and 9th
in females (Jemal et al., 2008). Age-adjusted incidence rates of HCC and intrahepatic
cholangiocarcinoma (ICC) are increasing, with a twofold increase in HCC over the past 20 years.
This increase is higher than expected from an expanded definition of liver cancer to include
primary or secondary neoplasms since International Classification of Disease (ICD)-9, incorrect
classification of hilar cholangiocarcinomas in ICD-O as ICC, or to improved detection methods
(El-Serag, 2007). It is estimated that 21,370 Americans will be diagnosed in 2008 with liver and
intrahepatic bile cancer; age-adjusted incidence rates for liver and intrahepatic bile duct cancer
for all races are 9.9 per 100,000 for males and 3.5 per 100,000 for females (Ries et al.. 2008).
Survival for liver and biliary tract cancers remains poor and age-adjusted mortality rates are just
slightly lower than incidence rates. While hepatitis B and C viruses and heavy alcohol
consumption are believed major risk factors for HCC and ICC, these risk factors cannot fully
account for roughly 10 and 20% of HCC cases (Kulkarni et al., 2004). Cirrhosis is considered a
premalignant condition for HCC; however, cirrhosis is not a sufficient cause for HCC since 10-
25% of HCC cases lack evidence of cirrhosis at time of detection (Kumar et al., 2007; Fattovich
et al., 2004; Chiesa et al., 2000). Nonalcoholic steatohepatitis reflecting obesity and metabolic
syndrome is recently suggested as contributing to liver cancer risk (El-Serag, 2007).
All cohort studies, except Zhao et al. (2005), present risk ratios (SIRs or SMRs) for liver
and biliary tract cancer. More rarely reported in cohort studies are risk ratios for primary liver
cancer (HCC) or for gallbladder and extrahepatic bile duct cancer. Four community studies also
presented risk ratios for liver and biliary tract cancer including a case-control study of primary
liver cancer of residents of Taiwanese community with solvent-contaminated drinking water
wells (ATSDR, 2006a; Lee et al., 2003: Morgan and Cassadv, 2002: Vartiainen et al., 1993).
Several population case-control studies examine liver cancer and organic solvents or
occupational job titles with possible TCE usage (Lindbohm et al., 2009; Ji and Hemminki, 2005;
4-224
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Kvam et al.. 2005: Weiderpass et al.. 2003: Porruet al.. 2001: Heinemann et al.. 2000: D0ssing
etal.. 1997: Hernberg et al.. 1988: Austin etal.. 1987: Hardell et al.. 1984: Hernberg et al.. 1984:
Stemhagen et al., 1983): however, the lack of detailed exposure assessment to TCE, specifically
in the population case-control studies as well as in geographic-based studies, or too few exposed
cases and controls in those studies that do present some information limits their usefulness for
evaluating hepatobiliary or gall bladder cancer and TCE exposure. Table 4-57 presents
observations from cohort, case-control, and community studies on liver and biliary tract cancer,
primary liver, and gallbladder and extrahepatic bile duct cancer and TCE.
Excess liver cancer incidence is observed in most studies in which there is a high
likelihood of TCE exposure in individual study subjects (e.g., based on JEMs or biomarker
monitoring) and which met, to a sufficient degree, the standards of epidemiologic design and
analysis were identified (Raaschou-Nielsen et al., 2003: Hansen et al., 2001: Anttila et al., 1995:
Axel son et al.. 1994) as is mortality (Radican et al.. 2008: Boice et al.. 2006b: ATSDR. 2004a:
Ritz, 1999a: Blair et al., 1998: Morgan et al., 1998). Risks for primary liver cancer and for
gallbladder and biliary tract cancers in females were statistically significantly elevated only in
Raaschou-Nielsen et al. (2003), the study with the largest number of observed cases without
suggestion of exposure duration-response patterns. Cohort studies with more uncertain exposure
assessment approaches, e.g., studies of all subjects working at a factory (Chang et al., 2005:
Chang etal.. 2003: Blair etal.. 1989: Costa etal.. 1989: Garabrant et al.. 1988). do not show
association but are quite limited given their lacking attribution of who may have higher or lower
exposure potentials. Ritz (1999a), the exception, found evidence of an exposure-response
relationship; mortality from hepatobiliary cancer was found to increase with degree and duration
of exposure and time since first exposure with a statistically significant but imprecise (wide CIs)
liver cancer risk for those with the highest exposure and longest time since first exposure. This
observation is consistent with association with TCE, but with uncertainty given one TCE
exposed case in the highest exposure group and correlation between TCE, cutting fluids, and
radiation exposures.
4-225
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Table 4-57. Selected results from epidemiologic studies of TCE exposure and liver cancer
Study
population
Exposure group
Liver and intrahepatic bile
ducts
RR (95% CI)
Number of
observable
events
Primary liver
RR (95% CI)
Number of
observable
events
Gallbladder and extrahepatic bile ducts
RR (95% CI)
Number of
observable
events
Reference
Cohort and PMR studies — incidence
Aerospace workers (Rocketdyne)
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
Not reported
Not reported
Not reported
Zhao et al. (2005)
Danish blue-collar workers with TCE exposure
Males + females
Males + females
Males, any exposure
5-yr employment duration
Females, any exposure
5-yr employment duration
1.3 (1.0, 1.6)a
1.4(1.0, 1.8)b
1.1(0.8, 1.5)b
1.2(0.7, 2. l)b
0.9 (0.5, 1.6)b
1.1(0.6, 1.7)b
2.8 (1.6, 4.6)b
2.5 (0.7, 6.5)b
4.5 (2.2, 8.3)b
1.1 (0.1,3.8)b
82
57
41
13
13
15
16
4
10
2
1.1(0.7, 1.6)
1.3 (0.6, 2.5)
1.0(0.5, 1.9)
1.1(0.5,2.1)
2.8(1.1,5.8)
2.8 (0.3, 10.0)
4.1 (1.1, 10.5)
1.3(0.0,7.1)
27
9
9
9
7
2
4
1
1.1 (0.6, 1.9)
1.1(0.3,2.9)
0.8(0.2,2.1)
1.4(0.5,3.1)
2.8(1.3,5.3)
2.3 (0.3, 8.4)
4.8 (1.7, 10.4)
0.9 (0.0, 5.2)
14
4
4
6
9
2
6
1
Raaschou-
Nielson et al.
(20031
4-226
-------
Table 4-57. Selected results from epidemiologic studies of TCE exposure and liver cancer (continued)
Study
population
Exposure group
Liver and intrahepatic bile
ducts
RR (95% CI)
Number of
observable
events
Primary liver
RR (95% CI)
Number of
observable
events
Gallbladder and extrahepatic bile ducts
RR (95% CI)
Number of
observable
events
Reference
Biologically -monitored Danish workers
Males + females
Males
Females
Cumulative exposure (Ikeda)
<17 ppm-yr
>17 ppm-yr
Mean concentration (Ikeda)
<4ppm
4+ppm
Employment duration
<6.25 yrs
>6.25
2.1 (0.7, 5.0)b
2.6 (0.8, 6.0)b
Not reported
Not reported
Not reported
5
5
0 (0.4 exp)
1.7 (0.2, 6.0)
1.8 (0.2, 6.6)
2
2
0(0.1 exp)
2.5(0.5,7.3)
3.3 (0.7, 9.7)
3
3
0 (0.3 exp)
Hansen et al.
(2001)
4-227
-------
Table 4-57. Selected results from epidemiologic studies of TCE exposure and liver cancer (continued)
Study
population
Exposure group
Liver and intrahepatic bile
ducts
RR (95% CI)
Number of
observable
events
Primary liver
RR (95% CI)
Number of
observable
events
Gallbladder and extrahepatic bile ducts
RR (95% CI)
Number of
observable
events
Reference
Aircraft maintenance workers from Hill Air Force Base
TCE subcohort
Not reported
9
Not reported
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative exposure
1.0C
0.6(0.1,3.1)
0.6(0.1,3.8)
1.1(0.2,4.8)
3
2
4
0
1.03
1.2(0.1,2.1)
1.0(0.1,16.7)
2.6(0.3,25.0)
2
1
3
0
Blair et al. (1998)
Biologically -monitored Finnish workers
All subjects
1.89 (0.86, 3.59)b
9
2.27 (0.74, 5.29)
5
1.56 (0.43, 4.00)
4
Mean air-TCE (Ikeda extrapolation from U-TCA)
<6ppm
6+ppm
Not reported
1.64 (0.20, 5.92)
2.74 (0.33, 9.88)
2
2
Anttila et al.
(1995)
Biologically -monitored Swedish workers
Males
Females
1.41 (0.38, 3.60)b
Not reported
4
Axelson et al.
(1994)
Cohort and PMR-mortality
Computer manufacturing workers (IBM),
New York
Not reported
1
Clapp and
Hoffman (2008)
4-228
-------
Table 4-57. Selected results from epidemiologic studies of TCE exposure and liver cancer (continued)
Study
population
Exposure group
Liver and intrahepatic bile
ducts
RR (95% CI)
Number of
observable
events
Primary liver
RR (95% CI)
Number of
observable
events
Gallbladder and extrahepatic bile ducts
RR (95% CI)
Number of
observable
events
Reference
Aerospace workers (Rocketdyne)
Any TCE (utility /eng flush)
Low cumulative TCE score
Med cumulative TCE score
High TCE score
p for trend
1.28 (0.35, 3.27)
Not reported
4
Boice et al.
(2006b)
Zhao et al.
(2005)
View-Master workers
Males
Females
2.45(0.50, 7.12)d
3
0
(2.61 exp)
1.01 (0.03, 5.63)d
1
0
(1.66 exp)
8.41 (1.01, 30.4)d
2
0
(0.95 exp)
ATSDR (2003b)
Electronic workers (Taiwan)
Primary liver, males
Primary liver, females
Not reported
Not reported
0
(0.69 exp)
0
(0.57 exp)
Chang et al.
(2005: 2003)
4-229
-------
Table 4-57. Selected results from epidemiologic studies of TCE exposure and liver cancer (continued)
Study
population
Exposure group
Liver and intrahepatic bile
ducts
RR (95% CI)
Number of
observable
events
Primary liver
RR (95% CI)
Number of
observable
events
Gallbladder and extrahepatic bile ducts
RR (95% CI)
Number of
observable
events
Reference
Uranium-processing workers
Any TCE exposure
Light TCE exposure, >2 yr-
duration
Mod TCE exposure, >2 yr-
duration
Light TCE exposure, >5 yr-
duration
Mod TCE exposure, >5 yr-
duration
Not reported
0.93(0.19, 4.53)e
4.97(0.48, 51. l)e
2.86 (0.48, 17.3)f
12.1(1.03, 144)f
3
1
3
1
Ritz (1999a)
Aerospace workers (Lockheed)
TCE routine exposure
0.54(0.15, 1.38)
4
TCE routine-intermittent
Oyr
Any exposure
5yrs
p for trend
1.00C
Not reported
0.53 (0.18, 1.60)
0.52(0.15, 1.79)
0.94 (0.36, 2.46)
>0.20
22
13
4
3
6
Boice et al.
(1999)
4-230
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Table 4-57. Selected results from epidemiologic studies of TCE exposure and liver cancer (continued)
Study
population
Exposure group
Liver and intrahepatic bile
ducts
RR (95% CI)
Number of
observable
events
Primary liver
RR (95% CI)
Number of
observable
events
Gallbladder and extrahepatic bile ducts
RR (95% CI)
Number of
observable
events
Reference
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)e
High intensity (>50 ppm)e
0.98(0.36,2.13)
1.32 (0.27, 3.85)
0.78(0.16,2.28)
6
3
3
TCE subcohort (Cox analysis)
Never exposed
Ever exposed
1.00C
1.48 (0.56, 3.91)8'h
14
6
Cumulative
Low
High
2.12(0.59, 7.66)h
1.19(0.34, 4.16)h
3
3
Peak
No/low
Medium/high
1.00C
0.98 (0.29, 3.35)h
17
3
Morgan et al.
(2000. 1998)
4-231
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Table 4-57. Selected results from epidemiologic studies of TCE exposure and liver cancer (continued)
Study
population
Exposure group
Liver and intrahepatic bile
ducts
RR (95% CI)
Number of
observable
events
Primary liver
RR (95% CI)
Number of
observable
events
Gallbladder and extrahepatic bile ducts
RR (95% CI)
Number of
observable
events
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
1.3 (0.5, 3.4)c
15
1.7 (0.2, 16.2)3
4
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0C
1.1(0.3,4.1)
0.9 (0.2, 4.3)
0.7 (0.2, 3.2)
6
3
3
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0C
1.6 (0.2, 18.2)
2.3 (0.3, 16.7)
1.12(0.57, 2. 19)c''
1.36(0.59, 3. ll)c
1.0°
1.17(0.45,3.09)
1.16(0.39,3.46)
1.72 (0.68, 4.38)
1
0
2
31
28
10
6
12
1.25(0.31, 4.97)c''
2.72 (0.34, 21.88)c
1.03
3.28 (0.37, 29,45)
4.05 (0.45, 36.41)
8
8
4
0
4
Reference
Blair et al. (1998)
Radican et al.
(2008)
4-232
-------
Table 4-57. Selected results from epidemiologic studies of TCE exposure and liver cancer (continued)
Study
population
Aircraft
maintenance
workers
(continued)
Exposure group
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Deaths reported to GE pension fund
(Pittsfield, Massachusetts)
Liver and intrahepatic bile
ducts
RR (95% CI)
0.74(0.18, 2.97)c
1.03
0.69 (0.08, 5.74)
0.98 (0.20, 4.90)
0.54(0.11,2.63)'
Number of
observable
events
3
1
0
2
9
Primary liver
RR (95% CI)
Number of
observable
events
0
Gallbladder and extrahepatic bile ducts
RR (95% CI)
Number of
observable
events
Reference
Radican et al.
(2008)
(continued)
Greenland et al.
(1994)
U.S. Coast Guard employees
Marine inspectors
Noninspectors
1.12(0.23,3.26)
Not reported
3
0 (2 exp)
Blair et al. (1989)
Aircraft manufacturing plant employees (Italy)
All subjects
0.70 (0.23, 1.64)
5
Costa et al.
(1989)
Aircraft manufacturing plant employees (San Diego, California)
All subjects
0.94 (0.40, 1.86)
8
Garabrant et al.
(1988)
Case-control studies
Residents of community with contaminated drinking water (Taiwan)
Village of residency, males
Upstream
Downstream
1.00
2.57(1.21,5.46)
26
Lee et al. (2003)
4-233
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Table 4-57. Selected results from epidemiologic studies of TCE exposure and liver cancer (continued)
Study
population
Exposure group
Liver and intrahepatic bile
ducts
RR (95% CI)
Number of
observable
events
Primary liver
RR (95% CI)
Number of
observable
events
Gallbladder and extrahepatic bile ducts
RR (95% CI)
Number of
observable
events
Reference
Geographic studies
Residents in two study areas in Endicott,
New York
Residents in 13 census tracts inRedlands,
California
0.71 (0.09, 2.56)
1.29 (0.74, 2.05)k
<6
28
ATSDR (2006a)
Morgan and
Cassidy (2002)
Finnish residents
Residents of Hausjarvi
Residents of Huttula
0.76(0.3, 1.4)
0.6 (0.2, 1.3)
7
6
Vartiainen et al.
(1993)
aICD-7, 155 and 156; primary liver (155.0), gallbladder, and biliary passages (155.1), and liver secondary and unspecified (156).
bICD-7, 155; primary liver, gallbladder, and biliary passages.
Internal referents, workers without TCE exposure.
dPMR.
eLogistic regression analysis with a 0-year lag for TCE exposure.
fLogistic regression analysis with a 15-year lag for TCE exposure.
gRisk ratio from Cox Proportional Hazard Analysis, stratified by age, sex, and decade in Environmental Health Strategies (1997).
hMorgan et al. (1998) do not identify if SIR is for liver and biliary passage or primary liver cancer; identified as primary liver in NRC (2006).
'Radican et al. (2008) provide results for TCE exposure for follow-up through 1990, comparing the Poisson model rate ratios as reported by Blair et al. (1998)
with Cox model hazard ratios. RR from Cox model adjusted for age and gender for liver and intrahepatic bile duct cancer was 1.2 (95% CI: 0.5, 3.4) and for
primary liver cancer was 1.3 (95% CI: 0.1, 12.0).
JOR.
k99% CIs.
exp = expected
4-234
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Observations in these studies provide some evidence of susceptibility of liver,
gallbladder, and biliary tract; these observations are consistent with pharmacokinetic processing
of TCE and the extensive intra- and extrahepatic recirculation of metabolites. Magnitude of risk
of gallbladder and biliary tract cancer is slightly higher than that for primary liver cancer in
Raaschou-Nielsen et al. (2003), the study with the most cases. Observations in Blair et al.
(1998), Hansen et al. (2001), and Radican et al. (2008), three smaller studies, suggest slightly
larger risk ratios for primary liver cancer compared to gallbladder and biliary tract cancer.
Overall, these studies are not highly informative for cross-organ comparison of relative
magnitude of susceptibility.
The largest geographic studies (Lee et al., 2003; Morgan and Cassady, 2002) are also
suggestive of association with the risk ratio (mortality OR) in Lee et al. (2003) as statistically
significantly elevated. The geographic studies do not include a characterization of TCE exposure
to individual subjects other than residency in a community with groundwater contamination by
TCE with potential for exposure misclassification bias dampening observations; these studies
lack characterization of TCE concentrations in drinking water and exposure characteristics such
as individual consumption patterns. For this reason, observations in Morgan and Cassidy (2002)
and Lee et al. (2003) are noteworthy, particularly if positive bias leading to false positive finding
is considered minimal, and the lack of association with liver cancer in the two other community
studies (ATSDR, 2006a; Vartiainen et al., 1993) does not detract from Morgan and Cassidy
(2002) or Lee et al. (2003). Lee et al. (2003), however, do not address possible confounding
related to hepatitis viral infection status, a risk factor for liver cancer, or potential
misclassification due to the inclusion of secondary liver cancer among the case series, factors
which may amplify observed association.
Meta-analysis is adopted as a tool for examining the body of epidemiologic evidence on
liver cancer and TCE exposure, to identify possible sources of heterogeneity and as an additional
means to identify cancer hazard. The meta-analyses of the overall effect of TCE exposure on
liver (and gall bladder/biliary passages) cancer suggest a small, statistically significant increase
in risk. The summary estimate from the primary random effects meta-analysis of the 9 (all
cohort) studies is 1.29 (95% CI: 1.07, 1.56) (see Figure 4-3). The study of Raaschou-Nielsen
et al. (2003) contributes about 57% of the weight; its removal from the analysis decreases
somewhat the RRm estimate and is no longer statistically significant (RRm = 1.22; 95% CI:
0.93, 1.61). The summary estimate was not overly influenced by any other single study, nor was
it overly sensitive to individual RR estimate selections. There is no evidence of publication bias
in this data set, and no observable heterogeneity (I2 = 0%) across the study results.
4-235
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TCE Exposure and Liver Cancer
Study Relative Ri:*
Boice (1999) . 0 —
Greenland (1994) • • — f
Morgan (1998)
Raaschou-Nielsen (20Q3)
Radican (2008) . —
OVERALL
ind95«CI RR LCL
JL
1
n
11
i
1,
-di-
ll
h';- -\
1.41 0.38
0.81 D.45
0.54 0.11
2.10 0.7D
1.4S 0.56
1 .35 1 .03
1.12 0.57
1 .23 1 .07
UCL
3.80
1.33
2.63
5.00
3.01
1.77
2.19
1.58
i ' ' > ' ' i i < < ' * ' i i > ' i
0.1 1 10
Random effects model; fixed effect model same. The summary estimate is in the
bottom row, represented by the diamond. Symbol sizes reflect relative weights of
the studies.
Figure 4-3. Meta-analysis of liver and biliary tract cancer and overall TCE
exposure.
Examination of sites individually (i.e., primary liver and intrahepatic bile ducts separate
from the combined liver and gallbladder/biliary passage grouping) resulted in the RRm estimate
for liver cancer alone (for the three studies for which the data are available; for the other studies,
results for the combined grouping were used) slightly lower than the one based entirely on
results from the combined cancer categories and was just short of statistical significance (1.25;
95% CI: 0.99, 1.57). This result is driven by the fact that the risk ratio estimate from the large
Raaschou-Nielsen et al. (2003) study decreased from 1.35 for liver and gall bladder/biliary
passage cancers combined to 1.28 for liver cancer alone.
The RRm estimate from the random effects meta-analysis of liver cancer in the highest
exposure groups in the six studies that provide risk estimates associated with highest exposure.
Primary liver cancer is 1.32 (95% CI: 0.93, 1.86), slightly lower than the RRm estimate
for liver and gallbladder/biliary cancer and any TCE exposure of 1.33 (95% CI: 1.09, 1.64), and
not statistically significant (see Figure 4-4). Again, the RRm estimate of the highest-exposure
groups is dominated by one study (Raaschou-Nielsen et al., 2003). Two studies lack reporting of
liver cancer risk associated with highest exposure, so consideration of reporting bias (considered
the primary analysis) lead to a result of 1.28 (95% CI: 0.93, 1.77), similar to that estimated in the
4-236
-------
more restricted set of studies presenting risk ratios association with highest exposure groups in
published papers.
TCE Exposure and Liver Cancer - highest exposure groups
Study
Anttila (19955 .
A\elson(1994)est "
Boice (1@09j
Morgan (1998)
Raaschou-Nielsen (2003) ,
Radican (2008)
Hansen(20D1)null
Ihao(2005»null
OVERALL
0.1
Relative Risk and 95% Cl
D
10
RR
1.20
1.49
1.00
LCL
2.74
3.70
0.94 0.36
1.19 0.34
0.70
0.67
0.32
1.00 0.08
1.28 0.93
UCL
0.33 9.88
0.09 21.00
2.46
4,16
1.90
3.34
3.10
11.00
1.77
Random effects model; fixed effect model same. The summary estimate is in the
bottom row, represented by the diamond. Symbol sizes reflect relative weights of
the studies. Assumed null RR estimates for Hansen and Zhao (see Appendix C
text).
Figure 4-4. Meta-analysis of liver cancer and TCE exposure—highest
exposure groups.
Different exposure metrics are used in the various studies, and the purpose of combining
results across the different highest exposure groups is not to estimate an RRm associated with
some level of exposure, but rather to examine impacts of combining RR estimates that should be
less affected by exposure misclassification. In other words, the highest exposure category is
more likely to represent a greater differential TCE exposure compared to people in the referent
group than the exposure differential for the overall (typically any vs. none) exposure comparison.
Thus, if TCE exposure increases the risk of liver and gallbladder/biliary cancer, the effects
should be more apparent in the highest exposure groups. The findings of a lower RRm
associated with highest exposure group reflects observations in Radican et al. (2008) and
Raaschou-Nielsen et al. (2003), the study contributing greatest weight to the meta-analysis, that
4-237
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RR estimates for the highest-exposure groups, although >1.0, are less than the RR estimates with
any TCE exposure.
Thus, while the finding of an elevated and statistically significant RRm for liver and
gallbladder/biliary cancer and any TCE exposure provides evidence of association, the statistical
significance of the summary estimates is dependent on one study, which provides the majority of
the weight in the meta-analyses. Furthermore, combining results from the highest-exposure
groups yields lower RRm estimates than for an overall effect. These results do not rule out an
effect of TCE on liver cancer, because the liver cancer results are relatively underpowered with
respect to numbers of studies and number of cases; overall, the meta-analysis provides only
minimal support for association between TCE exposure and liver and gallbladder/biliary cancer.
NRC (2006) deliberations on TCE commented on two prominent evaluations of the then-
current TCE epidemiologic literature using meta-analysis techniques, Wartenberg et al. (2000)
and Kelsh et al. (2005), submitted by Exponent-Health Sciences to NRC during their
deliberations and published afterwards in the open literature as Alexander et al. (2007a) adding
the than-published study of Boice et al. (2006b). NRC (2006) found weaknesses in the
techniques used in Wartenberg et al. (2000) and the Exponent analyses. EPA staff conducted
their analysis according to NRC (2006) suggestions for transparency, systematic review criteria,
and examination of both cohort and case-control studies. The EPA analysis of liver cancer
considered a similar set of studies as Alexander et al. (2007a), although treatment of these
studies differs between analyses. Alexander et al. (2007a) in their Table 2, for example, present
RRm estimates, grouping of studies with differing exposure potentials, for example, including
liver and biliary cancer risk estimates for all subjects, those exposed and unexposed to TCE, in
Boice et al.(1999). Blair et al. (1998). Morgan et al. (1998). and Boice et al. (2006b). with
biomarker studies (Hansen et al., 2001; Anttila et al., 1995; Axel son et al., 1994). The inclusion
of risk estimates for subjects who have little to no TCE exposure over background levels has the
potential to introduce misclassification bias and dampen observed risk ratios. Potential bias from
exposure misclassification may be substantial in Alexander et al. (2007a) since the percentage of
TCE exposed subjects to all cohort subjects in the four studies was 3, 23, 51 and 68% in Boice
et al. (1999). Morgan et al. (1998). Blair et al. (1998). and Boice et al. (2006b). respectively, and
is a likely alternative explanation for observed inconsistency across occupational groups reported
by the authors. Another difference between the EPA and previous meta-analyses is their
treatment of Ritz (1999a). included in Wartenberg et al. (2000). Kelsh et al. (2005). and
Alexander et al. (2007a), but not in this analysis. For a grouping of studies with subcohorts most
similar to those in EPA's analysis, summary liver and gall bladder/biliary tract cancer risk
estimates for overall TCE exposure for TCE subcohorts is of a similar magnitude as that
observed in EPA's updated and expanded analysis, Wartenberg et al. (2000), 1.1 (95% CI: 0.3,
4.8) for incidence and 1.1 (95% CI: 0.7, 1.7) for mortality, Kelsh et al. (2005). 1.32 (95% CI:
1.05, 1.66) and Alexander et al. (2007a). 1.30 (95% CI: 1.09-1.55).
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4.5.3. Experimental Studies of TCE in Rodents—Introduction
The previous sections have described available human data for TCE-induced noncancer
effects (e.g., disturbances in bile production) and whether an increased risk of liver cancer in
humans has been established from analysis of the epidemiological literature. A primary concern
for effects on the liver comes from a large database in rodents indicating that, not only TCE, but
also a number of its metabolites are capable of inducing hepatocellular adenomas and
carcinomas in rodent species. Thus, many of rodent bioassays have focused on the study of liver
cancer for TCE and its metabolites and possible early effects specifically that may be related to
tumor induction.
This section describes the hazard data for TCE effects in the rodent liver and inferences
from studies of its metabolites. For more detailed descriptions of the issues providing context for
these data in terms the state of the science of liver physiology (see Section E.I), cancer (see
Section E.3), liver cancer (see Section E.3), and the mode of action of liver cancer and other
TCE-induced effects (see Section E.3.4), please see Appendix E. A more comprehensive review
of individual studies of TCE-induced liver effects in laboratory animals is also provided in
Section E.2 that includes detailed analyses of the strengths and the limitations of these studies.
Issues have been raised regarding the relevance of mouse liver tumor data to human liver cancer
risk that are addressed in Sections E.3.2 and E.3.3. Given that activation of the PPARa receptor
has received great attention as a potential mode of action for TCE-induced liver tumors, the
current status of that hypothesis is reviewed in Section E.3.4.1. Finally, comparative studies of
TCE metabolites and the similarities and differences of such study results are described in
summary sections of Appendix E (i.e., Section E.2.4) as well as discussions of proposed modes
of action for TCE-induced liver cancer (i.e., Sections E.2.4 and E.3.4.2).
A number of acute and subchronic studies have been undertaken to describe the early
changes in the rodent liver after TCE administration, with the majority using the gavage route of
administration. Several key issues affect the interpretation of these data. The few drinking water
studies available for TCE have recorded a significant loss of TCE through volatilization in
drinking water solutions and thus, this route of administration is generally not used. Some short-
term studies of TCE have included detailed examinations, while others have reported primarily
liver weight changes as a marker of TCE response. The matching and recording of age, but
especially initial and final body weight, for control and treatment groups is of particular
importance for studies using liver weight gain as a measure of TCE response as differences in
these parameters affect TCE-induced liver weight gain. Most data are for TCE exposures of at
least 10-42 days. For many of the subchronic inhalation studies (Kj ell strand et al., 1983a:
Kjellstrand et al., 1983b: Kj ell strand et al., 1981b), issues associated with whole-body exposures
make determination of dose levels more difficult. The focus of the long-term studies of TCE is
primarily detection and characterization of liver tumor formation.
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For gavage experiments, death due to gavage errors and specifically from use of this
route of administration, especially at higher TCE exposure concentrations, has been a recurring
problem, especially in rats. Unlike inhalation exposures, the effects of vehicle can also be an
issue for background liver effects in gavage studies. Concerns regarding effects of oil vehicles,
especially corn oil, have been raised (Charbonneau et al., 1991; Kim etal., 1990a). Several oral
studies in particular document that use of corn oil as the vehicle for TCE gavage dosing induces
a different pattern of toxicity, especially in male rodents (see Merrick et al., 1989, Section
E.2.2.1). Several studies also report the effects of corn oil on hepatocellular DNA synthesis and
indices of lipid peroxidation (Rusyn et al., 1999; Channel et al., 1998). For example, Rusyn
et al. (1999) report that a single dose of dietary corn oil increases hepatocyte DNA synthesis 24
hours after treatment by ~3.5-fold of control, activates of NF-KB to a similar extent ~2 hours
after treatment almost exclusively in Kupffer cells, and induces an approximate three- to fourfold
increase of control NF-KB in hepatocytes after 8 hours and an increase in tumor necrosis factor
(TNF)-a mRNA between 8 and 24 hours after a single dose in female rats.
In regard to studies that have used the i.p. route of administration, as noted by
Kawamoto et al. (1988b), injection of TCE may result in paralytic ileus and peritonitis and that
subcutaneous treatment paradigm will result in TCE not immediately being metabolized but
retained in the fatty tissue. Wang and Stacey (1990) state that -^itraperitoneal injection is not
particularly relevant to humans" and suggest that intestinal interactions require consideration in
responses such as increase serum bile acid.
While studies of TCE metabolites have been almost exclusively conducted via drinking
water, and thus, have avoided vehicle effects and gavage error, they have issues of palatability at
high doses and decreased drinking water consumption as a result that raises issues not only of the
resulting internal dose of the agent, but also of effects of drinking water reduction.
Although there are data for both mice and rats for TCE exposure and studies of its
metabolites, the majority of the available information has been conducted in mice. This is
especially the case for long-term studies of DCA and TCA in rats. There is currently one study
each available for TCA and DCA in rats and both were conducted with such few numbers of
animals that the ability to detect and discern whether there was a treatment-related effect are very
limited (DeAngelo et al., 1997. 1996: Richmond et al.. 1995).
With regard to the sensitivity of studies used to detect a response, there are issues
regarding not only the number of animals used, but also the strain and weight of the animals. For
some studies of TCE strains were used that have less background rate of liver tumor
development and carcinogenic response. As for the B6C3Fi mouse, the strain most used in the
bioassays of TCE metabolites, the susceptibility of the B6C3Fi to hepatocarcinogenicity has
made the strain a sensitive biomarker for a variety of hepatocarcinogens. Moreover, Leakey
et al. (2003a) demonstrated that increased body weight at 45 weeks of life is an accurate
predictor of large background tumor rates. Unfortunately a 2-year study of CH (George et al..
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2000) and the only available 2-year study of TCA (De Angel o et al., 2008), which used the same
control animals, were both conducted in B6C3Fi mice that grew very large (-50 g) and prone to
liver cancer (64% background incidence of hepatocellular adenomas and carcinomas) and
premature mortality. Thus, these bioassays are of limited value for determination of the dose-
response for carcinogenicity.
Finally, as discussed below, the administration of TCE to laboratory animals as well as
environmental exposure of TCE in humans are effectively co-exposure studies. TCE is
metabolized to a number of hepatoactive as well as hepatocarcinogenic agents. A greater
variability of response is expected than from exposure to a single agent, making it particularly
important to look at the TCE database in a holistic fashion rather than the results of a single
study, especially for quantitative inferences. This approach is particularly useful given that the
number of animals in treatment groups in a variety of TCE and TCE metabolite studies have
been variable and small for control and treatment groups. Thus, their statistical power was
limited not only for detection of statistically significant changes, but also, in many cases, to be
able to determine whether there is not a treatment related effect (i.e., Type II error for power
calculation). Section E.2.4.2 provides detailed analyses of the database for liver weight
induction by TCE and its metabolites in mice and the results of those analyses are described
below. Specifically, the relationship of liver weight induction, but also other endpoints such as
peroxisomal enzyme activation and increases in DNA synthesis to liver tumor responses are also
addressed as well.
4.5.4. TCE-Induced Liver Noncancer Effects
A number of effects have been studied as indicators of TCE effects on the liver but also
as proposed events whose sequellae could be associated with resultant liver tumors after chronic
TCE exposure in rodents. Similar effects have been studied in rodents exposed to TCE
metabolites, which may be useful for determining not only whether such effects are associated
with liver tumors induced by these metabolites but also if they are similar to what has been
observed for TCE. Summaries of the laboratory animal studies of TCE noncancer effects in the
liver are provided in Table 4-58 (oral studies) and Table 4-59 (inhalation studies), along with the
types of effects discussed in the subsections below for each study.
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Table 4-58. Oral studies of TCE-induced liver effects in mice and rats
Reference"
Herman et al.
(1995)
Buben and
O'Flaherty (1985)
Channel et al.
(1998)
Dees and Travis
(1993)
Elcombe et al.
(1985)
Goel et al. (1992)
Animals (sex)
F344 rats (F)
Swiss-Cox mice
(M)
B6C3F!/CrlBR
mice (M)
B6C3FJ mice (M
andF)
B6C3FJ and
Alderley Park
(Swiss) mice (M)
Osborne-Mendel
and Alderley Park
(Wistar) rats (M)
Swiss albino mice
(M)
Exposure route
Corn oil gavage
Corn oil gavage
Corn oil gavage
Corn oil gavage
Corn oil gavage
Groundnut oil
gavage
Dose/exposure concentration
0, 150, 500, 1,500, or 5,000 mg/kgfor
Id
0, 50, 150, 500, or 1,500 mg/kg-dfor
14 d
0, 100, 200, 400, 800, 1,600, 2,400,
or 3.200 mg/kg-d, 5 d/wk for 6 wks
0 (water), 0 (corn oil), 400, 800, or
1,200 mg/kg-d, 5 d/wk for up to
8 wks
0, 100, 250, 500, or 1,000 mg/kg-d
for 10 d
0, 500, 1,000, or 1,500 mg/kg-d for
10 d
0, 500, 1,000, or 2,000 mg/kg-d,
5 d/wk for 28 d
Exposed
8/group
12-15/
group
77/group
5/group
6-10/
group
6/group
Section(s) where noncancer liver effects are
discussed
4.5.4.1 Liver weight
4.5.4.2 Cytotoxicity and histopathology
E.2. 1.11. Herman etal. (1995)
4.5.4.1 Liver weight
4.5.4.2 Cytotoxicity and histopathology
E.2.2.7. Buben and O'Flaherty (Buben and
O'Flahertv. 1985)
4.5.4.2 Cytotoxicity and histopathology
4.5.4.3 Measures of DNA Synthesis, Cellular
Proliferation, and Apoptosis
4.5.4.4 Peroxisome proliferation and related
effects
4.5.4.5 Oxidative stress
E.2.2.8. Channel et al. (1998)
4.5.4.1 Liver weight
4.5.4.2 Cytotoxicity and histopathology
4.5.4.3 Measures of DNA Synthesis
E.2. 1.9. Dees and Travis (1993)
4.5.4.1 Liver weight
4.5.4.2 Cytotoxicity and histopathology
4.5.4.3 Measures of DNA Synthesis, Cellular
Proliferation, and Apoptosis
4.5.4.4 Peroxisome proliferation and related
effects
E.2.1.8. Elcombe et al. (1985)
4.5.4.1 Liver weight
4.5.4.2 Cytotoxicity and histopathology
4.5.4.4 Peroxisome proliferation and related
effects
E.2.2.2. Goel et al. (1992)
4-242
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Table 4-58. Oral studies of TCE-induced liver effects in mice and rats (continued)
Reference"
Goldsworthy and
Popp (19871
Laughter et al.
(2004)
Melnick et al.
(1987)
Merrick et al.
(1989)
Mirsalis et al.
(1989)
Nakajima et al.
(2000)
NTP (1990)
NTP (1990)
Animals (sex)
F344 rats (M)
B6C3FJ mice (M)
Sv/129 and
PPARa-null mice
(M)
F344 rats (M)
B6C3FJ mice (M
andF)
B6C3FJ mice (M
andF)
F344 rats (M)
Sv/129 and
PPARa-null mice
(M and F)
B6C3FJ mice (M
andF)
F334/N rats (M
andF)
B6C3FJ mice (M
andF)
F334/N rats (M
andF)
Exposure route
Corn oil or methyl
cellulose gavage
Methyl-cellulose
gavage
Micro-
encapsulated in
feed
Corn oil gavage
Corn oil and 20%
Emulphor in
water gavage
Corn oil gavage
Corn oil gavage
Corn oil gavage
Corn oil gavage
Dose/exposure concentration
1,000 mg/kg-d for 10 d
0-1,500 mg/kg-d for 3 d; and 5 d/wk
for 3 wks
0, 0.055, 1.10, 2.21, or 4.41% in feed
for 14 d, equivalent to 0, 600, 1.300,
2.200, or 4.800 mg/kg-d
Males: 0, 600, 1,200, or 2,400 mg/kg-
d
Females: 0, 450, 900, or 1,800 mg/kg-
d
0, 50, 200, or 1,000 mg/kg (single
dose)
0 or 750 mg/kg-d for 14 d
Mice: 0, 375-6,000 mg/kg-d, 5 d/wk,
13 wks
Rats: 0, 62.5-1,000 mg/kg-d, 5 d/wk,
13 wks
Mice: 0, or 1,000 mg/kg-d, 5 d/wk,
103 wks
Rats: 0, 500, or 1,000 mg/kg-d,
5 d/wk, 103 wks
Exposed
5-7/group
4-5/group
10/group
12/group
3/group
6/sex/ group
10/group
50/group
Section(s) where noncancer liver effects are
discussed
4.5.4.1 Liver weight
4.5.4.4 Peroxisome proliferation and related
effects
E.2.1.7. Goldsworthy and Popp (1987)
4.5.4.1 Liver weight
4.5.4.2 Cytotoxicity and histopathology
4.5.4.3 Measures of DNA Synthesis, Cellular
Proliferation, and Apoptosis
4.5.4.4 Peroxisome proliferation and related
effects
E.2. 1. 13. Laughter et al. (2004)
4.5.4.1 Liver weight
4.5.4.2 Cytotoxicity and histopathology
4.5.4.4 Peroxisome proliferation and related
effects
E.2. 1. 12. Melnick et al. (1987)
4.5.4.1 Liver weight
4.5.4.2 Cytotoxicity and histopathology
E.2.2.1. Merrick et al. (1989)
4.5.4.3 Measures of DNA Synthesis, Cellular
Proliferation, and Apoptosis
E.2.4. 1 . Summary of Results for Short-term
Effects of TCE
4.5.4.1 Liver weight
4.5.4.4 Peroxisome proliferation and related
effects
E.2. 1. 10. Nakajima et al. (2000)
4.5.4.2 Cytotoxicity and histopathology
E.2.2.12.1 13-wk studies
4.5.4.2 Cytotoxicity and histopathology
E.2.2. 12.2 2-yr studies
4-243
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Table 4-58. Oral studies of TCE-induced liver effects in mice and rats (continued)
Reference"
Nunes et al. (2001)
Tao et al. (2000)
Tucker et al.
(1982)
Animals (sex)
Sprague-Dawley
rats (M)
B6C3FJ mice (F)
CD-I mice (M
andF)
Exposure route
Corn oil gavage
Corn oil gavage
Drinking water
with 1%
Emulphor
Dose/exposure concentration
2,000 mg/kg-d on d 10-16 (with and
without lead carbonate pretreatment
for 9 d)
1,000 mg/kg-d for 5 d
0 (untreated), 0 (vehicle), 0.1, 1.0,
2.5, or 5 mg/mL for 4 or 6 mo
M:0, 0, 18.4, 216.7, 393.0, or
660.2 mg/kg-d
F:0, 0, 17.9, 193.0, 437.1, or
793.3 mg/kg-d
Exposed
10/group
4-6/group
140/group
untreated
and TCE-
treated
260/group
vehicle-
treated
Section(s) where noncancer liver effects are
discussed
4.5.4.1. Liverweight
4.5.4.2. Cytotoxicity and histopathology
E.2.1.4. Nunes et al. (2001)
4.5.4.1. Liverweight
E.2.1.5. Tao etal. (2000)
4.5.4.1. Liverweight
E.2. 1.6. Tucker et al. (1982)
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
4-244
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Table 4-59. Inhalation and i.p. studies of TCE-induced liver effects in mice and rats
Reference"
Hamdan and
Stacey (1993)
Kaneko et al.
(2000)
Kjellstrand et al.
(1981b)
Kjellstrand et al.
(1983b)
Kjellstrand et al.
(1983a)
Kumar et al.
(2QQla)
Okino et al. (1991)
Ramdhan et al.
(2008)
Animals (sex)
Sprague-Dawley
rats (M)
MRL-lpr/lpr mice
(M)
NMRI mice.
Sprague-Dawley
rats
Mongolian
gerbils
wild, C57B1,
DBA, B6CBA,
A/sn, NZB, and
NMRI mice (M
andF)
NMRI mice (M
andF)
Wistar rats (M)
Wistar rats (M)
SV/129 mice (M)
CYP2El-null
mice (M)
Exposure route
i.p. in corn oil
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Dose/exposure concentration
0 or 131 mg/kg
0, 500, 1,000, or 2,000 ppm, 4 hrs/d,
6 d/wk, for 8 wks
150 ppm continuous for 2-30 d
150 ppm continuous for 30 d
0-3,600 ppm, variable time periods
of 1-24 hrs/d, for 30 or 120 d.
376 ppm, 4 hrs/d, 5 d/wk, 8-24 wks
0, 500 (8 hrs), 2,000 (2 or 8 hrs), or
8,000 ppm (2 hrs) (single exposure)
0, 1,000, or 2,000 ppm, 8 hrs/d, 7 d
Exposed
6/group
5/group
4-12/group
6/group
10-20/
group
6/group
5/group
6/group
Section(s) where noncancer liver effects are
discussed
4.5.4.6. Bile production
E.2.6. Serum Bile Acid Assays
4.5.4.2. Cytotoxicity and histopathology
4.5.4.1. Liverweight
E.2.2.3. Kjellstrand et al., (1981b)
4.5.4.1. Liverweight
E.2.2.5. Kjellstrand et al., (1983b)
4.5.4.1. Liverweight
4.5.4.2. Cytotoxicity and histopathology
E.2.2.6. Kjellstrand et al., (1983a)
4.5.4.2. Cytotoxicity and histopathology
E.2.2. 10. Kumar et al.(2001b)
4.5.4.2. Cytotoxicity and histopathology
E.2. 1.3. Okino etal. (1991)
4.5.4.2. Cytotoxicity and histopathology
4.5.6.2.1. Hepatomegally- qualitative and
quantitative comparisons
4.5.6.2.2. Cytotoxicity
E.2. 1. 14. Ramdhan et al. (2008)
4-245
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Table 4-59. Inhalation and i.p. studies of TCE-induced liver effects in mice and rats (continued)
Reference"
Ramdhan et al.
(2010)
Toraason et al.
(1999)
Wang and Stacey
(1990)
Watanabe and
Fukui (2000)
Woolhiser et al.
(2006)
Animals (sex)
Sv/129, PPARa-
null, and hPPARa
mice (M)
Fischer rats (M)
Sprague-Dawley
rats (M)
ddY mice (M)
Sprague-Dawley
rats (F)
Exposure route
Inhalation
i.p. in Alkamuls/
water
i.p. in corn oil
Inhalation
i.p. in corn oil
Inhalation
Dose/exposure concentration
0, 1,000, or 2,000 ppm, 8 hrs/d, 7 d
0, 100, 500, or 1,000 mg/kg
i.p.:0, 1.3-1,3 14 mg/kg-d for 3d
Inhalation: 0, 200, or 1,000 ppm,
6 hrs/d for 28 d
0, 158 mg/kg (single dose)
0, 100, 300, or 1,000 ppm, 6 hrs/d,
5 d/wk, for 4 wks
Exposed
6/group]
6/group
4-6/group
4/group
16/group
Section(s) where noncancer liver effects are
discussed
4.5.4.1. Liverweight
4.5.4.2. Cytotoxicity and histopathology
4.5.6.2.1. Hepatomegally- qualitative and
quantitative comparisons
4.5.6.2.2. Cytotoxicity
4.5.7.2. Peroxisome Proliterator Activated
Receptor Alpha (PPARa) Receptor Activation
E.2. 1. 15. Ramdhan et al. (2010)
4.5.4.5. Oxidative stress
E.2.4.3. Summary of TCE Subchronic and
Chronic Studies
E.3.4.2.3. Oxidative Stress
4.5.4.6. Bile production
E.2.2. Subchronic and Chronic Studies of TCE
4.5.4.4. Peroxisome proliferation and related
effects
4.5.4.1. Liverweight
E.2.2.4. Woolhiser et al. (2006)
aBolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
4-246
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4.5.4.1. Liver Weight
Increases in liver weight in mice, rats, and gerbils have been reported as a result of acute
short-term, and subchronic TCE treatment by inhalation and oral routes of exposure (Laughter et
al.. 2004: Nunesetal.. 2001: Nakaiima et al.. 2000: Tao et al.. 2000: Bermanetal.. 1995: Dees
and Travis. 1993: Goeletal.. 1992: Merricketal.. 1989: Goldsworthy andPopp. 1987: Melnick
etal.. 1987: Buben and O'Flaherty. 1985: Elcombe et al.. 1985: Kjellstrandetal.. 1983a:
Kj ell strand etal.. 1983b: Tucker etal.. 1982: Kjellstrandetal.. 1981b). The extent of TCE-
induced liver weight gain is dependent on species, strain, gender, nutrition status, duration of
exposure, route of administration, vehicle used in oral studies, and concentration of TCE
administered. Of great importance to the determination of the magnitude of response is whether
the dose of TCE administered also affects whole-body weight, and thus, liver weight and the
percentage liver/body weight ratio. Therefore, studies that employed high enough doses to
induce whole-body weight loss generally showed a corresponding decrease in percentage
liver/body weight at such doses and "flattening" of the dose-response curve, while studies that
did not show systemic toxicity reported liver/body weight ratios generally proportional to dose.
Chronic studies, carried out for longer durations, that examine liver weight are few and often
confounded by the presence of preneoplastic foci or tumors that also affect liver weight after an
extended period of TCE exposure. The number of studies that examine liver weight changes in
the rat are much fewer than for mouse. Overall, the database for mice provides data for
examination of the differences in TCE-induced effects from differing exposure levels, durations
of exposure, vehicle, strain, and gender. One study provided a limited examination of TCE-
induced liver weight changes in gerbils.
TCE-induced increases in liver weight have been reported to occur quickly.
Kjellstrand et al. (1981b) reported liver weight increases after 2 days of inhalation exposure in
NMRI mice, Laughter et al. (2004) reported increased liver weight in SV129 mice in their 3-day
study (see below), and Tao et al. (2000) reported a increased in percentage liver/body weight
ratio in female B6C3Fi mice for after 5 days. Elcombe et al. (1985) and Dees and Travis (1993)
reported gavage results in mice and rats after 10 days of exposure to TCE, which showed TCE-
induced increases in liver weight. Tucker et al. (1982) reported that 14 days of exposure to 24
and 240 mg/kg TCE via gavage to induce a dose-related increase in liver weight in male CD-I
mice but did not show the data.
For mice, the inhalation studies of Kjellstrand et al. provided the most information on the
affect of duration of exposure, dose of exposure, strain tested, gender, initial weight, and
variability in response between experiments on TCE-induced liver weight increases. These
experiments also provided results that were independent of vehicle effect. Although the
determination of the exact magnitude of response is limited by experimental design,
Kjellstrand et al. (1981b) reported that in NMRI mice, continuous TCE inhalation exposure
induced increased percentage liver/body weight by 2 days and that by 30 days (the last recorded
4-247
-------
data point) the highest percentage liver/body weight ratio was reported (~1.75-fold over controls)
in both male and female mice. Kjellstrand et al. (1983b) exposed seven different strains of mice
(wild, C57BL, DBA, B6CBA, A/sn, NZB, NMRI) to 150 ppm TCE for 30 days and
demonstrated that strain, gender, and toxicity, as reflected by changes in whole-body weight,
affected the percentage liver/body weight ratios induced by 30 days of continuous TCE
exposure. In general for the seven strains of mice examined, female mice had the less variable
increases in TCE-induced liver weight gain across duplicate experiments than male mice. For
instance, in strains that did not exhibit changes in body weight (reflecting systemic toxicity) in
either gender (wild-type and DBA), 150 ppm TCE exposure for 30 days induced 1.74-1.87-fold
of control percentage liver/body weight ratios in female mice and 1.45-2.00-fold of control
percentage liver/body weight ratios in male mice. The strain with the largest TCE-induced
increase in percentage liver/body weight increase was the NZB strain (~2.08-fold of control for
females and 2.34-3.57-fold of control for males). Kjellstrand et al. (1983a) provided dose-
response information for the NMRI strain of mice (A Swiss-derived strain) that indicated dose-
related increases in percentage liver/body weight ratios between 37 and 300 ppm TCE exposure
for 30 days. The 150 ppm dose was reported to induce a 1.66- and 1.69-fold increases in
percentage liver/body weight ratios in male and female mice, respectively. Interestingly, they
also reported similar liver weight increases among groups with the same cumulative exposure,
but with different daily exposure durations (1 hour/day at 3,600 ppm to 24 hours/day at 150 ppm
for 30 days).
Not only have most gavage experiments been carried out in male mice, which Kjellstrand
et al. (1983b) had demonstrated to have more variability in response than females, but also
vehicle effects were noted to occur in experiments that examined them. Merrick et al. (1989)
reported that corn oil induced a similar increase in percentage liver/body weight ratios in female
mice fed TCE in Emulphor and corn oil for 4 weeks; male mice TCE administered in the corn oil
vehicle induced a greater increase in liver weight than Emulphor but less mortality at a high
does.
Buben and O'Flaherty (1985) treated male, outbred Swiss-Cox mice for 6 weeks at doses
ranging from 100 to 3,200 mg/kg-day, and reported increased liver/body-weight ratios at all
tested doses (1.12-1.75-fold of controls). Given the large strain differences observed by
Kjellstrand et al. (1983b), the use of predominantly male mice, and the effects of vehicle in
gavage studies, interstudy variability in dose-response relationships is not surprising.
Dependence of PPARa activation for TCE-liver weight gain has been investigated in
PPARa null mice by Nakajima et al. (2000). Laughter et al. (2004). and Ramdhan et al. (2010).
the latter of which also investigated PPARa null mice with human PPARa inserted. Nakajima
et al. (2000) reported that after 2 weeks of 750 mg/kg TCE exposure to carefully matched SV129
wild-type or PPARa-null male and female mice (n = 6 group), there was a reported 1.50-fold
increase in wild-type and 1.26-fold of control percentage liver/body weight ratio in PPARa-null
4-248
-------
male mice. For female mice, there was ~1.25-fold of control percentage liver/body weight ratios
for both wild-type and PPARa-null mice. Thus, TCE-induced liver weight gain was not
dependent on a functional PPARa receptor in female mice and some portion of it may have been
in male mice. Both wild-type male and female mice were reported to have similar increases in
the number of peroxisome in the pericentral area of the liver and TCE exposure and, although
increased twofold, were still only -4% of cytoplasmic volume. Female wild-type mice were
reported to have less TCE-induced elevation of very long chain acyl-CoA synthetase, D-type
peroxisomal bifunctional protein, mitochondrial trifunctional protein a subunits a and P, and
CYP 4A1 than males mice, even though peroxisomal volume was similarly elevated in male and
female mice. The induction of PPARa protein by TCE treatment was also reported to be slightly
less in female than male wild-type mice (2.17- vs. 1.44-fold of control induction, respectively).
Thus, differences between genders in this study were for increased liver weight were not
associated with differences in peroxisomal volume in the hepatocytes but there was a gender-
related difference in induction of enzymes and proteins associated with PPARa.
The study of Laughter et al. (2004) used SV129 wild-type and PPARa-null male mice
treated with three daily doses of TCE in 0.1% methyl cellulose for either 3 days (1,500 mg/kg
TCE) or 3 weeks (0, 10, 50, 125, 500, 1,000, or 1,500 mg/kg TCE 5 days/week). However, the
paradigm is not strictly comparable to other gavage paradigms due to the different dose vehicle
and the documented impacts of vehicles such as corn oil on TCE-induced effects. In addition, no
initial or final body weights of the mice were reported and thus, the influence of differences in
initial body weight on percentage liver/body weight determinations could not be ascertained.
While control wild-type and PPARa-null mice were reported to have similar percentage
liver/body weight ratios (i.e., -4.5%) at the end of the 3-day study, at the end of the 3-week
experiment, the percentage liver/body weight ratios were reported to be larger in the control
PPARa-null male mice (5.1%). TCE treatment for 3 days was reported for percentage liver/body
weight ratio to be 1.4-fold of control in the wild-type mice and 1.07-fold of control in the null
mice. After 3 weeks of TCE exposure at varying concentrations, wild-type mice were reported
to have percentage liver/body weight ratios that were within -2% of control values with the
exception of the 1,000 and 1,500 mg/kg treatment groups (-1.18- and 1.30-fold of control,
respectively). For the PPARa-null mice, the variability in percentage liver/body weight ratios
was reported to be greater than that of the wild-type mice in most of the TCE groups and the
baseline levels of percentage liver/body weight ratio for control mice 1.16-fold of that of wild-
type mice. TCE exposure was apparently more toxic in the PPARa-null mice. Decreased
survival at the 1,500 mg/kg TCE exposure level resulted in the prevention of recording of
percentage liver/body weight ratios for this group. At 1,000 mg/kg TCE exposure level, there
was a reported 1.10-fold of control percentage liver/body weight ratio in the PPARa-null mice.
None of the increases in percentage liver/body weight in the null mice were reported to be
statistically significant by Laughter et al. (2004). However, the power of the study was limited
4-249
-------
due to low numbers of animals and increased variability in the null mice groups. The percentage
liver/body weight ratio after TCE treatment reported in this study was actually greater in the
PPARa-null mice than the wild-type male mice at the 1,000 mg/kg TCE exposure level
(5.6 ± 0.4 vs. 5.2 ± 0.5%, for PPARa-null and wild-type mice, respectively) resulting in a
1.18-fold of wild-type and 1.10-fold of PPARa-null mice. Although the results reported in
Laughter et al. (2004) for DC A and TCA were not conducted in experiments that used the same
paradigm, the TCE-induced increase in percentage liver/body weight more closely resembled the
dose-response pattern for DCA than for DCA wild-type SV129 and PPARa-null mice.
Ramdhan et al. (2010) examined TCE-induced hepatice steatosis and toxicity using male
wild type, PPARa-null, and human PPARa inserted (—huianized") mice exposed to high
inhalation concentrations of TCE for 7 days. Significant differences were observed among
control mice for each genotype with reduced body weight in untreated humanized mice.
Liver/body weight ratios were 11% higher in untreated PPARa-null mice than wild type mice.
Higher levels of liver triglycerides and hepatic steatosis were reported in the untreated
humanized mice and PPARa null mice than wild type mice. Background expression of a number
of genes and protein expression levels were significantly different between the untreated strains.
In particular, human PPARa protein levels were >10-fold greater in the humanized mice than
mouse PPARa in untreated wild type mice. Insertion of human PPARa in the null mice did not
return the mice to a normal state. Both PPARa null and humanized mice were more susceptible
to TCE toxicity. Hepatomegally was induced in all strains to a similar extent after TCE
exposure. However, urinary TCA concentrations were reported to be significantly lower and
TCOH levels significantly higher in TCE-treated PPARa-null mice in comparison to treated wild
type mice. This difference was not related to changes in expression of metabolic enzymes.
No study examined strain differences among rats, and cross-study comparisons are
confounded by heterogeneity in the age of animals, dosing regimen, and other design
characteristics that may affect the degree of response. For rats, TCE-induced percentage
liver/body weight ratios were reported to range from 1.16- to 1.46-fold of control values
depending on the study paradigm. The studies that employed the largest range of exposure
concentrations (Berman et al., 1995; Melnick et al., 1987) examined four doses in the rat. In
general, there was a dose-related increase in percentage liver/body weight in the rat, especially at
doses that did not cause concurrent decreased survival or significant body weight loss. For
gerbils, Kjellstrand et al. (1981b) reported a similar value of ~1.25-fold of control percentage
liver/body weight as for Sprague-Dawley rats exposed to 150 ppm TCE continuously for
30 days. Woolhiser et al. (2006) also reported inhalation TCE exposure to increase the
percentage liver/body weight ratios in female Sprague-Dawley rats, although this strain appeared
to be less responsive that others tested for induction of hepatomegaly from TCA exposure and to
also be less prone to spontaneous liver cancer.
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The size of the liver is under tight control and after cessation of a mitogenic stimulus or
one inducing hepatomegaly, the liver will return to its preprogrammed size (see Appendix E).
The increase in liver weight from TCE-exposure also appears to be reversible. Kjellstrand et al.
(1981b) reported a reduction in liver weight gain increases after cessation of TCE exposure for
5 or 30 days in male and female mice. However, experimental design limitations precluded
discernment of the magnitude of decrease. Kjellstrand et al. (1983a) reported that mice exposed
to 150 ppm TCE for 30 days and then examined 120 days after the cessation of exposure had
liver weights were 1.09-fold of control for TCE-exposed female mice and the same as controls
for TCE-exposed male mice. However, the livers were not the same as untreated liver in terms
of histopathology. The authors reported that —£§r exposure to 150 ppm for 30 days, followed
by 120 days of rehabilitation, the morphological picture was similar to that of the air-exposure
controls except for changes in cellular and nuclear sizes." Qualitatively, the reduction in liver
weight after treatment cessation is consistent with the report of Elcombe et al. (1985) in Alderly
Park mice. The authors report that the reversibility of liver effects after the administration of
TCE to Alderly Park mice for 10 consecutive days. Effects upon liver weight, DNA
concentration, and tritiated thymidine incorporation 24 and 48 hours after the last dose of TCE
were reported to still be apparent. However, 6 days following the last dose of TCE, all of these
parameters were reported to return to control values with the authors not showing the data to
support this assertion. Thus, cessation of TCE exposure would have resulted in a 75% reduction
in liver weight by 4 days in mice exposed to the highest TCE concentration. Quantitative
comparisons are not possible because Elcombe et al. (1985) did not report data for these results
(e.g., how many animals, what treatment doses, and differences in baseline body weights) and
such a large decrease in such a short period of time needs to be verified.
4.5.4.2. Cytotoxicity and Histopathology
Acute exposure to TCE appears to induce low cytotoxicity below sub chronically lethal
doses. Relatively high doses of TCE appear necessary to induce cytotoxicity after a single
exposure with two available studies reported in rats. Okino et al. (1991) reported small increases
in the incidence of hepatocellular necrosis in male Wistar rats exposed to 2,000 ppm (8 hours)
and 8,000 ppm (2 hours), but not at lower exposures. In addition, "swollen" hepatocytes were
noted at the higher exposure when rats were pretreated with ethanol or phenobarbital. Serum
transaminases increased only marginally at the 8,000-ppm exposure, with greater increases with
pretreatments. Berman et al. (1995) reported hepatocellular necrosis, but not changes in serum
markers of necrosis, after single gavage doses of 1,500 and 5,000 mg/kg TCE in female F344
rats. However, they did not report any indications of necrosis after 14 days of treatment at 50-
1,500 mg/kg-day nor the extent of necrosis.
At acute and subchronic exposure periods to multiple doses, the induction of cytotoxicity,
though usually mild, appears to differ depending on rodent species, strain, dosing vehicle, and
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duration of exposure, and the extent of reporting to vary between studies. For instance,
Elcombe et al. (1985) and Dees and Travis (1993), which used the B6C3Fi mouse strain and
corn oil vehicle, reported only slight or mild necrosis after 10 days of treatment with TCE at
doses up to 1,500 mg/kg-day. Elcombe et al. (1985) also reported cell hypertrophy in the
centrilobular region. Dees and Travis (1993) reported some loss of vacuolization in hepatocytes
of mice treated at 1,000 mg/kg-day. Laughter et al. (2004) reported that —wtd-type" SV129
mice exposed to 1,500 mg/kg TCE exposure for 3 weeks exhibited mild granuloma formation
with calcification or mild hepatocyte degeneration, but gave no other details or quantitative
information as to the extent of the lesions or what parts of the liver lobule were affected. The
authors noted that -wild-type mice administered 1,000 and 1,500 mg/kg exhibited centrilobular
hypertrophy" and that —the rice in the other groups did not exhibit any gross pathological
changes" after TCE exposure. Channel et al. (1998) reported no necrosis in B6C3Fi mice treated
with 400-1,200 mg/kg-day TCE by corn oil gavage for 2 days to 8 weeks.
However, as stated above, Merrick et al. (1989) reported that corn oil resulted in more
hepatocellular necrosis, as described by small focal areas of 3-5 hepatocytes, in male B6C3Fi
mice than use of Emulphor as a vehicle for 4-week TCE gavage exposures. Necrotic hepatocytes
were described as surrounded by macrophages and polymorphonuclear cells. The authors
reported that visible necrosis was observed in 30^0% of male mice administered TCE in corn
oil but not that there did not appear to be a dose-response. For female mice, the extent of
necrosis was reported to be 0 for all control and TCE treatment groups using either vehicle.
Serum enzyme activities for ALT, AST, and LDH (markers of liver toxicity) showed that there
was no difference between vehicle groups at comparable TCE exposure levels for male or female
mice. Except for LDH levels in male mice exposed to TCE in corn oil, there was not a
correlation with the extent of necrosis and the patterns of increases in ALT and AST enzyme
levels.
Ramdhan et al. (2008) assessed TCE-induced hepatotoxicity by measuring plasma ALT
and AST activities and histopathology in Sv/129 mice treated by inhalation exposure, which are
not confounded by vehicle effects. Despite high variability and only six animals per dose group,
all three measures showed statistically significant increases at the high dose of 2,000 ppm
(8 hours/day for 7 days), although a nonstatistically significant elevation is evident at the low
dose of 1,000 ppm. Even at the highest dose, cytotoxicity was not severe, with ALT and AST
measures increased twofold or less and an average histological score <2(range 0-4).
Using the same paradigm, Ramdhan et al. (2010) also reported increased in AST and
ALT liver injury biomarkers to be significantly increased in all exposed mice (Sv/129 wild type,
PPARa-null, and humanized PPARa mice) relative to controls (41-74 and 36-79% higher, for
ALT and AST, respectively). Mean levels within each treatment group were higher, though not
statistically significantly different, with exposure to 2,000 vs. 1,000 ppm TCE. Steatosis scores
were reported to be significantly higher in the 2,000 vs. 1,000 ppm TCE exposures to
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PPARa-null mice. The authors reported steatosis scored to be significantly correlated with liver
triglyceride levels of all mice examined in the study (r = 0.75). Macrovesicular steatosis was
reported to occur more frequently in hPPARa than PPARa-null mice. Necrosis scores were
reported to be significantly higher in TCE-exposed mice relative to controls in all three genotype
mice and to be significantly higher with 2,000 vs. 1,000 ppm TCE exposure in wild type mice
and hPPARa mice. Inflammation scores were reported to be significantly higher with exposed
group than control with 2,000 ppm TCE exposure than controls for each genotype group with a
difference between the 2,000 ppm and 1,000 ppm exposure groups in wild type mice.
Kjellstrand et al. (1983a) exposed male and female NRMI mice to 150 ppm for 30-
120 days. Kjellstrand et al. (1983a) reported more detailed light microscopic findings from their
study and stated that
After 150 ppm exposure for 30 days, the normal trabecular arrangement of the
liver cells remained. However, the liver cells were generally larger and often
displayed a fine vacuolization of the cytoplasm. The nucleoli varied slightly to
moderately in size and shape and had a finer, granular chromatin with a varying
basophilic staining intensity. The Kupffer cells of the sinusoid were increased in
cellular and nuclear size. The intralobular connective tissue was infiltrated by
inflammatory cells. There was not sign of bile stasis. Exposure to TCE in higher
or lower concentrations during the 30 days produced a similar morphologic
picture. After intermittent exposure for 30 days to a time-weighted-average
concentration of 150 ppm or continuous exposure for 120 days, the trabecular
cellular arrangement was less well preserved. The cells had increased in size and
the variations in size and shape of the cells were much greater. The nuclei also
displayed a greater variation in basophilic staining intensity, and often had one or
two enlarged nucleoli. Mitosis was also more frequent in the groups exposed for
longer intervals. The vacuolization of the cytoplasm was also much more
pronounced. Inflammatory cell infiltration in the interlobular connective tissue
was more prominent. After exposure to 150 ppm for 30 days, followed by
120 days of rehabilitation, the morphological picture was similar to that of the air-
exposure controls except for changes in cellular and nuclear sizes.
Although not reporting comparisons between male and female mice in the results section
of the paper for TCE-induced histopathological changes, the authors stated in the discussion
section that —However, livemass increase and the changes in liver cell morphology were similar
in TCE-exposed male and female mice." Kjellstrand et al. (1983a) did not present any
quantitative data on the lesions they described, especially in terms of dose-response. Most of the
qualitative description presented was for the 150-ppm exposure level and the authors suggest that
lower concentrations of TCE give a similar pathology as those at the 150-ppm level, but do not
present data to support that conclusion. Although stating that Kupffer cells were reported to be
increased in cellular and nuclear size, no differential staining was applied light microscopy
sections to distinguish Kupffer from endothelial cells lining the hepatic sinusoid in this study.
Without differential staining, such a determination is difficult at the light microscopic level.
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Indeed, Goel et al. (1992) describe proliferation of—sinusodal endothelial cells" after
1,000 and 2,000 mg/kg-day TCE exposure for 28 days in male Swiss mice. They reported that
histologically, —thdiver exhibits swelling, vacuolization, widespread degeneration/necrosis of
hepatocytes as well as marked proliferation of endothelial cells of hepatic sinusoids at 1,000 and
2,000 mg/kg TCE doses." Only one figure is given, at the light microscopic level, in which it is
impossible to distinguish endothelial cells from Kupffer cells and no quantitative measures or
proliferation were examined or reported to support the conclusion that endothelial cells are
proliferating in response to TCE treatment. Similarly, no quantitative analysis regarding the
extent or location of hepatocellular necrosis was given. The presence or absence of
inflammatory cells were not noted by the authors as well. In terms of white blood cell count, the
authors note that it was slightly increased at 500 mg/kg-day but decreased at 1,000 and
2,000 mg/kg-day TCE, perhaps indicating macrophage recruitment from blood to liver and
kidney, which was also noted to have pathology at these concentrations of TCE.
The inflammatory cell infiltrates described in the Kjellstrand et al. (1983a) study are
consistent with invasion of macrophages and well as polymophorphonuclear cells into the liver,
which could activate resident Kupffer cells. Although not specifically describing the changes as
consistent with increased polyploidization of hepatocytes, the changes in cell size and especially
the continued change in cell size and nuclear staining characteristics after 120 days of cessation
of exposure are consistent with changes in polyploidization induced by TCE. Of note is that in
the histological description provided by the authors, although vacuolization is reported and
consistent with hepatotoxicity or lipid accumulation, which is lost during routine histological
slide preparation, there is no mention of focal necrosis or apoptosis resulting from these
exposures to TCE.
Buben and O'Flaherty (1985) reported liver degeneration —a swollen hepatocytes" and to
be common with treatment of TCE to Male Swiss-Cox mice after 6 weeks. They reported that
—CeMhad indistinct borders; their cytoplasm was clumped and a vesicular pattern was apparent.
The swelling was not simply due to edema, as wet weight/dry weight ratios did not increase."
Karyorrhexis (the disintegration of the nucleus) was reported to be present in nearly all
specimens and suggestive of impending cell death. No Karyorrhexis, necrosis, or polyploidy
was reported in controls, but a low score Karyorrhexis was given for 400 mg/kg TCE and a
slightly higher one given for 1,600 mg/kg TCE. Central lobular necrosis was reported to be
present only at the 1,600 mg/kg TCE exposure level and was assigned a low score. Polyploidy
was described as characteristic in the central lobular region but with low scores for both
400 mg/kg and 1,600 mg/kg TCE exposures. The authors reported that —hpatic cells had two or
more nuclei or had enlarged nuclei containing increased amounts of chromatin, suggesting that a
regenerative process was ongoing" and that there were no fine lipid droplets in TCE exposed
animals. The finding of—no polypdidy" in control mouse liver in the study of Buben and
O'Flaherty (1985) is unexpected given that binucleate and polyploid hepatocytes are a common
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finding in the mature mouse liver. It is possible that the authors were referring to unusually high
instances of —polyploidy in comparison to what would be expected for the mature mouse. The
score given by the authors for polyploidy did not indicate a difference between the two TCE
exposure treatments and that it was of the lowest level of severity or occurrence. No score was
given for centrolobular hypertrophy, although the DNA content and liver weight changes
suggested a dose-response. The -Karyrrhexis" described in this study could have been a sign of
cell death associated with increased liver cell number or dying of maturing hepatocytes
associated with the increased ploidy, and suggests that TCE treatment was inducing
polyploidization. Consistent with enzyme analyses, centrilobular necrosis was only seen at the
highest dose and with the lowest qualitative score, indicating that even at the highest dose there
was little toxicity.
At high doses, Kaneko et al. (2000) reported sporadic necrosis in male Mrl-lpr/lpr mice,
which are —geneticallyikble to autoimmune disease," exposed to 500-2,000 ppm, 4 hours/day,
6 days/week, for 8 weeks (n = 5). Dose-dependent mild inflammation and associated changes
were reported to be found in the liver. The effects on hepatocytes were reported to be minimal
by the authors with 500 ppm TCE inducing sporadic necrosis in the hepatic lobule. Slight
mobilization and activation of sinusoid lining cells were also noted. These pathological features
were reported to increase with dose.
NTP (1990), which used the B6C3Fi mouse strain, reported centrilobular necrosis in
6/10 male and 1/10 female B6C3Fi mice treated at a dose of 6,000 mg/kg-day for up to 13 weeks
(all of the male mice and 8 of the 10 female mice died in the first week of treatment). At
3,000 mg/kg-day exposure level, although centrilobular necrosis was not observed, 2/10 males
had multifocal areas of calcification in their livers, which the authors suggest is indicative of
earlier hepatocellular necrosis. However, only 3/10 male mice at this dose survived to the end of
the 13-week study.
For the NTP (1990) 2-year study, B6C3Fi mice were reported to have no treatment-
related increase in necrosis in the liver. A slight increase in the incidence of focal necrosis was
noted TCE-exposed male mice (8 vs. 2%) with a slight reduction in fatty metamorphosis in
treated male mice (zero treated vs. two control animals) and, in female mice, a slight increase in
focal inflammation (29 vs. 19% of animals) and no other changes. Therefore, this study did not
show concurrent evidence of liver toxicity with TCE-induced neoplasia after 2 years of TCE
exposure in mice.
For the more limited database in rats, there appears to be variability in reported
TCE-induced cytotoxicity and pathology. Nunes et al. (2001) reported no gross pathological
changes in rats gavaged with corn oil or with corn oil plus 2,000 mg/kg TCE for 7 days.
Goldsworthy and Popp (1987) gave no descriptions of liver histology in this report for
TCE-exposed animals or corn-oil controls. Kjellstrand et al. (1981b) also did not provide
histological descriptions for livers of rats in their inhalation study.
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Elcombe et al. (1985) provided a description of the histopathology at the light
microscopy level in Osborne-Mendel rats and Alderly Park rats exposed to TCE via gavage for
10 days. However, they did not provide a quantitative analysis or specific information regarding
the variability of response between animals within group and there was no indication by the
authors regarding how many rats were examined by light microscopy. Hematoxylin and eosin
sections from Osborne-Mendel rats were reported to show that:
Livers from control rats contained large quantities of glycogen and isolated
inflammatory foci, but were otherwise normal. The majority of rats receiving
1,500 mg/kg body weight TCE showed slight changes in centrilobular
hepatocytes. The hepatocytes were more eosinophilic and contained little
glycogen. At lower doses these effects were less marked and were restricted to
fewer animals. No evidence of treatment-related hepatotoxicity (as exemplified
by single cell or focal necrosis) was seen in any rat receiving TCE. H&E
[hematoxylin and eosin] sections from Alderly Park Rats showed no signs of
treatment-related hepatotoxicity after administration of TCE. However, some
signs of dose-related increase in centrilobular eosinophilia were noted.
Thus, both mice and rats were reported to exhibit pericentral hypertrophy and
eosinophilia as noted from the histopathological examination in Elcombe et al. (1985).
Berman et al. (1995) reported that for female rats exposed to TCE for 14 days
hepatocellular necrosis was noted to occur in the 1,500 and 5,000 mg/kg groups in 6/7 and
6/8 female rats, respectively, but not to occur in lower doses. The extent of necrosis was not
noted by the authors for the two groups exhibiting a response after 1 day of exposure. Serum
enzyme levels, indicative of liver necrosis, were not presented and because only positive results
were presented in the paper, presumed to be negative. Therefore, the extent of necrosis was not
of a magnitude to affect serum enzyme markers of cellular leakage.
Melnick et al. (1987) reported that the only treatment-related lesion observed
microscopically in rats from either dosed-feed or gavage groups was individual cell necrosis of
the liver with the frequency and severity of this lesion similar at each dosage levels of TCE
microencapsulated in the feed or administered in corn oil. The severity for necrosis was only
mild at the 2.2 and 4.8 g/kg feed groups and for the six animals in the 2.8 g/kg group corn oil
group. The individual cell necrosis was reported to be randomly distributed throughout the liver
lobule with the change to not be accompanied by an inflammatory response. The authors also
reported that there was no histologic evidence of cellular hypertrophy or edema in hepatic
parenchymal cells. Thus, although there appeared to be TCE-treatment related increases in focal
necrosis after 14 days of exposure, the extent was mild even at the highest doses and involved
few hepatocytes.
For the 13-week NTP study (1990), only control and high dose F344/N rats were
examined histologically. Pathological results were reported to reveal that 6/10 males and
6/10 female rats had pulmonary vasculitis at the highest concentration of TCE. This change was
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also reported to have occurred in 1/10 control male and female rats. Most of those animals were
also reported to have had mild interstitial pneumonitis. The authors reported that viral liters were
positive during this study for Sendai virus.
Kumar et al. (200Ib) reported that male Wistar rats exposed to 376 ppm, 4 hours/day,
5 days/week for 8-24 weeks showed evidence of hepatic toxicity. The authors stated that, —after
8 weeks of exposure enlarged hepatocytes, with uniform presence of fat vacuoles were found in
all of the hepatocytes affecting the periportal, midzonal, and centriolobular areas, and fat
vacuoles pushing the pyknosed nuclei to one side of hepatocytes. Moreover, congestion was not
significant. After exposure of 12 and 24 weeks, the fatty changes became more progressive with
marked necrosis, uniformly distributed in the entire organ." No other description of pathology
was provided in this report. In regard to the description of fatty change, the authors only did
conventional H&E staining of sections with no precautions to preserve or stain lipids in their
sections. However, as noted below, the NCI study also reports long-term TCE exposure in rats
to result in hepatocellular fatty metamorphosis. The authors provided a table with histological
scoring of simply + or - for minimal, mild, or moderate effects and did not define the criteria for
that scoring. There is also no quantitative information given as to the extent, nature, or location
of hepatocellular necrosis. The authors reported that —no chage was observed in glutamic
oxoacetate transaminase and glutamic pyruvated transaminase levels of liver in all of the three
groups. The GSH level was significantly decreased while —ettal sulphydryl" level was
significantly increased during 8, 12, and 24 weeks of TCE exposure. The acid and ALPs were
significantly increased during 8, 12, and 24 weeks of TCE exposure." The authors presented a
series of figures, which were poor in quality, to demonstrate histopathological TCE-induced
changes. No mortality was observed from TCE exposure in any group, despite the presence of
liver necrosis.
Thus, in this limited database that spans durations of exposure from days to 24 weeks and
uses differing routes of administration, generally high doses for long durations of exposure are
required to induce hepatotoxicity from TCE exposure in the rat. The focus of 2-year bioassays in
rats has been the detection of a cancer response with little or no reporting of noncancer pathology
in most studies. Henschler et al. (1984) and Fukuda et al. (1983) do not report noncancer
histopathology, but both reported rare biliary-cell-derived tumors in rats in relatively insensitive
assays. For male rats, noncancer pathology in the NCI (1976) study was reported to include
increased fatty metamorphosis after TCE exposure and angiectasis or abnormally enlarged blood
vessels. Angiectasis can be manifested by hyperproliferation of endothelial cells and dilatation
of sinusoidal spaces. For the NTP (1990) study, there was little reporting of non-neoplastic
pathology or toxicity and no report of liver weight at termination of the study. In the NTP
(1988) study, the 2-year study of TCE exposure reported no evidence of TCE-induced liver
toxicity described as non-neoplastic changes in ACI, August, Marshal, and Osborne-Mendel rats.
Interestingly, for the control animals of these four strains, there was, in general, a low
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background level of focal necrosis in the liver of both genders. Obviously, the negative results in
this bioassay for cancer are confounded by the killing of a large portion of the animals accidently
by experimental error, but TCE-induced overt liver toxicity was not reported.
In sum, the cytotoxic effects in the liver of TCE treatment appear to include little or no
necrosis in the rodent liver, but rather, a number of histological changes such as mild focal
hepatocyte degeneration at high doses, cellular —welling" or hypertrophy, and enlarged nuclei.
Histological changes consistent with increased polyploidization and specific descriptions of
TCE-induced polyploidization have been noted in several experiments. Several studies noted
proliferation of nonparenchymal cells after TCE exposure as well. These results are more
consistently reported in mice, but also have been reported in some studies at high doses in rats,
for which fewer studies are available. In addition, the increase in cellular and nuclear sizes
appeared to persist after cessation of TCE treatment. In neither rats nor mice is there evidence
that TCE treatment results in marked necrosis leading to regenerative hyperplasia.
4.5.4.3. Measures of DNA Synthesis, Cellular Proliferation, and Apoptosis
The increased liver weight observed in rodents after TCE exposure may result from either
increased numbers of cells in the liver, increased size of cells in the liver, or a combination of
both. Studies of TCE in rodents have looked at whole-liver DNA content of TCE-treated
animals to determine whether the concentration of DNA/g of liver decreases as an indication of
hepatocellular hypertrophy (Dees and Travis, 1993; Buben and O'Flaherty, 1985; El combe et al.,
1985). While the slight decreases observed in some studies are consistent with hypertrophy, the
large variability in controls and lack of dose-response limits the conclusions that can be drawn
from these data. In addition, multiple factors beyond hypertrophy affect DNA concentration in
whole-liver homogenates, including changes in ploidy and the number of hepatocytes and
nonparenchymal cells.
The incorporation of tritiated thymidine or BrdU has also been analyzed in whole-liver
DNA and in individual hepatocytes as a measure of DNA synthesis. Such DNA synthesis can
occur from either increased numbers of hepatocytes in the liver or increased polyploidization.
Section E. 1.1 describes polyploidization in human and rodent liver and its impacts on liver
function, while Sections E.3.1.4 and E.3.3.1 discuss issues of target cell identification for liver
cancer and changes in ploidy as a key even in liver cancer using animals models, respectively.
Along with changes in cell size (hypertrophy), cell number (cellular proliferation), and the DNA
content per cell (cell ploidy), the rate of apoptosis has also been noted or specifically examined
in some studies of TCE and its metabolites. All of these phenomena have been identified in
proposed hypotheses as key events possibly related to carcinogenicity. In particular, changes in
cell proliferation and apoptosis have been postulated to be part of the mode of action for
PPARa-agonists by Klaunig et al. (2003) (see Section E.3.4).
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In regard to early changes in DNA synthesis, the data for TCE are very limited
Mirsalis et al. (1989) reported measurements of in vivo-in vitro hepatocyte DNA repair and
S-phase DNA synthesis in primary hepatocytes from male F344 rats and male and female
B6C3Fi mice administered single doses of TCE by gavage in corn oil. They reported negative
results 2-12 hours after treatment of 50-1,000 mg/kg TCE in rats and mice (male and female)
for UDS and repair using three animals per group. After 24 and 48 hours of 200 or 1,000 mg/kg
TCE in male mice (n = 3) and after 48 hours of 200 (n = 3) or 1,000 (n = 4) mg/kg TCE in
female mice, similar values of 0.30-0.69% of hepatocytes were reported as undergoing DNA
synthesis in primary culture. Only the 1,000 mg/kg TCE dose in male mice at 48 hours was
reported to give a result considered to be positive (-2.2% of hepatocytes), but no statistical
analyses were performed on these measurements. These results are limited by both the number
of animals examined and the relevance of the paradigm.
As noted above, TCE treatment in rodents has been reported to result in hepatocellular
hypertrophy and increased centrilobular eosinophilia. Elcombe et al. (1985) reported a small
decrease in DNA content with TCE treatment (consistent with hepatocellular hypertrophy) that
was not dose-related, increased tritiated thymidine incorporation in whole mouse liver DNA that
was that was treatment- but not dose-related (i.e., a two-, two-, and fivefold of control in mice
treated with 500, 1,000, and 1,500 mg/kg TCE), and slightly increased numbers of mitotic
figures that were treatment but not dose-related and not correlated with DNA synthesis as
measured by thymidine incorporation. Elcombe et al. (1985) reported no difference in response
between 500 and 1,000 mg/kg TCE treatments for tritiated thymidine incorporation. Dees and
Travis (1993) also reported that incorporation of tritiated thymidine in DNA from mouse liver
was elevated after TCE treatment with the mean peak level of tritiated thymidine incorporation
occurred at 250 mg/kg TCE treatment level and remaining constant for the 500 and 1,000 mg/kg
treated groups. Dees and Travis (1993) specifically report that mitotic figures, although very
rare, were more frequently observed after TCE treatment, found most often in the intermediate
zone, and found in cells resembling mature hepatocytes. They reported that there was little
tritiated thymidine incorporation in areas near the bile duct epithelia or close to the portal triad in
liver sections from both male and female mice. Channel et al. (1998) reported proliferating cell
nuclear antigen (PCNA) positive cells, a measure of cells that have undergone DNA synthesis,
was elevated only on day 10 (out of the 21 studied) and only in the 1,200 mg/kg-day TCE
exposed group with a mean of-60 positive nuclei per 1,000 nuclei for six mice (-6%). Given
that there was little difference in PCNA positive cells at the other TCE doses or time points
studied, the small number of affected cells in the liver could not account for the increase in liver
size reported in other experimental paradigms at these doses. The PCNA positive cells as well as
—ritotic figures" were reported to be present in centrilobular, midzonal, and periportal regions
with no observed predilection for a particular lobular distribution. No data were shown
regarding any quantitative estimates of mitotic figures and whether they correlated with PCNA
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results. Thus, whether the DNA synthesis phases of the cell cycle indicated by PCNA staining
were indentifying polyploidization or increased cell number cannot be determined.
For both rats and mice, the data from Elcombe et al. (1985) showed that tritiated
thymidine incorporation in total liver DNA observed after TCE exposure did not correlate with
mitotic index activity in hepatocytes. Both Elcombe et al. (1985) and Dees and Travis (1993)
reported small mitotic indices and evidence of periportal hepatocellular hypertrophy from TCE
exposure. Neither mitotic index nor tritiated thymidine incorporation data support a correlation
with TCE-induced liver weight increase in the mouse, but rather that the increase is most likely
due to hepatocellular hypertrophy. If higher levels of hepatocyte replication had occurred
earlier, such levels were not sustained by 10 days of TCE exposure. These data suggest that
increased tritiated thymidine levels were targeted to mature hepatocytes and in areas of the liver
where greater levels of polyploidization occur (see Section E. 1.1). Both Elcombe et al. (1985)
and Dees and Travis (1993) show that tritiated thymidine incorporation in the liver was
approximately twofold greater than controls between 250 and 1,000 mg/kg TCE, a result
consistent with a doubling of DNA. Thus, given the normally quiescent state of the liver, the
magnitude of this increase over control levels, even if a result of proliferation rather than
polyploidization, would be confined to a very small population of cells in the liver after 10 days
of TCE exposure.
Laughter et al. (2004) reported that there was an increase in DNA synthesis after aqueous
gavage exposure to 500 and 1,000 mg/kg TCE given as three boluses/day for 3 weeks with BrdU
given for the last week of treatment. An examination of DNA synthesis in individual
hepatocytes was reported to show that 1 and 4.5% of hepatocytes had undergone DNA synthesis
in the last week of treatment for the 500 and 1,000 mg/kg doses, respectively. Again, this level
of DNA synthesis is reported for a small percentage of the total hepatocytes in the liver and not
reported to be a result of regenerative hyperplasia.
Finally, Dees and Travis (1993) and Channel et al. (1998) reported evaluating changes in
apoptosis with TCE treatment. Dees and Travis (1993) enumerated identified by either
hematoxylin and eosin or feulgen staining in male and female mice after 10 days of TCE
treatment by. Only zero or one apoptosis was observed per 100 high power (400 x) fields in
controls and all dose groups except for those given 1,000 mg/kg-day, in which eight or nine
apoptoses per 100 fields were reported. None of the apoptoses were in the intermediate zones
where mitotic figures were observed, and all were located near the central veins. This is the
same region where one would expect endogenous apoptoses as hepatocytes —stain" from the
portal triad toward the central vein (Schwartz-Arad et al., 1989). In addition, this is the same
region where Buben and O'Flaherty (1985) noted necrosis and polyploidy. By contrast, Channel
et al. (1998) reported no significant differences in apoptosis at any treatment dose (400-
1,200 mg/kg-day) examined after any time from 2 days to 4 weeks.
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4.5.4.4. Peroxisomal Proliferation and Related Effects
Numerous studies have reported that TCE administered to mice and rats by gavage leads
to proliferation of peroxisomes in hepatocytes. Some studies have measured changes in the
volume and number of peroxisomes as measures of peroxisome proliferation, while others have
measured peroxisomal enzyme activity such as catalase and cyanide-insensitive PCO. Like liver
weight, the determination of a baseline level of peroxisomal volume, number, or enzyme activity
can be variable and have great effect on the ability to determine the magnitude of a treatment-
related effect.
Elcombe et al. (1985) reported increases in the percentage of the cytoplasm occupied by
peroxisomes in B6C3Fi and Alderley Park mice treated for 10 days at 500-1,500 mg/kg-day.
Although the increase over controls appeared larger in the B6C3Fi strain, this is largely due to
the twofold smaller control levels in that strain, as the absolute percentage of peroxisomal
volume was similar between strains after treatment. All of these results showed high variability,
as evidenced from the reported SDs. Channel et al. (1998) found a similar absolute percentage
of peroxisomal volume after 10 days treatment in the B6C3Fi mouse at 1,200 mg/kg-day TCE
but with the percentage in vehicle controls similar to the Alderley-Park mice in the Elcombe
et al. (1985) study. Interestingly, Channel et al. (1998) found that the increase in peroxisomes
peaked at 10 days, with lower values after 6 and 14 days of treatment. Furthermore, the vehicle
control levels also varied almost twofold depending on the number of days of treatment.
Nakajima et al. (2000) treated male wild-type SV129 mice at 750 mg/kg-day for 14 days, and
found even higher baseline values for the percentage of peroxisomal volume, but with an
absolute level after treatment similar to that reported by Channel et al. (1998) in B6C3Fi mice
treated at 1,200 mg/kg-day TCE for 14 days. Nakajima et al. (2000) also noted that the
treatment-related increases were smaller for female wild-type mice, and that there were no
increases in peroxisomal volume in male or female PPARa-null mice, although vehicle control
levels were slightly elevated (not statistically significant). Only Elcombe et al. (1985) examined
peroxisomal volume in rats, and reported smaller treatment-related increases in two strains (OM
and AP), but higher baseline levels. In particular, at 1,000 mg/kg-day, after 10 days of treatment,
the percentage peroxisomal volume was similar in OM and AP rats, with similar control levels as
well. While the differences from treatment were not statistically significant, only five animals
were used in each group, and variability, as can be seen by the SDs, was high, particularly in the
treated animals.
The activities of a number of different hepatic enzymes have also been as markers for
peroxisome proliferation and/or activation of PPARa. The most common of these are catalase
and cyanide-insensitive PCO. In various strains of mice (B6C3Fi, Swiss albino, SV129 wild-
type) treated at doses of 500-2,000 mg/kg-day for 10-28 days, increases in catalase activity have
tended to be more modest (1.3-1.6-fold of control) as compared to increases in PCO (1.4-
7.9-fold of control) (Laughter et al.. 2004: Nakajima et al.. 2000: Watanabe and Fukul 2000:
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Goel et al., 1992; Goldsworthy and Popp, 1987; Elcombe et al., 1985). In rats, Elcombe et al.
(1985) reported no increases in catalase or PCO activity in Alderley-Park rats treated at
1,000 mg/kg-day TCE for 10 days. In F344 rats, Goldsworthy and Popp (1987) and Melnick
et al. (1987) reported increases of up to 2-fold in catalase and 4.1-fold in PCO relative to controls
treated at 600-4,800 mg/kg-day for 10-14 days. The changes in catalase were similar to those in
mice at similar treatment levels, with 1.1-1.5-fold of control enzyme activities at doses of 1,000-
1,300 mg/kg-day (Melnick et al., 1987; Elcombe et al., 1985). However, the changes in PCO
were smaller, with 1.1-1.8-fold of control activity at these doses, as compared to 6.3-7.9-fold of
control in mice (Goldsworthy and Popp, 1987; Melnick et al., 1987).
In SV129 mice, Nakajima et al. (2000) and Laughter et al. (2004) investigated the
dependence of these changes on PPARa by using a null mouse. Nakajima et al. (2000) reported
that neither male nor female wild-type or PPARa null mice had significant increases in catalase
after 14 days of treatment at 750 mg/kg-day. However, given the small number of animals (four
per group) and the relatively small changes in catalase observed in other (wild-type) strains of
mice, this study had limited power to detect such changes. Several other markers of peroxisome
proliferation, including acyl-CoA oxidase and CYP4A1 (PCO was not investigated), were
induced by TCE in male wild-type mice, but not in male null mice or female mice of either type.
Unfortunately, none of these markers have been investigated using TCE in female mice of any
other strain, so it is unclear whether the lack of response is characteristic of female mice in
general, or just in this strain. Interestingly, as noted above, liver/body weight ratio increases
were observed in both sexes of the null mice in this study. Laughter et al. (2004) only quantified
activity of the peroxisome proliferation marker, PCO, in their study, and found in null mice a
slight decrease (0.8-fold of control) at 500 mg/kg-day TCE and an increase (1.5-fold of control)
at 1,500 mg/kg-day TCE after 3 weeks of treatment, with neither statistically significant (4-
5 mice per group). However, baseline levels of PCO were almost 2-fold higher in the null mice,
and the treated wild-type and null mice differed in PCO activity by only about 1.5-fold.
In sum, oral administration of TCE for up to 28 days causes proliferation of peroxisomes
in hepatocytes along with associated increases in peroxisomal enzyme activities in both mice and
rats. Male mice tend to be more sensitive in that at comparable doses, rats and female mice tend
to exhibit smaller responses. For example, for peroxisomal volume and PCO, the fold-increase
in rats appears to be lower by three- to sixfold than that in mice, but, for catalase, the changes
were similar between mice in F344 rats. No inhalation or longer-term studies were located, and
only one study examined these changes at more than one time-point. Therefore, little is known
about the route-dependence, time course, and persistence of these changes. Finally, two studies
in PPARa-null mice (Laughter et al., 2004; Nakajima et al., 2000) found diminished responses in
terms of increased peroxisomal volume and peroxisomal enzyme activities as compared to wild-
type mice, although there was some confounding due to baseline differences between null and
wild-type control mice in several measures.
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4.5.4.5. Oxidative Stress
Several studies have attempted to study the possible effects of —oxidafre stress" and
DNA damage resulting from TCE exposures. The effects of induction of metabolism by TCE, as
well as through co-exposure to ethanol, have been hypothesized to increase levels of—oxidative
stress" as a common effect for both exposures (see Sections E.3.4.2.3 and E.4.2.4). Oxidative
stress has been hypothesized to be a key event or mode of action for peroxisome proliferators as
well, but has been found to be correlated with neither cell proliferation nor carcinogenic potency
of peroxisome proliferators (see Section E.3.4.1.1). As a mode of action, it is not defined or
specific, as the term —oxidtave stress" is implicated as part of the pathophysiologic events in a
multitude of disease processes and is part of the normal physiologic function of the cell and cell
signaling.
In regard to measures of oxidative stress, Rusyn et al. (2006) noted that although an
overwhelming number of studies draw a conclusion between chemical exposure, DNA damage,
and cancer based on detection of 8-hydroxy-2'-deoxyguanosine (8-OHdG), a highly mutagenic
lesion, in DNA isolated from organs of in vivo treated animals, a concern exists as to whether
increases in 8-OHdG represent damage to genomic DNA, a confounding contamination with
mitochondrial DNA, or an experimental artifact. As noted in Sections E.2.1.1 and E.2.2.11,
studies of TCE which employ the i.p. route of administration can be affected by inflammatory
reactions resulting from that routes of administration and subsequent toxicity that can involve
oxygen radical formation from inflammatory cells. Finally, as described in Section E.2.2.8, the
study by Channel et al. (1998) demonstrated that corn oil as vehicle had significant effects on
measures of—oxiative stress" such as thiobarbiturate acid-reactive substances (TEARS).
The TEARS results presented by Channel et al. (1998) indicate suppression of TEARS
with increasing time of exposure to corn oil alone with data presented in such a way for 8-OHdG
and total free radical changes that the pattern of corn oil administration was obscured. It was not
apparent from that study that TCE exposure induced oxidative damage in the liver.
Toraason et al. (1999) measured 8-OHdG and a —freeadical-catalyzed isomer of
arachidonic acid and marker of oxidative damage to cell membranes, 8-epi-prostaglandin F2a
(8-epiPGF)," excretion in the urine and TEARS (as an assessment of malondialdehyde and
marker of lipid peroxidation) in the liver and kidney of male Fischer rats exposed to single i.p.
injections in of TCE in Alkamuls vehicle. Using this paradigm, 500-mg/kg TCE was reported to
induce Stage II anesthesia and 1,000 mg/kg TCE was reported to induce Level III or IV (absence
of reflex response) anesthesia and burgundy-colored urine with 2/6 rats at 24 hours comatose and
hypothermic. The animals were sacrificed before they could die and the authors suggested that
they would not have survived another 24 hours. Thus, using this paradigm, there was significant
toxicity and additional issues related to route of exposure. Urine volume declined significantly
during the first 12 hours of treatment and while water consumption was not measured, it was
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suggested by the authors to be decreased due to the moribundity of the rats. Given that this study
examined urinary markers of—oxidativeliess," the effects on urine volume and water
consumption, as well as the profound toxicity induced by this exposure paradigm, limit the
interpretation of the study. The issues of bias in selection of the data for this analysis, as well as
the issues stated above for this paradigm limit interpretation of these data while the authors
suggest that evidence of oxidative damage was equivocal.
4.5.4.6. Bile Production
Effects of TCE exposure in humans and in experimental animals is presented in
Section E.2.6. Serum bile acids (SBA) have been suggested as a sensitive indicator of
hepatotoxicity to a variety of halogenated solvents with an advantage of increased sensitivity and
specificity over conventional liver enzyme tests that primarily reflect the acute perturbation of
hepatocyte membrane integrity and —di leakage" rather than liver functional capacity (i.e.,
uptake, metabolism, storage, and excretion functions of the liver) (Neghab et al., 1997; Bai et al.,
1992a) While some studies have reported negative results, a number of studies have reported
elevated SBA in organic solvent-exposed workers in the absence of any alterations in normal
liver function tests. These variations in results have been suggested to arise from failure of some
methods to detect some of the more significantly elevated SBA and the short-lived and reversible
nature of the effect (Neghab etal., 1997). Neghab et al. (1997) reported that occupational
exposure to l,l,2-trichloro-l,2,2-trifluoroethane and TCE has resulted in elevated SBA and that
several studies have reported elevated SBA in experimental animals to chlorinated solvents such
as carbon tetrachloride, chloroform, hexachlorobutadiene, tetrachloroethylene, 1,1,1-trichloro-
ethane, and TCE at levels that do not induce hepatotoxicity (Hamdan and Stacey, 1993; Bai et
al., 1992b: Bai etal., 1992a: Wang and Stacey, 1990). Toluene, a nonhalogenated solvent, has
also been reported to increase SBA in the absence of changes in other hepatobiliary functions
(Neghab and Stacey, 1997). Thus, disturbance in SBA appears to be a generalized effect of
exposure to chlorinated solvents and nonchlorinated solvents and not specific to TCE exposure.
Wang and Stacey (1990) administered TCE in corn oil via i.p. injection to male Sprague-
Dawley rats with liver enzymes and SBA examined 4 hours after the last TCE treatment. The
limitations of i.p injection experiments have already been discussed. While reporting no overt
liver toxicity, there was, generally, a reported dose-related increase in cholic acid,
chenodeoxycholic acid, deoxycholic acid, taurocholic acid, tauroursodeoxycholic acid with
cholic acid, and taurochlolic acid increased at the lowest dose. The authors reported that
—exmination of liver sections under light microscopy yielded no consistent effects that could be
ascribed to trichloroethylene." In the same study, a rats were also exposed to TCE via inhalation
and using this paradigm, cholic acid and taurocholic acid were also significantly elevated but the
large variability in responses between rats and the low number of rats tested in this paradigm
limit its ability to determine quantitative differences between groups. Nevertheless, without the
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complications associated with i.p. exposure, inhalation exposure of TCE at relatively low
exposure levels that were not associated with other measures of toxicity were associated with
increased SB A level.
Hamdan and Stacey (1993) administered TCE in corn oil (1 mmol/kg) in male Sprague-
Dawley rats and followed the time-course of SBA elevation, TCE concentration, and TCOH in
the blood up to 16 hours. Liver and blood concentration of TCE were reported to peak at 4
hours, while those of TCOH peaked at 8 hours after dosing. TCE levels were not detectable by
16 hours in either blood or liver, while those of TCOH were still elevated. Elevations of SBA
were reported to parallel those of TCE with cholic acid, and taurochloate acid was reported to
show the highest levels of bile acids. The authors stated that liver injury parameters were
checked and were found to be unaffected by TCE exposure, but did not provide the data. Thus,
it was TCE concentration and not that of its metabolite that was most closely related to changes
in SBA after a single exposure and the effect appeared to be reversible. In an in vitro study by
Bai and Stacey (1993), TCE was studied in isolated rat hepatocytes with TCE reported to cause a
dose-related suppression of initial rates of cholic acid and taurocholic acid, but with no
significant effects on enzyme leakage and intracellular calcium contents, further supporting a
role for the parent compound in this effect.
4.5.4.7. Summary: TCE-Induced Noncancer Effects in Laboratory Animals
In laboratory animals, TCE leads to a number of structural changes in the liver, including
increased liver weight, small transient increases in DNA synthesis, cytomegaly in the form of
—swoHn" or enlarged hepatocytes, increased nuclear size probably reflecting polyploidization,
and proliferation of peroxisomes. Liver weight increases proportional to TCE dose are
consistently reported across numerous studies, and appear to be accompanied by periportal
hepatocellular hypertrophy. There is also evidence of increased DNA synthesis in a small
portion of hepatocytes at around 10 days in vivo exposure. The lack of correlation of
hepatocellular mitotic figures with whole-liver DNA synthesis or DNA synthesis observed in
individual hepatocytes supports the conclusion that cellular proliferation is not the predominant
cause of increased DNA synthesis. The lack of correlation of whole-liver DNA synthesis and
those reported for individual hepatocytes suggests that nonparenchymal cells also contribute to
such synthesis. Indeed, nonparenchymal cell activation or proliferation has been noted in several
studies. Moreover, the histological descriptions of TCE exposed liver are consistent with, and in
some cases specifically note, increased polyploidy after TCE exposure. Interestingly, changes in
TCE-induced hepatocellular ploidy, as indicated by histological changes in nuclei, have been
noted to remain after the cessation of exposure. In regard to apoptosis, TCE has been reported to
either not change apoptosis or to cause a slight increase at high doses. Some studies have also
noted effects from dosing vehicle alone (such as corn oil in particular) not only on liver
pathology, but also on DNA synthesis.
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Available data also suggest that TCE does not induce substantial cytotoxicity, necrosis, or
regenerative hyperplasia, as only isolated, focal necroses and mild to moderate changes in serum
and liver enzyme toxicity markers having been reported. Data on peroxisome proliferation,
along with increases in a number of associated biochemical markers, show effects in both mice
and rats. These effects are consistently observed across rodent species and strains, although the
degree of response at a given mg/kg-day dose appears to be highly variability across strains, with
mice on average appearing to be more sensitive.
In addition, like humans, laboratory animals exposed to TCE have been observed to have
increased serum bile acids, though the toxicological importance of these effects is unclear.
4.5.5. TCE-Induced Liver Cancer in Laboratory Animals
For 2-year or lifetime studies of TCE exposure, a consistent hepatocarcinogenic response
has been observed using mice of differing strains and genders and from differing routes of
exposure. However, some rat studies have been confounded by mortality from gavage error or
the toxicity of the dose of TCE administered. In some studies, a relative insensitive strain of rat
has been used. However, in general, it appears that the mouse is more sensitive than the rat to
TCE-induced liver cancer. Three studies had results that the authors considered to be negative
for TCE-induced liver cancer in mice, but have either design and/or reporting limitations, or are
in strains and paradigms with apparent low ability for liver cancer induction or detection.
Findings from these studies are shown in Tables 4-60 through 4-65, and discussed below.
4.5.5.1. Negative or Inconclusive Studies of Mice and Rats
Fukuda et al. (1983) reported a 104-week inhalation bioassay in female Crj:CD-l (ICR)
mice and female Crj:CD (Sprague-Dawley) rats exposed to 0-, 50-, 150-, and 450-ppm TCE
(n = 50). There were no reported incidences of mice or rats with liver tumors for controls
indicative of relatively insensitive strains and gender used in the study for liver effects. While
TCE was reported to induce a number of other tumors in mice and rats in this study, the
incidence of liver tumors was <2% after TCE exposure. Of note is the report of cystic
cholangioma reported in one group of rats.
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Table 4-60. Summary of liver tumor findings in gavage studies of TCE by
NTP H990)a
Sex
Dose (mg/kg)b
Adenoma
(overall; terminal0)
Adenocarcinoma (overall;
terminal0)
1/d, 5 d/wk, 103-wk study, F344/N rats
Male
Female
0
500
1,000
0
500
1,000
NAd
NA
NA
NA
NA
NA
0/49
0/49
1/49
0/50
1/48
1/48
1/d, 5 d/wk, 103-wk study, B6C3FJ mice
Male
Female
0
1,000
0
1,000
7/48; 6/33
14/50; 6/16
4/48; 4/32
16/49; ll/23e
8/48; 6/33
31/50; 14/16f
2/48; 2/32
13/49; 8/23g
"Liver tumors not examined in 13-week study, so data shown only for 103-week study.
bCorn oil vehicle.
°Terminal values not available for rats.
dData not available.
>< 0.003.
fp< 0.001.
sp< 0.002.
Table 4-61. Summary of liver tumor findings in gavage studies of TCE by
NCI (1976)
Sex
Dose (mg/kg)a
Hepatocarcinoma
1/d, 5 d/wk, 2-yr study, Osborne-Mendel rats
Males
Females
0
549
1,097
0
549
1,097
0/20
0/50
0/50
0/20
1/48
0/50
1/d, 5 d/wk, 2-yr study, B6C3F! mice
Males
Females
0
1,169
2,339
0
869
1,739
1/20
26/50b
31/48b
0/20
4/50
ll/47b
"Treatment period was 48 week for rats, 66 week for mice. Doses were changed several times during the study
based on monitoring of body weight changes and survival. Dose listed here is the TWA dose over the days on
which animals received a dose.
V<0.01.
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Table 4-62. Summary of liver tumor incidence in gavage studies of TCE by
NTP (1988)
Sex
Dose (mg/kg)a
Adenoma
Adenocarcinoma
1/d, 5 d/wk, 2-yr study, ACI rats
Male
Female
0
500
1,000
0
500
1,000
0/50
0/49
0/49
0/49
0/46
0/39
1/50
1/49
1/49
2/49
0/46
0/39
1/d, 5 d/wk, 2-yr study, August rats
Male
Female
0
500
1,000
0
500
1,000
0/50
0/50
0/48
0/48
0/48
0/50
0/50
1/50
1/48
2/48
0/48
0/50
1/d, 5 d/wk, 2-yr study, Marshall rats
Male
Female
0
500
1,000
0
500
1,000
1/49
0/50
0/47
0/49
0/48
0/46
1/49
0/50
1/47
0/49
0/48
0/46
1/d, 5 d/wk, 2-yr study, Osborne-Mendel rats
Male
Female
0
500
1,000
0
500
1,000
1/50
1/50
1/49
0/50
0/48
0/49
1/50
0/50
2/49
0/50
2/48
2/49
aCorn oil vehicle.
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Table 4-63. Summary of liver tumor findings in inhalation studies of TCE by
Maltoni et al. (1988; 1986V
Sex
Concentration (ppm)
Hepatoma
7 hrs/d, 5 d/wk, 8-wk exposure, observed for lifespan, Swiss mice
Male
Female
0
100
600
0
100
600
1/100
3/60
4/72
1/100
1/60
0/72
7 hrs/d, 5 d/wk, 78-wk exposure, observed for lifespan, Swiss mice
Male
Female
0
100
300
600
0
100
300
600
4/90
2/90
8/90
13/90
0/90
0/90
0/90
1/90
7 hrs/d, 5 d/wk, 78-wk exposure, observed for lifespan, B6C3FJ miceb
Male
Female
0
100
300
600
0
100
300
600
1/90
1/90
3/90
6/90
3/90
4/90
4/90
9/90
aThree inhalation experiments in this study found no hepatomas: BT302 (8-week exposure to 0, 100, or 600 ppm in
Sprague-Dawley rats); BT303 (8-week exposure to 0, 100, or 600 ppm in Swiss mice); and BT304 (78-week
exposure to 0, 100, 300, or 600 ppm in Sprague-Dawley rats).
bFemale incidences are from experiment BT306, while male incidences are from experiment BT306bis, which was
added to the study because of high, early mortality due to aggressiveness and fighting in males in experiment
BT306.
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Table 4-64. Summary of liver tumor findings in inhalation studies of TCE by
Henschler et al. (1980)" and Fukuda et al. (1983)
Sex
Concentration (ppm)
Adenomas
Adenocarcinomas
6 hrs/d, 5 d/wk, 18-mo exposure, 30-mo observation, Han:NMRI mice (Henschler et al., 1980)
Males
Females
0
100
500
0
100
500
l/30b
2/29b
0/29
0/29
0/30
0/28
1/30
0/30
0/30
0/29
0/30
0/28
6 hrs/d, 5 d/wk, 18-mo exposure, 36-mo observation, Han: WIST rats (Henschler et al., 1980)
Males
Females
0
100
500
0
100
500
1/29
1/30
0/30
0/28
1/30
2/30
0/29
0/30
0/30
0/28
1/30
0/30
7 hrs/d, 5 d/wk, 2-yr study, Crj:CD (Sprague-Dawley) rats (Fukuda et al.. 1983)
Females
0
50
150
450
0/50
1/50
0/47
0/51
0/50
0/50
0/47
1/50
7 hrs/d, 5 d/wk, 2-vr study, Crj:CD (ICR) mice (Fukuda et al., 1983)
Females
0
50
150
450
0/49
0/50
0/50
1/46
0/49
0/50
0/50
0/46
aHenschler et al. (1980) observed no liver tumors in control or exposed Syrian hamsters.
bOne additional hepatic tumor of undetermined class not included.
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Table 4-65. Summary of liver tumor findings in gavage studies of TCE by
Henschler et al. H984)a
Sex
(TCE
concentration)
TCE (Stabilizers if present)
Benignb
Malignant0
5 d/wk, 18-mo exposure, 24-mo observation, Swiss mice (Henschler et al., 1984)
Males
(2.4g/kgbody
weight)
Females
(1.8 g/kg body
weight)
Control (none)
TCE (triethanolamine)
TCE (industrial)
TCE (epichlorohydrin (0.8%))
TCE (1,2-epoxybutane [0.8%])
TCE (both epichlorohydrin [0.25%] and 1,2-
epoxybutane [0.25%])
Control (none)
TCE (triethanolamine)
TCE (industrial)
TCE (epichlorohydrin [0.8%])
TCE (1,2-epoxybutane [0.8%])
TCE (both epichlorohydrin [0.25%] and 1,2-
epoxybutane [0.25%])
5/50
7/50
9/50
3/50
4/50
5/50
1/50
7/50
9/50
3/50
2/50
4/50
0/50
0/50
0/50
1/50
0/50
0/50
0/50
0/50
0/50
0/50
0/50
1/50
aHenschler et al. (1984) due to poor condition of the animals resulting from the nonspecific toxicity of high doses of
TCE and/or the additives, gavage was stopped for all groups during week 35-40, 65 and 69-78, and all doses were
reduced by a factor of 2 from the 40th week on.
blncludes hepatocellular adenomas, hemangioendothelioma, cholangiocellular adenoma.
Includes HCC, malignant hemangiosarcoma, cholangiocellular carcinoma.
Henschler et al. (1980) exposed NMRI mice and WIST random bred rats to 0-, 100-, and
500-ppm TCE for 18 months (n = 30). Control male mice were reported to have one HCC and
one hepatocellular adenoma with the incidence rate unknown. In the 100 ppm group, two
hepatocellular adenomas and one mesenchymal liver tumor were reported. No liver tumors were
reported at any dose of TCE in female mice or controls. For male rats, only one hepatocellular
adenomas at 100 ppm was reported. For female rats, no liver tumors were reported in controls,
but one adenoma and one cholangiocarcinoma was reported at 100 ppm, and at 500 ppm, two
cholangioadenomas, a relatively rare biliary tumor, were reported. The difference in survival in
mice, did not affect the power to detect a response, as was the case for rats. However, the low
number of animals studied, abbreviated exposure duration, low survival in rats, and absent
background response (suggesting low intrinsic sensitivity to this endpoint) suggest a study of
limited ability to detect a TCE carcinogenic liver response. Of note is that despite their
limitations, both Fukuda et al. (1983) and Henschler et al. (1980) report rare biliary cell derived
tumors in TCE-exposed rats.
Van Duuren et al. (1979), exposed mice to 0.5 mg/mouse to TCE via gavage once a week
in 0.1 mL trioctanion (n = 30). Inadequate design and reporting of this study limit that ability to
use the results as an indicator of TCE carcinogenicity.
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The NCI (1976) study of TCE was initiated in 1972 and involved the exposure of
Osborne-Mendel rats to varying concentrations of TCE. A low incidence of liver tumors was
reported for controls and carbon tetrachloride positive controls in rats from this study. The
authors concluded that due to mortality, —thdiest is inconclusive in rats." They note the
insensitivity of the rat strain used to the positive control of carbon tetrachloride exposure.
The NTP (1990) study of TCE exposure in male and female F344/N rats, and B6C3Fi
mice (500 and 1,000 mg/kg for rats) is limited in the ability to demonstrate a dose-response for
hepatocarcinogenicity. For rats, the NTP (1990) study reported no treatment-related, non-
neoplastic liver lesions in males and a decrease in basophilic cytological change in female rats.
The results for detecting a carcinogenic response in rats were considered to be equivocal because
both groups receiving TCE showed significantly reduced survival compared to vehicle controls
and because of a high rate (e.g., 20% of the animals in the high-dose group) of death by gavage
error.
The NTP (1988) study of TCE exposure in four strains of rats to —diisoporpylamine-
stabilized TCE" was also considered inadequate for either comparing or assessing TCE-induced
liver carcinogenesis in these strains of rats because of chemically induced toxicity, reduced
survival, and incomplete documentation of experimental data. TCE gavage exposures of 0, 500,
or 1,000 mg/kg-day (5 days/week, for 103 weeks) male and female rats were also marked by a
large number of accidental deaths (e.g., for high-dose male Marshal rats, 25 animals were
accidentally killed).
Maltoni et al. (1988; 1986) reported the results of several studies of TCE via inhalation
and gavage in mice and rats. A large number of animals were used in the treatment groups but
the focus of the study was detection of a neoplastic response with only a generalized description
of tumor pathology phenotype and limited reporting of non-neoplastic changes in the liver.
Accidental death by gavage error was reported not to occur in this study. With regard to effects
of TCE exposure on rat survival, —aaonsignificant excess in mortality correlated to TCE
treatment was observed only in female rats (treated by ingestion with the compound)."
For rats, Maltoni et al. (1986) reported four liver angiosarcomas (one in a control male
rat, one both in a TCE-exposed male and female at 600 ppm TCE for 8 weeks, and one in a
female rat exposed to 600-ppm TCE for 104 weeks), but the specific results for incidences of
hepatocellular "hepatomas" in treated and control rats were not given. Although the Maltoni
et al. (1986) concluded that the small number was not treatment related, the findings were
brought forward because of the extreme rarity of this tumor in control Sprague-Dawley rats,
untreated or treated with vehicle materials. In rats treated for 104 weeks, there was no report of a
TCE treatment-related increase in liver cancer in rats. This study only presented data for positive
findings so it did not give the background or treatment-related findings in rats for liver tumors in
this study. Thus, the extent of background tumors and sensitivity for this endpoint cannot be
determined. Of note is that the Sprague-Dawley strain used in this study was also noted in the
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Fukuda et al. (1983) study to be relatively insensitive for spontaneous liver cancer and to also be
negative for TCE-induced hepatocellular liver cancer induction in rats. However, like Fukuda
et al. (1983) and Henschler et al. (1980), which reported rare biliary tumors in insensitive strains
of rat for hepatocellular tumors, Maltoni et al. (1986) reported a relatively rare tumor type,
angiosarcoma, after TCE exposure in a relatively insensitive strain for —hepatoias." As noted
above, many of the rat studies were limited by premature mortality due to gavage error or
premature mortality (NTP. 1990. 1988: Henschler et al.. 1980: NCL 1976). which was reported
not occur in Maltoni et al. (1986).
4.5.5.2. Positive TCE Studies of Mice
In the NCI (1976) study of TCE exposure in B6C3Fi mice, TCE was reported to increase
the incidence of HCCs in both doses and both genders of mice (-1,170 and 2,340 mg/kg for
males and 870 and 1,740 mg/kg for female mice). HCC diagnosis was based on histologic
appearance and metastasis to the lung. The tumors were described in detail and to be
heterogeneous —as described in thditerature" and similar in appearance to tumors generated by
carbon tetrachloride. The description of liver tumors in this study and tendency to metastasize to
the lung are similar to descriptions provided by Maltoni et al. (1986) for TCE-induced liver
tumors in mice via inhalation exposure.
The NTP (1990) study of TCE exposure in male and female B6C3Fi mice (1,000 mg/kg
for mice) reported decreased latency of liver tumors, with animals first showing carcinomas at
57 weeks for TCE-exposed animals and 75 weeks for control male mice. The administration of
TCE was also associated with increased incidence of HCC (tumors with markedly abnormal
cytology and architecture) in male and female mice. Hepatocellular adenomas were described as
circumscribed areas of distinctive hepatic parenchymal cells with a perimeter of normal
appearing parenchyma in which there were areas that appeared to be undergoing compression
from expansion of the tumor. Mitotic figures were sparse or absent but the tumors lacked typical
lobular organization. HCCs had markedly abnormal cytology and architecture with
abnormalities in cytology cited as including increased cell size, decreased cell size, cytoplasmic
eosinophilia, cytoplasmic basophilia, cytoplasmic vacuolization, cytoplasmic hyaline bodies, and
variations in nuclear appearance. Furthermore, in many instances several or all of the
abnormalities were present in different areas of the tumor and variations in architecture with
some of the HCCs having areas of trabecular organization. Mitosis was variable in amount and
location. Therefore, the phenotype of tumors reported from TCE exposure was heterogeneous in
appearance between and within tumors. However, because it consisted of a single-dose group in
addition to controls, this study is of limited utility for analyzing the dose-response for
hepatocarcinogenicity. There was also little reporting of non-neoplastic pathology or toxicity
and no report of liver weight at termination of the study.
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Maltoni et al. (1986) reported the results of several studies of TCE in mice. A large
number of animals were used in the treatment groups but the focus of the study was detection of
a neoplastic response with only a generalized description of tumor pathology phenotype given
and limited reporting of non-neoplastic changes in the liver. There was no accidental death by
gavage error reported to occur in mice, but a —nosignificant" excess in mortality correlated to
TCE treatment was observed in male B6C3Fi mice. TCE-induced effects on body weight were
reported to be absent in mice except for one experiment (BT 306 bis) in which a slight nondose-
correlated decrease was found in exposed animals. —Hepatma" was the term used to describe
all malignant tumors of hepatic cells, of different subhistotypes, and of various degrees of
malignancy and were reported to be unique or multiple, and have different sizes (usually
detected grossly at necropsy) from TCE exposure. In regard to phenotype, tumors were
described as usual type observed in Swiss and B6C3Fi mice, as well as in other mouse strains,
either untreated or treated with hepatocarcinogens and to frequently have medullary (solid),
trabecular, and pleomorphic (usually anaplastic) patterns. Swiss mice from this laboratory were
reported to have a low incidence of hepatomas without treatment (1%). The relatively larger
number of animals used in this bioassay (n = 90-100), in comparison to NTP standard assays,
allows for a greater power to detect a response.
TCE exposure for 8 weeks via inhalation at 100 or 600 ppm may have been associated
with a small increase in liver tumors in male mice in comparison to concurrent controls during
the life span of the animals. In Swiss mice exposed to TCE via inhalation for 78 weeks, there a
reported increase in hepatomas associated with TCE treatment that was dose-related in male but
not female Swiss mice. In B6C3Fi mice exposed via inhalation to TCE for 78 weeks, increases
in hepatomas were reported in both males and females. However, the experiment in males was
repeated with B6C3Fi mice from a different source, since in the first experiment, more than half
of the mice died prematurely due to excessive fighting. Although the mice in the two
experiments in males were of the same strain, the background level of liver cancer was
significantly different between mice from the different sources (1/90 vs. 19/90), though the early
mortality may have led to some censoring. The finding of differences in response in animals of
the same strain but from differing sources has also been reported in other studies for other
endpoints. However, for both groups of male B6C3Fi mice, the background rate of liver tumors
over the lifetime of the mice was no greater than about 20%.
There were other reports of TCE carcinogen!city in mice from chronic exposures that
were focused primarily on the detection of liver tumors, with limited reporting of tumor
phenotype or non-neoplastic pathology. Herren-Freund et al. (1987) reported that male B6C3Fi
mice given 40 mg/L TCE in drinking water had increased tumor response after 61 weeks of
exposure. However, concentrations of TCE fell by about half at this dose of TCE during the
twice a week change in drinking water solution so the actual dose of TCE the animals received
was <40 mg/L. The percentage liver/body weight was reported to be similar for control and
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TCE-exposed mice at the end of treatment. However, despite difficulties in establishing
accurately the dose received, an increase in adenomas per animal and an increase in the number
of animals with HCCs were reported to be associated with TCE exposure after 61 weeks of
exposure and without apparent hepatomegaly. Anna et al. (1994) reported tumor incidences for
male B6C3Fi mice receiving 800 mg/kg-day TCE via gavage (5 days/week for 76 weeks). All
TCE-treated mice were reported to be alive after 76 weeks of treatment. Although the control
group contained a mixture of exposure durations (76-134 weeks) and concurrent controls had a
very small number of animals, TCE treatment appeared to increase the number of animals with
adenomas and the mean number of adenomas and carcinomas, but with no concurrent
TCE-induced cytotoxicity.
4.5.5.3. Summary: TCE-induced Cancer in Laboratory Animals
Chronic TCE bioassays have consistently reported increased liver tumor incidences in
both sexes of B6C3Fi mice treated by inhalation and gavage exposure in a number of bioassays.
The only inhalation study of TCE in Swiss mice also showed an effect in males. Data in the rat,
while not reporting statistically significantly increased risks, are not entirely adequate due to low
numbers of animals, inadequate reporting, use of insensitive bioassays, increased systemic
toxicity, and/or increased mortality. Notably, several studies in rats noted a few very rare types
of liver or biliary tumors (cystic cholangioma, cholangiocarcinoma, or angiosarcomas) in treated
animals.
4.5.6. Role of Metabolism in Liver Toxicity and Cancer
It is generally thought that TCE oxidation by CYPs is necessary for induction of
hepatotoxicity and hepatocarcinogenicity (Bull, 2000). Direct evidence for this hypothesis is
limited, e.g., the potentiation of hepatotoxicity by pretreatment with CYP inducers such as
ethanol and phenobarbital (Okino et al., 1991; Nakajima et al., 1988). Rather, the presumption
that CYP-mediated oxidation is necessary for TCE hepatotoxicity and hepatocarcinogenicity is
largely based on similar effects (e.g., increases in liver weight, peroxisome proliferation, and
hepatocarcinogenicity) having been observed with TCE's oxidative metabolites. The discussion
below focuses the similarities and differences between the major effects in the liver of TCE and
of the oxidative metabolites CH, TCA, and DC A. In addition, CH is largely converted to TCOH,
TCA, and possibly DC A. DCA has been used in human clinical practice for a variety of severe
illnesses and no data on liver effects in humans have been reported (U.S. EPA, 2003b).
However, as noted in EPA (2003_b), data on DCA in humans are scarce and complicated by the
fact that available studies have predominantly focused on individuals who have a pre-existing
(usually severe) disease.
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4.5.6.1. Pharmacokinetics of CH, TCA, and DCA from TCE Exposure
As discussed in Chapter 3, in vivo data confirm that CH and TCA, are oxidative
metabolites of TCE, with available data on TCA incorporated into the PBPK modeling. In
addition, there are indirect data suggesting the formation of DCA. However, direct in vivo
evidence of the formation of DCA is confounded by its rapid clearance at low concentrations,
and analytical artifacts in its detection in vivo that have yet to be entirely resolved. PBPK
modeling (see Section 3.5) predicts that the proportions of TCE metabolized to CH and TCA
varies considerably in mice (ranging from 15 to 97 and 4 to 38%, respectively) and rats (ranging
7-75 and 0.5-22%, respectively). Therefore, a range of smaller concentrations of TCA or CH
may be relevant for comparisons with TCE-induced liver effects. For example, for 1,000 mg/kg-
day oral doses of TCE, the relevant comparisons would be approximately 0.25-1.5 g/L in
drinking water for TCA and CH. For DCA, a corresponding range is harder to determine and
has been suggested to be an upper limit of about 12% following oral exposures (Barton et al.,
1999). This is consistent with the range estimated from PBPK modeling attributing all of the
—unticked" oxidation (i.e., not producing TCOH or TCA) to DCA (95% CI: 0.2-16%, see
Figure 3-22).
Two studies have used analytic methods for DCA that are considered more reliable and
less confounded by artifactual formation. Kim et al. (2009), which was published too late to be
incorporated into the PBPK model, used an empirical pharmacokinetic model to analyze data on
male B6C3Fi mice exposed to a single dose of 2,100 mg/kg TCE by gavage. Peak levels of
TCA and DCA were found to be 64 and 18 ng/mL, respectively, a difference of more than
threefold. The kinetic rate constant they estimated for TCE —>• DCA were more than five orders
of magnitude smaller than the kinetic rate constant estimated for TCE —>• TCA. These data all
suggest that DCA is a minor metabolite of TCE as compared to TCA at high doses of around
2,000 mg/kg. Delinsky et al. (2005) reported that in male Sprague-Dawley rats, after a single
2,000 mg/kg dose by gavage, peak levels of DCA were 39.5 ng/mL. Delinsky et al. (2005) did
not report TCA levels for comparison. The only data available in rats in this range of gavage
doses (coincidentally also in male Sprague-Dawley rats) reported peak levels of TCA of 24 and
60 mg/mL at gavage doses of 600 and 3,000 mg/kg, respectively (Larson and Bull, 1992b). This
suggests a difference between DCA and TCA levels in rats exposed to TCE of about 1,000-fold,
albeit with more uncertainty as compared to Kim et al. (2009), in which both were measured
simultaneously in the same animals. However, liver toxicity in both rats and mice is evident at
much lower doses, so additional data are needed to inform whether the relative amount of TCA
and DCA changes at lower exposures.
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4.5.6.2. Comparisons Between TCE and TCA, DCA, and CH Noncancer Effects
4.5.6.2.1. Hepatomegaly—qualitative and quantitative comparisons
As discussed above, TCE causes hepatomegaly in rats, mice, and gerbils under both acute
and chronic dosing. Data from a few available studies suggest that oxidative metabolism is
important for mediating these effects. Buben and O'Flaherty (1985) collected limited
pharmacokinetic data in a sample of the same animals for which liver weight changes were being
assessed. While liver weight increases had similarly strong correlations with applied dose and
urinary metabolites for doses up to 1,600 mg/kg-day (R2 of 0.97 for both), above that dose, the
linear relationship was maintained with urinary metabolites but not with applied dose. Ramdhan
et al. (2008) conducted parallel experiments at TCE 1,000 and 2,000 ppm (8 hours/day, 7 days)
in wild-type and CYP2El-null mice, which did not exhibit increased liver/body weight ratios
with TCE treatment and excreted twofold lower amounts of oxidative metabolites TCA and
TCOH in urine as compared to wild-type mice. However, among control mice, those with the
null genotype had 1.32-fold higher absolute liver weights and 1.18-fold higher liver/body weight
ratios than wild-type mice, reducing the sensitivity of the experiment, particularly with only
six mice per dose group.
Ramdhan et al. (2010) reported that stated that urinary TCA levels in wild type mice were
incorrectly reported by Ramdhan et al. (2008) but were corrected in this study. The authors
reported no differences in urinary volume by genotype or exposure but did not show the data.
TCA and TCOH were detected in all exposed mice with no significant differences between the
1,000 and 2,000 ppm TCE levels. TCA concentrations were reported to be significantly lower
and TCOH levels significantly higher in PPARa-null mice relative to wild type mice, with no
differences in genotype between the sum of total TCA and TCOH concentrations between
genotypes. The authors reported that they measured hepatic protein expression of CYP2E1 and
ALDH2 enzymes and did not observe a significant difference among controls (data not shown)
and that TCE exposure did not alter hepatic CYP2E1 expression but did decrease ALDH2
expression to a comparable extent in all mouse lines (data not shown). Thus, changes in urinary
TCA levels in the differing strains were not related to changes in expression of these metabolic
enzymes.
As stated above, hepatomegally was increased by TCE exposure in all three strains. TCE
at both 1,000 and 2,000 ppm significantly increased liver weight in the three mouse lines to a
similar extent (i.e., 38 and 49% in wild type mice, 20 and 37% in PPAR-null mice, and 28 and
32% in hPPARa mice). The increases were not statistically significant between doses within
each strain. Liver/body weight ratios were also significantly increased with TCE exposure at
1,000 and 2,000 ppm relative to controls (i.e., 38 and 43% in wild type mice, 24 and 36% in
PPARa-null mice, and 27 and 39% in hPPARa mice, respectively). The difference between
2,000 and 1,000 ppm TCE exposure was statistically significant in PPARa-null mice. As to the
nature of the hepatomegally induced under these conditions, hepatic triglyceride levels were
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reported to be significantly correlated with liver/body weight ratios of all mice used in the study
(r = 0.54).
With respect to oxidative metabolites themselves, data from CH studies are not
informative—either because data were not shown (Sanders et al., 1982a) or, because at the time
points measured, liver weight increases were substantially confounded by foci and carcinogenic
lesions (Leakey et al., 2003b). TCA and DCA have both been found to cause hepatomegaly in
mice and rats, with mice being more sensitive to this effect. DCA also increases liver/body
weight ratios in dogs, but TCE and TCA have not been tested in this species (Cicmanec et al.,
1991).
As noted above, TCE-induced changes in liver weight appear to be proportional to the
exposure concentration across route of administration, gender and rodent species. As an
indication of the potential contribution of TCE metabolites to this effect, a quantitative
comparison of the shape of the dose-response curves for liver weight induction for TCE and its
metabolites is informative. The analysis below was reported in Evans et al. (2009).
A number of short-term (<4 weeks) studies of TCA and DCA in drinking water have
attempted to measure changes in liver weight induction, with the majority of these studies being
performed in male B6C3Fi mice. Studies conducted from 14 to 30 days show a consistent
increase in percentage liver/body weight induction by TCA or DCA. However, as stated in
many of the discussions of individual studies (see Appendix E), there is a limited ability to detect
a statistically significant change in liver weight change in experiments that use a relatively small
number of animals or do not match control and treatment groups for age and weight. The
experiments of Buben and O'Flaherty (1985) used 12-14 mice per group, giving them a greater
ability to detect a TCE-induced dose-response. However, many experiments have been
conducted with 4-6 mice per dose group. For example, the data from DeAngelo et al. (2008) for
TCA-induced percentage liver/body weight ratio increases in male B6C3Fi mice were only
derived from five animals per treatment group after 4 weeks of exposure. The 0.05 and 0.5 g/L
exposure concentrations were reported to give a 1.09- and 1.16-fold of control percentage
liver/body weight ratios, which were consistent with the increases noted in the cross-study
database above. However, a power calculation shows that the Type II error (which should be
>50% and thus, greater than the chances of—fliping a coin") was only a 6 and 7% and therefore,
the designed experiment could accept a false null hypothesis. In addition, some experiments
took greater care to age and weight match the control and treatment groups before the start of
treatment.
Therefore, given these limitations and the fact that many studies used a limited range of
doses, an examination of the combined data from multiple studies (Kato-Weinstein et al., 2001;
Parrish et al.. 1996: Carter etal.. 1995: Sanchez and Bull 1990: DeAngelo et al.. 1989. 2008)
can best inform/discern differences in DCA and TCA dose-response relationships for liver
weight induction (described in more detail in Section E.2.4.2). The dose-response curves for
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similar concentrations of DC A and TCA are presented in Figure 4-5 for durations of exposure
from 14 to 28 days in the male B6C3Fi mouse, which was the most common sex and strain used.
As noted in Appendix E, there appears to be a linear correlation between dose in drinking water
and liver weight induction up to 2 g/L of DC A. However, the shape of the dose-response curve
for TCA appears to be quite different. Lower concentrations of TCA induce larger increase that
does DC A, but the TCE response reaches an apparent plateau while that of DC A continues to
increase the response. TCA studies did not show significant duration-dependent difference in
liver weight induction in this duration range. Short-duration studies (10-42 days) were selected
because: (1) in chronic studies, liver weight increases are confounded by tumor burden;
(2) multiple studies are available; and (3) TCA studies do not show significant duration-
dependent differences in this duration range.
•TO 1.6 H
1.0 C
0.5 1.0 1.5 2.0
Concentration of DCA or TCA (g/l)
2.5
Sources: (Kato-Weinstein et al.. 2001: Parrish et al.. 1996: Carter et al.. 1995:
Sanchez and Bull 1990: DeAngelo et al.. 1989, 2008)).
Figure 4-5. Comparison of average fold-changes in relative liver weight to
control and exposure concentrations of 2 g/L or less in drinking water for
TCA and DCA in male B6C3Fi mice for 14-30 days.
Of interest is the issue of how the dose-response curves for TCA and DCA compare to
that of TCE in a similar model and dose range. Since TCA and DCA have strikingly different
dose-response curves, which one, if either, best fits that of TCE and thus, can give insight as to
which is causative agent for TCE's effects in the liver? The carcinogenicity of chronic TCE
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exposure has been predominantly studies in two mouse strains, Swiss and B6C3Fi, both of which
reportedly developed liver tumors. Rather than administered in drinking water, oral TCE studies
have been conducted via gavage and generally in corn oil for 5 days of exposure per week.
Factors adding to the increased difficulty in establishing the dose-response relationship for TCE
across studies and for comparisons to the DCA and TCA database include vehicle effects, the
difference between daily and weekly exposures, the dependence of TCE effects in the liver on its
metabolism to a variety of agents capable inducing effects in the liver, differences in response
between strains, and the inherent increased variability in use of the male mouse model. In
particular, these factors would add variability to any effort at a combined analysis, and make a
consistent dose-response pattern more difficult to discern. Nonetheless, despite such differences
in exposure route, vehicle, etc., a consistent pattern of dose-response emerges from combining
the available TCE data. The effects of oral exposure to TCE from 10 to 42 days on liver weight
induction is shown below in Figure 4-6 using the data of Elcombe et al. (1985), Dees and Travis
(1993). Goel et al. (1992). Merrick et al. (1989), Goldsworthy and Popp (1987). and Buben and
O'Flaherty (1985). Oral TCE administration in male B6C3Fi and Swiss mice appeared to induce
a dose-related increase in percentage liver/body weight that was generally proportional to the
increase in magnitude of dose, though as expected, with more variability than observed for a
similar exercise for DCA or TCA in drinking water. Some of the variability is due to the
inclusion of the 10-day studies, since as discussed in Section E.2.4.2, there was a greater increase
in TCE-induced liver weight at 28-42 days of exposure Swiss mice than the 10-day data in
B6C3Fi mice, and Kjellstrand et al. (1981b) noted that TCE-induced liver weight increases are
still increasing at 10 days inhalation exposure. A strain difference is not evident between the
Swiss and B6C3Fi males, as both the combined TCE data and that for only B6C3Fi mice show
similar correlation with the magnitude of dose and magnitude of percentage liver/body weight
increase. The correlation coefficients for the linear regressions presented for the B6C3Fi data
are R2 = 0.861 and for the combined data sets is R2 = 0.712. Comparisons of the slopes of the
dose-response curves suggest a greater consistency between TCE and DCA than between TCE
and TCA. There did not appear to be evidence of a plateau with higher TCE doses, and the
degree of fold-increase rises to higher levels with TCE than with TCA in the same strain of
mouse.
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2.0
1.8 -
.2> 1.6H
CD
1.4 -
1.2 -
1.0 -!-•—•-
0 500 1000 1500 2000 2500 3000
Male mice liver weight forTCE oral gavage - days 10-42
2.0
1.8 -
.°> 1.6-
CU
1.4 -
1.2 -
1.0
• B6C3F1 and Swiss
Plot 2 Regr
0 500 1000 1500 2000 2500 3000
Concentration of TCE (mg/kg/day)
Sources: Dees and Travis (1993): Merrick et al. (1989): Goldsworthy and Popp
(1987): Elcombe et al. (1985)
Figure 4-6. Comparisons of fold-changes in average relative liver weight and
gavage dose of (top panel) male B6C3Fi mice for 10-28 days of exposure and
(bottom panel) in male B6C3Fi and Swiss mice.
A more direct comparison would be on the basis of dose rather than drinking water
concentration. The estimations of internal dose of DC A or TCA from drinking water studies,
while varying considerably (DeAngelo et al., 2008: DeAngelo et al., 1989), nonetheless suggest
that the doses of TCE used in the gavage experiments were much higher than those of DC A or
TCA. However, only a fraction of ingested TCE is metabolized to DCA or TCA, as, in addition
4-281
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to oxidative metabolism, TCE is also cleared by GSH conjugation and by exhalation. While
DCA dosimetry is highly uncertain (see Sections 3.3 and 3.5), the mouse PBPK model, described
in Section 3.5 was calibrated using extensive in vivo data on TCA blood, plasma, liver, and
urinary excretion data from inhalation and gavage TCE exposures, and makes robust predictions
of the rate of TCA production. If TCA were predominantly responsible for TCE-induced liver
weight increases, then replacing administered TCE dose (e.g., mg TCE/kg/day) by the rate of
TCA produced from TCE (mg TCA/kg/day) should lead to dose-response curves for increased
liver weight consistent with those from directly administered TCA. Figure 4-7 shows this
comparison using the PBPK model-based estimates of TCA production for four TCE studies
from 28 to 42 days in the male NMRI, Swiss, and B6C3Fi mice (Goel etal., 1992; Merrick et al.,
1989: Buben and O'Flahertv, 1985: Kjellstrand et al., 1983a) and four oral TCA studies in
B6C3Fi male mice at <2 g/L drinking water exposure (2008: Kato-Weinstein et al., 2001: Parrish
etal., 1996: DeAngelo et al., 1989) from 14 to 28 days of exposure. The selection of the 28-42
day data for TCE was intended to address the decreased opportunity for full expression of
response at 10 days. PBPK modeling predictions of daily internal doses of TCA in terms of
mg/kg-day produced via TCE metabolism would indeed be lower than the TCE concentrations in
terms of mg/kg-day given orally by gavage. The predicted internal dose of TCA from TCE
exposure studies are of a comparable range to those predicted from TCA drinking water studies
at exposure concentrations in which palpability has not been an issue for estimation of internal
dose. Thus, although the TCE data are for higher exposure concentrations, they are predicted to
produce comparable levels of TCA internal dose estimated from direct TCA administration in
drinking water.
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o TCA Studies [14-28 d]
Linear (TCA Studies [14-28 d])
Linear (TCE Studies [28-42 d])
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.Q- "*" ""
8
100
400
200 300
mg TCA/kg-d
(produced [TCE studies] or administered [TCA studies])
500
Abscissa for TCE studies consists of the median estimates of the internal dose of
TCA predicted from metabolism of TCE using the PBPK model described in
Section 3.5 of the TCE risk assessment. Lines show linear regression with
intercept fixed at unity. All data were reported fold-change in mean liver
weight/body weight ratios, except for Kjellstrand et al. (1983a), which were the
fold-change in the ratio of mean liver weight to mean body weight. In addition, in
Kjellstrand et al. (1983a), some systemic toxicity as evidence by decreased total
body weight was reported in the highest-dose group.
Sources: Kjellstrand et al. (1983a): Goel et al. (1992): Merrick et al. (1989:
Maltoni et al.. 1988): Buben and O'Flaherty (1985): DeAngelo et al. (1999):
DeAngeleo et al. (2008): Kato-Weinstein et al. (2001): Parrish et al. (1996)
Figure 4-7. Comparison of fold-changes in relative liver weight for data sets
in male B6C3Fi, Swiss, and NRMI mice between TCE studies [duration 28-
42 days]) and studies of direct oral TCA administration to B6C3Fi mice
[duration 14-28 days]).
Figure 4-7 clearly shows that for a given amount of TCA produced from TCE, but going
through intermediate metabolic pathways, the liver weight increases are substantially greater
than, and highly inconsistent with, that expected based on direct TCA administration. In
particular, the response from direct TCA administration appears to "saturate" with increasing
TCA dose at a level of about 1.4-fold, while the response from TCE administration continues to
increase with dose to 1.75-fold at the highest dose administered orally in Buben and O'Flaherty
(1985) and over twofold in the inhalation study of Kjellstrand et al. (1983a). Because TCA liver
4-283
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concentrations are proportional to the dose TCA, and do not depend on whether it is
administered in drinking water or internally produced in the liver, the results of the comparison
using the TCA liver dose-metric are identical.
Furthermore, while as noted previously, oral studies appear to report a linear relationship
between TCE exposure concentration and liver weight induction, the inclusion of inhalation
studies on the basis of internal dose led to a highly consistent dose-response curve for among
TCE study. Therefore, it is unlikely that differing routes of exposure can explain the
inconsistencies in dose-response.
The bioavailability of TCA, which in the above analysis is assumed to be 100%, is
another factor that may impact the dose-response. Sweeney et al. (2009), in an analysis of the
potential role of TCA in the liver carcinogenesis of tetrachloroethylene, identified a number of
previously unpublished TCA kinetic data in mice exposed to TCA via drinking water for 3-
14 days. They concluded that fractional absorption of TCA via drinking water exposures is
much less than 100%—about 29% at low exposures and decreasing with increasing dose.
However, the conclusions of the Sweeney et al. (2009) were based on the Hack et al. (2006) TCE
PBPK model, which had a number of deficiencies, as noted in Section 3.5 and Appendix A.
Therefore, as discussed in Appendix A, Chiu (2011) reanalyzed those data using the updated
TCE PBPK model of Evans et al. (2009) and Chiu et al. (2009) and concluded that while there
was evidence of reduced absorption (80-90% at low exposures, and decreasing with increasing
dose), it was not as low as that estimated by Sweeney et al. (2009). As discussed in Appendix A,
it may be more accurate to characterize the fractional absorption as an empirical parameter
reflecting unaccounted-for biological processes as well as experimental variation.
Chiu (2011) also reanalyzed the data on TCE- and TCA-induced hepatomegaly using the central
estimates of the fractional absorption of TCA inferred from the analysis described above.
Figure 4-8 shows the results, comparing a fixed fractional absorption of 95% with the fitted
fractional absorption from Chiu (2011), here plotted using AUC of TCA in the liver as the dose-
metric. For reference, the dose-response for administered TCA with an assumption of fixed,
nearly complete absorption [analogous to the results from Evans et al. (2009), Figure 4-7] is also
included. While the reduced fractional absorption inferred from drinking water data reported by
Sweeney et al. (2009) accounts for part of the difference in dose-responses between TCE- and
TCA-induced hepatomegaly reported by Evans et al. (2009), it does not appear to be able to
account for the entire difference. In particular, the fraction of hepatomegaly contributed by TCA
is about 0.20 assuming nearly complete absorption, as compared to about 0.33 assuming the
best-fitting fractional absorption inferred from the PBPK model-based analysis. The inability of
TCA to account for TCE-induced hepatomegaly is confirmed statistically by analysis of variance
(ANOVA), with/?-values of <10"4. Therefore, assuming a reduced TCA bioavailability does not
change the conclusion that the available data are inconsistent with the toxicological hypothesis
that TCA can fully account for TCE-induced hepatomegaly.
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2.5
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n TCA Studies,
Fabs=0.95
• TCAStudies, Fabs
fit
0 500 1000 1500 2000 2500 3000 3500
Daily AUC of TCA in liver (mg-hr/L-d)
Fold-changes in relative liver weight for data sets in male B6C3Fi, Swiss, and
NRMI mice between TCE studies (duration 28-42 days) and studies of direct oral
TCA administration to B6C3Fi mice (duration 14-28 days). Linear regressions
were compared using ANOVA to assess whether the TCE studies were consistent
with the TCA studies, using TCA as the dose-metric. For each analysis of
drinking water fraction absorption, ANOVAp-values were <10"4 when comparing
the assumption that all of the data had a common slope with the assumption that
TCE and TCA data had different slopes.
Sources: Kjellstrand et al. (1983a): Goel et al. (1992): Merrick et al. (1989):
Buben and O'Flaherty (1985): DeAngelo et al. (2008: 1989): Kato-Weinstein et
al. (2001): Parrish et al. (1996): Green (2003).
Figure 4-8. Comparison of hepatomegaly as a function of AUC of TCA in
liver, using values for the TCA drinking water fractional absorption (Fabs).
Additional analyses do, however, support a role for oxidative metabolism in TCE-
induced liver weight increases, and that the parent compound TCE is not the likely active moiety
[suggested previously by Buben and O'Flaherty (1985)]. In particular, the same studies are
shown in Figure 4-9 using PBPK-model based predictions of the AUC of TCE in blood and total
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oxidative metabolism, which produces chloral, TCOH, DCA, and other metabolites in addition to
TCA. The dose-response relationship between TCE blood levels and liver weight increase,
while still having a significant trend, shows substantial scatter and a low R2 of 0.43. On the
other hand, using total oxidative metabolism as the dose-metric leads to substantially more
consistency dose-response across studies, and a much tighter linear trend with an R2 of 0.90 (see
Figure 4-9). A similar consistency is observed using liver-only oxidative metabolism as the
dose-metric, with R2 of 0.86 (not shown). Thus, while the slope is similar between liver weight
increase and TCE concentration in the blood and liver weight increase and rate of total oxidative
metabolism, the data are a much better fit for total oxidative metabolism.
IT)
IT)
R2=0.426£
IT)
-------
Although the qualitative similarity to the linear dose-response relationship between DCA
and liver weight increases is suggestive of DCA being the predominant metabolite responsible
for TCE liver weight increases, due to the highly uncertain dosimetry of DCA derived from
TCE, this hypothesis cannot be tested on the basis of internal dose. Similarly, another TCE
metabolite, CH, has also been reported to induce liver tumors in mice; however, there are no
adequate comparative data to assess the nature of liver weight increases induced by this TCE
metabolite (see Sections E.2.5 and 4.5.6.3.2). Whether its formation in the liver after TCE
exposure correlates with TCE-induced liver weight changes cannot be determined.
4.5.6.2.2. Cytotoxicity
As discussed above, TCE has sometimes been reported to cause minimal/mild focal
hepatocellular necrosis or other signs of hepatic injury, albeit of low frequency and mostly at
doses >1,000 mg/kg-day (Dees and Travis, 1993; Elcombe et al., 1985) or at exposures
>1,000 ppm in air (Ramdhan et al., 2010; Ramdhan et al., 2008) from 7 to 10 days of exposure.
Data from available studies are supportive of a role for oxidative metabolism in TCE-induced
cytotoxicity in the liver, though they are not informative as to the actual active moiety(ies).
Buben and O'Flaherty (1985) noted a strong correlation (R-squared between glucose-
6-phosphatase inhibition and total urinary oxidative metabolites). Ramdhan et al. (2008)
conducted parallel experiments at TCE 1,000 and 2,000 ppm (8 hours/day, 7 days) in wild-type
and CYP2El-null mice, the latter of which did not exhibit hepatotoxicity (assessed by serum
ALT, AST, and histopathology) and excreted twofold lower amounts of oxidative metabolites
TCA and TCOH in urine as compared to wild-type mice. In addition, urinary TCA and TCOH
excretion was correlated with serum ALT and AST measures, though the R-squared values
(square of the reported correlation coefficients) were relatively low (0.54 and 0.67 for TCOH and
TCA, respectively). Ramdhan et al. (2010) reported that TCA and TCOH were detected in the
urine of wild type and PPARa-null and humanized mice after TCE exposure with no significant
differences between the 1,000 and 2,000 ppm TCE treatments. TCA concentrations were
significantly lower and TCOH concentrations higher in exposed PPARa-null mice relative to
wild type mice. They stated that urinary TCA levels in wild type mice were incorrectly reported
by Ramdhan et al. (2008) but have been corrected in this study. AST and ALT levels were
significantly increased in all exposed mice relative to control 41-74 and 36-79% higher for AST
and ALT, respectively). Mean levels within each treatment group were higher, though not
statistically significantly different, with exposure to 2,000 versus 1,000 ppm TCE. Although
increased, such increases were small. Necrosis scores were reported to be significantly higher in
TCE-exposed mice relative to controls in all three genotype mice and to be significantly higher
with 2,000 vs. 1,000 ppm TCE exposure in wild type mice and hPPARa mice. Inflammation
scores were reported to be significantly higher with exposed group than control with 2,000 ppm
TCE exposure than controls for each genotype group with a difference between the 2,000 and
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1,000 ppm exposure groups in wild type mice. However, necrosis and inflammation score
means at the highest TCE exposure levels in any mouse strain were minimal (only occasional
necrotic cells in any lobule) for necrosis and mild for inflammation (<2 foci/field ).
With respect to CH (166 mg/kg-day) and DCA (-90 mg/kg-day), Daniel et al. (1992)
reported that after drinking water treatment, hepatocellular necrosis and chronic active
inflammation were reported to be mildly increased in both prevalence and severity in all treated
groups after 104 weeks of exposure. The histological findings, from interim sacrifices (n = 5),
were considered by the authors to be unremarkable and were not reported. TCA has not been
reported to induce necrosis in the liver under the conditions tested. Relatively high doses of
DCA (>1 g/L in drinking water) appear to result in mild focal necrosis with attendant reparative
proliferation at lesion sites, but no such effects were reported at lower doses (<0.5 g/L in
drinking water) more relevant for comparison with TCE (DeAngelo et al., 1999; Stauber et al.,
1998; Sanchez and Bull, 1990). Enlarged nuclei and changes consistent with increased ploidy,
are further discussed below in the context of DNA synthesis.
4.5.6.2.3. DNA synthesis and polyploidization
The effects on DNA synthesis and polyploidization observed with TCE treatment have
similarly been observed with TCA and DCA. With respect to CH, George et al. (2000) reported
that CH exposure did not alter DNA synthesis in rats and mice at any of the time periods
monitored (all well past 2 weeks), with the exception of 0.58 g/L CH at 26 weeks slightly
increasing hepatocyte labeling (-two- to threefold of controls) in rats and mice but the
percentage labeling still representing <3% of hepatocytes.
In terms of whole liver or hepatocyte label incorporation, the most comparable exposure
duration between TCE, TCA, and DCA studies is the 10- and 14-day period. Several studies
have reported that in this time period, peak label incorporation into individual hepatocytes and
whole liver for TCA and DCA have already passed (Pereira, 1996; Carter etal., 1995; Styles et
al., 1991; Sanchez and Bull, 1990). A direct time-course comparison is difficult, since data at
earlier times for TCE are more limited.
There are conflicting reports of DNA synthesis induction in individual hepatocytes for up
to 14 days of DCA or TCA exposure. In particular, Sanchez and Bull (1990) reported tritiated
thymidine incorporation in individual hepatocytes up to 2 g/L exposure to DCA or TCA induced
little increase in DNA synthesis except in instances and in close proximity to areas of
proliferation/necrosis for DCA treatment after 14 days of exposure in male mice. The largest
percentage of hepatocytes undergoing DNA synthesis for any treatment group was <1% of
hepatocytes. However, they reported treatment- and exposure duration-changes in hepatic DNA
incorporation of tritiated thymidine for DCA and TCA. For TCA treatment, the largest increases
over control levels for hepatic DNA incorporation (at the highest dose) was a threefold increase
after 5 days of treatment and a twofold increase over controls after 14 days of treatment. For
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DCA whole-liver tritiated thymidine incorporation was only slightly elevated at necrogenic
concentrations and decreased at the 0.3 g/L non-necrogenic level after 14 days of treatment. In
contrast to Sanchez and Bull (1990), Stauber and Bull (1997) reported increased tritiated
thymidine incorporation for individual hepatocytes after 14 days of treatment with 2 g/L DCA or
TCA in male mice. They used a more extended period of tritiated thymidine exposure of 3-
5 days and so these results represent aggregate DNA synthesis occurring over a more extended
period of time. A —4day labeling index" was reported as <1% for the highest level of increased
incorporation. However, after 14 days, the labeling index was reported to be increased by
~3.5-fold for TCA and -5.5-fold for DCA over control values. After 28 days, the labeling index
was reported to be decreased -2.3-fold by DCA and increased -2.5-fold after treatment with
TCA. Pereira (1996) reported that for female B6C3Fi mice, 5-day incorporation of BrDU, as a
measure of DNA synthesis, was increased at 0.86 and 2.58 g/L DCA treatment for 5 days
(-twofold at the highest dose) but that by Day 12 and 33 levels had fallen to those of controls.
For TCA exposures, 0.33, 1.10, and 3.27 g/L TCA all gave a similar -threefold increase in BrdU
incorporation by 5 days, but that by 12 and 33 days were not changed from controls.
Nonetheless, what is consistent is that these data report that, similar to TCE-exposed mice at
10 days of exposure, cells undergoing DNA synthesis in DCA- or TCA-exposed mice for up to
14 days of exposure to be confined to a very small population of cells in the liver. Thus, these
data are consistent with hypertrophy being primarily responsible for liver weight gains as
opposed to increases in cell number in mice.
Interestingly, a lack of correlation between whole liver label incorporation and that in
individual hepatocytes has been reported by several studies of DCA (Carter et al., 1995; Sanchez
and Bull, 1990). For example, Carter et al. (1995) reported no increase in labeling of
hepatocytes in comparison to controls for any DCA treatment group from 5 to 30 days of DCA
exposure. Rather than increase hepatocyte labeling, DCA induced no change from Days 5
though 15 but significantly decreased levels between days 20 and 30 for 0.5 g/L that were similar
to those observed for the 5 g/L exposures. However, for whole-liver DNA tritiated thymidine
incorporation, Carter et al. (1995) reported 0.5g/L DCA treatments to show trends of initial
inhibition of DNA tritiated thymidine incorporation followed by enhancement of labeling that
was not statistically significant from 5 to 30 days of exposure. Examination of individual
hepatocytes does not include the contribution of nonparenchymal cell DNA synthesis that would
be detected in whole-liver DNA. As noted above, proliferation of the nonparenchymal cell
compartment of the liver has been noted in several studies of TCE in rodents, and thus, this is
one possible reason for the reported discrepancy.
Another possible reason for this inconsistency with DCA treatment is polyploidization, as
was suggested above for TCE. Although this was not examined for DCA or TCA exposure by
Sanchez and Bull (1990), Carter et al. (1995) reported that hepatocytes from both 0.5 and 5 g/L
DCA treatment groups had enlarged, presumably polyploidy nuclei, with some hepatocyte nuclei
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labeled in the mid-zonal area. There were statistically significant changes in cellularity, nuclear
size, and multinucleated cells during 30 days of exposure to DCA. The percentage of
mononucleated cells hepatocytes was reported to be similar between control and DCA treatment
groups at 5- and 10-day exposure. However, at 15 days and beyond, DCA treatments were
reported to induce increases in mononucleated hepatocytes, with later time periods also showing
DCA-induced increases in nuclear area, consistent with increased polyploidization without
mitosis. The consistent reporting of an increasing number of mononucleated cells between
15 and 30 days could be associated with clearance of mature hepatocytes as suggested by the
report of DCA-induced loss of cell nuclei. The reported decrease in the numbers of binucleate
cells in favor of mononucleate cells is not typical of any stage of normal liver growth (Brodsky
and Uryvaeva, 1977). The pattern of consistent increase in percentage liver/body weight induced
by 0.5 g/L DCA treatment from days 5 though 30 was not consistent with the increased numbers
of mononucleate cells and increase nuclear area reported from day 20 onward. Specifically, the
large differences in liver weight induction between the 0.5 g/L and 5 g/L treatment groups at all
times studied also did not correlate with changes in nuclear size and percentage of mononucleate
cells. Thus, increased liver weight was not a function of cellular proliferation, but probably
included both aspects of hypertrophy associated with polyploidization and increased glycogen
deposition (see below) induced by DCA. Carter et al. (1995) suggested that although there is
evidence of DCA-induced cytotoxicity (e.g., loss of cell membranes and apparent apoptosis), the
0.5 g/L exposure concentration has been shown to increase hepatocellular lesions after
100 weeks of treatment without concurrent peroxisome proliferation or cytotoxicity (DeAngelo
etal.. 1999).
In sum, the observation of TCE treatment-related changes in DNA content, label
incorporation, and mitotic figures are generally consistent with patterns observed for both TCA
and DCA. In all cases, hepatocellular proliferation is confined to a very small fraction of
hepatocytes, and hepatomegaly observed with all three treatments probably largely reflects
cytomegaly rather than cell proliferation. Moreover, label incorporation likely largely reflects
polyploidization rather than hepatocellular proliferation, with a possible contribution from
nonparenchymal cell proliferation. As with TCE, histological changes in nuclear sizes and
number also suggest a significant degree of treatment-related polyploidization, particularly for
DCA.
4.5.6.2.4. Apoptosis
Both Elcombe et al. (1985) and Dees and Travis (1993) reported no changes in apoptosis
other than increased apoptosis only at a treatment level of 1,000 mg/kg TCE. Dees and Travis
(1993) reported that increased apoptoses from TCE exposure —od not appear to be in proportion
to the applied TCE dose given to male or female mice." Channel et al. (1998) reported that there
was no significant difference in apoptosis between TCE treatment and control groups with data
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not shown. However, the extent of apoptosis in any of the treatment groups, or which groups
and timepoints were studied for this effect cannot be determined. While these data are quite
limited, it is notable that peroxisome proliferators have been suggested inhibit, rather than
increase, apoptosis as part of their carcinogenic mode of action (Klaunig et al., 2003).
However, for TCE metabolites, DCA has been most studied, though it is clear that age
and species affect background rates of apoptosis. Snyder et al. (1995), in their study of DCA,
reported that control mice were reported to exhibit apoptotic frequencies ranging from -0.04 to
0.085% and that over the 30-day period of their study, the frequency rate of apoptosis declined; it
was suggested that this pattern is consistent with reports of the livers of young animals
undergoing rapid changes in cell death and proliferation. They reported the rat liver to have a
greater than estimated frequency of spontaneous apoptosis (-0.1%) and therefore, greater than
that of the mouse. Carter et al. (1995) reported that after 25 days of 0.5 g/L DCA treatment,
apoptotic bodies were reported as well as fewer nuclei in the pericentral zone and larger nuclei in
central and midzonal areas. This would indicate an increase in the apoptosis associated with
potential increases in polyploidization and cell maturation. However, Snyder et al. (1995)
reported that mice treated with 0.5 g/L DCA over a 30-day period had a similar trend as control
mice of decreasing apoptosis with age. The percentage of apoptotic hepatocytes decreased in
DCA-treated mice at the earliest time point studied and remained statistically significantly
decreased from controls from 5 to 30 days of exposure. Although the rate of apoptosis was very
low in controls, treatment with 0.5 g/L DCA reduced it further (-30-40% reduction) during the
30-day study period. The results of this study not only provide a baseline of apoptosis in the
mouse liver, which is very low, but also show the importance of taking into account the effects
of age on such determinations. The significance of the DCA-induced reduction in apoptosis
reported in this study, from a level that is already inherently low in the mouse, for the mode of
action for induction of DCA-induce liver cancer is difficult to discern.
4.5.6.2.5. Glycogen accumulation
As discussed in Sections E.3.2 and E.3.4.2.1, glycogen accumulation has been described
to be present in foci in both humans and animals as a result from exposure to a wide variety of
carcinogenic agents and predisposing conditions in animals and humans. The data from
Elcombe et al. (1985) included reports of TCE-induced pericentral hypertrophy and eosinophilia
for both rats and mice but with -^ewer animals affected at lower doses." In terms of glycogen
deposition, Elcombe report —amewhat" less glycogen pericentrally in the livers of rats treated
with TCE at 1,500 mg/kg than controls with less marked changes at lower doses restricted to
fewer animals. They do not comment on changes in glycogen in mice. Dees and Travis (1993)
reported TCE-induced changes to -4nclude an increase in eosinophilic cytoplasmic staining of
hepatocytes located near central veins, accompanied by loss of cytoplasmic vacuolization."
Since glycogen is removed using conventional tissue processing and staining techniques, an
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increase in glycogen deposition would be expected to increase vacuolization and thus, the report
from Dees and Travis (1993) is consistent with less, not more, glycogen deposition. Neither
study produced a quantitative analysis of glycogen deposition changes from TCE exposure.
Although not explicitly discussing liver glycogen content or examining it quantitatively in mice,
these studies suggest that TCE-induced liver weight increases did not appear to be due to
glycogen deposition after 10 days of exposure and any decreases in glycogen were not
necessarily correlated with the magnitude of liver weight gain either.
For TCE and TCA 500 mg/kg treatments in mice for 10 days, changes in glycogen were
not reported in the general descriptions of histopathological changes (Dees and Travis, 1993;
Styles etal., 1991; Elcombe etal., 1985) or were specifically described by the authors as being
similar to controls (Nelson et al., 1989). However, for DCA, glycogen deposition was
specifically noted to be increased with treatment, although no quantitative analyses was
presented that could give information as to the nature of the dose-response (Nelson et al., 1989).
In regard to cell size, although increased glycogen deposition with DCA exposure was
noted by Sanchez and Bull (1990) to occur to a similar extent in B6C3Fi and Swiss Webster
male mice despite differences in DCA-induced liver weight gain. Lack of quantitative analyses
of that accumulation in this study precludes comparison with DCA-induced liver weight gain.
Carter et al. (1995) reported that in control mice, there was a large variation in apparent glycogen
content, but did not perform a quantitative analysis of glycogen deposition. The variability of
this parameter in untreated animals and the extraction of glycogen during normal tissue
processing for light microscopy make quantitative analyses for dose-response difficult unless
specific methodologies are employed to quantitatively assess liver glycogen levels as was done
by Kato-Weinstein et al. (2001) and Pereira et al. (2004a).
Bull et al. (1990) reported that glycogen deposition was uniformly increased from 2 g/L
DCA exposure with photographs of TCA exposure showing slightly less glycogen staining than
controls. However, the abstract and statements in the paper suggest that there was increased
PAS-positive material from TCA treatment that has caused confusion in the literature in this
regard. Kato-Weinstein et al. (2001) reported that in male B6C3Fi mice exposed to DCA and
TCA, the DCA treatment increased glycogen and TCA decreased glycogen content of the liver
by using both chemical measurement of glycogen in liver homogenates and by using ethanol-
fixed sections stained with PAS, a procedure designed to minimize glycogen loss.
Kato-Weinstein et al. (2001) reported that glycogen-rich and -poor cells were scattered
without zonal distribution in male B6C3Fi mice exposed to 2 g/L DCA for 8 weeks. For TCA
treatments, they reported centrilobular decreases in glycogen and -25% decreases in whole liver
by 3 g/L TCA. Kato-Weinstein et al. (2001) reported whole-liver glycogen to be increased
~1.50-fold of control (90 vs. 60 mg glycogen/g liver) by 2 g/L DCA after 8 weeks of exposure to
male B6C3Fi mice, with a maximal level of glycogen accumulation occurring after 4 weeks of
DCA exposure. Pereira et al. (2004a) reported that after 8 weeks of exposure to 3.2 g/L DCA,
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liver glycogen content was 2.2-fold of control levels (155.7 vs. 52.4 mg glycogen/g liver) in
female B6C3Fi mice. Thus, the baseline level of glycogen content reported by (-60 mg/g) and
the increase in glycogen after DCA exposure was consistent between Kato-Weinstein et al.
(2001) and Pereira et al. (2004a). However, the increase in liver weight reported by Kato-
Weinstein et al. (2001) of 1.60-fold of control percentage liver/body weight cannot be accounted
for by the 1.50-fold of control glycogen content. Glycogen content only accounts for 5% of liver
mass so that 50% increase in glycogen cannot account for the 60% increase liver mass induced
by 2 g/L DCA exposure for 8 weeks reported by Kato-Weinstein (2001). Thus, DCA-induced
increases in liver weight are occurring from other processes as well. Carter et al. (2003) and
DeAngelo et al. (1999) reported increased glycogen after DCA treatment at much lower doses
after longer periods of exposure (100 weeks). Carter reported increased glycogen at 0.5 g/L
DCA and DeAngelo et al. (1999) at 0.03 g/L DCA in mice. However, there is no quantitation of
that increase.
4.5.6.2.6. Peroxisome proliferation and related effects
TCA and DCA have both been reported to induce peroxisome proliferation or increases
in related enzyme markers in rodent hepatocytes (Parrish et al., 1996; Mather et al., 1990;
DeAngelo et al., 1989, 1997). Between TCA and DCA, both induce peroxisome proliferation in
various strains of mice, but it clear that TCA and DCA are weak PPARa agonists and that DCA
is weaker than TCA in this regard (Nelson et al., 1989) using a similar paradigm.
George et al. (2000) reported that CH exposure did not hepatic PCO activity in rats and
mice at any of the time periods monitored. It is notable that the only time at which DNA
synthesis index was (slightly) increased, at 26 weeks, there remained a lack of induction of PCO.
A number of measures that may be related to peroxisome proliferation were investigated in
Leakey et al. (2003b). Of the enzymes associated with PPARa agonism (total CYP, CYP2B
isoform, CYP4A, or lauric acid p-hydroxylase activity), only CYP4A and lauric acid
p-hydroxylase activity were significantly increased at 15 months of exposure in the dietary-
restricted group administered the highest dose (100 mg/kg CH) with no other groups showing a
statistically significant increased response (n = 12/group). There is an issue of interpretation of
peroxisomal enzyme activities and other enzymes associated with PPARa receptor activation to
be a relevant event in liver cancer induction at a time period in which tumors or foci are already
present. Although not statistically significant, the 100 mg/kg CH exposure group of ad-libitum-
fed mice also had an increase in CH-induced increases of CYP4A and lauric acid P-hydroxylase
activity. Seng et al. (2003) described CH toxicokinetics and peroxisome proliferation-associated
enzymes in mice at doses up to 1,000 mg/kg-day for 2 weeks with dietary control or caloric
restriction. Lauric acid P-hydroxylase and PCO activities were reported to be induced only at
doses >100 mg/kg in all groups, with dietary-restricted mice showing the greatest induction.
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Differences in serum levels of TCA, the major metabolite remaining 24 hours after dosing, were
reported not to correlate with hepatic lauric acid p-hydroxylase activities across groups.
Direct quantitative inferences regarding the magnitude of response in these studies in
comparison to TCE, however, are limited by possible variability and confounding. In particular,
many studies used cyanide-insensitive PCO as a surrogate for peroxisome proliferation, but the
utility of this marker may be limited for a number of reasons. First, several studies have shown
that this activity is not well correlated with the volume or number of peroxisomes that are
increased as a result of exposure to TCE or it metabolites (Nakajima et al., 2000; Nelson et al.,
1989; Elcombe et al., 1985). In addition, this activity appears to be highly variable both as a
baseline measure and in response to chemical exposures. Laughter et al. (2004) presented data
showing WY-14,643 induced increases in PCO activity that varied up to sixfold between
different experiments in wild-type mice. They also showed that, in some instances, PCO activity
in untreated PPARa-null mice was up to sixfold greater than that in wild-type mice. Parrish
et al. (1996) noted that control values between experiments varied as much as a factor of twofold
for PCO activity and thus, their data were presented as percentage of concurrent controls.
Furthermore, Melnick et al. (1987) reported that corn oil administration alone can elevate PCO
(as well as catalase) activity, and corn oil has also been reported to potentiate the induction of
PCO activity of TCA in male mice (DeAngelo et al., 1989). Thus, quantitative inferences
regarding the magnitude of response in these studies are limited by a number of factors. For
example, in the studies reported in DeAngelo et al. (2008), a small number of animals was
studied for PCO activity at interim sacrifices (n = 5). PCO activity varied 2.7-fold as baseline
controls. Although there was a 10-fold difference in TCA exposure concentration, the increases
in PCO activity at 4 weeks were 1.3-, 2.4-, and 5.3-fold of control. More information on the
relationship of PCO enzyme activity and its relationship to carcinogenicity is discussed in
Section E.3.4 and below.
4.5.6.2.7. Oxidative stress
Very limited data are available as to oxidative stress and related markers induced by the
oxidative metabolites of TCE. As discussed in Appendix E, there are limited data that do not
indicate significant oxidative stress and associated DNA damage associated with acute and
subacute TCE treatment. In regard to DCA and TCA, Larson and Bull (1992b) exposed male
B6C3Fi mice or F344 rats to single doses TCA or DCA in distilled water by gavage (n = 4). In
the first experiment, TEARS was measured from liver homogenates and assumed to be
malondialdehyde. The authors stated that a preliminary experiment had shown that maximal
TEARS was increased 6 hours after a dose of DCA and 9 hours after a dose of TCA in mice and
that by 24 hours, TEARS concentrations had declined to control values. Time-course
information in rats was not presented. A dose of 100 mg/kg DCA (rats or mice) or TCA (mice)
did not elevate TEARS concentrations over that of control liver with this concentration of TCA
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not examined in rats. For TCA, there was a slight dose-related increase in TEARS over control
values starting at 300 mg/kg in mice with the increase in TEARS increasing at a rate that was
lower than the magnitude of increase in dose. Of note, is the report that the induction of TEARS
in mice is transient and subsided within 24 hours of a single dose of DCA or TCA, that the
response in mice appeared to be slightly greater with DCA than TCA at similar doses, and that
for DCA, there was similar TEARS induction between rats and mice at similar dose levels.
Austin et al. (1996) is a follow-up publication of the preliminary experiment cited in
Larson and Bull (1992b). Male B6C3Fi mice were treated with single doses of DCA or TCA via
gavage with liver examined for 8-OHdG. The authors stated that in order to conserve animals,
controls were not employed at each time point. There was a statistically significant increase over
controls in 8-OHdG for the 4- and 6-hour time points for DCA (-1.4- and 1.5-fold of control,
respectively) but not at 8 hours in mice. For TCA, there was a statistically significant increase in
8-OHdG at 8 and 10 hours for TCA (-1.4- and 1.3-fold of control, respectively).
Consistent results as to low, transient increases in markers of—oxidative strss" were also
reported by Parrish et al. (1996), who in addition to examining oxidative stress alone, attempted
to examine its possible relationship to PCO and liver weight in male B6C3Fi mice exposed to
TCA or DCA for 3 or 10 weeks (n = 6). The dose-related increase in PCO activity at 21 days for
TCA was not increased similarly for DCA. Only the 2.0 g/L dose of DCA was reported to
induce a statistically significant increase at 21-days of exposure of PCO activity over control
(-1.8-fold of control). After 71 days of treatment, TCA induced dose-related increases in PCO
activities that were approximately twice the magnitude as that reported at 21 days. Treatments
with DCA at the 0.1 and 0.5 g/L exposure levels produced statistically significant increases in
PCO activity of-1.5- and 2.5-fold of control, respectively. The administration of 1.25 g/L
clofibric acid in drinking water, used as a positive control, gave -six-sevenfold of control PCO
activity at 21 and 71 days of exposure. Parrish et al. (1996) reported that laurate hydroxylase
activity was elevated significantly only by TCA at 21 days and to approximately the same extent
(-1.4-1.6-fold of control) at all doses tested and at 71 days, both the 0.5 and 2.0 g/L TCA
exposures resulting in a statistically significant increase in laurate hydroxylase activity (i.e.,
1.6- and 2.5-fold of control, respectively). No change was reported after DCA exposure.
Laurate hydroxylase activity was within the control values, varying 1.7-fold between 21 and
71 days experiments. Levels of 8-OHdG in isolated liver nuclei were reported to not be altered
from 0.1, 0.5, or 2.0 g/L TCA or DCA after 21 days of exposure and this negative result was
reported to remain even when treatments were extended to 71 days of treatment. The authors
noted that the level of 8-OHdG increased in control mice with age (i.e., -twofold increase
between 71- and 21-day-old control mice). Thus, the increases in PCO activity noted for DCA
and TCA were not associated with 8-OHdG levels (which were unchanged) and also not with
changes in laurate hydrolase activity observed after either DCA or TCA exposure. Of note, is
that the authors report taking steps to minimize artifactual responses for their 8-OHdG
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determinations. The authors concluded that their data suggest that peroxisome proliferative
properties of TCA were not linked to oxidative stress or carcinogenic response.
4.5.6.3. Comparisons of TCE-Induced Carcinogenic Responses with TCA, DCA, and
CH Studies
4.5.6.3.1. Studies in rats
As discussed above, data on TCE carcinogenicity in rats, while not reporting statistically
significantly increased risks, are not entirely adequate due to low numbers of animals, increased
systemic toxicity, and/or increased treatment-related or accidental mortality. Notably, several
studies in rats noted a few very rare types of liver or biliary tumors (cystic cholangioma,
cholangiocarcinoma, or angiosarcomas) in treated animals. For TCA, DCA, and CH, there are
even fewer studies in rats, so there is a very limited ability to assess the consistency or lack
thereof in rat carcinogenicity among these compounds.
For TCA, the only available study in rats (DeAngelo et al., 1997) has been frequently
cited in the literature to indicate a lack of response in this species for TCA-induced liver tumors.
However, this study does report an apparent dose-related increase in multiplicity of adenomas
and an increase in carcinomas over control at the highest dose. The use by DeAngelo et al.
(1997) of a relatively low number of animals per treatment group (n = 20-24) limits this study's
ability to determine a statistically significant increase in tumor response. Its ability to determine
an absence of treatment-related effects is similarly limited. In particular, a power calculation of
the study shows that for most endpoints (incidence and multiplicity of all tumors at all exposure
DCA concentrations), the Type II error, which should be >50%, was <8%. The only exception
was for the incidence of adenomas and of adenomas and carcinomas for the 0.5 g/L treatment
group (58%), at which, notably, there was a reported increase in reported adenomas or adenomas
and carcinomas combined over control (15 vs. 4%). Therefore, the likelihood of a false null
hypothesis was not negligible. Thus, while suggesting a lower response than for mice for liver
tumor induction, this study is inconclusive for determining whether TCA induces a carcinogenic
response in the liver of rats.
For DCA, there are two long-term studies in rats (DeAngelo et al., 1996; Richmond et al.,
1995) that appear to have reported the majority of their results from the same data set and that
were consequently subject to similar design limitations and DCA-induced neurotoxicity in this
species. DeAngelo et al. (1996) reported increased hepatocellular adenomas and carcinomas in
male F344 rats exposed to DCA for 2 years. However, the data from exposure concentrations at
the 5 g/L dose had to be discarded and the 2.5 g/L DCA dose had to be continuously lowered
during the study due to neurotoxicity. There was a DCA-induced increased in adenomas and
carcinomas combined reported for the 0.5 g/L DCA (24.1 vs. 4.4% adenomas and carcinomas
combined in treated vs. controls) and an increase at a variable dose started at 2.5 g/L DCA and
continuously lowered (28.6 vs. 3.0% adenomas and carcinomas combined in treated vs.
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controls). Only combined incidences of adenomas and carcinomas for the 0.5 g/L DCA
exposure group was reported to be statistically significant by the authors although the incidence
of adenomas was 17.2 vs. 4% in treated vs. control rats. Hepatocellular tumor multiplicity was
reported to be increased in the 0.5 g/L DCA group (0.31 adenomas and carcinomas/animal in
treated vs. 0.04 in control rats) but was reported by the authors to not be statistically significant.
At the starting dose of 2.5 g/L that was continuously lowered due to neurotoxicity, the increased
multiplicity of HCCs was reported by the authors to be to be statistically significant
(0.25 carcinomas/animals vs. 0.03 in control) as well as the multiplicity of combined adenomas
and carcinomas (0.36 adenomas and carcinomas/animals vs. 0.03 in control rats). Issues that
affect the ability to determine the nature of the dose-response for this study include: (1) the use
of a small number of animals (n = 23, n = 21, and n = 23 at final sacrifice for the 2.0 g/L sodium
chloride control, 0.05 g/L and 0.5 g/L treatment groups) that limit the power of the study to both
determine statistically significant responses and to determine that there are not treatment-related
effects (i.e., power); (2) apparent addition of animals for tumor analysis not present at final
sacrifice (i.e., 0.05 and 0.5 g/L treatment groups); and (3) most of all, the lack of a consistent
dose for the 2.5 g/L DCA exposed animals.
Similar issues are present for the study of Richmond et al. (1995), which was conducted
by the same authors as DeAngelo et al. (1996) and appeared to be the same data set. There was a
small difference in reports of the results between the two studies for the same data for the 0.5 g/L
DCA group in which Richmond et al. (1995) reported a 21% incidence of adenomas and
DeAngelo et al. (1996) reported a 17.2% incidence. The authors did not report any of the results
of DCA-induced increases of adenomas and carcinomas to be statistically significant. The same
issues discussed above for DeAngelo et al. (1996) apply to this study. Similar to the DeAngelo
et al. (1997) study of TCA in rats, the use in these DCA studies (DeAngelo et al., 1996;
Richmond et al., 1995) of relatively small numbers of rats limits the detection of treatment-
related effects and the ability to determine whether there were treatment-related effects (Type II
error), especially at the low concentrations of DCA exposure.
For CH, George et al. (2000) exposed male F344/N rats to CH in drinking water for
2 years. Groups of animals were sacrificed at 13, 26, 52, and 78 weeks following the initiation
of dosing, with terminal sacrifices at week 104. Only a few animals received a complete
pathological examination. The number of animals surviving >78 weeks and the number
examined for hepatocellular proliferative appeared to differ (42-44 animals examined, but 32-
35 surviving until the end of the experiment). Only the lowest treatment group had increased
liver tumors that were marginally significantly increased.
Leuschner and Beuscher (1998) examined the carcinogenic effects of CH in male and
female Sprague-Dawley rats (69-79 g, 25-29 days old at initiation of the experiment)
administered 0, 15, 45, and 135 mg/kg CH in unbuffered drinking water 7 days/week
(n = 50/group) for 124 weeks in males and 128 weeks in females. Two control groups were
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noted in the methods section without explanation as to why they were conducted as two groups.
The authors reported no substance-related influence on organ weights and no macroscopic
evidence of tumors or lesions in male or female rats treated with CH for 124 or 128 weeks.
However, no data were presented on the incidence of tumors in either treatment or control
groups. The authors did report a statistically significant increase in the incidence of
hepatocellular hypertrophy in male rats at the 135 mg/kg dose (14/50 animals vs. 4/50 and 7/50
in Controls I and II). For female rats, the incidence of hepatocellular hypertrophy was reported
to be 10/50 rats (Control I) and 16/50 (Control II) rats with 18/50, 13/50 and 12/50 female rats
having hepatocellular hypertrophy after 15, 45, and 135 mg/kg CH, respectively. The lack of
reporting in regard to final body weights, histology, and especially background and treatment
group data for tumor incidences, limit the interpretation of this study. Whether this paradigm
was sensitive for induction of liver cancer cannot be determined.
Therefore, given the limitations in the available studies, a comparison of rat liver
carcinogenicity induced by TCE, TCA, DC A, and CH reveals no strong inconsistencies, but nor
does it provide much insight into the relative importance of different TCE metabolites in liver
tumor induction.
4.5.6.3.2. Studies in mice
Similar to TCE, the bioassay data in mice for DC A, TCA, and CH are much more
extensive and have shown that all three compounds induce liver tumors in mice. Several 2-year
bioassays have been reported for CH (Leakey et al., 2003b: George et al., 2000; Daniel et al.,
1992). For many of the DCA and TCA studies, the focus was not carcinogenic dose-response,
but rather investigation of the nature of the tumors and potential modes of action in relation to
TCE. As a result, studies often employed relatively high concentrations of DCA or TCA and/or
were conducted for <1 year. As shown previously in Section 4.5.6.2.1, the dose-response curves
for increased liver weight for TCE administration in male mice are more similar to those for
DCA administration and TCE oxidative metabolism than for direct TCA administration
(inadequate data were available for CH). An analogous comparison for DCA-, TCA-, and
CH-induced tumors would be informative, ideally using data from 2-year studies.
4.5.6.3.2.1. TCE carcinogenicity dose-response data
Unfortunately, the database for TCE, while consistently showing an induction of liver
tumors in mice, is very limited for making inferences regarding the shape of the dose-response
curve. For many of these experiments, only liver tumor incidence, not multiplicity, was
provided. NTP (1990), Bull et al. (2002), and Anna et al. (1994) conducted gavage experiments
in which they only tested one dose of-1,000 mg/kg-day TCE. NCI (1976) tested two doses that
were adjusted during exposure to an average of 1,169 and 2,339 mg/kg-day in male mice with
only twofold dose spacing in only two doses tested. Maltoni et al. (1988; 1986) conducted
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inhalation experiments in two sets of B6C3Fi mice and one set of Swiss mice at three exposure
concentrations that were threefold apart in magnitude between the low and mid-dose and twofold
apart in magnitude between the mid- and high dose. However, for one experiment in male
B6C3Fi mice (BT306), the mice fought and suffered premature mortality and for two the
experiments in B6C3Fi mice, although using the same strain, the mice were obtained from
differing sources with very different background liver tumor levels. For the Maltoni et al. (1988;
1986) study, a general descriptor of -h-epatoma" was used for liver neoplasia rather than
describing hepatocellular adenomas and carcinomas so that comparison of that data with those
from other experiments is difficult. More importantly, while the number of adenomas and
carcinomas may be the same between treatments or durations of exposure, the number of
adenomas may decrease as the number of carcinomas increase during the course of tumor
progression. Such information is lost by using only a hepatoma descriptor.
Given the limited database, it would be useful if different studies could be combined to
yield a more comprehensive dose-response curve, as was done for liver weight, above. However,
this is probably not appropriate for several reasons. First, only the NTP (1990) study was
performed with dosing duration and time of sacrifice both being the -standard" 104 weeks. NCI
(1976). Maltoni et al. (1988: 1986). Anna et al. (1994). and Bull et al. (2002) all had shorter
dosing periods and either longer (Maltoni et al., 1988; Maltoni et al., 1986) or shorter (the other
three studies) observation times. Therefore, because of potential dose-rate effects and differences
in the degree of expression of TCE-induced tumors, it is difficult to even come up with a
comparable administered dose-metric across studies. Moreover, the background tumor incidences
are substantially different across experiments, even controlling for mouse strain and sex. For
example, across gavage studies in male B6C3Fi mice, the incidence of HCCs ranged from 1.2 to
16.7% (Annaetal.. 1994: NTP. 1990: NCI. 1976) and the incidence of adenomas ranged from 1.2
to 14.6% (Annaetal.. 1994: NTP. 1990) in control B6C3Fi mice. After -1,000 mg/kg-day TCE
treatment, the incidence of carcinomas ranged from 19.4 to 62% (Bull et al., 2002: Anna et al.,
1994: NTP. 1990: NCI. 1976). with three of the studies (Annaetal.. 1994: NTP. 1990: NCI.
1976) reporting a range of incidences between 42.8 and 62.0%. The incidence of adenomas
ranged from 28 to 66.7% (Bull et al.. 2002: Annaetal.. 1994: NTP. 1990). In the Maltoni et al.
(1988: 1986) inhalation study as well, male B6C3Fi mice from two different sources had very
different control incidences of hepatomas (~2 vs. -20%).
Therefore, only data from the same experiment in which more than a single exposed dose
group was used provide reliable data on the dose-response relationship for TCE
hepatocarcinogenicity, and incidences from these experiments are shown in Figures 4-10 and
4-11. Except for one of the two Maltoni et al. (1988: 1986) inhalation experiments in male
B6C3Fi mice, all of these data sets show relatively proportional increases with dose, albeit with
somewhat different slopes as may be expected across strains and sexes. Direct comparison is
difficult, since the -h-epatomas" reported by Maltoni et al. (1988: 1986) are much more
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heterogeneous, including neoplastic nodules, adenomas, and carcinomas, than the carcinomas
reported by NCI (1976). Nonetheless, although the data limitations preclude a conclusive
statement, these data are generally consistent with the linear relationship observed with TCE-
induced liver weight changes.
4.5.6.3.2.2. DCA carcinogenicity dose-response data
Pereira (1996) reported that for 82-week exposures to DCA in female B6C3Fi mice,
DCA exposure concentrations of 0, 2, 6.67, and 20 mmol/L (0, 0.26, 0.86, and 2.6 g/L) led to
close, proportionally increasing adenoma prevalences of 2.2, 6, 25, and 84.2%, though adenoma
multiplicity increased more than linearly between the highest two doses. Unfortunately, too few
carcinomas were observed at these doses and duration to meaningfully inform the shape of the
dose-response relationship. More useful is DeAngelo et al. (1999), which reported on a study of
DCA hepatocarcinogenicity in male B6C3Fi mice over a lifetime exposure. DeAngelo et al.
(1999) used 0.05, 0.5, 1.0, 2.0, and 3.5 g/L exposure concentrations of DCA in their 100-week
drinking water study. The number of animals at final sacrifice was generally low in the DCA
treatment groups and variable. The multiplicity or number of HCCs/animals was significantly
increased over controls in a dose-related manner at all DCA treatments including 0.05 g/L DCA,
and a no-observed-effect level (NOEL) was not identified. Between the 0.5 and 3.5 g/L
exposure concentrations of DCA, the magnitude of increase in multiplicity was similar to the
increases in magnitude in dose. The incidence of HCCs was increased at all doses as well, but
was not statistically significant at 0.05 g/L. However, given that the number of mice examined
for this response (n = 33), the power of the experiment at this dose was only 16.9% to be able to
determine that there was not a treatment-related effect. Indeed, Figure 4-12 replots the data from
DeAngelo et al. (1999) with an abscissa drawn to scale (unlike the figure in the original paper,
which was not to scale), suggests even a slightly greater-than-linear effect at the lowest dose
(0.05 g/L, or 8 mg/kg-day) as compared to the next lowest dose (0.5 g/L, or 84 mg/kg-day),
though of course the power of such a determination is limited. The authors did not report the
incidence or multiplicity of adenomas for the 0.05 g/L exposure group in the study or the
incidence or multiplicity of adenomas and carcinomas in combination. For the animals surviving
from 79 to 100 weeks of exposure, the incidence and multiplicity of adenomas peaked at 1 g/L,
while HCCs continued to increase at the higher doses. This would be expected where some
portion of the adenomas would either regress or progress to carcinomas at the higher doses.
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100% -i
o
500 1000 1500 2000 2500
mg/kg-d (oral gavage)
^NCI76/B6C3F1 / F / oral
-^NCI76/B6C3F1 / M / oral
• NTP90/B6C3F1 / F / oral
» NTP90/B6C3F1 / M / oral
• Bull02 / B6C3F1 / M / oral (aqueous)
. A««-,Q/I ; QCI^QCI ; i\/i ; „.-,] /«„.-., «;i «n«tmi^\
25 n
500 1000 1500 2000 2500
mg/kg-d (oral gavage)
NCI76/B6C3F1 / F / oral
- NCI76 / B6C3F1 / M / oral
Figure 4-10. Dose-response relationship, expressed as (A) percentage
incidence and (B) fold-increase over controls, for TCE hepatocarcinogenicity
in NCI (1976). For comparison, incidences of carcinomas for NTP (1990), Anna
et al. (1994), and Bull et al. (2002) are included, but without connecting lines
since they are not appropriate for assessing the shape of the dose-response
relationship.
40% -
200 400 600
ppm (7 hr/d, 5 d/wk)
800
12 n
200 400 600
ppm (7 hr/d, 5 d/wk)
800
Maltoni86/B6C3F1 / F / inhal
Maltoni86 / B6C3F1 / M / inhal [BT306]
Maltoni86 / B6C3F1 / M / inhal [BT306bis]
Maltoni86 / B6C3F1 / F / inhal
- Maltoni86 / B6C3F1 / M / inhal [BT306]
Maltoni86 / B6C3F1 / M / inhal [BT306bis]
/ i\fl / ;»uni
Note that the BT306 experiment reported excessive mortality due to fighting, and
so the paradigm was repeated in experiment BT306bis using mice from a different
source.
Figure 4-11. Dose-response relationship, expressed as (A) incidence and
(B) fold-increase over controls, for TCE hepatocarcinogenicity in Maltoni
et al. (1988; 1986).
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100% -
0% -
0 100 200 300 400 500
DCA mg/kg-d
100 200 300 400
DCA mg/kg-d
500
Drinking water concentrations were 0, 0.05, 0.5, 1, 2, and 3.5 g/L, from which daily
average doses were calculated using observed water consumption in the study.
Figure 4-12. Dose-response data for HCCs (A) incidence and (B) multiplicity,
induced by DCA from DeAngelo et al. (1999).
Associations of DCA carcinogenicity with various noncancer, possibly precursor, effects
was also investigated. Importantly, the doses that induced tumors in DeAngelo et al. (1999)
were reported to not induce widespread cytotoxicity. An attempt was also made to relate
differing exposure levels to subchronic changes and peroxisomal enzyme induction.
Interestingly, DeAngelo et al. (1999) reported that peroxisome proliferation was significantly
increased at 3.5 g/L DCA only at 26 weeks, not correlated with tumor response, and not
increased at either 0.05 or 0.5 g/L treatments. The authors concluded that DCA-induced
carcinogenesis was not dependent on peroxisome proliferation or chemically sustained
proliferation, as measured by DNA synthesis. Slight hepatomegaly was present by 26 weeks in
the 0.5 g/L group and decreased with time. By contrast, increases in both percentage liver/body
weight and the multiplicity of HCCs increased proportionally with DCA exposure concentration
after 79-100 weeks of exposure. DeAngelo et al. (1999) presented a figure comparing the
number of HCCs/animal at 100 weeks compared with the percentage liver/body weight at
26 weeks that showed a linear correlation (r2 = 0.9977), while peroxisome proliferation and
DNA synthesis did not correlate with tumor induction profiles. The proportional increase in
liver weight with DCA exposure was also reported for shorter durations of exposure as noted
previously. Therefore, for DCA, both tumor incidence and liver weight appear to increase
proportionally with dose.
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4.5.6.3.2.3. TCA carcinogenicity dose-response data
Pereira (1996) reported that for 82-week exposures to TCA in female B6C3Fi mice, TCA
exposure concentrations of 0, 2, 6.67, and 20 mmol/L (0, 0.33, 1.1, and 3.3 g/L) led to increasing
incidences and multiplicity of adenomas and of carcinomas (see Figure 4-13). DeAngelo et al.
(2008) reported the results of three experiments exposing male B6C3Fi mice to neutralized TCA
in drinking water (incidences also in Figure 4-13). Rather than using five exposure levels that
were generally twofold apart, as was done in DeAngelo et al. (1999) for DC A, DeAngelo et al.
(2008) studied only three doses of TCA that were an order of magnitude apart, which limits the
elucidation of the shape of the dose-response curve. In addition, for the 104-week data,
DeAngelo et al. (2008) contained two studies, each conducted in a separate laboratories—the
two lower doses were studied in one study and the highest dose in another. The first 104-week
study was conducted using 2 g/L sodium chloride, or 0.05, 0.5, or 5 g/L TCA in drinking water
for 60 weeks (Study #1) while the other two were conducted for a period of 104 weeks (Study #2
with 2.5 g/L neutralized acetic acid or 4.5 g/L TCA exposure groups and Study #3 with
deionized water, 0.05 g/L TCA and 0.5 g/L TCA exposure groups). In addition, a relatively
small number of animals were used for the determination of a tumor response (n ~ 30 at final
necropsy).
100%
2 4
TCA concentration (g/l)
DeAngelo et al. (2008) 60 wk (Study #1, M)
Pereira (1996) 82 wk(F)
DeAngelo et al. (2008) 104 wk (Study #2, M)
DpAnnpIn pt al C9nnfiUfitiirl\/ #3 Ml
1.6 -
2 4
TCA concentration (g/l)
DeAngelo et al. (2008) 60 wk (Study #1, M)
Pereira (1996) 82 wk (F)
DeAngelo et al. (2008) 104 wk (Study #2, M)
fit al f^OORUSturlv #3 Ml
Combined HCA + HCC were not reported in Pereira (1996).
Sources: (DeAngelo et al.. 2008: Pereira. 1996)).
Figure 4-13. Reported incidences of HCCs and hepatocellular adenomas
plus carcinomas (HCA + HCC) in various studies in B6C3Fi mice.
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In Study #1, the incidence data for adenomas observed at 60 weeks at 0.05, 0.5, and
5.0 g/L TCA were 2.1-, 3.0- and 5.4-fold of control values, with similar fold increases in
multiplicity. As shown by Pereira (1996), 60 weeks does not allow for full tumor expression, so
whether the dose-response relationship is the same at 104 weeks is not certain. For instance,
Pereira (1996) examined the tumor induction in female B6C3Fi mice and demonstrated that foci,
adenoma, and carcinoma development in mice are dependent on duration of exposure (period of
observation in controls). In control female mice a 360- vs. 576-day observation period showed
that at 360 days, no foci or carcinomas and only 2.5% of animals had adenomas, whereas by
576 days of observation, 11% had foci, 2% adenomas, and 2% had carcinomas. For DCA and
TCA treatments, foci, adenomas, and carcinoma incidence and multiplicity did not reach full
expression until 82 weeks at the three doses employed. Although the numbers of animals were
relatively low and variable at the two highest doses (18-28 mice), there were 50-53 mice studied
at the lowest dose level and 90 animals studied in the control group.
Therefore, the 104-week DeAngelo et al. (2008) data from Studies #2 and #3 would
generally be preferred for elucidating the TCA dose-response relationship. However, Study #2
was only conducted at one dose, and although Study #3 used lower doses, it exhibited
extraordinarily high control incidences of liver tumors. In particular, while the incidence of
adenomas and carcinomas was 12% in Study #2, it was reported to be 64% in Study #3. The
mice in Study #3 were of very large size (weighing -50 g at 45 weeks) as compared to Study #1,
Study #2, or most other bioassays in general, and the large background rate of tumors reported is
consistent with the body-weight-dependence observed by Leakey et al. (2003a).
To put into context the 64% incidence data for carcinomas and adenomas reported in
DeAngelo et al. (2008) for the control group of Study #3, other studies cited in this review for
male B6C3Fi mice show a much lower incidence in liver tumors with: (1) NCI (1976) reporting
a colony control level of 6.5% for vehicle and 7.1% incidence of HCCs for untreated male
B6C3Fi mice (n = 70-77) at 78 weeks; (2) Herren-Freund et al. (1987) reporting a 9% incidence
of adenomas in control male B6C3Fi mice with a multiplicity of 0.09 ± 0.06 and no carcinomas
(n = 22) at 61 weeks; (3) NTP (1990) reporting an incidence of 14.6% adenomas and 16.6%
carcinomas in male B6C3Fi mice after 103 weeks (n = 48); and (4) Maltoni et al. (1988; 1986)
reporting that B6C3Fi male mice from the —NCsource" had a 1.1% incidence of—fapatoma"
(carcinomas and adenomas) and those from —Chads River Co." had a 18.9% incidence of
—heptoma" during the entire lifetime of the mice (n = 90 per group). The importance of
examining an adequate number of control or treated animals before confidence can be placed in
those results in illustrated by Anna et al. (1994) in which at 76 weeks, 3/10 control male B6C3Fi
mice that were untreated and 2/10 control animals given corn oil were reported to have
adenomas, but from 76 to 134 weeks, 4/32 mice were reported to have adenomas (multiplicity of
0.13 ± 0.06) and 4/32 mice were reported to have carcinomas (multiplicity of 0.12 ± 0.06).
Thus, the reported combined incidence of carcinomas and adenomas of 64% reported by
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DeAngelo et al. (2008) for the control mice of Study #3 not only is inconsistent and much higher
than those reported in Studies #1 and #2, but also is much higher than reported in a number of
other studies of TCE.
Therefore, this large background rate and the increased mortality for these mice limit
their use for determining the nature of the dose-response for TCA liver carcinogenicity. At the
two lowest doses of 0.05 and 0.5 g/L TCA from Study #3, the differences in the incidences and
multiplicities for all tumors were twofold at 104 weeks. However, there was no difference in any
of the tumor results (i.e., adenoma, carcinoma, and combinations of adenoma and carcinoma
incidence and multiplicity) between the 4.5 g/L dose group in Study #2 and the 0.5 g/L dose
group in Study #3 at 104 weeks. By contrast, at 60 weeks of exposure, but within the same study
(Study #1), there was a twofold increase in multiplicity for adenomas, and for adenomas and
carcinomas combined between the 0.5 and 5.0 g/L TCA exposure groups. These results are
consistent with the two highest exposure levels reaching a plateau of response after a long
enough duration of exposure for full expression of the tumors (i.e., -90% of animals having liver
tumors at the 0.5 and 5 g/L exposures). However, whether such a plateau would have been
observed in mice with a more —nonal" body weight, and hence a lower background tumor
burden, cannot be determined.
Because of the limitations of different studies, it is difficult to discern whether the liver
tumor dose-response curves of TCA and DCA are different in a way analogous to that for liver
weight (see Figure 4-14). Certainly, it is clear that at the same concentration in drinking water or
estimated applied dose, DCA is more potent than TCA, as DCA induces nearly 100% incidence
of carcinomas at a lower dose than TCA. Therefore, like with liver weight gains, DCA has a
steeper dose-response function than TCA. However, the evidence for a —piteau" in tumor
response at high doses with TCA, as was observed for liver weight, is equivocal, as it is
confounded by the highly varying background tumor rates and the limitations of the available
study paradigms.
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o
200 400
mg/kg-d
600
DeAngelo et al. (2008) (TCA Study #2)
DeAngelo et al. (2008) (TCA Study #3)
DeAngelo et al. (1999) (DCA)
Only carcinomas were reported in DeAngelo et al. (1999), so combined adenomas
and carcinomas could not be compared.
Sources: (DeAngelo et al.. 2008: DeAngelo et al.. 1999).
Figure 4-14. Reported incidence of HCCs induced by DCA and TCA in
104-week studies.
DeAngelo et al. (2008) attempted to identify a NOEL for tumorigenicity using tumor
multiplicity data and estimated TCA dose. However, it is not an appropriate descriptor for these
data, especially given that —tatistical significance" of the tumor response is the determinant used
by the authors to support the conclusions regarding a dose in which there is no TCA-induced
effect. Due to issues related to the appropriateness of use of the concurrent control in Study #3,
only the 60-week experiment (i.e., Study #1) is useful for the determination of tumor dose-
response. Not only is there not allowance for full expression of a tumor response at the 60-week
time point, but a power calculation of the 60-week study shows that the Type II error, which
should be >50% and thus, greater than the chances of —Ifpping a coin," was 41 and 71% for
incidence and 7 and 15% for multiplicity of adenomas for the 0.05 and 0.5 g/L TCA exposure
groups. For the combination of adenomas and carcinomas, the power calculation was 8 and 92%
for incidence and 6 and 56% for multiplicity at 0.05 and 0.5 g/L TCA exposure. Therefore, the
designed experiment could accept a false null hypothesis, especially in terms of tumor
multiplicity, at the lower exposure doses and erroneously conclude that there is no response due
to TCA treatment.
In terms of correlations with other noncancer, possibly precursor effects, DeAngelo et al.
(2008) also reported that PCO activity, which varied 2.7-fold as baseline controls, was 1.3-, 2.4-,
and 5.3-fold of control for the 0.05, 0.5, and 5 g/L TCA exposure groups in Study #1 at 4 weeks
4-306
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and adenomas incidence was 2.1-, 3.0-, and 5.4-fold of control and not similar at the lowest dose
level at 60 weeks. However, it is not clear whether the similarly between PCO and
carcinogenicity at 60 weeks would persist for tumor incidence at 104 weeks. DeAngelo et al.
(2008) provided regression analyses to compare —pKent of hepatocellular neoplasia," as
indicated by tumor multiplicity, with TCA dose, represented by estimations of the TCA dose in
mg/kg-day, and with PCO activity for the 60- and 104-week data. Whether adenomas and
carcinomas combined or individual tumor type were used in these analyse was not reported by
the authors. However, it would be preferable to compare —pacursor" levels of PCO at earlier
time points, rather than at a time when there was already a significant tumor response. In
addition, linear regression analyses of these data are difficult to interpret because of the wide
dose spacing of these experiments. In such a situation, for a linear regression, control and 5 g/L
exposure levels will basically determine the shape of the dose-response curve since the 0.05 and
0.5 g/L exposure levels are so close to the control (zero) value. Thus, dose-response appears to
be linear between control and the 5.0 g/L value with the two lowest doses not affectively
changing the slope of the line (i.e., -leveraging" the regression). Moreover, at the 5 g/L dose
level, there is potential for effects due to palatability, as reported in one study in which drinking
water consumption declined at this concentration (DeAngelo et al., 2008). Thus, the value of
these analyses is limited by: (1) the use of data from Study #3 in a tumor prone mouse that is not
comparable to those used in Studies #1 and #2; (2) the appropriateness of using PCO values from
later time points and the variability in PCO control values; (3) the uncertainty of the effects of
palatability on the 5 g/L TCA results, which were reported in one study to reduce drinking water
consumption; and (4) the dose-spacing of the experiment.
4.5.6.3.2.4. CH carcinogenic dose-response
Although a much more limited database in rodents than for TCA or DC A, there is
evidence that CH is also a rodent liver hepatocarcinogen [see also Section E.2.5 and Caldwell
and Keshava (2006)1.
Daniel et al. (1992) exposed adult male B6C3Fi 28-day-old mice to 1 g/L CH in drinking
water for 30 and 60 weeks (n = 5 for interim sacrifice) and for 104 weeks (n = 40). The
concentration of CH was 1 g/L and estimated to provide a 166-mg/kg-day dose. It is not clear
from the report what control group better matched the CH group, as the mean initial body
weights of the groups as well as the number of animals varied considerably in each group (i.e.,
-40% difference in mean body weights at the beginning of the study). Liver tumors were
increased by CH treatment. The percentage incidence of liver carcinomas and adenomas in the
surviving animals was 15% in control and 71% in CH-treated mice and the incidence of HCC
was reported to be 46% in the CH-treated group. The number of tumors/animals was also
significantly increased with CH treatment. However, because this was a single-dose study, a
comparison with the dose-response relationship with TCE, TCA, or DCA is not feasible.
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George et al. (2000) exposed male B6C3Fi mice to CH in drinking water for 2 years.
Groups of animals were sacrificed at 26, 52, and 78 weeks following the initiation of dosing,
with terminal sacrifices at week 104. Only a few animals received a complete pathological
examination. Preneoplastic foci and adenomas were increased in the livers of all CH treatment
groups at 104 weeks. The percentage incidences of hepatocellular adenomas were reported to be
21.4, 43.5, 51.3, and 50% in control, 13.5, 65.0 and 146.6 mg/kg-day CH treatment groups,
respectively. The percentage incidences of HCCs were reported to be 54.8, 54.3, 59.0 and 84.4%
in these same groups. The resulting percentage incidence of hepatocellular adenomas and
carcinomas were reported to be 64.3, 78.3, 79.5 and 90.6%. Of concern is the reporting of a 64%
incidence of HCCs and adenomas in the control group of mice for this experiment, which is the
same as that for another study published by this same laboratory (DeAngelo et al., 2008).
DeAngelo et al. (2008) did not identify them as being contemporaneous studies or sharing
controls, but a comparison of the control data published by DeAngelo et al. (2008) for TCA and
that published by George et al. (2000) for the CH studies shows them to be the same data set.
Therefore, as discussed above, this data set was derived from B6C3Fi mice that were large
(-50 g) and resultantly tumor prone, making determinations of the dose-response of CH from
this experiment difficult. Therefore, for the purposes of comparison of dose-response
relationships, this study has the same limitations as the DeAngelo et al. (2008) study, discussed
above.
Leakey et al. (2003_b)studied the effects of CH exposure (0, 25, 50, and 100 mg/kg-day,
5 days/week, 104-105 weeks via gavage) in male B6C3Fi mice with dietary control used to
manipulate body growth (n = 48 for 2-year study and n = 12 for the 15-month interim study).
Dietary control was reported to decrease background liver tumor rates (decreased by 15-20%)
and was reported to be associated with decreased variation in liver-to-body weight ratios, thereby
potentially increasing assay sensitivity. In dietary-controlled groups and groups fed ad libitum,
liver adenomas and carcinomas (combined) were reported to be increased with CH treatment.
With dietary restriction, there was a more discernable CH tumor-response with overall tumor
incidence reduced, and time-to-tumor increased by dietary control in comparison to ad-libitum-
fed mice. Incidences of hepatocellular adenoma and carcinoma overall rates were reported to be
33, 52, 49, and 46% for control, 25, 50, and 100 mg/kg ad-libitum-fed mice, respectively. For
dietary controlled mice the incidence rates were reported to be 22.9, 22.9, 29.2, and 37.5% for
controls, 25, 50, and 100 mg/kg CH, respectively. Body weights were matched and carefully
controlled in this study. These data are shown in Figure 4-15, relative to control incidences. It is
evident from these data that dietary control significantly changes the apparent shape of the dose-
response curve, presumably by reducing variability between animals. While the ad libitum dose
groups had an apparent -saturation" of response, this was not evident with the dietary controlled
group. Of note is that all of the other bioassays for TCE, TCA, DCA, and CH were in ad-
libitum-fed mice. Therefore, it is difficult to compare the dose-response curves for CH-treated
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mice on dietary restriction to those fed ad libitum. However, the rationale for dietary restriction
in the B6C3Fi mouse is to prevent the types of weight gain and corresponding high background
tumor levels observed in DeAngelo et al. (2008) and George et al. (2000). As stated previously,
most other studies of TCA, DCA, and TCE had background levels that, while varied, were lower
than the ad-libitum-fed mice studied in Leakey et al. (2003b)
60%
50% -
= 40%
;o
= 30% ^
o
20
40 60
CH mg/kg-d
80
100
ad libitum -H- dietary control
Source: Leakey et al. (2003b)
Figure 4-15. Effects of dietary control on the dose-response curves for
changes in liver tumor incidences induced by
Of note is that incidences of adenomas and carcinomas combined do not show
differences in tumor progression as carcinomas may increase and adenomas may regress. Liver
weight increases at 15 months did not correlate with 2-year tumor incidences in the ad libitum
group, but a consistent dose-response shape between these two measures is evident in the dietary
controlled group. However, of note is the reporting of liver weight at 15 months is for a time
period in which foci and liver tumors have been reported to have already occurred in other
studies, so hepatomegaly in the absence of these changes is hard to detect.
In terms of other noncancer effects that may be associated with tumor induction, it is
notable that while dietary restriction reduced the overall level of CH-mediated tumor induction,
it led to greater CH-mediated induction of peroxisome proliferation-associated enzymes.
Moreover, between control groups, dietary restricted mice appeared to have higher levels of
lauric acid co-hydrolase activity than ad-libitum-fed mice. Seng et al. (2003) report that lauric
acid p-hydroxylase and PCO were induced only at exposure levels >100 mg/kg CH, again with
dietary-restricted groups showing the greatest induction. Such data argue against the role of
peroxisome proliferation in CH liver tumor induction in mice.
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Leakey et al. (2003_b) gave no descriptions of liver pathology, other than incidence of
mice with fatty liver changes. Hepatic malondialdehyde concentration in ad-libitum-fed and
dietary-controlled mice did not change with CH exposure at 15 months, but the dietary-
controlled groups were all approximately half that of the ad-libitum-fed mice. Thus, while
overall increased tumors observed in the ad libitum diet correlated with increased
malondialdehyde concentration, there was no association between CH dose and malondialdehyde
induction for either diet.
Overall, from the CH studies in mice, there is an apparent increase in liver adenomas and
carcinomas induced by CH treatment by either drinking water or gavage with all available
studies performed in male B6C3Fi mice. However, the background levels of hepatocellular
adenomas and carcinomas in these mice in George et al. (2000) and body-weight data from this
study are high, consistent with the association between large body weight and background tumor
susceptibility shown with dietary control (Leakey et al., 2003b). With dietary control, Leakey
et al. (2003_b) report a dose-response relationship between exposure and tumor incidence that is
proportional to dose.
4.5.6.3.2.5. Degree of concordance among TCE, TCA, DCA, and CH dose-response
relationships
A quantitative comparison of the carcinogenicity dose-response relationships among
TCE, TCA, DCA, and CH—analogous to the quantitative comparison between TCE and TCA
hepatomegaly—was considered. This first step in such a comparison would an examination of
the dose-response data for TCE alone to see if they are consistent with a single dose-response
relationship. As shown in Figures 4-10 and 4-11, there is substantial variability among the
available liver tumor dose-response data that was not observed for hepatomegaly. The strain of
mice used in the bioassays had a difference in not only TCE liver tumor response, but also
background liver tumor incidence. Differences in exposure paradigms in the bioassays also leads
to difference in tumor incidence and reporting. In addition, unlike the case with TCE
hepatomegaly data in mice, the TCE dose-response data for liver tumors in mice exposed via
inhalation and gavage are not consistent with a common dose-response curve even on an internal
dose basis (e.g., Rhomberg, 2000) (Section 5.2). This heterogeneity is also evident for the TCA
dose-response data, as shown in Figure 4-13, which may in part be due to the differences in
study duration. Furthermore, among all of the available cancer bioassay data for TCE, TCA,
DCA, and CH, the control incidences for background liver tumors vary from about 1% to over
50%, and difference of >50-fold that adds substantial uncertainty to any joint analysis.
Therefore, differences within and across the databases of these compounds, such as the
comparability of study durations, control tumor incidences, and carcinogenic potency, preclude
either a quantitative analysis or a definitive conclusion. This question may be better addressed
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experimentally where similar animals are exposed to different compounds in the same
experimental setting.
4.5.6.3.3. Inferences from liver tumor phenotype and genotype
A number of studies have investigation tumor phenotypes, such as c-Jun staining,
tincture, and dysplacity, or genotypes, such as H-ras mutations, to inform both the identification
of the active agents of TCE liver tumor induction as well as what mode(s) of action may be
involved.
4.5.6.3.3.1. Tumor phenotype—staining and appearance
The descriptions of tumors in mice reported by the NCI (1976), NTP (1990), and Maltoni
et al. (1988; 1986) studies are also consistent with phenotypic heterogeneity as well as
spontaneous tumor morphology (see Section E.3.4.1.5). As noted in Section E.3.1, HCCs
observed in humans are also heterogeneous. For mice, Maltoni et al. (1986) described malignant
tumors of hepatic cells to be of different subhistotypes, of various degrees of malignancy, and
unique or multiple, and have different sizes (usually detected grossly at necropsy) from TCE
exposure. In regard to phenotype, tumors were described as usual type observed in Swiss and
B6C3Fi mice, as well as in other mouse strains, either untreated or treated with
hepatocarcinogens and to frequently have medullary (solid), trabecular, and pleomorphic
(usually anaplastic) patterns. For the NCI (1976) study, the mouse liver tumors were described
in detail and to be heterogeneous -as described in the literature" and similar in appearance to
tumors generated by carbon tetrachloride. The description of liver tumors in this study and
tendency to metastasize to the lung are similar to descriptions provided by Maltoni et al. (1986)
for TCE-induced liver tumors in mice via inhalation exposure. The NTP (1990) study reported
that TCE exposure is associated with increased incidence of HCC (tumors with markedly
abnormal cytology and architecture) in male and female mice. Hepatocellular adenomas were
described as circumscribed areas of distinctive hepatic parenchymal cells with a perimeter of
normal-appearing parenchyma in which there were areas that appeared to be undergoing
compression from expansion of the tumor. Mitotic figures were sparse or absent, but the tumors
lacked typical lobular organization. HCCs were reported to have markedly abnormal cytology
and architecture with abnormalities in cytology cited as including increased cell size, decreased
cell size, cytoplasmic eosinophilia, cytoplasmic basophilia, cytoplasmic vacuolization,
cytoplasmic hyaline bodies, and variations in nuclear appearance. Furthermore, in many
instances, several or all of the abnormalities were reported to be present in different areas of the
tumor and variations in architecture, with some of the HCCs having areas of trabecular
organization. Mitosis was variable in amount and location. Therefore, the phenotype of tumors
reported from TCE exposure was heterogeneous in appearance between and within tumors from
all three of these studies.
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Caldwell and Keshava (2006) reported that Bannasch (2001) and Bannasch et al. (2001)
describe the early phenotypes of preneoplastic foci induced by many oncogenic agents (DNA-
reactive chemicals, radiation, viruses, transgenic oncogenes and local hyperinsulinism) as
insulinomimetic. These foci and tumors have been described by tincture (after hematoxylin and
eosin staining of structural contents) as primarily eosinophilic (pink, reflecting eosin staining,
e.g., staining of intracellular and extracellular protein), basophilic (blue, reflecting hematoxylin
staining, e.g., staining of ribosomes and arginine rich basic nucleorprotein such as histones), and
to be heterogeneous. Primary eosin staining is associated with a less malignant state of the
hepatocyte with increased ribosomal content, decreased glycogen content, and increased
basophilia of the cytoplasm by hematoxylin staining to be indicative of a more malignant state or
tumor progression (Carter et al., 2003; Bannasch, 2001). Several studies do identify foci and
tumors as primarily eosinophilic or basophilic, but do not give specific criteria for how a foci or
tumor (which can be and usually is made up of a mixture of phenotypically heterogenous cells)
are assigned to be one category or another. Caldwell and Keshava (2006) noted that the tumors
observed after TCE exposure are consistent with the description for the main tumor lines of
development described by Bannasch et al. (2001). Thus, the response of liver to DCA
(glycogenesis with emergence of glycogen poor tumors) is similar to the progression of
preneoplastic foci to tumors induced from a variety of agents and conditions associated with
increased cancer risk. Furthermore Caldwell and Keshava (2006) note that Bull et al. (2002)
report expression of insulin receptor to be elevated in tumors of control mice or mice treated with
TCE, TCA, and DCA but not in nontumor areas, suggesting that this effect is not specific to
DCA.
There is a body of literature that has focused on the effects of TCE and its metabolites
after rats or mice were exposed to —mtagenic" agents to -4nitiate" hepatocarcinogenesis and this
is discussed in Section E.4.2. TCE and its metabolites were reported to affect tumor incidence,
multiplicity, and phenotype when given to mice as a co-exposure with a variety of —initiatig"
agents and with other carcinogens. Pereira and Phelps (1996) reported that methyl nitrosourea
(MNU) alone induced basophilic foci and adenomas. MNU and low concentrations of DCA or
TCA in female mice were reported to induce heterogeneous for foci and tumor with a higher
concentration of DCA inducing more eosinophilic and a higher concentration of TCA inducing
more tumors that were basophilic. Pereira et al. (2001) reported that not only dose, but also
gender affected phenotype in mice that had already been exposed to MNU and were then
exposed to DCA. As for other phenotypic markers, Lantendresse and Pereira (1997) reported
that exposure to MNU and TCA or DCA induced tumors that had some commonalities (i.e., were
hererogeneous), but differences were noted for female mice exposed to DCA or TCA as co-
exposures with MNU.
With regard to the phenotype of TCA and DCA-induced tumors, Stauber and Bull (1997)
reported the for male B6C3Fi mice, DCA-induced —dsions" contained a number of smaller
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lesions that were heterogeneous and more eosinophilic with larger "lesions" tending to be less
numerous and more basophilic. For TCA results using this paradigm, the —Mons" were
reported to be less numerous, more basophilic, and larger than those induced by DCA. Carter
et al. (2003) used tissues from the DeAngelo et al. (1999) study and examined the heterogeneity
of the DCA-induced lesions and the type and phenotype of preneoplastic and neoplastic lesions
pooled across all time points. Carter et al. (2003) examined the phenotype of liver tumors
induced by DCA in male B6C3Fi mice and the shape of the dose-response curve for insight into
its mode of action. They reported a dose-response of histopathologic changes (all classes of
premalignant lesions and carcinomas) occurring in the livers of mice at doses of 0.05-3.5 g/L
DCA for 26-100 weeks and suggested that foci and adenomas demonstrated neoplastic
progression with time at lower doses than observed DCA genotoxicity. Preneoplastic lesions
were identified as eosinophilic, basophilic and/or clear cell (grouped with clear cell and mixed
cell), and dysplastic. Altered foci were 50% eosinophilic with about 30% basophilic. As foci
became larger and evolved into carcinomas, they became increasingly basophilic. The pattern
held true throughout the exposure range. There was also a dose and length of exposure related
increase in atypical nuclei in -^oninvolved" liver. Glycogen deposition was also reported to be
dose-dependent with periportal accumulation at the 0.5 g/L exposure level. Carter et al. (2003)
suggested that size and evolution into a more malignant state are associated with increasing
basophilia, a conclusion consistent with those of Bannasch (1996) and that there is a greater
periportal location of lesions suggestive as the location from which they arose. Consistent with
the results of DeAngelo et al. (1999). Carter et al. (2003) reported that DCA (0.05-3.5 g/L)
increased the number of lesions per animal relative to animals receiving distilled water, that
DCA shortened the time to development of all classes of hepatic lesions, and that the phenotype
of the lesions were similar to those spontaneously arising in controls. Along with basophilic and
eosinophilic lesions or foci, Carter et al. (2003) concluded that DCA-induced tumors also arose
from isolated, highly dysplastic hepatocytes in male B6C3Fi mice chronically exposed to DCA
suggesting another direct neoplastic conversion pathway other than through eosinophilic or
basophilic foci.
Rather than male B6C3Fi mice, Pereira (1996) studied the dose-response relationship for
the carcinogenic activity of DCA and TCA and characterized their lesions (foci, adenomas, and
carcinomas) by tincture in females (the generally less sensitive gender). Like the studies of TCE
by Maltoni et al. (1988; 1986), female mice were also reported to have increased liver tumors
after TCA and DCA exposures. Pereira (1996) pool lesions were pooled for phenotype analysis
so the effect of duration of exposure could not be determined and adenomas could not be
separated from carcinomas for "tumors." However, Pereira (1996) reported that a decrease in the
concentration of DCA resulted in a decrease in the number of foci and a shift in the phenotype
from primarily eosinophilic foci (i.e., -95% eosinophilic at 2.58 g/L DCA) to basophilic foci
(-57% eosinophilic at 0.26 g/L). For TCA, the number of foci was reported to -40 basophilic
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and -60 eosinophilic, regardless of dose. Spontaneously occurring foci were more basophilic by
a ratio of 7/3. Pereira (1996) described the foci of altered hepatocytes and tumors induced by
DCA in female B6C3Fi mice to be eosinophilic at higher exposure levels, but at lower or
intermittent exposures, they were half eosinophilic and half basophilic. Regardless of exposure
level, half of the TCA-induced foci were reported to be half eosinophilic and half basophilic with
tumors 75% basophilic. In control female mice, the limited numbers of lesions were mostly
basophilic, with most of the rest being eosinophilic with the exception of a few mixed tumors.
The limitations of descriptions of tincture and especially for inferences regarding peroxisome
proliferator from the description of-basophilia" is discussed in Section E.3.4.1.5.
Thus, the results appear to differ between male and female B6C3Fi mice in regard to
tincture for DCA and TCA at differing doses. What is apparent is that the tincture of the lesions
is dependent on the stage of tumor progression, agent (DCA or TCA), gender, and dose. Also
what is apparent from these studies is that both DCA and TCA are heterogeneous in their
tinctoral characteristics.
Overall, tumors induced by TCA, DCA, CH, and TCE are all heterogeneous in their
physical and tinctural characteristics in a manner this not markedly distinguishable from
spontaneous lesions or those induced by a wide variety of chemical carcinogens. For instance,
Daniel et al. (1992), which studied DCA and CH carcinogenicity (discussed above), noted that
morphologically, there did not appear to be any discernable differences in the visual appearance
of the DCA- and CH-induced tumors. Therefore, these data do not provide strong insights into
elucidating the active agent(s) for TCE hepatocarcinogenicity or their mode(s) of action.
4.5.6.3.3.2. C-Jun staining
Stauber and Bull (1997) reported that in male B6C3Fi mice, the oncoproteins, c-Jun and
c-Fos, were expressed in liver tumors induced by DCA but not those induced by TCA. Although
Bull et al. (2004) suggested that the negative expression of c-Jun in TCA-induced tumors may be
consistent with a characteristic phenotype shown in general by peroxisome proliferators as a
class, as pointed out by Caldwell and Keshava (2006), there is no supporting evidence of this.
Nonetheless, the observation that TCA and DCA have different levels of oncogene expression
led to a number of follow-up studies by this group. No data on oncoprotein immunostaining are
available for CH.
Stauber et al. (1998) studied induction of—tnasformed" hepatocytes by DCA and TCE
treatment in vitro, including an examination of c-Jun staining. Stauber et al. (1998) isolated
primary hepatocytes from 5 to 8-week-old male B6C3Fi mice (n = 3) and subsequently cultured
them in the presence of DCA or TCA. In a separate experiment, 0.5 g/L DCA was given to mice
as pretreatment for 2 weeks prior to isolation. The authors assumed that the anchorage-
independent growth of these hepatocytes was an indication of an —irtiated cell." After 10 days
in culture with DCA or TCA (0, 0.2, 0.5, and 2.0 mM), concentrations of >0.5 mM DCA and
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TCA both induced an increase in the number of colonies that was statistically significant, with
DCA showing dose-dependence as well as slightly greater overall increases than TCA. In a
time-course experiment, the number of colonies from DCA treatment in vitro peaked by 10 days
and did not change through days 15-25 at the highest dose and, at lower concentrations of DCA,
increased time in culture induced similar peak levels of colony formation by days 20-25 as that
reached by 10 days at the higher dose. Therefore, the number of colonies formed was
independent of dose if the cells were treated long enough in vitro. However, not only did
treatment with DCA or TCA induce anchorage independent growth but untreated hepatocytes
also formed larger numbers of colonies with time, although at a lower rate than those treated
with DCA. The level reached by untreated cells in tissue culture at 20 days was similar to the
level induced by 10 days of exposure to 0.5 mM DCA. The time course of TCA exposure was
not tested to see if it had a similar effect with time as did DCA. The colonies observed at
10 days were tested for c-Jun expression with the authors noting that —ebonies promoted by
DCA were primarily c-Jun positive in contrast to TCA promoted colonies that were
predominantly c-Jun negative." Of the colonies that arose spontaneously from tissue culture
conditions, 10/13 (76.9%) were reported to be c-Jun+, those treated with DCA 28/34 (82.3%)
were c-Jun+, and those treated with TCA 5/22 (22.7%) were c-Jun+. Thus, these data show
heterogeneity in cell in colonies but with more that were c-Jun+ colonies occurring by tissue
culture conditions alone than in the presence of DCA, rather than in the presence of TCA.
Bull et al. (2002) administered TCE, TCA, DCA, and combinations of TCA and DCA to
male B6C3Fi mice by daily gavage (TCE) or drinking water (TCA, DCA, and TCA + DCA) for
52-79 weeks, in order to compare a number of tumor characteristics, including c-Jun expression,
across these different exposures. Bull et al. (2002) reported lesion reactivity to c-Jun antibody to
be dependent on the proportion of the DCA and TCA administered after 52 weeks of exposure.
Given alone, DCA was reported to produce lesions in mouse liver for which approximately half
displayed a diffuse immunoreactivity to a c-Jun antibody, half did not, and none exhibited a
mixture of the two. After TCA exposure alone, no lesions were reported to be stained with this
antibody. When given in various combinations, DCA and TCA co-exposure induced a few
lesions that were only c-Jun+, many that were only c-Jun-, and a number with a mixed
phenotype whose frequency increased with the dose of DCA. For TCE exposure of 79 weeks,
TCE-induced lesions were reported to also have a mixture of phenotypes (42% c-Jun+, 34%
c-Jun-, and 24% mixed) and to be most consistent with those resulting from DCA and TCA co-
exposure but not either metabolite alone.
A number of the limitations of the experiment are discussed in Caldwell et al. (2008b)
Specifically, for the DCA- and TCA-exposed animals, the experiment was limited by low
statistical power, a relatively short duration of exposure, and uncertainty in reports of lesion
prevalence and multiplicity due to inappropriate lesions grouping (i.e., grouping of hyperplastic
nodules, adenomas, and carcinomas together as -tumors"), and incomplete histopathology
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determinations (i.e., random selection of gross lesions for histopathology examination). For
determinations of immunoreactivity to c-Jun, Bull et al. (2002) combined hyperplastic nodules,
adenomas, and carcinomas in most of their treatment groups, so differences in c-Jun expression
across differing types of lesions were not discernable.
Nonetheless, these data collectively strongly suggest that TCA is not the sole agent of
TCE-induced mouse liver tumors. In particular, TCE-induced tumors that were, in order of
frequency, c-Jun+, c-Jun-, and of mixed phenotype, while c-Jun+ tumors have never been
observed with TCA treatment. Nor do these data support DC A as the sole contributor, since
mixed phenotypes were not observed with DC A treatment.
4.5.6.3.3.3. Tumor genotype: H-ras mutation frequency and spectrum
An approach to determine the potential modes of action of DC A and TCA through
examination of the types of tumors each -4nduced" or —seleted" was to examine H-ras activation
(Bull et al.. 2002: Ferreira-Gonzalez et al.. 1995: Anna et al.. 1994: Nelson et al.. 1989). No data
of this type were available for CH. This approach has also been used to try to establish an H-ras
activation pattern for —griotoxic" and "nongenotoxic" liver carcinogens compounds and to make
inferences concerning peroxisome proliferator-induced liver tumors. However, as noted by
Stanley et al. (1994), the genetic background of the mice used and the dose of carcinogen may
affect the number of activated H-ras containing tumors which develop. In addition, the stage of
progression of—Mons" (i.e., foci vs. adenomas vs. carcinomas) also has been linked the
observance of H-ras mutations. Fox et al. (1990) note that tumors induced by phenobarbital
(0.05% drinking water [EtzO], 1 year), chloroform (200 mg/kg corn oil gavage, 2 times weekly
for 1 year) or ciprofibrate (0.0125% diet, 2 years) had a much lower frequency of H-ras gene
activation than those that arose spontaneously (2-year bioassays of control animals) or induced
with the —griotoxic" carcinogen benzidine-2 hydrochloric acid (HC1) (120 ppm, drinking H2O,
1 year) in mice. In that study, the term —tmor" was not specifically defined, but a correlation
between the incidence of H-ras gene activation and the development of either a hepatocellular
adenoma or HCC was reported to be made with no statistically significant difference between the
frequency of H-ras gene activation in the hepatocellular adenomas and carcinomas.
Histopathological examination of the spontaneous tumors, tumors induced with benzidine-2 HC1,
phenobarbital, and chloroform was not reported to reveal any significant changes in morphology
or staining characteristics. Spontaneous tumors were reported to have 64% point mutation in
codon 61 (n = 50 tumors examined) with a similar response for benzidine of 59% (n = 22 tumors
examined), whereas the mutation rates were 7% (n = 15 tumors examined) for phenobarbital,
21% (n = 24 tumors examined) for chloroform, and 21% (n = 39 tumors examined) for
ciprofibrate. The ciprofibrate-induced tumors were reported to be more eosinophilic as were the
surrounding normal hepatocytes.
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Hegi et al. (1993) tested ciprofibrate-induced tumors in the NIH3T3 cotransfection-nude
mouse tumorigenicity assay, which the authors stated was capable of detecting a variety of
activated protooncogenes. The tumors examined (ciprofibrate-induced or spontaneously arising)
were taken from the Fox et al. (1990) study, screened previously, and found to be negative for
H-ras activation. With the limited number of samples examined, Hegi et al. (1993) concluded
that ras protooncogene activation or activation of other protooncogenes using the nude mouse
assay were not frequent events in ciprofibrate-induced tumors and that spontaneous tumors were
not promoted with it. Using the more sensitive methods, the H-ras activation rate was reported
to be raised from 21 to 31% for ciprofibrate-induced tumors and from 64 to 66% for spontaneous
tumors. Stanley et al. (1994) studied the effect of methylclofenapate (MCP) (25 mg/kg for up to
2 years), a peroxisome proliferator, in B6C3Fi (relatively sensitive) and C57BL/10J (relatively
resistant) mice for H-ras codon 61 point mutations in MCP-induced liver tumors (hepatocellular
adenomas and carcinomas). In the B6C3Fi mice, the number of tumors with codon 61 mutations
was 11/46 and for C57BL/10J mice 4/31. Unlike the findings of Fox et al. (1990). Stanley et al.
(1994) reported an increase in the frequency of mutation in carcinomas, which was reported to be
twice that of adenomas in both strains of mice, indicating that stage of progression was related to
the number of mutations in those tumors, although most tumors induced by MCP did not have
this mutation.
Anna et al. (1994) reported that the H-ras codon 61 mutation frequency was not
statistically different in liver tumors from DCA- and TCE-treated mice from a highly variable
number of tumors examined. From their concurrent controls, they reported that H-ras codon
61 mutations in 17% (n = 6) of adenomas and 100% (n = 5) of carcinomas. For historical
controls (published and unpublished), they reported mutations in 73% (n = 33) of adenomas and
mutations in 70% (n = 30) of carcinomas. For tumors from TCE-treated animals, they reported
mutations in 35% (n = 40) of adenomas and 69% (n = 36) of carcinomas, while for DCA-treated
animals, they reported mutations in 54% (n = 24) of adenomas and in 68% (n = 40) of
carcinomas. Anna et al. (1994) reported more mutations in TCE-induced carcinomas than
adenomas. In regard to mutation spectra in H-ras oncogenes in control or spontaneous tumors,
the patterns were slightly different but those from TCE treatment were mostly similar to that of
DCA-induced tumors (0.5% in drinking water).
The study of Ferreira -Gonzalez (1995) in male B6C3Fi mice has the advantage of
comparison of tumor phenotype at the same stage of progression (HCC), for allowance of the full
expression of a tumor response (i.e., 104 weeks), and an adequate number of spontaneous control
lesions for comparison with DCA or TCA treatments. However, tumor phenotype at an end
stage of tumor progression may not be indicative of earlier stages of the disease process. In
spontaneous liver carcinomas, 58% were reported to show mutations in H-61 as compared with
50% of tumor from 3.5 g/L DCA-treated mice and 45% of tumors from 4.5 g/L TCA-treated
mice. A number of peroxisome proliferators have been reported to have a much smaller
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mutation frequency that spontaneous tumors (e.g., 13-24% H-ras codon 61 mutations after
methylclofenopate depending on mouser strain, Stanely et al. [1994]: 21-31% for ciprofibrate-
induced tumors and 64-66% for spontaneous tumors, Fox et al. [1990] and Hegi et al. [1993]).
Thus, the heterogeneous response for H-ras mutations was similar for spontaneous and DCA-,
and TCA-induced HCCs and differed from the reduced H-ras mutation frequencies reported for a
number of peroxisome proliferators.
In his review, Bull (2000) suggested that —the iport by Anna et al. (1994) indicated that
TCE-induced tumors possessed a different mutation spectra in codon 61 of the H-ras oncogene
than those observed in spontaneous tumors of control mice." Bull (2000) stated that —results of
this type have been interpreted as suggesting that a chemical is acting by a mutagenic
mechanism" but went on to suggest that it is not possible to a priori rule out a role for selection
in this process and that differences in mutation frequency and spectra in this gene provide some
insight into the relative contribution of different metabolites to TCE-induced liver tumors. Bull
(2000) noted that data from Anna et al. (1994), Ferreira-Gonzalez et al. (1995), and Maronpot
et al. (1995b) indicated that mutation frequency in DCA-induced tumors did not differ
significantly from that observed in spontaneous tumors. Bull (2000) also noted that the mutation
spectra found in DCA-induced tumors has a striking similarity to that observed in TCE-induced
tumors, and DCA-induced tumors were significantly different than that of TCA-induced liver
tumors.
Bull et al. (2002) reported that mutation frequency spectra for the H-ras codon 61 in
mouse liver —timers" induced by TCE (n = 37 tumors examined) were significantly different
than that for TCA (n = 41 tumors examined), with DCA-treated mice tumors giving an
intermediate result (n = 64 tumors examined). In this experiment, TCA-induced —timers" were
reported to have more mutations in codon 61 (44%) than those from TCE (21%) and DC A
(33%). This frequency of mutation in the H-ras codon 61 for TCA is the opposite pattern as that
observed for a number of peroxisome proliferators in which the number of mutations at H-ras
codon 61 in tumors has been reported to be much lower than spontaneously arising tumors (see
above). Bull et al. (2002) noted that the mutation frequency for all TCE, TCA, or DCA tumors
was lower in this experiment than for spontaneous tumors reported in other studies (they had too
few spontaneous tumors to analyze in this study), but that this study utilized lower doses and was
of shorter duration than that of Ferreira-Gonzalez (1995). Furthermore, the disparities from
previous studies may also be impacted by lesion grouping, mentioned above, in which lower
stages of progression are grouped with more advanced stages.
Overall, in terms of H-ras mutation, TCE-induced tumors appears to be more like
DCA-induced tumors (which are consistent with spontaneous tumors), or those resulting from a
co-exposure to both DCA and TCA (Bull et al., 2002), than from those induced by TCA. As
noted above, Bull et al. (2002) reported the mutation frequency spectra for the H-ras codon 61 in
mouse liver tumors induced by TCE to be significantly different than that for TCA, with
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DCA-treated mice tumors giving an intermediate result and for TCA-induced tumors to have a
H-ras profile that is the opposite than those of a number of other peroxisome proliferators. More
importantly, however, these data, along with the measures discussed above, show that mouse
liver tumors induced by TCE are heterogeneous in phenotype and genotype in a manner similar
to that observed in spontaneous tumors.
4.5.6.3.4. "Stop" experiments
Several stop experiments, in which treatment is terminated early in some dose groups,
have attempted to ascertain the whether progression differences exist between TCA and DC A.
After 37 weeks of treatment and then a cessation of exposure for 15 weeks, Bull et al. (1990)
reported that after a combined 52-week period, liver weight and percentage of liver/body weight
were reported to still be statistically significantly elevated after DCA or TCA treatment. The
authors partially attribute the remaining increases in liver weight to the continued presence of
hyperplastic nodules in the liver. In terms of liver tumor induction, the authors stated that
—sttistical analysis of tumor incidence employed a general linear model ANOVA with contrasts
for linearity and deviations from linearity to determine if results from groups in which treatments
were discontinued after 37 weeks were lower than would have been predicted by the total dose
consumed." The multiplicity of tumors (incidence was not used) observed in male mice exposed
to DCA or TCA at 37 weeks and then sacrificed at 52 weeks were compared with those exposed
for a full 52 weeks. The response in animals that received the shorter duration of DCA exposure
was very close to that which would be predicted from the total dose consumed by these animals.
By contrast, the response to TCA exposure for the shorter duration was reported by the authors
to deviate significantly (p = 0.022) from the linear model predicted by the total dose consumed.
However, in the prediction of —dos€response," foci, adenomas, and carcinomas were combined
into one measure. Therefore, foci, a certain percentage of which have been commonly shown to
spontaneously regress with time, were included in the calculation of total —dsions." Moreover,
only a sample of lesions were selected for histological examination, and as is evident in the
sample, some lesions appeared —nonal" upon microscopic examination (see below). Therefore,
while suggesting that cessation of exposure diminished the number of—lesans," methodological
limitations temper any conclusions regarding the identity and progression of lesion with
continuous vs. noncontinuous DCA and TCA treatment.
Additionally, Bull et al. (1990) noted that after stopping treatment, DCA lesions appeared
to arrest their progression in contrast to TCA lesions, which appeared to progress. In particular,
those in the stop treatment group (at 2 g/L) with 0/19 lesions examined histologically were
carcinomas, while in the continuous treatment groups, a significant fraction of lesions examined
were carcinomas at the higher exposure (6/23 at 2 g/L). By contrast, at terminal sacrifice, a
larger fraction of the lesions examined were carcinomas in the stop treatment groups (3/5 at
2 g/L) than in the continuous treatment group (2/7 and 4/16 at 1 g/L and 2 g/L, respectively).
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However, as mentioned above, these inferences are based on examination of only a
subset of lesions. Specifically, for TCA treatment, the number of animals examined for
determination of which —dsions" were foci, adenomas, and carcinomas was 11/19 mice with
—dsions" at 52 weeks, while all 4 mice with lesions after 37 weeks of exposure and 15 weeks of
cessation were examined. For DCA treatment, the number of animals examined was only
10/23 mice with —dsions" at 52 weeks, while all 7 mice with lesions after 37 weeks of exposure
and 15 weeks of cessation were examined. Most importantly, when lesions were examined
microscopically, some did not turn out to be preneoplastic or neoplastic—for example, two
lesions appeared —to bfeistologically normal" and one necrotic.
While limited, the conclusions of Bull et al. (1990) are consistent with later experiments
performed by Pereira and Phelps (1996). They noted that in MNU-treated mice that were then
treated with DCA, the yield of altered hepatocytes decreases as the tumor yields increase
between 31 and 51 weeks of exposure, suggesting progression of foci to adenomas, but that
adenomas did not appear to progress to carcinomas. For TCA, Pereira and Phelps (1996)
reported that —MNUnitiated" adenomas promoted with TCA continued to progress. However,
the use of MNU initiation complicates direct comparisons with treatment with TCA or DCA
alone.
No similar data comparing stop and continued treatment of TCE are available to assess
the consistency or lack-thereof with TCA or DCA. Moreover, the informative of such a
comparison would be limited by designs of the available TCA and DCA studies, which have
used higher concentrations in conjunction with the much lower durations of exposure. While
higher doses allow for responses to be more easily detected, it introduces uncertainty as to the
effects of the higher doses alone. In addition, because the overall duration of the experiments is
also generally much less than 104 weeks, it is not possible to discern whether the differences in
results between those animals in which treatment was suspended in comparison to those in which
had not had been conducted would persist with longer durations.
4.5.6.4. Conclusions Regarding the Role of TCA, DCA, and CH in TCE-Induced
Effects in the Liver
In summary, it is likely that oxidative metabolism is necessary for TCE-induced effects in
the liver. However, the specific metabolite or metabolites responsible for both noncancer and
cancer effects is less clear. TCE, TCA, and DCA exposures have all been associated with
induction of peroxisomal enzymes but are all weak PPARa agonists. The available data strongly
support TCA not being the sole or predominant active moiety for TCE-induced liver effects.
With respect to hepatomegaly, TCE and TCA dose-response relationships are quantitatively
inconsistent, for TCE leads to greater increases in liver/body weight ratios that expected from
predicted rates of TCA production. In fact, above a certain dose of TCE, liver/body weight
ratios are greater than that observed under any conditions studied so far for TCA. Histological
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changes and effects on DNA synthesis are generally consistent with contributions from either
TCA or DCA, with a degree of polyploidization, rather than cell proliferation, likely to be
significant for TCE, TCA, and DCA. With respect to liver tumor induction, TCE leads to a
heterogeneous population of tumors, not unlike those that occur spontaneously or that are
observed following TCA-, DCA-, or CH-treatment. Moreover, some liver phenotype
experiments, particularly those utilizing immunostaining for c-Jun, support a role for both DCA
and TCA in TCE-induced tumors, with strong evidence that TCA cannot solely account for the
characteristics of TCE-induced tumors. In addition, H-ras mutation frequency and spectrum of
TCE-induced tumors more closely resembles that of spontaneous tumors or of those induced by
DCA, and were less similar in comparison to that of TCA-induced tumors. The heterogeneity of
TCE-induced tumors is similar to that observed to be induced by a broad category of
carcinogens, and to that observed in human liver cancer. Overall, then, it is likely that multiple
TCE metabolites, and therefore, multiple pathways, contribute to TCE-induced liver tumors.
4.5.7. Mode of Action for TCE Liver Carcinogenicity
This section will discuss the evidentiary support for several hypothesized modes of action
for liver carcinogenicity (including mutagenicity and peroxisome proliferation, as well as several
additional proposed hypotheses and key events with limited evidence or inadequate experimental
support), following the framework outlined in the Cancer Guidelines (U.S. EPA, 2005e, b).9
4.5.7.1. Mutagenicity
The hypothesis is that TCE acts by a mutagenic mode of action in TCE-induced
hepatocarcinogenesis. According to this hypothesis, the key events leading to TCE-induced liver
tumor formation constitute the following: TCE oxidative metabolite CH, after being produced in
the liver, cause direct alterations to DNA (e.g., mutation, DNA damage, and/or micronuclei
induction). Mutagenicity is a well-established cause of carcinogenicity.
4.5.7.1.1. Experimental support for the hypothesized mode of action
The genotoxicity, as described by the ability of TCE, CH, TCA, and DCA to induce
mutations, was discussed previously in Section 4.2. The strongest data for mutagenic potential
are for CH, thought to be a relatively short-lived intermediate in the metabolism of TCE that is
rapidly converted to TCA and TCOH in the liver (see Section 3.3). CH causes a variety of
9As recently reviewed (Guvton et al.. 2008X the approach to evaluating mode of action information described in
EPA's Cancer Guidelines (U.S. EPA. 2005e. 2005b) considers the issue of human relevance of a hypothesized
mode of action in the context of hazard evaluation. This excludes, for example, consideration of toxicokinetic
differences across species; specifically, the Cancer Guidelines state, —He toxicokinetic processes that lead to
formation or distribution of the active agent to the target tissue are considered in estimating dose but are not part of
the mode of action." In addition, information suggesting quantitative differences in the occurrence of a key event
between test species and humans are noted for consideration in the dose-response assessment, but is not considered
in human relevance determination.
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genotoxic effects in available in vitro and in vivo assays, with particularly strong data as to its
ability to induce aneuploidy. It has been argued that CH mutagenicity is unlikely to be the cause
of TCE carcinogenicity because the concentrations required to elicit these responses are
generally quite high, several orders of magnitude higher that achieved in vivo (Moore and
Harrington-Brock, 2000). For example, peak concentrations of CH in the liver of around 2-
3 mg/kg have been reported after TCE administration at doses that are hepatocarcinogenic in
chronic bioassays (Greenberg et al., 1999; Abbas and Fisher, 1997). Assuming a liver density of
about 1 kg/L, these concentrations are orders of magnitude less than the minimum concentrations
reported to elicit genotoxic responses in the Ames test and various in vitro measures of
micronucleus, aneuploidy, and chromosome aberrations, which are in the 100-1,000 mg/L range.
However, it is not clear how much of a correspondence is to be expected from concentrations in
genotoxicity assays in vitro and concentrations in vivo, as reported in vivo CH concentrations are
in whole-liver homogenate while in vitro concentrations are in culture media. In addition, a few
in vitro studies have reported positive results at concentrations as low as 1 or 10 mg/L, including
Furnus et al. (1990) for aneuploidy in Chinese hamster CHED cells (10 mg/L), Eichenlaub-Ritter
et al. (1996) for bivalent chromosomes in meiosis I in MF1 mouse oocytes (10 mg/L), and
Gibson et al. (1995) for cell transformation in Syrian hamster embryo cells after a 7-day
treatment. Moreover, some in vivo genotoxicity assays of CH reported positive results at doses
similar to those eliciting a carcinogenic response in chronic bioassays. For example, Nelson and
Bull (1988) reported increased DNA SSBs at 100 CH mg/kg (oral) in male B6C3Fi mice,
although the result was not replicated by Chang et al. (1992). In another example, four of six in
vivo mouse genotoxicity studies reported that CH induced micronuclei in mouse bone-marrow
erythrocytes, with the lowest effective doses in positive studies ranging from 83 to 500 mg/kg
[positive: Russo and Levis (1992a): Russo et al. (1992): Marrazzini et al. (1994): Beland et al.
(1999): and negative: Leuschner and Leuschner (1991): Leopardi et al. (1993)]. However, the
use of i.p. administration in these and many other in vivo genotoxicity assays complicates the
comparison with carcinogenicity data. Also, it is difficult with the available data to assess the
contributions from the genotoxic effects of CH along with those from the genotoxic and
nongenotoxic effects of other oxidative metabolites (discussed in Sections 4.5.5.2 and 4.5.5.3).
Furthermore, altered DNA methylation, another heritable mechanism by which gene
expression may be altered, is discussed in Section 4.5.7.3.7. As discussed previously, the
differential patterns of H-ras mutations observed in liver tumors induced by TCE, TCA, and
DCA may be more indicative of tumor selection and tumor progression resulting from exposure
to these agents rather than a particular mechanism of tumor induction. The state of the science of
cancer and the role of epigenetic changes, in addition to genetic changes, in the initiation and
progression of cancer and specifically liver cancer, are discussed in Section E.3.1.
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Therefore, while data are insufficient to conclude that a mutagenic mode of action
mediated by CH is operant, a mutagenic mode of action, mediated either by CH or by some other
oxidative metabolite of TCE, cannot be ruled out.
4.5.7.2. PPARa Receptor Activation
The hypothesis is that TCE acts by a PPARa agonism mode of action in TCE-induced
hepatocarcinogenesis. According to this hypothesis, the key events leading to TCE-induced liver
tumor formation constitute the following: the TCE oxidative metabolite TCA, after being
produced in the liver, activates the PPARa receptor, which then causes alterations in cell
proliferation and apoptosis and clonal expansion of initiated cells. This mode of action is
assumed to apply only to the liver.
4.5.7.2.1. Experimental support for the hypothesized mode of action
Proliferation of peroxisomes and increased activity of a number of related marker
enzymes has been observed in rodents treated with TCE, TCA, and DCA. The peroxisome-
related effects of TCE are most likely mediated primarily through TCA based on TCE
metabolism producing more TCA than DCA and the lower doses of TCA required to elicit a
response relative to DCA. However, Bull (2004a) and Bull et al. (2004) have recently suggested
that peroxisome proliferation occurs at higher exposure levels than those that induce liver tumors
for TCE and its metabolites. They report that a direct comparison in the no-effect level or low-
effect level for induction of liver tumors in the mouse and several other endpoints shows that, for
TCA, liver tumors occur at lower concentrations than peroxisome proliferation in vivo but that
PPARa activation occurs at a lower dose than either tumor formation or peroxisome
proliferation. A similar comparison for DCA shows that liver tumor formation occurs at a much
lower exposure level than peroxisome proliferation or PPARa activation. In vitro transactivation
studies have shown that human and murine versions of PPARa are activated by TCA and DCA,
while TCE itself is relatively inactive in the in vitro system, at least with mouse PPARa
(Maloney and Waxman, 1999; Zhou and Waxman, 1998). In addition, Laughter et al. (2004)
reported that the responses of acyl CoA oxidase (AGO), PCO, and CYP4A induction by TCE,
TCA, and DCA were substantially diminished in PPARa-null mice. Therefore, evidence
suggests that TCE, through its metabolites TCA and DCA, activate PPARa, and that at doses
relevant to TCE-induced hepatocarcinogenesis, the role of TCA in PPARa agonism is likely to
predominate.
It has been suggested that PPARa receptor activation is both the mode of action for TCA
liver tumor induction as well as the mode of action for TCE liver tumor induction, as a result of
the metabolism of TCE to TCA (Gorton. 2008: NRC, 2006). Section E.3.4 addressed the status
of the PPARa mode-of-action hypothesis for liver tumor induction and provides a more detailed
discussion. However, as discussed previously and in Section E.2.1.10, TCE-induced increases in
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liver weight have been reported in male and female mice that do not have a functional PPARa
receptor (Nakajima et al., 2000). The dose-response for TCE-induced liver weight increases
differs from that of TCA (see Section E.2.4.2). The phenotype of the tumors induced by TCE
have been described to differ from those by TCA and to be more like those occurring
spontaneously in mice, those induced by DC A, or those resulting from a combination of
exposures to both DCA and TCA (see Section E.2.4.4). As to whether TCA induces tumors
through activation of the PPARa receptor, the tumor phenotype of TC A-induced mouse liver
tumors has been reported to have a different pattern of H-ras mutation frequency from other
peroxisome proliferators (see Section E.2.4.4) (Bull et al., 2002; Stanley et al., 1994; Hegi et al.,
1993; Fox et al., 1990). While TCE, DCA, and TCA are weak peroxisome proliferators, liver
weight induction from exposure to these agents has not correlated with increases in peroxisomal
enzyme activity (e.g., PCO activity) or changes in peroxisomal number or volume. By contrast,
as discussed above, liver weight induction from subchronic exposures appears to be a more
accurate predictor of carcinogenic response for DCA, TCA, and TCE in mice (see also
Section E.2.4.4). The database for cancer induction in rats is much more limited than that of
mice for determination of a carcinogenic response to these chemicals in the liver and the nature
of such a response.
While many compounds known to cause rodent liver tumors with long-term treatment
also activate the nuclear receptor PPARa, the mechanisms by which PPARa activation
contributes to tumorigenesis are not completely known (Yang et al., 2007; NRC, 2006; Klaunig
et al., 2003). As reviewed by Keshava and Caldwell (2006), PPARa activation leads to a highly
pleiotropic response and may play a role in toxicity in multiple organs as well as in multiple
chronic conditions besides cancer (obesity, atherosclerosis, diabetes, inflammation). Klaunig
et al. (2003) and NRC (2006) proposed that the key causal events for PPARa agonist-induced
liver carcinogenesis, after PPARa activation, are perturbation of cell proliferation and/or
apoptosis, mediated by gene expression changes, and selective clonal expansion. It has also been
proposed that sufficient evidence for this mode of action consists of evidence of PPARa agonism
(i.e., in a receptor assay) in combination with either light- or electron-microscopic evidence for
peroxisome proliferation or both increased liver weight and one more of the in vivo markers of
peroxisome proliferation (Klaunig et al., 2003). However, it should be noted that peroxisome
proliferation and in vivo markers such as PCO are not considered causal events (NRC, 2006;
Klaunig et al., 2003), and that their correlation with carcinogenic potency is poor (Marsman et
al., 1988). Therefore, for the purposes of this discussion, peroxisome proliferation and its
markers are considered indicators of PPARa activation, as it is well established that these highly
specific effects are mediated through PPARa (Klaunig et al., 2003; Peters etal., 1997).
As recently reviewed by Guyton et al. (2009), recent data suggest that PPARa activation
along with these hypothesized causal events may not be sufficient for carcinogenesis. In
particular, Yang et al. (2007) reported comparisons between mice treated with Wy-14643 and
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transgenic mice in which PPARa was constitutively activated in hepatocytes without the
presence of ligand. Yang et al. (2007) reported that, in contrast to Wy-14643-treatment, the
transgene did not induce liver tumors at 11 months, despite inducing PPARa-mediated effects of
a similar type and magnitude seen in response to tumorigenic doses of Wy-14643 in wild-type
mice (decreased serum fatty acids, induction of PPARa target genes, altered expression of cell-
cycle control genes, and a sustained increase in cellular proliferation). Nonetheless, it is
important to discuss the extent to which PPARa activation mediates the effects proposed by
Klaunig et al. (2003) and NRC (2006), even if the hypothesized sequence of key events may not
be sufficient for carcinogenesis. Investigation continues into additional events that may also
contribute, such as nonparenchymal cell activation and micro-RNA-based regulation of
protooncogenes (Shah et al., 2007; Yang et al., 2007). Specifically addressed below are gene
expression changes, proliferation, clonal expansion, and mutation frequency or spectrum.
With respect to gene expression changes due to TCE, Laughter et al. (2004) evaluated
transcript profiles induced by TCE in wild-type and PPARa-null mice. As noted in
Sections E.3.4.1.3 and E.3.1.2, there are limitations to the interpretation of such studies, some of
which are discussed below. Also noted in Appendix E are discussions of how studies of
peroxisome proliferators, indicate of the need for phenotypic anchoring, especially since gene
expression is highly variable between studies and within studies using the same experimental
paradigm. Section E.3.4 also provides detailed discussions of the status of the PPARa
hypothesis. Of note, all null mice at the highest TCE dose (1,500 mg/kg-day) were moribund
prior to the end of the planned 3-week experiment (Laughter et al., 2004), and it was proposed
that this may reflect a greater sensitivity in PPARa-null mice to hepatotoxins due to defects in
tissue repair abilities. Laughter et al. (2004) also noted that four genes known to be regulated by
other peroxisome proliferators also had altered expression with TCE treatment in wild-type, but
not null mice. Ramdhan et al. (2010) report that not only do PPARa-null mice, but also
humanized mice (PPARa-null mice with inserted human PPARa) have underlying dysregulation
of lipid metabolism and gene expression. However, in a comparative analysis, Bartosiewicz
et al. (2001) concluded that TCE induced a different pattern of transcription than two other
peroxisome proliferators, di(2-ethylhexyl) phthalate (DEHP) and clofibrate. In addition,
Keshava and Caldwell (2006) compared gene expression data from Wy-14643, dibutyl phthalate
(DBF), gemifibrozil, and DEHP, and noted a lack of consistent results across PPARa agonists.
Thus, available data are insufficient to conclude that TCE gene expression changes are similar to
other PPAR agonists, or even that there are consistent changes (beyond the in vivo markers of
peroxisome proliferation, such as AGO, PCO, CYP4A, etc.) among different agonists. It should
also be noted that Laughter et al. (2004) did not compare baseline (i.e., control levels of) gene
expression between null and wild-type control mice, hindering interpretation of these results
(Keshava and Caldwell, 2006). The possible relationship between PPARa activation and
hypomethylation are discussed in Section 4.5.7.3.7.
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In terms of proliferation, mitosis itself has not been examined in PPARa-null mice, but
BrdU incorporation, a measure of DNA synthesis that may reflect cell division, polyploidization,
or DNA repair, was observed to be diminished in null mice as compared to wild-type mice at
500 and 1,000 mg/kg-day TCE (Laughter et al., 2004). However, BrdU incorporation in null
mice was still about threefold higher than controls, although it was not statistically significantly
different due to the small number of animals, high variability, and the two- to threefold higher
baseline levels of BrdU incorporation in control null mice as compared to control wild-type
mice. Therefore, while PPARa appears to contribute to the short-term increase in DNA
synthesis observed with TCE treatment, these results cannot rule out other contributing
mechanisms. However, since it is likely that both cellular proliferation and increased ploidy
contribute to the observed TCE-induced increases in DNA synthesis, it is not clear as to whether
the observed decrease in BrdU incorporation is due to reduced proliferation, reduced
polyploidization, or both.
With respect to clonal expansion, it has been suggested that tumor characteristics such as
tincture (i.e., the staining characteristics light microscopy sections of tumor using H&E stains)
and oncogene mutation status can be used to associate chemical carcinogens with a particular
mode of action such as PPARa agonism (NRC, 2006; Klaunig et al., 2003). This approach is
problematic primarily because of the lack of specificity of these measures. For example, with
respect to tincture, it has been suggested that TCA-induced foci and tumors resemble those of
other peroxisome proliferators in basophilia and lack of expression of GGT and GST-pi.
However, as discussed in Caldwell and Keshava (2006), the term —basphilic" in describing foci
and tumors can be misleading, because, for example, multiple lineages of foci and tumors exhibit
basophilia, including those not associated with peroxisome proliferators (Carter et al., 2003;
Bannasch et al., 2001; Bannasch, 1996). Moreover, a number of studies indicate that foci and
tumors induced by other —clasc" peroxisome proliferators may have different phenotypic
characteristics from that attributed to the class through studies of WY-14643, including DEHP
(Voss et al., 2005) and clofibric acid (Michel et al., 2007). Furthermore, even the combination of
GGT and GST-pi negative, basophilic foci are nonspecific to peroxisome proliferators, as they
have been observed in rats treated with AfBl and AfBl plus phenobarbital, none of which are
peroxisome proliferators (Grasl-Kraupp et al., 1993; Kraupp-Grasl etal., 1990). Finally, while
Bull et al. (2004) suggested that negative expression of c-Jun in TCA-induced tumors may be
consistent with a characteristic phenotype of peroxisome proliferators, no data could be located
to support this statement. Therefore, of phenotypic information does not appear to be reliable for
associating a chemical with a PPARa agonism mode of action.
Mutation frequency or spectrum in oncogenes has also been suggested to be an indicator
of a PPARa agonism mode of action being active (NRC, 2006), with the idea being that specific
genotypes are being promoted by PPARa agonists. Although not a highly specific marker, H-ras
codon 61 mutation frequency and spectra data do not support a similarity between mutations in
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TCE-, TCA-, or DCA-induced tumors and those due to other peroxisome proliferators. For
example, while ciprofibrate and methylclofenopate had lower mutation frequencies than
historical controls (Stanley etal., 1994; Hegi etal., 1993), TCA-induced tumors had mutation
frequencies similar to or higher than historical controls (Bull et al., 2002; Ferreira-Gonzalez et
al., 1995). Anna et al. (1994) and Ferreira-Gonzalez et al. (1995) also reported TCE and
DCA-induced tumors to have mutation frequencies similar to historical controls, although Bull
et al. (2002) reported lower frequencies for these chemicals. However, the data reported by Bull
et al. (2002) consist of mixed lesions at different stages of progression, and such differing stages,
in addition to differences in genetic background and dose, can influence the frequency of H-ras
mutations (Stanley etal., 1994). In addition, a greater frequency of mutations was reported in
carcinomas than adenomas, and Bull et al. (2002) stated that this suggested that H-ras mutations
were a late event. Moreover, Fox et al. (1990) noted that tumors induced by phenobarbital,
chloroform, and ciprofibrate all had a much lower frequency of H-ras gene activation than those
that arose spontaneously, so this marker does not have good specificity. Mutation spectrum is
similarly of low utility for supporting a PPARa agonism mode of action. First, because many
peroxisome proliferators been reported to have low frequency of mutations, the comparison of
mutation spectrum would be limited to a small fraction tumors. In addition to the low power due
to small numbers, the mutation spectrum is relatively nonspecific, as Fox et al. (1990) reported
that of the tumors with mutations, the spectra of the peroxisome proliferator ciprofibrate,
historical controls, and the genotoxic carcinogen benzidine-2 HC1 were similar.
In summary, TCE clearly activates PPARa, and some of the effects contributing to
tumorigenesis that Klaunig et al. (2003) and NRC (2006) propose to be the result of PPARa
agonism are observed with TCE, TCA, or DCA treatment. While this consistency is supportive a
role for PPARa, all of the proposed key causal effects with the exception of PPARa agonism
itself are nonspecific, and may be caused by multiple mechanisms. There is more direct
evidence that several of these effects, including alterations in gene expression and changes in
DNA synthesis, are mediated by multiple mechanisms in the case of TCE, and a causal linkage
to PPARa specifically is lacking. Therefore, because, as discussed further in the mode of action
discussion below, there are multiple lines of evidence supporting the role of multiple pathways
of TCE-induced tumorigenesis, the hypothesis that PPARa agonism and the key causal events
proposed by Klaunig et al. (2003) and NRC (2006) constitute the sole or predominant mode of
action for TCE-induced carcinogenesis is considered unlikely.
Furthermore, as reviewed by Guyton et al. (2009), recent data strongly suggest that
PPARa and key events hypothesized by Klaunig et al. (2003) are not sufficient for
carcinogenesis induced by the purported prototypical agonist, Wy-14643. Therefore, the
proposed PPARa mode of action is likely —incmplete" in the sense that the sequence of key
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events10 necessary for cancer induction has not been identified. A recent 2-year bioassay of the
peroxisome proliferator DEHP showed that it can induce a liver tumor response in mice lacking
PPARa similar to that in wild-type mice (Ito et al., 2007). Klaunig et al. (2003) previously
concluded that PPARa agonism was the sole mode of action for DEHP-induced liver
tumorigenesis based on the lack of tumors in PPARa-null mice after 11 months treatment with
Wy-14643 (Peters et al., 1997). They also assumed that due to the lack of markers of PPARa
agonism in PPARa-null mice after short-term treatment with DEHP (Ward et al., 1998), a long-
term study of DEHP in PPARa-null mice would yield the same results as for Wy-14643.
However, due the finding by Ito et al. (2007) that PPARa-null mice exposed to DEHP do
develop liver tumors, they concluded that DEHP can induce liver tumors by multiple
mechanisms (Takashima et al., 2008; Ito et al., 2007). Hence, since there is no 2-year bioassay
in PPARa-null mice exposed to TCE or its metabolites, it is not justifiable to use a similar
argument based on Peters et al. (1997) and short-term experiments to suggest that the PPARa
mode of action is operative. Therefore, the conclusion is supported that the hypothesized
PPARa mode of action is inadequately specified because the data do not adequately show the
proposed key events individually being required for hepatocarcinogenesis, nor do they show the
sequence of key events collectively to be sufficient for hepatocarcinogenesis.
4.5.7.2.2. Quantitative relationships between key events and tumor induction
The issues of whether there is a quantitative relationship between hypothesized key
events and tumor induction were recently examined in Guyton et al. (2009) and are discussed
below. Furthermore, IARC has recently concluded that additional mechanistic information has
become available, including studies with DEHP in PPARa-null mice, studies with several
transgenic mouse strains, carrying human PPARa or with hepatocyte-specific constitutively
activated PPARa and a study in humans exposed to DEHP from the environment that has
changed its conclusions regarding the relevance of rodent tumor data to human risk (Grosse et
al., 2011). Data from these new studies suggest that many molecular signals and pathways in
several cell types in the liver, rather than a single molecular event, contribute to cancer
development in rodents with IARC concluding that the human relevance of the molecular events
leading to DEHP induced cancer in several target tissues (e.g., liver and testis) in rats or mice
could not be ruled out, resulting in the evaluation of DEHP as a Group-2B agent, rather than
Group 3.
This following discussion is from Guyton et al. (2009):
10As defined by the EPA Cancer Guidelines (2005b) a -key event" is -an empirically observable precursor step that
is itself a necessary element of the mode of action or is a biologically based marker for such an element," and the
term -^node of action" (MOA) is defined as -a- sequence of key events and processes, starting with interaction of an
agent with a cell, proceeding through operational and anatomical changes, and resulting in cancer formation."
Therefore, a single key event alone is necessary, but not necessarily sufficient for carcinogenesis; however, the
sequence of key events constituting a MO A needs to be sufficient for carcinogenesis.
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Are key or associative events in the PPAR-a activation MOA quantitatively
predictive of hepatocarcinogenicity?
Another question to consider is whether potency for PPAR-a activation or
its attendant sequelae is quantitatively associated with carcinogenic activity or
potency. If so, differences in sensitivity for carcinogenesis (such as may occur
across species) could be predicted using quantitative information about the key
events alone. If robust correlations were established, then they could potentially
be used either to quantitatively account for pharmacodynamic differences that
impact carcinogenic potency or as precursor events in nonlinear dose response
assessment. However, there are limitations in the dose-response data available for
analyses of quantitative relationships between potencies for precursor events in
the proposed PPAR-a activation MOA and for liver tumor induction. Most tumor
data, including for the best characterized PPAR-a agonists, are for exposure
concentrations inducing well above 50% tumor incidence with less-than-lifetime
administration. Precursor events have typically been studied at a single dose,
often eliciting a near maximal response, thus precluding benchmark-based
comparisons across studies. This is especially true for Wy-14,643, which has
been administered most often at only one exposure concentration (1,000 ppm) that
elicits a 100% tumor incidence after 1 year or less (Peters et al., 1997) and that
also appears to be necrogenic (Woods et al., 2007a). On the other hand,
hypothesized precursor events such as hepatomegaly, peroxisome proliferation,
and increased DNA synthesis appear to have reached their maximal responses at
50 ppm Wy-14,643, with some statistically significant responses as low as 5 ppm
(Marsman et al., 1992; Wada et al., 1992). Potencies across compounds have
rarely been compared in a single study using the same experimental paradigm.
These deficits in the database notwithstanding, provided below is an assessment
of the quantitative predictive power of the potency for four proposed data
elements for establishing the hypothesized MOA for hepatocarcinogenesis:
PPAR-a activation in mice; and hepatomegaly, DNA synthesis, and increased
peroxisome proliferation in rats.
PPAR- a activation in mice
Table 2 [reproduced as Table 4-66] presents data for four peroxisome
proliferators in order of decreasing potency for inducing mouse liver tumors.
These compounds were selected because of their importance to environmental
human health risk assessments and because data to derive receptor activation
potency indicators were available from a single study (Maloney and Waxman,
1999). The transactivation potencies of MEHP, Wy-14,643, dichloroacetic acid
(DCA), and TCA for the mouse PPAR-a were monitored using a luciferase
reporter gene containing multiple PPAR response elements derived from the rat
hydratase/dehyrogenase promoter in transiently transfected COS-1 monkey
kidney cells. The derived potency indicators were compared to the TD50 (i.e., the
daily dose inducing tumors in half of the mice that would otherwise have
remained tumor-free) from the Carcinogenic Potency Database (CPDB) of Gold
et al. (2005). Note that for Wy-14,643, the dose listed yielded a maximal
response and thus represents an upper limit to the TD50 (indicated by "<"). Two
estimates of PPAR-a transactivation potency are given, the first based on 50% of
the maximal response (i.e., ECso) and the second based on the effective
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concentration required for a 2-fold increase in activity (i.e., EC2-fold) (Maloney
andWaxman, 1999). Orally administered DEHP undergoes presystemic
hydrolysis catalyzed by lipase to MEHP in the gut, with mice exhibiting higher
lipase activities in the small intestine compared to rats and marmosets (Ito et al.,
2005: Kessler et al.. 2004: Pollack et al.. 1985). Therefore, because the mouse
liver is likely exposed predominantly to MEHP rather than DEHP and
unmetabolized be explained by pharmacokinetics, i.e., hepatic
conversion of DEHP to its mono-ester MEHP, since studies in rats demonstrate
that orally administered DEHP undergoes presystemic hydrolysis to MEHP in the
gut (Kessler et al., 2004: Pollack et al., 1985). Possible explanations for these
results include one or more of the following: (1) the transactivation assay is not
an accurate quantitative indicator of in vivo receptor activation; (2) the rate and
nature of effects downstream of PPAR-a activation depends on the ligand; or
(3) there are rate-limiting events independent of PPAR-a activation that
contribute to mouse hepatocarcinogenesis by the agonists examined.
Hepatomegaly, DNA synthesis, and peroxisome proliferation in rats
Table 1 [reproduced as 4-67] compares potency indicators for various
precursor effects at the TD50 for four PPAR-a agonists and rat hepatocarcinogens.
Our analysis of whether there are consistent levels of in vivo precursor effect
induction across peroxisome proliferators at the TDso does not include all of the
data from a similar, prior analysis by Ashby et al. (1994) for several reasons.
First, unlike the CPDB, Ashby et al. (1994) did not adjust carcinogenicity data for
less-than-lifetime dosing, which is relevant for most compounds. Second, for
those mouse carcinogens reported in the CPDB, only acute data are available
regarding DNA synthesis effects from Ashby et al.. Therefore, our analysis was
restricted to rat precursor and potency data for the four compounds Wy-14,643,
nafenopin, clofibrate, and DEHP and included both 1-week and 13-week data to
separately address transient and sustained changes in DNA synthesis. Even for
this small set of compounds, several limitations in the rat database were apparent.
Because no single study provided comparative data for the precursor endpoints of
interest, four separate reports were used. In the Wada et al. (1992) and Tanaka
et al. (1992) studies of Wy-14,643 and clofibrate, respectively, administered doses
were within 10% of the TD50. However, nafenopin data were only available at a
single dose of 500 ppm (Lake et al., 1993), which was linearly interpolated to the
TD50. The highest administered dose of DEHP was 12,500 ppm (David et al.,
1999), a dose notably below the TDso, and thus a lower limit based on the
assumption of monotonicity with dose is shown. A further data limitation is that
in the CPDB, only the TD50 for one of the four compounds, DEHP, incorporates
data from studies administering more than one dose for two years.
The results shown in Table 1 [reproduced as Table 4-67] indicate that
potency for the occurrence of short-term in vivo markers of PPAR-a activation
varies widely in magnitude and lacks any apparent correlation with carcinogenic
potency. Such differences have been noted previously. Similar to the results
presented in Table 1 [reproduced as Table 4-67], Marsman et al. (1988) noted that
although DEHP (12,000 ppm) and Wy-14,643 (1,000 ppm) induced a similar
extent of hepatomegaly and peroxisome proliferation (measured either
morphologically or biochemically) after 1 year, the frequency of hepatocelluar
lesions was over 100-fold higher in Wy-14,643- relative to DEHP-exposed rats.
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In addition, a higher labeling index was reported for 12,500 ppm DEHP than the
maximal level attained after 50-1,000 ppm Wy-14,643 (David et al.. 1999:
Tanakaet al., 1992; Wadaet al., 1992). We did not examine such differences in
maximal responses in our analysis. We also do not present differences in
response with dose and time seen among PPAR-a agonists, which are prominent
enough to prevent displaying dose-response data on a common scale. For
instance, labeling index is increased in a dose-dependent manner at 1 week by
clofibrate (1.500, 4.500 and 9.000 ppm) but is decreased compared with controls
at 13 weeks at the two higher doses (Tanaka et al., 1992). Together, these
findings underscore the significant chemical-specific quantitative differences in
these markers that limit their utility for predicting carcinogenic dose-response
relationships.
Table 4-66. Potency indicators for mouse hepatocarcinogenicity and in vitro
transactivation of mouse PPARa for four PPARa agonists
Chemical
Carcinogenic potency indicators
(mg/kg-d)
TD50
Transactivation potency indicators (jiM)
ECso
ECtwofold
Hepatocarcinogens
Wy-14,643
DCA
TCA
DEHP/MEHP
<10.8
119
584
700
0.63
-300
-300
-0.7
-0.4
-300
-300
-0.7
Note: TD50 = the daily dose inducing tumors in half of the mice that would otherwise have remained tumor-
free, estimated from the Carcinogenic Potency Database (Goldetal.. 2005). EC50 = the effective
concentration yielding 50% of the maximal response; ECtw0foid = the effective concentration required for a
twofold increase in activity. Transactivation potencies were estimated from Maloney and Waxman (1999).
The "<" symbol denotes an upper limit due to maximal response. A "~" symbol indicates that the
transactivation potency was approximated from figures in Maloney and Waxman (1999).
MEHP = monoethylhexyl phthalate
Source: reproduced from Table 2 of Guyton et al. (2009).
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Table 4-67. Potency indicators for rat hepatocarcinogenicity and common
short-term markers of PPARa activation for four PPARa agonists
Chemical
Wy-14,643
Nafenopin
Clofibrate
DEHP
Tumor TDSO (ppm in
diet)
109
275
4.225
17.900
Fold-increase over control at tumor TD50
Iwk
RLW
1.8
1.4
1.4
>1.4
LI
12
3.6
4.4
>19
PCO
13
7.6
4.2
>3.6
13wks
RLW
2.6
1.5
1.4
>1.9
LI
6.8
1.12
0.95
>1.25
PCO
39
6.7
3.7
>4.9
Note: For ease of comparison with precursor effect studies, administered doses for the tumor TD50s in the
Carcinogenic Potency Database were back-converted to equivalent ppm in diet using the formula of Gold et al.
(2005), i.e., TD50 (mg/kg-day) = TD50(ppm in diet) x 0.04 (for male rats). Administered doses for precursor data on
Wy-14,643 (Wadaetal.. 1992) and clofibrate (Tanaka et al.. 1992) were within 10% of the TD50. Because
nafenopin precursor data were only available at 0 and 500 ppm (Lake etal. 1993). these doses were linearly
interpolated to the TD50. Because the highest administered dose of DEHP in precursor effect studies was
12,500 ppm (David etal.. 1999). a lower limit is shown, based on the assumption of monotonicity with dose.
RLW = relative liver weight, LI = labeling index, PCO = cyanide insensitive palmitoyl CoA oxidation
Source: reproduced from Table 1 of Guyton et al. (2009).
4.5.7.3. Additional Proposed Hypotheses and Key Events with Limited Evidence or
Inadequate Experimental Support
Several effects that been hypothesized to be associated with liver cancer induction are
discussed in more detail below, including increased liver weight, DNA hypomethylation, and
pathways involved in glycogen accumulation such as insulin signaling proteins. As discussed
above, TCE and its metabolites reportedly increase nuclear size and ploidy in hepatocytes, and
these effects likely account for much of the increases in labeling index and DNA synthesis
caused by TCE. Importantly, these changes appear to persist with cessation of treatment, with
liver weights, but not nuclear sizes, returning to control levels (Kj ell strand et al., 1983a). In
addition, glycogen deposition, DNA synthesis, increases in mitosis, or peroxisomal enzyme
activity do not appear correlated with TCE-induced liver weight changes.
4.5.7.3.1. Increased liver weight
Increased liver weight or liver/body weight ratios (hepatomegaly) is associated with
increased risk of liver tumors in rodents, but it is relatively nonspecific (Allen et al., 2004). The
evidence presented above for TCE and its metabolites suggest a similarity in dose-response
between liver weight increases at short-term durations of exposure and liver tumor induction
observed from chronic exposure. Liver weight increases may results from several concurrent
processes that have been associated with increase cancer risk (e.g., hyperplasia, increased ploidy,
and glycogen accumulation) and when observed after chronic exposure may result from the
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increased presence of foci and tumors themselves. Therefore, there are inadequate data to
adequately define a mode of action hypothesis for hepatocarcinogenesis based on liver weight
increases.
4.5.7.3.2. "Negative selection"
As discussed above, TCE, TCA, and DCA all cause transient increases in DNA synthesis.
This DNA synthesis has been assumed to result from proliferation of hepatocytes. However, the
dose-related TCA- and DCA-induced increases in liver weight not correlate with patterns of
DNA synthesis; moreover, there have been reports that DNA synthesis in individual hepatocytes
does not correlate with whole liver DNA synthesis measures (Carter et al., 1995; Sanchez and
Bull, 1990). With continued treatment, decreases in DNA synthesis have been reported for DCA
(Carter et al., 1995). More importantly, several studies show that transient DNA synthesis is
confined to a very small population of cells in the liver in mice exposed to TCE for 10 days or to
DCA or TCA for up to 14 days of exposure. Therefore, generalized mitogenic stimulation is not
likely to play a role in TCE-induced liver carcinogenesis.
Bull (2000) has proposed that the TCE metabolites TCA and DCA may contribute to
liver tumor induction through so-called —ngative selection" by way of several possible
processes. First, it is hypothesized that the mitogenic stimulation by continued TCA and DCA
exposure is downregulated in normal hepatocytes, conferring a growth advantage to initiated
cells that either do not exhibit the downregulation of response or are resistant to the
downregulating signals. This is implausible as both the normal rates of cell division in the liver
and the TCE-stimulated increases are very low. Polyploidization has been reported to decrease
the normal rates of cell division even further. That the transient and relatively low level of DNA
synthesis reported for TCE, DCA, and TCA is reflective of proliferation rather than
polyploidization is not supported by data on mitosis. A mechanism for such "downregulation"
has not been identified experimentally.
A second proposed contributor to —egative-selection" is direct enhancement by TCA and
DCA in the growth of certain populations of initiated cells. While differences in phenotype of
end stage tumors have been reported between DCA and TCA, the role of selection and
emergence of potentially different foci has not been elucidated. Neither have pathway
perturbations been identified that are common to liver cancer in human and rodent for TCE,
DCA, and TCA. The selective growth of clones of hepatocytes that may progress fully to cancer
is a general feature of cancer and not specific to at TCE, TCA, or DCA mode of action.
A third proposed mechanism by which TCE may enhance liver carcinogenesis within this
—neglave selection" paradigm is through changing apoptosis. However, as stated above, TCE
has been reported to either not change apoptosis or to cause a slight increase at high doses.
Rather than increases in apoptosis, peroxisome proliferators have been suggested to inhibit
apoptosis as part of their carcinogenic mode of action. However, the age and species studied
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appear to greatly affect background rates of apoptosis (Snyder et al., 1995) with the rat having a
greater rate of apoptosis than the mouse. DC A has been reported to induce decreases in
apoptosis in the mouse (Carter et al., 1995; Snyder etal., 1995). However, the significance of
the DCA-induced reduction in apoptosis, from a level that is already inherently low in the
mouse, for the mode of action for induction of DCA-induce liver cancer is difficult to discern.
Therefore, for a mode of action for hepatocarcinogenesis based on —neglave selection,"
there are inadequate data to adequately define the mode-of-action hypothesis, or the available
data do not support such a mode of action being operative.
4.5.7.3.3. Polyploidization
Polyploidization may be an important key event in tumor induction. For example, in
addition to TCE, partial hepatectomy, nafenopin, methylclofenopate, DEHP, diethylnitrosamine,
jV-nitrosomorpholine, and various other exposures that contribute to liver tumor induction also
shift the hepatocyte ploidy distribution to be increasingly diploid or polypoid (Hasmall and
Roberts. 2000: Miller et al.. 1996: Vickers and Lucier. 1996: Melchiorri et al.. 1993: Styles et al..
1988). As discussed by Gupta (2000), —[\Jorking models indicate that extensive polyploidy
could lead to organ failure, as well as to oncogenesis with activation of precancerous cell
clones." However, the mechanism(s) by which increased polypoidy enhances carcinogenesis is
not currently understood. Due to increased DNA content, polypoid cells will generally have
increased gene expression. However, polyploid cells are considered more highly differentiated
and generally divide more slowly and are more likely to undergo apoptosis, perhaps thereby
indirectly conferring a growth advantage to initiated cells (see Section E.I). Of note is that
changes in ploidy have been observed in transgenic mouse models that are also prone to develop
liver cancer (see Section E.3.3.1). It is likely that polyploidization occurs with TCE exposure
and it is biologically plausible that polyploidization can contribute to liver carcinogenesis,
although the mechanism(s) is (are) not known. However, whether polyploidization is necessary
for TCE-induced carcinogenesis is not known, as no experiment in which polyploidization
specifically is blocked or diminished has been performed and the extent of polyploidization has
not been quantified. Therefore, there are inadequate data to adequately define a mode-of-action
hypothesis for hepatocarcinogenesis based on polyploidization.
4.5.7.3.4. Glycogen storage
As discussed above, several studies have reported that DCA causes accumulation of
glycogen in mouse hepatocytes. Such glycogen accumulation has been suggested to be
pathogenic, as it is resistant to mobilization by fasting (Kato-Weinstein et al., 1998). In humans,
glycogenesis due to glycogen storage disease or poorly controlled diabetes has been associated
with increased risk of liver cancer (Rake et al., 2002: Wider off etal., 1997: Adami et al., 1996:
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La Vecchia et al., 1994). Glycogen accumulation has also been reported to occur in rats exposed
to DCA.
For TCE exposure in mice or rats, glycogen content of hepatocytes has been reported to
be somewhat less than or the same as controls, or not remarked upon in the studies. TCA
exposure has been reported to decrease glycogen content in rodent hepatocytes while DCA has
been reported to increase it (Kato-Weinstein et al., 2001). There is also evidence that
DCA-induced increases in glycogen accumulation are not proportional to liver weight increases
and only account for a relatively small portion of increases in liver mass. DCA-induced
increases in liver weight are not a function of cellular proliferation but probably include
hypertrophy associated with polyploidization, increased glycogen deposition, and other factors.
While not accounting for increases in liver weight, excess glycogen can still not only be
pathogenic, but also a predisposing condition for hepatocarcinogenesis. Some hypotheses
regarding the possible relationship between glycogenesis and carcinogenesis have been posed
that lend them biological plausibility. Evert et al. (2003), using an animal model of hepatocyte
exposure to a local hyperinsulinemia from transplanted islets of Langerhans with remaining
tissue is hypoinsulinemic, reported that insulin induces alterations resembling preneoplastic foci
of altered hepatocytes that develop into hepatocellular tumors in later stages of carcinogenesis.
Lingohr et al. (2001) suggested that normal hepatocytes downregulate insulin-signaling proteins
in response to the accumulation of liver glycogen caused by DCA and that the initiated cell
population, which does not accumulate glycogen and is promoted by DCA treatment, responds
differently from normal hepatocytes to the insulin-like effects of DCA. Bull et al. (2002)
reported increased insulin receptor protein expression in tumor tissues regardless of whether they
were induced by TCE, TCA, or DCA. Given the greater activity of DCA relative to TCA on
carbohydrate metabolism, it is unclear whether changes in these pathways are causes or simply
reflect the effects of tumor progression. Therefore, it is biologically plausible that changes in
glycogen status may occur from the opposing actions of TCE metabolites, but changes in
glycogen content due to TCE exposure has not been quantitatively studied. The possible
contribution of these effects to TCE-induced hepatocarcinogenesis is unclear. Therefore, there
are inadequate data to adequately define a mode-of-action hypothesis for TCE-induced
hepatocarcinogenesis based on changes in glycogen storage or even data to support increased
glycogen storage to result from TCE exposure.
4.5.7.3.5. Inactivation of GST-zeta
DCA has been shown to inhibit its own metabolism in that pretreatment in rodents prior
to a subsequent challenge dose leads to a longer biological half-life (Schultz et al., 2002). This
self-inhibition is hypothesized to occur through inactivation of GST-zeta (Schultz et al., 2002).
In addition, TCE has been shown to cause the same prolongation of DCA half-life in rodents,
suggesting that TCE inhibits GST-zeta, probably through the formation of DCA (Schultz et al..
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2002). DCA-induced inhibition of GST-zeta has also been reported in humans, with GST-zeta
polymorphisms reported to influence the degree of inactivation (Blackburn et al., 2001;
Blackburn et al., 2000; Tzeng et al., 2000). Board et al. (2001) reported one variant to have
significantly higher activity with DCA as a substrate than other GST-zeta isoforms, which could
affect DCA susceptibility.
GST-zeta, which is identical to maleylacetoacetate isomerase, is part of the tyrosine
catabolism pathway, which is disrupted in Type 1 hereditary tyrosinemia, a disease associated
with the development of HCC at a young age (Tanguay et al., 1996). In particular, GST-zeta
metabolizes maleylacetoacetate (MAA) to fumarylacetoacetate (FAA) and maleylacetone (MA)
to fumarylacetone (Cornett et al., 1999; Tanguay et al., 1996). It has been suggested that the
increased cancer risk with this disease, as well as through DCA exposure, results from
accumulation of MAA and MA, both alkylating agents, or FAA, which displays apoptogenic,
mutagenic, aneugenic, and mitogenic activities (Bergeron et al., 2003; Jorquera and Tanguay,
2001: Kim et al.. 2000: Cornett et al.. 1999: Tanguav et al., 1996). However, the possible effects
of DCA through this pathway will depend on whether MAA, MA, or FAA is the greater risk
factor, since inhibition of GST-zeta will lead to greater concentrations of MAA and MA and
lower concentrations of FAA. Therefore, if MAA is the more active agent, then DCA may
increase carcinogenic risk, while if FAA is the more active, then DCA may decrease
carcinogenic risk. Tzeng et al. (2000) proposed the latter based on the greater genotoxicity of
FAA, and in fact suggested that DCA may -^nerit consideration for trial in the clinical
management of hereditary tyrosinemia type 1."
Therefore, TCE-induced inactivation GST-zeta, probably through formation of DCA,
may play a role in TCE-induced hepatocarcinogenesis. However, this mode of action is not
sufficiently delineated at this point for further evaluation, as even the question of whether its
actions through this pathway may increase or decrease cancer risk has yet to be experimentally
tested.
4.5.7.3.6. Oxidative stress
Several studies have attempted to study the possible effects of —oxidafre stress" and
DNA damage resulting from TCE exposures. The effects of induction of metabolism by TCE, as
well as through co-exposure to ethanol, have been hypothesized to increase levels of—oxidative
stress" as a common effect for both exposures (see Section E.4.3.4). In terms of contributing to a
carcinogenic mode of action, the term —oxidtave stress" is a somewhat nonspecific term, as it is
implicated as part of the pathophysiologic events in a multitude of disease processes and is part
of the normal physiologic function of the cell and cell signaling. Commonly, it appears to refer
to the formation of reactive oxygen species leading to cellular or DNA damage. As discussed
above, however, measures of oxidative stress induced by TCE, TCA, and DCA appear to be
either not apparent, or at the very most, transient and nonpersistent with continued treatment
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(Toraason et al.. 1999: Channel et al.. 1998: Parrishetal.. 1996: Larson and Bull 1992b).
Therefore, while the available data are limited, there is insufficient evidence to support a role for
such effects in TCE-induced liver carcinogenesis.
Oxidative stress has been hypothesized to be part of the mode of action for peroxisome
proliferators, but has been found to be correlated with neither cell proliferation nor carcinogenic
potency of peroxisome proliferators (see Section E.3.4.1.1). For instance, Parrish et al. (1996)
reported that increases in PCO activity noted for DCA and TCA were not associated with
8-OHdG levels (which were unchanged) and also not with changes laurate hydrolase activity
observed after either DCA or TCA exposure. The authors concluded that their data do not
support an increase in steady-state oxidative damage to be associated with TCA initiation of
cancer and that extension of treatment to time periods sufficient to insure peroxisome
proliferation failed to elevate 8-OHdG in hepatic DNA. The authors thus, suggested that
peroxisome proliferative properties of TCA were not linked to oxidative stress or carcinogenic
response.
4.5.7.3.7. Changes in gene expression (e.g., hypomethylation)
Studies of gene expression as well as considerations for interpretation of studies of using
the emerging technologies of DNA, siRNA, and miRNA microarrays for mode-of-action
analyses are included in Sections E.3.1.2 and E.3.4.2.2. Caldwell and Keshava (2006) and
Keshava and Caldwell (2006) report on both genetic expression studies and studies of changes in
methylation status induced by TCE and its metabolites as well as differences and difficulties in
the patterns of gene expression between differing PPARa agonists. In particular are concerns for
the interpretation of studies that employ pooling of data as well as interpretation of —snapshotsri
time of multiple gene changes." For instance, in the Laughter et al. (2004) study, it is not clear
whether transcription arrays were performed on pooled data as well as the issue of phenotypic
anchoring as data on percentage liver/body weight indicates significant variability within TCE
treatment groups, especially in PPARa-null mice. For studies of gene expression using
microarrays Bartosiewicz et al. (2001) used a screening analysis of 148 genes for xenobiotic-
metabolizing enzymes, DNA repair enzymes, heat shock proteins, cytokines, and housekeeping
gene expression patterns in the liver in response TCE. The TCE-induced gene induction was
reported to be highly selective; only Hsp 25 and 86 and CYP were upregulated at the highest
dose tested. Collier et al. (2003) reported differentially expressed mRNA transcripts in
embryonic hearts from Sprague-Dawley rats exposed to TCE with sequences downregulated
with TCE exposure appearing to be those associated with cellular housekeeping, cell adhesion,
and developmental processes. TCE was reported to induce upregulated expression of numerous
stress-response and homeostatic genes.
For the Laughter et al. (2004) study, transcription profiles using macroarrays containing
approximately 1,200 genes were reported in response to TCE exposure with 43 genes reported to
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be significantly altered in the TCE-treated wild-type mice and 67 genes significantly altered in
the TCE-treated PPARa knockout mice. However, the interpretation of this information is
difficult because in general, PPARa knockout mice have been reported to be more sensitive to a
number of hepatotoxins partly because of defects in the ability to effectively repair tissue damage
in the liver (Shankar et al., 2003; Mehendale, 2000) and because a comparison of gene
expression profiles between controls (wild-type and PPARa knockout) were not reported. As
reported by Voss et al. (2006), dose-, time course-, species-, and strain-related differences should
be considered in interpreting gene array data. The comparison of differing PPARa agonists
presented in Keshava and Caldwell (2006) illustrates the pleiotropic and varying liver responses
of the PPARa receptor to various agonists, but did not imply that these responses were
responsible for carcinogenesis.
As discussed in Section E.3.3.5, aberrant DNA methylation is a common hallmark of all
types of cancers, with hypermethylation of the promoter region of specific tumor suppressor
genes and DNA repair genes leading to their silencing (an effect similar to their mutation) and
genome-wide hypomethylation (Pereira et al., 2004b: Ballestar and Esteller, 2002; Berger and
Daxenbichler, 2002; Rhee et al., 2002; Herman et al., 1998). Whether DNA methylation is a
consequence or cause of cancer is a long-standing issue (Ballestar and Esteller, 2002). Fraga
et al. (2005; 2004) reported global loss of monoacetylation and trimethylation of histone H4 as a
common hallmark of human tumor cells; they suggested, however, that genomewide loss of
5-methylcytosine (associated with the acquisition of a transformed phenotype) exists not as a
static predefined value throughout the process of carcinogenesis but rather as a dynamic
parameter (i.e., decreases are seen early and become more marked in later stages).
DNA methylation is a naturally occurring epigenetic mechanism for modulating gene
expression, and disruption of this mechanism is known to be relevant to human carcinogenesis.
As reviewed by Calvisi et al. (2007),
[a]berrant DNA methylation occurs commonly in human cancers in the forms of
genome-wide hypomethylation and regional hypermethylation. Global DNA
hypomethylation (also known as demethylation) is associated with activation of
protooncogenes, such as c-Jun, c-Myc, and c-HA-Ras, and generation of genomic
instability. Hypermethylation on CpG islands located in the promoter regions of
tumor suppressor genes results in transcriptional silencing and genomic
instability.
While clearly associated with cancer, it has not been conclusively established whether
these epigenetic changes play a causative role or are merely a consequence of transformation
(Tryndyak et al., 2006). However, as Calvisi et al. (2007) note, —Cusnt evidence suggests that
hypomethylation might promote malignant transformation via multiple mechanisms, including
chromosome instability, activation of protooncogenes, reactivation of transposable elements, and
loss of imprinting."
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Although little is known about how it occurs, a hypothesis has also been proposed that
that the toxicity of TCE and its metabolites may arise from its effects on DNA methylation
status. In regard to methylation studies, many are co-exposure studies as they have been
conducted in initiated animals with some studies being very limited in their reporting and
conduct. Cal dwell and Keshava (2006) reviewed the body of work regarding TCE, DC A, and
TCA. Methionine status has been noted to affect the emergence of liver tumors (Counts et al.,
1996). Tao et al. (2000) and Pereira et al. (2004a) have studied the effects of excess methionine
in the diet to see if it has the opposite effects as a deficiency (i.e., a reduction in a carcinogenic
response rather than enhancement). However, Tao et al. (2000) reported that the administration
of excess methionine in the diet is not without effect and can result in percentage liver/body
weight ratios. Pereira et al. (2004a) reported that methionine treatment alone at the 8 g/kg level
increased liver weight, decreased lauryl-CoA activity, and increased DNA methylation.
Pereira et al. (2004a) reported that very high levels of methionine supplementation to an
AIN-760A diet affected the number of foci and adenomas after 44 weeks of co-exposure to
3.2 g/L DCA. However, while the highest concentration of methionine (8.0 g/kg) was reported
to decrease both the number of DCA-induce foci and adenomas, the lower level of methionine
co-exposure (4.0 g/kg) increased the incidence of foci. Co-exposure of methionine (4.0 or
8.0 g/kg) with 3.2 g/L DCA was reported to decrease by -25% DCA-induced glycogen
accumulation, increase mortality, but not to have much of an effect on peroxisome enzyme
activity (which was not elevated by >33% over control for DCA exposure alone). The authors
suggested that their data indicate that methioninine treatment slowed the progression of foci to
tumors. Given that increasing hypomethylation is associated with tumor progression, decreased
hypomethylation from large doses of methionine are consistent with a slowing of progression.
Whether these results would be similar for lower concentrations of DCA and lower
concentrations of methionine that were administered to mice for longer durations of exposure
cannot be ascertained from these data. It is possible that in a longer-term study, the number of
tumors would be similar. Finally, a decrease in tumor progression by methionine
supplementation is not shown to be a specific event for the mode of action for DCA-induced
liver carcinogenicity.
Tao et al. (2000) reported that 7 days of gavage dosing of TCE (1,000 mg/kg in corn oil),
TCA (500 mg/kg, neutralized aqueous solution), and DCA (500 mg/kg, neutralized aqueous
solution) in 8-week-old female B6C3Fi mice resulted in not only increased liver weight, but also
increased hypomethylation of the promoter regions of c-Jun and c-Myc genes in whole-liver
DNA. However, data were shown for 1-2 mice per treatment. Treatment with methionine was
reported to abrogate this response only at a 300 mg/kg i.p dose, with 0-100 mg/kg doses of
methionine having no effect. Ge et al. (200la) reported DCA- and TCA-induced DNA
hypomethylation and cell proliferation in the liver of female mice at 500 mg/kg and decreased
methylation of the c-Myc promoter region in liver, kidney, and urinary bladder. However,
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increased cell proliferation preceded hypomethylation. Ge et al. (2002) also reported
hypomethylation of the c-Myc gene in the liver after exposure to the peroxisome proliferators
2,4-dichlorophenoxyacetic acid (1,680 ppm), DBF (20,000 ppm), gemfibrozil (8,000 ppm), and
Wy-14,643 (50-500 ppm, with no effect at 5 or 10 ppm) after 6 days in the diet. Caldwell and
Keshava (2006) concluded that hypomethylation did not appear to be a chemical-specific effect
at these concentrations. As noted Section E.3.3.5, chemical exposure to a number of differing
carcinogens have been reported to lead to progressive loss of DNA methylation.
After initiation by 7V-methyl-7V-nitrosourea (25 mg/kg) and exposure to 20 mmol/L DCA
or TCA (46 weeks), Tao et al. (2004a) report similar hypomethylation of total mouse liver DNA
by DCA and TCA with tumor DNA showing greater hypomethylation. A similar effect was
noted for the differentially methylated region-2 of the insulin-like growth factor-II (IGF-II) gene.
The authors suggest that hypomethylation of total liver DNA and the IGF-II gene found in
nontumorous liver tissue would appear to be the result of a more prolonged activity and not cell
proliferation, while hypomethylation of tumors could be an intrinsic property of the tumors. As
pointed out by Caldwell and Keshava (2006), overexpression of IGF-II gene in liver tumors and
preneoplastic foci has been shown in both animal models of hepatocarcinogenesis and humans,
and may enhance tumor growth, acting via the overexpressed IGF-I receptor (Scharf et al., 2001;
Werner and Le Roith, 2000).
Diminished hypomethylation was observed in Wy-14643-treated PPARa-null mice as
compared to wild-type mice, suggestive of involvement of PPARa in mediating hypomethylation
(Pogribny et al., 2007), but it is unclear how relevant these results are to TCE and its metabolites.
First, the doses of Wy-14643 administered are associated with substantial liver necrosis and
mortality with long-term treatment (Woods et al., 2007a), adding confounding factors to the
interpretation of their results. Hypomethylation by Wy-14643 progressively increased with time
up to 5 months (Pogribny et al., 2007), consistent with the sustained DNA synthesis caused by
Wy-14643 and a role for proliferation in causing hypomethylation. Regardless, as discussed
above, it is unlikely that PPARa is the mediator of the observed transient increase in DNA
synthesis by DCA, so even if it is important for hypomethylation by TCA, there may be more
than one pathway for this effect.
To summarize, aberrant DNA methylation status, including hypomethylation, is clearly
associated with both human and rodent carcinogenesis. Hypomethylation itself appears to be
sufficient for carcinogenesis, as diets deficient in choline and methionine that induce
hypomethylation have been shown to cause liver tumors in both rats and mice (Henning and
Swendseid, 1996: Wainfan and Poirier, 1992: Ghoshal andFarber, 1984: Mikol etal., 1983).
However, it is not known to what extent hypomethylation is necessary for TCE-induced
carcinogenesis. However, as noted by Bull (2004a) and Bull et al. (2004), the doses of TCA and
DCA that have been tested for induction of hypomethylation are quite high compared to doses at
which tumor induction occurs—at least 500 mg/kg-day. Whether these effects are still manifest
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at lower doses relevant to TCE carcinogenicity, particularly with respect to DCA, has not been
investigated. Finally, the role of PPARa in modulating hypomethylation, possibly through
increased DNA synthesis as suggested by experiments with Wy-14643, are unknown for TCE
and its metabolites.
4.5.7.3.8. Cytotoxicity
Cytotoxicity and subsequent induction of reparative hyperplasia have been proposed as
key events for a number of chlorinated solvents, such as chloroform and carbon tetrachloride.
However, as discussed above and discussed by Bull (2004a) and Bull et al. (2004), TCE
treatment at doses relevant to liver carcinogenicity results in relatively low Cytotoxicity. While a
number of histological changes with TCE exposure are observed, in most cases necrosis is
minimal or mild, associated with vehicle effects, and with relatively low prevalence. This is
consistent with the low prevalence of necrosis observed with TCA and DCA treatment at doses
relevant to TCE exposure. Therefore, it is unlikely that Cytotoxicity and reparative hyperplasia
play a significant role in TCE carcinogenicity
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4.5.7.4. Mode-of-Action Conclusions
The conclusions regarding the mode of action for TCE-induced liver carcinogenesis
described in the preceding sections are summarized in Table 4-68. Overall, although a role for
many of the proposed key events discussed above cannot be ruled out, there are inadequate data
to support the conclusion that any of the particular mode-of-action hypotheses reviewed above
are operant. The available data do suggest that the mode of action of liver tumors induced by
TCE is complex, as it is likely that key events from several pathways may operate. Nonetheless,
because a collection of key events sufficient to induce liver tumors has not been identified, the
answer to the first key question -t. Is the hypothesized mode of action sufficiently supported in
the test animals?" is —o" at this time. Consequently, the other key questions of -2. Is the
hypothesized mode of action relevant to humans?" and "J. Which populations or lifestages
can be particularly susceptible to the hypothesized mode of action?" will not be discussed in a
mode-of-action-specific manner. Rather, they are discussed below in more general terms, first
qualitatively and then quantitatively, using available relevant data.
4.5.7.4.1. Qualitative human relevance and susceptibility
No data exist that suggest that TCE-induced liver tumorigenesis is caused by processes
that are irrelevant in humans. In addition, as discussed above, several of the other effects such as
polyploidization, changes in glycogen storage, and inhibition of GST-zeta—are either clearly
related to human carcinogenesis or areas of active research as to their potential roles. For
example, the effects of DCA on glycogen storage parallel the observation that individuals with
conditions that lead to glycogenesis appear to be at an increased risk of liver cancer (Rake et al.,
2002: Wideroffetal.. 1997: Adamietal.. 1996: La Vecchia et al.. 1994). In addition, there may
be some relationship between the effects of DCA and the mechanism of increased liver tumor
risk in childhood in those with Type 1 hereditary tyrosinemia, though the hypotheses needs to be
tested experimentally. Similarly, with respect to PPARa activation and downstream events
hypothesized to be causally related to liver carcinogenesis, it is generally acknowledged that —a
point in the rat/mouse key events cascade where the pathway is biologically precluded in humans
cannot be identified, in principle" (NRC. 2006: Klaunig et al.. 2003).
In terms of human relevance and susceptibility, it is also useful to briefly review what is
known about human HCC. A number of risk factors have been identified for human HCC,
including ethanol consumption, hepatitis B and C virus infection, aflatoxin Bl exposure, and,
more recently, diabetes and perhaps obesity (El-Serag and Rudolph, 2007). However, it is also
estimated that a substantial minority of HCC patients, perhaps 15-50%, have no established risk
factors (El-Serag and Rudolph, 2007). In addition, cirrhosis is present in a large proportion of
HCC patients, but the prevalence of HCC without underlying cirrhosis, while not precisely
known, is still significant, with estimates based on relatively small samples ranging from 7 to
54% (Fattovich et al.. 2004).
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Table 4-68. Summary of mode-of-action conclusions for TCE-induced liver carcinogenesis
Hypothesized MOA
postulated key events
Experimental support
Human relevance
Weight-of-evidence
conclusion
Mutagenicity (Section 4.5.7.1)
One or more oxidative metabolites
produced in situ or delivered
systemically to liver.
Evidence that TCE or TCE metabolites induces key events:
• Multiple in vitro and in vivo studies demonstrate oxidation of
TCE, and availability to the liver (see Section 3.3.2).
• CH is a short-lived intermediate that is rapidly converted to
TCA and TCOH.
Necessity of key events for carcinogenesis:
• Based on analogy to demonstration that oxidation is
necessary for non-cancer effects in the liver. No TCE-
specific data.
Yes: demonstrated in
humans in vivo and in
human cells in vitro.
Known that both human
and rodent liver are
exposed to the oxidative
metabolites. CH is a
short-lived intermediate,
whereas TCA and TCOH
are more stable.
Mutagenicity induced by oxidative
metabolites advances acquisition of the
multiple critical traits contributing to
carcinogenesis.
Evidence that TCE or TCE metabolites induces key events:
• In rodents, TCE binds to and/or induces damage in DNA and
chromosome structure.
• TCE has a limited ability to induce mutation in bacterial
systems, even with metabolic activation that produce
oxidative metabolites.
• Oxidative metabolites, particularly CH, can cause a variety of
genotoxic effects (including aneuploidy) in available in vitro
and in vivo assays (see Section 4.2.1.5).
Necessity of key events for carcinogenesis:
• No TCE-specific data.
Yes: no basis for
discounting in vitro or
in vivo genotoxicity
results.
Evidence for mutagenicity
through CH is the
strongest, but difficult to
assess genotoxic
contributions from
nongenotoxic
contributions from CH
and other oxidative
metabolites.
Overall Conclusion
Sufficiency of MOA for carcinogenesis:
• Mutagenicity is assumed to cause cancer, as a sufficient
cause.
Yes: well established.
Data are inadequate to
support a conclusion that
a mutagenic MOA
mediated by CH is
operant; however, a
mutagenic MOA,
mediated either by CH or
other oxidative
metabolites of TCE,
cannot be ruled out.
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Table 4-68 Summary of mode-of-action conclusions for TCE-induced liver carcinogenesis (continued)
Hypothesized MOA
postulated key events
Experimental support
Human relevance
Weight-of-evidence
conclusion
Peroxisome proliferation activated receptor alpha activation (Section 4.5.7.2)
TCE oxidative metabolites (e.g.,
TCA), after being produced in the
liver, activate PPARa in the liver.
PPARa activation leads to
alterations in cell proliferation
and apoptosis in the liver.
Alterations in cell proliferation
and apoptosis causes clonal
expansion of initiated cells.
Increased number of initiated cells
causes cancer.
Evidence that TCE or TCE metabolites induces key events:
• Multiple in vitro and in vivo studies demonstrate oxidation of
TCE, and availability of metabolites TCA and DCA to the
liver (see Section 3.3.2).
• TCE, TCA and DCA activate PPARa, induce peroxisome
proliferation and hepatocyte proliferation in mice and rats
(e.g., DeAngelo et al.. 2008: Laughter et al. 2004: Nakaiima
et al.. 2000: Watanabe and Fukui. 2000: Stauber and Bull
1997: Pereira. 1996: Dees and Travis. 1994: Goeletal.. 1992:
Sanchez and Bull 1990: Goldsworthy and Popp. 1987:
Elcombeetal. 1985).
Necessity of key events for carcinogenesis:
• No studies of TCE or its metabolites (e.g., cancer bioassays in
PPARa-null mice). TCE induces increases in liver weight in
male and female mice lacking a functional PPARa receptor
(Ramdhanetal. 2010: Nakaiima etal.. 2000) and in
humanized null mice (Ramdhan et al.. 2010). Liver tumor
response from WY dramatically diminished in PPARa-null
mice (Peters etal. 1997): however, liver tumor response from
DEHP unchanged in PPARa-null mice (Ito et al.. 2007).
Thus, inferences regarding TCE are not possible.
Yes. Humans produce
oxidative metabolites of
TCE, PPARa is present
in the human liver.
Highly likely that PPARa
is activated in the liver,
but it is unlikely that
PPARa agonism and its
sequelae constitute the
sole or predominant MOA
for TCE-induced
carcinogenesis.
Overall
Sufficiency of MOA for carcinogenesis:
• No TCE-specific studies; PPARa activation in a transgenic
mouse model caused all the key events in the MOA, but not
carcinogenesis, suggesting that the MOA is not sufficient for
carcinogenesis (Yang etal.. 2007). Consistent with
hypothesis that TCE liver carcinogenesis involves multiple
mechanisms.
Yes. No evidence to
suggest that key events
are implausible in
humans.
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Table 4-68 Summary of mode-of-action conclusions for TCE-induced liver carcinogenesis (continued)
Hypothesized MOA
postulated key events
Experimental support
Human relevance
Weight-of-evidence
conclusion
Liver weight increases (Section 4.5.7.3.1)
• TCE oxidative metabolites, after
being produced in the liver, cause
liver weight increases.
• Further key events not specified.
Evidence that TCE or TCE metabolites induces key events:
• Multiple in vitro and in vivo studies demonstrate oxidation of
TCE, and availability of metabolites TCA and DCA to the
liver (see Section 3.3.2).
Necessity of key events for carcinogenesis:
• No studies of TCE or its metabolites.
Sufficiency of MOA for carcinogenesis:
• Hypothesis is inadequately specified for evaluation.
Yes. Humans produce
oxidative metabolites of
TCE. No evidence that
liver weight changes
would not occur in
humans.
Data are inadequate to
define a MOA hypothesis
for hepatocarcinogenesis
based on liver weight
increases.
Negative selection (Section 4.5.7.3.2)
• "Negative selection " confers a
growth advantage to initiated
cells.
• Increased number of initiated cells
causes cancer.
Evidence that TCE or TCE metabolites induces key events:
• Transient DNA synthesis is confined to a very small
population of cells in mouse liver (e.g.. Laughter et al, 2004;
Dees and Travis. 1993: Elcombe et al.. 1985). but no data on
whether this effect is — sektive" of initiated cells.
Necessity of key events for carcinogenesis:
• No studies of TCE or its metabolites.
Sufficiency of MOA for carcinogenesis:
• No studies of TCE or its metabolites.
Yes. Humans produce
oxidative metabolites of
TCE. No evidence that
negative selection
would not occur in
humans.
Data are inadequate to test
the MOA hypothesis for
hepatocarcinogenesis
based on liver weight
increases.
Negative selection (Section 4.5.7.3.3)
• TCE or its metabolites causes
polyploidization ofhepatocytes.
• Increased ploidy is associated with
carcinogenesis.
Evidence that TCE or TCE metabolites induces key events:
• Polyploidization likely occurs with TCE exposure, although
the evidence is limited (Buben and O'Flahertv. 1985).
Necessity of key events for carcinogenesis:
• No studies of TCE or its metabolites.
Sufficiency of MOA for carcinogenesis:
• No studies of TCE or its metabolites.
Yes. Increased ploidy
is associated with
cancer in humans as
well as rodents.
Although it is biologically
plausible that
polyploidization can
contribute to liver
carcinogenesis,
inadequate data are
available to support this
hypothesized MOA for
TCE.
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Table 4-68 Summary of mode-of-action conclusions for TCE-induced liver carcinogenesis (continued)
Hypothesized MOA
postulated key events
Experimental support
Human relevance
Weight-of-evidence
conclusion
Glycogen storage (Section 4.5.7.3.4)
• Increased glycogen storage.
• Glycogenosis in humans has been
associated with increased risk of
liver cancer.
Evidence that TCE or TCE metabolites induces key events:
• DCA increases glycogen deposition (Nelson et al.. 1989)
• For TCE and TCA, effects on glycogen were either not
reported (Dees and Travis, 1993; Styles et al., 1991; Elcombe
et al., 1985) or were described as similar to controls (Nelson
etal.. 1989).
Necessity of key events for carcinogenesis:
• No studies of TCE or its metabolites.
Sufficiency of MOA for carcinogenesis:
• No studies of TCE or its metabolites.
Yes. No evidence of
lack of relevance.
Data are inadequate to
define a MOA hypothesis
for TCE-induced
hepatocarcinogenesis
based on changes in
glycogen storage, or to
support changes in
glycogen storage as a
result of TCE exposure.
Inactivation of GST-zeta (Section 4.5.7.3.5)
• Inactivation of GST-zeta.
• Hereditary disruption of this
pathway in humans has been
associated with increased risk of
liver cancer, but the active agent
has not been identified.
Evidence that TCE or TCE metabolites induces key events:
• TCE prolongs DCA half -life in rodents, suggesting that TCE
may inhibit GST-zeta, likely through the formation of DCA
(Schultz etal.. 2002).
Necessity of key events for carcinogenesis:
• No studies of TCE or its metabolites.
Sufficiency of MOA for carcinogenesis:
• No studies of TCE or its metabolites.
Yes. No evidence of
lack of relevance.
Data are inadequate to
define a MOA hypothesis
for TCE-induced
hepatocarcinogenesis
based on inactivation of
GST-zeta.
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Table 4-68 Summary of mode-of-action conclusions for TCE-induced liver carcinogenesis (continued)
Hypothesized MOA
postulated key events
Experimental support
Human relevance
Weight-of-evidence
conclusion
Oxidative stress (Section 4.5.7.3.6)
• Oxidative stress.
• Further key events not specified.
Evidence that TCE or TCE metabolites induces key events:
• Measures of Oxidative stress induced by TCE, TCA, and
DCA either do not occur, or are transient and do not
persistent with continued treatment (Channel et al. 1998:
Parrish et al.. 1996: Larson and Bull 1992b).
Necessity of key events for carcinogenesis:
• No studies of TCE or its metabolites.
Sufficiency of MOA for carcinogenesis:
• No studies of TCE or its metabolites.
Yes. No evidence of
lack of relevance.
Available data are limited
to support a role for
oxidative stress in TCE-
induced liver
carcinogenesis.
Epigenetic changes (Section 4.5.7.3.7)
• Epigenetic changes, particularly
DNA methylation, induced by one
or more metabolites (TCA, DCA,
and other reactive species)
advance acquisition of multiple
critical traits contributing to
carcinogenesis.
Evidence that TCE or TCE metabolites induces key events:
• TCE, TCA and DCA decrease global DNA methylation and
promoter hypomethylation (e.g., of c-myc) in mouse liver
(Tao et al.. 2004a; Ge et al.. 200 Ib; Tao et al.. 1998).
Necessity of key events for carcinogenesis:
• No studies of TCE or its metabolites.
Sufficiency of MOA for carcinogenesis:
• No studies of TCE or its metabolites.
Yes. No evidence of
lack of relevance.
Although it is biologically
plausible that epigenetic
changes contribute to liver
carcinogenesis,
inadequate data are
available to support this
hypothesized MOA for
TCE.
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Table 4-68 Summary of mode-of-action conclusions for TCE-induced liver carcinogenesis (continued)
Hypothesized MOA
postulated key events
Experimental support
Human relevance
Weight-of-evidence
conclusion
Cytotoxicity and reparative hyperplasia(Section 4.5.7.3.8)
One or more reactive
intermediates induces
hepatotoxicity through cell death.
Cell proliferation increases in the
liver to repair damage.
Increased cell turnover increases
the rate of mutations.
Increased proliferation cause
clonal expansion of initiated
(premalignant) cells.
Increased number of mutations
and/or initiated cells causes
cancer.
Evidence that TCE or TCE metabolites induces key events:
• TCE treatment at doses relevant to liver carcinogenicity
results in relatively low cytotoxicity (Bull 2004a: Bull et al..
2004).
• No evidence that transient increases in DNA synthesis are
related to reparative proliferation.
Necessity of key events for carcinogenesis:
• No studies of TCE or its metabolites.
Sufficiency of MOA for carcinogenesis:
• No studies of TCE or its metabolites.
Yes. No evidence of
lack of relevance.
It is unlikely that
cytotoxicity and
reparative hyperplasia
play a significant role in
TCE carcinogenicity.
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However, despite the identification of numerous factors that appear to play a role in the
human risk of HCC, the mechanisms are still largely unclear (Yeh et al., 2007). Interestingly,
the observation by Leakey et al. (2003a: 2003b) that body weight significantly and strongly
impacts background liver tumor rates in B6C3Fi mice parallels the observed epidemiologic
associations between liver cancer and obesity (review in El-Serag and Rudolph, 2007). This
concordance suggests that similar pathways may be involved in spontaneous liver tumor
induction between mice and humans. The extent to which TCE exposure may interact with
known risk factors for HCC cannot be determined at this point, but several hypotheses can be
posed based on existing data. If TCE affects some of the same pathways involved in human
HCC, as suggested in the discussion of several TCE-induced effects above, then TCE exposure
may lead a risk that is additive to background.
As discussed above, there are several parallels between the possible key events in
TCE-induced liver tumors in mice and what is known about mechanisms of human HCC, though
none have been experimentally tested. Altered ploidy distribution and DNA hypomethylation
are commonly observed in human HCC (Calvisi et al., 2007; Lin etal., 2003; Zeppaetal., 1998).
Interestingly, El-Serag and Rudolph (2007) suggested that the risk of HCC increases with
cirrhosis in part because the liver parenchymal cells have decreased proliferative capacity,
resulting in an altered milieu that promotes tumor cell proliferation. This description suggests a
similarity in mode of action, though via different mechanisms, with the —egative selection"
hypothesis proposed by Bull (2000) for TCE and its metabolites although for TCE changes in
apoptosis and cell proliferation have not been noted or examined to such an extent to provide
evidence of a similar environment. Increased ploidy decreases proliferative capacity, so that
may be another mechanism through which the effects of TCE mimic the conditions thought to
facilitate the induction of human HCC.
In sum, from the perspective of hazard characterization, the available data support the
conclusion that the mode of action for TCE-induced mouse liver tumors is relevant to humans.
No data suggest that any of the key events are biologically precluded in humans, and a number of
qualitative parallels exist between hypotheses for the mode of action in mice and what is known
about the etiology and induction of human HCC. A number of risk factors have been identified
that appear to modulate the risk of human HCC, and these may also modulate the susceptibility
to the effects from TCE exposure. As noted in Section E.4, TCE exposure in the human
population is accompanied not only by external exposures to its metabolites, but brominated
analogues of those metabolites that are also rodent carcinogens, a number of chlorinate solvents
that are hepatocarcinogenic and alcohol consumption. The types of tumors and the heterogeneity
of tumors induced by TCE in rodents parallel those observed in humans (see Section E.3.1.8).
The pathways identified for induction of cancer in humans for cancer are similar to those for the
induction of liver cancer (see Section E.3.2.1). However, while risk factors have been identified
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for human liver cancer that have similarities to TCE-induced effects and those of its metabolites,
both the mechanism for human liver cancer induction and that for TCE-induced liver
carcinogenesis in rodents are not known.
4.5.7.4.2. Quantitative species differences
As a precursor to the discussion of quantitative differences between humans and rodents
and among humans, it should be noted that an adequate explanation for the difference in
response for TCE-liver cancer induction between rats and mice has yet to be established or for
that difference to be adequately described given the limitations in the rat database. For TCA,
there is only one available long-term study in rats that, while suggestive that TCA is less potent
in rats than mice, is insufficient to determine if there was a TCA-induced effect or what its
magnitude may be. While some have proposed that the lower rate of TCA formation in rats
relative to mice would explain the species difference, PBPK modeling suggests that the
differences (three-fivefold) may be inadequate to fully explain the differences in carcinogenic
potency. Moreover, inferences from comparing the effects of TCE and TCA on liver weight,
using PBPK model-based estimates of TCA internal dose-metrics as a result of TCE or TCA
administration, indicate that TCA is not likely to play a predominant role in hepatomegaly.
Combined with the qualitative correlation between rodent hepatomegaly and
hepatocarcinogenesis observed across many chemicals, this suggests that TCA similarly is not a
predominant factor in TCE-induced hepatocarcinogenesis. Indeed, there are multiple lines of
evidence that TCA is insufficient to account for TCE-induced tumors, including data on tumor
phenotype (e.g., c-Jun immunostaining) and genotype (e.g., H-ras mutation frequency and
spectrum). For DC A, only a single experiment in rats is available (reported in two publications),
and although it suggests lower hepatocarcinogenic potency in rats relative to mice, its relatively
low power limits the inferences that can be made as to species differences.
As TCA induces peroxisome proliferation in the mouse and the rat, some have suggested
that difference in peroxisomal enzyme induction is responsible for the difference in susceptibility
to TCA liver carcinogenesis. The study of DeAngelo et al. (1989) has been cited in the literature
as providing evidence of differences between rats and mice for peroxisomal response to TCA.
However, data from the most resistant strain of rat (Sprague-Dawley) have been cited in
comparisons of peroxisomal enzyme effects but the Osborne-Mendel and F344 rat were not
refractory and showed increased PCO activity so it is not correct to state that the rat is refractory
to TCA-induction of peroxisome activity (see Section E.2.3.1.5). In addition, as discussed
above, inferences based on PCO activity are limited by its high variability, even in control
animals, as well as its not necessarily being predictive of the peroxisome number or cytoplasmic
volume.
The same assumption of lower species sensitivity by measuring peroxisome proliferation
has been applied to humans, as peroxisome proliferation caused by therapeutic PPARa agonists
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such as fibrates in humans is generally lower (less than twofold induction) than that observed in
rodents (20-50-fold induction). However, as mentioned above, it is known that peroxisome
proliferation is not a good predictor of potency (Marsman et al., 1988).
Limited data exist on the relative sensitivity of the occurrence of key events for liver
tumor induction between mice and humans and among humans. Pharmacokinetic differences are
addressed with PBPK modeling to the extent that data allow, so the discussion here will
concentrate on pharmacodynamic differences. Most striking is the difference in -background"
rates of liver tumors. Data from NTP indicate that control B6C3Fi mice in 2-year bioassays
have a background incidence of HCCs of 26% in males and 10% in females, with higher
incidences for combined hepatocellular adenomas and carcinomas (Maronpot 2007). However,
as discussed above, Leakey et al. (2003a: 2003b) report that the background incidence rates are
very dependent on the weight of the mice. By contrast, the estimated lifetime risk of liver and
biliary tract cancer in the United States (about 75% of which are HCCs) is 0.97% for men and
0.43% for women (Ries et al., 2008). However, regions of the world where additional risk
factors (hepatitis infection, alflatoxin exposure) have high prevalence have liver cancer
incidences up to more than sixfold greater than the United States (Ferlay et al., 2004). Therefore,
one possible quantitative difference that can be flagged for use in dose-response assessment is
the background rate of liver tumors between species. Biologically-based dose-response
modeling by Chen (2000) suggested that the data were consistent with a purely promotional
model in which potency would be proportional to background tumor incidence. However, it is
notable that male Swiss mice, which have lower background liver tumor rates than the B6C3Fi
strain, were also positive in one long-term bioassay (Maltoni etal., 1988; Maltoni etal., 1986).
Similarly, in terms of intraspecies susceptibility, to the extent that TCE may
independently promote pre-existing initiated cells, it can be hypothesized that those with greater
risk for developing HCC due to one more of the known risk factors would have a proportional
increase in the any contributions from TCE exposure. In addition, in both humans and mice,
males appear to be at increased risk of liver cancer, possibly due to sexually dimorphism in
inflammatory responses (Lawrence et al., 2007; Naugler et al., 2007; Rakoff-Nahoum and
Medzhitov, 2007), suggesting that men may also be more susceptible to TCE-induced liver
tumorigenesis than women. It has been observed that human HCC is highly heterogeneous
histologically, but within patients and between patients, studies are only beginning to distinguish
the different pathways that may be responsible for this heterogeneity (Yeh et al., 2007; Chen et
al.. 2002b: Feitelson et al.. 2002).
Appropriate quantitative data are generally lacking on interspecies differences in the
occurrence of most other proposed key events, although many have argued that there are
significant quantitative differences between rodents and humans related to PPARa activation
(NRC, 2006; Klaunig et al., 2003). For instance, it has been suggested that lower levels of
PPARa receptor in human hepatocytes relative to rodent hepatocytes contributes to lower human
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sensitivity (Klaunig et al., 2003; Palmer et al., 1998; Tugwood et al., 1996). However, out of a
small sample of human livers (n = 6) show similar protein levels to mice (Walgren et al., 2000b).
Another proposed species difference has been ligand affinity, but while transactivation assays
showed greater affinity of Wy-14643 and perfluorooctanoic acid for rodent relative to human
PPARa, they showed TCA and DCA had a similar affinities between species (Maloney and
Waxman, 1999). Furthermore, it is not clear that receptor-ligand kinetics (capacity and affinity)
are rate-limiting for eliciting hepatocarcinogenic effects, as it is known that maximal receptor
occupation is not necessary for a maximal receptor mediated response (Stephenson, 1956) [see
also review by Danhof et al. (2007)1.
There is also limited in vivo and in vitro data suggesting that increases in cell
proliferation mediated by PPARa agonists are diminished in humans and other primates relative
to rodents (NRC, 2006: Hoivik et al.. 2004: Klaunig et al.. 2003). However, Walgren et al.
(2000a) reported that TCA and DCA were not mitogenic in either human or rodent hepatocytes
in vitro. Furthermore, TCE, TCA, and DCA all induce only transient increases in cell
proliferation, so the relevance to TCE of interspecies differences from PPARa agonists that to
produce sustained proliferation, such as Wy-14643, is not clear. In addition, comparisons
between primate and rodent models should take into account the differences in the ability to
respond to any mitogenic stimulation (see Section E.3.2). Primate and human liver respond
differently (and much more slowly) to a stimulus such as partial hepatectomy.
Recent studies in —huianized" mice (PPARa-null mice in which a human PPARa gene
was subsequently inserted and expressed in the liver) reported that treatment with a PPARa
agonist lead to greatly lower incidence of liver tumors as compared to wild-type mice (Morimura
et al., 2006). However, these experiments were performed with WY-14643 at a dose causing
systemic toxicity (reduced growth and survival), had a duration of <1 year, and involved a
limited number of animals. In addition, because liver tumors in mice at <1 year are extremely
rare, the finding a one adenoma in WY-14643-treated humanized mice suggests carcinogenic
potential that could be further realized with continued treatment (Keshava and Caldwell, 2006).
In addition, Yang et al. (2007) recently noted that let-7C, a microRNA involved in cell growth
and thought to be a regulatory target of PPARa (Shah et al., 2007), was inhibited by Wy-14643
in wild-type mice, but not in —huianized mice" in which human PPARa was expressed
throughout the body on a PPARa-null background. However, these humanized mice had about a
20-fold higher baseline expression of let-7C, as reported in control mice, potentially masking any
treatment effects. More generally, it is not known to what extent PPARa-related events are rate-
limiting in TCE-induced liver tumorigenesis, for which multiple pathways appear to be
operative. So even if quantitative differences mediated by PPARa were well estimated, they
would not be directly usable for dose-response assessment in the absence of way to integrate the
contributions from the different pathways.
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In sum, the only quantitative data and inter- and intraspecies susceptibility suitable for
consideration in dose-response assessment are differences background liver tumor risk. These
may modulate the effects of TCE if RR, rather than additional risk, is the appropriate common
inter- and intraspecies metric. However, the extent to which RR would provide a more accurate
estimate of human risk is unknown.
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4.6. IMMUNOTOXICITY AND CANCERS OF THE IMMUNE SYSTEM
Chemical exposures may result in a variety of adverse immune-related effects, including
immunosuppression (decreased host resistance), autoimmunity, and allergy-hypersensitivity, and
may result in specific diseases such as infections, systemic or organ-specific autoimmune
diseases, or asthma. Cell-mediated immune response, such as activation of macrophages, natural
killer (NK) cells, and cytokine production, can also influence a broader range of diseases, such as
cancer. Measures of immune function (e.g., T-cell counts, immunoglobulin [Ig] E levels,
specific autoantibodies, cytokine levels) may provide evidence of an altered immune response
that precedes the development of clinically expressed diseases. The first section of this section
discusses effects relating to immunotoxicity, including risk of autoimmune diseases, allergy and
hypersensitivity, measures of altered immune response, and lymphoid cancers. Studies
pertaining to effects in humans are presented first, followed by a section discussing relevant
studies in animals. The second section of this section discusses evidence pertaining to TCE in
relation to lymphoid tissue cancers, including childhood leukemia.
4.6.1. Human Studies
4.6.1.1. Noncancer Immune-Related Effects
4.6.1.1.1. Immunosuppression, asthma, and allergies
In 1982, Lagakos et al. conducted a telephone survey of residents of Woburn,
Massachusetts, collecting information on residential history and history of 14 types of medically
diagnosed conditions (Lagakos et al., 1986). The survey included 4,978 children born since
1960 who lived in Woburn before age 19. Completed surveys were obtained from
approximately 57% of the town residences with listed phone numbers. Two of the wells
providing the town's water supply from 1964 to 1979 had been found to be contaminated with a
number of solvents, including tetrachloroethylene (21 ppb) and TCE (267 ppb) (as cited in
Lagakos et al., 1986). Lagakos et al. (Lagakos et al., 1986) used information from a study by the
Massachusetts Department of Environmental Quality and Engineering to estimate the
contribution of water from the two contaminated wells to the residence of each participant, based
on zones within the town receiving different mixtures of water from various wells, for the period
in which the contaminated wells were operating. This exposure information was used to
estimate a cumulative exposure based on each child's length of residence in Woburn. A higher
cumulative exposure measure was associated with conditions indicative of immunosuppression
(e.g., bacterial or viral infections) or hypersensitivity (e.g., asthma). In contrast, a recent study
using the National Health and Nutrition Examination Survey data collected from 1999 to 2000 in
a representative sample of the U.S. population (n = 550) did not find an association between TCE
exposure and self-report of a history of physician-diagnosed asthma (OR: 0.94, 95% CI: 0.77,
1.14) (Arif and Shah, 2007). TCE exposure, as well as exposure to nine other VOCs, was
determined through a passive monitor covering a period of 48-72 hours. No clear trend was
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seen with self-reported wheeze episodes (OR: 1.29, 95% CI: 0.98, 1.68 for one to two episodes;
OR: 0.21, 95% CI: 0.04, 10.05 for three or more episodes in the past 12 months).
Allergy and hypersensitivity, as assessed with measures of immune system parameters or
immune function tests (e.g., atopy) in humans, have not been extensively studied with respect to
the effects of TCE (see Table 4-69). Lehmann et al. reported data pertaining to immunoglobulin
E (IgE) levels and response to specific antigens in relation to indoor levels of VOCs among
children (age 36 months) selected from a birth cohort study in Leipzig, Germany (Lehmann et
al., 2001). Enrollment into the birth cohort occurred between 1995 and 1996. The children in
this allergy study represent a higher-risk group for development of allergic disease, with
eligibility criteria that were based on low birth weight (between 1,500 and 2,500 g), or cord
blood IgE >0.9 kU/L with double positive family history of atopy. These eligibility criteria were
met by 429 children; 200 of these children participated in the allergy study described below, but
complete data (IgE and VOC measurements) were available for only 121 of the study
participants. Lehmann et al. (2001) measured 26 VOCs via passive indoor sampling in the
child's bedroom for a period of 4 weeks around the age of 36 months. The median exposure of
TCE was 0.42 ug/m3 (0.17 and 0.87 ug/m3 for the 25th and 75th percentiles, respectively). Blood
samples were taken at the 36-month study examination and were used to measure the total IgE
and specific IgE antibodies directed to egg white, milk, indoor allergens (house dust mites, cats,
and molds), and outdoor allergens (timothy-perennial grass and birch trees). There was no
association between TCE exposure and any of the allergens tested in this study, although some of
the other VOCs (e.g., toluene, 4-ethyltoluene) were associated with elevated total IgE levels and
with sensitization to milk or eggs.
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Table 4-69. Studies of immune parameters (IgE antibodies and cytokines) and TCE in humans
Parameter,
source of data
Results
Reference, location, diagnosis period,
sample size, age
IgE antibodies
blood sample, indoor air sampling of
28 volatile organic chemicals in child's
bedroom
TCE exposure not associated with sensitization to indoor or outdoor
allergens
Lehmann et al. (2001)
Germany. 1997-1999. n= 121 36-mo
old children
Cytokine secreting CD3+ T-cell
populations
cord blood, indoor air sampling of
28 volatile organic chemicals in child's
bedroom 4 wks after birth
In CD3+ cord blood cells, some evidence of association between increasing
TCE levels and
decreased IL-4 >75th percentile OR: 0.6 (95% CI: 0.2, 2.1),
<25th percentile OR 4.4 (95% CI: 1.1, 17.8)
increased IFN-y >75* percentile OR: 3.6 (95% CI: 0.9, 14.9)
<25* percentile OR: 0.7 (95% CI: 0.2, 2.2)
Similar trends not seen with tumor necrosis factor-a or IL-2
Lehmann et al. (2002)
Germany. 1995-1996. n = 85newborns
Cytokine secreting CD3+ and CD8+ T-
cell populations
blood sample, indoor air sampling of
28 volatile organic chemicals in child's
bedroom
TCE exposure not associated with percentages of IL-4 CD3+ or IFN-y
CD8+ T-cells
Lehmann et al. (2001)
Germany. 1995-1999. n = 200 36-mo
old children
Cytokine concentration—serum
urine sample (TCA concentration), blood
sample, questionnaire (smoking history,
age, residence), workplace TCE measures
(personal samples, four exposed and four
nonexposed workers)
Nonexposed workers similar to office controls for all cytokine measures.
Compared to nonexposed workers, the TCE exposed workers had
decreased IL-4 (mean 3.9 vs. 8.1 pg/mL)
increased IL-2 (mean 798 vs. 706 pg/mL)
increased IFN-y (mean 37.1 vs. 22.9 pg/mL)
lavicoli et al. (2005)
Italy, n = 35 printers using TCE,
30 nonexposed workers (in same
factory, did not use or were not near
TCE), 40 office worker controls. All
men. Mean age ~33 yrs
IFN = interferon; IL = interleukin
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4.6.1.1.2. Generalized hypersensitivity skin diseases, with or without hepatitis
Occupational exposure to TCE has been associated with a severe, generalized skin
disorder that is distinct from contact dermatitis in the clinical presentation of the skin disease
(which often involves mucosal lesions), and in the accompanying systemic effects that can
include lymphadenopathy, hepatitis, and other organ involvement. Kamijima et al. (2007)
recently reviewed case reports describing 260 patients with TCE-related generalized skin
disorders (Kamijima et al., 2007). Six of the patients were from the United States or Europe,
with the remainder occurring in China, Singapore, Philippines, and other Asian countries. One
study in Guangdong province, in southeastern China, included >100 of these cases in a single
year (Huang et al., 2002). Kamijima et al. (2007) categorized the case descriptions as indicative
of hypersensitivity syndrome (n = 124) or a variation of erythema multiforme, Stevens-Johnson
syndrome, and toxic epiderma necrolysis (n = 115), with 21 other cases unclassified in either
category. The fatality rate, approximately 10%, was similar in the two groups, but the
prevalence of fever and lymphadenopathy was higher in the hypersensitivity syndrome patients.
Hepatitis was seen in 92-94% of the multiforme, Stevens-Johnson syndrome, and toxic epiderma
necrolysis patients, but the estimates within the hypersensitivity syndrome group were more
variable (46-94%) (Kamijima et al.. 2007).
Some of the case reports reviewed by Kamijima et al. (2007) provided information on the
total number of exposed workers, working conditions, and measures of exposure levels. From
the available data, generalized skin disease within a worksite occurred in 0.25-13% of workers
in the same location, doing the same type of work (Kamijima et al., 2007). The measured
concentration of TCE ranged from <50 to >4,000 mg/m3, and exposure scenarios included
inhalation only and inhalation with dermal exposures. Disease manifestation generally occurred
within 2-5 weeks of initial exposure, with some intervals up to 3 months. Most of the reports
were published since 1995, and the geographical distribution of cases reflects the newly
industrializing areas within Asia.
Kamijima and colleagues recently conducted an analysis of urinary measures of TCE
metabolites (TCA and TCOH) in 25 workers hospitalized for hypersensitivity skin disease in
2002 (Kamijima et al., 2008). Samples taken within 15 days of the last exposure to TCE
exposure were available for 19 of the 25 patients, with a mean time of 8.4 days. Samples from
the other patients were not used in the analysis because the half-life of U-TCA is 50-100 hours.
In addition, 3-6 healthy workers doing the same type of work in the factories of the affected
worker, and 2 control workers in other factories not exposed to TCE were recruited in 2002-
2003 for a study of breathing zone concentration of volatile organochlorines and urinary
measures of TCE metabolites. Worksite measures of TCE concentration were also obtained.
Adjusting for time between exposure and sample collection, mean urinary concentration at the
time of last exposure among the 19 patients was 206 mg/mL for TCA. Estimates for TCOH
were not presented because of the shorter half-life for this compound. U-TCA levels in the
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healthy exposed workers varied among the 4 factories, with means (± SDs) of 41.6 (± 18.0),
131 (±90.2), 180 (±92), and 395 (±684). The lower values were found in a factory in which the
degreasing machine had been partitioned from the workers after the illnesses had occurred. TCE
concentrations (personal TWAs) at the factories of the affected workers ranged from 164 to
2,330 mg/m3 (30-431 ppm). At the two factories with no affected workers in the past 3 years,
the mean personal TWA TCE concentrations were 44.9 mg/m3 (14 ppm) and 1,803 mg/m3
(334 ppm). There was no commonality of additives or impurities detected among the affected
factories that could explain the occurrence of the hypersensitivity disorder.
To examine genetic influences on disease risk, Dai et al. (2004) conducted a case-control
study of 111 patients with TCE-related severe generalized dermatitis and 152 TCE-exposed
workers who did not develop this disease. Patients were recruited from May 1999 to November
2003 in Guangdong Province, and were employed in approximately 80 electronic and metal-
plating manufacturing plants. Initial symptoms occurred within 3 months of exposure. The
comparison group was drawn from the same plants as the cases, and had worked for >3 months
without development of skin or other symptoms. Mean age in both groups was approximately
23 years. A blood sample was obtained from study participants for genotyping of TNF-a,
TNF-P, and interleukin (IL)-4 genotypes. The genes were selected based on the role of TNF and
of IL-4 in hypersensitivity and inflammatory responses. The specific analyses included two
polymorphisms in the promoter region of TNF-a (G —> A substitution at position -308); and a
G —> A substitution at position -238), a polymorphism at the first intron on TNF-P, and a
polymorphism in the promoter region of IL-4 (C —> T substitution at -590). There was no
difference in the frequency of the TNF-a"238, TNF-P, or IL-4 polymorphisms between cases and
controls, but the wild-type TNF-a"308 genotype was somewhat more common among cases (94%
in cases and 86% in controls).
Kamijima et al. (2007) note the similarities, particular with respect to specific skin
manifestations, of the case presentations of TCE-related generalized skin diseases to conditions
that have been linked to specific medications (e.g., carbamazepine, allopurinol, antibacterial
sulfonamides), possibly in conjunction with reactivation of specific latent herpes viruses. A
previous review by these investigators discussed insights with respect to drug metabolism that
may be useful in developing hypotheses regarding susceptibility to TCE-related generalized skin
disorders (Nakajima et al., 2003). Based on consideration of metabolic pathways and
intermediaries, variability in CYP2E1, UDP-glucoronyltransferase, GST, andN-acetyl
transferase (NAT) activities could be hypothesized to affect the toxicity of TCE. NAT2 is most
highly expressed in liver, and the —bw" acetylation phenotype (which arises from a specific
mutation) has been associated with adverse effects of medications, including drug-induced lupus
(Lemke and McQueen, 1995) and hypersensitivity reactions (Spielberg, 1996). There are limited
data pertaining to genetic or other sources of variability in these enzymes on risk of TCE-related
generalized skin diseases, however. In a study in Guangdong province, CYP1A1, GSTM1,
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GSTP1, GSTT1, and NAT2 genotypes in 43 cases of TCE-related generalized skin disease were
compared to 43 healthy TCE-exposed workers (Huang et al., 2002). The authors reported that
the NAT2 slow acetylation genotype was associated with disease, but the data pertaining to this
finding were not presented.
4.6.1.1.3. Cytokine profiles and lymphocyte subsets
Cytokines are produced by many of the immune regulatory cells (e.g., macrophages,
dendritic cells), and have many different effects on the immune system. The T-helper Type 1
(Thl) cytokines, are characterized as —par-inflammatory" cytokines, and include TNF-a and
interferon (IFN)-y. Although this is a necessary and important part of the innate immune
response to foreign antigens, an aberrant pro-inflammatory response may result in a chronic
inflammatory condition and contribute to development of scarring or fibrotic tissue, as well as to
autoimmune diseases. Th2 cytokines are important regulators of humoral (antibody-related)
immunity. IL-4 stimulates production of IgE and thus influences IgE-mediated effects such as
allergy, atopy, and asthma. Th2 cytokines can also act as —bikes" on the inflammatory
response, so the balance between different types of cytokine production is also important with
respect to risk of conditions resulting from chronic inflammation. Several studies have examined
cytokine profiles in relation to occupational or environmental TCE exposure (see Table 4-69).
The Lehmann et al. (2001) study of 36-month-old children (described above) also
included a blood sample taken at the 3-year study visit, which was used to determine the
percentages of specific cytokine producing T-cells in relation to the indoor VOCs exposures
measured at birth. There was no association between TCE exposure and either IL-4 CD3+ or
IFN-y CD8+ T-cells (Lehmann et al.. 2001).
Another study by Lehmann et al. (2002) examined the relationship between indoor
exposures to VOCs and T-cell subpopulations measured in cord blood of newborns. The study
authors randomly selected 85 newborns (43 boys and 42 girls) from a larger cohort study of 997
healthy, full-term babies, recruited between 1997 and 1999 in Germany. Exclusion criteria
included a history in the mother of an autoimmune disease or infectious disease during the
pregnancy. Twenty-eight VOCs were measured via passive indoor sampling in the child's
bedroom for a period of 4 weeks after birth (a period that is likely to reflect the exposures during
the prenatal period close to the time of delivery). The levels were generally similar or slightly
higher than the levels seen in the previous study using samples from the bedrooms of the
36-month-old children. The highest levels of exposure were seen for limonene (median
24.3 ug/m3), a-pinene (median 19.3 ug/m3), and toluene (median 18.3 ug/m3), and the median
exposure of TCE was 0.6 ug/m3 (0.2 and 1.0 ug/m3 for the 25th and 75th percentiles,
respectively). Flow cytometry was used to measure the presence of CD3 T-cells obtained from
the cord blood labeled with antibodies against IFN-y, tumor necrosis factor-a, IL-2, and IL-4.
There was some evidence of a decreased level of IL-2 with higher TCE exposure in the
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univariate analysis, with median percentage of IL-2 cells of 46.1 and 33.0% in the groups that
were below the 75th percentile and above the 75th percentile of TCE exposure, respectively. In
analyses adjusting for family history of atopy, gender, and smoking history of the mother during
pregnancy, there was little evidence of an association with either IL-2 or IFN-y, but there was a
trend of increasing TCE levels associated with decreased IL-4 and increased IFN-y.
lavicoli et al. (2005) examined cytokine levels in 35 TCE-exposed workers (Group A)
from a printing area of a factory in Italy. Their work involved use of TCE in degreasing. Two
comparison groups were included. Group B consisted of 30 other factory workers who were not
involved in degreasing activities and did not work near this location, and Group C consisted of
40 office workers at the factory. All study participants were male and had worked at their
present position for at least 3 years, and all were considered healthy. Personal breathing zone air
samples from four workers in Group A and four workers in Group B were obtained in three
consecutive shifts (24 total samples) to determine air concentration of TCE. A urine sample was
obtained from each Group A and Group B worker (end of shift at end of work week) for
determination of TCA concentrations (corrected for creatinine), and blood samples were
collected for assessment of IL-2, IL-4, and IFN-y concentrations in serum using enzyme-linked
immunosorbent assays. Among exposed workers, the mean TCE concentration was
approximately 35 mg/m3 (30.75 ± SD 9.9, 37.75 ± 23.0, and 36.5 ± 8.2 mg/m3 in the morning,
evening, and night shifts, respectively). The U-TCA concentrations were much higher in
exposed workers compared with nonexposed workers (mean ± SD, Group A 13.3 ± 5.9 mg/g
creatinine; Group B 0.02 ± 0.02 mg/g creatinine). There was no difference in cytokine levels
between the two control groups, but the exposed workers differed significantly (all /^-values <
0.01 using Dunnett's test for multiple comparisons) from each of the two comparison groups.
The observed differences were a decrease in IL-4 levels (mean 3.9, 8.1, and 8.1 pg/mL for
Groups A, B, and C, respectively), and an increase in IL-2 levels (mean 798, 706, and
730 pg/mL for Groups A, B, and C, respectively) and in IFN-y levels (mean 37.1, 22.9, and
22.8 pg/mL for Groups A, B, and C, respectively).
The available data from these studies (lavicoli et al., 2005; Lehmann et al., 2002;
Lehmann et al., 2001) provide some evidence of an association between increased TCE exposure
and modulation of immune response involving an increase in pro-inflammatory cytokines (IL-2,
IFN-y) and a decrease in Th2 (allergy-related) cytokines (e.g., IL-4). These observations add
support to the influence of TCE in immune-related conditions affected by chronic inflammation.
Lan et al. (2010) examined lymphocyte subsets among 80 TCE-exposed workers and
96 controls in Guangdong, China. Six factories using TCE for cleaning metals, optical lenses, or
circuit boards were included in this study. These factories did not use other solvents (benzene,
styrene, ethylene oxide, formaldehyde, or epichlorohydrin), based on an exposure screening
using Drager tubes and 3M Badges. Eighty workers from these factories and 96 unexposed
controls (frequency matched by sex and 5-year age groups to controls) from clothes
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manufacturers, a food production factory, and a hospital, were included in the study. The study
was conducted in 2006. Study participants provided a blood sample, buccal cells, postshift and
overnight urine samples, and completed a questionnaire with demographic, alcohol and smoking
history, and occupational history data. A blood sample was used for a complete blood count and
differential lymphocyte subset analysis. At the time of the blood draw, a clinical examination,
including measurement of height and weight, and symptoms of recent respiratory infection
(which could affect the differential blood cell counts) was conducted. TCE monitoring was
conducted using full-shift personal air exposure measurements. The median level of exposure,
based on the mean of two measurements taken for each participant in the month before the blood
draw, among the 80 TCE-exposed workers was 12 ppm. The analysis used this level to
categorize workers into high (>12 ppm; mean 38 ppm) and low (<12 ppm; mean 5 ppm)
exposures. Among the controls, the mean TCE exposure was <0.03 ppm. The total number of
lymphocytes, T cells, CD4+ T cells, CD8+ T cells, B cells and NK cells was significantly lower
among TCE-exposed workers compared with controls, with the largest decrease seen in the
higher exposure group. For example, the age- and sex-adjusted lymphocyte count was 2,154,
2,012, and 1,671 cells/|iL blood in the controls, <12 and >12 ppm groups, respectively (trend
p = <0.0001). Plasma concentrations of soluble CD27 and CD30, two costimulators involved in
the regulation of T cells, were also decreased in both exposure groups compared with controls.
Similar patterns were seen when limited to the 77 workers with exposure levels <100 ppm, and
when limited to the 60 workers with exposure levels <25 ppm. Granulocytes, monocytes and
platelet counts did not differ by exposure. The authors noted that the immunosuppression and
decreased lymphocyte activation seen in this study provide support the biological plausibility of
a role of TCE exposure in NHL.
4.6.1.1.4. Autoimmune disease
4.6.1.1.4.1. Disease clusters and geographic-based studies
Reported clusters of diseases have stimulated interest in environmental influences on
systemic autoimmune diseases. These descriptions include investigations into reported clusters
of systemic lupus erythematosus (Dahlgren et al., 2007; Balluz et al., 2001) and Wegener
granulomatosis (Albert et al., 2005). Wegener granulomatosis, an autoimmune disease involving
small vessel vasculitis, usually with lung or kidney involvement, is a very rare condition, with an
incidence rate of 3-14 per million per year (Mahr et al., 2006). TCE was one of several
groundwater contaminants identified in a recent study investigating a cluster of seven cases of
Wegener granulomatosis around Dublin, Pennsylvania. Because of the multiple contaminants, it
is difficult to attribute the apparent disease cluster to any one exposure.
In addition to the study of asthma and infectious disease history among residents of
Woburn, Massachusetts (Lagakos et al., 1986) (see Section 4.6.1.1.1), Byers et al. (1988)
provided data pertaining to immune function from 23 family members of leukemia patients in
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Woburn, Massachusetts. Serum samples were collected in May and June of 1984 and in
November of 1985 (several years after 1979, when the contaminated wells had been closed).
Total lymphocyte counts and lymphocyte subpopulations (CDS, CD4, and CDS) and the
CD4/CD8 ratio were determined in these samples, and in samples from a combined control
group of 30 laboratory workers and 40 residents of Boston selected through a randomized
probability area sampling process. The study authors also assessed the presence of antinuclear
antibodies (ANA) or other autoantibodies (antismooth muscle, antiovarian, antithyroglobulin,
and antimicrosomal antibodies) in the family member samples and compared the results with
laboratory reference values. The age distribution of the control group, and stratified analyses by
age, are not provided. The lymphocyte subpopulations (CDS, CD4, and CDS) were higher and
the CD4/CD8 ratio was lower in the Woburn family members compared to the controls in both
of the samples taken in 1984. In the 1985 samples, however, the lymphocyte subpopulation
levels had decreased and the CD4/CD8 ratio had increased; the values were no longer
statistically different from the controls. None of the family member serum samples had
antithyroglobulin or antimicrosomal antibodies, but 10 family-member serum samples (43%) had
ANA (compared to <5% expected based on the reference value). Because the initial blood
sample was taken in 1984, it is not possible to determine the patterns at a time nearer to the time
of the exposure. The co-exposures that occurred also make it difficult to infer the exact role of
TCE in any alterations of the immunologic parameters.
Kilburn and Warshaw (1992a) reported data from a study of contamination by metal-
cleaning solvents (primarily TCE) and heavy metals (e.g., chromium) of the aquifer of the Santa
Cruz River in Tucson, Arizona (1992a). Exposure concentrations >5 ppb (6-500 ppb) had been
documented in some of the wells in this area. A study of neurological effects was undertaken
between 1986 and 1989 (Kilburn and Warshaw, 1993b), and two of the groups within this larger
study were also included in a study of symptoms relating to systemic lupus erythematosus.
Residents of Tucson (n = 362) were compared to residents of southwest Arizona (n = 158)
recruited through a Catholic parish. The Tucson residents were selected from the neighborhoods
with documented water contamination (>5 ppb TCE for at least 1 year between 1957 and 1981).
Details of the recruitment strategy are not clearly described, but the process included recruitment
of patients with lupus or other rheumatic diseases (Kilburn and Warshaw, 1993b, 1992a). The
prevalence of some self-reported symptoms (malar rash, arthritis/arthralgias, Raynaud syndrome,
skin lesions, and seizure or convulsion) was significantly higher in Tucson, but there was little
difference between the groups in the prevalence of oral ulcers, anemia, low white blood count or
low platelet count, pleurisy, alopecia, or proteinuria. The total number of symptoms reported
was higher in Tucson than in the other southwest Arizona residents (14.3 vs. 6.4% reported four
or more symptoms, respectively). Low-titer (1:80) ANA were seen in 10.6 and 4.7% of the
Tucson and other Arizona residents, respectively (p = 0.013). However, since part of the Tucson
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study group was specifically recruited based on the presence of rheumatic diseases, it is difficult
to interpret these results.
4.6.1.1.4.2. Case-control studies
Interest in the role of organic solvents, including TCE, in autoimmune diseases was
spurred by the observation of a scleroderma-like disease characterized by skin thickening,
Raynaud's phenomenon, and acroosteolysis and pulmonary involvement in workers exposed to
vinyl chloride (Gama and Meira, 1978). A case report in 1987 described the occurrence of a
severe and rapidly progressive case of systemic sclerosis in a 47-year-old woman who had
cleaned X-ray tubes in a tank of TCE for approximately 2.5 hours (Lockey etal., 1987).
One of the major impediments to autoimmune disease research is the lack of disease
registries, which makes it difficult to identify incident cases of specific diseases. There are no
cohort studies of the incidence of autoimmune diseases in workers exposed to TCE. Most of the
epidemiologic studies of solvents and autoimmune disease rely on general measures of
occupational exposures to solvents, organic solvents, or chlorinated solvents exposures. A two-
to threefold increased risk of systemic sclerosis (scleroderma) (Maitre et al., 2004; Garabrant et
al.. 2003: Aryal etal., 2001), rheumatoid arthritis (Sverdrup et al.. 2005: Lundberg et al.. 1994).
undifferentiated connective tissue disease (Lacey etal., 1999), and antineutrophil-cytoplasmic
antibody (ANCA)-related vasculitis (Beaudreuil et al., 2005: Lane et al., 2003) has generally
been seen in these studies, but there was little evidence of an association between solvent
exposure and systemic lupus erythematosus in two recent case-control studies (Finckh et al.,
2006: Cooper et al., 2004).
Two case-control studies of scleroderma (Bovenzi et al., 2004: Maitre et al., 2004) and
two of rheumatoid arthritis (Olsson et al., 2004: Olsson et al., 2000) provide data concerning
solvent exposure that occurred among metal workers or in jobs that involved cleaning metal (i.e.,
types of jobs that were likely to use TCE as a solvent). There was a twofold increased risk
among male workers in the two studies of rheumatoid arthritis from Sweden (Olsson et al., 2004:
Olsson et al., 2000). The results from the smaller studies of scleroderma were more variable,
with no exposed cases seen in one study with 93 cases and 206 controls (Maitre et al., 2004), and
an OR of 5.2 (95% CI: 0.7, 37) seen in a study with 56 cases and 171 controls (Bovenzi et al.,
2004).
Five other case-control studies provide data specifically about TCE exposure, based on
industrial hygienist review of job history data (see Table 4-70). Three of these studies are of
scleroderma (Garabrant etal., 2003: Diot et al., 2002: Nietert et al., 1998), one is of
undifferentiated connective tissue disease (Lacey etal., 1999), and one is of small vessel
vasculitidies involving ANCAs (Beaudreuil et al., 2005).
These studies included some kind of expert review of job histories, but only two studies
included a quantification of exposure (e.g., a cumulative exposure metric, or a "high" exposure
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group) (Plot et al., 2002; Nietert et al., 1998). Most of the studies present data stratified by sex,
and as expected, the prevalence of exposure (either based on type of job or on industrial
hygienist assessment) is considerably lower in women compared with men. In men, the studies
generally reported ORs between 2.0 and 8.0, and in women, the ORs were between 1.0 and 2.0.
The incidence rate of scleroderma in the general population is approximately 5-10 times higher
in women compared with men, which may make it easier to detect large RRs in men.
The EPA conducted a meta-analysis of the three scleroderma studies with specific
measures of TCE (Garabrant et al.. 2003: Diot et al.. 2002: Nietert et al.. 1998), examining
separate estimates for males and for females. The resulting combined estimate for "any"
exposure, using a random effects model to include the possibility of nonrandom error between
studies (DerSimonian and Laird. 1986). was OR: 2.5 (95% CI: 1.1, 5.4) for men and OR: 1.2
(95% CI: 0.58, 2.6) in women. (Because the —ayi" exposure variable was not included in the
published report, Dr. Paul Nietert provided the EPA with a new analysis with these results, e-
mail communication from Paul Nietert to Glinda Cooper, November 28, 2007.)
Specific genes may influence the risk of developing autoimmune diseases, and genes
involving immune response (e.g., cytokines, major histocompatibility complex, B- and T-cell
activation) have been the focus of research pertaining to the etiology of specific diseases. The
metabolism of specific chemical exposures may also be involved (Cooper et al., 1999).
Povey et al. (2001) examined polymorphisms of two CYP genes, CYP2E1 and CYP2C19, in
relation to solvent exposure and risk of developing scleroderma. These specific genes were
examined because of their hypothesized role in metabolism of many solvents, including TCE.
Seven scleroderma patients who reported a history of solvent exposure were compared to
71 scleroderma patients with no history of solvent exposure and to 106 population-based
controls. The CYP2E1*3 allele and the CYP2E1*4 allele were more common in the
seven solvent-exposed patients (each seen in two of the seven patients; 29%) than in either of the
comparison groups (approximately 5% for CYP2E1*3 and 14% for CYP2E1*4). The authors
present these results as observations that require a larger study for corroboration and further
elucidation of specific interactions.
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Table 4-70. Case-control studies of autoimmune diseases with measures of TCE exposure
Disease, source of data
Results:
exposure prevalence, OR, 95% CI
Reference, location, sample size,
age
Scleroderma
Structured interview (specific jobs and
materials; jobs held >1 yrs). Exposure
classified by self -report and by expert
review (JEM).
Structured interview (specific jobs and
materials; jobs held >6 mo). Exposure
classified by expert review.
Structured interview (specific jobs and
materials; jobs held >3 mo). Exposure
classified by serf-report and by expert
review.
Men
Maximum intensity 30% cases, 10% controls; OR: 3.3 (95% CI: 1.0, 10.3)
Cumulative intensity 32% cases, 21% controls; OR: 2.0 (95% CI: 0.7, 5.3)
Maximum probability 16% cases, 3% controls; OR: 5. 1 (95% CI: not calculated)
Women
Maximum intensity 6% cases, 7% controls; OR: 0.9 (95% CI: 0.3, 2.3)
Cumulative intensity 10% cases, 9% controls; OR: 1.2 (95% CI: 0.5, 2.6)
Maximum probability 4% cases, 5% controls; OR: 0.7 (95% CI: 0.2, 2.2)
Men and women
Any exposure: cases 16%, controls 8%; OR: 2.4 (95% CI: 1.0, 5.4)
High exposure:3 cases 9%, controls 1%; OR: 7.6 (95% CI: 1.5, 37.4)
Men
Any exposure: cases 64%, controls 27%; OR: 4.7 (95% CI: 0.99, 22.0)
Women
Any exposure: cases 9%, controls 4%; OR: 2.1 (95% CI: 0.65, 6.8)
Women
Self report: cases 1.3%, controls 0.7%; OR: 2.0 (95% CI: 0.8, 4.8)
Expert review: cases 0.7%, controls 0.4%; OR: 1.9 (95% CI: 0.6, 6.6)
Nietert et al. (1998)
South Carolina. Prevalent cases,
178 cases (141 women, 37 men),
200 hospital-based controls.
Mean age at onset 45.2 yrs
Diot et al. (2002)
France. Prevalent cases, 80 cases
(69 women, 11 men), 160
hospital controls. Mean age at
diagnosis 48 yrs
Garabrant et al. (2003)
Michigan and Ohio. Prevalent
cases, 660 cases (all women),
2,227 population controls.13
Ages 18 and older
Undifferentiated connective tissue disease
Structured interview (specific jobs and
materials; jobs held >3 mo).
Exposure classified by serf-report
and by expert review.
Women
Serf report: cases 0.5%, controls 0.7%; OR: 0.88 (95% CI: 0.11, 6.95)
Expert review: cases 0.5%, controls 0.4%; OR: 1.67 (95% CI: 0.19, 14.9)
Lacey et al. (1999).
Michigan and Ohio. Prevalent
cases, 205 cases (all women),
2,095 population controls.
Ages 18 and older
ANCA-related diseases0
Structured interview (specific jobs and
materials; jobs held >6 mo). Exposure
classified by expert review.
Men and women (data not presented separately by sex)
cases 18.3%, controls 17.5%; OR: 1.1 (95% CI: 0.5, 2.4)
Beaudreuil et al. (2005)
France. Incident cases, 60 cases
(-50% women), 120 hospital
controls. Mean age 61 yrs
""Cumulative exposure defined as product of probability x intensity x frequency x duration scores, summed across all jobs; scores of >1 classified as —Igh."
bTotal n; n with TCE data: serf -report 606 cases, 2,138 control; expert review 606 cases, 2,137 controls.
Diseases included Wegener glomerulonephritis (n = 20), microscopic polyangiitis (n = 8), pauci-immune glomerulonephritis (n = 10), uveitis (n = 6), Churg-
Strauss syndrome (n = 4), stroke (n = 4), and other diseases (no more than 2 each).
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4.6.1.2. Cancers of the Immune System, Including Childhood Leukemia
4.6.1.2.1. Description of studies
Human studies have reported cancers of the immune system resulting from TCE
exposure. Lymphoid tissue neoplasms arise in the immune system and result from events that
occur within immature lymphoid cells in the bone marrow or peripheral blood (leukemias), or
more mature cells in the peripheral organs (NHL). As such, the distinction between lymphoid
leukemia and NHL is largely distributional with overlapping entities, such that a particular
lymphoid neoplasm may manifest both lymphomatous and leukemic features during the course
of the disease (Weisenburger, 1992). The broad category of lymphomas can be divided into
specific types of cancers, including NHL, Hodgkin lymphoma, multiple myeloma, and various
types of leukemia (e.g., acute and chronic forms of lymphoblastic and myeloid leukemia). The
classification criteria for these cancers has changed over the past 30 years, reflecting improved
understanding of the underlying stem cell origins of these specific subtypes. Lymphomas are
grouped according to the World Health Organization (WHO) classification as B-cell neoplasms,
T-cell/NK-cell neoplasms, and Hodgkin lymphoma, formerly known as Hodgkin disease (Harris
et al., 2000). For example, hairy cell leukemia, CLL, NHL, and multiple myeloma may arise
from mature B cells and are types of NHLs according to the WHO's lymphoma classification
system (Morton et al., 2007, 2006). Most of the studies of TCE exposure evaluate NHL defined
as lymphosarcoma, reticulum-cell sarcoma, and other lymphoid tissue neoplasms with recently
published studies reporting on total B-cell or specific B-cell neoplasms.
Numerous studies are found in the published literature on NHL and either broad exposure
categories or occupational title. The NHL studies generally report positive associations with
organic solvents or job title as aircraft mechanic, metal cleaner or machine tool operator, and
printers, although associations are not observed consistently across all studies, specific solvents
are not identified, and different lymphoma classifications are adopted (Coccoetal., 2010; Orsi et
al.. 2010: Schenk et al.. 2009: Wang et al.. 2009: 'tMannetie et al.. 2008: Karunanavake et al..
2008: Richardson et al.. 2008: Alexander et al.. 2007b: Boffetta and de Vocht 2007: Seidler et
al.. 2007: Vineis et al.. 2007: Dryver et al.. 2004: Chiu and Weisenburger. 2003: Lynge et al..
1997: Tatham et al.. 1997: Figgsetal.. 1995: Blair etal.. 1993). A major use of TCE is the
degreasing, as vapor or cold state solvent, of metal and other products with potential exposure in
jobs in the metal industry, printing industry, and aircraft maintenance or manufacturing industry
(Bakke et al., 2007). The recent NHL case-control study of Purdue et al. (2009) examined
degreasing tasks, specifically, and reported an increasing positive trend between NHL risk in
males and three degreasing exposure surrogates: average frequency (hours/year) (p = 0.02),
maximal frequency (hours/year), (p = 0.06), or cumulative number of hours (p = 0.04).
As described in Appendix B, the EPA conducted a thorough and systematic search of
published epidemiological studies of cancer risk and TCE exposure using the PubMed,
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TOXNET®, and EMBASE® bibliographic database. The EPA also requested unpublished data
pertaining to TCE from studies that may have collected these data but did not include it in their
published reports. ATSDR and state health department peer-reviewed studies were also
reviewed. Information from each of these studies relating to specified design and analysis
criteria was abstracted. These criteria included aspects of study design, representativeness of
study subjects, participation rate/loss to follow-up, latency considerations, potential for biases
related to exposure misclassification, disease misclassification, and surrogate information,
consideration of possible confounding, and approach to statistical analysis. All studies were
considered for hazard identification, but those studies more fully meeting the objective criteria
provided the greater weight for identifying a cancer hazard.
The body of evidence on NHL and TCE is comprised of occupational cohort studies,
population-based case-control studies, and geographic studies. Four case-control studies and
four geographic studies also examined childhood leukemia and TCE. Most studies reported
observed risk estimates and associated CIs for NHL and overall TCE exposure. The studies
included a broad but sometimes slightly different group of lymphosarcoma, reticulum-cell
sarcoma, and other lymphoid tissue neoplasms, with the exception of the Nordstrom et al. (1998)
case-control study, which examined hairy cell leukemia, now considered a NHL, the Zhao et al.
(2005) cohort study, which reported only results for all lymphohematopoietic cancers, including
nonlymphoid types and excluding CLL, and the Greenland et al. (1994) nested case-control
study which reported results for NHL and Hodgkin lymphoma combined. Persson and
Fredrikson (1999) do not identify the classification system for defining NHL, and Hardell et al.
(1994) define NHL using the Rappaport classification system. Miligi et al. (2006) used an NCI
classification system and considered CLLs and NHL, classified as lymphosarcoma,
reticulosarcoma, and other lymphoid tissue neoplasms, together, while Cocco et al. (2010), used
the WHO classification system, which reclassifies lymphocytic leukemias and NHLs as
lymphomas of B-cell or T-cell origin. EPA staff, additionally, was able to obtain results
generally consistent with the traditional NHL definition from Dr. Cocco, although lymphomas
not otherwise specified were excluded (Cocco, 2010). The cohort studies [except for Zhao et al.
(2005)] and the nested case-control study of Greenland et al. (1994) have some consistency in
coding NHL, with NHL defined as lymphosarcoma and reticulum-cell sarcoma (ICD code 200)
and other lymphoid tissue neoplasms (ICD code 202) using the ICD Revisions 7, 8, or 9.
Revisions 7 and 8 are essentially the same with respect to NHL; under Revision 9, the definition
of NHL was broadened to include some neoplasms previously classified as Hodgkin lymphomas
(Banks, 1992). Wang et al. (2009) refer to their cases as —NHL cases and according to the ICD-
O classification system that they used, their cases are more specifically NHL subtypes such as
diffuse, lymphosarcoma, or follicular lymphoma (9590-9642, 9690-9701) or mast cell tumors
(9740-9750) which is consistent with the traditional definition of NHL (i.e., ICD-7, -8, -9 codes
200 + 202) (Morton et al.. 2003). NHL cases in Purdue et al. (2011) were also classified
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according to ICD-O (2nd Edition converted to ICO-O 3rd Edition codes), included diffuse,
follicular T-cell and all other NHL subtypes, which is generally consistent with the traditional
definition of NHL, although this grouping does not include the malignant lymphomas of
unspecified type coded as M-9590-9599. Fewer studies in published papers presented this
information for cell-specific lymphomas, leukemia, leukemia cell type, or multiple myeloma
(Gold etal.. 2011: Coccoetal.. 2010: Costantini et al.. 2008: Radican et al.. 2008: Boice et al.,
2006b: Hansenetal.. 2001: Raaschou-Nielsen et al.. 2001: Boice etal.. 1999: Morgan et al..
1998: Anttila et al.. 1995: Axel son et al.. 1994).
The seven cohort studies with data on the incidence of lymphopoietic and hematopoietic
cancer in relation to TCE exposure range in size from 803 (Hansen et al., 2001) to 86,868
(Chang et al., 2005), and were conducted in Denmark, Sweden, Finland, Taiwan, and the United
States (see Table 4-71; for additional study descriptions, see Appendix B). Some subjects in the
Hansen et al. (2001) study are also included in a study reported by Raaschou-Nielsen et al.
(2003): however, any contribution from the former to the latter are minimal given the large
differences in cohort sizes of these studies (Raaschou-Nielsen et al., 2003: Hansen etal., 2001).
The exposure assessment techniques used in all studies except Chang et al. (2005) and Sung et
al. (2007) included a detailed JEM (Zhao et al.. 2005: Blair etal.. 1998). biomonitoring data
(Hansenetal., 2001: Anttila et al., 1995: Axel son etal., 1994), or reference to industrial hygiene
records on TCE exposure patterns and factors that affected exposure, indicating a high
probability of TCE exposure potential (Raaschou-Nielsen et al., 2003) with high probability of
TCE exposure to individual subjects. Subjects in Chang et al. (2005) and Sung et al. (2007), two
studies with overlapping subjects employed at an electronics plant in Taiwan, have potential
exposure to several solvents including TCE; all subjects are presumed as —expostf' because of
employment in the plant although individual subjects would be expected to have differing
exposure potentials. The lack of attribution of exposure intensity to individual subjects yields a
greater likelihood for exposure misclassification compared to the six other studies with exposure
assessment approaches supported by information on job titles, tasks, and industrial hygiene
monitoring data. Incidence ascertainment in two cohorts began 21 (Blair et al., 1998) and
38 years (Zhao et al., 2005) after the inception of the cohort. Specifically, Zhao et al. (2005)
noted that their results may not accurately reflect the effects of carcinogenic exposure that
resulted in nonfatal cancers before 1988. Because of the issues concerning case ascertainment
raised by this incomplete coverage, observations must be interpreted in light of possible bias
reflecting incomplete ascertainment of incident cases.
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Table 4-71. Incidence cohort studies of TCE exposure and lymphopoietic and hematopoietic cancer risk
Population
exposure group
Lymphopoietic
cancer
RR (95% CI)a
na
NHL
RR
(95% CI)a
na
Leukemia
RR
(95% CI)a
na
Multiple
myeloma
RR
(95% CI)a
na
Aerospace workers (Rocketdyne), California
Any TCE exposure
Low cumulative TCE
score
Medium cumulative
TCE score
High cumulative TCE
score
(p for trend)
Not reported
Not reported
1.0 (referent)
0.88 (0.47, 1.65)
0.20 (0.03, 1.46)
(0.097)
28
16
1
Electronic workers, Taiwan
All employees
Males
Females
Females
0.67 (0.42, 1.01)
0.73 (0.27, 1.60)
0.65 (0.37, 1.05)
22
6
16
Not reported
Not reported
Not reported
Not reported
0.78(0.49, 1.17)
23
Not reported
Not reported
Not reported
Reference(s) and study
description13
Zhao et al. (2005)
n = 5,049 (2,689 with high
cumulative TCE exposure),
began work before 1980,
worked at least 2 yrs, alive
with no cancer diagnosis in
1988, follow-up from 1988 to
2000, JEM (intensity), internal
referents (workers with no
TCE exposure). Leukemia
and multiple myeloma
observations included in NHL
category.
Chang et al. (2005): Sung et al.
(2007)
n= 88,868 (n= 70,735
female), follow-up 1979-1997,
does not identify TCE
exposure to individual subjects
(Chang et al.. 2005).
n = 63,982 females, follow-up
1979-2001, does not identify
TCE exposure to individual
subjects (Sung et al.. 2007).
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Table 4-71. Incidence cohort studies of TCE exposure and lymphopoietic and hematopoietic cancer risk
(continued)
Population
exposure group
Lymphopoietic
cancer
RR (95% CI)a
na
NHL
RR
(95% CI)a
na
Leukemia
RR
(95% CI)a
na
Multiple
myeloma
RR
(95% CI)a
na
Blue-collar workers, Denmark
Any exposure
Subcohort w/higher
exposure"1
Employment duration
1-4.9 yrs
>5 yrs
1.1(1.0, 1.6)
Not reported
229
1.2(1.0,1.5)
1.5(1.2,2.0)
1.5(1.1,2.1)
1.6(1.1,2.2)
96
65
35
30
1.2 (0.9, 1.4)
Not reported
82
1.03 (0.70, 1.47)
Not reported
31
Biologically -monitored workers, Denmark
Any TCE exposure
Cumulative exposure
(Ikeda), males
<17 ppm-yr
>17 ppm-yr
Mean concentration
(Ikeda), males
<4ppm
4+ppm
Employment duration,
males
<6.25 yr
>6.25 yr
2.0(1.1,3.3)
Not reported
Not reported
Not reported
15
3.1 (1.3,6.1)
3.9(0.8, 11)
3.1 (0.6,9.1)
3.9(1.1, 10)
3.2(1.1, 10)
2.5 (0.3, 9.2)
4.2(1.1,11)
8
3
3
4
4
2
4
2.0 (0.7, 4.4)
Not reported
Not reported
Not reported
6
0.71 (0.02, 3.98)
Not reported
Not reported
Not reported
1
Reference(s) and study
description13
Raaschou-Nielsen et al. (2003)
n= 40,049 (14,360 with
presumed higher level
exposure to TCE), worked for
at least 3 mo, follow-up from
1968 to 1997, documented
TCEusec. EPA based the
lymphopoietic cancer category
on summation of ICD codes
200-204.
Hansenetal. (2001)
n = 803, U-TCA or air TCE
samples, follow-up 1968-1996
[subset of Raaschlou-Nielsen
et al. (2003) cohort]. EPA
based the lymphopoietic
cancer category on summation
of ICD codes 200-204.
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Table 4-71. Incidence cohort studies of TCE exposure and lymphopoietic and hematopoietic cancer risk
(continued)
Population
exposure group
Lymphopoietic
cancer
RR (95% CI)a
na
NHL
RR
(95% CI)a
na
Leukemia
RR
(95% CI)a
na
Multiple
myeloma
RR
(95% CI)a
na
Aircraft maintenance workers, Hill Air Force Base, Utah
TCE Subcohort
Males, cumulative
exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative
exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Not reported
1.0 (referent)
0.8 (0.4, 1.7)
0.7 (0.3, 1.8)
1.4 (0.6, 2.9)
1.0 (referent)
1.2 (0.3, 4.4)
1.9 (0.4, 8.8)
0.9(9.2,3.3)
12
7
17
3
2
3
Not reported
1.0 (referent)
0.9(0.3,2.6)
0.7 (0.2, 2.6)
1.0(0.4,2.9)
1.0 (referent)
0.6(0.1,5.0)
0.9(0.2,4.5)
8
4
7
1
0
2
Not reported
1.0 (referent)
0.4(0.1,2.0)
0.9 (0.2, 3.7)
1.0 (referent)
2.4(0.3,21.8)
2
0
4
0
1
0
Not reported
1.0 (referent)
0.8(0.1, 12.7)
3.8 (0.4, 37.4)
5.1(0.6,43.7)
1.0 (referent)
Not reported
Not reported
Not reported
9
1
3
5
2
1
1
Biologically -monitored workers, Finland
Any TCE exposure
1.51(0.92,2.33)
20
1.81 (0.78, 3.56)
8
1.08(0.35,2.53)
5
Mean air-TCE (Ikeda extrapolation)
<6ppm
6+ppm
1.36 (0.65, 2.49)
2.08 (0.95, 3.95)
10
9
2.01 (0.65, 4.69)
1.40(0.17,5.04)
5
2
0.39(0.01,2.19)
2.65 (0.72, 6.78)
1
4
1.62(0.44,4.16)
1.48(0.18,5.35)
2.41 (0.29, 6.78)
4
2
2
Reference(s) and study
description13
Blair et al. (1998)
n= 10,461 men and
3,605 women (total
n = 14,066, n = 7,204 with
TCE exposure), employed at
least 1 yrfrom 1952 to 1956,
follow-up 1973-1990, JEM
(intensity), internal referent
(workers with no chemical
exposures).
Anttila et al. (1995)
n = 3,089 men and women,
U-TCA samples, follow-up
1967-1992.
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Table 4-71. Incidence cohort studies of TCE exposure and lymphopoietic and hematopoietic cancer risk
(continued)
Population
exposure group
Lymphopoietic
cancer
RR (95% CI)a
na
NHL
RR
(95% CI)a
na
Leukemia
RR
(95% CI)a
na
Multiple
myeloma
RR
(95% CI)a
na
Biologically -monitored workers, Sweden
Males
0-17 ppm (Ikeda
extrapolation)
18-3 5 ppm (Ikeda
extrapolation)
>36 ppm (Ikeda
extrapolation)
Females
1.17(0.47,2.40)
Not reported
Not reported
7
1.56 (0.51, 3.64)
1.44 (0.30, 4.20)
(0, 8.58)
6.25(0.16,34.8)
Not reported
5
3
0
1
Not reported
Not reported
Not reported
0.57(0.01,3.17)
1
Reference(s) and study
description13
Axelson et al. (1994)
n = 1,421 men and 249 women
(total 1,670), U-TCA samples,
follow-up 1958-1987. EPA
based the lymphopoietic
cancer category includes ICD-
7200-203.
an = number of observed cases.
bSIRs using an external population referent group unless otherwise noted.
'Exposure assessment based on industrial hygiene data on TCE exposure patterns and factors that affect such exposure (Raaschou-Nielsen et al.. 2002). with high
probability of TCE exposure potential to individual subjects. Companies included iron and metal (48%), electronics (11%), painting (11%), printing
chemical (5%), dry cleaning (5%), and other industries.
dDefined as at least 1 year duration and first employed before 1980.
4-372
-------
Eighteen cohort or PMR studies describing mortality risks from lymphopoietic and
hematopoietic cancer are summarized in Table 4-72 (for additional study descriptions, see
Appendix B). Two studies examined cancer incidence, Radican et al. (2008), who updated
mortality in Blair et al. (1998) cohort, and Zhao et al. (2005), and are identified above. In 10 of
the 18 studies presenting mortality risks (Clapp and Hoffman, 2008; Sung et al., 2007; AT SDR,
2004a: Chang etal., 2003: Henschler et al., 1995: Sinks etal., 1992: Blair etal., 1989: Costa et
al., 1989: Garabrant et al., 1988: Wilcosky et al., 1984), a relatively limited exposure assessment
methodology was used, study participants may not represent the underlying population, or there
was a low exposure prevalence of TCE exposure. For reasons identified in the systematic
review, these studies are given less weight in the overall evaluation of the literature than the
eight other cohort studies that better met the ideals of evaluation criteria (Radican et al., 2008:
Boice et al., 2006b: Zhao et al., 2005: Boiceetal., 1999: Ritz, 1999a: Blair etal., 1998 and
extended follow-up by: Morgan etal., 1998: Greenland et al., 1994).
Case-control studies of NHL from United States (Connecticut), Germany, Italy, Sweden,
and Canada were identified, and are summarized in Table 4-73 (for additional study descriptions,
see Appendix B). These studies identified cases from hospital records (Coccoetal., 2010:
Costantini et al., 2008: Seidler et al., 2007: Mester et al., 2006: Miligi et al., 2006: Persson and
Fredrikson, 1999: Hardell et al., 1994: Siemiatvcki, 1991): the SEER Cancer Registry-
Connecticut residents (Wang et al., 2009), Iowa, Los Angeles County, and Seattle and Detroit
metropolitan area residents (Purdue et al., 2011), or Seattle and Detroit metropolitan area
residents (Gold etal., 2011): or the Swedish Cancer Registry (Nordstrom et al., 1998), and
hospital or population controls. These studies assign potential occupational TCE exposure to
cases and controls using self-reported information obtained from a mailed questionnaire (Persson
and Fredrikson, 1999: Nordstrom et al., 1998: Hardell et al., 1994) or from direct interview with
study subjects, with industrial hygienist ratings of exposure potential and a JEM (Purdue et al.,
2011: Coccoetal., 2010: Wang et al., 2009: Costantini et al., 2008: Seidler et al., 2007: Miligi et
al., 2006: Siemiatvcki, 1991). Additionally, large multiple center lymphoma case-control studies
examine specific types of NHL (Purdue etal., 2011: Cocco etal., 2010: Wang et al., 2009:
Miligi et al., 2006), leukemia (Costantini et al., 2008), or multiple myeloma (Purdue etal., 2011:
Cocco et al., 2010: Costantini et al., 2008).
4-373
-------
Table 4-72. Mortality cohort and PMR studies of TCE exposure and lymphopoietic and hematopoietic
cancer risk
Population,
exposure group
Lymphopoietic cancer
RR
(95% CI)
na
NHL
RR
(95% CI)
na
Leukemia
RR
(95% CI)
na
Multiple myeloma
RR
(95% CI)
na
Computer manufacturing workers (IBM), New York
Males
Females
2.24(1.01,4.19)
Not reported
9
0
Not reported
Not reported
Not reported
Not reported
Not reported
Not reported
3
0
Aerospace workers (Rocketdyne), California
Any TCE (utility/eng
flush)
0.74 (0.34, 1.40)
9
0.21(0.01, 1.18)
1
1.08(0.35,2.53)
5
0.50 (0.01, 2.77)
1
Reference(s) and study
description13
Clapp and Hoffman (2008)
n = 115 cancer deaths from
1969 to 2001, proportional
cancer mortality ratio, does
not identify TCE exposure
to individual subjects. EPA
based the lymphopoietic
cancer category on — al
lymphatic cancers."
Boice et al. (2006b)
n = 41,351 (1,111 Santa
Susana workers with any
TCE exposure), employed
on or after 1948-1999,
worked >6 mo, follow-up to
1999, JEM without
quantitative estimate of TCE
intensity.
4-374
-------
Table 4-72. Mortality cohort and PMR studies of TCE exposure and lymphopoietic and hematopoietic cancer
risk (continued)
Population,
exposure group
Lymphopoietic cancer
RR
(95% CI)
na
NHL
RR
(95% CI)
na
Leukemia
RR
(95% CI)
na
Multiple myeloma
RR
(95% CI)
na
Aerospace workers (Rocketdyne), California (continued)
Any TCE exposure
Low cumulative
TCE score
Medium
cumulative TCE
score
High TCE score
(p for trend)
Not reported
Not reported
1.0 (referent)
1.49 (0.86, 2.57)
1.30 (0.52, 3.23)
(0.370)
60
27
27
6
Not reported
Not reported
View-Master employees, Oregon
Males
Females
0.58(0.11, 1.69)
0.64(0.28, 1.26)
3
8
0.69 (0.08, 2.49)
0.52(0.14, 1.33)
2
4
0.50 (0.01, 2.79)
0.67(0.14, 1.96)
1
o
J
Reference(s) and study
description13
Zhao et al. (2005)
n = 6,044 (n = 2,689 with
high cumulative level
exposure to TCE), began
work and worked at least
2yrsinl950orlater-1993,
follow-up to 2001, JEM
(intensity), internal referents
(workers with no TCE
exposure). Leukemia and
multiple myeloma
observations included in
NHL category.
ATSDR (2004a)
n = 616 deaths from 1989 to
2001, PMR, does not
identify TCE exposure to
individual subjects. EPA
based the NHL cancer
category on -ether
lymphopoietic tissue" which
included NHL and multiple
myeloma.
4-375
-------
Table 4-72. Mortality cohort and PMR studies of TCE exposure and lymphopoietic and hematopoietic cancer
risk (continued)
Population,
exposure group
Lymphopoietic cancer
RR
(95% CI)
na
NHL
RR
(95% CI)
na
Leukemia
RR
(95% CI)
na
Multiple myeloma
RR
(95% CI)
na
Electronic workers, Taiwan
All employees
Males
Females
Not reported
Not reported
1.27 (0.41, 2.97)
1.14(0.55,2.10)
5
10
0.44 (0.05, 1.59)
0.54 (0.23, 1.07)
2
8
Not reported
Not reported
Aerospace workers (Lockheed), California
Routine TCE
Any TCE exposure
1.5 (0.81, 1.60)
36
1.19(0.65, 1.99)
14
1.05 (0.54, 1.84)
12
0.91 (0.34, 1.99)
6
Routine-intermittent
Any TCE exposure
Exposure duration
Oyr
5 yrs
p for trend
Not reported
Not reported
Not reported
1.0 (referent)
0.74 (0.32, 1.72)
1.33 (0.64, 2.78)
1.62 (0.82, 3.22)
0.20
32
7
10
14
Not reported
Not reported
1.0 (referent)
0.45(0.13, 1.54)
1.48 (0.64, 3.41)
0.51 (0.15, 1.76)
>0.20
24
3
8
3
Reference(s) and study
description13
Chang et al. (2003)
n = 88,868 (n = 70,735
female), began work 1978-
1997, lollow-up 1985—1997,
does not identify TCE
exposure to individual
subjects.
Boice et al. (1999)
n = 77,965 (n = 2,267 with
routine TCE exposure and
n — 3.016 with intermittent-
routine TCE exposure),
began work >1960, worked
at least 1 yr, follow-up from
1960 to 1996, JEM without
quantitative estimate of TCE
intensity.
4-376
-------
Table 4-72. Mortality cohort and PMR studies of TCE exposure and lymphopoietic and hematopoietic cancer
risk (continued)
Population,
exposure group
Lymphopoietic cancer
RR
(95% CI)
na
NHL
RR
(95% CI)
na
Leukemia
RR
(95% CI)
na
Uranium-processing workers (Fernald), Ohio
Any TCE exposure
No TCE exposure
Light TCE
exposure, >2 yrs
Moderate TCE
exposure, >2 yrs
Not reported
1.0 (referent)
1.45 (0.68, 3.06)c
1.17(0.15, 9.00)c
18
1
Not reported
Not reported
Not reported
Not reported
Not reported
Not reported
Not reported
Not reported
Aerospace workers (Hughes), California
TCE subcohort
TCE subcohort
Low intensity
(<50 ppm)
High intensity
(>50 ppm)
0.99 (0.64, 1.47)
1.07 (0.51, 1.96)
0.95 (0.53, 1.57)
25
10
15
0.96 (0.20, 2.81)d
1.01 (0.46, 1.92)e
1.79 (0.22, 6.46)d
0.50 (0.01, 2.79)d
3
9
2
1
1.05 (0.50, 1.93)
0.85(0.17,2.47)
1.17(0.47,2.41)
10
3
7
TCE subcohort (Cox Analysis)
Never exposed
Ever exposed
1.0 (referent)
1.05 (0.67, 1.65)f
82
25
1.0 (referent)
1.36 (0.35, 5.22)4f
8
3
1.0 (referent)
0.99 (0.48, 2.03)f
32
10
Peak
No/Low
Medium/High
1.0 (referent)
1.08 (0.64, 1.82)
90
17
1.0 (referent)
1.31(0.28, 6.08)d
9
2
1.0 (referent)
1.10(0.49,2.49)
35
7
Multiple myeloma
RR
(95% CI)
Not reported
Not reported
Not reported
Not reported
1.08(0.35, 2.53)e
na
5
Reference(s) and study
description13
Ritz (1999a)
n = 3,8 14 (n= 2,971 with
TCE), began work 195 1-
1972, worked >3 mo,
follow-up to 1989, internal
referents (workers with no
TCE exposure).
Morgan et al. (1998)
n = 20,508 (4,733 with TCE
exposure), worked >6 mo
1950-1985, follow-up to
1993, external and internal
(all non-TCE exposed
workers) workers referent,
JEM (intensity).
4-377
-------
Table 4-72. Mortality cohort and PMR studies of TCE exposure and lymphopoietic and hematopoietic cancer
risk (continued)
Population,
exposure group
Lymphopoietic cancer
RR
(95% CI)
na
NHL
RR
(95% CI)
na
Leukemia
RR
(95% CI)
na
Aerospace workers (Hughes), California (continued)
Cumulative
Referent
Low
High
1.0 (referent)
1.09(0.56,2.14)
1.03 (0.59, 1.79)
82
10
15
1.0 (referent)
2.25(0.46, ll.l)d
0.81(0.10, 6.49)d
8
2
1
1.0 (referent)
0.69 (0.21, 2.32)
1.14(0.5,2.60)
32
3
7
Aircraft maintenance workers, Hill Air Force Base, Utah
TCE subcohort
1.1(0.7, 1.8)g
66
2.0 (0.9, 4.6)g
28
0.6 (0.3, 1.2)g
16
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0 (referent)
1.1(0.6,2.1)
1.0(0.4,2.1)
1.3 (0.7, 2.5)
21
11
21
1.0 (referent)
1.8 (0.6, 5.4)
1.9 (0.6, 6.3)
1.1(0.3,3.8)
10
6
5
1.0 (referent)
1.0(0.3,3.2)
1.2 (0.4, 3.6)
7
0
7
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
1.0 (referent)
1.5 (0.6, 4.0)
0.7(0.1,4.9)
1.1(0.4,3.0)
1.06(0.75, 1.51)h
6
1
6
106
3.8(0.8, 18.9)
3.6 (0.8, 16.2)
1.36 (0.77, 2.39)h
3
0
4
46
1.0 (referent)
0.4(0.1,3.2)
0.3(0.1,2.4)
0.64(0.35, 1.18)h
1
0
1
27
Multiple myeloma
RR
(95% CI)
1.3 (0.5, 3.4)
1.0 (referent)
1.0 (0.2, 4.2)
0.8(0.1,4.4)
1.2 (0.3, 4.7)
1.0 (referent)
3.2(0.5, 19.8)
4.3 (0.4, 23.4)
1.3(0.1, 13.2)
1.35 (0.62, 2.93)
na
14
4
2
4
2
1
1
25
Reference(s) and study
description13
Blair et al. (1998): Radican
et al. (2008)
n = 14,066 (n = 7,204 ever
exposed to TCE), employed
at least 1 yr from 1952 to
1956, follow-up to 1990
(Blair etal.. 1998) or to
2000 (Radican et al.. 2008).
JEM, internal referent
(workers with no chemical
exposures).
4-378
-------
Table 4-72. Mortality cohort and PMR studies of TCE exposure and lymphopoietic and hematopoietic cancer
risk (continued)
Population,
exposure group
Lymphopoietic cancer
RR
(95% CI)
na
NHL
RR
(95% CI)
na
Leukemia
RR
(95% CI)
na
Aircraft maintenance workers, Hill Air Force Base, Utah (continued)
Males, cumulative
exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative
exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.12(0.72, 1.73)
1.0 (referent)
1.04 (0.63, 1.74)
1.06 (0.49, 1.88)
1.25 (0.75, 2.09)
1.00 (0.55, 1.83)
1.0 (referent)
1.10(0.48,2.54)
0.38 (0.05, 2.79)
1.11 (0.53,2.31)
88
34
21
33
18
7
1
10
1.56 (0.79, 4.21)
1.0 (referent)
1.83 (0.79, 4.21)
1.17(0.42,3.24)
1.50(0.61,3.69)
1.18(0.49,2.85)
1.0 (referent)
1.48 (0.47, 4.66)
1.30 (0.45, 3.77)
37
18
7
12
9
4
0
5
0.77 (0.37, 1.62)
1.0 (referent)
0.86 (0.36, 2.02)
0.51(0.16, 1.63)
0.87(0.35,2.14)
0.36(0.10, 1.32)
1.0 (referent)
0.35 (0.05, 2.72)
0.48(0.10,2.19)
24
11
4
9
3
1
0
2
Multiple myeloma
RR
(95% CI)
1.08(0.43,2.71)
1.0 (referent)
0.69 (0.21, 2.27)
1.58(0.53,4.71)
1.19(0.40,3.54)
2.37 (0.67, 8.44)
1.0 (referent)
2.20 (0.40, 12.02)
2.79(0.31,25.05)
2.38 (0.53, 10.67)
na
19
5
7
7
6
2
1
3
Cardboard manufacturing workers, Arnsburg, Germany
TCE-exposed subjects
Unexposed subjects
from same factory
1.10(0.03,6.12)
1.11 (0.03,6.19)
1
1
Reference(s) and study
description13
Henschler et al. (1995)
n = 169 TCE exposed and
n = 190 unexposed men,
employed >1 yr from 1956
to 1975, follow-up to 1992,
local population referent,
qualitative exposure
assessment.
4-379
-------
Table 4-72. Mortality cohort and PMR studies of TCE exposure and lymphopoietic and hematopoietic cancer
risk (continued)
Population,
exposure group
Lymphopoietic cancer
RR
(95% CI)
na
GE plant, Pittsfield, Massachusetts
NHL
RR
(95% CI)
0.76 (0.24, 2.42)1J
na
15
Leukemia
RR
(95% CI) na
1.1(0.46,2.66)'
22
Multiple myeloma
RR
(95% CI)
na
Cardboard manufacturing workers, Atlanta, Georgia
0.3 (0.0, 1.6)
1
Not reported
Not reported
Not reported
Reference(s) and study
description13
Greenland et al. (1994)
Nested case-control study,
n = 5 12 cancer (cases) and
1,202 noncancer (controls)
male deaths reported to
pension fund between 1969
and 1984 among workers
employed <1984 and with
job history record, JEM-ever
held job with TCE exposure.
Hodgkin lymphoma in NHL
grouping.
Sinks et al. (1992)
n = 2,050, employed on or
before 1957-1988, follow-
up to 1988, Material Data
Safety Sheets used to
identify chemicals used in
work areas.
4-380
-------
Table 4-72. Mortality cohort and PMR studies of TCE exposure and lymphopoietic and hematopoietic cancer
risk (continued)
Population,
exposure group
Lymphopoietic cancer
RR
(95% CI)
na
NHL
RR
(95% CI)
na
Leukemia
RR
(95% CI)
na
Multiple myeloma
RR
(95% CI)
na
U.S. Coast Guard employees
Marine inspectors
Noninspectors
1.57(0.91,2.51)
0.60 (0.24, 1.26)
17
7
1.75 (0.48, 4.49)
0.41(0.01,2.30)
4
1
1.55(0.62,3.19)
0.66(0.14, 1.94)
7
3
Not reported
Not reported
Aircraft manufacturing employees, Italy
All male subjects
0.80 (0.41, 1.40)
12
Not reported
Not reported
Not reported
Aircraft manufacturing, San Diego, California
All employees
0.82(0.56, 1.15)
32
0.82 (0.44, 1.41)d
0.65 (0.21, 1.52)k
13
5
0.82 (0.47, 1.32)
10
Not reported
Reference(s) and study
description13
Blair et al. (1989)
n= 3,781 males
(1,767 marine inspectors),
employed 1942-1970,
follow-up to 1980. TCE and
nine other chemicals
identified as potential
exposures; no exposure
assessment to individual
subjects.
Costa et al. (1989)
n = 7,676 males, employed
on or before 1954-1981,
followed to 1981, job titles
of white- and blue-collar
workers, technical staff, and
administrative clerks, does
not identify TCE exposure
to individual subjects.
Garabrant et al. (1988)
n = 14,067, employed at
least 4 yrs with company
and >1 d at San Diego plant
from 1958 to 1982, followed
to 1982, does not identify
TCE exposure to individual
subjects.
4-381
-------
Table 4-72. Mortality cohort and PMR studies of TCE exposure and lymphopoietic and hematopoietic cancer
risk (continued)
Population,
exposure group
Lymphopoietic cancer
RR
(95% CI)
na
NHL
RR
(95% CI)
na
Leukemia
RR
(95% CI)
na
Multiple myeloma
RR
(95% CI)
na
Solvent-exposed rubber workers
2.41
3
0.81
3
Reference(s) and study
description13
Wilcosky et al. (1984)
Nested case-control study,
n = 9 lymphosarcoma and
10 leukemia (cases) and
20% random sample of all
other deaths (controls)
between 1964 and 1973 in
cohort of n = 6,678,
exposure assessment by
company record for use in
work area.
an = number of observed cases.
bUnless otherwise noted, all studies reported standardized mortality ratios using an external population referent group.
'Logistic regression analysis with 15 lag for TCE exposure (Ritz. 1999a).
dln Morgan et al. (1998) and Garabrant et al. (1988). this category was based on lymphosarcoma and reticulosarcoma.
eAs presented in Mandel et al. (2006) for NHL, this category defined as ICD-7, ICDA-8, and ICD-9 codes of 200 and 202. As presented in Alexander et al.
(2006) for multiple myeloma.
fRisk ratio from Cox Proportional Hazard Analysis, stratified by age and sex, from Environmental Health Strategies (1997) Final Report to Hughes Corporation
(Communication from Paul A. Cammer, President, Trichloroethylene Issues Group to Cheryl Siegel Scott, U.S. EPA, December 22, 1997).
8Estimated RRs from Blair et al. (1998) from Poisson regression models adjusted for date of hire, calendar year of death and sex.
hEstimated RRs from Radican et al. (2008) from Cox proportional hazard models adjusted for age and sex.
'OR from nested case-control analysis.
JLymphomas, lymphosarcomas, reticulosarcomas, and Hodgkin lymphoma (ICDA-8 200-202) in Greenland et al. (1994).
kOther lymphatic and hematopoietic tissue neoplasms (Garabrant et al.. 1988).
4-382
-------
Table 4-73. Case-control studies of TCE exposure and lymphopoietic cancer, leukemia or multiple myeloma
Population
Men and women aged 20-74 in
Iowa, Los Angeles County
(California), Seattle and Detroit
metropolitan areas
Cancer type and exposure group
OR
(95% CI)
n exposed
cases
NHL
Any TCE exposure
Possible
Probable
Average weekly exposure3
0 ppm-hr/wk
1-60 ppm-hr/wk
61-150 ppm-hr/wk
>150 ppm-hr/wk
(p for linear trend)
Cumulative exposure3
0
1-46,800 ppm-hr
46,80 1-1 12,320 ppm-hr
>1 12,320 ppm-hr
(p for linear trend)
1.1 (0.9, 1.3)
1.4 (0.8, 2.4)
1.0
1.6 (0.7, 3.8)
0.5 (0.2, 1.4)
2.5(1.1,6.1)
0.02
1.0
1.4(0.6,3.3)
0.6 (0.2, 1.7)
2.3 (1.0, 5.0)
0.08
545
45
341
15
7
23
341
14
7
24
NHL types
Probable TCE exposure
Diffuse
Follicular
CLL
0.9 (0.5, 2.0)
2.1(1.0,4.2)
2.7(1.2,5.8)
155
13
11
Reference(s)
Gold et al. (2011): Purdue et al. (2011)
4-383
-------
Table 4-73. Case-control studies of TCE exposure and lymphopoietic cancer, leukemia or multiple myeloma
(continued)
Population
Men and women aged 20-74
(continued)
Men and women aged >17 yrs in
Czech Republic, Finland, France,
Germany, Ireland, Italy, and Spain
(Epilymph study)
Cancer type and exposure group
OR
(95% CI)
n exposed
cases
Multiple myeloma
Any TCE exposure
High confidence exposure13
Cumulative exposure13
0
1-471 ppm-hr
472-3,000 ppm-hr
3, 00 1-7,644 ppm-hr
7,645-570,000 ppm-hr
(p for linear trend)
1.4(0.9,2.1)
1.7(1.0,2.7)
1.0
1.1(0.4,2.9)
1.6(0.7,3.5)
1.5 (0.6, 3.9)
2.3(1.1,5.0)
0.03
66
43
139
6
11
7
17
All Centers:
B-cell NHLb
Any TCE exposure
Cumulative Exposure
Low
Medium
High
(p for linear trend)
0.8(0.6, 1.1)
0.9 (0.6, 1.6)
0.5(0.3,0.9),
1.0 (0.6, 1.6)
0.16
71
26
16
29
Reference(s)
Gold et al. (2011): Purdue et al. (2011)
(continued)
Cocco et al. (2010)
4-384
-------
Table 4-73. Case-control studies of TCE exposure and lymphopoietic cancer, leukemia or multiple myeloma
(continued)
Population
Men and women aged >17 yrs in
Czech Republic, Finland, France,
Germany, Ireland, Italy, and Spain
(Epilymph study) (continued)
Women aged 21-84 in Connecticut,
United States
Cancer type and exposure group
NHL types0
Diffuse large B-cell
Follicular
CLL
Multiple myeloma
T-cell lymphoma
German centers:
OR
(95% CI)
0.7(0.4, 1.1)
1.2 (0.6, 2.3)
0.9 (0.5, 1.5)
0.6 (0.3, 1.2)
0.9 (0.4, 2.2)
n exposed
cases
17
11
18
9
6
NHL
Any TCE exposure
Cumulative TCE
0 ppm-yr
>0- <4 ppm-yr
4.4- <35 ppm-yr
High exposure, >35 ppm-yr
(p for linear trend)
>35 ppm-yr, 10-yrlag
Not reported
1.0
0.7(0.4, 1.1)
0.7 (0.5, 1.2)
2.1(1.0,4.8)
0.14
2.2(1.0,4.9)
610
40
32
21
NHL
Any TCE exposure
Low intensity TCE exposure
Medium-high intensity TCE exposure
(p for linear trend)
Low probability TCE exposure
Medium-high probability TCE exposure
(p for linear trend)
Low intensity TCE exposure/low probability
Low intensity /medium-high probability
Medium-high intensity /low probability
Medium-high intensity /medium-high probability
1.2(0.9, 1.8)
1.1(0.8, 1.6)
2.2 (0.9, 5.4)
0.06
1.1(0.7, 1.8)
1.4 (0.9, 2.4)
0.37
0.9 (0.6, 1.5)
1.4 (0.9, 2.4)
2.2 (0.9, 5.4)
77
64
13
43
34
30
34
13
0
Reference(s)
Cocco et al. (2010) (continued)
Seidler et al. (2007): Mester et al. (2006)
Wang et al. (2009)
Wang et al. (2009) (continued)
4-385
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Table 4-73. Case-control studies of TCE exposure and lymphopoietic cancer, leukemia or multiple myeloma
(continued)
Population
Population in eight Italian regions
Cancer type and exposure group
OR
(95% CI)
n exposed
cases
NHL
Any TCE exposure
TCE exposure intensity
Very low/low
Medium/high
(p for linear trend)
Duration exposure, medium/high TCE intensity
<15 yrs
>15 yrs
(p for linear trend)
Not reported
0.8(0.5, 1.3)
1.2 (0.7, 2.0)
0.8
1.1(0.6,2.1)
1.0 (0.5, 2.6)
0.72
35
35
22
12
Other NHL
TCE exposure intensity, medium/high
Small lymphocytic NHL
FollicularNHL
Diffuse NHL
Other NHL
Multiple myeloma
0.9(0.4,2.1)
Not presented
1.9(0.9,3.7)
1.2 (0.6, 2.4)
0.9 (0.3, 2.4)
7
o
J
13
11
27
Reference(s)
Miligi et al. (2006); Costantini et al. (2008)
4-386
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Table 4-73. Case-control studies of TCE exposure and lymphopoietic cancer, leukemia or multiple myeloma
(continued)
Population
Population in eight Italian regions
(continued)
Population of Orebro and
Linkoping, Sweden
Population of Sweden
Population of Umea, Sweden
Population of Montreal, Canada
Cancer type and exposure group
OR
(95% CI)
n exposed
cases
Leukemia
Any TCE exposure
TCE exposure intensity
Very low/low
Medium/high
Not reported
1.0(0.5, 1.8)
0.7 (0.4, 1.5)
17
11
CLL
Any TCE exposure
TCE exposure intensity
Very low/low
Medium/high
Not reported
1.2 (0.5, 2.7)
0.9 (0.3, 2.6)
8
4
B-cell NHL
Any TCE exposure
1.2 (0.5, 2.4)
16
Hairy cell lymphoma
Any TCE exposure
1.5 (0.7, 3.3
9
NHL
Any exposure to TCE
7.2(1.3,42)
4
NHL
Any TCE exposure
Substantial TCE exposure
1.1(0.6, 2.3)d
0.8 (0.2, 2.5)d
6
2
Reference(s)
Miligi et al. (2006); Costantini et al. (2008)
(continued)
Persson and Fredrikson (1999)
Nordstrom et al. (1998)
Hardell et al. (1994)
Siemiatycki et al. (1991)
Tor Purdue et al. (2011). OR for subjects interviewed using computer-assisted personal interview with job modules and includes subjects assessed as unexposed
or with probably exposure, defined as holding one or more jobs with an assigned probability of TCE exposure of >50%.
bFor Gold et al. (2011) subjects with jobs assessed with low confidence considered as unexposed.
Tor Cocco et al. (2010). OR for subjects with high confidence assessment of TCE exposure.
d90% CI.
4-387
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Four geographic-based studies on NHL in adults are summarized in Table 4-74 (for
additional study descriptions, see Appendix B) and subjects in three studies are identified based
upon their residence in a community where TCE was detected in water serving the community
(ATSDR, 2006a; Cohn et al.. 1994b: Vartiainen et al.. 1993). Both Cohn et al. (1994b) and
ATSDR (2006a) also present estimates for childhood leukemia and these observations are
discussed below with other studies reporting on childhood leukemia. A subject is assumed to
have a probability of exposure due to residence likely receiving water containing TCE. Most
studies do not include statistical models of water distribution networks, which may influence
TCE concentrations delivered to a home, nor a subject's ingestion rate to estimate TCE exposure
to individual study subjects. ATSDR (2006a) adopts exposure modeling of soil vapor
contamination to define study area boundaries and to identify census tracts with a higher
probability of exposure to volatile organic solvents without identifying exposure concentrations
to TCE and other solvents. In these studies, one level of exposure to all subjects in a geographic
area is assigned, although there is some inherent measurement error and misclassification bias
because not all subjects are exposed uniformly.
NHL risk is statistically significantly elevated in three studies in which there is a high
likelihood of TCE exposure in individual study subjects (e.g., based on JEMs or biomarker
monitoring) and which met, to a sufficient degree, the standards of epidemiologic design and
analysis in a systematic review [3.1, 95% CI: 1.3, 6.1 (Hansen et al.. 2001): 1.5, 95% CI: 1.2,
2.0, subcohort with higher exposure (Raaschou-Nielsen et al.. 2003). 2.3, 95% CI: 1.0, 5.0,
>112,320-ppm hours cumulative TCE exposure, 2.5, 95% CI: 1.1, 6.1, >150-ppm hours average
weekly TCE exposure (Purdue et al., 2011)]. Two of these incidence studies report statistically
significantly associations for NHL for subjects with longer employment duration as a surrogate
of TCE exposure [>6.25 year, 4.2, 95% CI: 1.1, 11 (Hansen et al.. 2001): >5 year, 1.6, 95% CI:
1.1, 2.2 (Raaschou-Nielsen et al., 2003)] and Purdue et al. (2011) report a positive trend with
NHL and cumulative TCE exposure (p = 0.08) or average weekly TCE exposure (p = 0.02).
Hansen et al. (2001) also examined two other exposure surrogates, cumulative exposure and
exposure intensity, with estimated risk larger in low exposure groups than for high exposure
groups. A fourth study from Sweden reports a large and imprecise risk with TCE [7.2, 95% CI:
1.3, 42 (Hardell et al., 1994)] based on four exposed cases. Cohort mortality studies and other
case-control studies, except Cocco et al. (2010), observed a 10-50% increased risk between NHL
and any TCE exposure [1.2, 95% CI: 0.65, 1.99 (Boiceetal.. 1999): 1.36, 95% CI: 0.35, 5.22
(Morgan et al.. 1998): 1.5, 95% CI: 0.7, 3.3 (Nordstrom et al.. 1998): 1.2, 95% CI: 0.5, 2.4
(Persson and Fredrikson. 1999): 1.36, 95% CI: 0.77, 2.39 (Radican et al.. 2008): 1.1,
95% CI: 0.6, 2.3 (Siemiatvcki. 1991): 1.2, 95% CI: 0.9, 1.8 (Wang et al.. 2009)1.
4-388
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Table 4-74. Geographic-based studies of TCE and NHL or leukemia in adults
Population
Exposure group
Two study areas in Endicott, New York
Residents of 13 census tracts inRedlands, California
Population in New
Jersey
Population in Finland
NHL
RR
(95% CI)
0.54(0.22, 1.12)
1.09 (0.84, 1.38)
n exposed
cases
7
111
Leukemia
RR
(95% CI)
0.79 (0.34, 1.55)
1.02 (0.74, 1.35)
n exposed
cases
8
77
Males, maximum estimated TCE concentration (ppb) in municipal drinking water
<0.1
0.1-0.5
>5.0
1.00
1.28(1.10, 1.48)
1.20 (0.94, 1.52)
493
272
78
1.00
0.85 (0.71, 1.02)
1.10(0.84, 1.90)
438
162
63
Females, maximum estimated TCE concentration (ppb) in municipal drinking water
0.1
0.1-0.5
>5.0
Residents of Hausjarvi
Residents of Huttula
1.00
1.02 (0.87, 1.2)
1.36(1.08, 1.70)
0.6(0.3,1.1)
1.4(1.0,2.0)
504
26
87
14
13
1.00; 315
1.13 (0.93, 1.37)
1.43 (1.43, 1.90)
1.2 (0.8, 1.7)
0.7(0.4, 1.1)
156
56
33
19
Reference"
ATSDR (2006a)
Morgan and Cassady (2002)
Cohn et al. (1994b)
Vartiainen et al. (1993)
aNo geographic-based study reported an RR estimate for multiple myeloma except Vartiainen et al. (1993) who observed SIRs of 0.7 (95% CI: 0.3, 1.3) and 0.6 (95%
CI; 0.2, 1.3) for residents of Hausjarvi and Huttula, respectively..
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ORs are higher for diffuse or follicular NHL, primarily B-cell lymphomas, than for all
NHLs in both studies that examine forms of lymphoma, although based on few exposed cases
and inconsistently reported (Purdue etal., 2011; Cocco etal., 2010; Miligi et al., 2006) (see
Table 4-74). Observations in the two other studies of B-cell lymphomas (Wang et al., 2009;
Persson and Fredrikson, 1999) appear consistent with Miligi et al. (2006) and Purdue et al.
(2011). Together, these observations suggest that the associations between TCE and specific
NHL types are stronger than the associations seen with other forms of NHL, and that disease
misclassification may be introduced in studies examining TCE and NHL as a broader category.
Mortality observations in other occupational cohorts (Sung et al., 2007; Boice et al., 2006b;
AT SDR. 2004a; Chang et al.. 2003; Ritz, 1999a; Henschler et al.. 1995; Greenland et al.. 1994;
Costa etal.. 1989; Garabrant et al.. 1988; Wilcosky et al.. 1984) included a risk estimate of 1.0 in
95% CIs; these studies neither add to nor detract from the overall weight of evidence given their
lower likelihood for TCE exposure due to inferior exposure assessment approaches, lower
prevalence of exposure, lower statistical power, and fewer exposed deaths.
Seven studies presented estimated risks for leukemia and overall TCE exposure: Antilla
et al. (1995); Blair et al. (1998) and its update by Radican et al. (2008); Morgan et al. (1998);
Boice et al. (1999); Boice et al. (2006b); Hansen et al. (2001); and Raaschou-Nielsen et al.
(2003). Only three studies also presented estimated risks for a high exposure category (Blair et
al., 1998; Morgan etal., 1998; Anttila et al., 1995). Three case-control studies presented
estimated risk for leukemia categories and overall TCE exposure or low or high TCE exposure
category (Purdue etal., 2011; Cocco et al., 2010; Costantini et al., 2008). Risk estimates in these
cohort studies ranged from 0.64 (95% CI: 0.35, 1.18) (Radican et al.. 2008) to 2.0 (95% CI: 0.7,
4.44) (Hansen et al., 2001). The largest study, with 82 observed incident leukemia cases,
reported an RR estimate of 1.2 (95% CI: 0.9, 1.4) (Raaschou-Nielsen et al.. 2003). Case-control
studies which examined all leukemias (Costantini et al., 2008) or CLL (Purdue et al., 2011;
Cocco etal., 2010; Costantini et al., 2008), and TCE exposure are quite limited in statistical
power. Risk estimates in the four case-control studies ranged from 0.7 (95% CI: 0.4, 1.5) for all
leukemias and medium to high exposure intensity [Costantini et al. (2008) to 2.7 (95% CI: 1.2,
5.8) for CLL] and probable TCE exposure (Purdue et al., 2011).
Eight cohort studies presented estimated risks for multiple myeloma and overall TCE
exposure Antilla et al. (1995); Axelson et al. (1994); Blair et al. (1998) and its update by Radican
et al. (2008); Morgan et al. (1998); Boice et al. (1999); Boice et al. (2006b); Hansen et al.
(2001); and Raaschou-Nielsen et al. (2003). Only three studies also presented estimated risks for
a high exposure category (Radican et al., 2008; Boice etal., 1999; Anttila et al., 1995). Three
case-control studies presented estimated risk for multiple myeloma and overall TCE exposure or
low or high TCE exposure category (Gold et al., 2011; Cocco et al., 2010; Costantini et al.,
2008). Risk estimates in these cohort studies ranged from 0.57 (95% CI: 0.01, 3.17) (Axelson et
al., 1994) to 1.62 (95% CI: 0.44, 4.16) (Anttila et al., 1995). The largest cohort study, with
4-390
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31 observed incident multiple myeloma cases, reported an RR estimate of 1.03 (95% CI: 0.70,
1.47) (Raaschou-Nielsen et al., 2003). The largest case-control study of 43 exposed multiple
myeloma cases with high confidence TCE exposure reported an OR of 1.7 (95% CI: 1.0, 2.7) and
a positive trend with increasing cumulative TCE exposure (p = 0.03) (Gold et al., 2011).
The number of studies of childhood lymphoma including acute lymphatic leukemia and
TCE is much smaller than the number of studies of TCE and adult lymphomas, and consists of
four case-control studies (Costas et al., 2002; Shuetal., 1999; McKinney et al., 1991; Lowengart
etal.. 1987) and four geographic-based studies (ATSDR, 2008b, 2006a; APRS. 1995: Cohn et
al.. 1994b: Aickinetal., 1992: APRS. 1990) (see Table 4-75). An additional publication,
focusing on ras mutations, based on one of the case-control studies is also available (Shu et al.,
2004). All four case-control studies evaluate maternal exposure, and three studies also examine
paternal occupational exposure (Shu et al., 2004: Shu et al., 1999: McKinney et al., 1991:
Lowengart et al., 1987). There are relatively few cases with maternal exposure (range 0-16) in
these case-control studies, and only Shu et al. (2004: 1999) used a large number (n = 136) of
cases with paternal exposure. The small numbers of exposed case parents limit examination of
possible susceptibility time windows. Overall, evidence for association between parental TCE
exposure and childhood leukemia is not robust or conclusive.
The results from the studies of Costas et al. (2002) and Shu et al. (2004: 1999) suggest a
fetal susceptibility to maternal exposure during pregnancy, with RRs observed for this time
period equal or higher than the RRs observed for periods before conception or after birth (see
Table 4-75). The studies by Lowengart et al. (1987) and McKinney et al. (1991) do not provide
informative data pertaining to this issue due to the small number (n = <3) of exposed case
mothers. A recent update of a cohort study of electronics workers at a plant in Taiwan (2005:
Chang et al.. 2003) reported a fourfold increased risk (3.83; 95% CI: 1.17, 12.55) (Sung et al..
2008) for childhood leukemia risk among the offspring of female workers employed during the 3
months before to 3 months after conception. Exposures at this factory included TCE,
perchloroethylene, and other organic solvents (Sung et al., 2008). The lack of TCE assignment
to individual subjects in this study decrease its weight in the overall analysis.
4-391
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Table 4-75. Selected results from epidemiologic studies of TCE exposure and
childhood leukemia
RR
(95% CI)
n observed
events
Reference(s)
Cohort studies (solvents)
Childhood leukemia among offspring of electronic workers
Nonexposed
Exposed pregnancy to organic solvents
1.0a
3.83(1.17, 12.55)
9
6
Sung et al. (2008)
Case-control studies
Children's Cancer Group Study (children <15 yrs old)
Acute lymphocytic leukemia
Maternal occupational exposure to TCE
Anytime
Preconception
During pregnancy
Postnatal
1.8(0.8,4.1)
1.8 (0.8, 5.2)
1.8 (0.5, 6.4)
1.4(0.5,4.1)
15
9
6
9
Paternal occupational exposure to TCE
Anytime
Preconception
During pregnancy
Postnatal
1.1 (0.8, 1.5)
1.1 (0.8, 1.5)
0.9 (0.6, 1.4)
1.0(0.7, 1.3)
136
100
56
77
K-ras + acute lymphocytic leukemia
Maternal occupational exposure to TCE
Anytime
Preconception
During pregnancy
Postnatal
1.8 (0.6, 4.8)
2.0 (0.7, 6.3)
3.1(1.0,9.7)
5
4
4
0
Paternal occupational exposure to TCE
Anytime
Preconception
During pregnancy
Postnatal
0.6 (0.3, 1.4)
0.6(0.3, 1.5)
0.3(0.1, 1.2)
0.4(0.1, 1.4)
9
8
2
3
Shu et al. (1999)
Shu et al., (2004)
4-392
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Table 4-75. Selected results from epidemiologic studies of TCE exposure and
childhood leukemia (continued)
RR
(95% CI)
n observed
events
Residents of ages <19 in Woburn, Massachusetts
Maternal exposure 2 yrs before conception to diagnosis
Never
Least
Most
(p for linear trend)
1.00
5.00 (0.75, 33.5)
3.56(0.51,24.8)
>0.05
3
9
7
Maternal exposure 2 yrs before conception
Never
Least
Most
(p for linear trend)
1.00
2.48 (0.42, 15.2)
2.82 (0.30, 26.4)
>0.05
11
4
4
Birth to diagnosis
Never
Least
Most
(p for linear trend)
1.00
1.82(0.31, 10.8)
0.90(0.18,4.56)
>0.05
7
7
5
Maternal exposure during pregnancy
Never
Least
Most
(p for linear trend)
1.00
3.53(0.22,58.1)
14.3 (0.92, 224)
O.05
9
o
J
1
Population <14 yrs of age in 3 areas north England, United Kingdom
Acute lymphocytic leukemia and NHL
Maternal occupation exposure to TCE
Preconception
Paternal occupational exposure to TCE
Preconception
Periconception and gestation
Postnatal
1.16(0.13,7.91)
2.27(0.84,6.16)
4.49(1,15,21)
2.66(0.82,9.19)
2
9
7
7
Reference(s)
Costas et al. (2002)
McKinney et al. (1991)
4-393
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Table 4-75. Selected results from epidemiologic studies of TCE exposure and
childhood leukemia (continued)
RR
(95% CI)
n observed
events
Los Angeles Cancer Surveillance Program
Acute lymphocytic and nonlymphocytic leukemia, <10 yrs old
Maternal occupational exposure to TCE
Paternal occupational exposure to TCE
One yr before pregnancy
During pregnancy
After delivery
2.0 (p = 0.16)
2.0(^ = 0.16)
2.7 (0.64, 15.6)
0
6/3b
6/3b
8/3b
Reference(s)
Lowengart et al. (1987)
Geographic-based studies
Two study areas in Endicott, New York
Leukemia, <19 yrs old
Population in New Jersey
Not reported
<6
Acute lymphocytic leukemia
Maximum estimated TCE concentration (ppb) in municipal drinking water
Males
<0.1
0.1-0.5
>5.0
1.00
0.91 (0.53, 1.57)
0.54(0.17, 17.7)
45
16
o
J
Females
<0.1
0.1-0.5
>5.0
1.00
1.85 (1.03, 3.70)
2.36(1.03,5.45)
25
22
7
Resident of Tucson Airport Area, Arizona
Leukemia, <19 yrs old
1970-1986
1987-1991
1.48 (0.74, 2.65)
0.80(0.31,2.05)
11
o
3
Resident of West Central Phoenix, Arizona
Leukemia, <19 yrs old
1.95(1.43,2.63)
38
ATSDR (2006a)
Cohnetal. (1994b)
ADHS (1995. 1990)
Aickin et al. (1992)
"Internal referents, live born children among female workers not exposed to organic solvents.
bDiscordant pairs.
The evidence for an association between childhood leukemia and paternal exposure to
solvents is quite strong (Colt and Blair, 1998): however, for studies of TCE exposure, the small
numbers of exposed case fathers in two studies (McKinney et al., 1991; Lowengart et al., 1987)
and, for all three studies, likelihood of misclassification resulting from a high percentage of
paternal occupation information obtained from proxy interviews, limits observation
interpretations. Both Lowengart et al. (1987) and McKinney et al. (1991) provide some evidence
for a two- to fourfold increase of childhood leukemia risk and paternal occupational exposure
although the population study of Shu et al. (2004; 1999), with 13% of case father's occupation
reported by proxy respondents, does not appear to support the earlier and smaller studies.
4-394
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The geographic-based studies for adult lymphopoietic (see Table 4-74) or childhood
leukemias (see Table 4-75) do not greatly contribute to the overall weight of evidence. While
some studies observed statistically significantly elevated risks for NHL or childhood cancer,
these studies generally fulfilled only the minimal of evaluation criteria with questions raised
about subject selection (Morgan and Cassady, 2002), their use of less sophisticated exposure
assessment approaches and associated assumption of an average exposure to all subjects (all
studies), and few cases with high level parental exposure (all studies).
4.6.1.2.2. Meta-analysis of NHL risk
Meta-analysis is adopted as a tool for examining the body of epidemiologic evidence on
NHL and TCE exposure and to identify possible sources of heterogeneity. The meta-analysis of
NHL examines 17 cohort and case-control studies identified through a systematic review and
evaluation of the epidemiologic literature on TCE exposure (Purdue etal., 2011; Cocco et al.,
2010; Wang et al.. 2009; Radican et al.. 2008; Miligi et al.. 2006; Zhao et al.. 2005; Raaschou-
Nielsen etal..2003; Hansen etal.. 2001; Boiceetal.. 1999; Persson and Fredrikson. 1999;
Morgan et al., 1998; Nordstrom et al., 1998; Anttila et al., 1995; Axel son et al., 1994; Greenland
et al., 1994; Hardell et al., 1994; Siemiatycki, 1991) and two studies as alternatives (Boice et al.,
2006b; Blair etal.. 1998). These 19 studies of NHL and TCE had high likelihood of exposure,
were judged to have met, to a sufficient degree, the criteria of epidemiologic design and analysis,
and reported estimated risks for overall TCE exposure; 13 of these studies, also, presented
estimated NHL risk with high level TCE exposure (Purdue et al., 2011; Cocco etal., 2010; Wang
et al.. 2009; Radican et al.. 2008; Miligi et al.. 2006; Zhao et al.. 2005; Raaschou-Nielsen et al..
2003; Hansen etal.. 2001; Boiceetal.. 1999; Morgan et al.. 1998; Anttila et al.. 1995; Axel son et
al., 1994; Siemiatycki, 1991). Full details of the systematic review, criteria to identify studies for
including in the meta-analysis, and meta-analysis methodology and findings are discussed in
Appendices B and C.
The meta-analyses of the overall effect of TCE exposure on NHL suggest a small, robust,
and statistically significant increase in NHL risk. The summary estimate from the primary
random effect meta-analysis (RRm) was 1.23 (95% CI: 1.07, 1.42) (see Figure 4-16). This result
and its statistical significance were not influenced by individual studies. Removal of individual
studies resulted in RRm estimates between 1.18 (with the removal of Hansen et al., 2001) and
1.27 (with the removal of Miligi et al. (2006) or Cocco et al. (2010)). and lower 95% CIs
excluded 1.0 (all ^-values were/? < 0.02). The result is similarly not sensitive to individual risk
ratio estimate selections. Use of six alternative selections, individually, resulted in RRm
estimate that ranged from 1.20 (95% CI: 1.03, 1.39) (with estimated overall RR for incidence in
Zhao et al.. 2005) to 1.28 (95% CI: 1.09, 1.49) (with Raaschou-Nielsen et al. (2003) subgroup).
Nor was the RRm estimate highly sensitive to restriction of the meta-analysis to only those
studies for which RR estimates for the traditional definition of NHL were available. An alternate
4-395
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analysis that omitted Miligi (which included CLLs), Nordstrom (which was a study of hairy cell
leukemias), Persson and Frederikson (for which the classification system not specified), and
Greenland (which included Hodgkin lymphomas) and which included Boice (2006b) instead of
Zhao (which included all lymphohematopoietic cancers) yielded an RRm estimate of 1.27 (95%
CI: 1.05, 1.55). Meta-analysis of the highest exposure groups, either duration, intensity, or their
product, cumulative exposure, results in an RRm of 1.43 (95% CI: 1.13, 1.82), which is greater
than the RRm from the overall exposure analysis, and provides additional support for an
association between NHL and TCE (see Figure 4-17). No single study was overly influential;
removal of individual studies resulted in RRm estimates that were all statistically significant (all
with/? < 0.025) and that ranged from 1.38 [with the removal of Purdue et al. (2011)] to 1.57
[with the removal of Cocco et al. (2010)]. In addition, the RRm estimate was not highly
sensitive to alternate RR estimate selections. Use of the nine alternate selections, individually,
resulted in RRm estimates that were all statistically significant (all with/? < 0.025) and all in the
narrow range from 1.40 (95% CI: 09, 1.80) [with Blair et al. (1998) incidence RR instead of
Radican et al. (2008) mortality hazard ratio] to 1.49 (95% CI: 1.14, 1.93) [with Hansen et al.
(2001) duration]. The highest exposure category groups have a reduced likelihood for exposure
misclassification because they are believed to represent a greater differential TCE exposure
compared to people identified with overall TCE exposure. Observation of greater risk associated
with higher exposure category compared to overall (typically any vs. none) exposure comparison
additionally suggests an exposure-response gradient between NHL and TCE, although estimation
of a level of exposure associated with the RRm is not possible.
4-396
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TCE Exposure and Non-Hodgkin Lyrnphorna
Study Relative Risk and 95H Cl RR
A n-H-i 1 n f 1 QQfl^ 1 1 •"* 1
A-u-f. I*-.-, r. MQQ,d.^
Axeison ^lyy^tj
Boice(1999)
Aroonl and r"1ClQJ.^ 1
Hansen(2QQ1)
Mnrn an C1QQQ^ I
Raaschou-Nielsen (2003)
P -j i-i i .-• i n fsnnfl1!
Zhao (2005)
Cocco(2010) — D—
Hardell (1994)
Miligi(2006) — n-
MnrHHrnm r"1QCl^^
PHI in nm 1^
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OVERALL
D*l ^^
1 .O*i
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D1 fin
01 Tl
D1 -LI
01 1 n
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• 1 *^n
| \ 1.23
0.1 1 10
LCL
0.78
0.49
0.83
0.24
1.30
0.46
1.01
0.77
0.90
0.50
1.30
0.70
0.70
0.50
0.80
0.50
0.90
1.07
UCL
3.56
3.53
1.65
2.42
6.10
1.92
1.52
2.39
2.30
1.10
42.00
1.30
3.30
2.40
2.40
2.50
1.80
1.42
The summary estimate is in the bottom row. Symbol sizes reflect relative weights of the studies. The horizontal
midpoint of the bottom diamond represents the RRm estimate.
Figure 4-16. Meta-analysis of NHL and overall TCE exposure.
4-397
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TCE Exposure and Non Hodgkin L
Study Relative RisJ<
•' r.++il-, ^1QQ^"S
Avalc-nn M GCld^
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oiemidTycKi ^ iyy i j U
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ymphoma - highest exposure gn
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0.52
0.40
0.70
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0.20
0.90
1.13
UCL
5.04
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3.22
3.00
6.49
2.20
2.80
3.23
1.30
2.00
10.10
3.30
5.40
1.82
The summary estimate is in the bottom row. Symbol sizes reflect relative weights of the studies. The horizontal
midpoint of the bottom diamond represents the RRm estimate.
Figure 4-17. Meta-analysis of NHL and TCE exposure—highest exposure groups.
4-398
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Low-to-moderate heterogeneity in RRm is observed across the results of the 17 studies in
the meta-analysis of the overall effect of TCE and the 13 studies with highest exposure groups,
but it was not statistically significant (p = 0.16 andp = 0.30, respectively). The /-values were
26% for overall exposure and 14% for highest exposure groups, suggesting low-to-moderate and
low heterogeneity, respectively. To investigate the heterogeneity, subgroup analyses were done
examining the cohort and case-control studies separately. Difference between cohort and case-
control studies could explain much of the observed heterogeneity. In the subgroup analysis of
overall exposure and of highest exposure groups, increased risk of NHL was strengthened in
analysis limited to cohort studies and reduced in the case-control study analysis. Examination of
heterogeneity in cohort and case-control studies of overall exposure separately was not
statistically significant in either case (/2-values for the cohort studies were 12%, suggesting low
heterogeneity and 27% for the case-control studies, suggesting low-to-moderate heterogeneity),
although some may be present given that statistical tests of heterogeneity are generally
insensitive in cases of minor heterogeneity. Subgroup analyses examining the cohort and case-
control studies highest exposure groups, separately, showed no residual heterogeneity in the
cohort subgroup (I2 = 0%) and moderate heterogeneity in the case-control subgroup (/-value
was 53%) that was not statistically significant (p = 0.08). Although no further attempt was made
to quantitatively investigate potential sources of heterogeneity, the removal of the Cocco et al.
(2010) study, an influential study, eliminates all of the heterogeneity, suggesting that the RR
estimate for the highest exposure group from that study is a relative outlier.
In general, sources of heterogeneity are uncertain and may reflect several features known
to influence epidemiologic studies. Study design itself is unlikely to be an underlying cause of
heterogeneity and, to the extent that it may explain some of the differences across studies, is
more probably a surrogate for some other difference(s) across studies that may be associated
with study design. Furthermore, other potential sources of heterogeneity may be masked by the
broad study design subgroupings. The true source(s) of heterogeneity across these studies is an
uncertainty.
One reason may be differences in exposure assessment and in overall TCE exposure
concentration between cohort and case-control studies. Several cohort and case-control studies
included TCE assignment from information on job and task exposures, e.g., a JEM (Purdue et al.,
2011: Cocco etal.. 2010: Wang et al.. 2009: Radican et al.. 2008: Boice et al.. 2006b: Miligi et
al.. 2006: Zhao et al.. 2005: Boice etal.. 1999: Morgan et al.. 1998: Siemiatvcki. 199IX or from
an exposure biomarker in either breath or urine (Hansen et al., 2001: Anttila et al., 1995: Axel son
etal., 1994). Three case-control studies (Persson and Fredrikson, 1999: Nordstrom et al., 1998:
Hardell etal., 1994) relied on self-reported TCE exposure. No information is available to judge
the degree of possible misclassification bias associated with a particular exposure assessment
approach; it is quite possible that in some cohort studies, in which past exposure is inferred from
various data sources, exposure misclassification may be as great as in population- or hospital-
4-399
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based case-control studies. In addition, a low overall TCE exposure prevalence is anticipated in
population case-control studies, which would typically assess a large number of workplaces and
operations, where exposures are less well defined, and where case and control subjects identified
as exposed to TCE probably have minimal contact (NRC, 2006). Observed higher risk ratios
with higher exposure categories in NHL case-control studies support exposure differences as a
source of heterogeneity.
Diagnostic inaccuracies are likely another source of heterogeneity in the meta-analysis
through study differences in NHL groupings and in lymphoma classification schemes, although
restricting the meta-analysis to only those studies for which RR estimates based on the traditional
NHL definition were available did not eliminate all heterogeneity. All studies include a broad
but slightly different group of lymphosarcoma, reticulum-cell sarcoma, and other lymphoid
tissue neoplasms (Codes 200 and 202), except Nordstrom et al. (1998), Zhao et al. (2005), and
Greenland et al. (1994). Cohort studies have some consistency in coding NHL, with NHL
defined as lymphosarcoma and reticulum-cell sarcoma (200) and other lymphoid tissue
neoplasms (202) using the ICD, Revision 7, 200 and 202—four studies (Raaschou-Nielsen et al.,
2003: Hansenetal., 2001: Anttila et al., 1995: Axel son et al., 1994), ICD-Adapted, Revision 8
(Blair et al., 1998), and ICD-7, -8, -9, and -10, per the version in use at the time of death (as
presented in Morgan et al. (1998) [as presented in Mandel et al. (2006), Boice et al. (1999),
Radican et al. (2008)], as does the case-control study of Siemiatycki (1991) whose coding
scheme for NHL is consistent with ICD 9, 200 and 202. Case-control studies, on the other hand,
have adopted other classification systems for defining NHL including the NCI Working
Formulation (Miligi et al., 2006), Rappaport (Hardell etal., 1994), or else do not identify the
classification system for defining NHL (Persson and Fredrikson, 1999). Cocco et al. (2010) used
the WHO/Revised European-American Lymphoma (REAL) classification system, which
reclassifies lymphocytic leukemias and NHLs as lymphomas of B-cell or T-cell origin and
considers CLLs and multiple myelomas as (non-Hodgkin) lymphomas; however, U.S. EPA was
able to obtain results generally consistent with the traditional NHL definition from Dr. Cocco,
although lymphomas not otherwise specified were excluded. Wang et al. (2009) defined NHL
using ICD-O-2 codes (M-9590-9595, 9670-9688, 9690-9698, 9700-9723), which is consistent
with the traditional definition of NHL (i.e., ICD-7, -8, -9 codes 200 + 202). Purdue et al. (2011)
used ICD-O-3 codes 967-972, which is generally consistent with the traditional definition of
NHL, although this grouping does not include the malignant lymphomas of unspecified type
coded as M-9590-9599.
There is some evidence of potential publication bias in this data set; however, it is
uncertain that this is actually publication bias rather than an association between SE and effect
size resulting for some other reason, e.g., a difference in study populations or protocols in the
smaller studies. Furthermore, if there is publication bias in this data set, it does not appear to
account completely for the finding of an increased NHL risk.
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NRC (2006) deliberations on TCE commented on two prominent evaluations of the then-
current epidemiologic literature using meta-analysis techniques. These studies were by
Wartenberg et al. (2000), and by Kelsh et al. (2005), submitted by Exponent-Health Sciences to
NRC during their deliberations and subsequently published in a paper on NHL (Mandel et al.,
2006) and a paper on multiple myeloma and leukemia (Alexander et al., 2006). The NRC found
weaknesses in the techniques used in each of these studies, and suggested that EPA conduct a
new meta-analysis of the epidemiologic data on TCE using objective and transparent criteria so
as to improve on the past analyses. EPA staff conducted their analysis according to NRC (2006)
suggestions for transparency, systematic review criteria, and examination of both cohort and
case-control studies. The EPA analysis of NHL analysis considered a larger number of studies
than in the previous analyses (Mandel et al., 2006; Wartenberg et al., 2000), includes recently
published studies (Purdue et al., 2011: Cocco et al., 2010: Wang et al., 2009: Radican et al.,
2008: Boice et al., 2006b: Miligi et al., 2006: Zhao et al., 2005), and combines both cohort and
case-control studies.
4.6.2. Animal Studies
The immunosuppressive and immunomodulating potential of TCE has not been fully
evaluated in animal models across various exposure routes, over various relevant durations of
exposure, across representative lifestages, and/or across a wide variety of endpoints.
Nevertheless, the studies that have been conducted indicate a potential for TCE-induced
immunotoxicity, both following exposures in adult animals and during immune system
development (i.e., in utero and preweaning exposures).
4.6.2.1. Immunosuppression
A number of animal studies have indicated that moderate to high concentrations of TCE
over long periods have the potential to result in immunosuppression in animal models, dependant
on species and gender. These studies are described in detail below and summarized in
Table 4-76.
4.6.2.1.1. Inhalation exposures
Mature cross-bred dogs (5/group) were exposed to 0-, 200-, 500-, 700-, 1,000-, 1,500-, or
2,000 ppm TCE for 1 hour or to 700 ppm TCE for 4 hours, by tracheal intubation under i.v.
sodium pentobarbital anesthesia. An additional group of dogs was exposed by venous injection
of 50 mg/kg TCE administered at a rate of 1 mL/minute (Hobara et al., 1984). Blood was
sampled pre- and postexposure for erythrocyte and leukocyte counts. Marked, transient
decreases in leukocyte counts were observed at all exposure levels 30 minutes after initiation of
exposure. At the end of the exposure period, all types of leukocytes were decreased (by 85%);
neutrophils were decreased 33%, and lymphocytes were increased 40%. There were no
treatment-related changes in erythrocyte counts, hematocrit values, or thrombocyte counts.
4-401
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Table 4-76. Summary of TCE immunosuppression studies
Exposure route/vehicle,
duration, dose
NOAEL; LOAEL"
Results
Reference, species/strain
sex/number
Inhalation exposure studies
Single 1-hr exposure to all dose
groups; plus single 4-hr exposure
at 700 ppmc
0, 200, 500, 700, 1,000, 1,500, or
2,000 ppm
LOAEL: 200 ppm
Marked transient J, leukocyte counts at all exposure levels
30 min after initiating exposure. At end of exposure, 85% J,
leukocyte counts (33% J, neutrophils, 40% J, lymphocytes).
Hobara et al. (1984)
Dog, cross-bred, both sexes, 5/group
Single 3-hr exposure. Also,
3 hrs/d on 5 d at lowest dose
0, 2.6, 5.2, 10.6, 25.6, or 48 ppm
NOAEL: 2.6 ppm
LOAEL: 5.2 ppm
Challenged with Streptococcus zooepidemicus to assess
susceptibility to infection and Klebsiella pneumoniae to
assess bacterial clearance. For single exposure: dose-related
statistically significant t mortality at >5.2 ppm over 14 d.
Statistically significant J, in bactericidal activity at 10.6 ppm.
Aranyi et al. (1986)
Mouse, CD-I females, 4-5 wks old,
approximately 30 mice/group, 5-
10 replications; for pulmonary
bactericidal activity assay, 17-
24 mice/group
Single 3-hr exposure.
0, 5, 10, 25, 50, 100, 200 ppm
NOAEL: 25 ppm
LOAEL: 50 ppm
Challenged with Streptococcus zooepidemicus to assess
susceptibility to infection and bacterial clearance. For single
exposure: dose-related statistically significant t mortality at
>50 ppm over 20 d. Dose dependent responses also observed
in the clearance of bacteria from the lung at >50 ppm, the
number of mice with delayed bacterial clearance at various
postinfection time points at >50 ppm, and the phagocytic
function of alveolar macrophages at 200 ppm.
Selgrade and Gilmour (2010)
Mouse, CD-I females, 5-6 wks old,
at least 38 mice/group
Single 3-hr exposure, 50-
200 ppmd
Challenged with Streptococcus zooepidemicus. Dose-related
t mortality, bacterial antiphagocytic capsule formation, and
bacterial survival. Dose-related impairment of alveolar
macrophages; increased neutrophils in bronchoalveolar fluid
at 3 d postinfection.
Park et al. (1993) (abstract)
Mouse, CD-I, (sex and
number/group not specified)
4-wk, 6 hrs/d, 5 d/wk
0,100,300, or 1,000 ppm
NOAEL: 300 ppm
LOAEL: 1,000 ppm
At 1,000 ppm, 64% J, plaque-forming cell assay response.
Woolhiser et al. (2006)
Rat, Sprague-Dawley, female,
16/group
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Table 4-76. Summary of TCE immunosuppression studies (continued)
Exposure route/vehicle,
duration, dose
NOAEL; LOAEL3
Results
Reference, species/strain
sex/number
Oral exposure studies
Gavage in 10% Emulphor, 14 d,
daily, 0, 24, or 240 mg/kg-d
LOAEL: 24 mg/kg-d
Statistically significant J, cell-mediated immune response to
SRBC at both dose levels.
Sanders et al. (1982b)
Mouse, CD-I, male, 9-12/group
Drinking water with 1%
Emulphor, 4-6 mo
0,0.1,1.0,2.5, or 5.0 mg/niL
LOAEL: 0.1 mg/kg-d
In females, humoral immunity J, at 2.5 and 5 mg/mL
TCE, whereas cell-mediated immunity J, and bone
marrow stem cell colonization J, at all four
concentrations. The males were relatively unaffected
after both 4 and 6 mo.
Sanders et al. (1982b)
Mouse, CD-I, male and female, 7-
25/group
Gavage, 14 d, 0, 14.4, or
144 mg/kg-d CH
NOAEL: 144 mg/kg-d
No treatment-related effects.
Kauffmann et al. (1982)
Mouse, CD-I, male, 12/group
Drinking water, 90 d, 0, 0.07, or
0.7 mg/mL CH. (M: 0, 16, or
160 mg/kg-d; F: 0, 18, or
173 mg/kg-d)
NOAEL: 0.07 mg/mL
LOAEL: 0.7 mg/mL
Statistically significant J, cell-mediated immune response
(plasma hemagglutination liters and spleen antibody-
producing cells of mice sensitized to SRBC) in females at
0.7 mg/mL.
Kauffmann et al. (1982)
Mouse, CD-I, male and female, 15-
20/group
Drinking water, From mating to
PND 21 or 56, (Emulphor
concentration not provided)
0 (Emulphor), 1, or 10 ppm
LOAEL: 1 ppm
At 10 ppm, I body weight and length at PND 21. IgM
antibody response to SRBC challenge suppressed in both $
and $ pups at 10 ppm, and <3 pups at 1 ppm, J, in splenic
CD4+CD8-T-cells. At 56 PND, striking | in NK cell activity
seen at both doses.
Adams et al. (2003) (abstract)
Mouse, B6C3F!, both sexes,
numbers of pups not stated
Drinking water, from GDs 0 to 3
or 8 wks of age, 0, 1,400, or
14,000 ppb
LOAEL: 1,400 ppb
Suppressed PFC responses in both sexes and ages at
14,000 ppb, in males at both ages at 1,400 ppb, and in
females at 8 wks at 1,400 ppb. Numbers of spleen B220+
cells J, at 3 wks at 14,000 ppb. Pronounced t thymus T-cell
populations at 8 wks.
Peden-Adams et al. (2006)
Mouse, B6C3F!, dams and both
sexes offspring, 5 litters/group; 5-
7 pups/group at 3 wks; 4-
5 pups/sex/group at 8 wks
Drinking water, from GD 0 to 7-
8 wks of age; 0, 0.5, or 2.5 mg/mL
LOAEL: 0.5 mg/mL
At 0.5 mg/mL: statistically significant J, postweaning weight;
statistically significantt IFNy produced by splenic CD4+
cells at 5-6 wks; statistically significant J, splenic CD8+ and
B220+ lymphocytes; statistically significantt IgG2a and
histone; statistically significant altered CD4-/CD8- and
CD4+/CD8+ thymocyte profile
At 2.5 mg/mL: statistically significant J, postweaning weight;
statistically significant t IFNy produced by splenic CD4+
and CD8+ cells at 4-5 and 5-6 wks; statistically significant
| splenic CD4+, CD8+, and B220+ lymphocytes; statistically
significant altered CD4+/CD8+ thymocyte profile.
Blossom and Doss (2007)
Mouse, MRL +/+, dams and both
sexes offspring, 3 litters/group; 8-
12 pups/group
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Table 4-76. Summary of TCE immunosuppression studies (continued)
Exposure route/vehicle,
duration, dose
Drinking water, from GD 0 to
PND42;Oor0.1mg/mL;
maternal dose = 25.7 mg/kg-d;
offspring PNDs 24-42 dose =
3 1.0 mg/kg-d
Drinking water, from GD 0 to
12 mo of age; 0 (1% Emulphor),
1,400, or 14,000 ppb
NOAEL; LOAEL3
LOAEL:0.1mg/mL
LOAEL: 1,400 ppb
Results
At 0. 1 mg/mL: at PND 20, statistically significant
t thymocyte cellularity and distribution, associated with
statistically significant t in thymocyte subset distribution;
statistically significant t reactive oxygen species generation
in total thymocytes; statistically significant t in splenic
CD4+ T-cell production of IFN-y and IL-2 in females and
TNF-a in males at PND 42.
At 1,400 ppb: splenic CD4-/CD8- cells statistically
significant t in females; thymic CD4+/CD8+ cells
statistically significant J, in males; 18% f in male kidney
weight.
At 14,000 ppb: thymic T-cell subpopulations (CD8+,
CD4/CD8-, CD4+) statistically significant | in males.
Reference, species/strain
sex/number
Blossom et al. (2008)
Mouse, MRL +/+, dams and both
sexes offspring, 8 litters/group; 3-
8 pups/group
Peden- Adams et al. (2008)
Mouse, MRL +/+, dams and both
sexes offspring, unknown number
litters/group, 6-10
offspring/sex/group
i.p. injection exposure studies
3 d, single daily injection, 0, 0.05,
0.5, or 5 mmol/kg-d
3 d, single daily injection, 0 or
10 mmol/kg-d
NOAEL: 0.05
mmol/kg-d
LOAEL: 0.5 mmol/kg-
d
LOAEL: 10 mmol/kg-d
1 NK cell activity at 0.5 and 5 mmol/kg-d. J, splenocyte
counts at 5 mmol/kg-d.
1 NK cell activity and J, spleen weights at 10 mmol/kg-d.
Wright et al. (19911
Rat, Sprague-Dawley
Wright et al. (19911
Mouse, B6C3FJ
aNOAEL and LOAEL are based upon reported study findings.
bBolded studies carried forward for consideration in dose-response assessment (see Chapter 5).
Inhalation, trachea! intubation under anesthesia.
dExact dose levels not specified.
I, t = decreased, increased; PFC = plaque-forming cell; SRBC = sheep red blood cells
4-404
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In a study that examined the effects of a series of inhaled organic chemical air
contaminants on murine lung host defenses, Aranyi et al. (Aranyi etal., 1986) exposed female
CD-I mice to single 3-hour exposures of TCE at time-weighted concentrations of 0, 2.6, 5.2,
10.6, 25.6, or 48 ppm. Additionally, at the dose at which no adverse treatment-related effect
occurred with a single exposure (i.e., 2.6 ppm), a multiple exposure test (5 days, 3 hours/day)
was conducted. Susceptibility to Streptococcus zooepidemicus aerosol infection and pulmonary
bactericidal activity to inhaled Klebsiellapneumoniae were evaluated. There was a significant
(p < 0.0001) treatment by concentration interaction for mortality, with the magnitude of the
effect increasing with concentration. A significant (p < 0.0001) treatment by concentration
interaction was also found for bactericidal activity. Single 3-hour exposures at 10.6, 25.6, and
48 ppm resulted in significant increases in mortality, although increases observed after single
exposures at 5.2 or 2.6 ppm or five exposures at 2.6 ppm were not significant. Pulmonary
bactericidal activity was significantly decreased after a single exposure at 10.6 ppm, but single
exposures to 2.6 or 5.2 ppm resulted in significant increases.
Suppression of pulmonary host defenses and enhanced susceptibility to respiratory
bacterial infection was studied in female CD-I mice by Selgrade and Gilmour (2010). The mice
(5-6 weeks of age; at least 38 per exposure group) were exposed via inhalation for 3 hours to
concentrations of 0, 5, 10, 25, 50, 100, or 200 ppm TCE. The mice were then challenged by
aerosol doses of S. zooepidemicus. Bacterial clearance (based upon organisms present in lung
lavage fluid) and a phagocytic index (percentage of phagocytic cells in lung lavage fluid and the
number of bacteria ingested per phagocytic cell) were assessed. Mortality due to infection was
significantly increased with TCE exposure concentration at exposures of 50 ppm and higher
(NOAEL = 25 ppm). Dose-dependent responses were also observed for the clearance of bacteria
from the lung at >25 ppm, the number of mice with delayed bacterial clearance at various
postinfection time points at >25 ppm, and the phagocytic function of alveolar macrophages at
200 ppm. The higher NOAEL for mortality observed in this study compared to Aranyi et al.
(1986) (i.e., 25 vs. 5 ppm) was attributed to the use of unencapsulated bacteria in this study; the
study authors suggested that this may be more representative of the human condition.
In a host-resistance assay, CD-I mice (sex and number/group not specified) exposed to
TCE by inhalation for 3 hours at 50-200 ppm were found to be more susceptible to increased
infection following challenge with S. zooepidemicus administered via aerosol (Park et al., 1993).
Dose-related increases in mortality, bacterial antiphagocytic capsule formation, and bacterial
survival were observed. Alveolar macrophage phagocytosis was impaired in a dose-responsive
manner, and an increase in neutrophils in bronchoalveolar lavage fluid was observed in exposed
mice 3 days post infection.
A guideline (OPPTS 870.3800) 4-week inhalation immunotoxicity study was conducted
in female Sprague-Dawley rats (Woolhiser et al., 2006). The animals (16/group) were exposed
to TCE at nominal levels of 0, 100, 300, or 1,000 ppm for 6 hours/day, 5 days/week. Effects on
4-405
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the immune system were assessed using an antigen response assay, relevant organs weights,
histopathology of immune organs, and hematology parameters. Four days prior to study
termination, the rats were immunized with sheep red blood cells (SRBC), and within 24 hours
following the last exposure to TCE, a plaque-forming cell (PFC) assay was conducted to
determine effects on splenic anti-SRBC IgM response. Minor, transient effects on body weight
and food consumption were noted in treated rats for the first 2 weeks of exposure. Mean relative
liver and kidney weights were significantly (p = 0.05) increased at 1,000 ppm as compared to
control, while lung, spleen, and thymus weights were similar to control. No treatment-related
effects were observed for hematology, white blood cell differential counts, or histopathological
evaluations (including spleen, thymus, and lung-associated lymph nodes). At 1,000 ppm, rats
demonstrated a 64% decrease in PFC assay response. LDH, total protein levels, and cellular
differentiation counts evaluated from bronchoalveolar lavage (BAL) samples were similar
between control and treated groups. A phagocytic assay using BAL cells showed no alteration in
phagocytosis, although these data were not considered fully reliable since: (1) the number of
retrieved macrophage cells was lower than expected and pooling of samples was conducted and
(2) samples appear to have been collected at 24 hours after the last exposure (rather than within
approximately 2 hours of the last exposure), thereby allowing for possible macrophage recovery.
The NOAEL for this study was considered by the study authors to be 300 ppm, and the LOAEL
was 1,000 ppm; however, the effect level may have actually been lower. It is noted that the
outcome of this study does not agree with the studies by Aranyi et al. (1986) and Park et al.
(1993), both of which identified impairment of macrophage phagocytic activity in BAL
following inhalation TCE exposures.
4.6.2.1.2. Oral exposures
In a study by Sanders et al. (1982b), TCE was administered to male and female CD-I
mice for 4 or 6 months in drinking water at concentrations of 0, 0.1, 1, 2.5, or 5 mg/mL(Sanders
et al., 1982b). In females, humoral immunity was suppressed at 2.5 and 5 mg/mL, while cell-
mediated immunity and bone marrow stem cell activity were inhibited at all dose levels. Male
mice were relatively unaffected either at 4 or 6 months, even though a preliminary study in male
CD-I mice (exposed to TCE for 14 days by gavage at 0, 24, or 240 mg/kg-day) had
demonstrated a decrease in cell-mediated immune response to SRBC in male mice at both
treatment levels.
A significant decrease in humoral immunity (as measured by plasma hemagglutination
liters and the number of spleen antibody producing cells of mice sensitized to sheep
erythrocytes) was observed by Kaufmann et al. (1982) in female CD-I mice (15-20/group)
following a 90-day drinking water exposure to 0, 0.07, or 0.7 mg/mL (equivalent to 0, 18, or
173 mg/kg) CH, a metabolite of TCE. Similar responses were not observed in male CD-I mice
4-406
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exposed for 90 days in drinking water (at doses of 0, 16, or 160 mg/kg-day), or when
administered CH by gavage to 12/group for 14 days at 14.4 or 144 mg/kg-day.
The potential for developmental immunotoxicity was assessed in B6C3Fi mice
administered TCE in drinking water at dose levels of 0, 1,400 or 14,000 ppb from GD 0 to either
3 or 8 weeks of age (Peden-Adams et al., 2006; Adams et al., 2003 [preliminary data]). At 3 and
8 weeks of age, offspring lymphocyte proliferation, NK cell activity, SRBC-specific IgM
production (PFC response), splenic B220+ cells, and thymus and spleen T-cell immunopheno-
types were assessed. Delayed-typed hypersensitivity and autoantibodies to double-stranded
DNA (dsDNA) were evaluated in offspring at 8 weeks of age. Observed positive responses
consisted of suppressed PFC responses in males at both ages and both TCE treatment levels, and
in females at both ages at 14,000 ppb and at 8 weeks of age at 1,400 ppb. Spleen numbers of
B220+ cells were decreased in 3-week-old pups at 14,000 ppb. Pronounced increases in all
thymus T-cell subpopulations (CD4+, CD8+, CD4+/CD8+, and CD4-/CD8-) were observed at
8 weeks of age. Delayed hypersensitivity response was increased in 8-week-old females at both
treatment levels and in males at 14,000 ppb only. No treatment-related increase in serum anti-
dsDNA antibody levels was found in the offspring at 8 weeks of age.
In a study designed to examine potential susceptibility of the young (Blossom and Doss,
2007), TCE was administered to groups of pregnant MRL +/+ mice in drinking water at
occupationally-relevant levels of 0, 0.5, or 2.5 mg/mL. A total of 3 litters per treatment group
were maintained following delivery (i.e., a total of 11 pups at 0 mg/mL TCE, 8 pups at
0.5 mg/mL TCE, and 12 pups at 2.5 mg/mL TCE), and TCE was continuously administered to
the offspring until young adulthood (i.e., 7-8 weeks of age). Although there were no effects on
reproduction, offspring postweaning body weights were significantly decreased in both treated
groups. Additionally, TCE exposure was found to modulate the immune system following
developmental and early life exposures. Decreased spleen cellularity and reduced numbers of
CD4+, CD8+, and B220+ lymphocyte subpopulations were observed in the postweaning
offspring. Thymocyte development was altered by TCE exposures, as evidenced by significant
alterations in the proportions of double-negative subpopulations and inhibition of in vitro
apoptosis in immature thymocytes. TCE was also shown to induce a dose-dependent increase in
CD4+ and CD8+ T-lymphocyte IFNy in peripheral blood by 4-5 weeks of age, although these
effects were no longer observed at 7-8 weeks of age. Serum antihistone autoantibodies and total
IgG2a were significantly increased in treated offspring; however, no histopathological signs of
autoimmunity were observed in the liver and kidneys at sacrifice.
This increase in T-cell hyperactivity was further explored in a study by Blossom et al.
(2008). In this study, MRL +/+ mice were treated with 0 or 0.1 mg/mL TCE in the drinking
water. Based on drinking water consumption data, average maternal doses of TCE were
25.7 mg/kg-day, and average offspring (PNDs 24-42) doses of TCE were 31.0 mg/kg-day.
Treatment was initiated at the time of mating, and continued in the females (8/group) throughout
4-407
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gestation and lactation. Pups were weaned at PND 24, and the offspring were continued on
drinking water treatment in a group-housed environment until study termination (PND 42).
Subsets of offspring were sacrificed at PNDs 10 and 20, at which time developmental and
functional endpoints in the thymus were evaluated (i.e., total cellularity, CD4+/CD8+ ratios,
CD24 differentiation markers, and double-negative subpopulation counts). Indicators of
oxidative stress were measured in the thymus at PNDs 10 and 20, and in the brain at PND 42.
Mitogen-induced intracellular cytokine production by splenic CD4+ and CD8+ T-cells was
evaluated in juvenile mice and brain tissue was examined at PND 42 for evidence of
inflammation. Behavioral testing was also conducted; these methods and results are described in
Section 4.3. TCE treatment did not affect reproductive capacity, parturition, or ability of dams to
maintain litters. The mean body weight of offspring was not different between the control and
treated groups. Evaluation of the thymus identified a significant treatment-related increase in
cellularity, accompanied by alterations in thymocyte subset distribution, at PND 20 (sexes
combined). TCE treatment also appeared to promote T-cell differentiation and maturation at
PND 42, and ex vivo evaluation of cultured thymocytes indicated increased reactive oxygen
species generation. Evaluation of peripheral blood indicated that splenic CD4+ T-cells from
TCE-exposed PND 42 mice produced significantly greater levels of IFN-y and IL-2 in males and
TNF-a in both sexes. There was no effect on cytokine production on PND 10 or 20. The dose of
TCE that resulted in adverse offspring outcomes in this study (i.e., 0.1 mg/mL, equivalent to
25.7-31.0 mg/kg-day) is comparable to that which has been previously demonstrated to result in
immune system alterations and autoimmunity in adult MRL +/+ mice (i.e., 0.1 mg/mL,
equivalent to 21 mg/kg-day (Griffin et al., 2000b).
Another study that examined the effects of developmental exposure to TCE on the
MRL+/+ mouse was conducted by Peden-Adams et al. (2008). In this study, MRL/MpJ (i.e.,
MRL +/+) mice (unspecified number of dams/group) were exposed to TCE (solubilized with 1%
Emulphor) in drinking water at levels of 0, 1,400, or 14,000 ppb from GD 0 and continuing until
the offspring were 12 months of age. TCE concentrations in the drinking water were reported to
be analytically confirmed. Endpoints evaluated in offspring at 12 months of age included final
body weight; spleen, thymus, and kidney weights; spleen and thymus lymphocyte
immunophenotyping (CD4 or CDS); splenic B-cell counts; mitogen-induced splenic lymphocyte
proliferation; serum levels of autoantibodies to dsDNA and glomerular antigen, periodically
measured from 4 to 12 months of age; and urinary protein measures. Reported sample sizes for
the offspring measurements varied from 6 to 10 per sex per group; the number of source litters
represented within each sample was not specified. The only organ weight alteration was an 18%
increase in kidney weight in the 1,400 ppb males. Splenic CD4-/CD8- cells were altered in
female mice (but not males) at 1,400 ppm only. Splenic T-cell populations, numbers of B220+
cells, and lymphocyte proliferation were not affected by treatment. Populations of thymic T-cell
subpopulations (CD8+, CD4-/CD8-, and CD4+) were significantly decreased in male but not
4-408
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female mice following exposure to 14,000-ppb TCE, and CD4+/CD8+ cells were significantly
reduced in males by treatment with both TCE concentrations. Autoantibody levels (anti-dsDNA
and anti-glomerular antigen) were not increased in the offspring over the course of the study,
indicating that TCE did not contribute to the development of autoimmune disease markers
following developmental exposures that continued into adult life.
Overall, the studies by Peden-Adams et al. (2008: 2006). Blossom and Doss (2007). and
Blossom et al. (2008), which examined various immunotoxicity endpoints following exposures
that spanned the critical periods of immune system development in the rodent, were generally
not designed to assess issues such as posttreatment recovery, latent outcomes, or differences in
severity of response that might be attributed to the early life exposures.
4.6.2.1.3. i.p. administration
Wright et al. reported that following 3 days of single i.p. injections of TCE in Sprague-
Dawley rats at 0, 0.05, 0.5, or 5 mmol/kg-day and B6C3Fi mice at 0 or 10 mmol/kg-day, NK cell
activity was depressed in the rats at the mid- and high-dose levels, and in the mice at the high-
dose level (Wright et al., 1991). Also at the highest dose levels tested, decreased splenocyte
counts and relative spleen weight were observed in the rats and mice, respectively. In vitro
assays demonstrated treatment-related decreases in splenocyte viability, inhibition of
lipopolysaccharide-stimulated lymphocyte mitogenesis, and inhibited NEC cell activity suggesting
the possibility that compromised immune function may play a role in carcinogenic responses of
experimental animals treated with TCE.
4.6.2.2. Hypersensitivity
Evidence of a treatment-related increase in delayed hypersensitivity response has been
observed in guinea pigs following dermal exposures with TCE and in mice following exposures
that occurred both during development and postnatally (see Table 4-77).
In a modified guinea pig maximization test, Tang et al. (2002) evaluated the contact
allergenicity potential of TCE and three metabolites (TCA, TCOH, and CH) in four animals
(FMMU strain, sex not specified) per group (Tang et al., 2002). Edema and erythema indicative
of skin sensitization (and confirmed by histopathology) were observed. Sensitization rates were
reported to be 71.4% for TCE and 58.3% for TCA, as compared to a reference positive control
response rate (i.e., 100% for 2,4-dinitrochlorobenzene). In this study, the mean response scores
for TCE, TCA, and 2,4-dinitrochlorobenzene were 2.3, 1.1, and 6.0, respectively. TCE was
judged to be a strong allergen and TCA was a moderate allergen, according to the criteria of
Magnusson and Kligman (1969). TCOH and CH were not found to elicit a dermal
hypersensitivity response.
4-409
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Table 4-77. Summary of TCE hypersensitivity studies3
Exposure route/vehicle, duration,
dose
NOAEL; LOAELb
Results
Reference, species/strain
sex/number
Induction by single intradermal
injection, then challenge by dermal
application at 21 d
0 or 0.1 mL induction; 0 or 0.2 mL
challenge
TCE, TCA, TCOH, and CH
Edema and erythema (confirmed by
histopathology) indicative of skin sensitization
for TCE (strong sensitizer) and TCA (moderate
sensitizer)
Tang et al. (2002)
Guinea pig, FMMU strain, sex not
specified, 4/group
Intradermal injection, 0, 167, 500,
1,500, or 4,500 mg/kg
Dermal patch, 0 or 900 mg/kg
Hypersensitivity: total dose from
induction through challenge <340
mg/kg
Intradermal NOAEL:
500 mg/kg
Intradermal LOAEL:
1,500 mg/kg
Dermal patch NOAEL:
900 mg/kg
Intradermal injection: At 1,500 mg/kg:
statistically significant t AST; at 4,500 mg/kg,
statistically significant t ALT and AST,
statistically significant J, total protein and
globulin; fatty degeneration of liver
Dermal patch: no effects of treatment
Hypersensitivity: sensitization rate of 66%
(strong sensitizer), with edema and erythema;
statistically significant t ALT, AST, and LDH;
statistically significant t relative liver weight;
statistically significant J, albumin, IgA, and
GOT; hepatic lesions (ballooning changes)
Tang et al. (2008)
Guinea pig, FMMU strain, female,
5-6/group for intradermal/dermal
patch study, 10/group for
hypersensitivity study, female
Drinking water, from GD 0 to 8 wks
of age
0,1,400, or 14,000 ppb
LOAEL: 1,400 ppb
Statistically significant | swelling of foot pad
in females at 1,400 and in both sexes at
14,000 ppb
Peden-Adams et al. (2006)
Mouse, B6C3Fj, both sexes, 5
litters/group; 4-5 pups/sex/group
at 8 wksc
"Bolded study carried forward for consideration in dose-response assessment (see Chapter 5).
bNOAEL and LOAEL are based upon reported study findings.
°Subset of immunosuppression study.
4-410
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Immune-mediated hepatitis associated with dermal hypersensitivity reactions in the
guinea pig following TCE exposures was characterized by Tang et al. (2008). In this study,
FMMU strain female guinea pigs (5-6/group) were treated with intradermal injection of 0, 167,
500, 1,500, or 4,500 mg/kg TCE or with a dermal patch containing 0 or 900 mg/kg TCE and
sacrificed at 48 hours posttreatment. At the intradermal dose of 1,500 mg/kg, a significant
increase (p < 0.05) in serum AST level was observed. At 4,500 mg/kg, significantly (p < 0.01)
increased ALT and AST levels were reported, and total protein and globulin decreased
significantly (p < 0.05). Histopathological examination of the liver revealed fatty degeneration,
hepatic sinusoid dilation, and inflammatory cell infiltration. No changes were observed at the
intradermal doses of <500 mg/kg, or the dermal patch dose of 900 mg/kg. A Guinea Pig
Maximization Test was also conducted according to the procedures of Magnusson and Kligman
on 10 FMMU females/group, in which the total TCE dosage from induction through challenge
phases was below 340 mg/kg. TCE treatment resulted in dermal erythema and edema, and the
sensitization rate was 66% (i.e., classified as a strong sensitizer). Significant increases (p < 0.05)
in ALT, AST, LDH, and relative liver weight, and significant decreases (p < 0.05) in albumin,
IgA, and GOT were observed. Additionally, hepatic lesions (diffuse ballooning changes without
lymphocyte infiltration and necrotic hepatocytes) were noted. It was concluded that TCE
exposure to guinea pigs resulted in delayed type hypersensitivity reactions with hepatic injury
that was similar to occupational medicamentosa-like dermatitis disorders observed in human
occupational studies.
Also, as indicated in Section 4.6.2.1.2, in a developmental immunotoxicity-type study in
B6C3Fi mice, administration of TCE in drinking water at dose levels of 0, 1,400, or 14,000 ppb
from GD 0 through to 8 weeks of age resulted in an increased delayed hypersensitivity response
in 8-week-old female offspring at both treatment levels and in males at the high dose of
14,000 ppb (Peden-Adams et al.. 2006).
In an in vitro study that evaluated a number of chlorinated organic solvents, nonpurified
rat peritoneal mast cells (NPMC) and rat basophilic leukemia (RBL-2H3) cells were sensitized
with anti-DNP (dinitrophenol) monoclonal IgE antibody and then stimulated with
DNP-conjugated bovine serum albumin plus TCE (Seo et al., 2008). TCE enhanced antigen-
induced histamine release from NPMC and RBL-2H3 cells in a dose-related manner, and
increased IL-4 and TNF-a production from the RBL-2H3 cells. In an in vivo study, i.p.-injected
TCE was found to markedly enhance passive cutaneous anaphylaxis reaction in antigen-
challenged rats. These results suggest that TCE increases histamine release and inflammatory
mediator production from antigen-stimulated mast cells via the modulation of immune
responses; TCE exposure may lead to the enhancement of allergic disease through this response.
4-411
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4.6.2.3. Autoimmunity
A number of studies have been conducted to examine the effects of TCE exposure in
mouse strains (i.e., MRL +/+, MRL -Ipr, or NZB x NZW) which are all known to be genetically
susceptible to autoimmune disease. The studies have demonstrated the potential for TCE to
induce autoimmune disease (as demonstrated in Table 4-78, which summarizes those studies
which assessed serology, ex vivo assays of cultured splenocytes, and/or clinical or
histopathology). These and other studies conducted in susceptible mouse strains have proven to
be useful tools in exploring various aspects of the mode of action for this response.
Khan et al. (1995) used the MRL +/+ mouse model to evaluate the potential for TCE and
one of its metabolites, DCAC to elicit an autoimmune response. Female mice (4-5/group) were
dosed by i.p. injection with 10 mmol/kg TCE or 0.2 mmol/kg DCAC every 4th day for 6 weeks
and then sacrificed. Spleen weights and IgG were increased. ANA and anti-ssDNA (single-
stranded DNA) antibodies were detected in the serum of TCE- and DCAC-treated mice;
anticardiolipin antibodies were detected in the serum of DCAC-treated mice. A greater
magnitude of response observed with DCAC treatment suggested that the metabolite may be
important to the mechanism of TCE-induced autoimmunity.
Other studies in female MRL +/+ mice (8/group) examined exposure via drinking water.
In one of these studies, mice were treated with 2.5 or 5.0 mg/mL (455 or 734 mg/kg-day) TCE in
drinking water for up to 22 weeks (Griffin et al., 2000a: Gilbert et al., 1999). Serial sacrifices
were conducted at weeks 4, 8, and 22. Significant increases in ANA and total serum
immunoglobulin were found at 4 weeks of TCE treatment (indicating an autoimmune response),
but not at 22 weeks. Increased expression of the activation marker C44 on splenic CD4+ cells
was observed at 4 weeks, with the highest expression seen in the highest exposure group. In
addition, at 4 weeks, splenic T-cells from treated mice secreted more IFN-y and less IL-4 than
control T-cells (significant at 0.5 and 2.5 mg/mL), consistent with a Thl immune or
inflammatory response. By 22 weeks of TCE treatment, a specific immune serum antibody
response directed against dichloroacetylated proteins was activated in hepatic tissues, indicating
the presence of protein adducts.
In a subsequent study that assessed occupationally relevant concentrations, TCE was
administered to female MRL +/+ mice (8/group) in drinking water at treatment levels of 0.1, 0.5,
or 2.5 mg/mL (21, 100, or 400 mg/kg-day) for 4 and 32 weeks (Griffin et al.. 2000b). At 4
weeks, significant increases in serum antinuclear antibody levels were observed at 0.1 and
0.5 mg/kg-day. A dose-related increase in the percentage of activated CD4+ T-cells in lymph
nodes of treated mice was observed at 32 weeks, and a dose-related increase in secretion in IFN-
y by the CD4+ T-cells was also observed at 4 and 32 weeks. There was a slight but statistically
significant increase in serum ALT levels at 32 weeks at 0.5 mg/mL. Histopathological
evaluation at 32 weeks revealed extensive hepatic lymphocytic cell infiltration at 0.5 and
2.5 mg/mL; all treated groups contained significantly more hepatocyte reactive changes (i.e.,
4-412
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presence of multinucleated hepatocytes, variations in hepatocyte morphology, and hepatocytes in
mitosis) than controls.
A similar response was observed by Cai et al. following chronic (48 weeks) exposure of
TCE to female MRL +/+ mice (5/group) in drinking water at 0 or 0.5 mg/mL (approximately
60 ug/g/day) (Cai et al., 2008). After 11 weeks of treatment, a statistically significant decrease
in body-weight gain was observed. After 24 weeks of exposure, serum ANA was consistently
elevated in treated mice as compared to controls, although statistical significance was not
achieved. Apparent treatment-related effects on serum cytokines included decreased IL-6 after
36 and 48 weeks, decreased TNF-a after 48 weeks, and increased granulocyte colony stimulating
factor (G-CSF) after 36 weeks of treatment. After 36 weeks of treatment, ex vivo cultured
splenocytes secreted higher levels of IFN-y than control splenocytes. Although there were no
observed effects on serum aminotransferase liver enzymes at termination, statistically significant
incidences of hepatocytic necrosis and leukocyte infiltration (including CD3+ T lymphocytes)
into liver lobules were observed in treated mice after 48 weeks of exposure. Hepatocyte
proliferation was also increased. TCE treatment for 48 weeks also induced necrosis and
extensive infiltration of leukocytes in the pancreas, infiltration of leukocytes into the perivascular
and peribronchial regions of the lungs, and thickening of the alveolar septa in the lungs. At
36 and 48 weeks of exposure, massive perivascular infiltration of leukocytes (including CD3+
T lymphocytes) was observed in the kidneys, and immunoglobulin deposits were found in the
glomeruli.
4-413
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Table 4-78. Summary of autoimmune-related studies of TCE and metabolites in mice and rats (by sex, strain,
and route of exposure)8
Number/group, vehicle, dose,
duration
NOAEL; LOAELb
Results
Serology
Ex vivo assays of cultured
splenocytes
Clinical and
histopathology
Reference
Autoimmune-prone: female MRL +/+ mice, drinking water
8 per group, 0, 2.5, or
5 mg/mL TCE (average 0,
455, or 734 mg/kg-d), 4, 8, or
22wks
8 per group, 0, 0.1, 0.5, or
2.5 mg/mL TCE (0, 21, 100,
or 400 mg/kg-d), 4 or 32 wks
6-8 per group, 0, 0. 1, or
0.9 mg/mL trichloroacetalde-
hyde hydrate (0, 24, or
220 mg/kg-d) or TCA (0, 27,
or 205 mg/kg-d), 4 wks
8 per group, 0, 0.1, 0.3, or
0.9 mg/mL
trichloroacetaldehyde hydrate
(0, 13, 46, or 143 mg/kg-d),
40 wks
LOAEL:
2.5 mg/mL
LOAEL:
0.1 mg/mL
LOAEL:
0.1 mg/mL
LOAEL:
0.9 mg/mL
Increased ANA at 4 and
8 wks, no difference
between groups at 22 wks
Increased ANA in all
treated groups at 4 wks,
but not at 32 wks
Increased ANA and
antihistone antibodies at
0.9 mg/mL
trichloroacetaldehyde
hydrate0
Slightly suppressed anti-
ssDNA, anti-dsDNA, and
antihistone antibody
expression; differences not
statistically significant
Increased activated CD4+
T-cells and IFN-y secretion
across doses at 4 wks, these
effects were reversed at
22 wks; decreased IL-4
secretion (4 and 22 wks)
Increased activated CD4+
T-cells (32 wks), IFN-y
secretion (4 and 32 wks), no
effect on IL-4 secretion
Increased activated CD4+
T-cells at 0.1 and 0.9 g/mL
doses of both metabolites. At
0.9 mg/mL, increased IFN-y
secretion, no effect on IL-4
secretion
Increased activated CD4+
T-cells and increased INF-y
secretion, no effect on IL-4
secretion
No evidence of liver or renal
damage, based on serum ALT,
SDH, and BUN.
Extensive hepatic
mononuclear cellular
infiltrate in 0.5 and
2.5 mg/mL groups, and
hepatocyte reactive changes
in all treated groups at
32 wks.
No evidence of liver of kidney
damage, based on serum ALT,
liver and kidney histology.
Diffuse alopecia, skin
inflammation and ulceration,
mononuclear cell infiltration,
mast cell hyperplasia, dermal
fibrosis. Statistically
significant increase at
0.9 mg/mL dose group, but
also increased at lower doses.
No liver or kidney
histopathology effects seen.
Griffin et al.
(2000a)
Griffin et al.
(2000b)
Blossom et al.
(2004)
Blossom et al.
(20071
4-414
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Table 4-78. Summary of autoimmune-related studies of TCE and metabolites (by sex, strain, and route of
exposure) (continued)
Number/group, vehicle, dose,
duration
5 per group, 0 or 0.5 mg/mL
TCE (mean 60 fig/g-d),
48wks
NOAEL; LOAELb
LOAEL:
0.5 mg/mL
Results
Serology
Increased ANA after
24 wks but not
statistically significant
Ex vivo assays of cultured
splenocytes
Increased INF-y secretion
after 36 wks but not
statistically significant
Clinical and
histopathology
Hepatic necrosis; hepatocyte
proliferation; leukocyte
infiltrate in the liver, lungs,
and kidneys; no difference
in serum aminotransferase
liver enzymes.
Reference
Cai et al.
(2008)
Autoimmune-prone: male and female offspring MRL +/+ mice, drinking water
3 litters/group, 8-
12 offspring/group; 0, 0.5, or
2.5 mg/mL, GD 0 to 7-8 wks
of age
8 litters/group, 8-
12 offspring/group; 0 or
0.1 mg/mL; maternal dose =
25.7 mg/kg-d; offspring
PNDs 24-42 dose =
3 1.0 mg/kg-d; GDO to
PND42
Unknown number of litters/
group, 6-10 offspring/
sex/group; 0 (l%Emulphor),
1,400, or 14,000 ppb; GD 0 to
12 mo of age
LOAEL:
0.5 mg/mL
LOAEL:
0.1 mg/mL
NOAEL: 1,400 ppb
Increased antihistone
antibodies and total IgG2a
in treated groups
Not evaluated
No increase in
autoantibody levels
Dose-dependent increase in
IFN-y secretion at 4-5 wks of
age but not 7-8 wks of age
Increased IFN-y and IL-2 in
females, increased TNF-a in
both sexes
Not evaluated
No histopathological effects in
liver or kidneys.
Not evaluated
Not evaluated
Blossom and
Doss (2007)
Blossom et al.
(2008)
Peden-Adams et
al. (2008)
4-415
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Table 4-78. Summary of autoimmune-related studies of TCE and metabolites (by sex, strain, and route of
exposure) (continued)
Number/group, vehicle, dose,
duration
NOAEL; LOAELb
Results
Serology
Ex vivo assays of cultured
splenocytes
Clinical and
histopathology
Reference
Autoimmune-prone: female MRL +/+ mice, i.p. injection
4-5 per group, 0 (corn oil),
10 mmol/kg TCE, or
0.2 mmol/kg DCAC, every
4th d for 6 wks
6 per group, 0 (corn oil),
0.2 mmol/kg DCAC, or
0.2 mmol/kg dichloroacetic
anhydride, 2 times per wk for
6 wks
LOAEL:
10 mmol/kg TCE,
0.2 mmol/kg
DCAC
LOAEL:
0.2 mmol/kg TCE,
0.2 mmol/kg
dichloroacetic
anhydride
In both groups, increased
ANA and anti-ssDNA
antibodies. In DCAC
group, anticardiolipin
antibodies. No difference
in antihistone, -Sm, or -
DNA antibodies
In both treated groups,
increased ANA
Not evaluated
In both treated groups,
increased IL-lo, IL-lp, IL-3,
IL-6, IFN-y, G-CSF and KC
secretion; decreased IL-5. In
DCAC group, increased IL-17
and INF-ad
Not evaluated
In both treated groups,
increased lymphocytes in
spleen, thickening of alveolar
septa with lymphocytic
interstitial infiltration.
Khan et al.
(1995)
Cai et al. (2006)
Autoimmune-prone: female NZB x NZW mice, drinking water
6 per group, 0, 1,400, or
14,000 ppb TCEe'f, 27 wks
exposure
10 per group, 0, 1,400, or
14,000 ppb TCEf, 27 wks
exposure
LOAEL: 1,400 ppb
LOAEL:
1,400 ppb
Increased anti-dsDNA
antibodies at 19 wks and at
32-32 wks in the 1,400 ppb
group
Increased anti-dsDNA
antibodies at 19 wks and
at 32-32 wks in the
1,400 ppb group
Not evaluated
No effect on splenocyte NK
activity
At 14,000 ppb, proteinuria
increased beginning at
20 wks; renal pathology
scores increased, no evidence
of liver disease.
No effect on renal pathology
score; liver disease not
examined.
Gilkeson et al.
(2004)
Keil et al.
(2009)
4-416
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Table 4-78. Summary of autoimmune-related studies of TCE and metabolites (by sex, strain, and route of
exposure) (continued)
Number/group, vehicle, dose,
duration
NOAEL; LOAELb
Results
Serology
Ex vivo assays of cultured
splenocytes
Clinical and
histopathology
Reference
Autoimmune-prone: male MRL — Ipr/lpr mice, inhalation
5 per group, 0, 500, 1,000, or
2,000 ppm TCE, 4 hrs/d,
6 d/wk, 8 wks
LOAEL: 500 ppm
At >500 ppm, dose-related
liver inflammation,
splenomegaly and
hyperplasia of lymphatic
follicles; at 1,000 ppm,
immunoblastic cell
formation in lymphatic
follicles, no changes in
thymus.
Kaneko et al.
(2000)
Autoimmune-inducible: female brown Norway Rat, gavage
6-8 per group, 0, 100, 200,
400 mg/kg, 5 d/wk, 6 wks
followed by 1 mg/kg HgCl2
challenge
NOAEL 500 mg/kg
Not reported8
Not evaluated
Not evaluated
White et al.
(2000)
Nonautoimmune-prone: female B6C3Fj mice, drinking water
6 per group, 0, 1,400, or
14,000 ppb TCE,e'f 30 wks
exposure
LOAEL: 1,400 ppb
Anti-dsDNA increased in
1,400 ppb group beginning
at age 32 wks and in the
14,000 ppb group
beginning at age 26 wks
No effect on splenocyte NK
activity
No renal disease observed.
Gilkeson et al.
(2004)
4-417
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Table 4-78. Summary of autoimmune-related studies of TCE and metabolites (by sex, strain, and route of
exposure) (continued)
Number/group, vehicle, dose,
duration
10 per group, 0, 1,400, or
14,000 ppb TCE/ 30 wks
exposure
NOAEL; LOAELb
LOAEL: 1,400
ppb
Results
Serology
Anti-dsDNA increased
beginning at 26 wks in the
14,000 ppb group and at
32 wks of age in the
1,400 ppb group;
increases in anti-ssDNA
antibodies seen in both
groups at 32 wks. Anti-
glomerular antigen were
not affected
Ex vivo assays of cultured
splenocytes
No effect on splenocyte NK
activity
Clinical and
histopathology
Increased renal pathology
scores in 1,400 ppb group;
Significant decrease in
thymus weight in both
groups
Reference
Keil et al.
(2009)
"Bolded studies carried forward for consideration in dose-response assessment (see Chapter 5); selected endpoints, based on those reported across the majority
of studies. Lupus-prone mouse strains develop lupus-like condition spontaneously, with virtually complete penetrance. The autoimmune-inducible (Brown
Norway) rat has been used as a model of mercuric chloride induced glomerulonephritis and experimental autoimmune myasthenia gravis.
bNOAEL and LOAEL are based upon reported study findings.
°No difference reported in anti-dsDNA, -ssDNA, -ribonucleosome, -SSA, -SSB, -Sm, -Jo-1, or -Scl-70 antibodies.
dNo difference reported in secretion of other cytokines measured: IL-2, IL-4, IL-10, IL-12, TNF-a, granulocyte monocyte colony stimulating factor, macrophage
inflammatory protein-la, andRANTES (CCL-5).
Dose levels cited in the report (Gilkeson et al.. 2004) were incorrect; corrections provided by personal communication from Margie Peden-Adams (Medical
University of South Carolina) to Glinda Cooper (U.S. EPA) on 13 August 2008; dose levels in this table are correctly reported.
fDose in mg/kg-day not given.
8 Anti-dsDNA tests were described in the methods section; no effect of TCE on serum IgE levels was seen, and it is not clear if the additional serological tests
were conducted in the TCE portion of this study or if they were conducted but not reported because no effect was seen.
G-CSF = granulocyte colony stimulating factor; KC = keratinocyte-derived chemokine; SDH = sorbitol dehydrogenase
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To examine the role of metabolic activation in the autoimmune response, Griffin et al.
(2000c) treated MRL +/+ mice with 2.5 mg/mL (300 mg/kg-day) TCE in drinking water for
4 weeks (Griffin et al., 2000c). Immune responses were examined in the presence or absence of
subcutaneous doses of 200 mg/kg-day diallyl sulfide, a specific inhibitor of CYP2E1, which is
known to be a primary CYP that is active in TCE metabolism. With diallyl sulfide cotreatment
that resulted in a decreased level of CYP2E1 apoprotein in liver microsomes, the enhanced
mitogen-induced proliferative capacity of T-cells was inhibited and the reduction in IL-4 levels
secreted by CD4+ T-cells was reversed for TCE-treated MRL +/+ mice. This study suggests that
metabolism of TCE by CYP2E1 is responsible, at least in part, for the treatment-related CD4+
T-cell alterations.
The TCE metabolite, trichloroacetaldehyde (TCAA) or trichloroacetaldehyde hydrate
(TCAH), was also evaluated in MRL +/+ mice (Blossom et al., 2007; Blossom and Gilbert,
2006; Gilbert et al., 2004) in order to determine if outcomes similar to the immunoregulatory
effects of TCE would be observed, and to attempt to further characterize the role of metabolism
in the mode of action for TCE. At concentrations ranging from 0.04 to 1 mM, TCAA stimulated
proliferation of murine Thl cells treated with anti-CD3 antibody or antigen in vitro. At similar
concentrations, TCAA induced phenotypic alterations consistent with upregulation of CD28 and
downregulation of CD62L in cloned memory Thl cells and DC4+ T-cells from untreated MRL
+/+ mice. Phosphorylation of activating transcription factor 2 (ATF-2) and c-Jun (two
components of the activator protein-a transcription factor) was also observed with
TCAA-induced Thl cell activation. Higher concentrations of TCAA formed a Schiff base on
T-cells, which suppressed the ability of TCAA to phosphorylate ATF-2. These findings
suggested that TCAA may promote T-cell activation by stimulating the mitogen-activated
protein kinase pathway in association with Schiff base formation on T-cell surface proteins
(Gilbert et al.. 2004).
In order to determine whether metabolites of TCE could mediate the immunoregulatory
effects previously observed with TCE treatment (i.e., the generation of lupus and autoimmune
hepatitis, associated with activation of IFN-y-producing CD4+ T-cells), Blossom et al. (2004)
administered TCE metabolites, TCAH and TCA, to MRL +/+ mice (6-8/group) in drinking
water for 4 weeks. Drinking water concentrations were 0, 0.1, or 0.9 mg/mL; average daily
doses were calculated as 0, 24, or 220 mg/kg-day for TCAH and 0, 27, or 205 mg/kg-day for
TCA. These treatment levels were considered to be physiologically relevant and to reflect
occupational exposure. A phenotypic analysis of splenic and lymph node cells, cytokine profile
analysis, evaluation of apoptosis in CD4+ T-cells, and examination of serum markers of
autoimmunity (anti-ssDNA, antihistone, or ANA) were conducted. Exposure to TCAH or TCA
at both treatment levels was found to promote CD4+ T-cell activation, as shown by significant
(p < 0.05) increases in the percentage of CD62L10 CD4+ T-cells in the spleens and lymph nodes
of the MRL +/+ mice. Increased levels of IFN-y were secreted by CD4+ T-cells from mice
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treated by TCAH and TCA. No significant changes in body weight were observed; spleen
weights were similar between control and treated mice with the exception of a significant
decrease in spleen weight from mice treated with 0.9 mg/mL TCA. Liver and kidney histology
were not affected, and serum ALT levels were similar for control and treated mice. A
generalized trend towards an increase in serum autoantibodies (anti-ssDNA) was observed in
TCAH-treated mice, and slight but significant increases in antihistone and antinuclear antibody
production were observed in mice treated with 0.9 mg/mL-day TCAH.
The autoimmune response of female MRL +/+ mice to DC AC, a metabolite of TCE, and
to dichloroacetic anhydride (DCAA) a similar acylating agent, was evaluated by Cai et al.
(2006). Six mice/group were injected intraperitoneally, twice weekly for 6 weeks, with
0.2 mmol/kg DCAC or DCAA in corn oil. Body weight gain was significantly decreased after
5 or 6 weeks treatment with DCAC and DCAA. DCAC treatment resulted in significant
increases in total serum IgG (77% increase over control) and IgGl (172% increase over control),
as well as the induction of DCAC-specific IgG and IgGl. Serum IgM levels were significantly
decreased by 25 and 18% in DCAC and DCAA-treated mice, respectively. IgE levels were
increased 100% over controls in DCAC-treated mice. Of eight Thl/Th2 cytokines measured,
only IL-5 was decreased in DCAC- and DCAA-treated mice. Serum ANA were detected in both
DCAC- and DCAA-treated mice. Treatment-related increases in cytokine and chemokine
secretion in cultured splenocytes were observed for DCAC and DCAA (IL-1, G-CSF,
keratinocyte-derived chemokine, IL-3, and IL-6). DCAC-treated splenocytes also secreted more
IL-17 and IFN-a than controls. Histopathological changes were observed in the spleens of
DCAC and DCAA-treated mice (lymphocyte population increases in the red pulp). With both
DCAC and DCAA treatment, the alveolar septa were thickened in the lungs, moderate levels of
lymphocytic interstitial infiltrates were present in tissues, and alveolar capillaries were clogged
with erythrocytes. These findings were attributed both to the predisposition of the MRL +/+
mice towards autoimmune disease and to the treatment-related induction of autoimmune
responses.
Fas-dependant activation-induced cell death leading to autoimmune disease has been
shown to be related to impaired Fas or FasL ligand expression in humans and mice, and defects
in the Fas-signaling pathways have been described in autoimmune disease models. The study by
Blossom and Gilbert (2006) examined the effects of TCAH on Fas-dependent autoimmune cell
death). In this study, TCAH: (1) inhibited apoptosis of antigen-activated cells; (2) did not
protect CD4+ T-cells from Fas-independent apoptosis; (3) did not inhibit autoimmune cell death
induced by direct engagement of the Fas receptor; (4) inhibited the expression of FasL but not
Fas on the surface of activated CD4+ T-cell; (5) increased release of FasL from CD4+ cells in a
metalloprotein-dependent manner; and (6) increased metalloprotein MMP-7 expression.
Gilbert et al. (2006) studied the effect of treatment on apoptosis in CD4+ T-lymphocytes
isolated from MRL +/+ female mice that had been exposed to TCE (0, 0.1, 0.5, or 2.5 mg/mL) in
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the drinking water for 4 or 32 weeks or to TCAH (0.1, 0.3, or 0.9 mg/mL) in drinking water for 4
or 40 weeks. After only 4 weeks, decreased activation-induced apoptosis was associated with
decreased FasL expression in the CD4+ T-cells, suggesting that TCE- and TCAH-induced
autoimmune disease was promoted through suppression of the process that would otherwise
delete activated self-reactive T-lymphocytes. By 32 weeks of treatment, TCE had induced
autoimmune hepatitis, which was associated with the promotion of oxidative stress, the
formation of liver protein adducts, and the stimulated production of antibodies to those adducts.
TCAH-treated mice did not exhibit autoimmune hepatitis by 40 weeks, but developed a dose-
dependent alopecia and skin inflammation (Blossom et al., 2007). TCAH appeared to modulate
the CD4+ T-cell subset by promoting the expression of an activated/effector phenotype with an
increased capacity to secrete the proinflammatory cytokine IFN-y. A 4-week exposure to TCAH
attenuated activation-induced cell death and the expression of the death receptor Fas in CD4+
cells; these effects were not seen after a 40-week exposure period. Differences in response were
tentatively attributed to higher levels of metalloproteinases (specifically MMP-7) at 4 weeks of
treatment, suggesting a possible mechanism for the promotion of skin pathology by TCAH.
The role of protein adduct formation in autoimmune response has been pursued by
various researchers. Halmes et al. (1997) administered a single i.p. dose of TCE in corn oil to
male Sprague-Dawley rats (2/group) at 0 or 1,000 mg/kg. Using antiserum that recognizes TCE
covalently bound to protein, a single 50 kDa microsomal adduct was detected by Western blot in
livers of treated rats. Using affinity chromatography, a 50 kDa dichloroacetyl protein was also
isolated from rat plasma. The protein was reactive immunochemically with anti-CYP2El
antibodies. The data suggest that the protein adduct may be CYP2E1 that has been released from
TCE-damaged hepatocytes.
Cai et al. (2007) examined the role of protein haptenization in the induction of immune
responses. In this study, MRL +/+ mice were immunized with albumin adducts of various TCE
reactive intermediates of oxidative metabolism. Serum immunoglobulins and cytokine levels
were measured to evaluate immune responses against the haptenized albumin. Antigen-specific
IgG responses (subtypes: IgGl, IgG2a, and IgG2b) were found. Serum levels of G-CSF were
increased in immunized mice, suggesting macrophage activation. Following immunization with
formyl-albumin, lymphocyte infiltration in the hepatic lobule and portal area was increased.
This study suggests that proteins that are haptenized by metabolites of TCE may act as antigens
to induce humoral immune responses and T-cell-mediated hepatitis.
A possible role for oxidative stress in inflammatory autoimmune disease was proposed by
Khan et al. (2001). A study was performed in which female MRL +/+ mice were treated with
10 mmol/kg TCE or 0.2 mmol/kg DCAC via i.p. injection every 4th day for 2, 4, 6, or 8 weeks.
Antimalondialdehyde serum antibodies, a marker of lipid peroxidation and oxidative stress, were
measured and were found to increase by 4 weeks of treatment, marginally for TCE and
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significantly for DCAC. It was reported that antimalondialdehyde antibodies has also been
found to be present in the serum of systemic lupus erythematosus-prone MRL-lpr/lpr mice.
In another study that addressed the association of oxidative and nitrosative stress, and the
role of lipid peroxidation and protein nitration, in TCE-mediated autoimmune response,
Wang et al. (2007b) treated female MRL +/+ mice with 0.5 mg/mL TCE in drinking water for
48 weeks . The formation of antibodies in the serum to lipid peroxidation-derived aldehyde
protein adducts was evaluated. With TCE treatment, the serum levels of antimalondialdehyde
and anti-4-hydroxynonenal protein adduct antibodies, inducible nitric oxide synthase, and
nitrotyrosine were increased. These were associated with increases in antinuclear-, anti-ssDNA-,
and anti-dsDNA antibodies. The involvement of lipid peroxidati on-derived aldehyde protein
adducts in TCE autoimmunity was further explored, using female MRL +/+ mice that were
administered by i.p. injections of TCE at 10 mmol/kg, either every 4th day for 6 or 12 weeks
(Wang et al., 2007a) or once per week for 4 weeks (Wang et al., 2008). Significant increases in
malondialdehyde and 4-hydroxynonenal protein adducts, as well as significant induction of
specific antibodies directed against these antigens were observed in both studies. Wang et al.
(2008) also demonstrated a significant proliferation of CD4+ T-cells in TCE-treated mice, and
splenic lymphocytes from TCE-treated mice released more IL-2 and IFN-y when stimulated with
MDA- or 4-hydroxynonenal-adducted mouse serum albumin. Overall, the result of these studies
suggest a role for lipid peroxidation aldehydes in the induction and/or exacerbation of
autoimmune response in the MRL +/+ animal model, and the involvement of Thl cell activation.
In studies conducted in other rodent strains, less consistent outcomes have been observed.
Inhalation exposure of an autoimmune-prone strain of male mice (MRL-lpr/lpr) to 0, 500, 1,000,
or 2,000 ppm TCE for 4 hours/day, 6 days/week, for 8 weeks resulted in depressed serum IgG
levels and increased numbers of lymphoblastoid cells (Kaneko et al., 2000). Also at 2,000 ppm,
changes in T-cell helper to suppressor cell ratios were observed. At histopathological evaluation,
dose-dependent inflammation and associated changes were noted in the liver at >500 ppm,
hyperplasia of the lymphatic follicles of the spleen and splenomegaly were observed at
>500 ppm, and the spleen exhibited the development of an immunoblastic-cell-like structure at
1,000 ppm.
A 26-week drinking water study of TCE in NZB x NZW (NZBWF1) autoimmune-prone
mice demonstrated an increase in anti-dsDNA antibodies at 19 weeks and at 32 and 34 weeks in
the 1,400 ppb group, and increased kidney disease at 14,000 ppb (i.e., increased proteinuria at
20 weeks; increased renal pathology scores were noted at termination, based upon glomerular
proliferation, inflammation, and necrosis) (Gilkeson et al., 2004)." Also in that study, a small
increase in anti-dsDNA antibody production, without kidney disease, was observed in B6C3Fi
nThe study was reported in symposium proceedings. Dose levels cited in the proceedings were incorrect; however,
corrections were provided by personal communication from Margie Peden-Adams (Medical University of South
Carolina) to Glinda Cooper (U.S. EPA) on 13 August 2008, and dose levels are correctly reported here.
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mice, with statistically significant (p < 0.05) or borderline (p = 0.07) effects seen in the
1,400 ppb group at observations between 32 and 39 weeks of age, and in the 14,000 ppb group at
observations between 26 and 39 weeks of age.
Keil et al. (2009) also assessed the effects of TCE exposure on NZWBF1 mice,
comparing the responses to those of TCE-exposed B6C3Fi mice, which are not autoimmune
prone (Keil et al.. 2009). In this study, groups of NZWBF1 and B6C3Fi female mice (10/dose
level) were administered 0, 1,400, or 14,000 ppb TCE in the drinking water. Treatment was
initiated at 9 weeks of age and continued until 36 weeks of age for the NZBWF1 mice and until
39 weeks of age for the B6C3Fi mice. Body weight; spleen, thymus, liver, and kidney weight;
spleen and thymus cellularity; and renal pathology were assessed. Splenic lymphocyte
proliferation, autoantibody production (anti-dsDNA, anti-ssDNA, and antiglomerular), total
serum IgG, NK cell activity, and mitogen-induced lymphocyte proliferation were conducted.
Administration of TCE did not result in alterations in NK cell activity or T- or B-cell
proliferation in either strain of mice. In the NZBWF1 mice, there was little evidence of an
increase or of an acceleration in ssDNA antibody production with TCE exposure, but as was seen
in the earlier study by these investigators (Gilkeson et al., 2004), dsDNA antibodies were
increased at 19 weeks and at 32-34 weeks in the 1,400 ppb group. However, antiglomerular
antibody levels were increased in NZBWF1 mice early in the study, returning to control levels
by 23 weeks of age. In the B6C3Fi mice, the number of activated T-cells (CD4++/CD44+) was
increased (significantly at 14,000 ppb;/> < 0.05) and thymus weights were significantly
decreased (p < 0.05) in a dose-responsive manner. Renal pathology (as indicated by renal score
based on assessment of glomerular inflammation, proliferation, crescent formation, and necrosis)
was significantly increased (p < 0.05) at 1,400 ppb. Also in the B6C3Fi mice, autoantibodies to
dsDNA were increased relative to controls beginning at 26 weeks in the 14,000 ppb group and at
32 weeks of age in the 1,400 ppb group; increases in anti-ssDNA antibodies were seen in both
groups at 32 weeks. Antiglomerular antibodies were not affected in B6C3Fi mice. In summary,
the authors concluded that this study showed that 27-30 weeks of TCE drinking water
administration to NZBWF1 (autoimmune-prone) mice did not contribute to the progression of
autoimmune disease, while similar administration to B6C3Fi (nonautoimmune-prone) mice
increased the expression of a number of markers that are associated with autoimmune disease.
This study is important in that it demonstrates that autoimmune responses to TCE exposure in
animal models are not solely dependent upon a genetic predisposition to autoimmune disease.
White et al. (2000) conducted a study in female Brown Norway rats, which have been
shown to be susceptible to development of chemically-induced IgE mediated glomerulonephritis
that is similar to the nephritic damage seen in systemic lupus erythematosus. TCE administered
by gavage 5 days/week at 100, 200, or 400 mg/kg did not increase in IgE levels after 6 weeks
exposure, or after an additional challenge with 1 mg/kg
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Several studies have examined the potential for autoimmune response following oral
exposures during pre- and postnatal immune system development, as described in
Section 4.6.2.1.2. Peden-Adams et al. (2008; 2006) conducted two such studies. In the first
study, B6C3Fi mice were treated with either 1,400 or 14,000 ppb TCE in drinking water from
GD 0 to postnatal week 8 (Peden-Adams et al., 2006). No treatment-related increases in serum
anti-dsDNA antibody levels were observed in the 8-week-old offspring, although it is noted that
the mouse strain used in the experiment is not an autoimmune-prone animal model. A more
recent study (Peden-Adams et al., 2008) exposed pregnant MRL +/+ mice to TCE in drinking
water at levels of 0, 1,400, or 14,000 ppb from GD 0 and continued the exposures until the
offspring were 12 months of age. Consistent with the findings of the 2006 publication,
autoantibody levels (anti-dsDNA and antiglomerular) were not increased in the offspring over
the course of the study. Contrasting with these negative studies, the lupus-prone MRL +/+
mouse model was utilized in two additional drinking water studies with developmental exposures
in which there was some indication of a positive association between developmental exposures
to TCE and the initiation of autoimmune disease. Blossom and Doss (2007) administered TCE
to pregnant MRL +/+ mice in drinking water at levels of 0, 0.5, or 2.5 mg/mL and continued
administration to the offspring until approximately 7-8 weeks of age. TCE exposure induced a
dose-dependent increase in T-lymphocyte IFN-y in peripheral blood at 4-5 weeks of age, but this
effect was not observed in splenic T-lymphocytes at 7-8 weeks of age. Serum antihistone
autoantibodies and total IgG2a were significantly increased in the TCE-treated offspring;
however, histopathological evaluation of the liver and kidneys did not reveal any treatment-
related signs of autoimmunity. In a study by Blossom et al. (2008), pregnant MRL +/+ mice
were administered TCE in the drinking water at levels of 0 or 0.1 mg/mL from GD 0 through
lactation, and continuing postweaning in the offspring until GD 42. Significant treatment-related
increases in pro-inflammatory cytokines (IFN-y and 11-2 in males and TNF-a in both sexes)
produced by splenic CD4+ T-cells were observed in PND 42 offspring.
In summary, TCE treatment induces and exacerbates autoimmune disease in genetically
susceptible strains of mice, and has also been shown to induce signs of autoimmune disease in a
nongenetically predisposed strain. Although the mechanism for this response is not fully
understood, a number of studies have been conducted to examine this issue. The primary
conclusion to date is that metabolism of the TCE to its chloral or DC A metabolites is at least
partially responsible for activating T-cells or altering T-cell regulation and survival associated
with polyclonal disease in susceptible mice strains.
4.6.2.4. Cancers of the Immune System
Cancers of the immune system that have been observed in animal studies and are
associated with TCE exposure are summarized in Tables 4-79 and 4-80. The specific cancer
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types observed are malignant lymphomas, lymphosarcomas, and reticulum cell sarcomas in mice
and leukemias in rats.
Table 4-79. Malignant lymphomas incidence in mice exposed to TCE in
gavage and inhalation exposure studies
Cancer type, species, and sex
Prevalence in exposure groups:
n affected/n total (% affected)
Reference
Gavage exposure
Malignant lymphomas
B6C3F! mice, male
B6C3F! mice, female
Lymphosarcomas and reticulum
cell sarcomas
B6C3F! mice, male
B6C3F! mice, female
Malignant lymphomas
Swiss (ICR/HA) mice, male
Swiss (ICR/HA) mice, female
Vehicle control
11/50(22%)
7/48 (15%)
Vehicle control
1/20 (5%)
1/20 (5%)
Control
19/50
(38%)
28/50
(56%)
TCE-
pure
16/50
(32%)
21/50
(42%)
1,000 mg/kg-d
13/50 (26%)
13/49 (27%)
Low dose
4/50 (8%)
5/50 (10%)
TCE-
indust
17/49
(35%)
19/50
(38%)
Inhalation exposure
Malignant lymphomas
Han:NMRI mice, male
Han:NMRI mice, female6
Control
7/30 (23%)
9/29(31%)
TCE-
EPC
11/49
(22%)
20/50
(40%)
High dose
2/48 (4%)
5/47(11%)
TCE-
BO
11/49
(22%)
23/48
(48%)
96
7/29 (24%)
17/30 (57%)
TCE-
EPC-BO
12/49
(24%)
18/50
(36%)
NTP (1990)
NCI (1976)b
Henschler et al.
(1984) c
480
6/30 (20%)
18/28 (64%)
Henschler et al.
(1980)d
"After 103 weeks of gavage exposure, beginning at 8 weeks of age.
bAfter 90 weeks of gavage exposure, beginning at 5 weeks of age. Low dose is 1,200 mg/kg-d for male mice,
900 mg/kg-d for female mice (5 days/week). High dose is 2,400 mg/kg-d for male mice, 1,800 mg/kg-d for female
mice (5 days/week).
0 After 72 weeks of gavage exposure (corn oil), beginning at 5 weeks of age. Male mice received 2,400 mg/kg-d,
female mice received 1,800 mg/kg-d. Stabilizers were added in the percentage w/w: TCE-EPC, 0.8%, TCE-BO,
0.8%, TCE-EPC-BO, 0.25 and 0.25%.
dAfter 78 weeks of inhalation exposure. Administered daily concentration: low dose is 96 (mg/m3) and high dose is
480 (mg/m3), equivalent to 100 and 500 ppm (100 ppm = 540 mg/m3), adjusted for 6 hours/day, 5 days/week
exposure.
Statistically significant by Cochran-Armitage trend test (p < 0.05).
Sources: NTP (1990) Tables 8 and 9; NCI (1976) Table -^CXXa"; Henschler et al. (1980) Table 3a.
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Table 4-80. Leukemia incidence in rats exposed to TCE in gavage and
inhalation exposure studies
Species and sex
Gavage exposure
Sprague-Dawley rats, male
Sprague-Dawley rats, female
August rats, female
Inhalation exposure
Sprague-Dawley rats, male
Sprague-Dawley rats, female
Prevalence in exposure groups:
n affected/n total (% affected)
Control
0/30
(0%)
1/30
(3.3%)
Control
0/50
(0%)
Control
9/135
(6.7)
7/145
(4.8)
50 mg/kg
2/30 (6.7%)
0/30
(0%)
500 mg/kg
1/50
(2%)
100 ppm
13/130
(10.0)
9/130
(6.9)
250 mg/kg
3/30
(10.0%)
0/30
(0%)
1,000 mg/kg
5/50
(10%)
300 ppm
14/130
(10.8)
2/130
(1.5)
600 ppm
15/130
(11.5)
11/130
(8.5)
Reference
Maltoni et al. (1988:
1986)a
NTP (1988)b
Maltoni et al. (1988:
1986)°
aAfter 52 weeks of gavage exposure, beginning at 13 weeks of age, olive oil vehicle. Percentage affected and
starting n given in reported; EPA calculated n affected.
bAfter 104 weeks of gavage exposure, beginning at 6.5-8 weeks of age, corn oil vehicle.
0 After 104 weeks of inhalation exposure, BT304 and BT304bis. Percentage affected and starting n given in
reported; EPA calculated n affected.
In the NCI (1976) study, the results for Osborne-Mendel rats were considered
inconclusive due to significant early mortality, but exposure to B6C3Fi mice were also analyzed.
Limited increases in lymphomas over controls were observed in both sexes of mice exposed (see
Table 4-79). The NCI study (1976) used technical-grade TCE, which contained two known
carcinogenic compounds as stabilizers (epichlorohydrin and 1,2-epoxybutane). A later study
(Henschler et al., 1984) in which mice were given TCE that was pure, industrial, and stabilized
with one or both of these stabilizers did not find significant increases in lymphomas over
historical controls. A gavage study by NTP (1988), which used TCE stabilized with
diisopropylamine, did not see an increase in lymphomas in all four strains of rats (ACI, August,
Marshall, and Osborne-Mendel). The final NTP study (1990) in male and female F344 rats and
B6C3Fi mice, using epichlorohydrin-free TCE, again reported early mortality in male rats. This
study did not observe a significant increase in lymphomas over that of controls. Henschler et al.
(1980) tested NMRI mice, WIST rats, and Syrian hamsters of both sexes, and observed a variety
of tumors in both sexes, consistent with the spontaneous tumor incidence in these strains
(Deerberg et al., 1974; Deerberg and Muller-Peddinghaus, 1970). Henschler et al. (1980) did not
show an increase in lymphomas in rats or hamsters of either sex. Background levels of
lymphomas in this mouse strain are high, making it difficult to determine if the increased
lymphomas in female mice is a treatment effect. In a follow-up study, Henschler et al. (1984)
examined the role of stabilizers of TCE in the lymphomas demonstrated in female mice in the
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1980 paper. Each exposure group had -50 SPF-bred ICR/HA-Swiss mice and exposure was for
18 months. Background incidence of tumors was high in all groups. Focusing just on malignant
lymphomas (see Table 4-79), the high background incidence in unexposed animals again makes
it difficult to determine if there is TCE and/or stabilizer-related incidence of lymphomas. There
are no data at any other timepoint than 18 months. A high mortality rate in all animals as well as
the increased incidence of =ba:kground' lymphomas in that report was also a problem and may
have been related to the shorter time frame.
Maltoni et al. (1988; 1986) reported a nonsignificant increase in leukemias in male rats
exposed via inhalation. Maltoni et al. (1988; 1986) demonstrates a borderline higher frequency
of leukemias in male Sprague-Dawley rats following exposure by ingestion for 52 weeks,
believed by the authors to be related to an increase in lymphoblastic lymphosarcomas (see Table
4-80). The gavage study by NTP (1988), which used TCE stabilized with diisopropylamine,
observed leukemia in female August rats with a positive trend, but was not significantly greater
than the vehicle controls.
In summary, overall there is limited available data in animals on the role of TCE in
lymphomas and leukemias. There are few studies that analyze for lymphomas and/or leukemias.
Lymphomas were described in four studies (NTP, 1990; Henschler et al., 1984; Henschler et al.,
1980; NCI, 1976), but study limitations (high background rate) in most studies make it difficult
to determine if these are TCE-induced. Three studies found positive trends in leukemia in
specific strains and/or gender (Maltoni et al., 1988: NTP, 1988: Maltoni etal., 1986). Due to
study limitations, these trends cannot be determined to be TCE-induced.
4.6.3. Summary
4.6.3.1. Noncancer Effects
The human and animal studies of TCE and immune-related effects provide strong
evidence for a role of TCE in autoimmune disease and in a specific type of generalized
hypersensitivity syndrome. The data pertaining to immunosuppressive effects is weaker. It
should also be noted that immune-related and inflammatory effects, particularly cell-mediated
immunity involving cytokine production and activation of macrophages and NK cells, may
influence a variety of other conditions of considerable public health importance, including cancer
(tumor surveillance) and atherosclerosis. Thus, the relevance of immune-related effects of TCE
are not limited to diseases affecting organs and tissues within the immune system. The relation
between systemic autoimmune diseases, such as scleroderma, and occupational exposure to TCE
has been reported in several recent studies. A meta-analysis of scleroderma studies (Garabrant et
al., 2003: Diot et al., 2002: Nietert et al., 1998) conducted by the EPA resulted in a statistically
significant combined OR for any exposure in men (OR: 2.5, 95% CI: 1.1, 5.4), with a lower RR
seen in women in women (OR: 1.2, 95% CI: 0.58, 2.6). The incidence of systemic sclerosis
among men is very low (approximately 1 per 100,000 per year), and is approximately 10 times
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lower than the rate seen in women (Cooper and Stroehla, 2003). Thus, the human data, at this
time, do not allow for the determination of whether the difference in effect estimates between
men and women reflects the relatively low background risk of scleroderma in men, gender-
related differences in exposure prevalence or in the reliability of exposure assessment (Messing
et al., 2003), a gender-related difference in susceptibility to the effects of TCE, or chance.
Changes in levels of inflammatory cytokines were reported in an occupational study of
degreasers exposed to TCE (lavicoli et al., 2005) and a study of infants exposed to TCE via
indoor air (Lehmann et al., 2002; Lehmann et al., 2001). Experimental studies support the
biological plausibility of these effects. Numerous studies have demonstrated accelerated
autoimmune responses in autoimmune-prone mice (Cai et al., 2008; Blossom et al., 2007;
Blossom et al.. 2004: Griffin et al.. 2000a: Griffin et al.. 2000b). With shorter exposure periods,
effects include changes in cytokine levels similar to those reported in human studies. More
severe effects, including autoimmune hepatitis, inflammatory skin lesions, and alopecia, were
manifest at longer exposure periods, and interestingly, these effects differ somewhat from the
—nonal" expression in these mice. Immunotoxic effects, including increases in anti-dsDNA
antibodies in adult animals and decreased PFC response with prenatal and neonatal exposure,
have been also reported in B6C3Fi mice, which do not have a known particular susceptibility to
autoimmune disease (Peden-Adams et al., 2006; Gilkeson et al., 2004). Recent mechanistic
studies have focused on the roles of various measures of oxidative stress in the induction of these
effects by TCE (Wang et al.. 2008: Wang et al.. 2007b).
There have been a large number of case reports of a severe hypersensitivity skin disorder,
distinct from contact dermatitis and often accompanied by hepatitis, associated with occupational
exposure to TCE, with prevalences as high as 13% of workers in the same location (Kamijima et
al., 2008; Kamijima et al., 2007). Evidence of a treatment-related increase in delayed
hypersensitivity response accompanied by hepatic damage has been observed in guinea pigs
following intradermal injection (Tang et al., 2008: Tang et al., 2002), and hypersensitivity
response was also seen in mice exposed via drinking water pre- and postnatally (GD 0 through to
8 weeks of age) (Peden-Adams et al., 2006).
Human data pertaining to TCE-related immunosuppression resulting in an increased risk
of infectious diseases is limited to the report of an association between reported history of
bacteria of viral infections in Woburn, Massachusetts (Lagakos et al., 1986). Evidence of
localized immunosuppression, as measured by pulmonary response to bacterial challenge (i.e.,
risk of Streptococcal pneumonia-related mortality and clearance ofKlebsiella bacteria) was seen
in an acute exposure study in CD-I mice (Aranyi et al., 1986). A 4-week inhalation exposure in
Sprague-Dawley rats reported a decrease in PFC response at exposures of 1,000 ppm (Woolhiser
et al., 2006).
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4.6.3.2. Cancer
Associations observed in epidemiologic studies of lymphoma and TCE exposure suggest
a causal relation between TCE exposure and NHL. Issues of study heterogeneity, potential
publication bias, and weaker exposure-response results contribute uncertainty to the evaluation
of the available data.
In a review of the NHL studies, studies in which there is a high likelihood of TCE
exposure in individual study subjects (e.g., based on JEMs, biomarker monitoring, or industrial
hygiene data on TCE exposure patterns and factors that affect such exposure) and which met, to
a sufficient degree, the standards of epidemiologic design and analysis were identified. These
studies generally reported excess RR estimates for NHL between 0.8 and 3.1 for overall TCE
exposure. Statistically significant elevated RR estimates with NHL and overall TCE exposure
were observed in two cohort (Raaschou-Nielsen et al., 2003; Hansen et al., 2001) and one case-
control (Hardell et al., 1994) study. Both cohort studies reported statistically significant
associations with NHL for subjects with longer employment duration as a surrogate of TCE
exposure as does a second case-control study with high-quality exposure-assessment
methodology reported statistically significant associations with highest cumulative TCE
exposure or highest average-weekly TCE exposure (Purdue et al., 2011). Hardell et al. (1994)
reported a strong but imprecise association, in part reflecting possible bias from subject-reported
exposure history and few exposed cases. Other identified studies reported a 10-50% elevated
RR estimate with overall TCE exposures that were not statistically significant, except for two
population case-control studies of NHL, one of which did not report RR estimates with overall
TCE exposure but did for medium-high intensity or cumulative TCE exposure (Purdue et al.,
2011: Coccoetal., 2010: Wang et al., 2009: Radican et al., 2008: Miligi et al., 2006: Zhao et al.,
2005: Boiceetal., 1999: Persson and Fredrikson, 1999: Morgan et al., 1998: Nordstrom et al.,
1998: Anttila et al., 1995: Axel son et al., 1994: Greenland et al., 1994: Siemiatvcki, 1991).
Fifteen additional studies were given less weight because of their lesser likelihood of TCE
exposure and other design limitations that would decrease study power and sensitivity (Clapp
and Hoffman, 2008: ATSDR, 2006a; Chang et al., 2005: ATSDR, 2004a: Chang etal., 2003:
Morgan and Cassadv, 2002: Ritz, 1999a: Henschler et al., 1995: Cohnetal., 1994b: Vartiainen et
al., 1993: Sinks etal., 1992: Blair etal., 1989: Costa etal., 1989: Garabrant et al., 1988:
Wilcosky et al., 1984)The observed lack of association with NHL in these studies likely reflects
study design and exposure assessment limitations and is not considered inconsistent with the
overall evidence on TCE and NHL.
Consistency of the association between TCE exposure and NHL is further supported by
the results of meta-analyses of 17 studies reporting risk estimates for overall TCE exposure that
met the meta-analysis inclusion criteria. These meta-analyses found a statistically significant
increased RRm estimate for NHL of 1.23 (95% CI: 1.07, 1.42) for overall TCE exposure. The
analysis of NHL was robust to the removal of individual studies and the use of alternate RR
4-429
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estimates from individual studies, and in only one cases was the resulting RRm no longer
statistically significant (lower 95% confidence bounds of 1.00). Some evidence heterogeneity
was observed, particularly between cohort and case-control studies, but it was not statistically
significant (p = 0.10); and, in addition, there was some evidence of potential publication bias.
Analyzing the cohort and case-control studies separately resolved most of the heterogeneity, but
the result for the summary case-control studies was only a 7% increased RR estimate and was
not statistically significant. The sources of heterogeneity are uncertain but may be the result of
some bias associated with exposure assessment and/or disease classification, or from differences
between cohort and case-control studies in average TCE exposure.
Exposure-response relationships are examined in the TCE epidemiologic studies only to a
limited extent. Many studies examined only overall "exposed" vs. "unexposed" groups and did
not provide exposure information by level of exposure. Others do not have adequate exposure
assessments to confidently distinguish between levels of exposure. The NHL case-control study
of Purdue et al. (2011) reported a statistically significant trend with TCE exposure (p = 0.02 for
average-weekly TCE exposure), and NHL risk in Boice et al. (1999) appeared to increase with
increasing exposure duration (p = 0.20 for routine-intermittent exposed subjects). The borderline
statistically significant trend with TCE intensity in the case-control study of Wang et al. (2009
\p = 0.06]) and with cumulative TCE exposure in the case-control study of Purdue et al. (2011
\p = 0.08] is consistent with that observed with average weekly TCE exposure in Purdue et al.
(2011). Further support was provided by meta-analyses using only the highest exposure groups,
which yielded a higher RRm estimate (1.43 [95% CI: 1.13, 1.82]) than for overall TCE exposure
(1.23 [95% CI: 1.07, 1.42]).
Few risk factors are recognized for NHL, with the exception of viruses,
immunosuppression, or smoking, which are associated with specific NHL subtypes (Besson et
al., 2006). Associations between NHL and TCE exposure are based on groupings of several
subtypes. Two of the seven NHL case-control studies adjusted for age, sex, and smoking in
statistical analyses (Wang et al., 2009; Miligi et al., 2006), two others adjusted for age and sex
(Purdue et al., 2011; Cocco et al., 2010), and the other three case-control studies presented only
unadjusted OR estimates (Persson and Fredrikson, 1999; Nordstrom et al., 1998; Hardell et al.,
1994).
Animal studies describing rates of lymphomas and/or leukemias in relation to TCE
exposure (NTP, 1990: Maltoni et al., 1988: NTP, 1988: Maltoni etal., 1986: Henschler et al.,
1984: Henschler et al., 1980: NCI, 1976) are available. Henschler et al. (1980) reported
statistically significant increases in lymphomas in female Han:NMRI mice treated via inhalation.
While Henschler et al. (1980) suggested that these lymphomas were of viral origin specific to
this strain, subsequent studies reported increased lymphomas in female B6C3Fi mice treated via
corn oil gavage (NTP, 1990) and leukemias in male Sprague-Dawley and female August rats
(Maltoni etal., 1988: NTP, 1988: Maltoni etal., 1986). However, these tumors had relatively
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modest increases in incidence with treatment, and were not reported to be increased in other
studies.
4.7. RESPIRATORY TRACT TOXICITY AND CANCER
4.7.1. Epidemiologic Evidence
4.7.1.1. Chronic Effects: Inhalation
Two reports of a study of 1,091 gun-manufacturing workers are found on noncancer
pulmonary toxicity (Saygun et al., 2007; Cakmak et al., 2004). A subset of these workers
(n = 411) had potential exposure to multiple organic solvents including toluene, acetone, butanol,
xylene, benzene, and TCE used to clean gun parts; however, both papers lacked information on
exposure concentration. Mean exposure duration in Cakmak et al. (2004) was 17 years
(SD = 7.9) for nonsmokers and 16 years (SD = 7.1) for smokers. Cakmak et al. (2004) indicated
effects of smoking and exposure to solvents, with smoking having the most important effect on
asthma-related symptoms (smoking, OR = 2.8, 95% CI: 2.0, 3.8; solvent exposure, OR = 1.4,
95% CI: 1.1, 1.9). Similarly, smoking, but not solvent exposure, was shown as a statistically
significant predictor of lung function decrements. Saygun et al. (2007) reported on a 5-year
follow-up of 393 of the original 1,091 subjects, 214 of who were exposed to solvents. Of the
393 original subjects, the prevalence of definitive asthma symptoms, a more rigorous definition
than used by Cakmak et al. (2004), was 3.3% among exposed and 1.1% among nonexposed
subjects,/* > 0.05. Saygun et al. (2007) presents observations on lung function tests for
697 current workers, a group which includes the 393 original study subjects. Smoking, but not
solvent exposure, was a predictor of mean annual forced expiratory volume (FEVi) decrease.
4.7.1.2. Cancer
Cancers of the respiratory tract including the lung, bronchus, and trachea were examined
in 25 cohort, community studies and case-control studies of TCE. Twelve of the 25 studies
approached standards of epidemiologic design and analysis identified in the review of the
epidemiologic body of literature on TCE and cancer (Radican et al., 2008; Boice et al., 2006b;
Zhao et al.. 2005: Raaschou-Nielsen et al.. 2003: Hansen et al.. 2001: Boice etal.. 1999: Blair et
al.. 1998: Morgan et al.. 1998: Anttila et al.. 1995: Axel son et al.. 1994: Greenland etal.. 1994:
Siemiatycki, 1991). Cancers at other sites besides lung, bronchus, and trachea in the respiratory
system are more limitedly reported in these studies. Some information is available on laryngeal
cancer; however, only 9 of the 16 occupational cohort studies providing information on lung
cancer also reported findings for this site. Case-control studies of lung or laryngeal cancers and
occupational title or organic solvent exposure were found in the literature. Two case-control
studies of lung cancer, one population-based and the other nested within a cohort, were of TCE
exposure specifically. Lung and laryngeal cancer risk ratios reported in cohort, community and
case-control studies are found in Table 4-81.
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Table 4-81. Selected results from epidemiologic studies of TCE exposure and
lung cancer
Exposure group
RR
(95% CI)
Number of
observable
events
Reference
Cohort studies — incidence
Aerospace workers (Rocketdyne)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
All employees at electronics factory (Taiwan)
Not reported
1.00a
1.36(0.86,2.14)
1.11(0.60,2.06)
0.60
1.07 (0.72, 1.52)
43
35
14
30
Danish blue-collar worker with TCE exposure
Any exposure, all subjects
Any exposure, males
Any exposure, females
1.4(1.32, 1.55)
1.4(1.28, 1.51)
1.9(1.48,2.35)
632
559
73
Employment duration
5yrs
1.7(1.46, 1.93)
1.3(1.16, 1.52)
1.4(1.23, 1.63)
209
218
205
Biologically -monitored Danish workers
Any TCE exposure, males
Any TCE exposure, females
Cumulative exposure (Ikeda)
<17 ppm-yr
>17 ppm-yr
Mean concentration (Ikeda)
<4ppm
4+ppm
Employment duration
<6.25 yr
>6.25 yr
0.8 (0.5, 1.3)
0.7(0.01,3.8)
Not reported
Not reported
Not reported
16
1
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
Not reported
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
1.0(0.6,2.0)
0.8 (0.4, 1.6)
0.8 (0.4, 1.7)
24
11
15
Zhao et al. (2005)
Chang et al. (2005)
Raaschou-Nielsen et al.
(2003)
Hansen et al. (2001)
Blair etal. (1998)
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Table 4-81. Selected results from epidemiologic studies of TCE exposure
and lung cancer (continued)
Exposure group
RR
(95% CI)
Number of
observable
events
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
1
1
1
Biologically -monitored Finnish workers
All subjects
0.92 (0.59, 1.35)
25
Mean air-TCE (Ikeda extrapolation)
<6ppm
6+ppm
1.02 (0.58, 1.66)
0.83 (0.33, 1.71)
16
7
Biologically -monitored Swedish workers
Any TCE exposure, males
Any TCE exposure, females
0.69(0.31, 1.30)
Not reported
9
Reference
Blair et al. (1998) (continued)
Anttila et al. (1995)
Axelson et al. (1994)
Cohort and PMR-mortality
Computer manufacturing workers (IBM), New York
Males
Females
1.03 (0.71, 1.42)
0.95 (0.20, 2.77)
35
3
Aerospace workers (Rocketdyne)
Any TCE (utility or engine flush workers)
1.24 (0.92, 1.63)
51
Engine flush — duration of exposure
Referent
0 yr (utility workers with TCE exposure)
<4yrs
>4yrs
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
1.0a
0.5 (0.22, 1.00)
0.8 (0.50, 1.26)
0.8 (0.46, 1.41)
Not reported
1.00a
1.05 (0.76, 1.44)
1.02 (0.68, 1.53)
0.91
472
7
27
24
99
62
33
View-Master employees
Males
Females
0.81 (0.42, 1.42)b
0.99 (0.71, 1.35)b
12
41
Clapp and Hoffman (2008)
Boice et al. (2006b)
Zhao et al. (2005)
ATSDR (2004a)
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Table 4-81. Selected results from epidemiologic studies of TCE exposure
and lung cancer (continued)
Exposure group
RR
(95% CI)
Number of
observable
events
United States uranium-processing workers (Fernald)
Any TCE exposure
Light TCE exposure, >2-yr duration0
Moderate TCE exposure, >2-yr duration0
Not reported
Not reported
Not reported
Aerospace workers (Lockheed)
Routine exposure
Routine-intermittent exposure3
0.76 (0.60, 0.95)
Not reported
78
173
Duration of exposure
Oyr
5yrs
Trend test
1.0
0.85(0.65, 1.13)
0.98 (0.74, 1.30)
0.64 (0.46, 0.89)
^<0.05
288
66
63
44
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)
High intensity (>50 ppm)
1.10(0.89, 1.34)
1.49(1.09, 1.99)
0.90 (0.67, 1.20)
97
45
52
TCE subcohort (Cox Analysis)13
Never exposed
Ever exposed
1.00a
1.14(0.90, 1.44)
291
97
Peak
No/Low
Medium/High
1.00a
1.07 (0.82, 1.40)
324
64
Cumulative
Referent
Low
High
1.00a
1.47 (1.07, 2.03)
0.96 (0.72, 1.29)
291
45
52
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
Any TCE exposure
0.9 (0.6, 1.3)a
109
Reference
Ritz (1999a)
Boice et al. (1999)
Morgan et al. (1998)
Blair et al. (1998)
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Table 4-81. Selected results from epidemiologic studies of TCE exposure
and lung cancer (continued)
Exposure group
RR
(95% CI)
Number of
observable
events
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
1.0 (0.7, 1.6)
0.9 (0.5, 1.6)
1.1 (0.7, 1.8)
51
43
23
38
Females, Cumulative exp
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
0.6(0.1,2.4)
0.6(0.1,4.7)
0.4(0.1,1.8)
2
2
11
2
TCE subcohort
Any TCE exposure
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
0.83 (0.63, 1.08)
0.91 (0.67, 1.24)
1.0a
0.96 (0.67, 1.37)
0.71(0.46, 1.11)
1.00 (0.69, 1.45)
0.53 (0.27, 1.07)
1.0a
0.69 (0.27, 1.77)
0.65(0.16,2.73)
0.39(0.14, 1.11)
166
155
66
31
58
11
5
2
4
Cardboard manufacturing workers in Arnsburg, Germany
TCE-exposed workers
Unexposed workers
Deaths reported to GE pension fund (Pittsfield,
Massachusetts)
1.38 (0.55, 2.86)
1.06 (0.34, 2.47)
1.01 (0.69, 1.47)d
7
5
139
U.S. Coast Guard employees
Marine inspectors
Noninspectors
0.52(0.31,0.82)
0.81 (0.55, 1.16)
18
30
Aircraft manufacturing employees (Italy)
All employees
0.99 (0.73, 1.32)
99
Aircraft manufacturing plant employees (San Diego, California)
All subjects
0.80 (0.68, 0.95)
138
Reference
Blair et al. (1998) (continued)
Radican et al. (2008)
Henschler et al. (1995)
Greenland et al. (1994)
Blair etal. (1998)
Costa et al. (1989)
Garabrant et al. (1988)
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Table 4-81. Selected results from epidemiologic studies of TCE exposure
and lung cancer (continued)
Exposure group
Lamp manufacturing workers (GE)
Rubber industry workers (Ohio)
RR
(95% CI)
0.58 (0.27, 1.27)
0.64 (p > 0.05)c
Number of
observable
events
6
11
Reference
Shannon et al. (1988)
Wilcosky et al. (1984)
Case-control studies
Population of Montreal, Canada
Any TCE exposure
Substantial TCE exposure
0.9 (0.6, 1.5)e
0.6 (0.3, 1.2)e
21
9
Siemiatycki et al. (1991)
Geographic-based studies
Two study areas in Endicott, New York
1.28 (0.99, 1.62)
68
Residents of 13 census tracts
In Redlands, California
0.71(0.61, 0.81)f
356
Iowa residents with TCE in water supply
Males
<0.15 ug/L
>0. 15 ug/L
343. lg
345.7g
1,181
299
Females
<0.15 ug/L
>0. 15 ug/L
58.7g
47.8g
289
59
ATSDR (2006a)
Morgan and Cassidy (2002)
Isacson et al. (1985)
Internal referents, workers not exposed to TCE.
bRisk ratio from Cox Proportional Hazard Analysis, stratified by age, sex, and decade (EHS. 1997).
°OR from nested case-control study.
dOR from nested case-control analysis.
e90% CI.
f99% CI.
8Average annual age-adjusted incidence (per 100,000).
Lung cancer RRs were reported in 11 of 12 cohort studies of aircraft manufacturing,
aircraft maintenance, aerospace, and metal workers, with potential exposure to TCE as a
degreasing agent, and in occupational cohort studies employing biological markers of TCE
exposures. All 11 studies had a high likelihood of TCE exposure in individual study subjects
and were judged to have met, to a sufficient degree, the standards of epidemiologic design and
analysis (Radican et al., 2008; Boice et al., 2006b: Zhao et al., 2005; Raaschou-Nielsen et al.,
2003: HansenetaL 2001: Boice etal.. 1999: Blair etal.. 1998: Morgan et al.. 1998: Anttila et
al., 1995: Axelson et al., 1994: Greenland et al., 1994). Lung cancer risks were not reported for
Fernald uranium processing workers with potential TCE exposure (Ritz, 1999a), a study of less
weight than the other 11 studies. The incidence study of Raaschou-Nielsen et al. (2003) was the
largest cohort, with 40,049 subjects identified as potentially exposed to TCE in several industries
(primarily, in the iron/metal and electronic industries), including 14,360 who had presumably
higher level exposures to TCE. The study included 632 lung cancer cases and reported a 40%
4-436
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elevated incidence in TCE exposed males and females combined (95% CI: 1.32, 1.55), with no
exposure duration gradient. The 95% CIs in other studies of lung cancer incidence included a
risk ratio of 1.0 (Zhao et al.. 2005: Hansenetal.. 2001: Blair etal.. 1998: Anttila et al.. 1995:
Axelson et al., 1994). Lung cancer mortality risks in studies of TCE exposure to aircraft
manufacturing, aircraft maintenance, and aerospace workers included a RR of 1.0 in their 95%
CIs (Radican et al.. 2008: Boice et al.. 2006b: Zhao et al.. 2005: Blair etal.. 1998: Morgan et al..
1998). Boice et al. (1999) observed a 24% decrement (95% CI: 0.60, 0.95) for subjects with
routine TCE exposure. Exposure-response analyses using internal controls (unexposed subjects
at the same company) showed a statistically significant decreasing trend between lung cancer
risk and routine or intermittent TCE exposure duration. The routine or intermittent category is
broader and includes more subjects with potential TCE exposure. Five other studies with
internal controls do not provide evidence of either an increasing or decreasing pattern between
TCE and lung cancer incidence or mortality (Radican et al., 2008: Boice et al., 2006b: Zhao et
al.. 2005: Blair etal.. 1998: Morgan et al.. 1998).
The population studied by Garabrant et al. (1988). ATSDR (2004a) and Chang et al.
(2005) are all employees (white- and blue-collar) at a manufacturing facility or plant with
potential TCE exposures. Garabrant et al. (1988) observed a 20% deficit in lung cancer
mortality (95% CI: 0.68, 0.95) in their study of all employees working for >4 years at an aircraft
manufacturing company. Blair et al. (1998), a study of Coast Guard marine inspectors with
potential for TCE exposure but lacking assessment to individual subjects, observed a 48% deficit
in lung cancer mortality (95% CI: 0.31, 0.82). Confidence intervals (95% CI) in Costa et al.
(1989). Chang et al. (2005). and ATSDR (2004a) included a risk of 1.0. TCE exposure was not
known for individual subjects in these studies. A wide potential for TCE exposure is likely
ranging from subjects with little to no TCE exposure potential to those with some TCE exposure
potential. Exposure misclassification bias, typically considered as a negative bias, is likely
greater in these studies compared to studies adopting more sophisticated exposure assessment
approaches, which are able to assign quantitative exposure metrics to individual study subjects.
All three studies were of lower likelihood for TCE exposure, in addition to limited statistical
power and other design limitations, and these aspects, in addition to potential exposure
misclassification bias, were alternative explanations of observed findings.
One population case-control study examined the relationship between lung cancer and
TCE exposure (Siemiatvcki. 1991) with risk ratios of 0.9 (95% CI: 0.6, 1.5) for any TCE
exposure and 0.6 (95% CI: 0.3, 1.2) for substantial TCE exposure after adjustment for cigarette
smoking. TCE exposure prevalence in cases in this study was 2.5% for any exposure. Only 1%
had —subtotal" (author's term) exposure, limiting the sensitivity of this study. RRs >2.0 could
only be detected with sufficient (80%) statistical power. The finding of no association of lung
cancer with TCE exposure, therefore, is not surprising. One nested case-control study of rubber
4-437
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workers observed a smoking unadjusted risk of 0.64 (95% CI: not presented in paper) in those
who had >1 year cumulative exposure to TCE (Wilcosky et al., 1984).
Three geographic-based studies reported lung cancer incidence or mortality risks for
drinking water contamination with TCE (ATSDR, 2006a; Morgan and Cassady, 2002; Isacson et
al.. 1985). Morgan and Cassidy (2002) observed a RR of 0.71 (99% CI: 0.61, 0.81) for lung
cancer among residents of Redlands (San Bernardino County), California, whose drinking water
was contaminated with TCE and perchlorate. However, ATSDR (2006a) reported a 28%
increase (95% CI: 0.99, 1.62) in lung cancer incidence among residents living in a area in
Endicott, New York, whose drinking water was contaminated with TCE and other solvents. No
information on smoking patterns is available for individual lung cancer cases as identified by the
New York State Department of Health (NYS DOH) for other cancer cases in this study (ATSDR.
2008b). Isacson et al. (1985) presented lung cancer age-adjusted incidence rates for Iowa
residents by TCE level in drinking water supplies and did not observe an exposure-response
gradient. Exposure information is inadequate in all three of these studies, with monitoring data,
if available, based on few samples and for current periods only, and no information on water
distribution, consumption patterns, or temporal changes. Thus, TCE exposure potential to
individual subjects was not known with any precision, introducing misclassification bias, and
greatly limiting their ability to inform evaluation of TCE and lung cancer.
Laryngeal cancer risks are presented in a limited number of cohort studies involving TCE
exposure. No case-control or geographic-based studies of TCE exposure were found in the
published literature. All but one of the cohort studies providing information on laryngeal cancer
observed less that five incident cases or deaths. Accordingly, these studies are limited for
examining the relationship between TCE exposure and laryngeal cancer. Risk ratios for
laryngeal cancer are found in Table 4-82.
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Table 4-82. Selected results from epidemiologic studies of TCE exposure and
laryngeal cancer
Exposure group
RR
(95% CI)
Number of
observable
events
Reference
Cohort studies — incidence
Aerospace workers with TCE exposure
Not reported
Danish blue-collar worker with TCE exposure
Any exposure, males
Any exposure, females
Employment duration
5yrs
1.2 (0.87, 1.52)
1.7(0.33,4.82)
Not reported
53
3
Biologically -monitored Danish workers
Any TCE exposure, males
Any TCE exposure, females
Cumulative exposure (Ikeda)
<17 ppm-yr
>17 ppm-yr
Mean concentration (Ikeda)
<4 ppm
4+ppm
Employment duration
<6.25 yr
>6.25 yr
1.1(0.1,3.9)
Not reported
Not reported
Not reported
2
0
(0.1 exp)
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
Any exposure
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Not reported
Not reported
Not reported
Zhao et al. (2005)
Raaschou-Nielsen et al. (2003)
Hansen et al. (2001)
Blair et al. (1998)
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Table 4-82. Selected results from epidemiologic studies of TCE exposure
and laryngeal cancer (continued)
Exposure group
Biologically -monitored Finnish workers
Mean air-TCE (Ikeda extrapolation from U-
TCA)
<6ppm
6+ppm
RR
(95% CI)
Not reported
Not reported
Number of
observable
events
Biologically -monitored Swedish workers
Any TCE exposure, males
Any TCE exposure, females
1.39(0.17,5.00)
Not reported
2
Reference
Anttila et al. (1995)
Axelson et al. (1994)
Cohort and PMR-mortality
Computer manufacturing workers (IBM), New York
Not reported
Aerospace workers (Rocketdyne)
Any TCE (utility or engine flush workers)
Engine flush — duration of exposure
Referent
0 yr (utility workers with TCE exposure)
<4yrs
>4yrs
Any exposure to TCE
View-Master employees
Males
Females
1.45(0.18,5.25)
Not reported
Not reported
Not reported
2
All employees at electronic factory (Taiwan)
Males
Females
0
0
(0.90 exp)
0
(0.23 exp)
United States uranium-processing workers (Fernald)
Any TCE exposure
Light TCE exposure, >2-yr duration
Moderate TCE exposure, >2-yr duration
Not reported
Not reported
Not reported
Aerospace workers (Lockheed)
Routine exposure
Routine-intermittent exposure
1.10(0.30,2.82)
Not reported
4
Clapp and Hoffman (2008)
Boice et al. (2006b)
Zhao et al. (2005)
ATSDR (2004a)
Chang et al. (2003)
Ritz (1999a)
Boice et al. (1999)
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Table 4-82. Selected results from epidemiologic studies of TCE exposure
and laryngeal cancer (continued)
Exposure group
RR
(95% CI)
Number of
observable
events
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)
High intensity (>50 ppm)
Peak
No/low
Medium/high
Cumulative
Referent
Low
High
Not reported
Not reported
Not reported
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Cardboard manufacturing workers in Arnsburg,
Germany
Deaths reported to GE pension fund (Pittsfield,
Massachusetts)
Not reported
Not reported
Not reported
Not reported
Not examined
U.S. Coast Guard employees
Marine inspectors
Noninspectors
0.57(0.01,3.17)
0.58 (0.01, 3.20)
1
1
Aircraft manufacturing employees (Italy)
All employees
0.27 (0.03, 0.98)
2
Aircraft manufacturing plant employees (San Diego, California)
All subjects
0
(7.41 exp)
Reference
Morgan et al. (1998)
Blair et al. (1998)
Henschler et al. (1995)
Greenland et al. (1994)
Blair et al. (1998)
Costa et al. (1989)
Garabrant et al. (1988)
In summary, studies in humans examining lung and laryngeal cancer and TCE exposure
are inconclusive and do not support either a positive or a negative association between TCE
exposure and lung cancer or laryngeal cancer. Raaschou-Nielsen et al. (2003), with the largest
numbers of lung cancer cases of all studies, was the only one to observe a statistically
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significantly elevated lung cancer risk with TCE exposure. Raaschou-Nielsen et al. (2003) also
noted several factors that may have confounded or biased their results in either a positive or
negative direction. This study and other cohort studies, as with almost any occupational study,
were not able to control confounding by exposure to chemicals other than TCE (although no
such chemical was apparent in the reports). Information available for factors related to SES
status (e.g., diet, smoking, alcohol consumption) was also not available. Such information may
positively confound smoking-related cancers such as lung cancer, particularly in those studies,
which adopted national rates to derive expected numbers of site-specific cancer, if greater
smoking rates were over-represented in blue-collar workers or residents of lower SES status.
The finding of a larger risk among subjects with shortest exposure also argues against a causal
interpretation for the observed association for all subjects (NRC, 2006).
Four studies reported a statistically significant deficit in lung cancer incidence (Morgan
and Cassadv. 2002: Boice et al.. 1999: Blair et al.. 1998: Garabrant et al.. 1988). Absence of
smoking information in these studies would introduce a negative bias if the studied population
smoked less than the referent population and may partially explain the lung cancer decrements
observed in these studies. Morgan and Cassidy (2002) noted the relatively high education, high
income levels, and high access to health care of subjects in this study compared to the averages
for the county as a whole, likely leading to a lower smoking rate compared to their referent
population. Garabrant et al. (1988) similarly attributed their observations to negative selection
bias introduced when comparison is made to national mortality rates, also known as a —hekhy
worker effect." The statistically significant decreasing trend in Boice et al. (1999) with exposure
duration to intermittent or routine exposure may reflect a protective effect between TCE and lung
cancer. The use of internal controls in this analysis reduces bias associated with use of an
external population who may have different smoking patterns than an employed population.
However, the exposure assessment approach in this study is limited due to inclusion of subjects
identified with intermittent TCE exposure (i.e., workers who would be exposed only during
particular shop runs or when assisting other workers during busy periods) (Boice etal., 1999).
The Boice et al. (1999) analysis is based on twice as many lung cancer deaths (i.e., 173 lung
cancer deaths) among subjects with routine or intermittent TCE exposure compared to only
routinely exposed subjects (78 deaths). Subjects identified as intermittently exposed are
considered as having a lower exposure potential than routinely exposed subject and their
inclusion in exposure-response analyses may introduce exposure misclassification bias. Such
bias is a possible explanation for the decreasing trend observation, particularly if workers with
lower potential for TCE exposure have longer exposure (employment) durations.
Thus, a qualitative assessment suggests the epidemiological literature on respiratory
cancer and TCE, although limited and of sufficient power to detect only large RRs, does not
provide strong evidence for any association between TCE exposure and lung cancer. These
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studies can only rule out risks of a magnitude of >2.0 for lung cancer and RRs >3.0 or 4.0 for
laryngeal cancer for exposures to studied populations.
4.7.2. Laboratory Animal Studies
4.7.2.1. Respiratory Tract Animal Toxicity
Limited studies are available to determine the effects of TCE exposure on the respiratory
tract (summarized in Table 4-83). Many of these studies in mice have examined acute effects
following i.p. administration at relatively high TCE doses. However, effects on the bronchial
epithelium have been noted in mice and rats with TCE administered via gavage, with doses
>1,000 mg/kg-day reported to cause rales and dyspnea (Narotsky et al., 1995) and pulmonary
vasculitis (NTP, 1990) in rats. Mice appear to be more sensitive than rats to histopathological
changes in the lung via inhalation; pulmonary effects are also seen in rats with gavage exposure.
It is difficult to compare i.p. to oral and inhalation routes of exposure given the risk of peritonitis
and paralytic ileus. Any inflammatory response from this route of administration can also affect
the pulmonary targets of TCE exposure such as the Clara cells.
This section reviews the existing literature on TCE, and the role of the various TCE
metabolites in TCE-induced lung effects. The most prominent toxic effect reported is damage to
Clara cells in mouse lung. The nonciliated, columnar Clara cells comprise the majority of the
bronchiolar and terminal bronchiolar epithelium in mice, and alveolar Type I and Type II cells
constitute the alveolar epithelium. These cells have been proposed as a progenitor of lung
adenocarcinomas in both humans and mice (Kim et al., 2005). Long-term studies have not
focused on the detection of pulmonary adenoma carcinomas but have shown a consistently
positive response in mice but not rats. However, chronic toxicity data on noncancer effects are
very limited.
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Table 4-83. Animal toxicity studies of TCE
Reference
Green et al.
(1997b)
Forkert and
Forkert (1994)
Villaschi et al.
(1991)
Odum et al. (1992)
Kurasawa (1988)
(translation)
Forkert et al.
(2006)
Animals (sex)
CD-I mice (F)
CD-I mice (M)
BC3F1 mice (M)
CD-I mice (F)
Alpk APfSD rats
(F)
Ethanol-treated
(130) and
nontreated(HO)
Wistar rats (M)
CD-I mice (M);
wild-type (mixed
129/Sv and
C57BL) and
CYP2El-null
mice (M)
Exposure route
Inhalation
i.p. injection
Single inhalation
Inhalation
Inhalation
Inhalation
i.p. injection
Dose/exposure concentration
450 ppm, 6 hrs/d, 5 d with 2 d break
then 5 more d; sacrificed 18 hrs after
1, 5, 6, or 10 exposures
2,000 mg/kg in corn oil (0.01 mL/g
body weight); sacrificed 15, 30, 60,
and 90 d after single exposure
30 min 500, 1,000, 2,000, 3,500, and
7,000 ppm; sacrificed 2 hrs, 24 hrs, 2,
5, or 7 d post exposure
6 hrs/d; separate repeated study in
mice: 450 ppm for 6 hrs/d, 5 d/wk for
2 wks; sacrificed 24 hrs after
exposure; repeat study sacrificed at 2,
5, 6, 8, 9, 12, or 13 d; mice: 20, 100,
200, 450, 1,000, or 2,000 ppm
6 hrs/d; repeat study sacrificed at 2, 5,
6, 8, 9, 12, or 13 d; rats: 500, or
1,000 ppm
500, 1,000, 2,000, 4,000, and
8,000 ppm for 2 hrs; sacrificed 22 hrs
after exposure
500, 750, and 1,000 mg/kg in corn
oil; for inhibition studies mice
pretreated with 100 mg/kg diallyl
sulfone; for immunoblotting, 250,
500, 750, and 1,000 mg/kg; forPNP
hydroxylation, 50, 100, 250, 500, 750,
and 1,000 mg/kg; sacrificed 4 hrs
after exposure
Exposed
5/group
10/group
3/group
4/group
4/group
10/group
4/group
Results
Increased vacuolation and proliferation of Clara
cells caused by accumulation of chloral.
Increased fibrotic lesions, with early signs visible
at 15 d postexposure.
Increased vacuolation and proliferation of
nonciliated bronchial cells. Injury was maximal
at 24 hrs with some repair occurring between
24 and 48 hrs.
Dose-dependent increase in Clara cell
vacuolation in mice after a single exposure,
resolved after 5 d repeated exposures but
recurred following a 2-d break from exposure.
Changes accompanied by decrease in CYP
activity in mice. Exposure to chloral alone
demonstrated similar response as TCE exposure
in mice. No changes were seen in rats.
TCE exposure resulted in highly selective
damage to Clara cells that occurred between
8 and 22 hrs after the highest exposure with
repair by 4 wks post exposure.
TCE bioactivation by CYP2E1 and/or 2F2
correlated with bronchiolar cytotoxicity in mice.
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Table 4-83. Animal toxicity studies of TCE (continued)
Reference
Forkert et al.
(1985)
Forkert and Birch
(1982)
Stewart et al.
(1972);
Le Mesurier et al.
(1980)
Lewis et al. (1984)
Scott et al. (1988)
NTP (1990)
Animals (sex)
CD-I mice (M)
CD-I mice (M)
WistarRats(F)
Mice
CD-I mice (M)
F344 rats (M,F)
B6C3FJ mice
(M,F)
Exposure route
i.p. injection
i.p. injection
Inhalation (whole-
body chamber)
Inhalation (Pyrex
bell jars)
i.p. injection
Gavage
Dose/exposure concentration
2,000, 2,500 or 3,000 mg/kg in
mineral oil; sacrificed 24 hrs
postexposure for dose response; time
course sacrificed 1, 2, 12, and 24 hrs
postexposure
2,000 mg/kg in corn oil; sacrificed 1,
2, 4, 8, 12, and 24 hrs postexposure
30 min, 48.5 g/m3 (9,030 ppm);
sacrificed at 5 and 15 d postexposure
10,000 ppm, 1-4 hrs daily for
5 consecutive d; sacrificed 24 hrs
after last exposure
single injection of 2,500-
3,000 mg/kg, sacrificed 24 hrs
postexposure
Male rats: 0, 125, 250, 500, 1,000,
and 2,000 mg/kg body weight (corn
oil); female rats: 0, 62.5, 125, 250,
500 or 1,000 mg/kg body weight
(corn oil); Mice: 0, 375, 750, 1,500,
3,000, and 6,000 mg/kg body weight
(corn oil); dosed 5d/w for 13 wks
Exposed
10/group
10/group
5/group
~28/group
4/group
10/group
Results
Clara cell injury was increased following
exposure at all doses tested; time course
demonstrated a rapid and marked reduction in
pulmonary microsomal CYP content and aryl
hydrocarbon hydroxylase activity. Alveolar
Type II cells were also affected.
Necrotic changes seen in Clara cells as soon as
1 hr postexposure; increased vacuolation was
seen by 4 hrs postexposure; covalent binding of
TCE to lung macromolecules peaked at 4 hrs and
reached a plateau at 12 and 24 hrs post exposure.
Decreased recovery of pulmonary surfactant
(dose-dependent).
Increased vacuolation and reduced activity of
pulmonary mixed function oxidases.
Clara cells were damaged and exfoliated from
the epithelium of the lung.
Increased pulmonary vasculitis in the high-dose
groups of male and female rats (6/10 group as
compared to 1/10 in controls). No pulmonary
effects described in mice at this time point.
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Table 4-83. Animal toxicity studies of TCE (continued)
Reference
Prendergast et al.
(1967)
Narotsky et al.
(1995)
Animals (sex)
Sprague-Dawley
or Long-Evans
rats; Hartley
Guinea pigs; New
Zealand albino
rabbits; beagle
dogs; squirrel
monkeys (sex not
given for any
species)
F344 rats (F)
Exposure route
Inhalation
Gavage
Dose/exposure concentration
730 ppm for 8 hrs/d, 5 d/w, 6 wks or
35 ppm for 90 d constant
0, 1,125, or 1,500 mg/kg-d
Exposed
Rats (15);
guinea pigs
(15); rabbit
(3); dog (2);
monkey (3)
21, 16, or
17 per group
Results
No histopathological changes observed, although
rats were described to show a nasal discharge in
the 6-wk study. No quantification was given.
Rales and dyspnea were observed in the TCE
high-dose group; two females with dyspnea
subsequently died.
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4.7.2.1.1. Acute and short-term effects: inhalation
Relatively high-dose single and multiple inhalation exposures to TCE result in dilation of
endoplasmic reticulum and vacuolation of nonciliated (Clara) cells throughout the bronchial tree
in mice. A single study in rats reported similar findings. In mice, single exposure experiments
show vacuolation at all dose levels tested with the extent of damage increasing with dose.
Villaschi et al. (1991) reported similar degrees of vacuolation in B6C3Fi mice (3/group) at
24 hours after the start of exposure across all tested doses (500, 1,000, 2,000, 3,500, and
7,000 ppm, 30 minutes), with the percentage of the nonciliated cells remaining vacuolated at
48 hours increasing with dose. Clara cell vacuolation was reported to be resolved 7 days after
single 30-minute exposures to TCE. Odum et al. (1992) reported that, when observed 24 hours
after the start of 6 hours exposure, the majority of Clara cells in mice were unaffected at the
lowest dose of 20 ppm exposures, while marked vacuolation was observed at 200 ppm (no
quantitative measures of damage given and only three animals per group were examined).
In rats, Odum et al. (1992) reported no morphological changes in the female Alpk APfSD
rat epithelium after 6 hours exposure (500 or 1,000 ppm) when observed 24 hours after the start
of exposure (n = 3/group). However, Kurasawa reported pronounced dose-related morphological
changes in Clara cells at the highest dose (8,000 ppm) for 2 hours in Wistar rats (n = 10 per
group). At 500 and 1,000 ppm, slight dilation of the apical surface was reported, but
morphological measurements (the ratio of the lengths of the apical surface to that of the base line
of apical cytoplasm) were not statistically-significantly different from controls. From 2,000 to
8,000 ppm, a progressively increasing flattening of the apical surface was observed. In addition,
at 2,000 ppm, slight dilation of the smooth endoplasmic reticulum was also observed, with
marked dilation and possible necrosis at 8,000 ppm. Kurasawa (1988) also examined the time-
course of Clara cell changes following a single 8,000-ppm exposure, reporting the greatest
effects at 1 day to 1 week, repair at 2 weeks, and nearly normal morphology at 4 weeks. The
only other respiratory effect that has been reported from one study in rats exposed via inhalation
is a reduction in pulmonary surfactant yield following 30-minute exposures at 9,030 ppm for 5 or
15 days (Stewart et al., 1979). Therefore, single inhalation experiments (Odum et al., 1992;
Villaschi et al., 1991; Kurasawa, 1988) suggest that the Clara cell is the target for TCE exposure
in both rats and mice and that mice are more susceptible to these effects. However, the database
is limited in its ability to discern quantitative differences in susceptibility or the nature of the
dose-response after a single dose of TCE.
Other experiments examined the effects of several days of TCE inhalation exposure in
mice and potential recovery. While single exposures require 1-4 weeks for complete recovery,
after short-term repeated exposure, the bronchial epithelium in mice appears to either adapt to or
become resistant to damage. Odum et al. (1992) and Green et al. (1997b) observed Clara cells in
mice to be morphologically normal at the end of exposures 6 hours/day for 4 or 5 days. As with
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single-dose experiments, the extent of recovery in multidose exposures may be dose-dependent.
Using a very high dose, Lewis et al. (1984) reported vacuolation of bronchial epithelial cells
after 4 hours/day, but not 1 hour/day (10,000 ppm), for 5 days in mice. In addition, Odum et al.
(1992) reported that the damage to Clara cells that resolved after repeated exposures of 5 days, a
sign of adaptation to TCE exposure, returned when exposure was resumed after 2 days.
In rats, only one inhalation study reported in two published articles (Le Mesurier et al.,
1980; Stewart et al., 1979) using repeated exposures examined pulmonary histopathology.
Interestingly, this study reported vacuolation in Type 1 alveolar cells, but not in Clara cells, after
5 days of exposure to approximately 9,030 ppm for 30 minutes/day (only dose tested). In
addition, abnormalities were observed in the endothelium (bulging of thin endothelial segments
into the microcirculatory lumen) and minor morphological changes in Type 2 alveolar cells.
Although exposures were carried out for 5 consecutive days, histopathology was recorded up to
15 days postexposure, giving cell populations time to recover. Because earlier time points were
not examined, it is not possible to discern whether the lack of reported Clara cell damage in rats
following repeated exposure is due to recovery or lack of toxicity in this particular experiment.
Although recovery of individual damaged cells may occur, cell proliferation, presumed
from labeling index data suggestive of increased DNA synthesis, contributes, at least in part, to
the recovery of the bronchial epithelium in mice. Villaschi et al. (1991) observed a dose-
dependent increase in labeling index as compared to controls in the mouse lung at 48 hours after
a single TCE exposure (30 minutes; 500, 1,000, 2,000, 3,500, or 7,000 ppm), which decreased to
baseline values at 7 days postexposure. Morphological analysis of cells was not performed,
although the authors stated that the dividing cells had the appearance of Clara cells.
Interestingly, Green et al. (1997b) reported no increase in BrdU labeling 24 hours after a single
exposure (6 hours, 450 ppm), but did see increased BrdU labeling at the end of multiple
exposures (I/day, 5 days) while Villaschi et al. (1991) reported increased [3H]-thymidine
labeling 2, 5, and 7 days after single 30-minute exposures to 500-7,000 ppm. Therefore, the data
for single exposures at 450-500 ppm may be consistent if increased cell proliferation occurred
only for a short period of time around 48 hours postexposure, and was thereby effectively
washed-out by the longer —averaging tim" in the experiments by Green et al. (1997b). Also,
these contradictory results may be due to differences in methodology. Green et al. (1997b) and
Villaschi et al. (1991) reported very different control labeling indices (6 and 0%, respectively)
while reporting similar absolute labeling indices at 450-500 ppm (6.5 and 5.2%, respectively).
The different control values may be a result of substantially different times over which the label
was incorporated: the mice in Green et al. (1997b) were given BrdU via a surgically-implanted
osmotic pump over 4 days prior to sacrifice, while the mice in Villaschi et al. (1991) were given
a single i.p. dose of [3H]-thymidine 1 hour prior to sacrifice. Stewart et al. (1979) observed no
stimulation of thymidine incorporation after daily exposure to TCE (9,000 ppm) for up to
4-448
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15 days. This study did, however, report a nonstatistically significant reduction in orotate
incorporation, an indicator of RNA synthesis, after 15 days, although the data were not shown.
At the biochemical level, changes in pulmonary metabolism, particularly with respect to
CYP activity, have been reported following TCE exposure via inhalation or i.p. administration in
mice. Odum et al. (1992) reported reduced enzyme activity in Clara cell sonicates of
ethoxycoumarin O-deethylase, aldrin epoxidation, and NADPH cytochrome c reductase after
6 hour exposures to 20-2,000 ppm TCE, although the reduction at 20 ppm was not statistically
significant. No reduction of GST activity as determined by chlorodinitrobenzene as a substrate
was detected. With repeated exposure at 450 ppm, the results were substrate-dependent, with
ethoxycoumarin (9-deethylase activity remaining reduced, while aldrin epoxidation and NADPH
cytochrome c reductase activity showing some eventual recovery by 2 weeks. The results
reported by Odum et al. (1992) for NADPH cytochrome c reductase were consistent with those
of Lewis et al. (1984), who reported similarly reduced NADPH cytochrome c reductase activity
following a much larger dose of 10,000 ppm for 1 and 4 hours/day for 5 days in mice (strain not
specified). TCE exposure has also been associated with a decrease in pulmonary surfactant.
Repeated exposure of female Wistar rats to TCE (9,000 ppm, 30 minutes/day) for 5 or 15 days
resulted in a significant decrease in pulmonary surfactant as compared to unexposed controls (Le
Mesurier et al., 1980).
4.7.2.1.1.1. Acute and short-term effects: i.p. injection and gavage exposure
As stated previously, the i.p. route of administration is not a relevant paradigm for human
exposure. A number of studies used this route of exposure to study the effects of acute TCE
exposure in mice. In general, similar lung targets are seen following inhalation or i.p. treatment
in mice (Forkert et al.. 2006: Forkert and Birch. 1989: Scott etal.. 1988: Forkertetal.. 1985).
Inhalation studies generally reported the Clara cell as the target in mice. No lung histopathology
from i.p. injection studies in rats is available. Forkert et al. (1985) and Forkert and Birch (1989)
reported vacuolation of Clara cells as soon as 1 hour following i.p. administration of a single
dose of 2,000 mg/kg in mice. At 2,500 mg/kg, both Forkert et al. (1985) and Scott et al. (1988)
reported exfoliation of Clara cells and parenchymal changes, with morphological distortion in
alveolar Type II cells and inconsistently observed minor swelling in Type I cells at 24 hours
postexposure. Furthermore, at 3,000 mg/kg, Scott et al. (1988) also reported a significant (85%)
decrease in intracellularly stored surfactant phospholipids at 24 hours postexposure. These data
indicate that both Clara cells and alveolar Type I and II cells are targets of TCE toxicity at these
doses using this route of administration. Recently, Forkert et al. (2006) reported Clara cell
toxicity that showed increased severity with increased dose (pyknotic nuclei, exfoliation) at 500-
1,000 mg/kg i.p. doses as soon as 4 hours postexposure in mice. Even at 500 mg/kg, a few Clara
cells were reported with pyknotic nuclei that were in the process of exfoliation. Damage to
alveolar Type II cells was not observed in this dose range. The study by Scott et al. (1988)
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examined surfactant phospholipids and phospholipase A2 activity in male CD-I mice exposed by
i.p. injection of TCE (2,500 or 3,000 mg/kg, 24 hours). The lower concentration led to damage
to and exfoliation of Clara cells from the epithelial lining into the airway lumen, while only the
higher concentration led to changes in surfactant phospholipids. This study demonstrated an
increase in total phospholipid content in the lamellar body fractions in the mouse lung.
The study by Narotsky et al. (1995) exposed F344 timed-pregnant rats to TCE (0, 1,125,
and 1,500 mg/kg body weight) by gavage and examined both systemic toxicity and
developmental effects at 14 days postexposure. Rales and dyspnea in the dams were observed in
the high-dose group, with two of the animals with dyspnea subsequently dying. The
developmental effects observed in this study are discussed in more detail in Section 4.8.
4.7.2.1.1.2. Subchronic and chronic effects
There are a few reports of the subchronic and chronic noncancer effects of TCE on the
respiratory system from i.p. exposure in mice and from gavage exposure in rats. Forkert and
Forkert (1994) reported pulmonary fibrosis in mice 90 days after i.p. administration of a single
2,000 mg/kg dose of TCE. The effects were in the lung parenchyma, not the bronchioles where
Clara cell damage has been observed after acute exposure. It is possible that fibrotic responses
in the alveolar region occur irrespective of where acute injury occurs. Effects upon Clara cells
can also impact other areas of the lung via cytokine regulation (Elizur et al., 2008).
Alternatively, the alveolar and/or capillary components of the lung may have been affected by
TCE in a manner that was not morphologically apparent in short-term experiments. In addition
effects from a single or a few short-term exposures may take longer to manifest. The latter
hypothesis is supported by the alveolar damage reported by Odum et al. (1992) after chloral
administration by inhalation, and by the adducts reported in alveolar Type II cells by Forkert et
al. (2006) after 500-1,000 mg/kg TCE i.p. administration.
As noted previously, rats have responded to short-term inhalation exposures of TCE with
Clara cell and alveolar Type I and II effects. After repeated inhalation exposures over 6 weeks
(8 hours/day, 5 days/week, 730 ppm) and continuous exposures over 90 days (35 ppm),
Prendergast et al. (1967) noted no histopathologic changes in rats, guinea pigs, rabbits, dogs, or
monkeys after TCE exposure, but did describe qualitatively observing some nasal discharge in
the rats exposed for 6 weeks. The study details in Prendergast et al. (1967) are somewhat
limited. Exposed animals are described as —jtpically" 15 Long-Evans or Sprague-Dawley rats,
15 Hartley guinea pigs, 3 squirrel monkeys, 3 New Zealand albino rabbits, and 2 beagle dogs.
Controls were grouped between studies. In a 13-week NTP study in F344/N rats (n = 10/group)
exposed to TCE (0-2,000 mg/kg-day 5 days/week) by gavage, pulmonary vasculitis was
observed in 6/10 animals of each sex of the highest dose group (2,000 mg/kg-day), in contrast
tol/10 in controls of each sex (NTP. 1990).
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4.7.2.2. Respiratory Tract Cancer
Limited studies have been performed examining lung cancer following TCE exposure
(summarized in Table 4-84). TCE inhalation exposure was reported to cause statistically
significant increase in pulmonary tumors (i.e., pulmonary adenocarcinomas) in some studies in
mice, but not in studies in rats and hamsters. Oral administration of TCE frequently resulted in
elevated lung tumor incidences in mice, but not in any tested species was there a statistically
significant increase. This section will describe the data regarding TCE induction of pulmonary
tumors in rodent models. The next sections will consider the role of metabolism and potential
modes of action for inhalation carcinogenicity, primarily in mice.
4.7.2.2.1. Inhalation
There are three published inhalation studies examining the carcinogenicity of TCE at
exposures from 0 to 600 ppm, two of which reported statistically significantly increased lung
tumor incidence in mice at the higher concentrations (Maltoni et al., 1988; Maltoni et al., 1986;
Fukuda et al., 1983; Henschler et al., 1980). Rats and hamsters did not show an increase in lung
tumors following exposure.
The inhalation studies by Fukuda et al. (1983), which involved female ICR mice and
Sprague-Dawley rats, observed a threefold increase in lung tumors per mouse in those exposed
to the two higher concentrations (150-450 ppm), but reported no increase in lung tumors in the
rats. Maltoni et al. (1988; 1986) reported statistically-significantly increased pulmonary tumors
in male Swiss and female B6C3Fi mice at the highest dose of 600 ppm, but no significant
increases in any of the other species/strains/sexes tested. Henschler et al. (1980) tested NMRI
mice, Wistar rats, and Syrian hamsters of both sexes, and reported no observed increase in
pulmonary tumors any of the species tested (see Appendix E for details on the conduct of these
studies).
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Table 4-84. Animal carcinogenicity studies of TCE
Reference
Fukuda et al.
(1983)
Maltoni et al.
(1988: 1986)
Henschler et al.
(1980)
Henschler et al.
(1984)
Van Duuren et
al. (1979)
NCI (1976)
Animals (sex)
ICR mice (F)
Sprague-Dawley
rats (F)
Sprague-Dawley
rats (M, F)
Swiss mice (M, F)
B6C3FJ mice (M,
F)
Wistar rats (M, F)
Syrian hamsters
(M,F)
NMRI mice
Swiss mice (M, F)
Swiss mice (M, F)
Osborne-Mendel
rats (M, F)
B6C3FJ mice
(M,F)
Exposure route
Inhalation, 7 hrs/d,
5 d/wk, 104 wks,
hold until 107 wks
Inhalation, 7 hrs/d,
5 d/wk, 104 wks,
hold until death
Inhalation, 6 hrs/d,
5 d/wk, 78 wks, hold
until 130 wks (mice
and hamsters) or
156 wks (rats)
Gavage, 5/wk,
72 wks hold 104 wks
Gavage, 1/wk,
89 wks
Gavage, 5/wk,
78 wks, hold until
110 wks (rats) or
90 wks (mice)
Dose/exposure concentration
(stabilizers, if any)
0,50, 150, or450ppm
(epichlorohydrin)
0, 100, 300, or 600 ppm
0, 100, or 500 ppm
(triethanolamine)
2.4 g/kg body weight (M),
1.8 g/kg body weight (F) all
treatments; (control,
triethanolamine, industrial,
epichlorohydrin, 1,2-
epoxybutane, both)
0 or 0.5 mg (unknown)
Rats: TWA: 0, 549, or
1,097 mg/kg
Mice: TWA: M: 0, 1,169, or
2,339 mg/kg; F: 0, 869, or
1,739 mg/kg (epoxybutane,
epichlorohydrin)
Pulmonary tumor incidences
Benign + malignant
Mice: 6/49, 5/50, 13/50, 11/46
Rats: 0/50, 0/50, 1/47, 1/51
Rats: 0/280, 0/260, 0/260, 0/260
Swiss Mice: M: 10/90, 11/90,
23/903, 27/90b;F: 15/90, 15/90,
13/90, 20/90
B6C3FJ Mice: M: 2/90, 2/90,
3/90, 1/90; F: 4/90, 6/90, 7/90,
15/903
Rats: M: 1/29, 1/30, 1/30; F: 0/28;
1/30; 0/30
Hamsters: 0/60, 0/59, 0/60
Mice: M: 1/30, 3/29, 1/30; F: 3/29,
0/30,1/28
M: 18/50, 17/50, 14/50, 21/50,
15/50, 18/50; F: 12/50, 20/50,
21/50, 17/50, 18/50, 18/50
0/30 for all groups
Rats: M: 1/20, 0/50, 0/50; F: 0/20,
1/47, 0/50
Mice: M: 0/20, 5/50, 2/48; F: 1/20,
4/50, 7/47
Malignant only
Mice: 1/49, 3/50, 8/50a, 7/46a
Rats: none
Rats: 0/280, 0/260, 0/260, 0/260
Swiss Mice: M: 0/90, 0/90,
0/90, 1/90;F: 2/90, 0/90, 0/90,
2/90
B6C3FJ Mice M: 0/90, 0/90,
0/90, 0/90; F: 0/90, 1/90, 0/90,
0/90
Rats: M: 1/29, 1/30, 1/30; F:
0/28; 1/30; 0/30
Hamsters: 0/60, 0/59, 0/60
Mice: M: 5/30, 3/29, 1/30;
F: 1/29, 3/30,0/28
M: 8/50, 6/50, 7/50, 5/50, 7/50,
7/50; F: 5/50, 11/50, 8/50, 3/50,
7/50, 7/50
0/30 for all groups
Rats: M: 0/20, 0/50, 0/50; F:
0/20, 1/47, 0/50
Mice: M: 0/20, 0/50, 1/48; F:
0/20, 2/50, 2/47
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Table 4-84. Animal carcinogenicity studies of TCE (continued)
Reference
NTP (1988)
NTP (19901
Maltoni et al.
(1988: 1986)
Animals (sex)
ACI, August,
Marshall,
Osborne-Mendel
rats
F344 rats (M, F)
B6C3FJ mice
(M,F)
Sprague-Dawley
rats (M, F)
Exposure route
Gavage, 1/d, 5 d/wk,
103 wks
Gavage, 1/d, 5 d/wk,
103 wks
Gavage, 1/d, 4-
5 d/wk, 56 wks; hold
until death
Dose/exp cone
(stabilizers, if any)
0, 500, or 1,000 mg/kg
(diisopropylamine)
Mice: 0 or 1,000 mg/kg
Rats: 0, 500, or 1,000 mg/kg
0, 50, or 250 mg/kg
Pulmonary tumor incidences
Benign + malignant
ACI M: 1/50, 4/47, 0/46; F: 0/49,
2/47, 2/42
August M: 1/50, 1/50, 0/49; F:
1/50, 1/50, 0/50
Marshall M: 3/49, 2/50, 2/47; F:
3/49, 3/49, 1/46
Osborne-Mendel M: 2/50, 1/50,
1/50; F: 0/50, 3/50, 2/50
Mice: M: 7/49, 6/50; F: 1/48, 4/49
Rats: M: 4/50, 2/50, 3/49; F: 1/50,
1/49, 4/50
M: 0/30, 0/30, 0/30; F: 0/30, 0/30,
0/30
Malignant only
ACI M: 1/50, 2/47, 0/46; F: 0/49,
1/47, 2/42
August M: 0/50, 1/50, 0/49; F:
1/50, 0/50, 0/50
Marshall M: 3/49, 2/50, 2/47; F:
3/49, 3/49, 1/46
Osborne-Mendel M: 1/50, 1/50,
0/50; F: 0/50, 3/50, 1/50
Mice: M: 3/49, 1/50; F: 1/48,
0/49
Rats: M: 3/50, 2/50, 3/49; F:
0/50, 0/49, 2/50
M: 0/30, 0/30, 0/30; F: 0/30,
0/30, 0/30
aStatistically-significantly different from controls by Fisher's exact test (p < 0.05).
bStatistically-significantly different from controls by Fisher's exact test (p < 0.01).
M = males, F = females.
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4.7.2.2.2. Gavage
None of the six chronic gavage studies (NTP. 1990: Maltoni etal.. 1988: NTP. 1988:
Maltoni etal.. 1986: Henschler et al.. 1984: Van Duuren etal.. 1979: NCI. 1976). which exposed
multiple strains of rats and mice to 0-3,000 mg/kg TCE for at least 56 weeks, reported a
statistically-significant excess in lung tumors, although nonstatistically significant increases were
frequently observed in mice.
The study by Van Duuren et al. (1979) examined TCE along with 14 other halogenated
compounds for carcinogenicity in both sexes of Swiss mice. While no excess tumors were
observed, the dose rate of 0.5 mg once per week is equivalent to an average dose rate of
approximately 2.4 mg/kg-day for a mouse weighing 30 g, which is about 400-fold smaller than
that in the other gavage studies. In the NCI (1976) study, the results for Osborne-Mendel rats
were considered inconclusive due to significant early mortality, but female B6C3Fi mice (though
not males) exhibited a nonstatistically-significant elevation in pulmonary tumor incidence. The
NCI study (1976) used technical-grade TCE, which contained two known carcinogenic
compounds as stabilizers (epichlorohydrin and 1,2-epoxybutane), but a later study by Henschler
et al. (1984) in which mice were given TCE that was either pure, industrial, or stabilized with
one or both of these stabilizers found similar pulmonary tumors regardless of the presence of
stabilizers. In this study, female mice (n = 50) had elevated, but again not statistically
significant, increases in pulmonary tumors. A later gavage study by NTP (1988), which used
TCE stabilized with diisopropylamine, observed no pulmonary tumors, but chemical toxicity and
early mortality rendered this study inadequate for determining carcinogenicity. The final NTP
study (1990) in male and female F344 rats and B6C3Fi mice, using epichlorohydrin-free TCE,
again showed early mortality in male rats. Similar to the other gavage studies, a nonstatistically
significant elevation in (malignant) pulmonary tumors was observed in mice, in this case in both
sexes. These animal studies show that while there is a limited increase in lung tumors following
gavage exposure to TCE in mice, the only statistically significant increase in lung tumors occurs
following inhalation exposure in mice.
4.7.3. Role of Metabolism in Pulmonary Toxicity
TCE oxidative metabolism has been demonstrated to play a main role in TCE pulmonary
toxicity in mice. However, data are not available on the role of specific oxidative metabolites in
the lung. The Clara cell is thought to be the cell type responsible for much of the CYP
metabolism in the lung. Therefore, damage to this cell type would be expected to also affect
metabolism. More direct measures of CYP and isozyme-specific depression following TCE
exposure have been reported following i.p. administration in mice. Forkert et al. (1985) reported
significant reduction in microsomal aryl hydrocarbon hydroxylase activity as well as CYP
content between 1 and 24 hours after exposure (2,000-3,000 mg/kg i.p. TCE). Maximal
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depression occurred between 2 and 12 hours, with aryl hydrocarbon hydroxylase activity (a
function of CYP) <50% of controls and CYP content <20% of controls. While there was a trend
towards recovery from 12 to 24 hours, depression was still significant at 24 hours. Forkert et al.
(2005) reported decreases in immunoreactive CYP2E1, CYP2F2, and CYP2B1 in the 4 hours
after TCE treatment with 750 mg/kg i.p. injection in mice. The amount and time of maximal
reduction was isozyme dependent (CYP2E1: 30% of controls at 2 hours; CYP2F2: abolished at
30 minutes; CYP2B1: 43% of controls at 4 hours). Catalytic markers for CYP2E1, CYP2F2, and
CYP2B enzymes showed rapid onset (<15 minutes after TCE administration) of decreased
activity, and continued depression through 4 hours. Decrease in CYP2E1 and CYP2F2 activity
(measured by PNP hydroxylase activity) was greater than that of CYP2B (measured by
pentoxyresorufin O-dealkylase activity). Forkert et al. (2006) reported similar results in which
4 hours after treatment, immunodetectable CYP2E1 protein was virtually abolished at doses of
250-1,000 mg/kg and immunodetectable CYP2F2 protein, while still detectable, was reduced.
PNP hydroxylase activity was also reduced 4 hours after treatment to 37% of controls at the
lowest dose tested of 50 mg/kg, with further decreases to around 8% of control levels at doses of
500 mg/kg and higher. These results correlate with previously described increases in Clara cell
cytotoxicity, as well as dichloroacetyl lysine (DAL) protein adduct formation. DAL adducts
were observed in the bronchiolar epithelium of CD-I mice and most prominent in the cellular
apices of Clara cells (Forkert et al., 2006). This study also examined the effect of TCE in vitro
exposure on the formation of CH in lung microsomes from male CD-I mice and CYP2E1 knock-
out mice. The rates of CH formation were the same for lysosomes from both CD-I and CYP2E1
knockout mice from 0.25 mM to 0.75 mM, but the CH formation peaked earlier for in the wild-
type lysosomes (0.75 mM) as compared to CYP2El-null lysosomes (1 mM).
The strongest evidence for the necessary role of TCE oxidation is that pretreatment of
mice with diallyl sulfone (DASO2), an inhibitor of CYP2E1 and CYP2F2, protected against
TCE-induced pulmonary toxicity. In particular, following an i.p. TCE dose of 750 mg/kg, Clara
cells and the bronchiolar epithelium in mice pretreated with the CYP2E1/CYP2F2 inhibitor
appeared normal. In naive mice given the same dose, the epithelium was attenuated due to
exfoliation and there was clear morphological distortion of Clara cells (Forkert et al., 2005). In
addition, the greater susceptibility of mouse lungs relative to rat lungs is consistent with their
larger capacity to oxidize TCE, as measured in vitro in lung microsomal preparations (Green et
al., 1997b). Analysis by immunolocalization also found considerably higher levels of CYP2E1
in the mouse lung, heavily localized in Clara cells, as compared to rat lungs, with no detectable
CYP2E1 in human lung samples (Green etal., 1997b). In addition, both Green et al. (1997b) and
Forkert et al. (2006) report substantially lower metabolism of TCE in human lung microsomal
preparations than either rats or mice. It is clear that CYP2E1 is not the only CYP enzyme
involved in pulmonary metabolism, as lung microsomes from CYP2El-null mice showed greater
or similar rates of CH formation compared to those from wild-type mice. Recent studies have
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suggested a role for CYP2F2 in TCE oxidative metabolism, although more work is needed to
make definitive conclusions. In addition, there may be substantial variability in human lung
oxidative metabolism, as Forkert et al. (2006) reported that in microsomal samples from eight
individuals, five exhibited no detectable TCE oxidation (<0.05 pmol/mg protein/20 minutes),
while others exhibited levels well above the limit of detection (0.4-0.6 pmol/mg protein/minute).
In terms of direct pulmonary effects of TCE metabolites, Odum et al. (1992) reported that
mice exposed to 100 ppm via inhalation of chloral for 6 hours resulted in bronchiolar lesions
similar to those seen with TCE, although with a severity equivalent to 1,000 ppm TCE
exposures. In addition, some alveolar necrosis, alveolar oedema, and desquamation of the
epithelium were evident. In the same study, TCOH (100 and 500 ppm) also produced Clara cell
damage, but with lower incidence than TCE, and without alveolar lesions, while TCA treatment
produced no observable pulmonary effects. Therefore, it has been proposed that chloral is the
active metabolite responsible for TCE pulmonary toxicity, and the localization of damage to
Clara cells (rather than to other cell types, as seen with direct exposure to chloral) is due to the
localization of oxidative metabolism in that cell type (Green, 2000; Green etal., 1997b: Odum et
al., 1992). However, the recent identification by Forkert et al. (2006) of DAL adducts, also
localized with Clara cells, suggests that TCE oxidation to DCAC, which is not believed to be
derived from chloral, may also contribute to adverse health effects.
Due to the histological similarities between TCE- and chloral-induced pulmonary
toxicity, consistent with chloral being the active moiety, it has been proposed that the limited or
absent capacity for reduction of chloral (rapidly converted to CH in the presence of water) to
TCOH and glucuronidation of TCOH to TCOG in mouse lungs leads to —acumulation" of
chloral in Clara cells. However, the lack of TCOH glucuronidation capacity of Clara cells
reported by Odum et al. (1992), while possibly an important determinant of TCOH
concentrations, should have no bearing on CH concentrations, which depend on the production
and clearance of CH only. While isolated mouse Clara cells form smaller amounts of TCOH
relative to CH (Odum etal., 1992), the cell-type distribution of the enzymes metabolizing CH is
not clear. Indeed, cytosolic fractions of mouse, rat, and human whole lungs show significant
activity for CH conversion to TCOH (Green etal., 1997b). In particular, in mouse lung
subcellular fractions, 1 micromole of TCE in a 1.3 mL reactivial was converted to CH at a rate of
1 nmol/minute/mg microsomal protein, while 10 nmol CH in a 1.3 mL reactivial was converted
to TCOH at a rate of 0.24 nmol/minute/mg cytosolic protein (Green etal., 1997b). How this
fourfold difference in activity would translate in vivo is uncertain given the 100-fold difference
in substrate concentrations, lack of information as to the concentration-dependence of activity,
and uncertain differences between cytosolic and microsomal protein content in the lung. It is
unclear whether local pulmonary metabolism of chloral is the primary clearance process in vivo,
as in the presence of water, chloral rapidly converts to CH, which is soluble in water and hence
can rapidly diffuse to surrounding tissue and to the blood, which also has the capacity to
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metabolize CH (Lipscomb etal., 1996). Nonetheless, experiments with isolated perfused lungs
of rats and guinea pigs found rapid appearance of TCOH in blood following TCE inhalation
exposure, with no detectable CH or TCOG (Dalbey and Bingham, 1978). Therefore, it appears
likely that chloral in the lung either is rapidly metabolized to TCOH, which then diffuses to
blood, or diffuses to blood as CH and is rapidly metabolized to TCOH by erythrocytes
(Lipscomb et al., 1996).
This hypothesis is further supported by in vivo data. No in vivo data in rats on CH after
TCE administration were located, and Fisher et al. (1998) reported CH in blood of volunteers
exposed to TCE via inhalation were below detection limits. In mice, however, after both
inhalation and gavage exposure to TCE, CH has been reported in whole-lung tissue at
concentrations similar to or somewhat greater than that in blood (Greenberg et al., 1999; Abbas
and Fisher, 1997). A peak concentration (1.3 |ig/g) of pulmonary CH was reported after
inhalation exposure to 600 ppm—at or above exposures where Clara cell toxicity was reported in
acute studies (Green et al., 1997b: Odum et al., 1992). However, this was fivefold less than the
reported pulmonary CH concentration (6.65 |ig/g) after gavage exposures of 1,200 mg/kg.
Specifically, 600- or 450-ppm exposures reported in the Maltoni et al. (1988; 1986) and
Fukuda et al. (1983) studies result in a greater incidence in lung tumors than the 1,000-
1,200 mg/kg-day exposures in the NTP (1990) and NCI (1976) bioassays. However, the peak
CH levels measured in whole-lung tissues after inhalation exposure to TCE at 600 ppm were
reported to be about fivefold lower than that at 1,200 mg/kg by gavage, therefore, showing the
opposite pattern (Greenberg et al., 1999; Abbas and Fisher, 1997). No studies of Clara cell
toxicity after gavage exposures were located, but several studies in mice administered TCE via
i.p. injection did show Clara cell toxicity at around a dose of 750 mg/kg (Forkert et al., 2006) or
above (e.g., Forkert and Forkert, 1994; Forkert and Birch, 1989). However, as noted previously,
i.p. exposures are subject to an inflammatory response, confounding direct comparisons of dose
via other routes of administration.
Although whole-lung CH concentrations may not precisely reflect the concentrations
within specific cell types, as discussed above, the water solubility of CH suggests rapid
equilibrium between cell types and between tissues and blood. Both Abbas and Fisher (1997)
and Greenberg et al. (1999) were able to fit CH blood and lung levels using a PBPK model that
did not include pulmonary metabolism, suggesting that lung CH levels may be derived largely by
systemic delivery (i.e., from CH formed in the liver). However, a more detailed PBPK model-
based analysis of this hypothesis has not been performed, as CH is not included in the PBPK
model developed by Hack et al. (2006) that was updated in Section 3.5.
Two studies have reported formation of reactive metabolites in pulmonary tissues as
assessed by macromolecular binding after TCE i.p. administration. Forkert and Birch (1989)
reported temporal correlations between the severity of Clara cell necrosis with increased levels
of covalent binding macromolecules in the lung of TCE or metabolites with a single 2,000 mg/kg
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dose of [14C]-TCE. The amount of bound TCE or metabolites/g of lung tissue, DNA, or protein
peaked at 4 hours and decreased progressively at 8, 12, and 24 hours. The fraction of
radioactivity in lung tissue macromolecules that was covalently bound reached a plateau of about
20% from 4 to 24 hours, suggesting that clearance of total and covalently bound TCE or
metabolites was similar. The amount of covalent binding in the liver was 3-10-fold higher than
in the lung, although hepatic cytotoxicity was not apparent. This tissue difference could either
be due to greater localization of metabolism in the lung, so that concentrations of reactive
metabolites in individual Clara cells are greater than both the lung as a whole and hepatocytes, or
because of greater sensitivity of Clara cells as compared to hepatocytes to reactive metabolites.
More recently, Forkert et al. (2006) examined DAL adducts resulting from metabolism of TCE
to DCAC as an in vivo marker of production of reactive metabolites. Following i.p.
administration of 500-1,000 mg/kg TCE in CD-I mice, the authors found localization of DAL
adducts believed to be from oxidative metabolism within Clara cell apices, with dose-dependent
increase in labeling with a polyclonal anti-DAL antibody that correlated with increased Clara
cell damage. Dose-dependent DAL adducts were also found in alveolar Type II cells, although
no morphologic changes in those cells were observed. Both Clara cell damage (as discussed
above) and DAL labeling were abolished in mice pretreated with DASO2, an inhibitor of
CYP2E1 and CYP2F2. However, Clara cell damage in treated CYP2El-null mice was more
severe than in CD-I mice. Although DAL labeling was less pronounced in CYP2El-null mice
as compared to CD-I mice, this was due in part to the greater histopathologic damage leading to
attenuation of the epithelium and loss of Clara cells in the null mice. In addition, protein
immunoblotting with anti-DAL, anti-CYP2El, and anti-CYP2F2 antibodies suggested that a
reactive TCE metabolite including DCAC was formed that is capable of binding to CYP2E1 and
CYP2F2 and changing their protein structures. Follow-up studies are needed in the lung and
other target tissues to determine the potential role of the DAL adducts in TCE-induced toxicity.
Finally, although Green (2000) and others have attributed species differences in
pulmonary toxicity to differences in the capacity for oxidative metabolism in the lung, it should
be noted that the concentration of the active metabolite is determined by both its production and
clearance (Clewell et al., 2000). Therefore, while the maximal pulmonary capacity to produce
oxidative metabolites is clearly greater in the mouse than in rats or humans, there is little
quantitative information as to species differences in clearance, whether by local chemical
transformation/metabolism or by diffusion to blood and subsequent systemic clearance. In
addition, existing in vitro data on pulmonary metabolism are at millimolar TCE concentrations
where metabolism is likely to be approaching saturation, so the relative species differences at
lower doses has not been characterized. Studies with recombinant CYP enzymes examined
species differences in the catalytic efficiencies of CYP2E1, CYP2F, and CYP2B1, but the
relative contributions of each isoform to pulmonary oxidation of TCE in vivo remains unknown
(Forkert et al., 2005). Furthermore, systemic delivery of oxidative metabolites to the lung may
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contribute, as evidenced by respiratory toxicity reported with i.p. administration. Therefore,
while the differences between mice and rats in metabolic capacity are correlated with their
pulmonary sensitivity, it is not clear that differences in capacity alone are accurate quantitative
predictors of toxic potency. Thus, while it is likely that the human lung is exposed to lower
concentrations of oxidative metabolites, quantitative estimates for differential sensitivity made
with currently available data and dosimetry models are highly uncertain.
In summary, it appears likely that pulmonary toxicity is dependent on in situ oxidative
metabolism; however, the active agent has not been confidently identified. The similarities in
histopathologic changes in Clara cells between TCE and chloral inhalation exposure, combined
with the wider range of cell types affected by direct chloral administration relative to TCE, led
some to hypothesize that chloral is the toxic moiety in both cases, but with that generated in situ
from TCE in Clara cells —acumulating" in those cells (Green, 2000). However, chemical and
toxicokinetic data suggest that such —acumulation" is unlikely for several reasons. These
include the rapid conversion of chloral to CH in the presence of water, the water solubility of CH
leading to rapid diffusion to other cell types and blood, the likely rapid metabolism of CH to
TCOH either in pulmonary tissue or in blood erythrocytes, and in vivo data showing lack of
correlation across routes of exposure between whole-lung CH concentrations and pulmonary
carcinogenicity and toxicity. However, additional possibilities for the active moiety exist, such
as DC AC, which is derived through a TCE oxidation pathway independent of chloral and
appears to result in adducts with lysine localized in Clara cells.
4.7.4. Mode of Action for Pulmonary Carcinogenicity
A number of effects have been hypothesized to be key events in the pulmonary
carcinogenicity of TCE, including cytotoxicity leading to increased cell proliferation, formation
of DAL protein adducts, and mutagenicity. As stated previously, the target cell for pulmonary
adenocarcinoma formation has not been established. Much of the hazard and mode-of-action
information has focused on Clara cell effects from TCE, which is a target in both susceptible and
nonsusceptible rodent species for lung tumors. However, the role of Clara cell susceptibility to
TCE-induced lung toxicity or to other potential targets such as lung stem cells that are activated
to repopulate both Clara and Type II alveolar cells after injury, has not been determined for
pulmonary carcinogenicity. While all of the events described above may be plausibly involved
in the mode of action for TCE pulmonary carcinogenicity, none have been directly shown to be
necessary for carcinogenesis.
4.7.4.1. Mutagenicity via Oxidative Metabolism
The hypothesis is that TCE acts by a mutagenic mode of action in TCE-induced lung
tumors. According to this hypothesis, the key events leading to TCE-induced lung tumor
formation constitute the following: the oxidative metabolism of TCE producing chloral/CH
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delivered to pulmonary tissues, causes direct alterations to DNA (e.g., mutation, DNA damage,
and/or micronuclei induction). Mutagenicity is a well-established cause of carcinogenicity.
4.7.4.1.1. Experimental support for the hypothesized mode of action
Pulmonary toxicity has been proposed to be dependent on in situ oxidative metabolism;
however, the active agent has not been confidently identified. The similarities in histopathologic
changes in Clara cells between TCE and chloral inhalation exposure, combined with the wider
range of cell types affected by direct chloral administration relative to TCE, led some to
hypothesize that chloral is the toxic moiety. Chloral that is formed from the metabolism of TCE
is quickly converted to CH upon hydration under physiological conditions. As discussed in
Section 4.2.4, CH clearly induces aneuploidy in multiple test systems, including bacterial and
fungal assays in vitro (Crebelli et al., 1991; Kappas, 1989; Kafer, 1986), mammalian cells in
vitro (Sbrana et al., 1993; Vagnarelli et al., 1990), and mammalian germ-line cells in vivo (Miller
and Adler, 1992; Russo et al., 1984). Conflicting results were observed in in vitro and in vivo
mammalian studies of micronuclei formation (Beland, 1999; Nesslany andMarzin, 1999; Giller
et al., 1995; Russo and Levis, 1992b, a; Degrassi and Tanzarella, 1988) with positive results in
germ-line cells (Nutley et al., 1996; Allen et al., 1994). In addition, it is mutagenic in the Ames
bacterial mutation assay for some strains (Beland, 1999; Giller etal., 1995; Ni et al., 1994;
Haworth et al., 1983). Structurally related chlorinated aldehydes 2-chloroacetyaldehyde and 2,2-
dichloroacetaldehyde are both alkylating agents, are both positive in a genotoxic assay (Bignami
etal., 1980), and both interact covalently with cellular macromolecules (Guengerich et al.,
1979).
As discussed in the section describing the experimental support for the mutagenic mode
of action for liver carcinogenesis (see Section 4.5.7.1), it has been argued that CH mutagenicity
is unlikely to be the cause of TCE carcinogenicity because the concentrations required to elicit
these responses are several orders of magnitude higher that achieved in vivo (Moore and
Harrington-Brock, 2000). Similar to the case of the liver, it is not clear how much of a
correspondence is to be expected from concentrations in genotoxicity assays in vitro and
concentrations in vivo, as reported in vivo CH concentrations are in whole-lung homogenate,
while in vitro concentrations are in culture media. None of the available in vivo genotoxicity
assays used the inhalation route that elicited the greatest lung tumor response under chronic
exposure conditions, so direct in vivo comparisons are not possible. Finally, as discussed in
Section 4.5.7.1, the use of i.p. administration in many other in vivo genotoxicity assays
complicates the comparison with carcinogenicity data.
As discussed above (see Section 4.7.3), chemical and toxicokinetic data are not
supportive of CH being the active agent of TCE-induced pulmonary toxicity, and directly
contradict the hypothesis of chloral -accumulation." Nonetheless, CH has been measured in the
mouse lung following inhalation and gavage exposures to TCE (Greenberg et al., 1999; Abbas
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and Fisher, 1997), possibly the result of both in situ production and systemic delivery.
Therefore, in principle, CH could cause direct alterations in DNA in pulmonary tissue.
However, as discussed above, the relative amounts of CH measured in whole-lung tissue from
inhalation and oral exposures do not appear to correlate with sensitivity to TCE lung tumor
induction across exposure routes. While these data cannot rule out a role for mutagenicity
mediated by CH due to various uncertainties, such as whether whole-lung CH concentrations
accurately reflect cell-type specific concentrations and possible confounding due to strain
differences between inhalation and oral chronic bioassays, they do not provide support for this
mode of action.
Additional possibilities for the active moiety exist, such as DCAC, which is derived
through a TCE oxidation pathway independent of chloral and which appears to result in adducts
with lysine localized in Clara cells (Forkert et al., 2006). DCA, which has some genotoxic
activity, is, also, presumed to be formed through this pathway (see Section 3.3). Currently,
however, there are insufficient data to support a role for these oxidative metabolites in a
mutagenic mode of action.
4.7.4.2. Cytotoxicity Leading to Increased Cell Proliferation
The hypothesis is that TCE acts by a cytotoxicity mode of action in TCE-induced
pulmonary carcinogenesis. According to this hypothesis, the key events leading to TCE-induced
lung tumor formation constitute the following: TCE oxidative metabolism in situ leads to
currently unknown reactive metabolites that cause cytotoxicity, leading to compensatory cellular
proliferation and subsequently increased mutations and clonal expansion of initiated cells.
4.7.4.2.1. Experimental support for the hypothesized mode of action
Evidence for the hypothesized mode of action consists primarily of: (1) the
demonstration of acute cytotoxicity and transient cell proliferation following TCE exposure in
laboratory mouse studies; (2) toxicokinetic data supporting oxidative metabolism being
necessary for TCE pulmonary toxicity; and (3) the association of lower pulmonary oxidative
metabolism and lower potency for TCE-induced cytotoxicity with the lack of observed
pulmonary carcinogenicity in laboratory rats. However, there is a lack of experimental support
linking TCE acute pulmonary cytotoxicity to sustained cellular proliferation of chronic
exposures or clonal expansion of initiated cells.
As discussed above, a number of acute studies have shown that TCE is particularly
cytotoxic to Clara cells in mice, which has been suggested to be involved in the development of
mouse lung tumors (Kim et al., 2005; Buckpitt et al., 1995; Forkert and Forkert, 1994). In
addition, studies examining cell labeling by either BrdU (Green et al., 1997b) or [3H]-thymidine
incorporation (Villaschi etal., 1991) suggest increased cellular proliferation in mouse Clara cells
following acute inhalation exposures to TCE. Moreover, in short-term studies, Clara cells appear
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to become resistant to cytotoxicity with repeated exposure, but regain their susceptibility after
2 days without exposure. This observation led to the hypothesis that the 5 days/week inhalation
dosing regime (Maltoni et al.. 1988: 1986: Fukudaetal.. 1983: Henschler et al.. 1980) in the
chronic mouse studies leads to periodic cytotoxicity in the mouse lung at the beginning of each
week followed by cellular regeneration, and that the increased rate of cell division leads to
increased incidence of tumors by increasing the overall mutation rate and by increasing the
division rate of already initiated cells (Green, 2000). However, longer-term studies to test this
hypothesis have not been carried out.
As discussed above (see Section 4.7.3), there is substantial evidence that pulmonary
oxidative metabolism is necessary for TCE-induced pulmonary toxicity, although the active
moiety remains unknown. In addition, the lower capacity for pulmonary oxidative metabolism
in rats as compared to mice is consistent with studies in rats not reporting pulmonary cytotoxicity
until exposures higher than those in the bioassays, and the lack of reported pulmonary
carcinogenicity in rats at similar doses to mice. However, rats also have a lower background rate
of lung tumors (Green, 2000), and so would be less sensitive to carcinogenic effects in that tissue
to the extent that RRs is the important metric across species. In addition, this mode-of-action
hypothesis requires a number of additional key assumptions for which there are currently no
direct evidence. First, the cycle of cytotoxicity, repair, resistance to toxicity, and loss of
resistance after exposure interruption, has not been documented and under the proposed mode of
action should continue under chronic exposure conditions. This cycle has, thus far, only been
observed in short-term (up to 13-day) studies. In addition, although Clara cells have been
identified as the target of toxicity whether they or endogenous stem cells in the lung are the cells
responsible for mouse lung tumors has not been established. There are currently no data as to the
cell type of origin for TCE-induced lung tumors.
This hypothesized mode of action has been proposed for other compounds that induce
mouse lung tumors, such as coumarin, naphthalene, and styrene (e.g., Cruzan et al., 2009).
Among these, only for styrene have there been studies of chronic duration linking cytotoxicity
with hyperplasia, and no studies appear to provide experimental linkage to clonal expansion of
initiated cells.
4.7.4.3. Additional Hypothesized Modes of Action with Limited Evidence or
Inadequate Experimental Support
4.7.4.3.1. Role of formation of DAL protein adducts
As discussed above, Forkert et al. (2006) recently observed dose-dependent formation of
DAL protein adducts in the Clara cells of mice exposed to TCE via i.p. injection. While adducts
were highly localized in Clara cells, they were also found in alveolar Type II cells, though these
cells did not show signs of cytotoxicity in this particular experimental paradigm. In terms of the
mode of action for TCE-induced pulmonary carcinogenicity, these adducts may either be
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causally important in and of themselves, or they may be markers of a different causal effect. For
instance, it is possible that these adducts are a cause for the observed Clara cell toxicity, and
Forkert et al. (2006) suggested that the lack of toxicity in alveolar Type II cells may indicate that
—here may be a threshold in adduct formation and hence bioactivation at which toxicity is
manifested." In this case, they are an additional precursor event in the same causal pathway
proposed above. Alternatively, these adducts may be indicative of effects related to
carcinogenesis but unrelated to cytotoxicity. In this case, the Clara cell need not be the cell type
of origin for mouse lung tumors.
Because of their recent discovery, there are little additional data supporting, refuting, or
clarifying the potential role for DAL protein adducts in the mode of action for TCE-induced
pulmonary carcinogenesis. For instance, the presence and localization of such adducts in rats has
not been investigated, and could indicate the extent to which the level of adduct formation is
correlated with existing data on species differences in metabolism, cytotoxicity, and
carcinogenicity. In addition, the formation of these adducts has only been investigated in a
single dose study using i.p. injection. As stated above, i.p. injection may involve the initiation of
a systemic inflammatory response that can activate lung macrophages or affect Clara cells.
Experiments with repeated exposures over chronic durations and by inhalation or oral of
administration would be highly informative. Finally, the biological effects of these adducts,
whether cytotoxicity or something else, have not been investigated.
4.7.4.4. Conclusions About the Hypothesized Modes of Action
4.7.4.4.1. Is the hypothesized mode of action sufficiently supported in the test animals?
4.7.4.4.1.1. Mutagenicity
CH is clearly genotoxic, as there are substantial data from multiple in vitro and in vivo
assays supporting its ability induce aneuploidy, with more limited data as to other genotoxic
effects, such as point mutations. CH is also clearly present in pulmonary tissues of mice
following TCE exposures similar to those inducing lung tumors in chronic bioassays. However,
chemical and toxicokinetic data are not supportive of CH being the predominant metabolite for
TCE carcinogenicity. Such data include the water solubility of CH leading to rapid diffusion to
other cell types and blood, it's likely rapid metabolism to TCOH either in pulmonary tissue or in
blood erythrocytes, and in vivo data showing lack of correlation across routes of exposure
between whole-lung CH concentrations and pulmonary carcinogenicity. Therefore, while a role
for mutagenicity via CH in the mode of action of TCE-induced lung tumors cannot be ruled
about, available evidence is inadequate to support the conclusion that direct alterations in DNA
caused by CH produced in or delivered to the lung after TCE exposure constitute a mode of
action for TCE-induced lung tumors.
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4.7.4.4.1.2. Cytotoxicity
The mode-of-action hypothesis for TCE-induced lung tumors involving cytotoxicity is
supported by relatively consistent and specific evidence for cytotoxicity at tumorigenic doses in
mice. However, the majority of cytotoxicity-related key events have been investigated in studies
<13 days, and none has been shown to be causally related to TCE-induced lung tumors. In
addition, the cell type (or types) of origin for the observed lung tumors in mice has not been
determined, so the contribution to carcinogenicity of Clara cell toxicity and subsequent
regenerative cell division is not known. Similarly, the relative contribution from recently
discovered dichloroacetyl-lysine protein adducts to the tumor response has not been investigated
and has currently only been studied in i.p. exposure paradigms of short duration. In summary,
while there are no data directly challenging the hypothesized mode of action described above,
the existing support for their playing a causal role in TCE-induced lung tumors is largely
associative, and based on acute or short term studies. Therefore, there are inadequate data to
support a cytotoxic mode of action based on the TCE-induced cytotoxicity in Clara cells in the
lungs of test animals.
4.7.4.4.1.3. Additional hypothesis
Inadequate data are available to develop a mode-of-action hypothesis based on recently
discovered DAL adducts induced by TCE inhalation and i.p. exposures. It will, therefore, not be
considered further in the conclusions below.
Overall, therefore, the mode of action for TCE-induced lung tumors is considered
unknown at this time.
4.7.4.4.2. Is the hypothesized mode of action relevant to humans?
4.7.4.4.2.1. Mutagenicity
The evidence discussed above demonstrates that CH is mutagenic in microbial as well as
test animal species. There is, therefore, the presumption that they would be mutagenic in
humans. Therefore, this mode of action is considered relevant to humans.
4.7.4.4.2.2. Cytotoxicity
No data from human studies are available on the cytotoxicity of TCE and its metabolites
in the lung, and no causal link between cytotoxicity and pulmonary carcinogenicity has been
demonstrated in animal or human studies. Nonetheless, in terms of human relevance, no data
suggest that the proposed key events are not biologically plausible in humans; therefore,
qualitatively, TCE-induced lung tumors are considered relevant to humans. This conclusion that
this hypothesized mode of action is qualitatively relevant has also been reached for other
compounds for which the mode of action has been postulated (Cruzan et al., 2009). Information
about the relative pharmacodynamic sensitivity between rodents and humans is absent, but
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information on pharmacokinetic differences in lung oxidative metabolism does exist and will be
considered in dose-response assessment when extrapolating between species (see
Section 5.2.1.2).
4.7.4.4.3. Which populations or lifestages can be particularly susceptible to the
hypothesized mode of action?
4.7.4.4.3.1. Mutagenicity
The mutagenic mode of action is considered relevant to all populations and lifestages.
According to EPA's Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005b) and
Supplemental Guidance for Assessing Susceptibility From Early-Life Exposure to Carcinogens
(U.S. EPA, 2005e), there may be increased susceptibility to early-life exposures for carcinogens
with a mutagenic mode of action. However, because the weight of evidence is inadequate to
support a mutagenic mode of action for TCE pulmonary carcinogenicity, and in the absence of
chemical-specific data to evaluate differences in susceptibility, the ADAFs should not be
applied, in accordance with the Supplemental Guidance.
4.7.4.4.3.2. Cytotoxicity
No information is available as to which populations or lifestages may be particularly
susceptible to TCE-induced lung tumors. However, pharmacokinetic differences in lung
oxidative metabolism among humans do exist, and because of the association between lung
oxidative metabolism and toxicity, these differences will be considered in dose-response
assessment when extrapolating within species.
4.7.5. Summary and Conclusions
The studies described here show pulmonary toxicity found mainly in Clara cells in mice
(Green etal.. 1997b: Odumetal.. 1992: Villaschi et al.. 1991: Forkert and Birch. 1989: Forkert
etal., 1985) and rats (Kurasawa, 1988). The most convincing albeit limited data regarding this
type of toxicity were demonstrated predominantly in mice exposed via inhalation, although some
toxicity was shown in i.p. injection studies. Increased vacuolation of Clara cells was often seen
within the first 24 hours of exposure, depending on dose, but with cellular repair occurring
within days or weeks of exposure. Continued exposure led to resistance to TCE-induced Clara
cell toxicity, but damage recurred if exposure was stopped after 5 days and then resumed after
2 days without exposure. However, Clara cell toxicity has only been observed in acute and
short-term studies, and it is unclear whether they persist with subchronic or chronic exposure,
particularly in mice, which are the more sensitive species. With respect to pulmonary
carcinogenicity, statistically significantly increased incidence of lung tumors from chronic
inhalation exposures to TCE was observed female ICR mice (Fukuda et al., 1983), male Swiss
mice, and female B6C3Fi mice (Maltoni etal., 1988: Maltoni etal., 1986), though not in other
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sex/strain combinations, or in rats (Maltoni et al., 1988; Maltoni et al., 1986; Henschler et al.,
1980). However, lung toxicity and Clara cell effects have also been observed in rats. Overall,
the limited carcinogenesis studies described above are consistent with TCE causing mild
increases in pulmonary tumor incidence in mice, but not in other species tested such as rats and
hamsters.
The epidemiologic studies are quite limited for examining the role of TCE in cancers of
the respiratory system, with no studies found on TCE exposure specifically examining toxicity of
the respiratory tract. The two studies found on organic solvent exposure which included TCE
suggested smoking as a primary factor for observed lung function decreases among exposed
workers. Animal studies have demonstrated toxicity in the respiratory tract, particularly damage
to the Clara cells (nonciliated bronchial epithelial cells), as well as decreases in pulmonary
surfactant following both inhalation and i.p. exposures, especially in mice. Dose-related
increases in vacuolation of Clara cells have been observed in mice and rats as early as 24 hours
postexposure (2006: Odumetal.. 1992: Forkert and Birch. 1989: Kurasawa, 1988: Scott et al..
1988: Forkert et al., 1985). Mice appear to be more sensitive to these changes, but both species
show a return to normal cellular morphology at 4 weeks postexposure (Odum etal., 1992).
Studies in mice have also shown an adaptation or resistance to this damage after only 4-5 days
of repeated exposures (Green etal., 1997b: Odum etal., 1992). The limited epidemiological
literature on lung and laryngeal cancer in TCE-exposed groups is inconclusive due to study
limitations (low power, null associations, CIs on RRs that include 1.0). These studies can only
rule out risks of a magnitude of >2.0 for lung cancer and RRs >3.0 or 4.0 for laryngeal cancer for
exposures to studied populations and thus, may not detect a level of response consistent with
other endpoints. Animal studies demonstrated a statistically significant increase in pulmonary
tumors in mice following chronic inhalation exposure to TCE (Maltoni et al., 1988: Maltoni et
al., 1986: Fukudaetal., 1983). These results were not seen in other species tested (rats,
hamsters; (Maltoni et al.. 1988: Maltoni et al.. 1986: Fukudaetal.. 1983: Henschler et al.. 1980).
By gavage, elevated, but not statistically significant, incidences of benign and/or malignant
pulmonary tumors have been reported in B6C3Fi mice (NTP, 1990: Henschler et al., 1984: NCI,
1976). No increased pulmonary tumor incidences have been reported in rats exposed to TCE by
gavage (NTP. 1990. 1988: NCI. 1976). although all of the studies suffered from early mortality
in at least one sex of rat.
Although no epidemiologic studies on the role of metabolism of TCE in adverse
pulmonary health effects have been published, animal studies have demonstrated the importance
of the oxidative metabolism of TCE by CYP2E1 and/or CYP2F2 in pulmonary toxicity.
Exposure to DASO2, an inhibitor of both enzymes protects against pulmonary toxicity in mice
following exposure to TCE (Forkert et al., 2005). The increased susceptibility in mice correlates
with the greater capacity to oxidize TCE based on increased levels of CYP2E1 in mouse lungs
relative to lungs of rats and humans (Forkert et al., 2006: Green et al., 1997b), but it is not clear
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that these differences in capacity alone are accurate quantitative predictors of sensitivity to
toxicity. In addition, available evidence argues against the previously proposed hypothesis (e.g.,
Green, 2000) that —acamulation" of chloral in Clara cells is responsible for pulmonary toxicity,
since chloral is first converted the water-soluble compounds, CH and TCOH, which can rapidly
diffuse to surrounding tissue and blood. Furthermore, the observation of DAL protein adducts,
likely derived from DCAC and not from chloral, that were localized in Clara cells suggests an
alternative to chloral as the active moiety. While CH has shown substantial genotoxic activity,
chemical and toxicokinetic data on CH as well as the lack of correlation across routes of
exposure between in vivo measurements of CH in lung tissues and reported pulmonary
carcinogenicity suggest that evidence is inadequate to conclude that a mutagenic mode of action
mediated by CH is operative for TCE-induced lung tumors. Another mode of action for TCE-
induced lung tumors has been plausibly hypothesized to involve cytotoxicity leading to increased
cell proliferation, but the available evidence is largely associative and based on short-term
studies, so a determination of whether this mode of action is operative cannot be made. The
recently discovered formation of DAL protein adducts in pulmonary tissues may also play a role
in the mode of action of TCE-induced lung tumors, but an adequately defined hypothesis has yet
to be developed. Therefore, the mode of action for TCE-induced lung tumors is currently
considered unknown, and this endpoint is thus considered relevant to humans. Moreover, none
of the available data suggest that any of the currently hypothesized mechanisms would be
biologically precluded in humans.
4.8. REPRODUCTIVE AND DEVELOPMENTAL TOXICITY
4.8.1. Reproductive Toxicity
An assessment of the human and experimental animal data, taking into consideration the
overall weight of evidence, demonstrates a concordance of adverse reproductive outcomes
associated with TCE exposures. Effects on male reproductive system integrity and function are
particularly notable and are discussed below. Cancers of the reproductive system in both males
and females have also been identified and are discussed below.
4.8.1.1. Human Reproductive Outcome Data
A number of human studies have been conducted that examined the effects of TCE on
male and female reproduction following occupational and community exposures. These are
described below and summarized in Table 4-85. Epidemiological studies of female human
reproduction examined infertility and menstrual cycle disturbances related to TCE exposure.
Other studies of exposure to pregnant women are discussed in the section on human
developmental studies (see Section 4.8.3.1). Epidemiological studies of male human
reproduction examined reproductive behavior, altered sperm morphology, altered endocrine
function, and infertility related to TCE exposure.
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Table 4-85. Human reproductive effects
Subjects
Exposure
Effect
Reference
Female and male combined effects
Reproductive behavior
15 men and 71 women
living near Rocky
Mountain Arsenal,
Colorado
Low: <5.0 ppb
Medium: >5.0-<10.0 ppb
High: <10.0 ppb
Highest: <15 ppb
Altered libido3
Low: referent
Medium: ORadj: 0.67 (95% CI: 0.18-2.49)
High: OR^y: 1.65 (95% CI: 0.54-5.01)
Highest: ORadl: 2.46 (95% CI: 0.59-10.28)
ATSDR
(2001)
Female effects
Infertility
197 women
occupationally exposed to
solvents in Finland 1973-
1983
7 1 women living near
Rocky Mountain Arsenal,
Colorado
U-TCA (umol/L)b
Median: 48.1
Mean: 96.2 ± 19.2
Low: <5.0 ppb
Medium: >5.0-<10.0 ppb
High: <10.0 ppb
Reduced incidence of fecundability in the
high exposure group0 as measured by time to
pregnancy
Low: IDR= 1.21 (95%CI: 0.73-2.00)
High: IDR = 0.61 (95%CI: 0.28-1.33)
No effect on lifetime infertility3
Low: referent
Medium: ORadj: 0.45 (95% CI: 0.02-8.92)
High: ORadl: 0.88 (95% CI: 0.13-6.22)
Sallmen et al.
(1995)
ATSDR
(2001)
Menstrual cycle disturbance
7 1 women living near
Rocky Mountain Arsenal,
Colorado
184 women working in a
factory assembling small
electrical parts in Poland
32 women working in dry
cleaning or metal
degreasing in
Czechoslovakia"1
20-yr-old woman
occupationally exposed to
TCE via inhalation
Low: <5.0 ppb
Medium: >5.0-<10.0 ppb
High: <10.0 ppb
Mean indoor air TCE:
200 mg/m3
0.28-3.4 mg/L TCE for
0.5-25 yrs
U-TTCs3.2ng/mL(21-
25 d after exposure)
Increase in abnormal menstrual cycle (defined
as <26 d or >30 d)
Low: referent
Medium: ORadj: 4.17 (95% CI: 0.31-56.65)
High: ORadl: 2.39 (95% CI: 0.41-13.97)
18% reporting increase in amenorrhea in
exposed group (n = 140), compared to 2%
increase in unexposed group (n = 44)
3 1% reporting increase in menstrual
disturbances3
Amenorrhea, followed by irregular
menstruation and lack of ovulation
ATSDR
(2001)
Zielinski
(1973)
Bardodej and
Vyskocil
(1956)
Sagawa et al.
(1973)
Male effects
Reproductive behavior
43 men working in dry
cleaning or metal
degreasing in
Czechoslovakia
30 male workers in a
money printing shop in
Egypt
0.28-3.4 mg/L TCE for
0.5-25 yrs
38-172 ppm TCE
30% reporting decreased potency3
Decreased libido reported in 10 men (33%),
compared to 3 men in the control group (10%)
Bardodej and
Vyskocil
(1956)
El Ghawabi
et al. (1973)
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Table 4-85. Human reproductive effects (continued)
Subjects
42 yr-old male aircraft
mechanic in UK
Exposure
TCE exposure reported but
not measured; exposure for
25yrs
Effect
Gynaecomastia, impotence
Reference
Saihan et al.
(1978)
Altered sperm quality
15 men working as metal
degreasers in Denmark
85 men of Chinese
descent working in an
electronics factory
TCE exposure reported but
not measured
Mean personal air TCE:
29.6 ppm; Mean U-TCA:
22.4 mg/g creatinine
Nonsignificant increase in percentage of two
YFF in spermatozoa; no effect on sperm
count or morphology
Decreased normal sperm morphology and
hyperzoospermia
Rasmussen et
al. (1988)
Chia et al.
(1996)
Altered endocrine function
85 men of Chinese
descent working in an
electronics factory
85 men of Chinese
descent working in an
electronics factory
Mean personal air TCE:
29.6 ppm; Mean U-TCA:
22.4 mg/g creatinine
Mean personal air TCE:
29.6 ppm; Mean U-TCA:
22.4 mg/g creatinine
Increased DHEAS and decreased FSH,
SHBG and testosterone levels; dose-response
observed
Decreased serum levels of testosterone and
SHBG were significantly correlated with
years of exposure to TCE; increased insulin
levels for exposure <2 yrs
Chia et al.
(1997)
Goh et al.
(1998)
Infertility
282 men occupationally
exposed to solvents in
Finland 1973-1983
8 male mechanics seeking
treatment for infertility in
Canada
75 men living near Rocky
Mountain Arsenal,
Colorado
U-TCA (umol/L):
High exposure:0
Mean: 45 (SD 42)
Median 3 1
Low exposure:0
Mean: 41 (SD 88)
Median: 15
Urine (umol/):
TCA: O.30-4.22
TCOH: O.60-0.89
Seminal fluid (pg/extract):
TCE: 20.4-5,419.0
Chloral: 61.2-1,739.0
TCOH 2.7-25.5
TCA: <100-5,504
DCA: <100-13,342
Low: <5.0 ppb
Medium: >5.0 to <10.0 ppb
High: <10.0 ppb
No effect on fecundability0 (as measured by
time to pregnancy)
Low: FDR: 0.99 (95% CI: 0.63-1.56)
Intermediate/High: FDR:° 1.03 (95%
CI: 0.60-1.76)
Infertility could not be associated with TCE
as controls were five men also in treatment
for infertility
No effect on lifetime infertility (not defined)
Low: referent
Medium: NA
High: ORadl: 0.83 (95% CI: 0.11-6.37)
Sallmen et al.
(1998)
Forkert et al.
(2003)
ATSDR
(2001)
aNot defined by the authors.
bAs reported in Lindbohm et al.
1990).
°Low/intermediate exposure indicated use of TCE <1 or 1-4 days/week, and biological measures indicated high
exposure. High exposure indicated daily use of TCE, or if biological measures indicated high exposure.
dNumber inferred from data provided in Tables 2 and 3 in Bardodej and Vyskocil (1956).
Bolded study(ies) carried forward for consideration in dose-response assessment (see Chapter 5).
DHEAS = dehydroepiandrosterone sulphate; FSH = follicle-stimulating hormone; ORadj = adjusted odds ratio;
SHBG = sex-hormone binding globulin
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4.8.1.1.1. Female and male combined human reproductive effects
Reproductive behavior
A residential study of individuals living near the Rocky Mountain Arsenal in Colorado
examined the reproductive outcomes in 75 men and 71 women exposed to TCE in drinking water
(ATSDR, 2001). TCE exposure was classified as high (>10.0 ppb), medium (>5.0-<10.0 ppb),
and low (<5.0 ppb). Altered libido for men and women combined was observed in a dose-
response fashion, although the results were nonsignificant. The results were not stratified by
gender.
4.8.1.1.2. Female human reproductive effects
4.8.1.1.2.1. Infertility
Sallmen et al. (1995) examined maternal occupational exposure to organic solvents and
time-to-pregnancy. Cases of spontaneous abortion and controls from a prior study of maternal
occupational exposure to organic solvents in Finland during 1973-1983 and pregnancy outcome
(Lindbohm et al., 1990) were used to study time-to-pregnancy of 197 couples. Exposure was
assessed by questionnaire during the first trimester and confirmed with employment records.
Biological measurements of TCA in urine in 64 women who held the same job during pregnancy
and measurement (time of measurement not stated) had a median value of 48.1 jimol/L (mean:
96.2 ± 19.2 |imol/L) (Lindbohm et al., 1990). Nineteen women had low exposure to TCE (used
<1 or 1-4 times/week), and 9 had high exposure to TCE (daily use). In this follow-up study, an
additional questionnaire on time-to-pregnancy was answered by the mothers (Sallmen et al.,
1995). The incidence density ratio (IDR) was used in this study to estimate the ratio of average
incidence rate of pregnancies for exposed women compared to nonexposed women; therefore, a
lower IDR indicates infertility. For TCE, a reduced incidence of fecundability was observed in
the high-exposure group (IDR: 0.61, 95% CI: 0.28-1.33) but not in the low-exposure group
(IDR: 1.21, 95% CI: 0.73-2.00). A similar study of paternal occupational exposure (Sallmen et
al.. 1998) is discussed in Section 4.8.1.1.3.4.
The residential study in Colorado discussed above did not observe an effect on lifetime
infertility in the medium- (ORadj: 0.45; 95% CI: 0.02-8.92) or high-exposure groups (ORadj:
0.88; 95% CI: 0.13-6.22) (ATSDR, 2001). Curiously, exposed women had more pregnancies
and live births than controls.
4.8.1.1.2.2. Menstrual cycle disturbance
The ATSDR (2001) study discussed above also examined effects on the menstrual cycle
(ATSDR, 2001). Nonsignificant associations without a dose-response were seen for abnormal
menstrual cycle in women (ORadj: 2.23, 95% CI: 0.45-11.18).
Other studies have examined the effect of TCE exposure on the menstrual cycle. One
study examined women working in a factory assembling small electrical parts (Zielinski, (1973),
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translated). The mean concentration of TCE in indoor air was reported to be 200 mg/m3. Of the
140 exposed women, 18% suffered from amenorrhea, compared to only 2% of the 44
nonexposed workers. The other study examined 75 men and women working in dry cleaning or
metal degreasing (Bardodej and Vyskocil, 1956). Exposures ranged from 0.28 to 3.4 mg/L, and
length of exposure ranged from 0.5 to 25 years. This study reported that many women
experienced menstrual cycle disturbances, with a trend for increasing air concentrations and
increasing duration of exposure.
There is also an additional case study of a 20-year-old woman who was occupationally
exposed to TCE via inhalation. The exposure was estimated to be as high as 10 mg/mL or
several thousand ppm, based on urine samples 21-25 days after exposure of 3.2 ng/mL of TTCs.
The primary effect was neurological, although she also experienced amenorrhea, followed by
irregular menstruation and lack of ovulation as measured by basal body temperature curves
(Saeawa et al.. 1973).
4.8.1.1.3. Male human reproductive effects
4.8.1.1.3.1. Reproductive behavior
One study reported the effect of TCE exposure on the male reproductive behavior in
75 men working in dry cleaning or metal degreasing (Bardodej and Vyskocil, 1956). Exposures
ranged from 0.28 to 3.4 mg/L, and length of exposure ranged from 0.5 to 25 years. This study
found that men experienced decreased potency or sexual disturbances; the authors speculated
that the effects on men could be due to the CNS effects of TCE exposure. This study also
measured serial neutral 17-ketosteroid determinations, but they were found to be not statistically
significant (Bardodej and Vyskocil, 1956).
In an occupational study, 30 men working in a money printing shop were exposed to TCE
for
-------
measurement of TCE exposure was reported. Sperm count, morphology, and spermatozoa
Y-chromosomal nondisjunction during spermatogenesis were examined, along with
chromosomal aberrations in cultured lymphocytes. A nonsignificant increase in percentage of
two fluorescent Y-bodies (YFF) in spermatozoa were seen in the exposed group (p > 0.10), and
no difference was seen in sperm count or morphology compared to controls.
An occupational study of men using TCE for electronics degreasing (Goh et al., 1998;
Chiaetal., 1997; Chiaetal., 1996) examined subjects (n = 85) who were offered a free medical
exam if they had no prior history related to endocrine function, no clinical abnormalities, and
normal liver function tests; no controls were used. These participants provided urine, blood, and
sperm samples. The mean urine TCA level was 22.4 mg/g creatinine (range: 0.8-136.4 mg/g
creatinine). In addition, 12 participants provided personal 8-hour air samples, which resulted in
a mean TCE exposure of 29.6 ppm (range: 9-131 ppm). Sperm samples were divided into two
exposure groups: low for urine TCA <25 mg/g creatinine and high for urine TCA >25 mg/g
creatinine. A decreased percentage of normal sperm morphology was observed in the sperm
samples in the high-exposure group (n = 48) compared to the low-exposure group (n = 37).
However, TCE exposure had no effect on semen volume, sperm density, or sperm motility.
There was also an increased prevalence of hyperzoospermia (sperm density of >120 million
sperm per mL ejaculate) with increasing urine TCA levels (Chiaetal., 1996).
4.8.1.1.3.3. Altered endocrine function
Two studies followed up on the study by Chia et al. (1996) to examine endocrine function
(Goh et al., 1998; Chia et al., 1997). The first examined serum testosterone, follicle-stimulating
hormone (FSH), dehydroepiandrosterone sulphate (DFIEAS), and sex-hormone binding globulin
(SHBG) (Chiaetal., 1997). With increased number of years of exposure to TCE, increases in
DFIEAS levels were seen, from 255 ng/mL for <3 years to 717.8 ng/mL >7 years of exposure.
Also with increased number of years of exposure to TCE, decreased FSH, SHBG, and
testosterone levels were seen. The authors speculated that these effects could be due to
decreased liver function related to TCE exposure (Chia et al., 1997).
The second follow-up study of this cohort studied the hormonal effects of chronic low-
dose TCE exposure in these men (Goh et al., 1998). Because urine TCE measures only indicate
short-term exposure, long-term exposure was indicated by years of exposure. Hormone levels
examined include androstenedione, cortisol, testosterone, aldosterone, SHBG, and insulin.
Results show that a decrease in serum levels of testosterone and SHBG were significantly
correlated with years of exposure to TCE, and an increase in insulin levels were seen in those
exposed for <2 years. Androstenedione, cortisol, and aldosterone were in normal ranges and did
not change with years of exposure to TCE.
4-472
-------
4.8.1.1.3.4. Infertility
Sallmen et al. (1998) examined paternal occupational exposure and time-to-pregnancy
among their wives. Cases of spontaneous abortion and controls from a prior study of pregnancy
outcome (Taskinen et al., 1989) were used to study time-to-pregnancy of 282 couples. Exposure
was determined by biological measurements of the father who held the same job during
pregnancy and measurement (time of measurement not stated) and questionnaires answered by
both the mother and father. An additional questionnaire on time-to-pregnancy was answered by
the mother for this study 6 years after the original study (Sallmen et al., 1998). The level of
exposure was determined by questionnaire and classified as 4ew/intermediate" if the chemical
was used <1 or 1-4 days/week and biological measures indicated high exposure (defined as
above the reference value for the general population), and "high" if used daily or if biological
measures indicated high exposure. For 13 men highly exposed, mean levels of urine TCA were
45 |imol/L (SD 42 |imol/L; median 31 |imol/L); for 22 men low/intermediately exposed, mean
levels of urine TCA were 41 |imol/L (SD 88 |imol/L; median 15 |imol/L). The terminology IDR
was replaced by fecundability density ratio (FDR) in order to reflect that pregnancy is a desired
outcome; therefore, a high FDR indicates infertility. No effect was seen on fertility in the low-
exposure group (FDR: 0.99, 95% CI: 0.63-1.56) or in the intermediate-/high-exposure group
(FDR: 1.03, 95% CI: 0.60-1.76). However, the exposure categories were grouped by
low/intermediate vs. high, whereas the outcome categories were grouped by low vs.
intermediate/high, making a dose-response association difficult.
A small occupational study reported on eight male mechanics exposed to TCE for at least
2 years who sought medical treatment for infertility (Forkert et al., 2003). The wives were
determined to have normal fertility. Samples of urine from two of the eight male mechanics
contained TCA and/or TCOH, demonstrating the rapid metabolism in the body. However,
samples of seminal fluid taken from all eight individuals detected TCE and the metabolites CH
and TCOH, with two samples detecting DCA and one sample detecting TCA. Five unexposed
controls also diagnosed with infertility did not have any TCE or metabolites in samples of
seminal fluid. There was no control group that did not experience infertility. Increased levels of
TCE and its metabolites in the seminal fluid of exposed workers compared to lower levels found
in their urine samples was explained by cumulative exposure and mobilization of TCE from
adipose tissue, particularly that surrounding the epididymis. In addition, CYP2E1 was detected
in the epididymis, demonstrating that metabolism of TCE can occur in the male reproductive
tract. However, this study could not directly link TCE to the infertility, as both the exposed and
control populations were selected due to their infertility.
The ATSDR (2001) study discussed above on the reproductive effects from TCE in
drinking water of individuals living near the Rocky Mountain Arsenal in Colorado did not
observe infertility or other adverse reproductive effects for the high exposure group compared to
4-473
-------
the low exposure group (ORadj: 0.83; 95% CI: 0.11-6.37). Curiously, exposed men had more
pregnancies and live births than controls.
4.8.1.1.4. Summary of human reproductive toxicity
Following exposure to TCE, observed adverse effects on the female reproductive system
include reduced incidence of fecundability (as measured by time-to-pregnancy) and menstrual
cycle disturbances. Observed adverse effects on the male reproductive system include altered
sperm morphology, hyperzoospermia, altered endocrine function, decreased sexual drive and
function, and altered fertility. These are summarized in Table 4-85.
4.8.1.2. Animal Reproductive Toxicity Studies
A number of animal studies have been conducted that examined the effects of TCE on
reproductive organs and function following either inhalation or oral exposures. These are
described below and summarized in Tables 4-86 and 4-87. Other animal studies of offspring
exposed during fetal development are discussed in the section on animal developmental studies
(see Section 4.8.3.2).
4.8.1.2.1. Inhalation exposures
Studies in rodents exposed to TCE via inhalation are described below and summarized in
Table 4-86. These studies focused on various aspects of male reproductive organ integrity,
spermatogenesis, or sperm function in rats or mice. In the studies published after the year 2000,
the effects of either 376 or 1,000 ppm TCE were studied following exposure durations ranging
from 1 to 24 weeks, and adverse effects on male reproductive endpoints were observed.
Table 4-86. Summary of mammalian in vivo reproductive toxicity studies—
inhalation exposures
Reference"
Forkert et al.
(2002)
Species/strain/
sex/number
Mouse, CD-I,
male, 6/group
Exposure
level/duration
0 or 1,000 ppm
(5,374 mg/m3)c
6 hrs/d, 5 d/wk,
19 d over
4 wks
NOAEL;
LOAELb
LOAEL:
1,000 ppm
Effects
U-TCA and U-TCOH increased by 2nd and
3rd wk, respectively. CYP 2E1 and p-
nitrophenol hydroxylation in epididymal
epithelium > testicular Leydig cells. Choral
also generated from TCE in epididymis >
testis. Sloughing of epididymal epithelial
cells after 4-wk exposure.
4-474
-------
Table 4-86. Summary of mammalian in vivo reproductive toxicity
studies—inhalation exposures (continued)
Reference"
Kan et al.
(2007)
Kumar et al.
(2000b)
Kumar et al.
(2000a)
Kumar et al.
(2001b)
Land et al.
(1981)
Xu et al.
(2004)
Species/strain/
sex/number
Mouse, CD-I,
male, 4/group
Rat, Wistar,
male, 12-
13/group
Rat, Wistar,
males, 12-
13/group
Rat, Wistar,
male, 6/group
Mouse,
C57BlxC3H
(Fl), male, 5
or 10/group
Mouse, CD-I,
male, 4-
27/group
Exposure
level/duration
0 or 1,000 ppm
6 hrs/d,5 d/wk,
\-4 wks
0 or 376 ppm
4 hrs/d, 5 d/wk,
2-10 wks
exposure, 2-8-
wk rest period
0 or 376 ppm
4 hrs/d, 5 d/wk,
12 and 24 wks
0 or 376 ppm
4 hrs/d, 5 d/wk,
12 and 24 wks
0,0.02%, or
0.2%
4 hrs/d, 5 d, 23-
d rest
0 or 1,000 ppm
(5.37 mg/L)c
6 hrs/d, 5 d/wk,
1-6 wks
NOAEL;
LOAEL a
LOAEL:
1,000 ppm
LOAEL:
376 ppm
LOAEL:
376 ppm
LOAEL:
376 ppm
NOAEL:
0.02%
LOAEL: 0.2%
LOAEL:
1,000 ppm
Effects
Light microscopy findings: degeneration and
sloughing of epididymal epithelial cells as
early as 1 wk into exposure; more severe by
4 wks. Ultrastructional findings: vesiculation
in cytoplasm, disintegration of basolateral
cell membranes, sloughing of epithelial cells.
Sperm found in situ in cytoplasm of
degenerated epididymal cells. Abnormalities
of the head and tail in sperm located in the
epididymal lumen.
Alterations in testes histopathology (smaller,
necrotic spermatogenic tubules), | sperm
abnormalities, and statistically significant
| pre- and/or postimplantation loss in litters
observed in the groups with 2 or 10 wks of
exposure, or 5 wks of exposure with 2-wk
rest.
Statistically significant J, in total epididymal
sperm count and sperm motility, with
statistically significant J, in serum testosterone,
statistically significant \ in testes cholesterol,
statistically significant \ of glucose 6-phosphate
dehydrogenase and 17-p-hydroxy steroid
dehydrogenase at 12 and 24 wks of exposure.
Body weight gain statistically significant \.
Testis weight, sperm count and motility
statistically significant j, effect stronger with
exposure time. After 12 wks, numbers of
spermatogenic cells and spermatids j, some
of the spermatogenic cells appeared necrotic.
After 24 wks, testes were atrophied, tubules
were smaller, had Sertoli cells, and were
almost devoid of spermatocytes and
spermatids. Leydig cells were hyperplastic.
SDH, G6PDH statistically significant j, GGT
and p-glucuronidase statistically significant
t; effects stronger with exposure time.
Statistically significant \ percentage
morphologically abnormal epididymal
sperm.
Statistically significant \ in vitro sperm-
oocyte binding and in vivo fertilization
"Bolded studies carried forward for consideration in dose-response assessment (see Chapter 5).
bNOAEL and LOAEL are based upon reported study findings.
Dose conversion calculations by study author(s).
G6PDH = glucose 6-p dehydrogenase
4-475
-------
Table 4-87. Summary of mammalian in vivo reproductive toxicity studies—
oral exposures
Reference"
Species/strain/
sex/number
Dose
level/exposure
duration
Route/vehicle
NOAEL;
LOAELb
Effects
Studies assessing male reproductive outcomes
DuTeaux et al.
(2003)
DuTeaux et
al. (2004a)
Veera-
machaneni et
al. (2001)
Zenick et al.
(1984)
Rat, Sprague-
Dawley, male,
3/group
Rat, Sprague-
Dawley, male,
3/group, or
Simonson
albino (UC-
Davis), male,
3/group
Rabbit, Dutch
belted, females
and offspring;
7-9 offspring/
group
Rat, Long-
Evans, male,
10/group
0, 0.2, or 0.4%
(0, 143, or
270 mg/kg-d)
0, 0.2, or 0.4%
(0, 143, or
270 mg/kg-d)
14 d
9.5 or 28. 5 ppm
TCEC
GD 20 through
lactation, then to
offspring thru
postnatal wk 15
0, 10, 100, or
1,000 mg/kg-d
6 wk, 5 d/wk;
4 wks recovery
Drinking
water; 3%
ethoxylated
castor oil
vehicle
Drinking
water, 3%
ethoxylated
castor oil
vehicle
Drinking
water
Gavage, corn
oil vehicle
LOEL: 0.2%
LOAEL: 0.2%
LOAEL:
9.5 ppm
NOAEL:
100 mg/kg-d
LOAEL:
1,000 mg/kg-d
TCE metabolite-protein
adducts formed by a CYP-
mediated pathway were
detected by fluorescence
imunohistochemistry in the
epithelia of corpus epididymis
and in efferent ducts.
Dose-dependent J, in ability
of sperm to fertilize oocytes
collected from untreated $s.
Oxidative damage to sperm
membrane in head and mid-
piece was indicated by dose-
related | in oxidized
proteins and lipid
peroxidation.
Decreased copulatory
behavior; acrosomal
dysgenesis, nuclear
malformations; statistically
significant J, LH and
testosterone.
At 1,000 mg/kg, body weight
j, liver/body weight ratiosf,
and impaired copulatory
behavior. Copulatory
performance returned to
normal by 5th wk of
exposure. At wk 6, TCE
and metabolites
concentrated to a significant
extent in male reproductive
organs.
Studies assessing female reproductive outcomes
Berger and
Homer (2003)
Cosby and
Dukelow
(1992)
Rat, Simonson
(Sprague-
Dawley
derived),
female, (5-
6); x 3/group
Mouse,
B6D2F1,
female, 7-
12/group
0 or 0.45%
2 wks
0, 24, or
240 mg/kg-d
CDs 1-5, 6-10, or
11-15
Drinking
water, 3%
Tween vehicle
Gavage, corn
oil vehicle
LOAEL:
0.45%
NOAEL:
240 mg/kg-d
In vitro fertilization and
sperm penetration of oocytes
statistically significant J, with
sperm harvested from
untreated males.
No treatment-related effects
on in vitro fertilization in
dams or offspring.
4-476
-------
Table 4-87. Summary of mammalian in vivo reproductive toxicity studies—
oral exposures (continued)
Reference"
Manson et al.
(1984)
Wu and Berger
(20071
Wu and Berger
(20081
Species/strain/
sex/number
Rat, Long-
Evans, female,
23-25/group
Rat, Simonson
(Sprague-
Dawley derived),
female,
(number/group
not reported)
Rat, Simonson
(Sprague-
Dawley derived),
female,
(number/group
not reported)
Dose
level/exposure
duration
0, 10, 100, or
1,000 mg/kg-d
6 wks: 2 wks
premating, 1 wk
mating period,
CDs 1-21
0 or 0.45%
(0.66 g/kg-d)d
Preovulation d 1-
5, 6-10, 11-14, or
1-14
0 or 0.45%
(0.66 g/kg-d)d
Ior5d
Route/vehicle
Gavage, corn
oil vehicle
Drinking
water, 3%
Tween vehicle
Drinking
water, 3%
Tween vehicle
NOAEL;
LOAEL3
NOAEL:
100 mg/kg-d
LOAEL:
1,000 mg/kg-d
LOAEL: 0.45%
NOEL: 0.45%
Effects
Female fertility and mating
success was not affected. At
1,000 mg/kg-d group,
5/23 females died, gestation
body weight gain was
statistically significant j.
After subchronic oral TCE
exposure, TCE was detected
in fat, adrenals, and ovaries;
TCA levels in uterine tissue
were high.
At 1,000 mg/kg-d, neonatal
deaths (female pups) were
| on PNDs 1, 10, and 14.
Dose-related | seen in TCA
in blood, liver and milk in
stomach of 2 pups, not c?s.
In vitro fertilization and sperm
penetration of oocytes
statistically significant J, with
sperm harvested from
untreated males.
Ovarian mRNA expression for
ALCAM and Cudzl protein
were not altered.
4-477
-------
Table 4-87. Summary of mammalian in vivo reproductive toxicity studies—
oral exposures (continued)
Reference"
Species/strain/
sex/number
Dose
level/exposure
duration
Route/vehicle
NOAEL;
LOAEL3
Effects
Studies assessing fertility and reproductive outcome in both sexes
George et al.
(1985)
Mouse, CD-I,
male and
female,
20 pairs/
treatment
group;
40 controls/sex
0, 0.15, 0.30, or
0.60%e micro-
encapsulated
TCE
(TWA dose
estimates: 0, 173,
362, or
737 mg/kg-d)d
Breeders exposed
1 wk premating,
then for 13 wks;
pregnant females
throughout
gestation (i.e.,
18 wks total)
Dietary
Parental
systemic
toxicity:
NOAEL:
0.30%
LOAEL:
0.60%
Parental
reproductive
function:
LOAEL: 0.60%d
Offspring
toxicity:
NOAEL: 0.30%
LOAEL: 0.60%
At 0.60%, in FO: statistically
significant! liver weights in
both sexes; statistically
significant J, testis and
seminal vesicle weight;
histopathology of liver and
kidney in both sexes.
At 0.60%, in Fl: statistically
significant J, body weight on
PND 74, and in postpartum
Fl dams; statistically
significant | liver, testis, and
epididymis weights in males,
statistically significant
| kidney weights in both
sexes; statistically significant
J, testis and seminal vesicle
weight; histopathology of
liver and kidney in both
sexes.
At 0.60%, in FO and Fl males:
statistically significant
J, sperm motility.
At 0.60%, in Fl pups:
statistically significant J, live
birth weights, statistically
significant J, PND 4 pup body
weight; perinatal mortality
t (PNDsO-21).
4-478
-------
Table 4-87. Summary of mammalian in vivo reproductive toxicity studies—
oral exposures (continued)
Reference"
George et al.
(1986)
Species/strain/
sex/number
Rat, F334,
males and
female,
20 pairs/
treatment
group,
40 controls/sex
Dose
level/exposure
duration
0, 0.15, 0.30, or
0.60%e micro-
encapsulated
TCE
Breeders exposed
1 wk premating,
then for 13 wks;
pregnant females
throughout
gestation (i.e.,
18 wks total)
Route/vehicle
Dietary
NOAEL;
LOAEL3
Parental
systemic
oxicity:
LOAEL:
0.15%
Parental
reproductive
'unction:
LOAEL:
0.60%e
Offspring
oxicity:
LOAEL:
0.15%
Effects
At 0.60%, in FO: statistically
significant J, postpartum
dam body weight;
statistically significant
J, term, body weight in both
sexes; statistically significant
| liver, and kidney/adrenal
weights in both sexes;
statistically significant
| testis/epididymis weights;
in Fl: statistically significant
J, testis weight.
At all doses in Fl:
statistically significant
J, postpartum dam body
weight; statistically
significant^ term, body
weight in both sexes,
statistically significant
| liver weight in both sexes.
At 0.30 and 0.60%, in Fl:
statistically significant
t liver weight in females.
At 0.60%, sig 1 mating in FO
males and females (in cross-
over mating trials).
At 0.60%, statistically
significant J, Fl body weight
on PNDs 4 and 14.
At all doses, statistically
significant J, Fl body weight
on PNDs 21 and 80.
At 0.3 and 0.60%,
statistically significant J, live
Fl pups/litter.
Statistically significant trend
towards J, live litters per pair
At 0.15 and 0.60%, trend
toward J, Fl survival from
PNDs 21-80.
aBolded studies carried forward for consideration in dose-response assessment (see Chapter 5).
bNOAEL, LOAEL, NOEL, and LOEL are based upon reported study findings.
Concurrent exposure to several groundwater contaminants; values given are for TCE levels in the mixture.
dDose conversion calculations by study author(s).
Tertiliry and reproduction assessment of last litter from continuous breeding phase and cross-over mating
assessment (rats only) were conducted for 0 or 0.60% dose groups only.
LH = luteinizing hormone
4-479
-------
Kumar et al. (2000b) exposed male Wistar rats in whole-body inhalation chambers to
376-ppm TCE for 4 hours/day, 5 days/week over several duration scenarios. These were
2 weeks (to observe the effect on the epididymal sperm maturation phase), 10 weeks (to observe
the effect on the entire spermatogenic cycle), 5 weeks with 2 weeks of rest (to observe the effect
on primary spermatocytes differentiation to sperm), 8 weeks with 5 weeks of rest (to observe
effects on an intermediate stage of spermatogenesis), and 10 weeks with 8 weeks of rest (to
observe the effect on spermatogonial differentiation to sperm). Control rats were exposed to
ambient air. Weekly mating with untreated females was conducted. At the end of the
treatment/rest periods, the animals were sacrificed; testes and cauda epididymes tissues were
collected. Alterations in testes histopathology (smaller, necrotic spermatogenic tubules),
increased sperm abnormalities, and significantly increased pre- and/or postimplantation loss in
litters were observed in the groups with 2 or 10 weeks of exposure, or 5 weeks of exposure with
2 of weeks rest. It was hypothesized that postmeiotic cells of spermatogenesis and epididymal
sperm were affected by TCE exposure, leading to reproductive impairment.
To test the hypothesis that TCE exposure adversely affects sperm function and
fertilization, Xu et al. (2004) conducted a study in which male CD-I mice were exposed by
inhalation to atmospheres containing 1,000 ppm (5.37 mg/L) TCE for 1-6 weeks (6 hours/day,
5 days/week). After each TCE exposure, body weights were recorded. Following termination,
the right testis and epididymis of each treated male were weighed, and sperm was collected from
the left epididymis and vas deferens for assessment of the number of total sperm and motile
sperm. Sperm function was evaluated in the following experiments: (1) suspensions of
capacitated vas deferens/cauda epididymal sperm were examined for spontaneous acrosome
reaction; (2) in vitro binding of capacitated sperm to mature eggs from female CF-1 mice
(expressed as the number of sperm bound per egg) was assessed; and (3) in vivo fertilization was
evaluated via mating of male mice to superovulated female CF-1 mice immediately following
inhalation exposure; cumulus masses containing mature eggs were collected from the oviducts of
the females, and the percentage of eggs fertilized was examined. Inhalation exposure to TCE did
not result in altered body weight, testis and epididymis weights, sperm count, or sperm
morphology or motility. Percentages of acrosome-intact sperm populations were similar
between treated and control animals. Nevertheless, for males treated with TCE for >2 weeks
decreases were observed in the number of sperm bound to the oocytes in vitro (significant at
2 and 6 weeks,/? < 0.001). In a follow-up assessment, control sperm were incubated for
30 minutes in buffered solutions of TCE or metabolites (CH or TCOH); while TCE-incubation
had no effect on sperm-oocyte binding, decreased binding capacity was noted for the metabolite-
incubated sperm. The ability for sperm from TCE-exposed males to bind to and fertilize oocytes
in vivo was also found to be significantly impaired (p < 0.05).
4-480
-------
A study designed to investigate the role of testosterone, and of cholesterol and ascorbic
acid (which are primary precursors of testosterone) in TCE-exposed rats with compromised
reproductive function was conducted by Kumar et al. (2000a). Male Wistar rats (12-13/group)
were exposed (whole body) to 376 ppm TCE by inhalation for 4 hours/day, 5 days/week, for
either 12 or 24 weeks and then terminated. Separate ambient-air control groups were conducted
for the 12- and 24-week exposure studies. Epididymal sperm count and motility were evaluated,
and measures of 17-p-hydroxy steroid dehydrogenase (17-P-HSD), testicular total cholesterol
and ascorbic acid, serum testosterone, and glucose 6-p dehydrogenase (G6PDH) in testicular
homogenate were assayed. In rats exposed to TCE for either 12 or 24 weeks, total epididymal
sperm count and motility, serum testosterone concentration, and specific activities of both
17-P-HSD and G6PDH were significantly decreased (p < 0.05), while total cholesterol content
was significantly (p < 0.05) increased. Ascorbic acid levels were not affected.
In another study, Kumar et al. (200Ib) utilized the same exposure paradigm to examine
cauda epididymal sperm count and motility, testicular histopathology, and testicular marker
enzymes: sorbitol dehydrogenase (SDH), G6PDH, glutamyl transferase (GT), and glucuronidase,
in Wistar rats (6/group). After 24 weeks of exposure, testes weights and epididymal sperm count
and motility were significantly decreased (p < 0.05). After 12 weeks of TCE exposure,
histopathological examination of the testes revealed a reduced number of spermatogenic cells in
the seminiferous tubules, fewer spermatids as compared to controls, and the presence of necrotic
spermatogenic cells. Testicular atrophy, smaller tubules, hyperplastic Leydig cells, and a lack of
spermatocytes and spermatids in the tubules were observed after 24 weeks of TCE exposure.
After both 12 and 24 weeks of exposure, SDH and G6PDH were significantly (p < 0.05)
reduced, while GT and p-glucuronidase were significantly (p < 0.05) increased.
In a study by Land et al. Q981X 8-10-week-old male mice (C57BlxC3H)Fl (5 or
10/group) were exposed (whole body) by inhalation to a number of anesthetic agents for
5 consecutive days at 4 hours/day and sacrificed 28 days after the first day of exposure.
Chamber concentration levels for the TCE groups were 0.02 and 0.2%. The control group
received ambient air. Epididymal sperm were evaluated for morphological abnormalities. At
0.2% TCE, the percentage of abnormal sperm in a sample of 1,000 was significantly (p < 0.01)
increased as compared to control mice; no treatment-related effect on sperm morphology was
observed at 0.02% TCE.
Forkert et al. (2002) exposed male CD-I mice by inhalation to 1,000-ppm TCE
(6 hours/day, 5 days/week) for 4 consecutive weeks and observed sloughing of portions of the
epithelium upon histopathological evaluation of testicular and epididymal tissues.
Kan et al. (2007) also demonstrated that damage to the epididymal epithelium and sperm
of CD-I mice (4/group) resulted from exposure to 0 or 1,000 ppm TCE by inhalation for
6 hours/day, 5 days/week, for 1-4 weeks. Segments of the epididymis (caput, corpus, and cauda)
were examined by light and electron microscope. As early as 1 week after TCE exposure,
4-481
-------
degeneration and sloughing of epithelial cells from all three epididymal areas were observed by
light microscopy; these findings became more pronounced by 4 weeks of exposure. Vesiculation
in the cytoplasm, disintegration of basolateral cell membranes, and epithelial cell sloughing were
observed with electron microscopy. Sperm were found in situ in the cytoplasm of degenerated
epididymal cells. A large number of sperm in the lumen of the epididymis were abnormal,
including head and tail abnormalities.
4.8.1.2.2. Oral exposures
A variety of studies were conducted to assess various aspects of male and/or female
reproductive capacity in laboratory animal species following oral exposures to TCE. These are
described below and summarized in Table 4-87. They include studies that focused on male
reproductive outcomes in rats or rabbits following gavage or drinking water exposures (DuTeaux
et al.. 2004a: DuTeaux et al.. 2003: Veeramachaneni et al.. 2001: Zenicketal.. 1984), studies
that focused on female reproductive outcomes in rats following gavage or drinking water
exposures (Wu and Bergen 2008. 2007: Berger and Horner, 2003: Cosby and Dukelow, 1992:
Manson etal., 1984), and studies that assessed fertility and reproductive outcome in both sexes
following dietary exposures to CD-I mice or F344 rats (George et al., 1986: George et al., 1985).
4.8.1.2.2.1. Studies assessing male reproductive outcomes
Zenick et al. (1984) conducted a study in which sexually experienced Long-Evans
hooded male rats were administered 0, 10, 100, or 1,000 mg/kg-day TCE by gavage in corn oil
for 6 weeks. A 4-week recovery phase was also incorporated into the study design. Endpoints
assessed on weeks 1 and 5 of treatment included copulatory behavior, ejaculatory plug weights,
and ejaculated or epididymal sperm measures (count, motility, and morphology). Sperm
measures and plug weights were not affected by treatment, nor were Week 6 plasma testosterone
levels found to be altered. TCE effects on copulatory behavior (ejaculation latency, number of
mounts, and number of intromissions) were observed at 1,000 mg/kg-day; these effects were
recovered by 1-4 weeks posttreatment. Although the effects on male sexual behavior in this
study were believed to be unrelated to narcotic effects of TCE, a later study by Nelson and
Zenick (1986) showed that naltrexone (an opioid receptor antagonist, 2.0 mg/kg, i.p.,
administered 15 minutes prior to testing) could block the effect. Thus, it was hypothesized that
the adverse effects of TCE on male copulatory behavior in the rat at 1,000 ppm may, in fact, be
mediated by the endogenous opioid system at the CNS level.
In a series of experiments by DuTeaux et al. (2004a; 2003), adult male rats were
administered 0, 0.2, or 0.4% TCE (v/v) (equivalent to 0, 2.73, or 5.46 mg/L) in a solution of 3%
ethoxylated castor oil in drinking water for 14 days. These concentrations were within the range
of measurements obtained in formerly contaminated drinking water wells, as reported by
ATSDR (1997b). The average ingested doses of TCE (based upon animal body weight and
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average daily water consumption of 28 mL) were calculated to be 143 or 270 mg/kg-day for the
low- and high-dose groups, respectively (DuTeaux et al., 2003). Cauda epididymal and vas
deferens sperm from treated males were incubated in culture medium with oviductal cumulus
masses from untreated females to assess in vitro fertilization capability. Treatment with TCE
resulted in a dose-dependent decrease in the ability of sperm to fertilize oocytes. Terminal body
weights and testis/epididymal weights were similar between control and treated groups.
Evaluation of sperm concentration or motility parameters did not reveal any treatment-related
alterations; acrosomal stability and mitochondrial membrane potential were not affected by
treatment. Although no histopathological changes were observed in the testis or in the caput,
corpus, or cauda epididymis, exposure to 0.2 and 0.4% TCE resulted in slight cellular alterations
in the efferent ductule epithelium.
Veeramachaneni et al. (2001) evaluated the effects of drinking water containing
chemicals typical of groundwater near hazardous waste sites (including 9.5 or 28.5 ppm TCE) on
male reproduction. In this study, pregnant Dutch-belted rabbits were administered treated
drinking water starting on GD 20; treatment continued through the lactation period and to
weaned offspring (7-9/group) through postnatal week 15. Deionized water was administered
from postnatal weeks 16-61, at which time the animals were terminated. At 57-61 weeks of
age, ejaculatory capability, and seminal, testicular, epididymal, and endocrine characteristics
were evaluated. In both treated groups, long-term effects consisted of decreased copulatory
behavior (interest, erection, and/or ejaculation), significant increases in acrosomal dysgenesis
and nuclear malformations (p < 0.03), and significant decreases in serum concentration of
luteinizing hormone (LH) (p < 0.05) and testosterone secretion after human chorionic
gonadotropin administration (p < 0.04). There were no effects on total spermatozoa per ejaculate
or on daily sperm production. The contribution of individual drinking water contaminants to
adverse male reproductive outcome could not be discerned in this study. Additionally, it was not
designed to distinguish between adverse effects that may have resulted from exposures in late
gestation (i.e., during critical period of male reproductive system development) vs. postnatal life.
4.8.1.2.2.2. Studies assessing female reproductive outcomes
In a study that evaluated postnatal growth following gestational exposures, female
B6D2F1 mice (7-12/group) were administered TCE at doses of 0, 1% LD50 (24 mg/kg-day), and
10% LD50 (240 mg/kg-day) by gavage in corn oil on GDs 1-5, 6-10, or 11-15 (day of mating
was defined as GD 1) (Cosby and Dukelow, 1992). Litters were examined for pup count, sex,
weight, and crown-rump measurement until GD 21. Some offspring were retained to 6 weeks of
age, at which time they were killed and the gonads were removed, weighed, and preserved. No
treatment-related effects were observed in the dams or offspring. In a second series of studies
conducted by (Cosby and Dukelow) and reported in the same paper, TCE and its metabolites,
DCA, TCA, and TCOH, were added to culture media with capacitated sperm and cumulus
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masses from B6D2F1 mice to assess effects on in vitro fertilization. Dose-related decreases in
fertilization were observed for DC A, TCA, and TCOH at 100 and 1,000 ppm, but not with TCE.
Synergystic effects were not observed with TCA and TCOH.
A study was conducted by Manson et al. (1984) to determine if subchronic oral exposure
to TCE affected female reproductive performance, and if TCE or its metabolites, TCA or TCOH,
accumulated in female reproductive organs or neonatal tissues. Female Long-Evans hooded rats
(22-23/group) were administered 0 (corn oil vehicle), 10, 100, or 1,000 mg/kg-day of TCE by
gavage for 2 weeks prior to mating, throughout mating, and to GD 21. Delivered pups were
examined for gross anomalies, and body weight and survival were monitored for 31 days. Three
maternal animals per group and 8-10 neonates per group (killed on GDs 3 and 31) were analyzed
for TCE and metabolite levels in tissues. TCE exposure resulted in five deaths and decreased
maternal body weight gain at 1,000 mg/kg-day, but did not affect estrous cycle length or female
fertility at any dose level. There were no evident developmental anomalies observed at any
treatment level; however, at 1,000 mg/kg-day, there was a significant increase in the number of
pups (mostly female) born dead, and the cumulative neonatal survival count through PND 18
was significantly decreased as compared to control. TCE levels were uniformly high in fat,
adrenal glands, and ovaries across treatment groups, and TCA levels were high in uterine tissue.
TCE levels in the blood, liver, and milk contents of the stomach increased in female PND-3
neonates across treatment groups. These findings suggest that increased metabolite levels did
not influence fertility, mating success, or pregnancy outcome.
In another study that examined the potential effect of TCE on female reproductive
function, Berger and Horner (2003) conducted 2-week exposures of Sprague-Dawley derived
female Simonson rats to tetrachloroethylene, TCE, several ethers, and 4-vinylcyclohexene
diepoxide in separate groups. The TCE-treated group received 0.45% TCE in drinking water
containing 3% Tween vehicle; control groups were administered either untreated water, or water
containing the 3% Tween vehicle. There were 5-6 females/group, and three replicates were
conducted for each group. At the end of exposure, ovulation was induced, the rats were killed,
and the ovaries were removed. The zona pellucida was removed from dissected oocytes, which
were then placed into culture medium and inseminated with sperm from untreated males. TCE
treatment did not affect female body weight gain, the percentage of females ovulating, or the
number of oocytes per ovulating female. Fertilizability of the oocytes from treated females was
reduced significantly (46% for TCE-treated females vs. 56% for vehicle controls). Oocytes from
TCE-treated females had reduced ability to bind sperm plasma membrane proteins compared
with vehicle controls.
In subsequent studies, Wu and Berger (Wu and Berger, 2008, 2007) examined the effect
of TCE on oocyte fertilizibility and ovarian gene expression. TCE was administered to female
Simonson rats (number of subjects not reported) in the drinking water at 0 or 0.45% (in 3%
Tween vehicle); daily doses were estimated to be 0.66 g TCE/kg body weight/day. In the oocyte
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fertilizibility study (Wu and Berger, 2007), the female rats were treated on days 1-5, 6-10, 11-
14, or 1-14 of the 2-week period preceding ovulation (on day 15). Oocytes were extracted and
fertilized in vitro with sperm from a single male donor rat. With any duration of TCE exposure,
fertilization (as assessed by the presence of decondensed sperm heads) was significantly
(p < 0.05) decreased as compared to controls. After exposure on days 6-10, 11-14, or 1-14, the
oocytes from TCE-treated females had a significantly decreased ability to bind sperm (p < 0.05)
in comparison to oocytes from vehicle controls. Increased protein carbonyls (an indicator of
oxidatively modified proteins) were detected in the granulosa cells of ovaries from females
exposed to TCE for 2 weeks. The presence of oxidized protein was confirmed by Western blot
analysis. Microsomal preparations demonstrated the localization of CYP 2E1 and GST (TCE-
metabolizing enzymes) in the ovary. Ovarian mRNA transcription for ALCAM and Cuzdl
protein was not found to be altered after 1 or 5 days of exposure (Wu and Berger, 2008),
suggesting that the posttranslational modification of proteins within the ovary may partially
explain the observed reductions in oocyte fertilization.
4.8.1.2.2.3. Studies assessing fertility and reproductive outcomes in both sexes
Assessments of reproduction and fertility with continuous breeding were conducted in
NTP studies in CD-I mice (George et al.. 1985) and F344 rats (George et al.. 1986). TCE was
administered to the mice and rats at dietary levels of 0, 0.15, 0.30, or 0.60%, based upon the
results of preliminary 14-day dose-range finding toxicity studies. Actual daily intake levels for
the study in mice were calculated from the results of dietary formulation analyses and body
weight/food consumption data at several time points during study conduct; the most conservative
were from the second week of the continuous breeding study: 0, 52.5, 266.3, and 615.0 mg/kg-
day. No intake calculations were presented for the rat study. In these studies, which were
designed as described by Chapin and Sloane (1997), the continuous breeding phase in FO adults
consisted of a 7-day premating exposure, 98-day cohabitation period, and 28-day segregation
period. In rats, a crossover mating trial (i.e., control males x control females; 0.60% TCE males
x control females; control males x 0.60% TCE females) was conducted to further elucidate
treatment-related adverse reproductive trends observed in the continuous breeding phase. The
last litter of the continuous breeding phase was raised to sexual maturity for an assessment of
fertility and reproduction in control and high-dose groups; for the rats, this included an open field
behavioral assessment of Fl pups. The study protocol included terminal studies in both
generations, including sperm evaluation (count, morphology, and motility) in 10 selected males
per dose level, macroscopic pathology, organ weights, and histopathology of selected organs.
In the continuous breeding phase of the CD-I mouse study (George et al., 1985), no
clinical signs of toxicity were observed in the parental (FO) animals, and there were no treatment-
related effects on the proportion of breeding pairs able to produce a litter, number of live pups
per litter, percentage born live, proportion of pups born live, sex of pups born live, absolute live
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pup weights, or adjusted female pup weights. At the high-dose level of 0.60%, a number of
adverse outcomes were observed. In the parental animals, absolute and body-weight-adjusted
male and female liver weight values were significantly increased (p < 0.01), and right testis and
seminal vesicle weights were decreased (p < 0.05), but kidney/adrenal weights were not affected.
Sperm motility was significantly (p < 0.01) decreased by 45% in treated males as compared to
controls. Histopathology examination revealed lesions in the liver (hypertrophy of the
centrilobular liver cells) and kidneys (tubular degeneration and karyomegaly of the
corticomedullary renal tubular epithelium) of FO males and females. In the pups at 0.60%,
adjusted live birth weights for males and both sexes combined were significantly decreased
(p < 0.01) as compared to control. The last control and high-dose litters of the continuous
breeding assessment were raised to the age of sexual maturity for a further assessment of
reproductive performance. In these Fl pups, body weights (both sexes) were significantly
decreased at PND 4, and male offspring body weights were significantly (p < 0.05) less than
controls at PND 74 (±10). It was reported that perinatal mortality (PNDs 0-21) was increased,
with a 61.3% mortality rate for TCE-treated pups vs. a 28.3% mortality rate for control pups.
Reproductive performance was not affected by treatment, and postmortem evaluations of the Fl
adult mice revealed significant findings at 0.60% TCE that were consistent with those seen in the
FO adults and additionally demonstrated renal toxicity (i.e., elevated liver and kidney/adrenal
weights and hepatic and renal histopathological lesions in both sexes) elevated testis and
epididymis weights in males, and decreased sperm motility (18% less than control).
The F344 rat study continuous breeding phase demonstrated no evidence of treatment-
related effects on the proportion of breeding pairs able to produce a litter, percentage of pups
born alive, the sex of pups born alive, or absolute or adjusted pup weights (George et al., 1986).
However, the number of live pups per litter was significantly (p < 0.05) decreased at 0.30 and
0.60% TCE, and a significant (p < 0.01) trend toward a dose-related decrease in the number of
live litters per pair was observed; individual data were reported to indicate a progressive decrease
in the number of breeding pairs in each treatment group producing third, fourth, and fifth litters.
The crossover mating trial conducted in order to pursue this outcome demonstrated that the
proportion of detected matings was significantly depressed (p < 0.05) in the mating pairs with
TCE-treated partners compared to the control pairs. In the FO adults at 0.60% TCE, postpartum
dam body weights were significantly decreased (p < 0.01 or 0.05) in the continuous breeding
phase and the crossover mating trials, and terminal body weights were significantly decreased
(p < 0.01) for both male and female rats. Postmortem findings for FO adults in the high-dose
group included significantly increased absolute and body-weight-adjusted liver and
kidney/adrenal weights in males, increased adjusted liver and kidney/adrenal weights in females,
and significantly increased adjusted left testis/epididymal weights. Sperm assessment did not
identify any effects on motility, concentration, or morphology, and histopathological
examination was negative. The last control and high-dose litters of the continuous breeding
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assessment were raised to the age of sexual maturity for assessment of open field behavior and
reproductive performance. In these Fl pups at 0.60% TCE, body weights of male and females
were significantly (p < 0.05 or 0.01, respectively) decreased at PNDs 4 and 14. By PND 21, pup
weights in both sexes were significantly reduced in all treated groups, and this continued until
termination (approximately PND 80). A tendency toward decreased postweaning survival (i.e.,
from PND 21 to PND 81 ± 10) was reported for Fl pups at the 0.15 and 0.60% levels. Open
field testing revealed a significant (p < 0.05) dose-related trend toward an increase in the time
required for male and female Fl weanling pups to cross the first grid in the testing device,
suggesting an effect on the ability to react to a novel environment. Reproductive performance
assessments conducted in this study phase were not affected by treatment. Postpartum Fl dam
body weights were significantly decreased (p < 0.05 or 0.01) in all of the TCE-treated groups as
compared to controls, as were terminal body weights for both adult Fl males and females.
Postmortem evaluations of the Fl adult rats revealed significantly (p < 0.01) decreased left
testis/epididymis weight at 0.60% TCE, and significantly (p < 0.05 or 0.01) increased adjusted
mean liver weight in all treated groups for males and at 0.30 and 0.60% for females. Sperm
assessments for Fl males revealed a significant increase (p < 0.05) in the percentage of abnormal
sperm in the 0.30% TCE group, but no other adverse effects on sperm motility, concentration, or
morphology were observed. As with the FO adults, there were no adverse treatment-related
findings revealed at histopathological assessment. The study authors concluded that the
observed effects to TCE exposure in this study were primarily due to generalized toxicity and not
to a specific effect on the reproductive system; however, based upon the overall toxicological
profile for TCE, which demonstrates that the male reproductive system is a target for TCE
exposures, this conclusion is not supported.
4.8.1.3. Discussion/Synthesis of Noncancer Reproductive Toxicity Findings
The human epidemiological findings and animal study evidence consistently indicate that
TCE exposures can result in adverse reproductive outcomes. Although the epidemiological data
may not always be robust or unequivocal, they demonstrate the potential for a wide range of
exposure-related adverse outcomes on female and male reproduction. In animal studies, there is
some evidence for female-specific reproductive toxicity; but there is strong and compelling
evidence for adverse effects of TCE exposure on male reproductive system and function.
4.8.1.3.1. Female reproductive toxicity
Although few epidemiological studies have examined TCE exposure in relation to female
reproductive function (see Table 4-88), the available studies provide evidence of decreased
fertility, as measured by time to pregnancy (Sallmen etal., 1995) and effects on menstrual cycle
patterns, including abnormal cycle length (ATSDR, 2001), amenorrhea (Sagawa et al., 1973;
Zielinski, 1973), and menstrual —disturbanc'e (Bardodej and Vyskocil, 1956). In experimental
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animals, the effects on female reproduction include evidence of reduced in vitro oocyte
fertilizability in rats (Wu and Berger, 2007; Berger and Horner, 2003). However, in other
studies that assessed reproductive outcome in female rodents (Cosby and Dukelow, 1992;
George et al., 1986; George et al., 1985; Manson et al., 1984), there was no evidence of adverse
effects of TCE exposure on female reproductive function. Overall, although the data are
suggestive, there are inadequate data to make conclusions as to whether adverse effects on
human female reproduction are caused by TCE.
Table 4-88. Summary of adverse female reproductive outcomes associated
with TCE exposures
Finding
Menstrual cycle disturbance
Reduced fertility
Species
Human
Human3
Ratb
References
ATSDR (200 IV
Bardodej and Vyskocil (1956)
Sagawa et al. (1973)
Zielinski (1973)
Sallmen et al. (1995)
Berger and Horner (2003)
Wu and Berger (2007)
aNot significant.
bln vitro oocyte fertilizability.
4.8.1.3.2. Male reproductive toxicity
Notably, the results of a number of studies in both humans and experimental animals
have suggested that exposure to TCE can result in targeted male reproductive toxicity (see
Table 4-89). The adverse effects that have been observed in both male humans and male animal
models include altered sperm count, morphology, or motility (Kumar et al., 200lb:
Veeramachaneni et al.. 2001: Kumar et al.. 2000a: Kumar et al.. 2000b: Chiaetal., 1996:
Rasmussen et al., 1988: George et al., 1985: Landetal., 1981): decreased libido or copulatory
behavior (Veeramachaneni et al., 2001: George et al., 1986: Zenick et al., 1984: Saihan et al.,
1978: El Ghawabi et al., 1973: Bardodej and Vyskocil, 1956): alterations in serum hormone
levels (Veeramachaneni et al., 2001: Kumar et al., 2000a: Gohetal., 1998: ChiaetaL, 1997):
and reduced fertility (George et al., 1986). However, other studies in humans did not see
evidence of altered sperm count or morphology (Rasmussen et al., 1988) or reduced fertility
(Forkert et al., 2003: Sallmen et al., 1998), and some animal studies also did not identify altered
sperm measures (Xu et al., 2004: Cosby and Dukelow, 1992: George et al., 1986: Zenick et al.,
1984). Additional adverse effects observed in animals include histopathological lesions of the
testes (Kumar et al., 200lb: Kumar et al., 2000b: George et al., 1986) or epididymides (Kan et
al., 2007: Forkert et al., 2002) and altered in vitro sperm-oocyte binding and/or in vivo
fertilization for TCE and/or its metabolites (DuTeaux et al., 2004a: Xu et al., 2004).
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Table 4-89. Summary of adverse male reproductive outcomes associated
with TCE exposures
Finding
Testicular toxicity /pathology
Epididymal toxicity /pathology
Decreased sperm quantity/quality
Altered in vitro sperm-oocyte binding or in vivo fertilization
Altered sexual drive or function
Altered serum testosterone levels
Reduced fertility
Gynaecomastia
Species
Rat
Mouse
Mouse
Human
Rat
Mouse
Rabbit
Rat
Mouse
Human
Rat
Rabbit
Human
Rat
Rabbit
Rat
Human
References
George et al. (1986)
Kumar et al. QQQQb)
Kumar et al. (200 lb)
Kan etal. (2007)
Forkert et al. (2002)
Chia et al. (1996)
Rasmussen et al. (1988)a
Kumar et al. (200 Ib; 2000a; 2000b)
George et al. (1985)
Land et al. (1981)
Veeramachaneni et al. (2001)
DuTeaux et al. (2004a)
Cosby and Dukelow (1992)b
Xu et al. (2004)b
El Ghawabi et al. (1973)
Saihan etal. (1978)°
Bardodej and Vyskocil (1956)
George et al. (1986)
Zenick et al. (1984)
Veeramachaneni et al. (2001)
Chia et al. (1997)d
Goh et al. (1998)e
Kumar et al. (2000a)
Veeramachaneni et al. (2001)
George et al. (1986)
Saihan et al. (1978)°
"Nonsignificant increase in percentage of two YFF in spermatozoa; no effect on sperm count or morphology.
bObserved with metabolite(s) of TCE only.
°Case study of one individual.
dAlso observed altered levels of DHEAS, FSH, and SHBG.
eAlso observed altered levels of SHBG.
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In spite of the preponderance of studies demonstrating effects on sperm parameters, there
is an absence of overwhelming evidence in the database of adverse effects of TCE on overall
fertility in the rodent studies. That is not surprising, however, given the redundancy and
efficiency of rodent reproductive capabilities. Nevertheless, the continuous breeding
reproductive toxicity study in rats (George etal., 1986) did demonstrate a trend towards
reproductive compromise (i.e., a progressive decrease in the number of breeding pairs producing
third, fourth, and fifth litters).
It is noted that in the studies by George et al. (1986; George etal., 1985), adverse
reproductive outcomes in male rats and mice were observed at the highest dose level tested
(0.060% TCE in diet), which was also systemically toxic (i.e., demonstrating kidney toxicity and
liver enzyme induction and toxicity, sometimes in conjunction with body weight deficits).
Because of this, the study authors concluded that the observed reproductive toxicity was a
secondary effect of generalized systemic toxicity; however, this conclusion is not supported by
the overall toxicological profile of TCE, which provides significant evidence indicating that TCE
is a reproductive toxicant.
4.8.1.3.2.1. The role of metabolism in male reproductive toxicity
There has been particular focus on evidence of exposure to male reproductive organs by
TCE and/or its metabolites, as well as the role of TCE metabolites in the observed toxic effects.
In humans, a few studies demonstrating male reproductive toxicity have measured levels
of TCE in the body. U-TCA was measured in men employed in an electronics factory, and
adverse effects observed included abnormal sperm morphology and hyperzoospermia and altered
serum hormone levels (Gohetal.. 1998: Chiaetal.. 1997: Chiaetal.. 1996). U-TCA was also
measured as a marker of exposure to TCE in men occupationally exposed to solvents, although
this study did not report any adverse effects on fertility (Sallmen et al., 1998).
In the study in Long-Evans male rats by Zenick et al. (1984), blood and tissue levels of
TCE, TCA, and TCOH were measured in three rats/group following 6 weeks of gavage treatment
at 0, 10, 100, and 1,000 mg/kg-day. Additionally, the levels of TCE and metabolites were
measured in seminal plugs recovered following copulation at week 5. Marked increases in TCE
levels were observed only at 1,000 mg/kg-day, in blood, muscle, adrenals, and seminal plugs. It
was reported that dose-related increases in TCA and TCOH concentrations were observed in the
organs evaluated, notably including the reproductive organs (epididymis, vas deferens, testis,
prostate, and seminal vesicle), thus creating a potential for interference with reproductive
function.
This potential was explored further in a study by Forkert et al. (2002), in which male
CD-I mice were exposed by inhalation to 1,000 ppm TCE (6 hours/day, 5 days/week) for
4 consecutive weeks. Urine was obtained on days 4, 9, 14, and 19 of exposure and analyzed for
concentrations of TCE and TCOH. Microsomal preparations from the liver, testis, and
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epididymis were used for immunoblotting, determining/>-nitrophenol hydroxylase and CYP2E1
activities, and evaluating the microsomal metabolism of TCE.
Subsequent studies conducted by the same laboratory (Forkert et al., 2003) evaluated the
potential of the male reproductive tract to accumulate TCE and its metabolites including chloral,
TCOH, TCA, and DCA. Human seminal fluid and urine samples from eight mechanics
diagnosed with clinical infertility and exposed to TCE occupationally were analyzed. Urine
samples from two of the eight subjects contained TCA and/or TCOH, suggesting that TCE
exposure and/or metabolism was low during the time just prior to sample collection. TCE,
chloral, and TCOH were detected in seminal fluid samples from all eight subjects, while TCA
was found in one subject, and DCA was found in two subjects. Additionally, TCE and its
metabolites were assessed in the epididymis and testis of CD-I mice (4/group) exposed by
inhalation (6 hours/day, 5 days/week) to 1,000 ppm TCE for 1, 2, and 4 weeks. TCE, chloral,
and TCOH were found in the epididymis at all timepoints, although TCOH levels were increased
significantly (tripled) at 4 weeks of exposure. This study showed that the metabolic disposition
of TCE in humans is similar to that in mice, indicating that the murine model is appropriate for
investigating the effects of TCE-induced toxicity in the male reproductive system. These studies
provide support for the premise that TCE is metabolized in the human reproductive tract, mainly
in the epididymis, resulting in the production of metabolites that cause damage to the epididymal
epithelium and affect the normal development of sperm.
Immunohistochemical experiments (Forkert et al., 2002) confirmed the presence of
CYP2E1 in the epididymis and testis of mice; it was found to be localized in the testicular
Leydig cells and the epididymal epithelium. Similar results were obtained with the
immunohistochemical evaluation of human and primate tissue samples. CYP2E1 has been
previously shown by Lipscomb et al. (1998a) to be the predominant CYP enzyme catalyzing the
hepatic metabolism of TCE in both animals and rodents. These findings support the role of
CYP2E1 in TCE metabolism in the male reproductive tract of humans, primates, and mice.
4.8.1.3.2.2. Mode of action for male reproductive toxicity
A number of studies have been conducted to attempt to characterize various aspects of
the mode of action for observed male reproductive outcomes.
Studies by Kumar et al. (2001b: 2000a) suggest that perturbation of testosterone
biosynthesis may have some role in testicular toxicity and altered sperm measures. Significant
decreases in the activity of G6PDH and accumulation of cholesterol are suggestive of an
alteration in testicular steroid biosynthesis. Increased testicular lipids, including cholesterol,
have been noted for other testicular toxicants such as lead (Saxena et al., 1987),
triethylenemelamine (Johnson et al., 1967), and quinalphos (Ray et al., 1987), in association with
testicular degeneration and impaired spermatogenesis. Since testosterone has been shown to be
essential for the progression of spermatogenesis (O'Donnell et al., 1994), alterations in
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testosterone production could be a key event in male reproductive dysfunction following TCE
exposure. Additionally, the observed TCE-related reduction of 17-P-HSD, which is involved in
the conversion of androstenedione to testosterone, has also been associated with male
reproductive insufficiency following exposure to phthalate esters (Srivastava, 1991), quinalphos
(Ray etal., 1987), and lead (Saxena et al., 1987). Reductions in SDH, which are primarily
associated with the pachytene spermatocyte maturation of germinal epithelium, have been shown
to be associated with depletion of germ cells (Chapin et al., 1982; Mills and Means, 1972), and
the activity of G6PDH is greatest in premeiotic germ cells and Leydig cells of the interstitium
(Blackshaw, 1970). The increased GT and glucuronidase observed following TCE exposures
appear to be indicative of impaired Sertoli cell function (Sherins and Hodgen, 1976; Hodgen and
Sherins, 1973). Based upon the conclusions of these studies, Kumar et al. (200 Ib) hypothesized
that the reduced activity of G6PDH and SDH in testes of TCE-exposed male rats is indicative of
the depletion of germ cells, spermatogenic arrest, and impaired function of the Sertoli cells and
Leydig cells of the interstitium.
In the series of experiments by DuTeaux et al. (2004a; 2003), protein dichloroacetyl
adducts were found in the corpus epididymis and in the efferent ducts of rats administered TCE;
this effect was also demonstrated following in vitro exposure of reproductive tissues to TCE.
Oxidized proteins were detected on the surface of spermatozoa from TCE-treated rats in a dose-
response pattern; this was confirmed using a Western blotting technique. Soluble (but not
mitochondrial) cysteine-conjugate p-lyase was detected in the epididymis and efferent ducts of
treated rats. Following a single i.p. injection of DCVC, no dichloroacetylated protein adducts
were detected in the epididymis and efferent ducts. The presence of CYP2E1 was found in
epididymis and efferent ducts, suggesting a role of CYP-dependent metabolism in adduct
formation. An in vitro assay was used to demonstrate that epididymal and efferent duct
microsomes are capable of metabolizing TCE; TCE metabolism in the efferent ducts was found
to be inhibited by anti-CYP2El antibody. Lipid peroxidation in sperm, presumably initiated by
free radicals, was increased in a significant (p < 0.005) dose-dependent manner after TCE
exposure.
Overall, it has been suggested (DuTeaux et al., 2004a) that reproductive organ toxicities
observed following TCE exposure are initiated by metabolic bioactivation, leading to subsequent
protein adduct formation. It has been hypothesized that epoxide hydrolases in the rat epididymis
may play a role in the biological activation of metabolites (DuTeaux et al., 2004b). Disruption
of colony stimulating factor and of macrophage development may also play a role in sperm
production (Cohen etal., 1999), and thus, may be another route through which immune-related
effects of TCE may operate. In addition, the potential for epigenetic changes, through which
heritable changes in gene mutations occur without changes in DNA sequencing, should also be
considered in the evaluation of transgenerational effects (Guerrero-Bosagna and Skinner, 2009).
4-492
-------
4.8.1.3.3. Summary of noncancer reproductive toxicity
The toxicological database for TCE includes a number of studies that demonstrate
adverse effects on the integrity and function of the reproductive system in females and males.
Both the epidemiological and animal toxicology databases provide suggestive, but limited,
evidence of adverse outcomes to female reproductive outcomes. However, much more extensive
evidence exists in support of an association between TCE exposures and male reproductive
toxicity. The available epidemiological data and case reports that associate TCE with adverse
effects on male reproductive function are limited in size and provide little quantitative dose data
(Lamb and Hentz, 2006). However, the animal data provide extensive evidence of TCE-related
male reproductive toxicity. Strengths of the database include the presence of both functional and
structural outcomes, similarities in adverse treatment-related effects observed in multiple species,
and evidence that metabolism of TCE in male reproductive tract tissues is associated with
adverse effects on sperm measures in both humans and animals (suggesting that the murine
model is appropriate for extrapolation to human health risk assessment). Additionally, some
aspects of a putative mode of action (e.g., perturbations in testosterone biosynthesis) appear to
have some commonalities between humans and animals.
4.8.2. Cancers of the Reproductive System
The effects of TCE on cancers of the reproductive system have been examined for males
and females in both epidemiological and experimental animal studies. The epidemiological
literature includes data on prostate in males and cancers of the breast and cervix in females. The
experimental animal literature includes data on prostate and testes in male rodents; and uterus,
ovary, mammary gland, vulva, and genital tract in female rodents. The evidence for these
cancers is generally not robust.
4.8.2.1. Human Data
The epidemiologic evidence on TCE and cancer of the prostate, breast, and cervix is from
cohort and geographic-based studies. Two additional case-control studies of prostate cancer in
males are nested within cohorts (Krishnadasan et al., 2007; Greenland et al., 1994). The nested
case-control studies are identified in Tables 4-90 through 4-92 with cohort studies given their
source population for case and control identification. One population-based, case-control study
examined on TCE exposure and prostate (Siemiatycki, 1991): however, no population case-
control studies on breast or cervical cancers and TCE exposure were found in the peer-reviewed
literature.
4-493
-------
Table 4-90. Summary of human studies on TCE exposure and prostate
cancer
Studies
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Cohort studies — incidence
Aerospace workers (Rocketdyne)
Low/moderate TCE score
High TCE score
p for trend
Low/moderate TCE score
High TCE score
p for trend
All employees at electronics factory (Taiwan)
1.3 (0.81, 2.1fb
2.1(1.2, 3.9)a'b
0.02
1.3(0.81,2.1)a'c
2.4 (1.3, 4.4)a'c
0.01
0.14(0.00, 0.76)d
90
45
1
Danish blue-collar worker with TCE exposure
Any exposure
0.9 (0.79, 1.08)
163
Biologically -monitored Danish workers
Any TCE exposure, females
0.6 (0,2, 1.3)
6
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
Cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
Cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Biologically -monitored Finnish workers
Mean air-TCE (Ikeda extrapolation
<6ppm
6+ppm
Not reported
1.0e
1.1 (0.7, 1.6)
1.0 (0.6, 1.6)
1.2 (0.8, 1.8)
1.2 (0.92, 1.76)
1.0"
1.03 (0.65, 1.62)
1.33(0.82,2.15)
1.31(0.84,2.06)
1.38(0.73,2.35)
1.43 (0.62, 2.82)
0.68 (0.08, 2.44)
158
64
38
56
116
41
42
43
13
8
2
Krishnadasan et al. (2007)
Chang et al. (2005)
Raaschou-Nielsen et al.
(2003)
Hansen et al. (2001)
Blair et al. (1998)
Radican et al. (2008)
Anttila et al. (1995)
4-494
-------
Table 4-90. Summary of human studies on TCE exposure and prostate
cancer (continued)
Studies
Exposure group
RR (95% CI)
Number of
observable
events
Cardboard manufacturing workers in Arnsburg, Germany
Exposed workers
Biologically -monitored Swedish workers
Cardboard manufacturing workers, Atlanta area,
Georgia
Not reported
1.25 (0.84, 1.84)
Not reported
26
Reference
Henschler et al. (1995)
Axelson et al. (1994)
Sinks et al. (1992)
Cohort and PMR-mortality
Aerospace workers (Rocketdyne)
Any TCE (utility/eng flush)
View-Master employees
All employees at electronics factory (Taiwan)
0.82 (0.36, 1.62)
1.69 (0.68, 3.48)f
Not reported
8
8
0
Fernald workers
Any TCE exposure
Light TCE exposure, >2-yr duration
Moderate TCE exposure, >2-yr duration
Not reported
0.91(0.38, 2.18)e'g
1.44(0.19, 11.4)e'g
10
1
Aerospace workers (Lockheed)
Routine exposure to TCE
Routine-intermittent
1.31 (0.52,2.69)
Not reported
7
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)
High intensity (>50 ppm)
1.18(0.73, 1.80)
1.03 (0.51, 1.84)
0.47(0.15, 1.11)
21
7
14
TCE subcohort (Cox Analysis)
Never exposed
Ever exposed
1.00e
1.58 (0.96, 2.62)h
Peak
No/low
Medium/high
1.00e
1.39 (0.80, 2.41)h
Cumulative
Referent
Low
High
1.00e
1.72 (0.78, 3.80)h
1.53 (0.85, 2.75)h
Boice et al. (2006b)
ATSDR (2004a)
Chang et al. (2003)
Ritz ( 1999a)
Boice et al. (1999)
Morgan et al. (2000. 1998)
4-495
-------
Table 4-90. Summary of human studies on TCE exposure and prostate
cancer (continued)
Studies
Exposure group
RR (95% CI)
Number of
observable
events
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
1.1 (0.6, 1.8)
54
Cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0e
0.9(0.5, 1.8)
1.0(0.5,2.1)
1.3 (0.7, 2.4)
19
13
22
Cardboard manufacturing workers in Arnsburg, Germany
TCE exposed workers
Deaths reported to GE pension fund (Pittsfield,
Massachusetts)
Cardboard manufacturing workers, Atlanta area,
Georgia
Not reported
0.82 (0.46, 1.46)a
Not reported
58
0
U.S. Coast Guard employee
Marine inspectors
Noninspectors
1.06 (0.51, 1.95)
0.57(0.15, 1.45)
10
7
Aircraft manufacturing plant employees (Italy)
Aircraft manufacturing plant employees (San Diego,
California)
Lamp manufacturing workers (GE)
0.93 (0.60, 1.37)
1.56 (0.63, 3.22)
25
7
Rubber workers
Any TCE exposure
0.62 (not reported)
3
Reference
Blair et al. (1998)
Henschler et al. (1995)
Greenland et al. (1994)
Sinks et al. (1992)
Blair et al. (1989)
Costa et al. (1989)
Garabrant et al. (1988)
Shannon et al. (1988)
Wilcosky et al. (1984)
Case-control studies
Population of Montreal, Canada
Any TCE exposure
Substantial TCE exposure
1.1(0.6,2.1)'
1.8 (0.8, 4.0)1
11
7
Siemiatycki (1991)
Geographic-based studies
Residents in two study areas in Endicott, New York
Residents of 13 census tracts inRedlands, California
1.05 (0.75, 1.43)
1.11(0.98, 1.25)1
40
483
Finnish residents
Residents of Hausjarvi
Residents of Huttula
Not reported
Not reported
ATSDR (2006a)
Morgan and Cassady (2002)
Vartiainen et al. (1993)
aOR from nested case-control study.
bOR, zero lag.
COR, 20-year lag.
dChang et al. (2005) presents SIRs for a category site of all cancers of male genital organs.
Internal referents, workers without TCE exposure.
fPMR.
8Analysis for >2 years exposure duration and a lagged TCE exposure period of 15 years.
hRisk ratio from Cox Proportional Hazard Analysis, stratified by age and sex, from Environmental Health Strategies
(1997) Final Report to Hughes Corporation (Communication from Paul A. Cammer, President, Trichloroethylene
Issues Group to Cheryl Siegel Scott, U.S. EPA, December 22, 1997).
'90% CI.
J99% CI.
4-496
-------
Table 4-91. Summary of human studies on TCE exposure and breast cancer
Studies
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Cohort studies — incidence
Aerospace workers (Rocketdyne)
Any TCE exposure
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
All employees at electronics factory (Taiwan)
Females
Females
Not reported
1.09 (0.96, 1.22)a
1.19(1.03, 1.36)
286
215
Danish blue-collar worker with TCE exposure
Any exposure, males
Any exposure, females
0.5 (0.06, 1.90)
1.1 (0.89, 1.24)
2
145
Biologically -monitored Danish workers
Any TCE exposure, males
Any TCE exposure, females
0.9 (0.2, 2.3)
0
(0.2 exp)
4
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Biologically -monitored Finnish workers
Not reported
1.0b
0.3 (0.1, 1.4)
0.4 (0.1,2.9)
0.4 (0.4, 1.2)
Not reported
34
20
11
o
J
Cardboard manufacturing workers in Arnsburg, Germany
Exposed workers
Biologically -monitored Swedish workers
Cardboard manufacturing workers, Atlanta area,
Georgia
Not reported
Not reported
Not reported
Zhao et al. (2005)
Sung et al. (2007)
Chang et al. (2005)
Raaschou-Nielsen et al.
(2003)
Hansenetal. (2001)
Blair et al. (1998)
Anttila et al. (1995)
Henschler et al. (1995)
Axelson et al. (1994)
Sinks et al. (1992)
4-497
-------
Table 4-91. Summary of human studies on TCE exposure and breast cancer
(continued)
Studies
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Cohort and PMR-mortality
Aerospace workers (Rocketdyne)
Any TCE (utility/eng flush)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
Not reported
Not reported
Not reported
Not reported
Not reported
View-Master employees
Males
Females
1.02 (0.67, 1.49)c
0
(0.05 exp)
27
Fernald workers
Any TCE exposure
Light TCE exposure, >2-yr duration
Moderate TCE exposure, >2-yr duration
Not reported
Not reported
Not reported
Aerospace workers (Lockheed)
Routine exposure to TCE
Routine-intermittent3
1.31(0.52, 2.69)d
Not reported
7
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)
High intensity (>50 ppm)
0.75 (0.43, 1.22)d
1.03 (0.51, 1.84)d
0.47(0.15, l.ll)d
16
11
5
TCE subcohort (Cox Analysis)
Never exposed
Ever exposed
1.00d
0.94 (0.51, 1.75)d'e
NR
NR
Peak
No/low
Medium/high
1.00d
1.14(0.48, 2.70)d'e
NR
Cumulative
Referent
Low
High
1.00b
1.20 (0.60, 2.40)d'e
0.65 (0.25, 1.69)d'e
NR
NR
Boice et al. (2006b)
Zhao et al. (2005)
ATSDR (2004a)
Ritz (1999a)
Boice et al. (1999)
Morgan et al. (1998)
4-498
-------
Table 4-91. Summary of human studies on TCE exposure and breast cancer
(continued)
Studies
Exposure group
RR (95% CI)
Number of
observable
events
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort (females)
2.0 (0.9, 4.6)
20
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Low level intermittent exposure
Low level continuous exposure
Frequent peaks
TCE subcohort (females)
1.0b
2.4(1.1,5.2)
1.2(0.3,5.4)
1.4 (0.6, 3.2)
3.1(1.5,6.2)
3.4(1.4,8.0)
1.4 (0.7, 3.2)
1.23 (0.73, 2.06)
10
21
8
15
8
10
26
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Low level intermittent exposure
Low level continuous exposure
Frequent peaks
1.0b
1.57 (0.81, 3.04)
1.01(0.31,3.30)
1.05 (0.53, 2.07)
1.92 (1.08, 3.43)
1.71 (0.79, 3.71)
1.08 (0.57, 2.02)
12
3
11
18
8
14
Cardboard manufacturing workers in Arnsburg, Germany
TCE exposed workers
Deaths reported to GE pension fund (Pittsfield,
Massachusetts)
Cardboard manufacturing workers, Atlanta area,
Georgia
Not examined
Not reported
Not reported
0
U.S. Coast Guard employees
Marine inspectors
Noninspectors
Aircraft manufacturing plant employees (Italy)
Not reported
Not reported
Not reportedf
Aircraft manufacturing plant employees (San Diego, California)
All subjects, females
Lamp manufacturing workers (GE)
Coil/wire drawing
Other areas
0.81 (0.52, 1.48)d
2.04 (0.88, 4.02)
0.97 (0.57, 1.66)
16
8
13
Reference
Blair et al. (1998)
Radican et al. (2008)
Henschler et al. (1995)
Greenland et al. (1994)
Sinks et al. (1992)
Blair et al. (1989)
Costa et al. (1989)
Garabrant et al. (1988)
Shannon et al. (1988)
Case-control studies
Population of Montreal, Canada
Any TCE exposure
Substantial TCE exposure
Not reported
Not reported
Siemiatycki (1991)
4-499
-------
Table 4-91. Summary of human studies on TCE exposure and breast cancer
(continued)
Studies
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Geographic-based studies
Residents in two study areas in Endicott, New York
Residents of 13 census tracts inRedlands, California
0.88(0.65, 1.18)
1.09 (0.97, 1.21)
46
536
Finnish residents
Residents of Hausjarvi
Residents of Huttula
Not reported
Not reported
ATSDR (2006a)
Morgan and Cassady (2002)
Vartiainen et al. (1993)
al5-year lag.
blnternal referents, workers not exposed to TCE.
CPMR.
dln Garabrant et al. (1988). Morgan et al. (1998). and Boice et al. (1999). breast cancer risk is for males and females
combined (ICD-9, 174, 175).
eRisk ratio from Cox Proportional Hazard Analysis, stratified by age and sex, from Environmental Health Strategies
(1997) Final Report to Hughes Corporation c,c.CEa.
fThe cohort of Blair et al. (1989) and Costa et al. (1989) are composed of males only.
NR = not reported
4-500
-------
Table 4-92. Summary of human studies on TCE exposure and cervical
cancer
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Cohort studies — incidence
Aerospace workers (Rocketdyne)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
All employees at electronics factory (Taiwan)
Not reported
Not reported
0.96 (0.86, 1.22)a
337
Danish blue-collar worker w/TCE exposure
Any exposure
1.9 (1.42,2.37)
62
Exposure lag time
20yrs
1.5(0.7,2.9)
9
Employment duration
5yrs
2.5(1.7,3.5)
1.6(1.0,2.4)
1.3(0.6,2.4)
30
22
10
Biologically -monitored Danish workers
Any TCE exposure
3.8(1.0,9.8)
4
Cumulative exposure (Ikeda)
<17 ppm-yr
>17 ppm-yr
2.9 (0.04, 16)
2.6 (0.03, 14)
1
1
Mean concentration (Ikeda)
<4ppm
4+ppm
3.4 (0.4, 12)
4.3 (0.5, 16)
2
2
Employment duration
<6.25 yrs
>6.25 yrs
3.8(0.1,21)
2.1 (0.03, 12)
1
1
Aircraft maintenance workers from Hill Air Force Base, Utah
TCE subcohort
Cumulative exposure
Not reported
Not reported
Zhao et al. (2005)
Sung et al. (2007)
Raaschou-Nielsen et al.
(2003)
Hansen et al. (2001)
Blair et al. (1998)
4-501
-------
Table 4-92. Summary of human studies on TCE exposure and cervical
cancer (continued)
Exposure group
RR (95% CI)
Number of
observable
events
Biologically -monitored Finnish workers
All subjects
2.42 (1.05, 4.77)
8
Mean air-TCE (Ikeda extrapolation)
<6ppm
6+ppm
1.86 (0.38, 5.45)
4.35(1.41, 10.1)
3
5
Cardboard manufacturing workers in Arnsburg, Germany
Exposed workers
Not reported
Biologically -monitored Swedish workers
Any TCE exposure
Not reported
Cardboard manufacturing workers, Atlanta area, Georgia
All subjects
Not reported
Reference
Anttila et al. (1995)
Henschler et al. (1995)
Axelson et al. (1994)
Sinks et al. (1992)
Cohort studies-mortality
Aerospace workers (Rocketdyne)
Any TCE (utility /eng flush)
Any exposure to TCE
Not reported
Not reported
View-Master employees
Females
1.77 (0.57, 4.12)b
5
United States uranium-processing workers (Fernald, Ohio)
Any TCE exposure
Light TCE exposure, >2-yr duration
Moderate TCE exposure, >2-yr duration
Not reported
Not reported
Not reported
Aerospace workers (Lockheed)
Routine exposure
Routine-intermittent
- (0.00, 5.47)
Not reported
0
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)
High intensity (>50 ppm)
(0.00, 1.07)
0
(3.5 exp)
0
(1.91 exp)
0
(1.54 exp)
Boice et al. (2006b)
Zhao et al. (2005)
ATSDR (2004a)
Ritz (1999a)
Boice et al. (1999)
Morgan et al. (1998)
4-502
-------
Table 4-92. Summary of human studies on TCE exposure and cervical
cancer (continued)
Exposure group
RR (95% CI)
Number of
observable
events
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
1.8(0.5, 6.5)c
5
Cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
1.0C
0.9(0.1,8.3)
3.0(0.8, 11.7)
1.67 (0.54, 5.22)
1
0
4
6
Cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0C
0.76 (0.09, 6.35)
2.83 (0.86, 9.33)
1
0
5
Cardboard manufacturing workers in Arnsburg, Germany
TCE exposed workers
Unexposed workers
Deaths reported to GE pension fund (Pittsfield,
Massachusetts)
Cardboard manufacturing workers, Atlanta area,
Georgia
U.S. Coast Guard employees
Aircraft manufacturing plant employees (Italy)
Not reported
Not reported
Not examinedd
Not reported
Not reported6
Not reported6
Aircraft manufacturing plant employees (San Diego, California)
All subjects
0.61 (0.25, 1.26)f
7
Lamp manufacturing workers (GE)
Coil/wire drawing
Other areas
1.05 (0.03, 5.86)
1.16(0.32,2.97)
1
4
Reference
Blair et al. (1998)
Radican et al. (2008)
Henschler et al. (1995)
Greenland et al. (1994)
Sinks et al. (1992)
Blair et al. (1989)
Costa et al. (1989)
Garabrant et al. (1988)
Shannon et al. (1988)
4-503
-------
Table 4-92. Summary of human studies on TCE exposure and cervical
cancer (continued)
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Case-control studies
Geographic-based studies
Residents in two study areas in Endicott, New York
Residents in Texas
Counties reporting any air TCE release
Countries not reporting any air TCE release
Residents of 13 census tracts inRedlands, California
1.06 (0.29, 2.71)
66.4g
60.8g
0.65 (0.38, 1.02)
<6
29
Finnish residents
Residents of Hausjarvi
Residents of Huttula
Not reported
Not reported
ATSDR (2006a)
Coyle et al. (2005)
Morgan and Cassady (2002)
Vartiainen et al. (1993)
"SIR for females in Sung et al. (2007) reflects a 15-year lag period.
bPMR.
Internal referents, workers not exposed to TCE.
dNested case-control analysis.
eMales only in cohort.
fSMR is for cancer of the genital organs (cervix, uterus, endometrium, etc.).
8Median annual age-adjusted breast cancer rate (1995-2000).
4.8.2.1.1. Prostate cancer
Sixteen cohort or PMR studies, two nested case-control, one population case-control, and
two geographic-based studies present RR estimates for prostate cancer (Radican et al., 2008;
Krishnadasan et al.. 2007: ATSDR, 2006a; Boice et al.. 2006b: Chang et al.. 2005: ATSDR.
2004a: Chang etal.. 2003: Raaschou-Nielsen et al.. 2003: Morgan and Cassadv, 2002: Hansen et
al.. 2001: Boice etal.. 1999: Ritz, 1999a: Blair etal.. 1998: Morgan et al.. 1998: Anttila et al..
1995: Axel son et al.. 1994: Greenland et al.. 1994: Siemiatvcki. 1991: Blair etal.. 1989:
Garabrant et al.. 1988: Shannon et al.. 1988: Wilcosky et al.. 1984). Three small cohort studies
(Henschler et al., 1995: Sinks etal., 1992: Costa etal., 1989), one multiple-site population case-
control (Siemiatvcki, 1991), and one geographic-based study (Vartiainen et al., 1993) do not
report estimates for prostate cancer in their published papers. Twelve of the 19 studies with
prostate cancer RR estimates had high likelihood of TCE exposure in individual study subjects
and were judged to have met, to a sufficient degree, the standards of epidemiologic design and
analysis (Radican et al., 2008: Krishnadasan et al., 2007: Boice et al., 2006b: Raaschou-Nielsen
etal.. 2003: Hansen et al.. 2001: Morgan et al.. 2000: Boice etal.. 1999: Blair etal.. 1998:
Morgan et al.. 1998: Anttila et al.. 1995: Axel son et al.. 1994: Greenland et al.. 1994:
Siemiatycki, 1991). Krishnadasan et al. (2007), in their nested case-control study of prostate
cancer, observed a twofold OR estimate with high cumulative TCE exposure score (2.4, 95% CI:
1.3, 4.4, 20-year lagged exposure) and an increasing positive relationship between prostate
4-504
-------
cancer incidence and TCE cumulative exposure score (p = 0.02). TCE exposure was positively
correlated with several other occupational exposures, and Krishnadasan et al. (2007) adjusted for
possible confounding from all other chemical exposures as well as age at diagnosis, occupational
physical activity, and SES status in statistical analyses. RR estimates in studies other than
Krishnadasan et al. (2007) were >1.0 for overall TCE exposure [1.8, 95% CI: 0.8, 4.0
(Siemiatvckl 1991): 1.1, 95% CI: 0.6, 1.8 (Blair etal.. 1998) and 1.20, 95% CI: 0.92, 1.76, with
an additional 10-year follow-up (Radican et al.. 2008): 1.58, 95% CI: 0.96, 2.62 (Morgan et al..
2000. 1998: EHS. 1997): 1.3, 95% CI: 0.52, 2.69 (BoiceetaL 1999): 1.38, 95% CI: 0.73, 2.35
(Anttila et al., 1995)1 and prostate cancer risks did not appear to increase with increasing
exposure. Four studies observed RR estimates below 1.0 for overall TCE exposure (0.93, 95%
CI: 0.60, 1.37 (Garabrant et al.. 1988): 0.6, 95% CI: 0.2, 1.3 (Hansen et al.. 2001): 0.9, 95% CI:
0.79, 1.08 (Raaschou-Nielsen et al.. 2003): 0.82, 95% CI: 0.36, 1.62 (Boice et al.. 2006bX and
are not considered inconsistent because alternative explanations are possible and included
observations are based on few subjects, lowering statistical power, or to poorer exposure
assessment approaches that may result in a higher likelihood of exposure misclassification.
Seven other cohort, PMR, and geographic-based studies were given less weight in the
analysis because of their lesser likelihood of TCE exposure and other study design limitations
that would decrease statistical power and study sensitivity (ATSDR, 2006a; Chang et al., 2005:
AT SDR. 2004a: Morgan and Cassadv. 2002: Blair etal.. 1989: Shannon et al.. 1988: Wilcosky et
al., 1984). Chang et al. (2005) observed a statistically significant deficit in prostate cancer risk,
based on one case, and an insensitive exposure assessment (0.14, 95% CI: 0.00, 0.76). Relative
risks in the other five studies ranged from 0.62 (CI not presented in paper) (Wilcosky et al.,
1984) to 1.11 (95% CI: 0.98, 1.25) (Morgan and Cassadv. 2002).
Risk factors for prostate cancer include age, family history of prostate cancer, and
ethnicity as causal with inadequate evidence for a relationship with smoking or alcohol (Wigle et
al., 2008). All studies except Krishnadasan et al. (2007) were not able to adjust for possible
confounding from other chemical exposures in the work environment. None of the studies
including Krishnadasan et al. (2007) accounted for other well-established nonoccupational risk
factors for prostate cancer such as race, prostate cancer screening, and family history. There is
limited evidence that physical activity may provide a protective effect for prostate cancer (Wigle
et al., 2008). Krishnadasan et al. (2008) examined the effect of physical activity in the
Rocketdyne aerospace cohort (Krishnadasan et al., 2007: Zhao et al., 2005). Their finding of a
protective effect with high physical activity (0.55, 95% CI: 0.32, 0.95, p trend = 0.04) after
control for TCE exposure provides additional evidence (Krishnadasan et al., 2008) and suggests
that underlying risk may be obscured in studies lacking adjustment for physical activity.
4-505
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4.8.2.1.2. Breast cancer
Fifteen studies of TCE exposure reported findings on breast cancer in males and females
combined (Boiceetal., 1999; Greenland et al., 1994; Garabrant et al., 1988), in males and
females, separately (Clapp and Hoffman. 2008: ATSDR, 2004a: Raaschou-Nielsen et al.. 2003:
Hansenetal.. 2001). or in females only (Radican et al.. 2008: Sung et al.. 2007: ATSDR, 2006a;
Chang et al.. 2005: Coyle et al.. 2005: Blair etal.. 1998: Morgan et al.. 1998: Shannon et al..
1988). Six studies have high likelihood of TCE exposure in individual study subjects and met, to
a sufficient degree, the standards of epidemiologic design and analysis (Radican et al., 2008:
Raaschou-Nielsen et al.. 2003: Hansen etal.. 2001: Boiceetal.. 1999: Blair etal.. 1998: Morgan
etal., 1998). Four studies with risk estimates for other cancer sites did not report risk estimates
for breast cancer (Boice et al., 2006b: Anttila et al., 1995: Axelson et al., 1994: Siemiatycki,
1991). No case-control studies were found on TCE exposure, although several studies examined
occupational title or organic solvent as a class (7i et al., 2008: Rennix et al., 2005: Band et al.,
2000: Weiderpass et al., 1999). While association is seen with occupational title or industry and
breast cancer [employment in aircraft and aircraft part industry, 2.48, 95% CI: 1.14, 5.39 (Band
et al., 2000): solvent user: 1.48, 95% CI: 1.03, 2.12 (Rennix et al., 2005)1, TCE exposure is not
uniquely identified. The two studies suggest that an association between organic solvents and
female breast cancer needs further investigation of possible risk factors.
Relative risk estimates in the five studies in which there is a high likelihood of TCE
exposure in individual study subjects and which met, to a sufficient degree, the standards of
epidemiologic design and analysis in a systematic review ranged from 0.75 (0.43, 1.22) (females
and males; (Morgan et al., 1998)) to 2.0 (0.9, 4.6) (mortality in females; (Blair etal., 1998)).
Blair et al. (1998) additionally observed stronger risk estimates for breast cancer mortality
among females with low-level, intermittent (3.1, 95% CI: 1.5, 6.2) and low-level, continuous
(3.4, 95% CI: 1.4, 8.0) TCE exposures, but not with frequent peaks (1.4, 95% CI: 0.7, 3.2). A
similar pattern of risks was also observed by Radican et al. (2008) who studied mortality in this
cohort and adding 10 years of follow-up, although the magnitude of breast cancer risk in females
was lower than that observed in Blair et al. (1998). Risk estimates did not appear to increase
with increasing cumulative exposure in the two studies that included exposure-response analyses
(Blair et al., 1998; Morgan et al., 1998). None of these five studies reported a statistically
significant deficit in breast cancer and CIs on RRs estimates included 1.0 (no risk). Few female
subjects in these studies appear to have high TCE exposure. For example, Blair et al. (1998)
identified 8 of the 28 breast cancer deaths and 3 of the 34 breast cancer cases with high
cumulative exposure.
Relative risk estimates in six studies of lower likelihood TCE exposure and other design
deficiencies ranged from 0.81 (95% CI: 0.52, 1.48) (Garabrant et al., 1988) to 1.19 (1.03, 1.36)
(Chang et al., 2005). These studies lack a quantitative surrogate for TCE exposure to individual
subjects and instead classify all subjects as —ptentially exposed," with resulting large dilution of
4-506
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actual risk and decreased sensitivity (Sung et al.. 2007: AT SDR, 2006a; NRC, 2006: Chang et
al.. 2005: Morgan and Cassadv, 2002: Garabrant et al.. 1988: Shannon et al.. 1988).
Four studies reported on male breast cancer separately (Clapp and Hoffman, 2008:
AT SDR. 2004a: Raaschou-Nielsen et al.. 2003: Hansen et al.. 2001) and a total of three cases
were observed. Breast cancer in men is a rare disease and is best studied using a case-control
approach (Weiss et al., 2005). Reports exist of male breast cancer among former residents of
Camp Lejuene (ATSDR, 2010, 2007b). Further assessment of TCE exposure and male breast
cancer is warranted.
Overall, the epidemiologic studies on TCE exposure and breast cancer are quite limited in
statistical power; observations are based on few breast cancer cases or on inferior TCE exposure
assessment in studies with large numbers of observed cases. Additionally, adjustment for
nonoccupational breast cancer risk factors is less likely in cohort and geographic-based studies
given their use of employment and public records. Breast cancer mortality observations in Blair
et al. (1998) and further follow-up of this cohort by Radican et al. (2008) of an elevated risk with
overall TCE exposure, particularly low-level intermittent and continuous TCE exposure, provide
evidence of an association with TCE. No other study with high likelihood of TCE exposure in
individual study subjects reported a statistically significant association with breast cancer,
although few observed cases leading to lower statistical power or examination of risk for males
and females combined are alternative explanations for the null observations in these studies.
Both Chang et al. (2005) and Sung et al. (2007), two overlapping studies of female electronics
workers exposed to TCE, perchloroethylene, and mixed solvents, reported association with
breast cancer incidence, with breast cancer risk in Chang et al. (2005) appearing to increase with
employment duration. Both studies, in addition to association provided by studies of exposure to
broader category of organic solvents (Rennix et al., 2005: Band et al., 2000), support Blair et al.
(1998) and Radican et al. (2008), although the lack of exposure assessment is an uncertainty.
The epidemiologic evidence is limited for examining TCE and breast cancer, and while these
studies do not provide any strong evidence for association with TCE exposure, they in turn do
not provide evidence of an absence of association.
4.8.2.1.3. Cervical cancer
Eleven cohort or PMR studies and two geographic-based studies present RR estimates
(Radican et al., 2008: Sung et al., 2007: ATSDR, 2006a, 2004a: Raaschou-Nielsen et al., 2003:
Morgan and Cassadv, 2002: Hansen et al., 2001: Boiceetal., 1999: Blair etal., 1998: Morgan et
al., 1998: Anttila et al., 1995: Garabrant et al., 1988: Shannon et al., 1988). Seven of these
studies had high likelihood of TCE exposure in individual study subjects and were judged to
have met, to a sufficient degree, the standards of epidemiologic design and analysis (Radican et
al., 2008: Raaschou-Nielsen et al., 2003: Hansen et al., 2001: Boiceetal., 1999: Blair et al.,
1998: Morgan et al., 1998: Anttila et al., 1995). Three small cohort studies (Henschler et al..
4-507
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1995: Sinks etal.. 1992: Costa etal.. 1989) as well as three studies with high likelihood of TCE
exposure in individual study subjects (Boice et al., 2006b: Zhao et al., 2005: Axel son et al.,
1994) did not present RR estimates for cervical cancer. Additionally, one population case-
control and one geographic study of several site-specific cancers did not present information on
cervical cancer (Vartiainen et al., 1993: Siemiatycki, 1991).
Five studies with high likelihood of TCE exposure in individual study subjects and which
met, to a sufficient degree, the standards of epidemiologic design and analysis in a systematic
review observed elevated risk for cervical cancer and overall TCE exposure [2.42, 95% CI: 1.05,
4.77 (Anttila et al., 1995): 1.8, 95% CI: 0.5, 6.5 (Blair etal.. 1998) that changed little with an
additional 10 years follow-up, 1.67, 95% CI: 0.54, 5.22 (Radican et al.. 2008): 3.8, 95% CI: 1.0,
9.8 (Hansen etal., 2001): 1.9, 95% CI: 1.42, 2.37 (Raaschou-Nielsen et al.. 2003)1. The
observations of a three- to fourfold elevated cervical cancer risk with high mean TCE exposure
compared to subjects in the low exposure category [6+ ppm: 4.35, 95% CI: 1.41, 10.1 (Anttila et
al.. 1995): 4+ ppm: 4.3, 95% CI: 0.5, 16 (Hansen etal., 2001)1 or with high cumulative TCE
exposure (0.25-ppm year: 3.0, 95% CI: 0.8, 11.7 (Blair etal.. 1998). 2.83, 95% CI: 0.86, 9.33
(Radican et al., 2008)) provide additional support for association with TCE. Cervical cancer risk
was lowest for subjects in the high-exposure duration category (Raaschou-Nielsen et al., 2003:
Hansen etal., 2001): however, duration of employment is a poor exposure metric given that
subjects may have differing exposure intensity with similar exposure duration (NRC, 2006). No
deaths due to cervical cancer were observed in two other studies (Boice et al., 1999: Morgan et
al., 1998): less than four deaths were expected, suggesting that these cohorts contained few
female subjects with TCE exposure.
Human papilloma virus and low SES status are known risk factors for cervical cancer
(American Cancer Society, 2008). Subjects in Raaschou-Nielsen et al. (2003) are blue-collar
workers and low SES status likely explains observed associations in this and the other studies.
The use of internal controls in Blair et al. (1998) who are similar in SES status as TCE subjects is
believed to partly account for possible confounder related to SES status; however, direct
information on individual subjects is lacking.
Six other cohort, PMR, and geographic-based studies were given less weight in the
analysis because of their lesser likelihood of TCE exposure and other study design limitations
that would decrease statistical power and study sensitivity (Sung et al., 2007: ATSDR, 2006a,
2004a: Morgan and Cassadv, 2002: Garabrant et al.. 1988: Shannon et al.. 1988). Cervical
cancer risk estimates in these studies ranged between 0.65 (95% CI: 0.38, 1.02) (Morgan and
Cassadv, 2002) and 1.77 [PMR; 95% CI: 0.57, 4.12 (ATSDR. 2004a)1. No study reported a
statistically significant deficit in cervical cancer risk.
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4.8.2.2. Animal Studies
Histopathology findings have been noted in reproductive organs in various cancer
bioassay studies conducted with TCE. A number of these findings (summarized in Table 4-93)
do not demonstrate a treatment-related profile.
Table 4-93. Histopathology findings in reproductive organs
Tumor incidence in mice after 18 mo inhalation exposure"
Males
Females
Tissue
Finding
Number examined:
Prostate
Testis
Myoma
Carcinoma
Cyst
Number examined:
Uterus
Ovary
Adenocarcinoma
Adenocarcinoma
Adenoma
Carcinoma
Granulosa cell tumor
Control
30
1
0
0
29
1
1
3
0
4
100 ppm
29
0
0
0
30
0
0
1
2
0
500 ppm
30
0
1
1
28
0
0
o
J
2
2
Tumor incidence in rats after 18 mo inhalation exposure"
Males
Females
Tissue
Finding
Number examined:
Testis
Interstitial cell tumors
Number examined:
Mammary
Uterus
Ovary
Genital tract
Fibroadenoma
Adenocarcinoma
Adenocarcinoma
Carcinoma
Granulosa cell tumor
Squamous cell carcinoma
Control
29
4
28
2
3
3
4
1
0
100 ppm
30
0
30
0
2
1
0
0
2
500 ppm
30
3
30
0
2
4
1
0
0
Tumor incidence in hamsters after 18 mo inhalation exposure"
Females
Tissue
Finding
Number examined:
Ovary
Cystadenoma
Control
30
1
100 ppm
29
0
Tumor incidence in mice after 18 mo gavage administration1"
Females
Tissue
Finding
Number examined:
Mammary
Ovary
Vulva
Carcinoma
Granulosa cell tumor
Squamous cell carcinoma
Con- TCE
trol Pure
50 50
1 2
0 1
0 0
TCE
Industrial
50
0
0
0
500 ppm
30
0
TCE +
EPC
50
0
0
0
TCE+ TCE + EPC
BO +BO
48 50
0 0
0 0
1 1
aHenschler et al. (1980).
bHenschler et al. (1984).
EPC = epichlorohydrin; BO = 1,2-epoxybutane
4-509
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Cancers of the reproductive system that are associated with TCE exposure and observed
in animal studies are comprised of testicular tumors (interstitial cell and Ley dig cell). A
summary of the incidences of testicular tumors observed in male rats is presented in Table 4-94.
Table 4-94. Testicular tumors in male rats exposed to TCE, adjusted for
reduced survival3
Interstitial cell tumors after 103-wk gavage exposure, beginning at 6.5-8 wks of age (NTP, 1988, 1990)
Administered dose (mg/kg-d)
Male ACI rats
Male August rats
Male Marshall ratsb
Male Osborne-Mendel rats
Male F344/N rats
Untreated
control
38/45 (84%)
36/46 (78%)
16/46 (35%)
1/30 (3%)
44/47 (94%)
Vehicle control
36/44 (82%)
34/46 (74%)
17/46 (37%)
0/28 (0%)
47/48 (98%)
500
23/26 (88%)
30/34 (88%)
21/33 (64%)
0/25 (0%)
47/48 (98%)
1,000
17/19 (89%)
26/30 (87%)
32/39 (82%)
1/19 (5%)
32/44 (73%)
Levdig cell tumors after 104-wk inhalation exposure, beginning at 12 wks of age (Maltoni et al., 1986)
Administered daily concentration
(mg/m3)c
Male Sprague-Dawley ratsb
Control
6/114(5%)
112.5
16/105 (15%)
337.5
30/107 (28%)
675
31/113(27%)
aACI rats alive at week 70, August rats at week 65, Marshall rats at week 32, Osborne-Mendel rats at week 97,
F344/N rats at week 32, Sprague-Dawley rats at week 81 (except BT304) or week 62 (except BT304 bis).
Equivalent to 100, 300, or 600 ppm (100 ppm = 540 mg/m3), adjusted for 7 hours/day, 5 days/week exposure.
Statistically significant by Cochran-Armitage trend test (p < 0.05).
Sources: NTP (19881 Tables A2, C2, E2, G2; NTP (19901 Table A3; Maltoni et al. (1986) IV/IV Table 21, IV/V
Table 21.
4.8.2.3. Mode of Action for Testicular Tumors
The database for TCE does not include an extensive characterization of the mode of
action for Ley dig cell tumorigenesis in the rat, although data exist that are suggestive of
hormonal disruption in male rats. A study by Kumar et al. (2000a) found significant decreases in
serum testosterone concentration and in 17-P-HSD, G6PDH, and total cholesterol and ascorbic
acid levels in testicular homogenate from male rats that had been exposed via inhalation to
376 ppm TCE for 12 or 24 weeks. In a follow-up study, Kumar et al. (200Ib) also identified
decreases in SDH in the testes of TCE-treated rats. These changes are markers of disruption to
testosterone biosynthesis. Evidence of testicular atrophy, observed in the 2001 study by
Kumar et al., as well as the multiple in vivo and in vitro studies that observed alterations in
spermatogenesis and/or sperm function, could also be consistent with alterations in testosterone
levels. Therefore, while the available data are suggestive of a mode of action involving
hormonal disruption for TCE-induced testicular tumors, the evidence is inadequate to specify
and test a hypothesized sequence of key events.
4-510
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Leydig cell tumors can be chemically induced by alterations of steroid hormone levels,
through mechanisms such as agonism of estrogen, gonadotropin releasing hormone, or dopamine
receptors; antagonism of androgen receptors; and inhibition of 5a-reductase, testosterone
biosynthesis, or aromatase (Cook et al., 1999). For those plausible mechanisms that involve
disruption of the hypothalamic-pituitary-testis (HPT) axis, decreased testosterone or estradiol
levels or recognition is involved, and increased LH levels are commonly observed. Although
there is evidence to suggest that humans are quantitatively less sensitive than rats in their
proliferative response to LH, evidence of treatment-related Leydig cell tumors in rats that are
induced via HPT disruption is considered to represent a potential risk to humans (with the
possible exception of GnRh or dopamine agonists), since the pathways for regulation of the HPT
axis are similar in rats and humans (Clegg et al., 1997).
4.8.3. Developmental Toxicity
An evaluation of the human and experimental animal data for developmental toxicity,
considering the overall weight and strength of the evidence, suggests a potential for adverse
outcomes associated with pre- and/or postnatal TCE exposures.
4.8.3.1. Human Developmental Data
Epidemiological developmental studies (summarized in Table 4-95) examined the
relationship between TCE exposure and prenatal developmental outcomes including spontaneous
abortion and perinatal death; decreased birth weight, small for gestational age (SGA), and
postnatal growth; congenital malformations; and other adverse birth outcomes. Postnatal
developmental outcomes examined include developmental neurotoxicity, developmental
immunotoxicity, other developmental outcomes, and childhood cancer.
4.8.3.1.1. Adverse fetal/birth outcomes
4.8.3.1.1.1. Spontaneous abortion and perinatal death
Spontaneous abortion or miscarriage is defined as nonmedically induced premature
delivery of a fetus prior to 20 weeks of gestation. Perinatal death is defined as stillbirths and
deaths before 7 days after birth. Available data comes from several studies of occupational
exposures in Finland and Santa Clara, California, and by geographic-based studies in areas with
known contamination of water supplies in Woburn, Massachusetts; Tucson Valley, Arizona;
Rocky Mountain Arsenal, Colorado; Endicott, New York; and New Jersey.
4-511
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Table 4-95. Developmental studies in humans
Subjects
Exposure
Effect
Reference
Adverse fetal/birth outcomes
Spontaneous abortion and perinatal death
371 men occupationally exposed to
solvents in Finland 1973-1983
535 women occupationally exposed to
solvents in Finland 1973-1986
3,265 women occupationally exposed
to organic solvents in Finland 1973-
1983
361 women occupationally and
residentially exposed to solvents in
Santa Clara County, California
June 1986-February 1987 (735
controls)
4,396 pregnancies among residents of
Woburn, Massachusetts 1960-1982
707 parents of children with congenital
heart disease in Tucson Valley,
Arizona 1969-1987
75 men and 71 women living near
Rocky Mountain Arsenal, Colorado
1981-1986
Questionnaire
Low/rare used <1 d/wk;
Intermediate used 1-4 d/wk or
intermediate/low TCA urine
levels;
High/frequent used daily or high
TCA urine levels
Questionnaire
Rare used 1-2 d/wk;
Frequent used >3 d/wk
Questionnaire
U-TCA: median: 48.1 umol/L; mean
96.2 ± 19.2 umol/L
Questionnaire
TCE: 267 ug/L
Tetrachloroethylene: 21 ug/L
Chloroform: 12 ug/L
6-239 ppb TCE, along with DCA and
chromium
Low: <5.0 ppb
Medium: >5.0-<10.0 ppb
High: <10.0 ppb
No risk of spontaneous abortion after paternal exposure,
based on 17 cases and 35 controls exposed to TCE
OR: 1.0, 95% CI: 0.6-2.0
Increased risk of spontaneous abortion among frequently-
exposed women, based on 7 cases and 9 controls exposed to
TCE
OR: 1.6, 95% CI: 0.5-4.8
No increased risk of spontaneous abortion based on 3 cases
and 13 controls exposed to TCE
OR: 0.6, 95% CI: 0.2-2.3
Increased risk of spontaneous abortion based on 6 cases and
4 controls exposed to TCEa
OR: 3.1, 95% CI: 0.92-10.4
Increased risk of perinatal death (n = 67) after 1970
(p = 0.55) but not before 1970 (OR: 10, p = 0.003)
No increased risk of spontaneous abortion (n = 520;
p = 0.66)
No increased risk of fetal death (not quantified) based on
246 exposed and 461 unexposed cases
Increased risk of miscarriage
ORadj: 4.44, 95% CI: 0.76-26.12
Increased risk of no live birth
ORadl: 2.46, 95% CI: 0.24-24.95
Taskinen et al.
(1989)
Taskinen et al.
(1994)
Lindbohm et al.
(1990)
Windham et al.
(1991)
Lagakos et al.
(1986)
Goldberg et al.
(1990)
ATSDR (2001)
4-512
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Table 4-95. Developmental studies in humans (continued)
Subjects
1,440 pregnancies among residents of
Endicott, New York
1978-2002
81,532 pregnancies among residents of
75 New Jersey towns
1985-1988 (3 control groups)
Exposure
Indoor air from soil vapor: 0.18-
140 mg/m3
55 ppb TCE, along with many other
compounds
Effect
No increase in spontaneous fetal death
SIR: 0.66, 95% CI: 0.22-1. 55
No increased risk of fetal death for >10 ppb
OR: 1.12
Reference
ATSDR (2008b,
2006a)
Bove (1996): Bove
et al. (1995)
Decreased birth weight, SGA, and postnatal growth
361 women occupationally and
residentially exposed to solvents in
Santa Clara County, California
June 1986-February 1987 (735
controls)
3,462 births in Woburn, Massachusetts
1960-1982
1,099 singleton birthsb to residents of
three census tracts near Tucson
International Airport
1979-1 98 1(877 controls)
1,440 births0 to residents of Endicott,
New York
1978-2002
6,289 pregnancies among women
residing at Camp Lejeune, North
Carolina 1968-1985 (141 short-term
and 3 1 long-term TCE-exposed,
5,681 unexposed controls)"1
Questionnaire
267 ug/L TCE in drinking water,
along with tetrachloroethylene and
chloroform
<5-107 ug/L
Indoor air from soil vapor: 0.18-
140 mg/m3
Tarrawa Terrace:
TCE: 8 ppb
1,2-DCE: 12 ppb
Perchloroethylene: 215 ppb
Hadnot Point:
TCE: 1,400 ppb
1,2-DCE: 407 ppb
Increased risk of IUGR based on one case exposed to both
TCE and tetrachloroethylene
OR: 12.5
No increase in low birth weight (p = 0.77)
No increase in full-term low birth weight (OR: 0.81)
No increase in low birth weight (OR: 0.9)
Increase in very low birth weight
OR: 3.3, 95% CI: 0.53-20.6
Small increase in low birth weight
OR: 1.26, 95% CI: 1.00-1.59
Small increase in SGA
OR: 1.22, 95% CI: 1.02-1.45
Increase in full-term low birth weight
OR: 1.41, 95% CI: 1.01-1.95
Change in mean birth weight
Long-term total: -139 g, 90% CI: -277, -1
Long-term males: -312 g, 90% CI: -540, -85
Short term total: +70g, 90% CI: -6, 146
Increase in SGA
Long-term total: OR: 1.5, 90% CI: 0.5, 3.8
Long-term males: OR: 3.9, 90% CI: 1.1-11.9
Short term total: OR: 1.1, 90% CI: 0.2-1.1
Windham et al.
(1991)
Lagakos et al.
(1986)
Rodenbeck et al.
(2000)
ATSDR (2008b,
2006a)
ATSDR (1998a)
4-513
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Table 4-95. Developmental studies in humans (continued)
Subjects
81,532 pregnancies6 among residents
of 75 New Jersey towns 1985-1988
Exposure
55 ppb TCE, along with many other
compounds
Effect
Decreased birth weight at >5 ppb by 17.9g
No increase in prematurity at >10 ppb: OR: 1.02
Increase in low birth weight, term
>10 ppb: OR: 1.23, 50% CI: 1.09-1.39
No risk for very low birth weight
Reference
Bove (1996): Bove
et al. (1995)
Congenital malformations
1,148 men and 969 women
occupationally exposed to TCE in
Finland 1963-1976
371 men occupationally exposed to
solvents in Finland 1973-1983
100 babies with oral cleft defects born
to women occupationally exposed in
Europe 1989-1992
4,396 pregnancies among residents of
Woburn, Massachusetts
1960-1982
707 children with congenital heart
disease in Tucson Valley, Arizona
1969-1987 (246 exposed, 461
unexposed)
75 men, 71 women living near Rocky
Mountain Arsenal, Colorado
1981-1986
U-TCA:
<10 to >500 mg/L
Low/rare used <1 d/wk;
Intermediate used 1-4 d/wk or if
biological measures indicated high
exposure;
High/frequent used daily or if
biological measures indicated high
exposure
Questionnaire
TCE: 267 ug/L
Tetrachloroethylene: 21 ug/L
Chloroform: 12 ug/L
Wells contaminated with TCE (range:
6-239 ppb), along with DCA and
chromium
Low: <5.0 ppb
Medium: >5.0-<10.0 ppb
High: <10.0 ppb
No congenital malformations reported
No increase in congenital malformations based on 17 cases
and 35 controls exposed to TCE
OR: 0.6, 95% CI: 0.2-2.0
Increase in cleft lip based on 2 of 4 TCE-exposed women
ORadj: 3.21, 95% CI: 0.49-20.9
Increase in cleft palate based on 2 of 4 TCE-exposed women
ORadj : 4.47, 95% CI: 1.02-40.9
Increase in eye/ear birth anomalies: OR: 14.9, p <0.0001
Increase in CNS/chromosomal/oral cleft anomalies:
OR: 4.5, p = 0.01
Increase in kidney /urinary tract disorders:
OR: 135, p = 0.02
Small increase in lung/respiratory tract disorders:
OR: 1.16, p = 0.05
No increase in cardiovascular anomalies (n =5):p = 0.91
Increase in congenital heart disease
<1981:OR:~3 (p< 0.005)
>1981:OR:~1
Increased prevalence after maternal exposure during first
trimester (p < 0.001, 95% CI: 1.14-4.14)
Increase in total birth defects (n = 9)
OR: 5.87, 95% CI: 0.59-58.81
Tola et al. (1980)
Taskinen et al.
(1989)
Lorente et al.
(2000)
Lagakos et al.
(1986)
Goldberg et al.
(1990)
ATSDR (2001)
4-514
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Table 4-95. Developmental studies in humans (continued)
Subjects
Births to residents of Endicott, New
York 1983-2000f
81,532 pregnancies among residents of
75 New Jersey towns
1985-1988
1,623 children <20 yrs old dying from
congenital anomalies in Maricopa
County, Arizona
1966-1986
4,025 infants born with congenital
heart defects in Milwaukee, Wisconsin
1997-1999
12 children exposed to TCE in well
water in Michigan
Exposure
Indoor air from soil vapor: 0.18-
140 mg/m3
55 ppb TCE, along with many other
compounds
8.9 and 29 ppb TCE in drinking water
Maternal residence within 1.32 miles
from at least one TCE emissions
source
5-10 yrs to 8-14 ppm
Effect
No increase in total birth defects:
RR: 1.08, 95% CI: 0.82-1.42
Increase in total cardiac defects:
RR: 1.94, 95% CI: 1.21-3.12
Increase in major cardiac defects:
RR: 2.52, 95% CI: 1.2-5.29
Increase in conotruncal heart defects:
RR: 4.83, 95% CI: 1.81-12.89
No increase in total birth defects: >10 ppb: OR: 1. 12
Increase in total CNS defects at high dose
>l-5 ppb: OR: 0.93, 90% CI: 0.47-1.77
>10 ppb: OR: 1.68, 90% CI: 0.76-3.52
Increase in neural tube defects
>l-5 ppb: OR: 1.58, 90% CI: 0.69-3.40
>10 ppb: OR: 2.53, 90% CI: 0.91-6.37
Increase in oral clefts:
>5 ppb: OR: 2.24, 95% CI: 1.16-4.20
Increase in major cardiac defects:
>10 ppb: OR: 1.24, 50% CI: 0.75-1.94
Increase in ventrical septal defects
>5ppb: OR: 1.30, 95% CI: 0.88-1.87
Increase in deaths due to congenital anomalies in East
Central Phoenix
1966-1969: RR: 1.4, 95% CI: 1.1-1.7
1970-1981: RR: 1.5, 95% CI: 1.3-1.7
1982-1986: RR: 2.0, 95% CI: 1.5-2.5
Increase in congenital heart defects for mothers >38 yrs old
Exposed: OR: 6.2, 95% CI: 2.6-14.5
Unexposed: OR: 1.9, 95% CI: 1.1-3.5
No increase in congenital heart defects for exposed mothers
<38 yrs old: OR: 0.9, 95% CI: 0.6-1.2
One born with multiple birth defects
Reference
ATSDR (2008b,
2006a)
Bove (1996); Bove
et al. (1995)
AZ DHS (Flood.
1988)
Yauck et al. (2004)
Bernad et al.
(1987). abstract
Other adverse birth outcomes
34 live births for which inhalation of
TCE for anesthesia was used in Japan
1962-1967
2-8 mL (mean 4.3 mL) for 2-98 min
(mean: 34.7 min)
One case of asphyxia; 3 -sleepy babies" with Apgar scores
of 5-9; delayed appearance of newborn reflexes
Beppu (1968)
4-515
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Table 4-95. Developmental studies in humans (continued)
Subjects
5 1 U.K. women whose fetus was
considered to be at risk for hypoxia
during labor administered TCE as an
analgesic (50 controls)
Exposure
Amount and route of exposure not
reported
Effect
TCE caused fetal pH to fall more, base deficit increased
more, and PO2 fell more than the control group by fourfold
or more compared to other analgesics used
Reference
Phillips and
Macdonald (1971)
Postnatal developmental outcomes
Developmental neurotoxicity
54 individuals from 3 residential
cohorts in the United States exposed to
TCE in drinking water
284 cases of ASD diagnosed <9 yrs old
and 657 controls born in the San
Francisco Bay Area
1994
948 children (<18 yrs) in the TCE
Subregistry
12 children exposed to TCE in well
water in Michigan
Woburn, Massachusetts
63^00 ppb for <1-12 yrs
Alpha, Ohio
3.3-330 ppb for 5-17 yrs
Twin Cities, Minnesota
261-2,440 ppb for 0.25-25 yrs
Births geocoded to census tracts, and
linked to hazardous air pollutants data
0.4->5,000 ppb TCE
5-10 yrs to 8-14 ppm
Woburn, Massachusetts
Verbal naming/language impairment in 6/13 children
(46%)
Alpha, Ohio
Verbal naming/language impairment in 1/2 children
(50%)
Twin Cities, Minnesota
Verbal naming/language impairment in 4/4 children
(100%)
Memory impairment in 4/4 children (100%)
Academic impairment in 4/4 children (100%)
Moderate encephalopathy in 4/4 children (100%)
Poor performance on reading/spelling test in 3/4 children
(75%)
Poor performance on information test in 3/4 children
(75%)
Increase in ASD
upper 3rd quartile: OR: 1.37, 95% CI: 0.96-1.95
upper 4th quartile: OR: 1.47, 95% CI: 1.03-2.08
Increase in speech impairment:
0-9 yrs old: RR: 2.45, 99% CI: 1.31-1.58
10-17 yrs old: RR: 1.14, 99% CI: 0.46-2.85
Increase in hearing impairment:
0-9 yrs old: RR: 2.13, 99% CI: 1.12-1.07
10-17 yrs old: RR: 1.12, 99% CI: 0.52-2.24
9 of 12 children (75%) had poor learning ability, aggressive
behavior, and low attention span
White et al. (1997)
Windham et al.
(2006)
ATSDR (2002):
Burg et al. (1995):
Burg and Gist
(1999)
Bernad et al.
(1987). abstract
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Table 4-95. Developmental studies in humans (continued)
Subjects
Exposure
Effect
Reference
Developmental immunotoxicity
200 children aged 36 mo old born
prematurely8 and at risk of atopyh in
Lepzig, Germany
1995-1996
85 healthy1 full-term neonates born in
Lepzig, Germany
1997-1999
Median air level in child's bedroom:
0.42 ug/m3
Median air level in child's bedroom
3-4 wks afterbirth: 0.6 ug/m3
No association with allergic sensitization to egg white and
milk, or to cytokine producing peripheral T-cells
Significant reduction of Thl IL-2 producing T-cells
Lehmann et al.
(2001)
Lehmann et al.
(2002)
Other developmental outcomes
55 children (6 mo to 10 yrs old) were
anesthetized for operations to repair
developmental defects of the jaw and
face in Poland
1964
>10 mL TCE
Reports of bradycardia, accelerated heart rate, and
respiratory acceleration observed; no arrhythmia was
observed
Jasinka (1965).
translation
Childhood cancer
98 children (<10 yrs old) diagnosed
with brain tumors in Los Angeles
County 1972-1977
22 children (<19 yrs old) diagnosed
with neuroblastoma in United States
and Canada
1992-1994 (12 controls)
61 boys and 62 girls (<10 yrs old)
diagnosed with leukemia and
123 controls in Los Angeles County
1980-1984
1,842 children (<15 yrs old) diagnosed
with ALL in United States and Canada
1989-1993 (1986 controls)
Questionnaire of parental
occupational exposures
Questionnaire of parental
occupational exposures
Questionnaire of parents for
occupational exposure
Questionnaire of parents for
occupational exposure
Two cases were reported for TCE exposure, one with methyl
ethyl ketone
Increase in neuroblastoma after paternal exposure
OR: 1.4, 95% CI: 0.7-2.9
Maternal exposure not reported
Increase in leukemia after paternal exposure
Preconception (1 yr): OR: 2.0, p = 0.16
Prenatal: OR: 2.0, p = 0.16
Postnatal: OR: 2.7, /? = 0.7
Maternal exposure not reported
Increase in ALL after maternal exposure
Preconception: OR: 1.8, 95% CI: 0.6-5.2
Pregnancy: OR: 1.8, 95% CI: 0.5-6.4
Postnatal: OR: 1.4, 95% CI: 0.5-U
Anytime: OR: 1.8, 95% CI: 0.8^.1
No increase in ALL after paternal exposure
Anytime: OR: 1.1, 95% CI: 0.8-1.5
Peters and Preston-
Martin (198T)
De Roos et al.
(2001)
Lowengart et al.
(1987)
Shu et al. (1999)
4-517
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Table 4-95. Developmental studies in humans (continued)
Subjects
109 children (<15 yrs old) born in the
U.K. 1974-1988 (218 controls)
22 children (<15 yrs old) diagnosed
with childhood cancer in California
1988-1998
1,190 children (<20 yrs old) diagnosed
with leukemia in 4 counties in New
Jersey
1979-1987
24 children (<15 yrs old) diagnosed
with leukemia in Woburn,
Massachusetts
1969-1997
347 children (<20 yrs old) diagnosed
with cancer in Endicott, New York
1980-2001
189 children (<20 yrs old) diagnosed
with cancer in Maricopa County,
Arizona 1965-1990
16 children (<20 yrs old) diagnosed
with cancer in East Phoenix, Arizona
1965-1986
Exposure
Questionnaire of parents for
occupational exposure
0.09-97 ppb TCE in drinking water
0-67 ppb TCE in drinking water
267 ug/L TCE in drinking water,
along with tetrachloroethylene,
arsenic, and chloroform
indoor air from soil vapor: 0.18-
140 mg/m3
8.9 and 29 ppb TCE in drinking water
TCE, TCA, and other contaminants in
drinking water
Effect
Increase in leukemia and NHL after paternal exposure
Preconception: OR: 2.27, 95% CI: 0.84-6.16
Prenatal: OR: 4.40, 95% CI: 1.15-21.01
Postnatal: OR: 2.66, 95% CI: 0.82-9.19
No increase in leukemia and NHL after maternal exposure
Preconception: OR: 1.16, 95% CI: 0.13-7.91
No increase in total cancer: SIR: 0.83, 99% CI: 0.44-1.40
No increase in CNS cancer: SIR: 1.05, 99% CI: 0.24-2.70
No increase in leukemia: SIR: 1.09, 99% CI: 0.38-2.31
Increase in ALL in girls with >5 ppb exposure
<20 yrs old: RR: 3.36, 95% CI: 1.29-8.28
<5 yrs old: RR: 4.54, 95% CI: 1.47-10.6
Increase in childhood leukemia
Preconception: ORadj: 2.61, 95% CI: 0.47-14.97
Pregnancy: ORad]: 8.33, 95% CI: 0.73-94.67
Postnatal: ORadj: 1.18, 95% CI: 0.28-5.05
Ever: ORadl: 2.39, 95% CI: 0.54-10.59
No increase in cancer (<6 cases, similar to expected)
Increase in leukemia:
1965-1986: SIR: 1.67, 95% CI: 1.20-2.27
1982-1986: SIR: 1.91, 95% CI: 1.11-3.12
No increase in total childhood cancers, lymphoma,
brain/CNS, or other cancers
No increase in leukemia: SIR: 0.85, 95% CI: 0.50-1.35
Reference
McKinney et al.
(1991)
Morgan and
Cassady (2002)
Cohn et al. (1994b)
Costas et al.
(2002): Cutler et al.
(1986): Lagakos et
al. (1986): MDPH
(1997cV
ATSDR (2008b,
2006a)
AZ DHS (Flood.
1997a: Flood.
1988) (1990)k
AZ DHS (Kioski et
al., 1990b)
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Table 4-95. Developmental studies in humans (continued)
Subjects
Exposure
Effect
Reference
37 children (<20 yrs old) diagnosed
with cancer in Pima County, Arizona
1970-1986
1.1-239 ppb TCE, along with
1,1-DCE, chloroform and chromium
in drinking water
Increase in leukemia (n = 11):
SIR: 1.50, 95% CI: 0.76-2.70
No increase in testicular cancer (n = 6):
SIR: 0.78, 95% CI: 0.32-1.59
No increase in lymphoma (n = 2):
SIR: 0.63, 95% CI: 0.13-1.80
No increase in CNS/brain cancer (n = 3):
SIR: 0.84, 95% CI: 0.23-2.16
Increase in other cancer (n = 15):
SIR: 1.40, 95% CI: 0.79-2.30
AZ DHS (Kioski et
al. 1990a)
aOf those exposed to TCE, four were also exposed to tetrachloroethylene and one was also exposed to paint strippers and thinners.
bFull term defined as between 35 and 46 weeks gestation, low birth weight as <2,501 g, and very low birth weight as <1,501 g.
°Low birth weight defined as <2,500, moderately low birth weight (1,500-<2,500 g), term low birth weight (>37 weeks gestation and <25,000 g).
dUnexposed residents resided at locations not classified for long-term or short-term TCE exposure. Long-term TCE exposed mothers resided at Hospital Point
during 1968-1985 for at least 1 week prior to birth. Short-term TCE exposed mothers resided at Berkeley Manor, Midway Park, Paradise Point, and Wakins
Village at the time of birth and at least 1 week during January 27 to February 7, 1985. In addition, the mother's last menstrual period occurred on or before
January 31, 1985 and the birth occurred after February 2, 1985.
eLow birth weight defined as <2,500 g, very low birth weight as <1,500 g.
f 1,440 births reported for years 1978-2002, but number not reported for years 1983-2000.
gPremature defined as 1,500-2,500 g at birth.
hRisk of atopy defined as cord blood IgE >0.9 kU/L; double positive family atopy history.
'Healthy birth defined as >2,500 g and >37 weeks gestation.
JOnly results from Costas et al. (2002) are reported in the table.
kOnly results from AZ DHS (1990) are reported in the table.
ALL = acute lymphoblastic leukemia; IUGR = intrauterine growth restriction
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4.8.3.1.1.1.1. Occupational studies
The risks of spontaneous abortion and congenital malformations among offspring of men
occupationally exposed to TCE and other organic solvents were examined by Taskinen et al.
(1989). This nested case-control study was conducted in Finland from 1973 to 1983. Exposure
was determined by biological measurements of the father and questionnaires answered by both
the mother and father. The level of exposure was classified as —low^cre" if the chemical was
used <1 day/week, -4ntermediate" if used 1-4 days/week or if TCA urine measurements
indicated intermediate/low exposure, and —hjh/frequent" if used daily or if TCA urine
measurements indicated clear occupational exposure (defined as above the reference value for
the general population). There was no risk of spontaneous abortion from paternal TCE exposure
(OR: 1.0, 95% CI: 0.6-2.0), although there was a significant increase for paternal organic solvent
exposure (OR: 2.7, 95% CI: 1.3-5.6) and a nonsignificant increase for maternal organic solvent
exposure (OR: 1.4, 95% CI: 0.6-3.0). (Also see section below for results from this study for
congenital malformations.)
Another case-control study in Finland examined pregnancy outcomes in 1973-1986
among female laboratory technicians aged 20-34 years (Taskinen et al., 1994). Exposure was
reported via questionnaire, and was classified as ^are" if the chemical was used 1-2 days/week,
and "frequent" if used at least 3 days/week. Cases of spontaneous abortion (n = 206) were
compared with controls who had delivered a baby and did not report prior spontaneous abortions
(n = 329). A nonstatistically significant increased risk was seen between spontaneous abortion
and TCE use at least 3 days/week (OR: 1.6, 95% CI: 0.5-4.8).
The association between maternal exposure to organic solvents and spontaneous abortion
was examined in Finland for births 1973-1983 (Lindbohm et al., 1990). Exposure was assessed
by questionnaire and confirmed with employment records, and the level of exposure was either
high, low, or none based on the frequency of use and known information about typical levels of
exposure for job type. Biological measurements of TCA in urine were also taken on 64 women,
with a median value of 48.1 |imol/L (mean: 96.2 ± 19.2 |imol/L). Three cases and 13 controls
were exposed to TCE, with no increased risk seen for spontaneous abortion (OR: 0.6, 95%
CI: 0.2-2.3,^ = 0.45).
A case-control study in Santa Clara County, California, examined the association
between solvents and adverse pregnancy outcomes in women >18 years old (Windham et al.,
1991). For pregnancies occurring between June 1986 and February 1987, 361 cases of
spontaneous abortion were compared to 735 women who had a live birth during this time period.
Telephone interviews included detailed questions on occupational solvent exposure, as well as
additional questions on residential solvent use. For TCE exposure, 6 cases of spontaneous
abortion were compared to 4 controls of live births; of these 10 TCE-exposed individuals,
4 reported exposure to tetrachloroethylene, and 1 reported exposure to paint strippers and
thinners. An increased risk of spontaneous abortions was seen with TCE exposure (OR: 3.1,
4-520
-------
95% CI: 0.92-10.4), with a statistically significant increased risk for those exposed
>0.5 hours/week (OR: 7.7, 95% CI: 1.3-47.4). An increased risk for spontaneous abortion was
also seen for those reporting a more —inetnse" exposure based primarily on odor, as well as skin
contact or other symptoms (OR: 3.9,p = 0.04). (Also see section below from this study on low
birth weight.)
4.8.3.1.1.1.2. Geographic-based studies
A community in Woburn, Massachusetts with contaminated well water experienced an
increased incidence of adverse birth outcomes and childhood leukemia (Lagakos et al., 1986). In
1979, the wells supplying drinking water were found to be contaminated with 267 ppb TCE,
21 ppb tetrachloroethylene, and 12 ppb chloroform, and were subsequently closed. Pregnancy
and childhood outcomes were examined from 4,396 pregnancies among residents (Lagakos et
al., 1986). No association between water access and incidence of spontaneous abortion (n = 520)
was observed (p = 0.66). The town's water distribution system was divided into five zones,
which was reorganized in 1970. Prior to 1970, no association was observed between water
access and incidence of perinatal deaths (n = 46 stillbirths and 21 deaths before 7 days)
(p = 0.55). However, after 1970, a statistically significant positive association between access to
contaminated water and perinatal deaths was observed (OR: 10.0,/> = 0.003). The authors could
not explain why this discrepancy was observed, but speculated that contaminants were either not
present prior to 1970, or were increased after 1970. (Also see sections below on decreased birth
weight, congenital malformations, and childhood cancer for additional results from this cohort.)
A community in Tucson Valley, Arizona with contaminated well water had a number of
reported cases of congenital heart disease. The wells were found to be contaminated with TCE
(range = 6-239 ppb), along with DCE and chromium (Goldberg et al., 1990). This study
identified 707 children born with congenital heart disease during the years 1969-1987. Of the
study participants, 246 families had parental residential and occupational exposure during
1 month prior to conception and during the first trimester of pregnancy, and 461 families had no
exposure before the end of the first trimester. In addition to this control group, two others were
used: (1) those that had contact with the contaminated water area, and (2) those that had contact
with the contaminated water area and matched with cases for education, ethnicity, and
occupation. Among these cases of congenital heart disease, no significant difference was seen
for fetal death (not quantified) for exposed cases compared to unexposed cases. (Also see
section below on congenital malformations for additional results from this cohort.)
A residential study of individuals living near the Rocky Mountain Arsenal in Colorado
examined the outcomes in offspring of 75 men and 71 women exposed to TCE in drinking water
(ATSDR. 2001). TCE exposure was stratified by high (>10.0 ppb), medium (>5.0-<10.0 ppb),
and low (<5.0 ppb). Among women with >5 ppb exposure experiencing miscarriage (n = 22/57)
compared to unexposed women experiencing miscarriage (n = 2/13) an elevated nonsignificant
4-521
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association was observed (ORadj: 4.44, 95% CI: 0.76-26.12). For lifetime number of
miscarriages reported by men and women, results were increased but without dose-response for
women (medium: ORadj: 8.56, 95% CI: 0.69-105.99; high: ORadj: 4.16, 95% CI: 0.61-25.99), but
less for men (medium: ORadj: 1.68, 95% CI: 0.26-10.77; high: ORadj: 0.65, 95% CI: 0.12-3.48).
Among women with >5 ppb exposure experiencing no live birth (n = 9/57) compared to
unexposed women experiencing no live birth (n = 1/13) an elevated nonsignificant association
was observed (ORadj: 2.46, 95% CI: 0.24-24.95). (Also see below for results from this study on
birth defects.)
NYS DOH and ATSDR conducted a study in Endicott, New York to examine childhood
cancer and birth outcomes in an area contaminated by a number of VOCs, including -thousands
of gallons" of TCE (ATSDR. 2006a). Soil vapor levels tested ranged from 0.18 to 140 mg/m3 in
indoor air. A follow-up study by ATSDR (2008b) reported that during the years 1978-1993 only
five spontaneous fetal deaths occurring >20 weeks gestation were reported when 7.5 were
expected (SIR: 0.66, 95% CI: 0.22-1.55). (See sections on low birth weight, congenital
malformations, and childhood cancer for additional results from this cohort.)
Women were exposed to contaminated drinking water while pregnant and living in
75 New Jersey towns during the years 1985-1988 (Bove. 1996: Bove et al.. 1995). The water
contained multiple trihalomethanes, including an average of 55 ppb TCE, along with
tetrachloroethylene, 1,1,1-trichloroethane, carbon tetrachloride, 1,2-dichloroethane, and benzene.
A number of birth outcomes were examined for 81,532 pregnancies, which resulted in
80,938 live births and 594 fetal deaths. No association was seen for exposure to >10 ppb TCE
and fetal death (ORadj: 1.12). (See below for results from this study on decreased birth weight
and congenital malformations.)
4.8.3.1.1.2. Decreased birth weight, SGA, and postnatal growth
Available data pertaining to birth weight and other growth-related outcomes come from
the case-control study in Santa Clara, California (discussed above), and by geographic-based
studies as well as geographic areas with known contamination of water supplies areas in
Woburn, Massachusetts; Tucson, Arizona, Endicott, New York; Camp Lejeune, North Carolina;
and New Jersey.
4.8.3.1.1.2.1. Occupational studies
The case-control study of the relationship between solvents and adverse pregnancy
outcomes discussed above (Windham et al., 1991) also examined intrauterine growth restriction
(IUGR). Telephone interviews included detailed questions on occupational solvent exposure, as
well as additional questions on residential solvent use. An increased risk of IUGR was observed
(OR: 12.5), although this was based only on one case that was exposed to both TCE and
tetrachloroethylene (also see section above on spontaneous abortion).
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4.8.3.1.1.2.2. Geographic-based studies
The study of Woburn, Massachusetts with contaminated well water discussed above
(Lagakos et al., 1986) examined birth weight. Of 3,462 live births surviving to 7 days, 220 were
<6 pounds at birth (6.4%). No association was observed between water access and low birth
weight (p = 0.77). (See section on spontaneous abortion for study details, and see sections on
spontaneous abortion, congenital malformations, and childhood cancer for additional results
from this cohort.)
An ecological analysis of well water contaminated with TCE in Tucson and birth-weight
was conducted by Rodenbeck et al. (2000). The source of the exposure was a U.S. Air Force
plant and the Tucson International Airport. The wells were taken out of service in 1981 after
concentrations of TCE were measured in the range of <5-107 |ig/L. The study population
consisted of 1,099 babies born within census tracts between 1979 and 1981, and the comparison
population consisted of 877 babies from nearby unexposed census tracts. There was a
nonsignificant increased risk for maternal exposure to TCE in drinking water and very-low-birth-
weight (<1,501 g) (OR: 3.3, 95% CI: 0.53-20.6). No increases were observed in the low-birth-
weight (<2,501 g) (OR: 0.9) or full-term (>35-<46-week gestation) low-birth-weight (OR: 0.81).
The study of VOC exposure in Endicott, New York reported data on low birth weight and
SGA (ATSDR, 2006a), see section on spontaneous abortion for study details). For births
occurring during the years 1978-2002, low birth weight was slightly but statistically elevated
(OR: 1.26, 95% CI: 1.00-1.59), as was SGA (OR: 1.22, 95% CI: 1.02-1.45), and full-term low
birth weight (OR: 1.41, 95% CI: 1.01-1.95). (Also see sections on spontaneous abortion,
congenital malformations, and childhood cancer for additional results from this cohort.)
Well water at the U.S. Marine Corps Base in Camp Lejeune, North Carolina was
identified to be contaminated with TCE, tetrachloroethylene, and 1,2-dichloroethane in April,
1982 and the wells were closed in December, 1984. ATSDR examined pregnancy outcomes
among women living on the base during the years 1968-1985 (ATSDR, 1998a). Compared to
unexposed residents12 (n = 5,681), babies exposed to TCE long-term13 (n = 31) had a lower mean
birth weight after adjustment for gestational age (-139 g, 90% CL = -277, -1), and babies
exposed short-term14 (n = 141) had a slightly higher mean birth weight (+70 g, 90% CL = -6,
146). For the long-term group, no effect was seen for very low birth weight (<1,500 g) or
prematurity (>5 ppb, OR: 1.05). No preterm births were reported in the long-term group and
those (n = 8) in the short-term group did not have an increased risk (OR: 0.7, 90% CI: 0.3-1.2).
12Unexposed residents resided at locations not classified for long- or short-term TCE exposure.
13Long-term TCE exposed mothers resided at Hospital Point during 1968-1985 for at least 1 week prior to birth.
14Short-term TCE exposed mothers resided at Berkeley Manor, Midway Park, Paradise Point, and Wakins Village at
the time of birth and at least 1 week during January 27 to February 7, 1985. In addition, the mother's last menstrual
period occurred on or before January 31, 1985 and the birth occurred after February 2, 1985.
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A higher prevalence of SGA15 was seen in the long-term exposed group (n = 3; OR 1.5, 90%
CL: 0.5, 3.8) compared to the short-term exposed group (OR: 1.1, 90% CI: 0.2-1.1). When the
long-term group was stratified by gender, male offspring were at more risk for both reduced birth
weight (-312 g, 90% CL = -632, -102) and SGA (OR: 3.9, 90% CL: 1.1-11.8). This study is
limited due the mixture of chemicals in the water, as well as it small sample size. ATSDR is
currently reanalyzing the findings because of an error in the exposure assessment related to the
start-up date of a water treatment plant (ATSDR. 2009. 2007a: U.S. GAP. 2007a. b)
Pregnancy outcomes among women were exposed to contaminated drinking water while
pregnant and living in 75 New Jersey towns during the years 1985-1988 were examined by
Bove et al. (Bove, 1996; Boveetal., 1995). The water contained multiple trihalomethanes,
including an average of 55 ppb TCE, along with tetrachloroethylene, 1,1,1-trichloroethane,
carbon tetrachloride, 1,2-dichloroethane, and benzene. A number of birth outcomes were
examined for 81,532 pregnancies, which resulted in 80,938 live births and 594 fetal deaths. A
slight decrease of 17.9 g in birth weight was seen for exposure >5 ppb, with a slight increase in
risk for exposure >10 ppb (OR: 1.23), but no effect was seen for very low birth weight or
SGA/prematurity (>5 ppb, OR: 1.05). However, due to the multiple contaminants in the water, it
is difficult to attribute the results solely to TCE exposure. (See below for results from this study
on congenital malformations.)
4.8.3.1.1.3. Congenital malformations
Three studies focusing on occupational solvent exposure and congenital malformations
from Europe provide data pertaining to TCE. Analyses of risk of congenital malformations were
also included in the studies in the four geographic areas described above (Woburn,
Massachusetts; Tucson, Arizona; Rocky Mountain Arsenal, Colorado; Endicott, New York; and
New Jersey), as well as additional sites in Phoenix, Arizona; and Milwaukee, WI. Specific
categories of malformations examined include cardiac defects, as well as cleft lip or cleft palate.
4.8.3.1.1.3.1. Occupational studies
A study of 1,148 men and 969 women occupationally exposed to TCE in Finland from
1963 to 1976 examined congenital malformations of offspring (Tola et al., 1980). U-TCA
measurements available for 2,004 employees ranged from <10 to >500 mg/L, although 91% of
the samples were <100 mg/L. No congenital malformations were seen in the offspring of women
between the ages of 15-49 years, although 3 were expected based on the national incidence.
Expected number of cases for the cohort could not be estimated because the number of
pregnancies was unknown.
5The criteria for SGA being singleton births less than the 10 percentile of published sex-specific growth curves.
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Men from Finland occupationally exposed to organic solvents including TCE did not
observe a risk of congenital malformations from paternal organic solvent exposure based on
17 cases and 35 controls exposed to TCE (OR: 0.6, 95% CI: 0.2-2.0) (Taskinen et al.. 1989).
(Also see section above on spontaneous abortion for study details and additional results from this
cohort.)
An occupational study of 100 women who gave birth to babies born with oral cleft
defects and 751 control women with normal births were examined for exposure to a number of
agents including TCE during the first trimester of pregnancy (Lorente et al., 2000). All women
were participants in a multicenter European case-referent study whose children were born
between 1989 and 1992. Four women were exposed to TCE, resulting in two cases of cleft lip
(ORadj: 3.21, 95% CI: 0.49-20.9), and two cases of cleft palate (ORadj: 4.47, 95% CI: 1.02-40.9).
Using logistic regression, the increased risk of cleft palate remained high (OR: 6.7, 95% CI: 0.9-
49.7), even when controlling for tobacco and alcohol consumption (OR: 7.8, 95% CI: 0.8-71.8).
However, the number of cases was small, and exposure levels were not known.
4.8.3.1.1.3.2. Geographic-based studies
A community in Woburn, Massachusetts with contaminated well water experienced an
increased incidence of adverse birth outcomes and childhood leukemia (Lagakos et al., 1986, see
section on spontaneous abortion for study details). Statistically significant positive association
between access to contaminated water and eye/ear birth anomalies (OR: 14.9, p < 0.0001),
CNS/chromosomal/oral cleft anomalies (OR: 4.5, p = 0.01), kidney/urinary tract disorders
(OR: 1.35,/> = 0.02) and lung/respiratory tract disorders (OR: I.l6,p = 0.05) were observed.
There were also five cases of cardiovascular anomalies, but there was not a significant
association with TCE (p = 0.91). However, since organogenesis occurs during gestational
weeks 3-5 in humans, some of these effects could have been missed if fetal loss occurred. (Also
see sections on spontaneous abortion, perinatal death, decreased birth weight, and childhood
cancer for additional results from this cohort.)
A high prevalence of congenital heart disease was found within an area of Tucson Valley,
Arizona (Goldberg et al., 1990, see section on spontaneous abortion for study details and
additional results). Of the total 707 case families included, 246 (35%) were exposed to wells
providing drinking water found to be contaminated with TCE (range = 6-239 ppb), along with
DCE and chromium. Before the wells were closed after the contamination was discovered in
1981, the OR of congenital heart disease was 3 times higher for those exposed to contaminated
drinking water compared to those not exposed; after the wells were closed, there was no
difference seen. This study observed 18 exposed cases of congenital heart disease when
16.4 would be expected (RR: 1.1). Prevalence of congenital heart disease in offspring after
maternal exposure during the first trimester (6.8 in 1,000 live births) was significantly increased
compared to nonexposed families (2.64 in 1,000 live births) (p < 0.001, 95% CI: 1.14-4.14). No
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difference in prevalence was seen if paternal data were included, and there was no difference in
prevalence by ethnicity. In addition, no significant difference was seen for cardiac lesions.
A residential study of individuals living near the Rocky Mountain Arsenal in Colorado
examined the outcomes in offspring of 75 men and 71 women exposed to TCE in drinking water
(ATSDR. 2001). The risk was elevated for the nine birth defects observed (OR: 5.87,
95% CI: 0.59-58.81), including one nervous system defect, one heart defect, and one incidence
of cerebral palsy. The remaining cases were classified as -ether," and the authors speculate
these may be based on inaccurate reports. (See above for study details and results on
spontaneous abortion.)
The study of VOC exposure in Endicott, New York examined a number of birth defects
during the years 1983-2000 (ATSDR, 2006a), see section on spontaneous for study details).
These include total reportable birth defects, structural birth defects, surveillance birth defects,
total cardiac defects, major cardiac defects, cleft lip/cleft palate, neural tube defects, and choanal
atresia (blocked nasal cavities). There were 56 expected cases of all birth defects and 61 were
observed resulting in no elevation of risk (rate ratio: 1.08, 95% CI: 0.82-1.42). There were no
cases of cleft lip/cleft palate, neural tube defects, or choanal atresia. Both total cardiac defects
(n = 15; rate ratio: 1.94, 95% CI: 1.21-3.12) and major cardiac defects (n = 6; rate ratio: 2.52,
95% CI: 1.2-5.29) were statistically increased. A follow-up study by ATSDR (2008b) reported
that conotruncal heart malformations were particularly elevated (n = 4; rate ratio: 4.83, 95% CI:
1.81-12.89). The results remained significantly elevated (rate ratio: 3.74; 95% CI: 1.21-11.62)
when infants with Down syndrome were excluded from the analysis. (Also see sections on
spontaneous abortion, decreased birth weight, and childhood cancer for additional results from
this cohort.)
In the New Jersey study described previously, the prevalence of birth defects reported by
surveillance systems was examined among the women exposed to TCE and other contaminants
in water while pregnant between 1985 and 1988 (Bove, 1996; Bove et al., 1995). For exposure
>10 ppb (n = 1,372), an increased risk, with relatively wide CIs, was seen for all birth defects
(OR: 2.53, 95% CI: 0.77-7.34). An increased risk was also seen for CNS defects (>10 ppb:
OR: 1.68), specifically 56 cases of neural tube defects (10 ppb: OR: 2.53, 95% CI: 0.77-7.34). A slight increase was seen in major cardiac defects
(>10 ppb: OR: 1.24, 50% CI: 0.75-1.94), including ventrical septal defects (>5 ppb: OR: 1.30,
95% CI: 0.88-1.87). An elevated risk was seen for nine cases of oral clefts (<5 ppb: OR: 2.24,
95% CI: 1.04-4.66), although no dose-response was seen (>10 ppb, OR: 1.30). However, due to
the multiple contaminants in the water, it is difficult to attribute the results solely to TCE
exposure. (See above for results from this study on fetal death and decreased birth weight.)
Arizona Department of Health Services (AZ DHS) conducted studies of contaminated
drinking water and congenital malformations (<20 years old) in Maricopa County, which
encompasses Phoenix and the surrounding area (Flood, 1988). TCE contamination was
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associated with elevated levels of deaths in children <20 years old due to total congenital
anomalies in East Central Phoenix from 1966 to 1969 (RR: 1.4, 95% CI: 1.1-1.7), from 1970 to
1981 (RR: 1.5, 95% CI: 1.3-1.7), and from 1982 to 1986 (RR: 2.0, 95% CI: 1.5-2.5), as well as
in other areas of the county. (See below for results from this study on childhood leukemia.)
A study was conducted of children born in 1997-1999 with congenital heart defects in
Milwaukee, Wisconsin (Yauck et al., 2004). TCE emissions data were ascertained from state
and EPA databases, and distance between maternal residence and the emission source was
determined using a GIS. Exposure was defined as those within 1.32 miles from at least one site.
Results showed that an increased risk of congenital heart defects was seen for the offspring of
exposed mothers >38 years old (OR: 6.2, 95% CI: 2.6-14.5), although an increased risk was also
seen for offspring of unexposed mothers >38 years old (OR: 1.9, 95% CI: 1.1-3.5), and no risk
was seen for offspring of exposed mothers <38 years old (OR: 0.9, 95% CI: 0.6-1.2). The
authors speculate that studies that did not find a risk only examined younger mothers. The
authors also note that statistically significant increased risk was seen for mothers with
preexisting diabetes, chronic hypertension, or alcohol use during pregnancy.
An abstract reported that 28 people living in a Michigan town were exposed for 5-
10 years to 8-14 ppm TCE in well water (Bernad et al., 1987, abstract). One child was born with
multiple birth defects, with no further details.
4.8.3.1.1.4. Other adverse birth outcomes
TCE was previously used as a general anesthetic during pregnancy. One study measured
the levels of TCE in maternal and newborn blood after use during 34 vaginal childbirths (Beppu,
1968). TCE was administered through a vaporizer from two to 98 minutes (mean 34.7 minutes)
at volumes of 2 to 8 mL (mean 4.3 mL). Mean blood TCE concentrations were 2.80 ±
1.14 mg/dL in maternal femoral arteries; 2.36 ±1.17 mg/dL in maternal cubital veins; 1.83 ±
1.08 mg/dL in umbilical vein; and 1.91 ± 0.95 mg/dL in the umbilical arteries. A significant
correlation was seen for maternal arterial blood and infants' venous blood, and the concentration
of the fetal blood was lower than that of the mother. Of these newborns, one had asphyxia and
three —depy babies" had Apgar scores of 5-9; however, these results could not be correlated to
length of inhalation and there was no difference in the TCE levels in the mother or newborn
blood compared to those without adverse effects. Discussion included delayed newborn reflexes
(raising the head and buttocks, bending the spine, and sound reflex), blood pressure, jaundice,
and body weight gain; however, the results were compared to newborns exposed to other
compounds, not to an unexposed population. This study also examined the concentration of TCE
in one mother at 22-weeks gestation exposed for four minutes, after which the fetus was
—aificially delivered." Maternal blood concentration was 3.0 mg/dL, and 0.9 mg/dL of TCE
was found in the fetal heart, but not in other organs.
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Another study of TCE administered during childbirth to the mother as an analgesic
examined perinatal measures, including fetal pH, fetal partial pressure carbon dioxide (PCO2,)
fetal base deficit, fetal partial pressure oxygen (PO2), Apgar scores, and neonatal capillary blood
(Phillips and Macdonald, 1971). The study consisted of 152 women whose fetus was considered
to be at risk for hypoxia during labor. Out of this group, 51 received TCE (amount and route of
exposure not reported). TCE caused fetal pH to fall more, base deficit increased more, and PO2
fell more than the control group by fourfold or more compared to other analgesics used.
4.8.3.1.2. Postnatal developmental outcomes
4.8.3.1.2.1. Developmental neurotoxicity
The studies examining neurotoxic effects from TCE exposure are discussed in
Section 4.3, and the human developmental neurotoxic effects are reiterated here.
4.8.3.1.2.1.1. Occupational studies
An occupational study examined the neurodevelopment of the offspring of 32 women
exposed to various organic solvents during pregnancy (Laslo-Baker et al., 2004; Till et al.,
Three of these women were exposed to TCE; however, no levels were measured and the
results for examined outcomes are for total organic solvent exposure, and are not specific to
TCE.
4.8.3.1.2.1.2. Geographic-based studies
A study of three residential cohorts (Woburn, Massachusetts; Alpha, Ohio; and Twin
Cities, Minnesota) examined the neurological effects of TCE exposure in drinking water (White
et al., 1997). For Woburn, Massachusetts, 28 individuals ranging from 9 to 55 years old were
assessed, with exposure from a tanning factor and chemical plant at levels of 63-400 ppb for <1-
12 years; the time between exposure and neurological examination was about 5 years. In this
cohort, 6/13 children (46%) had impairments in the verbal naming/language domain. For Alpha,
Ohio, 12 individuals ranging from 12 to 68 years old were assessed, with exposure from
degreasing used at a manufacturing operation at levels of 3.3-330 ppb for 5-17 years; the time
between exposure and neurological examination was 5-17 years. In this cohort, one of two
children (50%) had impairments in the verbal naming/language domain. For Twin Cities,
Minnesota, 14 individuals ranging from 8 to 62 years old were assessed, with exposure from an
army ammunition plant at levels of 261-2,440 ppb for 0.25-25 years; the time between exposure
and neurological examination was 4-22 years. In this cohort, four of four children (100%) had
impairments in the verbal naming/language, memory, and academic domains and were
diagnosed with moderate encephalopathy; and three of four children (75%) performed poorly on
the WRAT-R Reading and Spelling and WAIS-R Information tests.
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A case-control study was conducted to examine the relationship between multiple
environmental agents and ASD (Windham et al., 2006). Cases (n = 284) and controls (n = 657)
were born in 1994 in the San Francisco Bay Area. Cases were diagnosed before age 9 years.
Exposure was determined by geocoding births to census tracts, and linking to hazardous air
pollutants data. An elevated risk was seen for TCE in the upper 3rd quartile (OR: 1.37, 95% CI:
0.96-1.95), and a statistically significant elevated risk was seen for the upper 4th quartile
(OR: 1.47, 95% CI: 1.03-2.08).
The TCE Subregistry (Burg and Gist 1999: BurgetaL 1995). including 948 children
<18 years old from 13 sites located in 3 states, was examined for any association of ingestion of
drinking water contaminated with TCE and various health effects (ATSDR, 2003b: Burg and
Gist 1999; Burg et al., 1995). Exposure groups included: (1) maximum TCE exposure;
(2) cumulative TCE exposure; (3) cumulative chemical exposure; and (4) duration of exposure.
Exposed children 0-9 years old had statistically increased hearing impairment compared to
controls (RR: 2.13, 99% CI: 1.12-4.07), with children <5 years old having a 5.2-fold increase
over controls. Exposed children 0-9 years old also had statistically increased speech impairment
(RR: 2.45, 99% CI: 1.31-4.58). In addition, anemia and other blood disorders were statistically
higher for males 0-9 years old. The authors noted that exposure could have occurred prenatally
or postnatally. There was further analysis on the 116 exposed children and 182 controls who
were under 10 years old at the time that the baseline study was conducted by ATSDR. This
analysis did not find a continued association with speech and hearing impairment in these
children; however, the absence of acoustic reflexes (contraction of the middle ear muscles in
response to sound) remained significant (ATSDR, 2003b). No differences were seen when
stratified by prenatal and postnatal exposure.
Twenty-eight people living in a Michigan town were exposed for 5-10 years to 8-14 ppm
TCE in well water (Bernad et al., 1987, abstract). Ten adults and 12 children completed a
questionnaire on neurotoxic endpoints. Nine of the 12 children had poor learning ability,
aggressive behavior, and low attention span.
4.8.3.1.2.2. Developmental immunotoxicity
The studies examining human immunotoxic effects from TCE exposure are discussed in
Section 4.6.1. The studies reporting developmental effects are reiterated briefly here.
Two studies focused on immunological development in children after maternal exposure
to VOCs (Lehmann et al., 2002; Lehmann et al., 2001). The first examined premature neonates
(1,500-2,500 g) and neonates at risk of atopy (cord blood IgE >0.9 kU/L; double positive family
atopy history) at 36 months of age (Lehmann et al., 2001). The median air level in children's
bedrooms measured 0.42 |ig/m3. There was no association with allergic sensitization to egg
white and milk, or to cytokine producing peripheral T-cells. The second examined healthy, full-
term neonates (>2,500 g; >37 weeks gestation) born in Lepzig, Germany (Lehmann et al., 2002).
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The median air level in the children's bedrooms 3-4 weeks after birth measured 0.6 |ig/m3. A
significant reduction of Thl IL-2 producing T-cells was observed.
Byers et al. (1988) observed altered immune response in family members of children
diagnosed with leukemia in Woburn, Massachusetts (Lagakos et al., 1986, see below for results
of this study). The family members included 13 siblings under 19 years old at the time of
exposure; however, an analysis looking at only these children was not done. This study is
discussed in further detail in Section 4.6.1.
4.8.3.1.2.3. Other developmental outcomes
A study demonstrated the adverse effects of TCE used as an anesthetic in children during
operations during 1964 in Poland to repair developmental defects of the jaw and face (Jasinska,
1965, translation). Fifty-five children ranging from 6 months to 10 years old were anesthetized
with at least 10 mL TCE placed into an evaporator. Bradycardia occurred in two children, an
accelerated heart rate of 20-25 beats per minute occurred in seven children, no arrhythmia was
observed, and arterial blood pressure remained steady or dropped by 10 mmHg only.
Respiratory acceleration was observed in 25 of the children, and was seen more in infants and
younger children.
4.8.3.1.2.4. Childhood cancer
Several studies of parental occupational exposure were conducted in North America and
the United Kingdom to determine an association with childhood cancer. A number of
geographic-based studies were conducted in California; New Jersey; Woburn, Massachusetts;
Endicott, New York; Phoenix, Arizona; and Tucson, Arizona. Specific categories of childhood
cancers examined include leukemia, NHL, and CNS tumors.
4.8.3.1.2.4.1. Occupational studies
Brain tumors were observed in 98 children >10 years old at diagnosis from 1972-1977 in
Los Angeles County (Peters et al., 1985; Peters et al., 1981). Exposure was determined by
questionnaire. Two cases whose father had TCE exposure were reported: one case of
oligodendroglioma in an 8-year-old whose father was a machinist, and astrocytoma in a 7-year-
old whose father was an inspector for production scheduling and parts also exposed to methyl
ethyl ketone (Peters etal., 1981). Peters et al. (1985) also briefly mentioned five cases of brain
tumors in the offspring and no controls of paternal exposure to TCE (resulting in an inability to
calculate an OR), but without providing any additional data.
A case-control study was conducted to assess an association between parental
occupational exposure and neuroblastoma diagnosed in offspring <19 years old in the United
States and Canada from May 1992 to April 1994 (De Roos et al.. 2001). Paternal self-reported
exposure to TCE was reported in 22 cases and 12 controls, resulting in an elevated risk of
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neuroblastoma in the offspring (OR: 1.4, 95% CI: 0.7-2.9). Maternal exposure to TCE was not
reported.
A case-control study of parental occupational exposure and childhood leukemia was
conducted in Los Angeles County (Lowengart et al., 1987). Children (61 boys and 62 girls)
diagnosed at <10 years old (mean age 4 years) from 1980 to 1984 were included in the analysis.
Paternal occupation exposure to TCE was elevated for 1 year preconception (OR: 2.0, p = 0.16),
prenatal (OR: 2.0,/> = 0.16), and postnatal (OR: 2.1, p = 0.7) exposure periods. Maternal
exposure to TCE was not reported.
A case-control study children diagnosed with acute lymphoblastic leukemia (ALL)
examined parental occupational exposure to hydrocarbons in the United States and Canada (Shu
et al., 1999). Children were under the age of 15 years at diagnosis during the years 1989-1993.
Cases were confirmed with a bone marrow sample. Questionnaires on maternal and paternal
exposures were given to 1,842 case-control pairs, resulting in 15 cases and 9 controls maternally
exposed and 136 cases and 104 controls paternally exposed to TCE. There was an increased but
nonsignificant risk for maternal exposure to TCE during preconception (OR: 1.8, 95% CI: 0.6-
5.2), pregnancy (OR: 1.8, 95% CI: 0.5-6.4), postnatally (OR: 1.4, 95% CI: 0.5-4.1), or any of
these periods (OR: 1.8, 95% CI: 0.8-4.1). However, there was no increased risk for paternal
exposure to TCE.
Occupational exposure in communities in the United Kingdom was examined to
determine an association with leukemia and NHL diagnosed in the offspring (McKinney et al.,
1991). Paternal occupational exposure was elevated for exposure occurring during
preconception (OR: 2.27, 95% CI: 0.84-6.16), prenatal (OR: 4.40, 95% CI: 1.15-21.01), and
postnatal (OR: 2.66, 95% CI: 0.82-9.19) exposure periods. Risk from maternal preconception
exposure was not elevated (OR: 1.16, 95% CI: 0.13-7.91). However, the number of cases
examined in this study was low, particularly for maternal exposure.
4.8.3.1.2.4.2. Geographic-based studies
A California community exposed to TCE (0.09-97 ppb) in drinking water from
contaminated wells was examined for cancer (Morgan and Cassady, 2002). A specific emphasis
was placed on the examination of 22 cases of childhood cancer diagnosed before 15 years old.
However, the incidence did not exceed those expected for the community for total cancer
(SIR: 0.83, 99% CI: 0.44-1.40), CNS cancer (SIR: 1.05, 99% CI: 0.24-2.70), or leukemia
(SIR: 1.09, 99% CI: 0.38-2.31).
An examination of drinking water was conducted in four New Jersey counties to
determine an association with leukemia and NHL (Cohn et al., 1994b). A number of
contaminants were reported, including VOCs and trihalomethanes. TCE was found as high as
67 ppb, and exposure categories were assigned to be >0.1, 0.1-5, and >5 ppb. A significantly
elevated dose-response risk for ALL was observed for girls diagnosed before 20 years old (RR:
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3.36, 95% CI: 1.29-8.28), which was increased among girls diagnosed before 5 years old
(RR:4.54, 95% CI: 1.47-10.6). A significantly elevated dose-response risk for girls was also
observed for total leukemia (RR: 1.43, 95% CI: 1.07-1.98).
The Woburn, Massachusetts community with contaminated well water experienced an
increase in the incidence of childhood leukemia (Costas et al., 2002; MDPH, 1997b: Cutler et al.,
1986; Lagakos et al., 1986). An initial study examined 12 cases of childhood leukemia
diagnosed in children <15 years old between 1969 and 1979, when 5.2 cases were expected, and
a higher risk was observed in boys compared to girls; however, no factors were observed to
account for this increase (Cutler et al., 1986). Another study observed statistically significant
positive association between access to contaminated water; 20 cases of childhood cancer were
observed for both cumulative exposure metric (OR: 1.39,/? = 0.03), and none vs. some exposure
metric (OR: 3.03, p = 0.02) (Lagakos et al., 1986). Massachusetts Department of Public Health
(MDPH, 1997b) conducted a case-control study of children <20 years old living in Woburn and
diagnosed with leukemia between 1969 and 1989 (n = 21) and observed that consumption of
drinking water increased the risk of leukemia (OR: 3.03, 95% CI: 0.82-11.28), with the highest
risk from exposure during fetal development (OR: 8.33, 95% CI: 0.73-94.67). This study found
that paternal occupational exposure to TCE was not related to leukemia in the offspring (MDPH,
1997b). In the most recent update, Costas et al. (2002) reported that between the years 1969 and
1997, 24 cases of childhood leukemia were observed when 11 were expected. Risk was
calculated for cumulative exposure to contaminated drinking water 2 years prior to conception
(ORadj: 2.61, 95% CI: 0.47-14.97), during pregnancy (ORadj: 8.33, 95% CI: 0.73-94.67),
postnatal (ORadj: 1.18, 95% CI: 0.28-5.05), and any of these time periods (ORadj: 2.39, 95%
CI: 0.54-10.59). A dose-response was observed during pregnancy only. Cases were more likely
to be male (76%), <9 years old at diagnosis (62%), breast-fed (OR: 10.17, 95% CI: 1.22-84.50),
and exposed during pregnancy (adjusted OR [ORadj]: 8.33, 95% CI: 0.73-94.67). A dose-
response was seen during the pregnancy exposure period, with the most exposed having an ORadj
of 14.30 (95% CI: 0.92-224.52). Other elevated risks observed included maternal alcohol intake
during pregnancy (OR: 1.50, 95% CI: 0.54-4.20), having a paternal grandfather diagnosed with
cancer (OR: 2.01, 95% CI: 0.73-5.58), father employed in a high risk industry (OR: 2.55, 95%
CI: 0.78-8.30), and public water being the subject's primary beverage (OR: 3.03, 95% CI: 0.82-
11.28). (Also see sections on spontaneous abortion, perinatal death, decreased birth weight, and
congenital malformations for additional results from this cohort.)
The study of VOC exposure in Endicott, New York discussed above observed fewer than
six cases of cancer that were diagnosed between 1980 and 2001 in children <20 years old, and
did not exceed expected cases or types (ATSDR, 2006a). (See section on spontaneous abortion
for study details, and sections on spontaneous abortion, decreased birth weight, and congenital
malformations for additional results from this cohort.)
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The AZ DHS conducted a number of studies of contaminated drinking water and 189
cases of childhood cancer (<20 years old) (Flood. 1997a: APRS. 1990: Kioski etaL 1990a:
Kioski et al., 1990b: Flood, 1988). In Maricopa County, which encompasses Phoenix and the
surrounding area, TCE contamination (8.9 and 29 ppb in two wells) was associated with elevated
levels of childhood leukemia (n = 67) in west central Phoenix during 1965-1986 (SIR: 1.67,
95% CI: 1.20-2.27) and 1982-1986 (SIR: 1.91, 95% CI: 1.11-3.12), but did not observe a
significant increase in total childhood cancers, lymphoma, brain/CNS, or other cancers during
these time periods (ADHS, 1990). (See above for results from this study on congenital
anomalies.) A follow-up study retrospectively asked parents about exposures and found that
residence within 2 miles of wells contaminated with TCE was not a risk factor for childhood
leukemia, but identified a number of other risk factors (Flood, 1997a). A further study of East
Phoenix, reported on TCE contamination found along with 1,1,1-trichloroethane and 25 other
contaminants in well water (levels not reported) and found no increase in incidence of childhood
leukemia (SIR: 0.85, 95% CI: 0.50-1.35) based on 16 cases (Kioski et al.. 1990b). There were
also 16 cases of other types of childhood cancer, but were too few to be analyzed separately. In
Pima County, which encompasses Tucson and the surrounding area, TCE was found in drinking
wells (1.1-239 ppb), along with 1,1-DCE, chloroform, and chromium and found a
nonstatistically elevated risk of leukemia was observed (SIR: 1.50, 95% CI: 0.76-2.70), but no
risk was observed for testicular cancer, lymphoma, or CNS/brain cancer (Kioski etal., 1990a).
4.8.3.1.3. Summary of human developmental toxicity
Epidemiological developmental studies examined the association between TCE exposure
and a number of prenatal and postnatal developmental outcomes. Prenatal developmental
outcomes examined include spontaneous abortion and perinatal death; decreased birth weight,
SGA, and postnatal growth; congenital malformations; and other adverse birth outcomes.
Postnatal developmental outcomes examined include developmental neurotoxicity,
developmental immunotoxicity, other developmental outcomes, and childhood cancer related to
TCE exposure.
More information on developmental outcomes is expected. A follow-up study of the
Camp Lejeune cohort (ATSDR, 1998a) for birth defects and childhood cancers was initiated in
1999 (ATSDR. 2003a) and expected to be completed soon (ATSDR. 2009: U.S. GAP. 2007a.
b). Out of a total of 106 potential cases of either birth defects or childhood cancer, 57 have been
confirmed and will constitute the cases. These will be compared 548 control offspring of
mothers who also lived at Camp Lejeune during their pregnancy from 1968 to 1985. As part of
this study, a drinking water model was developed to determine a more accurate level and
duration of exposure to these pregnant women (ATSDR, 2007a). Additional health studies have
been suggested, including adverse neurological or behavioral effects or pregnancy loss.
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4.8.3.2. Animal Developmental Toxicology Studies
A number of animal studies have been conducted to assess the potential for
developmental toxicity of TCE. These include studies conducted in rodents by prenatal
inhalation or oral exposures (summarized in Tables 4-96 and 4-97), as well as assessments in
nonmammalian species (e.g., avian, amphibian, and invertebrate species) exposed to TCE during
development. Studies have been conducted that provide information on the potential for effects
on specific organ systems, including the developing nervous, immune, and pulmonary systems.
Additionally, a number of research efforts have focused on further characterization of the mode
of action for cardiac malformations that have been reported to be associated with TCE exposure.
Table 4-96. Summary of mammalian in vivo developmental toxicity
studies—inhalation exposures
Reference"
Carney et
al. (2006)
Dorfmueller
etal. 0979)
Hardin et al.
(1981)
Healy et al.
(1982)
Species/strain/
sex/number
Rat, Sprague-
Dawley, females,
27 dams/group
Rat, Long-Evans,
females,
30 dams/group
Rat, Sprague-
Dawley, female,
nominal 30/group
Rabbit, New
Zealand white,
female, nominal
20/group
Rat, Wistar,
females, 31-
32 dams/group
Exposure level/
duration
0, 50, 150, or
600 ppm
(600 ppm =
3.2 mg/L)c
6 hrs/d;
CDs 6-20
0 or 1,800 +
200 ppm
(9,674 +
1,075 mg/m3)c
2 wks, 6 hrs/d,
5 d/wk; prior to
mating and/or
on CDs 0-20
0 or 500 ppm
6-7 hrs/d;
CDs 1-19
0 or 500 ppm
6-7 hrs/d;
CDs 1-24
0 or 100 ppm
4 hrs/d;
CDs 8-21
NOAEL; LOAELb
Maternal NOAEL:
150 ppm
Maternal LOAEL:
600 ppm
Developmental
NOAEL: 600 ppm
Maternal NOAEL:
1,800 +200 ppm
Developmental
LOAEL: 1,800 +
200 ppm
Maternal NOAEL:
500 ppm
Developmental
NOAEL: 500 ppm
Maternal NOAEL:
500 ppm
Developmental
LOAEL: 500 ppm
Maternal NOAEL:
100 ppm
Developmental
LOAEL: 100 ppm
Effects
J, Body weight gain (22% less than
control) on CDs 6-9 at 600 ppm.
No evidence of developmental
toxicity, including heart defects.
No maternal abnormalities.
Statistically significant t skeletal and
soft tissue anomalies in fetuses from
dams exposed during pregnancy only.
No statistically significant treatment
effects on behavior of offspring 10,
20, or 100 d postpartum. Body weight
gains statistically significant J, in pups
from dams with pregestational
exposure.
No maternal toxicity.
No embryonic or fetal toxicity.
No maternal toxicity.
Hydrocephaly observed in two fetuses
of two litters, considered equivocal
evidence of teratogenic potential.
No maternal abnormalities.
Litters with total resorptions
statistically significant |.
Statistically significant J, fetal
weight, and | bipartite or absent
skeletal ossification centers.
4-534
-------
Table 4-96. Summary of mammalian in vivo developmental toxicity
studies—inhalation exposures
Reference"
Schwetz et
al. (1975)
Westergren
et al. (1984)
Species/strain/
sex/number
Rat, Sprague-
Dawley, female,
20-35/group
Mouse, Swiss-
Webster, females,
30-40 dams/group
Mouse, NMRI, male
and female, 6-
12 offspring/group
Exposure level/
duration
0 or 300 ppm
7 hrs/d;
CDs 6-15
0 or 150 ppm
24 hrs/d;
30 d (during 7 d
of mating and
until GD 22)
NOAEL; LOAEL3
Maternal LOAEL:
300 ppm
Developmental
NOAEL: 300 ppm
Developmental
LOAELd: 150 ppnf
Effects
4-5% J, maternal body weight
No embryonic or fetal toxicity; not
teratogenic.
Specific gravity of brains statistically
significant | at PNDs 0, 10, and 20-
22. Similar effects at PNDs 20-22 in
occipital cortex and cerebellum. No
effects at 1 mo of age.
aBolded studies carried forward for consideration in dose-response assessment (see Chapter 5).
bNOAEL and LOAEL are based upon reported study findings.
Dose conversions provided by study author(s).
dParental observations not reported.
Table 4-97. Ocular defects observed (Narotsky et al., 1995)
Dose TCE (mg/kg-d)
0
10.1
32
101
320
475
633
844
1,125
Incidence
(number affected pups/total number pups)3
1/197
0/71
0/85
3/68
3/82
6/100
6/100
7/58
12/44
Percentage of pups with
eye defects
0.51
0.00
0.00
4.41
3.66
6.00
6.00
12.07
27.27
"Reported in Barton and Das (1996).
4.8.3.2.1. Mammalian studies
Studies that have examined the effects of TCE on mammalian development following
either inhalation or oral exposures are described below and summarized in Tables 4-96 and 4-98,
respectively.
4-535
-------
Table 4-98. Summary of mammalian in vivo developmental toxicity
studies—oral exposures
Reference"
Blossom
and Doss
(2007)
Blossom et
al. (20081
Species/strain/
sex/number
Mouse, MRL +/+,
dams and both sexes
offspring,
3 litters/group, 8-
12 offspring/group
Mouse, MRL +/+,
dams and both sexes
offspring,
8 litters/group, 3-
8 offspring/group
Dose level/
exposure
duration
0, 0.5, or
2.5 mg/mL
Parental mice
and/or offspring
exposed from
GD 0 to 7-8 mo of
age
0 or 0.1 mg/mL
(maternal dose =
25.7 mg/kg-d;
offspring
PNDs 24^2
dose —
3 1.0 mg/kg-d
Parental mice
and/or offspring
exposed from
GD 0 to PND 42
Route/
vehicle
Drinking
water
Drinking
water
NOAEL;
LOAELb
Developmental
LOAEL =
0.5 mg/mLc
Developmental
LOAEL =
1,400 ppbc
Effects
At 0.5 mg/mL: statistically
significant J, postweaning
weight; statistically
significantt IFNy produced
by splenic CD4+ cells at 5-
6 wks; statistically significant
| splenic CD8+and B220+
lymphocytes; statistically
significant t IgG2a and
histone; statistically
significant altered CD4-
/CD8- and CD4+/CD8+
thymocyte profile.
At 2.5 mg/mL: statistically
significant J, postweaning
weight; statistically
significant t IFNy produced
by splenic CD4+ and CD8+
cells at 4-5 and 5-6 wks;
statistically significant
| splenic CD4+, CD8+, and
B220+ lymphocytes;
statistically significant altered
CD4+/CD8+ thymocyte
profile.
At 0. 1 mg/mL: at PND 20,
statistically significant
t thymocyte cellularity and
distribution, associated with
statistically significant t in
thymocyte subset distribution;
statistically significant
t reactive oxygen species
generation in total
thymocytes; statistically
significant t in splenic CD4+
T-cell production of IFN-y
and IL-2 in females and TNF-
a in males at PND 42.
Significantly impaired nest-
building behaviors at
PND 35. Increased
aggressive activities, and
increased oxidative stress and
impaired thiol status in the
cerebellar tissue of male
offspring at PND 40.
4-536
-------
Table 4-98. Summary of mammalian in vivo developmental toxicity
studies—oral exposures (continued)
Reference"
Collier et al.
(2003)
Cosby and
Dukelow
(1992J
Dawson et al.
(1991)
Fisher et al.
(2001):
Warren et al.
(2006)
Species/strain/
sex/number
Rat, Sprague-
Dawley, female,
number dams/group
not reported
Mouse, B6D2F1,
female, 28-
62 dams/group
Rat, Sprague-
Dawley, 116 females
allocated to
1 1 groups
Rat, Sprague-
Dawley, female, 20-
25 dams/group
Dose level/
exposure
duration
0,0.11, or
1 . 1 mg/mL
(0, 830, or
8,300 ugM)d
CDs 0-11
0, 24, or
240 mg/kg-d
CDs 1-5, 6-10, or
11-15
0, 1.5, or
l,100ppm
2 mo before
mating and/or
during gestation
0 or 500 mg/kg-d
CDs 6-15
Route/
vehicle
Drinking
water
Gavage in
corn oil
Drinking
water
Gavage in
soybean oil
NOAEL;
LOAEL3
Developmental
LOEL:
O.llmg/mL
Maternal
NOAEL: 240
mg/kg-d
Developmental
NOAEL:
240 mg/kg-d
Maternal
NOAEL:
l,100ppm
Developmental
LOAEL:
1.5 ppm
Maternal
NOAEL:
500 mg/kg-d
Developmental
NOAEL:
500 mg/kg-d
Effects
Embryos collected between
CDs 10.5 and 11. Gene
expression at 1.1 mg/mL
TCE: 8 housekeeping genes
t, and one gene |; 3 stress
response genes t, IL-10 J,;
2 cyto-skeletal/cell
adhesion/blood related genes
t, 3 genes |; 2 heart-specific
genes |. Effects at
0.11 mg/mL reduced
considerably. Two possible
markers for fetal TCE
exposure identified as Serca-2
Ca+2 ATPase and GPI-pl37.
No maternal toxicity.
No effects on embryonic or
fetal development.
No maternal toxicity.
Statistically significant t in
heart defects, primarily atrial
septal defects, found at both
dose levels in groups exposed
prior to pregnancy and during
pregnancy, as well as in group
exposed to 1,100 ppm dose
during pregnancy only. No
statistically significant t in
congenital heart defects in
groups exposed prior to
pregnancy only.
No maternal toxicity.
No developmental toxicity.
The incidence of heart
malformations for fetuses
from TCE-treated dams (3-
5%) did not differ from
negative controls. No eye
defects observed.
4-537
-------
Table 4-98. Summary of mammalian in vivo developmental toxicity
studies—oral exposures (continued)
Reference"
Fredriksson
et al. (1993)
George et al.
(1986)
Isaacson and
Taylor
(1989)
Johnson et
al. (2003)
Narotsky et
al. (1995)
Species/strain/
sex/number
Mouse, NMRI,
male pups, 12 pups
from 3-4 different
litters/group
Rat, F334, male
and female,
20 pairs/treatment
group,
40 controls/sex
Rat, Sprague-
Dawley, females,
6 dams/group
Rat, Sprague-
Dawley, female, 9-
13/group, 55 in
control group
Rat, F344, females,
8-12 dams/group
Dose level/
exposure
duration
0, 50, or
290 mg/kg-d
PNDs 10-16
0, 0.15, 0.30, or
0.60% micro-
encapsulated
TCE
Breeders
exposed 1 wk
premating, then
for 13 wks;
pregnant $s
throughout
pregnancy (i.e.,
18 wks total)
0,312, or
625 mg/L
(0, 4.0, or
8.1 mg/d)d
Dams (and pups)
exposed from
14 d prior to
mating until end
of lactation.
0, 2.5, 250, 1.5,
or 1,100 ppm
(0, 0.00045,
0.048, 0.218, or
129 mg/kg-d)d
CDs 0-22
0,10.1,32,101,
320, 475, 633,
844, or
1,125 mg/kg-d
CDs 6-15
Route/
vehicle
Gavage in
a 20% fat
emulsion
prepared
from egg
lecithin
and
peanut oil
Dietary
Drinking
water
Drinking
water
Gavage in
corn oil
NOAEL;
LOAEL"
Developmental
LOAEL:
50 mg/kg-d
LOAEL:
0.15%
Developmental
LOAEL:
312 mg/Lb
Developmental
NOAEL:
2.5 ppb
Developmental
LOAEL:
250 ppbb
Maternal
LOAEL:
475 mg/kg-d
Effects
Rearing activity statistically
significant J, at both dose
levels on PND 60.
Open field testing in pups: a
statistically significant dose-
related trend toward | time
required for male and
female pups to cross the
first grid in the test devise.
Statistically significant
J, myelinated fibers in the
stratum lacunosum-
moleculare of pups.
Reduction in myelin in the
hippocampus.
Statistically significant | in
percentage of abnormal
hearts and the percentage of
litters with abnormal hearts
at >250 ppb.
Statistically significant dose-
related J, dam body weight
gain at all dose levels on
CDs 6-8 and 6-20. Delayed
parturition at >475 mg/kg-
d; ataxia at >633 mg/kg-d;
mortality at 1,125 mg/kg-d.
4-538
-------
Table 4-98. Summary of mammalian in vivo developmental toxicity
studies—oral exposures (continued)
Reference"
Narotsky et
al. (1995)
(continued)
Narotsky and
Kavlock
(1995)
Noland-
Gerbec et al.
(1986)
Species/strain/
sex/number
Rat, F344, females,
16-21 dams/group
Rat, Sprague-
Dawley, females,
9-11 dams/group
Dose level/
exposure
duration
0, 1,125, or
1,500 mg/kg-d
CDs 6-19
Oor312mg/L
(Average total
intake of dams:
825 mg TCE over
61d)d
Dams (and pups)
exposed from
14 d prior to
mating until end
of lactation
Route/
vehicle
Gavage in
corn oil
Drinking
water
NOAEL;
LOAEL3
Developmental
NOAEL:
32 mg/kg-d
Developmental
LOAEL:
101 mg/kg-d
Maternal
LOAEL:
1,1 25 mg/kg-d
Developmental
LOAEL:
1,1 25 mg/kg-d
Developmental
LOEL:
312mg/Lb
Effects
| full litter resorption and
postnatal mortality at
>425 mg/kg-d. Statistically
significant prenatal loss at
1,125 mg/kg-d. Pup body
weight J, (not statistically
significant) on PNDs 1 and
6. Statistically significant
t in pups with eye defects at
1,125 mg/kg-d. Dose-
related (not statistically
significant) | in pups with
eye defects at >101 mg/kg-d.
Ataxia, J, activity, piloerection;
dose-related J, body weight
gain.
Statistically significant t full
litter resorptions, J, live
pups/litter; statistically
significant J, pup body weight
onPND ^statistically
significant t incidences of
microophthalmia and
anophthalmia.
Statistically significant
| uptake of [3H]-2-DG in
whole brains and cerebella
(no effect in hippocampus)
of exposed pups at 7, 11,
and 16 d, but returned to
control levels by 21 d.
4-539
-------
Table 4-98. Summary of mammalian in vivo developmental toxicity
studies—oral exposures (continued)
Reference"
Peden-
Adams et al.
(2006)
Peden-Adams
et al. (2008)
Taylor et al.
(1985)
Species/strain/
sex/number
Mouse, B6C3Fi,
dams and
both sexes
offspring,
5 dams/group; 5-
7 pups/group at
3 wks; 4-
5 pups/sex/group at
8 wks
Mouse, MRL +/+,
dams and both sexes
offspring, unknown
number litters/group,
6-10 offspring/sex/
group
Rat, Sprague-
Dawley, females,
number
dams/group not
reported
Dose level/
exposure
duration
0, 1,400, or
14,000 ppb
Parental mice
and/or offspring
exposed during
mating, and
from GD 0 thru
3 or 8 wks of age
0, 1,400, or
14,000 ppb
(vehicle = 1%
Emulphor)
Parental mice
and/or offspring
exposed from
GD 0 to 12 mo of
age
0,312, 625, or
1,250 mg/L
Dams (and pups)
exposed from
14 d prior to
mating until end
of lactation
Route/
vehicle
Drinking
water
Drinking
water
Drinking
water
NOAEL;
LOAEL"
Developmental
LOAEL:
1,400 ppbc
Developmental
LOAEL =
1,400 ppbc
Developmental
LOAEL:
312 mg/Lc
Effects
At 1,400 ppb: Suppressed
PFC responses in males at
3 and 8 wks of age and in
females at 8 wks of age.
Delayed hypersensitivity
response increased at 8 wks
of age in females.
At 14,000 ppb: Suppressed
PFC responses in males and
females at 3 and 8 wks of
age. Splenic cell population
decreased in 3-wk-old pups.
Increased thymic T-cells at
8 wks of age. Delayed
hypersensitivity response
increased at 8 wks of age in
males and females.
At 1,400 ppb: splenic CD4-
/CD8- cells statistically
significant t in females;
thymic CD4+/CD8+ cells
statistically significant J, in
males; 18% f in male kidney
weight.
At 14,000 ppb: thymic T-cell
subpopulations (CD8+,
CD4/CD8-, CD4+)
statistically significant J, in
males.
Exploratory behavior
statistically significant | in
60- and 90-d old male rats
at all treatment levels.
Locomotor activity was
higher in rats from dams
exposed to 1,250 ppm TCE.
aBolded studies carried forward for consideration in dose-response assessment (see Chapter 5).
bNOAEL, LOAEL, and LOEL are based upon reported study findings.
°Dose conversions provided by study author(s).
dMaternal observations not reported.
4.8.3.2.1.1. Inhalation exposures
Dorfmueller et al. (1979) conducted a study in which TCE was administered by
inhalation exposure to groups of approximately 30 female Long-Evans hooded rats at a
concentration of 1,800 ± 200 ppm before mating only, during gestation only, or throughout the
premating and gestation periods. Half of the dams were killed at the end of gestation and half
4-540
-------
were allowed to deliver. There were no effects on body weight change or relative liver weight in
the dams. The number of corpora lutea, implantation sites, live fetuses, fetal body weight,
resorptions, and sex ratio were not affected by treatment. In the group exposed only during
gestation, a significant increase in four specific sternebral, vertebral, and rib findings, and a
significant increase in displaced right ovary were observed upon fetal skeletal and soft tissue
evaluation. Mixed function oxidase enzymes (ethoxycoumarin and ethoxyresorbin), which are
indicative of CYP and P448 activities, respectively, were measured in the livers of dams and
fetuses, but no treatment-related findings were identified. Postnatal growth was significantly
(p < 0.05) decreased in the group with gestation-only exposures. Postnatal behavioral studies,
consisting of an automated assessment of ambulatory response in a novel environment on
GDs 10, 20, and 100, did not identify any effect on general motor activity of offspring following
in utero exposure to TCE.
In a study by Schwetz et al. (1975), pregnant Sprague-Dawley rats and Swiss Webster
mice (30-40 dams/group) were exposed to TCE via inhalation at a concentration of 300 ppm for
7 hours/day on GDs 6-15. The only adverse finding reported was a statistically significant 4-5%
decrease in maternal rat body weight. There were no treatment-related effects on pre- and
postimplantation loss, litter size, fetal body weight, crown-rump length, or external, soft tissue,
or skeletal findings.
Hardin et al. (1981) summarized the results of inhalation developmental toxicology
studies conducted in pregnant Sprague-Dawley rats and New Zealand white rabbits for a number
of industrial chemicals, including TCE. Exposure concentrations of 0 or 500 ppm TCE were
administered for 6-7 hours/day, on GDs 1-19 (rats) or 1-24 (rabbits), and cesarean sections were
conducted on GDs 21 or 30, respectively. There were no adverse findings in maternal animals.
No statistically significant increase in the incidence of malformations was reported for either
species; however, the presence of hydrocephaly in two fetuses of two TCE-treated rabbit litters
was interpreted as a possible indicator of teratogenic potential.
Healy et al. (1982) did not identify any treatment-related fetal malformations following
inhalation exposure of pregnant inbred Wistar rats to 0 or 100 ppm (535 mg/m3) on GDs 8-21.
In this study, significant differences between control and treated litters were observed as an
increased incidence of total litter loss (p < 0.05), decreased mean fetal weight (p < 0.05), and
increased incidence of minor ossification variations (p = 0.003) (absent or bipartite centers of
ossification).
Carney et al. (2006) investigated the effects of whole-body inhalation exposures to
pregnant Sprague-Dawley rats at nominal (and actual) chamber concentrations of 0, 50, 150, or
600 ppm TCE for 6 hours/day, 7 days/week, on GDs 6-20. This study was conducted under
Good Laboratory Practice regulations according to current EPA and Organisation for Economic
Co-operation and Development (OECD) regulatory testing guidelines (i.e., OPPTS 870.3700 and
OECD GD 414). Maternal toxicity consisted of a statistically significant decrease (22%) in body
4-541
-------
weight gain during the first 3 days of exposure to 600-ppm TCE, establishing a no-observed-
effect concentration (NOEC) of 150 ppm for dams. No significant difference between control
and TCE-treated groups was noted for pregnancy rates, number of corpora lutea, implantations,
viable fetuses per litter, percentage pre- and postimplantation loss, resorption rates, fetal sex
ratios, or gravid uterine weights. External, soft tissue, and skeletal evaluation of fetal specimens
did not identify any treatment-related effects. No cardiac malformations were identified in
treated fetuses. The fetal NOEC for this study was established at 600 ppm.
Westergren et al. (1984) examined brain specific gravity of NMRI mice pups following
developmental exposures to TCE. Male and female mice were separately exposed 24 hours/day
(except for limited periods of animal husbandry activities) to 0 or 150 ppm TCE for 30 days and
mated during exposure for 7 days. Exposure of the females was continued throughout gestation,
until the first litter was born. Offspring (6-12/group; litter origin not provided in report) were
terminated on PNDs 1, 10, 21-22, or 30. The specific gravity of the brain frontal cortex, cortex,
occipital cortex, and cerebellum were measured. The cortex specific gravity was significantly
decreased at PND 1 (p < 0.001) and 10 (p < 0.01) in pups from exposed mice. There were also
significant differences (p < 0.05) in the occipital cortex and cerebellum at PNDs 20-22. This
was considered suggestive of delayed maturation. No significant differences between control
and treated pups were observed at 1 month of age.
4.8.3.2.1.2. Oral exposures
A screening study conducted by Narotsky and Kavlock (1995) assessed the
developmental toxicity potential of a number of pesticides and solvents, including TCE. In this
study, F344 rats were administered TCE by gavage at 0, 1,125, and 1,500 mg/kg-day on GDs 6-
19, and litters were examined on GDs 1, 3, and 6. TCE-related increased incidences of full-litter
resorptions, decreased litter sizes, and decreased mean pup birth weights were observed at both
treatment levels. Additionally, TCE treatment was reported to be associated with increased
incidences of eye abnormalities (microphthalmia or anophthalmia). Increased incidences of fetal
loss and percentage of pups with eye abnormalities were confirmed by Narotsky et al. (1995) in a
preliminary dose-setting study that treated F344 rats with TCE by gavage doses of 475, 633, 844,
or 1,125 mg/kg-day on GDs 6-15, and then in a 5 x 5 x 5 mixtures study that used TCE doses of
0, 10.1, 32, 101, and 320 mg/kg-day on GDs 6-15. In both studies, dams were allowed to
deliver, and pups were examined postnatally. The incidence of ocular defects observed across all
TCE treatment levels tested is presented in Table 4-97.
Other developmental findings in this study included increased full litter resorption at 475,
844, and 1,125 mg/kg-day; increased postnatal mortality at 425 mg/kg-day. Pup body weights
were decreased (not significantly) on PNDs 1 and 6 at 1,125 mg/kg-day. In both the Narotsky
and Kavlock (1995) and Narotsky et al. (1995) studies, significantly decreased maternal body
weight gain was observed at the same treatment levels at which full litter resorption was noted.
4-542
-------
Additionally, in Narotsky et al. (1995), maternal observations included delayed parturition at
475, 844, and 1,125 mg/kg-day, ataxia at 633 mg/kg-day, and mortality at 1,125 mg/kg-day.
Cosby and Dukelow (1992) administered TCE in corn oil by gavage to female B6D2F1
mice (28-62/group) on GDs 1-5, 6-10, or 11-15 (where mating = GD 1). Dose levels were 0,
1/100, and 1/10 of the oral LD50 (i.e., 0, 24.02, and 240.2 mg/kg body weight). Dams were
allowed to deliver; litters were evaluated for pup count sex, weight, and crown-rump length until
weaning (PND 21). Some litters were retained until 6 weeks of age, at which time gonads (from
a minimum of 2 litters/group) were removed, weighed, and examined. No treatment-related
reproductive or developmental abnormalities were observed.
A single dose of TCE was administered by gavage to pregnant CD-I mice (9-19/group)
at doses of 0, 0.1, or 1.0 ug/kg in distilled water, or 0, 48.3, or 483 mg/kg in olive oil, 24 hours
after premating human chorionic gonadotropin (hCG) injection (Coberly et al., 1992). At
53 hours after the hCG-injection, the dams were terminated, and the embryos were flushed from
excised oviducts. Chimera embryos were constructed, cultured, and examined. Calculated
proliferation ratios did not identify any differences between control and treated blastomeres. A
lack of treatment-related adverse outcome was also noted when the TCE was administered by i.p.
injection to pregnant mice (16-39/group) at 24 and 48 hours post-hCG at doses of 0, 0.01, 0.02,
or 10 ug/kg body weight.
In a study intended to confirm or refute the cardiac teratogenicity of TCE that had been
previously observed in chick embryos, Dawson et al. (1990) continuously infused the gravid
uterine horns of Sprague-Dawley rats with solutions of 0, 15, or 1,500 ppm TCE (or 1.5 or
150-ppm DCE) on GDs 7-22. At terminal cesarean section on GD 22, the uterine contents were
examined, and fetal hearts were removed and prepared for further dissection and examination
under a light microscope. Cardiac malformations were observed in 3% of control fetuses, 9% of
the 15-ppm TCE fetuses (p = 0.18), and 14% of the 1,500-ppm TCE fetuses, (p = 0.03). There
was a >60% increase in the percentage of defects with a 100-fold increase in dose. No individual
malformation or combination of abnormalities was found to be selectively induced by treatment.
To further examine these TCE-induced cardiac malformations in rats, Dawson et al.
(1993) administered 0, 1.5 or 1,100-ppm TCE in drinking water to female Sprague-Dawley rats.
Experimental treatment regimens were: (1) a period of approximately 2 months prior to
pregnancy plus the full duration of pregnancy; (2) the full duration of pregnancy only; or (3) an
average of 3 months before pregnancy only. The average total daily doses of TCE consumed for
each exposure group at both dose levels were
Group 1
Group 2
Group 3
1.5 ppm
23.5 uL
0.78 uL
3.97 uL
1,100 ppm
1,206 uL
261 uL
1,185 uL
4-543
-------
The study also evaluated 0, 0.15, or 110 ppm DCE in drinking water, with treatment
administered: (1) 2 months prior to pregnancy plus the full duration of pregnancy, or (2) an
average of 2 months before pregnancy only. At terminal cesarean section, uterine contents were
examined, fetuses were evaluated for external defects, and the heart of each fetus was removed
for gross histologic examination under a dissecting microscope, conducted without knowledge of
treatment group. There were no differences between TCE-treated and control group relative to
percentage of live births, implants, and resorptions. The percentage of cardiac defects in
TCE-treated groups ranged from 8.2 to 13.0%, and was statistically significant as compared to
the control incidence of 3%. The dose-response was relatively flat, even in spite of the extensive
difference between the treatment levels. There was a broad representation of various types of
cardiac abnormalities identified, notably including multiple transposition, great artery, septal,
and valve defects (see Table 4-99). No particular combination of defects or syndrome
predominated. Exposure before pregnancy did not appear to be a significant factor in the
incidence of cardiac defects.
Table 4-99. Types of congenital cardiac defects observed in TCE-exposed
fetuses
Cardiac abnormalities
d-Transposition (right chest)
1-Transposition (left chest)
Great artery defects
Atrial septal defects
Mitral valve defects
Tricuspid valve defects
Control
2
1
TCE concentrations
Premating
1,100 ppm
7
1
1.5 ppm
3
Premating/gestation
1,100 ppm
1
19
5
1
1.5 ppm
2
2
5
8
2
Gestation only
1,100 ppm
7
1.5 ppm
1
1
4
Ventricular septal defects
Subaortic
Membranous
Muscular
Endocardia! cushion defect
Pulmonary valve defects
Aortic valve defects
Situs inversus
Total abnormalities
Total abnormal hearts
1
2
1
7
7
1
9
9
1
3
1
8
8
4
2
4
2
2
1
41
40
1
1
2
23
23
1
4
1
2
15
11
2
1
1
10
9
Source: (Dawson et al.. 1993. Table 3).
In an attempt to determine a threshold for cardiac anomalies following TCE exposures,
Johnson et al. (Johnson et al., 2005, 2003) compiled and reanalyzed data from five studies
4-544
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conducted from 1989 to 1995. In these studies, TCE was administered in drinking water to
Sprague-Dawley rats throughout gestation (i.e., a total of 22 days) at levels of 2.5 ppb (0.0025
ppm), 250 ppb (0.25 ppm), 1.5, or 1,100 ppm. The dams were terminated on the last day of
pregnancy and fetuses were evaluated for abnormalities of the heart and great vessels. The
control data from the five studies were combined prior to statistical comparison to the individual
treated groups, which were conducted separately. The study author reported that significant
increases in the percentage of abnormal hearts and the percentage of litters with abnormal hearts
were observed in a generally dose-responsive manner at >250 ppb (see Table 4-100).
Table 4-100. Types of heart malformations per 100 fetuses
Type of defect/100 fetuses
Abnormal looping
Coronary artery /sinus
Aortic hypoplasia
Pulmonary artery hypoplasia
Atrial septal defect
Mitral valve defect
Tricuspid valve defect
Ventricular septal defect
Perimembranous (subaortic)
Muscular
Atriventricular septal defect
Pulmonary valve defect
Aortic valve defects
Fetuses with abnormal hearts (n)
Total fetuses (n)
Litters with fetuses with abnormal hearts/litter (n)
Litter with fetuses with abnormal hearts/number litters
(%)
Control
0.33
1.16
0.17
0.33
0.33
0.17
13
606
9/55
16.4
TCE dose group
1,100 ppm
6.67
2.86
0.95
0.95
1.9
11
105
6/9
66.7
1.5 ppm
1
0.55
0.55
2.21
1.66
0.55
9
181
5/13
38.5
250 ppb
1.82
0.91
0.91
0.91
0.91
5
110
4/9
44.4
2.5 ppb
0
144
0/12
0.0
Source: (Johnson et al.. 2003. Table 2. p. 290).
In a study by Fisher et al. (2001), pregnant Sprague-Dawley rats were administered daily
gavage doses on GDs 6-15 of TCE (500 mg/kg-day), TCA (300 mg/kg-day), or DCA
(300 mg/kg-day). Cesarean delivery of fetuses was conducted on GD 21. Water and soybean oil
negative control groups, and a retinoic acid positive control group were also conducted
simultaneously. Maternal body weight gain was not significantly different from control for any
of the treated groups. No significant differences were observed for number of implantations,
resorptions, or litter size. Mean fetal body weight was reduced by treatment with TCA and
DCA. The incidence of heart malformations was not significantly increased in treated groups as
4-545
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compared to controls. The fetal rate of cardiac malformations ranged from 3 to 5% across the
TCE, TCA, and DCA dose groups and from 6.5 to 2.9% for the soybean and water control dose
groups, respectively. It was suggested that the apparent differences between the results of this
study and the Dawson et al. (1993) study may be related to factors such as differences in purity
of test substances or in the rat strains, or differences in experimental design (e.g., gavage vs.
drinking water, exposure only during the period of organogenesis versus during the entire
gestation period, or the use of a staining procedure). The rats from this study were also
examined for eye malformations to follow-up on the findings of Narotsky (1995). As reported in
Warren et al. (2006), gross evaluation of the fetuses as well as computerized morphometry
conducted on preserved and sectioned heads revealed no ocular anomalies in the groups treated
with TCE. This technique allowed for quantification of the lens area, global area, medial
canthus, distance, and interlocular distance. DCA treatment was associated with statistically
significant reductions in the lens area, globe area, and interlocular distance. All four measures
were reduced in the TCA-treated group, but not significantly. The sensitivity of the assay was
demonstrated successfully with the use of a positive control group that was dosed on GDs 6-15
with a known ocular teratogen, retinoic acid (15 mg/kg-day).
Johnson et al. (1998b: 1998a) conducted a series of studies to determine whether specific
metabolites of TCE or DCE were responsible for the cardiac malformations observed in rats
following administration during the period of organogenesis. Several metabolites of the two
chemicals were administered in drinking water to Sprague-Dawley rats from GDs 1 to 22. These
included carboxy methylcystine, dichloroacetaldehyde, dichlorovinyl cystine, monochloroacetic
acid, TCA, trichloroacetaldehyde, and TCOH. DCA, a primary common metabolite of TCE and
DCE, was not included in these studies. The level of each metabolite administered in the water
was based upon the dosage equivalent expected if 1,100 ppm (the limit of solubility) TCE broke
down completely into that metabolite. Cesarean sections were performed on GD 22, uterine
contents were examined, and fetuses were processed and evaluated for heart defects according to
the procedures used by Dawson et al. (1993). No treatment-related maternal toxicity was
observed for any metabolite group. Adverse fetal outcomes were limited to significantly
increased incidences of fetuses with abnormal hearts (see Table 4-101). Significant increases in
fetuses with cardiac defects (on a per-fetus and per-litter basis) were observed for only one of the
metabolites evaluated (i.e., TCA [2,730 ppm, equivalent to a dose of 291 mg/kg-day]). Notably,
significant increases in fetuses with cardiac malformations were also observed with 1.5 or
1,100 ppm TCE (0.218 or 129 mg/kg-day), or with 0.15 or 110 ppm DCE (0.015 or
10.64 mg/kg-day), but in each case, only with prepregnancy-plus-pregnancy treatment regimens.
The cardiac abnormalities observed were diverse and did not segregate to any particular anomaly
or grouping. Dose related increases in response were observed for the overall number of fetuses
with any cardiac malformation for both TCE and DCE; however, no dose-related increase
occurred for any specific cardiac anomaly (Johnson et al., 1998a).
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Table 4-101. Congenital cardiac malformations
Heart abnormalities
Abnormal looping
Aortic hypoplasia
Pulmonary artery hypoplasia
Atrial septal defects
Mitral valve defects, hypoplasia or ectasia
Tricuspid valve defects, hypoplasia or ectasia
Ventricular septal defects
Perimembranous3
Muscular
Atrioventricual septal defects
Pulmonary valve defects
Aortic valve defects
Situs inversus
Total
Abnormal hearts
Fetuses with abnormal hearts
Fetuses
Treatment group
Normal
water
2
-
-
7
1
-
2
2
1
-
-
-
15
13
605
TCE
p+p
1,100
ppm
-
1
-
19
5
1
6
4
-
2
2
1
41
40b
434
TCE
p+p
1.5
ppm
2
1
1
5
8
1
2
-
-
1
2
-
23
22b
255
TCE
P
1,100
ppm
-
-
-
7
-
-
1
4
1
-
2
-
15
llb
105
DCE
p+p
110
ppm
-
1
-
11
4
1
4
2
1
1
2
-
25
24b
184
DCE
p+p
0.15
ppm
-
-
-
7
o
J
-
1
1
-
-
3
-
15
14b
121
TCA
P
2,730
ppm
-
1
2
o
J
1
-
4
1
-
1
-
-
13
12b
114
MCA
P
1,570
ppm
-
-
1
3
-
-
-
-
-
3
-
-
7
6
132
TCEth
P
1,249
ppm
-
1
-
-
1
1
-
1
-
1
1
-
6
5
121
TCAld
P
1,232
ppm
-
-
-
2
2
-
o
J
-
-
1
-
-
8
8
248
DCAld
P
174
ppm
-
1
2
-
-
-
-
-
-
-
-
-
3
3
101
CMC
P
473
ppm
-
-
-
-
-
-
1
2
-
-
1
-
4
4
85
DCVC
P
50
ppm
-
1
-
1
1
-
-
2
-
-
-
-
5
5
140
"Subaortic.
bPer-fetus statistical significance (Fisher's exact test).
p = pregnancy; p+p = pregnancy and prepregnancy
Source: (Johnson et al.. 1998b. Table 2. p. 997).
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The TCE metabolites TCA and DCA were also studied by Smith et al. (1992: 1989).
Doses of 0, 330, 800, 1,200, or 1,800 mg/kg TCA were administered daily by gavage to Long-
Evan hooded rats on GDs 6-15. Similarly, DCA was administered daily by gavage to Long-
Evans rats on GDs 6-15 in two separate studies, at 0, 900, 1,400, 1,900, or 2,400 mg/kg-day and
0, 14, 140, or 400 mg/kg-day. Embryo lethality and statistically or biologically significant
incidences of orbital anomalies (combined soft tissue and skeletal findings) were observed for
TCA at >800 mg/kg-day, and for DCA at >900 mg/kg-day. Fetal growth (body weight and
crown-rump length) was affected at >330 mg/kg-day for TCE and at >400 mg/kg-day for DCA.
For TCA, the most common cardiac malformations observed were levocardia at >330 mg/kg-day
and interventricular septal defect at >800 mg/kg-day. For DCA, levocardia was observed at
>900 mg/kg-day, interventricular septal defect was observed at >1,400 mg/kg-day, and a defect
between the ascending aorta and right ventricle was observed in all treated groups (i.e.,
>14 mg/kg-day, although the authors appeared to discount the single fetal finding at the lowest
dose tested). Thus, NOAELs were not definitively established for either metabolite, although it
appears that TCA was generally more potent than DCA in inducing cardiac abnormalities.
These findings were followed up by a series of studies on DCA reported by Epstein et al.
(1992), which were designed to determine the most sensitive period of development and further
characterize the heart defects. In these studies, Long-Evans hooded rats were dosed by gavage
with a single dose of 2,400 mg/kg-day on selected GDs (6-8, 9-11, or 12-15); with a single dose
of 2,400 mg/kg on days 10, 11, 12, or 13; or with a single dose of 3,500 mg/kg on days 9, 10, 11,
12, or 13. The heart defects observed in these studies were diagnosed as high interventricular
septal defects rather than membranous type interventricular septal defects. The authors
hypothesized that high intraventricular septal defects are a specific type of defect produced by a
failure of proliferating interventricular septal tissue to fuse with the right tubercle of the
atrioventricular cushion tissue. This study identified GDs 9 through 12 as a particularly sensitive
period for eliciting high interventricular septal defects. It was postulated that DCA interferes
with the closure of the tertiary interventricular foramen, allowing the aorta to retain its
embryonic connection with the right ventricle. Further, it was suggested that the selectivity of
DCA in inducing cardiac malformations may be due to the disruption of a discrete cell
population.
TCE, DCE, and TCA were administered in drinking water to pregnant Sprague-Dawley
rats on GDs 0-11 (Collier et al.. 2003). Treatment levels were 0, 110, or 1,100 ppm (i.e., 0, 830,
or 8,300 ugM) TCE; 0, 11, or 110 ppm (i.e., 0, 110, or 1,100 ugM) DCE; 0, 2.75, or 27.3 mg/mL
(i.e., 0, 10, or 100 mM) TCA. Embryos (including hearts) were harvested between embryonic
days 10.5-11, since this is the stage at which the developmental processes of myoblast
differentiation, cardiac looping, atrioventricular valve formation, and trabeculation would
typically be occurring. A PCR-based subtraction scheme was used to identify genes that were
differentially regulated with TCE or metabolite exposure. Numerous differentially regulated
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gene sequences were identified. Upregulated transcripts included genes associated with stress
response (Hsp 70) and homeostasis (several ribosomal proteins). Downregulated transcripts
included extracellular matrix components (GPI-pl37 and vimentin) and Ca2+ responsive proteins
(Serca-2 Ca2+-ATPase and p-catenin). Serca-2 Ca2+ and GPI-pl37 were identified as two
possible markers for fetal TCE exposure. Differential regulation of expression of these markers
by TCE was confirmed by dot blot analysis and semi quantitative real time PCR with decreased
expression seen at levels of TCE exposure between 100 and 250 ppb (0.76 and 1.9 uM).
4.8.3.2.1.2.1. Developmental neurotoxicity and developmental immunotoxicity
Several studies were conducted that included assessments of the effects of TCE oral
exposure on the developing nervous system (Blossom et al., 2008; Fredriksson et al., 1993;
Isaacson and Taylor, 1989; George et al., 1986; Noland-Gerbec et al., 1986; Dorfmueller et al.,
1979) or immune system (Blossom et al., 2008; Peden-Adams et al., 2008; Blossom and Doss,
2007; Peden-Adams et al., 2006). These studies, summarized below, are addressed in additional
detail in Sections 4.3 (nervous system) and 4.6.2.1.2 (immune system).
4.8.3.2.1.2.2. Developmental neurotoxicity
Fredriksson et al. (1993) conducted a study in male NMRI weanling mice (12/group,
selected from 3-4 litters), which were exposed to TCE by gavage at doses of 0 (vehicle), 50, or
290 mg/kg-day TCE in a fat emulsion vehicle, on PNDs 10-16. Locomotor behavior (horizontal
movement, rearing, and total activity) were assessed over three 20-minute time periods at
GDs 17 and 60. There were no effects of treatment in locomotor activity at PND 17. At
PND 60, the mice treated with 50 and 290 mg/kg-day TCE showed a significant (p < 0.01)
decrease in rearing behavior at the 0-20- and 20-40-minute time points, but not at the 40-
60 minute time point. Mean rearing counts were decreased by over 50% in treated groups as
compared to control. Horizontal activity and total activity were not affected by treatment.
Open field testing was conducted in control and high-dose Fl weanling F344 rat pups in
an NTP reproduction and fertility study with continuous breeding (George et al., 1986). In this
study, TCE was administered at dietary levels of 0, 0.15, 0.30, or 0.60%. The open field testing
revealed a significant (p < 0.05) dose-related trend toward an increase in the time required for
male and female pups to cross the first grid in the testing device, suggesting an effect on the
ability to react to a novel environment.
Taylor et al. (1985) administered TCE in drinking water (0, 312, 625, or 1,250 ppm) to
female Sprague-Dawley rats for 14 days prior to breeding, and from GD 0 through PND 21. The
number of litters/group was not reported, nor did the study state how many pups per litter were
evaluated for behavioral parameters. Exploratory behavior was measured in the pups in an
automated apparatus during a 15-minute sampling period on PNDs 28, 60, and 90. Additionally,
wheel-running, feeding, and drinking behavior was monitored 24 hours/day on PNDs 55-60.
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The number of exploratory events was significantly increased by approximately 25-50% in
60- and 90-day old male TCE-treated rats at all dose levels, with the largest effect observed at
the highest dose level tested, although there were no effects of treatment on the number of
infrared beam-breaks. No difference between control and treated rats was noted for pups tested
on PND 28. Wheel-running activity was increased approximately 40% in 60-day-old males
exposed to 1,250-ppm TCE as compared to controls. It is notable that adverse outcomes
reported in the developmentally-exposed offspring on this study were observed long after
treatment ceased.
Using a similar treatment protocol, the effects of TCE on development of myelinated
axons in the hippocampus was evaluated by Isaacson and Taylor (1989) in Sprague-Dawley rats.
Female rats (6/group) were exposed in the drinking water from 14 days prior to breeding and
through the mating period; the dams and their pups were then exposed throughout the prenatal
period and until PND 21, when they were sacrificed. The dams received 0, 312, or 625 ppm (0,
4, or 8.1 mg/day) TCE in the drinking water. Myelinated fibers were counted in the
hippocampus of 2-3 pups per treatment group at PND 21, revealing a decrease of approximately
40% in myelinated fibers in the CA1 area of the hippocampus of pups from dams at both
treatment levels, with no dose-response relationship. There was no effect of TCE treatment on
myelination in several other brain regions including the internal capsule, optic tract, or fornix.
A study by Noland-Gerbec et al. (1986) examined the effect of pre- and perinatal
exposure to TCE on 2-deoxyglucose (2-DG) uptake in the cerebellum, hippocampus, and whole
brain of neonatal rats. Sprague-Dawley female rats (9-1 I/group) were exposed via drinking
water to 0 or 312 mg TCE/L distilled water from 14 days prior to mating until their pups were
euthanized at GD 21. The total TCE dose received by the dams was 825 mg over the 61-day
exposure period. Pairs of male neonates were euthanized on PNDs 7, 11, 16, and 21. There was
no significant impairment in neonatal weight or brain weight attributable to treatment, nor were
other overt effects observed. 2-DG uptake was significantly reduced from control values in
neonatal whole brain (9-11%) and cerebellum (8-16%) from treated rats at all ages studied, and
hippocampal 2-DG uptake was significantly reduced (7-21% from control) in treated rats at all
ages except at PND 21.
In a study by Blossom et al. (2008), MRL +/+ mice were treated in the drinking water
with 0 or 0.1 mg/mL TCE from maternal GD 0 through offspring PND 42. Based on drinking
water consumption data, average maternal doses of TCE were 25.7 mg/kg-day, and average
offspring (PNDs 24-42) doses of TCE were 31.0 mg/kg-day. In this study, a subset of offspring
(three randomly selected neonates from each litter) was evaluated for righting reflex on PNDs 6,
8, and 10; bar-holding ability on PNDs 15 and 17; and negative geotaxis on PNDs 15 and 17;
none of these were impaired by treatment. In an assessment of offspring nest building on
PND 35, there was a significant association between impaired nest quality and TCE exposure;
however, TCE exposure did not have an effect on the ability of the mice to detect social and
4-550
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nonsocial odors on PND 29 using olfactory habituation and dishabituation methods. Resident
intruder testing conducted on PND 40 to evaluate social behaviors identified significantly more
aggressive activities (i.e., wrestling and biting) in TCE-exposed juvenile male mice as compared
to controls. Cerebellar tissue homogenates from the male TCE-treated mice had significantly
lower GSH levels and GSH:GSSG ratios, indicating increased oxidative stress and impaired thiol
status; these have been previously reported to be associated with aggressive behaviors (Franco et
al., 2006). Qualitative histopathological examination of the brain did not identify alterations
indicative of neuronal damage or inflammation. Although the study author attempted to link the
treatment-related alterations in social behaviors to the potential for developmental exposures to
TCE to result in autism in humans, this association is not supported by data and is considered
speculative at this time.
As previously noted, postnatal behavioral studies conducted by Dorfmueller et al. (1979)
did not identify any changes in general motor activity measurements of rat offspring on
PNDs 10, 20, and 100 following maternal gestational inhalation exposure to TCE at 1,800 ±
200 ppm.
4.8.3.2.1.2.3. Developmental immunotoxicity
Peden-Adams et al. (2006) assessed the potential for developmental immunotoxicity
following TCE exposures. In this study, B6C3Fi mice (5/sex/group) were administered TCE via
drinking water at dose levels of 0, 1,400 or 14,000 ppb from maternal GD 0 to either PND 3 or 8,
when offspring lymphocyte proliferation, NK cell activity, SRBC-specific IgM production (PFC
response), splenic B220+ cells, and thymus and spleen T-cell immunophenotypes were assessed.
(A total of 5-7 pups per group were evaluated at week 3, and the remainder were evaluated at
week 8.) Observed positive responses consisted of suppressed PFC responses in males at both
ages and both TCE treatment levels, and in females at both ages at 14,000 ppb and at 8 weeks of
age at 1,400 ppb. Spleen numbers of B220+ cells were decreased in 3-week-old pups at
14,000 ppb. Pronounced increases in all thymus T-cell subpopulations (CD4+, CD8+,
CD4+/CD8+, and CD4-/CD8-) were observed at 8 weeks of age. Delayed hypersensitivity
response, assessed in offspring at 8 weeks of age, was increased in females at both treatment
levels and in males at 14,000 ppb only. No treatment-related increase in serum anti-dsDNA
antibody levels was found in the offspring at 8 weeks of age.
In a study by Blossom and Doss (2007), TCE was administered to groups of pregnant
MRL +/+ mice in drinking water at levels of 0, 0.5, or 2.5 mg/mL. TCE was continuously
administered to the offspring until young adulthood (i.e., 7-8 weeks of age). Offspring
postweaning body weights were significantly decreased in both treated groups. Decreased
spleen cellularity and reduced numbers of CD4+, CD8+, and B220+ lymphocyte subpopulations
were observed in the postweaning offspring. Thymocyte development was altered by TCE
exposures (significant alterations in the proportions of double-negative subpopulations and
4-551
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inhibition of in vitro apoptosis in immature thymocytes). A dose-dependent increase in CD4+
and CD8+ T-lymphocyte IFNy was observed in peripheral blood by 4-5 weeks of age, although
these effects were no longer observed at 7-8 weeks of age. Serum antihistone autoantibodies
and total IgG2a were significantly increased in treated offspring; however, no histopathological
signs of autoimmunity were observed in the liver and kidneys at sacrifice.
Blossom et al. (2008) administered TCE to MRL +/+ mice (8 dams/group) in the drinking
water at levels of 0 or 0.1 mg/mL from GD 0 through PND 42. Average maternal doses of TCE
were 25.7 mg/kg-day, and average offspring (PNDs 24-42) doses of TCE were 31.0 mg/kg-day.
Subsets of offspring were sacrificed at PNDs 10 and 20, and thymus endpoints (i.e., total
cellularity, CD4+/CD8+ ratios, CD24 differentiation markers, and double-negative
subpopulation counts) were evaluated. Evaluation of the thymus identified a significant
treatment-related increase in cellularity, accompanied by alterations in thymocyte subset
distribution, at PND 20 (sexes combined). TCE treatment also appeared to promote T-cell
differentiation and maturation at PND 42. Indicators of oxidative stress were measured in the
thymus at PNDs 10 and 20, and in the brain at PND 42, and ex vivo evaluation of cultured
thymocytes indicated increased reactive oxygen species generation. Mitogen-induced
intracellular cytokine production by splenic CD4+ and CD8+ T-cells was evaluated in juvenile
mice and brain tissue was examined at PND 42 for evidence of inflammation. Evaluation of
peripheral blood indicated that splenic CD4+ T-cells from TCE-exposed PND 42 mice produced
significantly greater levels of LFN-y and IL-2 in males and TNF-a in both sexes. There was no
effect on cytokine production on PND 10 or 20.
Peden-Adams et al. (2008) administered TCE to MRL+/+ mice (unspecified number of
dams/group) in drinking water at levels of 0, 1,400, or 14,000 ppb from GD 0 and continuing
until the offspring were 12 months of age. At 12 months of age, final body weight; spleen,
thymus, and kidney weights; spleen and thymus lymphocyte immunophenotyping (CD4 or
CDS); splenic B-cell counts; mitogen-induced splenic lymphocyte proliferation; serum levels of
autoantibodies to dsDNA and GA, periodically measured from 4 to 12 months of age; and
urinary protein measures were recorded. Reported sample sizes for the offspring measurements
varied from 6 to 10 per sex per group; the number of source litters represented within each
sample was not specified. The only organ weight alteration was an 18% increase in kidney
weight in the 1,400 ppb males. Splenic CD4-/CD8- cells were altered in female mice (but not
males) at 1,400 ppm only. Splenic T-cell populations, numbers of B220+ cells, and lymphocyte
proliferation were not affected by treatment. Populations of thymic T-cell subpopulations
(CD8+, CD4-/CD8-, and CD4+) were significantly decreased in male but not female mice
following exposure to 14,000 ppb TCE, and CD4+/CD8+ cells were significantly reduced in
males by treatment with both TCE concentrations. Autoantibody levels (anti-dsDNA and anti-
GA) were not increased in the offspring over the course of the study.
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Although all of the developmental immunotoxicity studies with TCE (Peden-Adams et
al., (2006). (2008): Blossom and Doss, (2007): Blossom et al., (2008)) exposed the offspring
during critical periods of pre- and postnatal immune system development, they were not
designed to assess issues such as posttreatment recovery, latent outcomes, or differences in
severity of response that might be attributed to the early life exposures.
4.8.3.2.1.3. i.p. exposures
The effect of TCE on pulmonary development was evaluated in a study by Das and Scott
(1994). Pregnant Swiss-Webster mice (5/group) were administered a single i.p. injection of TCE
in peanut oil at doses of 0 or 3,000 mg/kg on GD 17 (where mating = day 1). Lungs from
GDs 18 and 19 fetuses and from neonates on PNDs 1, 5, and 10 were evaluated for phospholipid
content, DNA, and microscopic pathology. Fetal and neonatal (PND 1) mortality was
significantly increased (p < 0.01) in the treated group. Pup body weight and absolute lung
weight were significantly decreased (p < 0.05) on PND 1, and mean absolute and relative (to
body weight) lung weights were significantly decreased on GDs 18 and 19. Total DNA content
(ug/mg lung) was similar between control and treated mice, but lung phospholipid was
significantly (p < 0.05) reduced on GD 19 and significantly increased (p < 0.05) on PND 10 in
the TCE-treated group. Microscopic examination revealed delays in progressive lung
morphological development in treated offspring, first observed at GD 19 and continuing at least
through PND 5.
4.8.3.2.2. Studies in nonmammalian species
4.8.3.2.2.1. Avian
Injection of White Leghorn chick embryos with 1, 5, 10, or 25 umol TCE per egg on
days 1 and 2 of embryogenesis demonstrated mortality, growth defects, and morphological
anomalies at evaluation on day 14 (Bross etal., 1983). These findings were consistent with a
previous study that had been conducted by Elovaara et al. (1979). Up to 67% mortality was
observed in the treated groups, and most of the surviving embryos were malformed (as compared
to a complete absence of malformed chicks in the untreated and mineral-oil-treated control
groups). Reported anomalies included subcutaneous edema, evisceration (gastroschisis), light
dermal pigmentation, beak malformations, club foot, and patchy feathering. Retarded growth
was observed as significantly (p < 0.05) reduced crown-rump, leg, wing, toe, and beak lengths as
compared to untreated controls. This study did not identify any liver damage or cardiac
anomalies.
In a study by Loeber et al. (1988), 5, 10, 15, 20, or 25 umol TCE was injected into the air
space of White Longhorn eggs at embryonic stages 6, 12, 18, or 23. Embryo cardiac
development was examined in surviving chicks in a double-blinded manner at stages 29, 34, or
44. Cardiac malformations were found in 7.3% of TCE-treated hearts, compared to 2.3% of
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saline controls and 1.5% of mineral oil controls. The observed defects included septal defects,
cor biloculare, conotruncal abnormalities, atrioventricular canal (AVC) defects, and abnormal
cardiac muscle.
Drake et al. (2006b) injected embryonated White Leghorn chicken eggs (Babcock or
Bovan strains) with 0, 0.4, 8, or 400 ppb TCE per egg during the period of cardiac valvuloseptal
morphogenesis (i.e., 2-3.3 days incubation). The injections were administered in four aliquots at
Hamberger and Hamilton (HH) stages 13, 15, 17, and 20, which spanned the major events of
cardiac cushion formation, from induction through mesenchyme transformation and migration.
Embryos were harvested 22 hours after the last injection (i.e., HH 24 or HH 30) and evaluated
for embryonic survival, apoptosis, cellularity and proliferation, or cardiac function. Survival was
significantly reduced for embryos at 8 and 400 ppb TCE at HH 30. Cellular morphology of
cushion mesenchyme, cardiomyocytes, and endocardiocytes was not affected by TCE treatment;
however, the proliferative index was significantly increased in the AVC cushions at both
treatment levels and in the outflow tract (OFT) cushions at 8 ppb. This resulted in significant
cushion hypercellularity for both the OFT and AVC of TCE-treated embryos. Similar outcomes
were observed in embryos when TCA or TCOH was administered, and the effects of TCA were
more severe than for TCE. Doppler ultrasound assessment of cardiac hemodynamics revealed no
effects of TCE exposure on cardiac cycle length or heart rate; however, there was a reduction in
dorsal aortic blood flow, which was attributed to a 30.5% reduction in the active component of
atrioventricular blood flow. Additionally the passive-to-active atrioventricular blood flow was
significantly increased in treated embryos, and there was a trend toward lower stroke volume.
The overall conclusion was that exposure to 8 ppb TCE during cushion morphogenesis reduced
the cardiac output of the embryos in this study. The findings of cardiac malformations and/or
mortality following in ovo exposure to chick embryos with 8 ppb TCE during the period of
valvuloseptal morphogenesis has also been confirmed by Rufer et al. (2010; 2008).
In a follow-up study, Drake et al. (2006a) injected embryonated White Leghorn chicken
eggs with TCE or TCA during the critical window of avian heart development, beginning at HH
stage 3+ when the primary heart field is specified in the primitive streak and ending
approximately 50 hours later at HH stage 17, at the onset of chambering. Total dosages of 0, 0.2,
2, 4, 20, or 200 nmol (equivalent to 0, 0.4, 4, 8, 40, or 400 ppb) were injected in four aliquots
into each egg yolk during this window (i.e., at stages 3+, 6, 13, and 17: hours 16, 24, 46, and 68).
Embryos were harvested at 72 hours, 3.5 days, 4 days or 4.25 days (HH stages 18, 21, 23, or 24,
respectively) and evaluated for embryonic survival, cardiac function, or cellular parameters.
Doppler ultrasound technology was utilized to assess cardiovascular effects at HH 18, 21, and
23. In contrast to the results of Drake et al. (2006b), all of the functional parameters assessed
(i.e., cardiac cycle length, heart rate, stroke volume, and dorsal aortic and atrioventricular blood
flow) were similar between control and TCE- or TCA-treated embryos. The authors attributed
this difference in response between studies to dependence upon developmental stage at the time
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of exposure. In this case, the chick embryo was relatively resistant to TCE when exposure
occurred during early cardiogenic stages, but was extremely vulnerable when TCE exposure
occurred during valvuloseptal morphogenesis. It was opined that this could explain why some
researchers have observed no developmental cardiac effects after TCE exposure to mammalian
models, while others have reported positive associations.
4.8.3.2.2.2. Amphibian
The developmental toxicity of TCE was evaluated in the Frog Embryo Teratogenesis
Assay: Xenopus by Fort et al. (1993; 1991). Late Xenopus laevis blastulae were exposed to TCE,
with and without exogenous metabolic activation systems, or to TCE metabolites (DCA, TCA,
TCOH, or oxalic acid), and developmental toxicity ensued. Findings included alterations in
embryo growth, and increased types and severity of induced malformations. Findings included
cardiac malformations that were reportedly similar to those that had been observed in avian
studies. It was suggested that a mixed function oxidase-mediated reactive epoxide intermediate
(i.e., TCE-oxide) may play a significant role in observed developmental toxicity in in vitro tests.
Likewise, McDaniel et al. (2004) observed dose-dependent increases in developmental
abnormalities in embryos of four North American amphibian species (wood frogs, green frogs,
American toads, and spotted salamanders) following 96-hour exposures to TCE. The median
effective concentration (ECso) for malformations was 40 mg/L for TCE in green frogs, while
American toads were less sensitive (with no ECso at the highest concentration tested—85 mg/L).
Although significant mortality was not observed, the types of malformations noted would be
expected to compromise survival in an environmental context.
4.8.3.2.2.3. Invertebrate
The response of the daphnid Ceriodaphnia dubia to six industrial chemicals, including
TCE, was evaluated by Niederlehner et al. (1998). Exposures were conducted for 6-7 days,
according to standard EPA testing guidelines. Lethality, impairment of reproduction, and
behavioral changes, such as narcosis and abnormal movement, were observed with TCE
exposures. The reproductive sublethal effect concentration value for TCE was found to be
82 uM.
4.8.3.2.3. In vitro studies
Rat whole embryo cultures were used by Saillenfait et al. (1995) to evaluate the
embryotoxicity of TCE, tetrachloroethylene, and four metabolites (TCA, DCA, CH, and
trichloroacetyl chloride). In this study, explanted embryos of Sprague-Dawley rats were cultured
in the presence of the test chemicals for 46 hours and subsequently evaluated. Concentration-
dependant decreases in growth and differentiation, and increases in the incidence of
morphologically abnormal embryos were observed for TCE at >5 mM.
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Whole embryo cultures were also utilized by Hunter et al. (1996) in evaluating the
embryotoxic potential of a number of disinfection byproducts, including the TCE metabolites
DC A and TCA. CD-I mouse conceptuses (GD 9; 3-6 somites) were cultured for 24-26 hours in
treated medium. DCA levels assessed were 0, 734, 1,468, 4,403, 5,871, 7,339, 11,010, or
14,680 uM; TCA levels assessed were 0, 500, 1,000, 2,000, 3,000, 4,000, and 5,000 uM. For
DCA, neural tube defects were observed at levels >5,871 uM, heart defects were observed at
>7,339 uM, and eye defects were observed at levels >11,010 uM. For TCA, neural tube defects
were observed at levels >2,000 uM, heart and eye defects were observed at >3,000 uM. The
heart defects for TCA were reported to include incomplete looping, a reduction in the length of
the heart beyond the bulboventricular fold, and a marked reduction in the caliber of the heart
tube lumen. Overall benchmark concentrations (i.e., the lower limit of the 95% CI required to
produce a 5% increase in the number of embryos with neural tube defects) were 2,451.9 uM for
DCA and 1,335.8 uM for TCA (Richard and Hunter. 1996).
Boyer et al. (2000) used an in vitro chick-AVC culture to test the hypothesis that TCE
might cause cardiac valve and septal defects by specifically perturbing epithelial-mesenchymal
cell transformation of endothelial cells in the AVC and outflow tract areas of the heart. AV
explants from Stage 16 White Leghorn chick embryos were placed in hydrated collagen gels,
with medium and TCE concentrations of 0, 50, 100, 150, 200, or 250 ppm. TCE was found to
block the endothelial cell-cell separation process that is associated with endothelial activation as
well as to inhibit mesenchymal cell formation across all TCE concentrations tested. TCE did
not, however, have an effect on the cell migration rate of fully formed mesenchymal cells.
TCE-treatment was also found to inhibit the expression of transformation factor Mox-1 and
extracellular matrix protein fibrillin 2, two protein markers of epithelial-mesenchyme cell
transformation.
4.8.3.3. Discussion/Synthesis of Developmental Data
In summary, an overall review of the weight of evidence in humans and experimental
animals is suggestive of the potential for developmental toxicity with TCE exposure. A number
of developmental outcomes have been observed in the animal toxicity and the epidemiological
data, as discussed below. These include adverse fetal/birth outcomes including death
(spontaneous abortion, perinatal death, pre- or postimplantation loss, resorptions), decreased
growth (low birth weight, SGA, IUGR, decreased postnatal growth), and congenital
malformations, in particular cardiac defects. Postnatal developmental outcomes include
developmental neurotoxicity, developmental immunotoxicity, and childhood cancer.
4.8.3.3.1. Adverse fetal and early neonatal outcomes
Studies that demonstrate adverse fetal or early neonatal outcomes are summarized in
Table 4-102. In human studies of prenatal TCE exposure, increased risk of spontaneous abortion
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was observed in some studies (ATSDR, 2001; Taskinen et al., 1994; Windham et al., 1991), but
not in others (ATSDR, 2008b, 2001: Goldberg et al.. 1990: Lindbohm et al.. 1990: Taskinen et
al., 1989: Lagakos et al., 1986). In addition, perinatal deaths were observed after 1970, but not
before 1970 (Lagakos et al., 1986). In rodent studies that examined offspring viability and
survival, there was an indication that TCE exposure may have resulted in increased pre-and/or
postimplantation loss (Kumar et al., 2000b: Narotsky and Kavlock, 1995: Healy et al., 1982), and
in reductions in live pups born as well as in postnatal and postweaning survival (George et al.,
1986: George etal., 1985).
Table 4-102. Summary of adverse fetal and early neonatal outcomes
associated with TCE exposures
Positive finding
Spontaneous abortion, miscarriage, pre-and/or
postimplantation loss
Perinatal death, reduction in live births
Postnatal and postweaning survival
Decreased birth weight, SGA, postnatal growth
Species
Human
Rat
Human
Mouse
Rat
Mouse
Rat
Human
Mouse
Rat
Reference
ATSDR (200 l)a: Taskinen et al. Q994)a;
Windham et al. (1991)
Kumar et al. (2000b): Healy et al. (1982):
Narotsky and Kavlock (1995): Narotsky et al.
(1995)
Lagakos et al. (1986)b
George et al. (1985)
George et al. (1986)
George et al. (1985)
George et al. (1986)
ATSDR (1998a): ATSDR (2006a): Rodenbeck et
al. (2000)°: Windham et al. (1991)
George et al. (1985)
George et al. (1986): Healy et al. (1982):
Narotsky and Kavlock (1995): Narotsky et al.
(1995)
aNot significant.
bObserved for exposures after 1970, but not before.
Increased risk for very low birth weight but not low birth weight or full-term low birth weight.
Decreased birth weight and SGA was observed (ATSDR, 2006a: Rodenbeck et al., 2000:
ATSDR, 1998a: Windham et al., 1991): however, no association was observed in other studies
(Bove, 1996: Bove et al., 1995: Lagakos et al., 1986). While comprising both occupational and
environmental exposures, these human studies are, overall, not highly informative due to their
small numbers of cases and limited exposure characterization or to the fact that exposures to
mixed solvents were involved. However, decreased fetal weight, live birth weights and postnatal
growth were also observed in rodents, (Narotsky and Kavlock, 1995: George etal., 1986: George
et al., 1985: Healy et al., 1982), adding to the weight of evidence for this endpoint. It is noted
that the rat studies reporting effects on fetal or neonatal viability and growth used F344 or Wistar
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rats, while several other studies, which used Sprague-Dawley rats, reported no increased risk in
these developmental measures (Carney et al., 2006; Hardinet al., 1981; Schwetz etal., 1975).
Overall, based on weakly suggestive epidemiologic data and fairly consistent laboratory
animal data, it can be concluded that TCE exposure poses a potential hazard for prenatal losses
and decreased growth or birth weight of offspring.
4.8.3.3.2. Cardiac malformations
A discrete number of epidemiological studies and studies in laboratory animal models
have identified an association between TCE exposures and cardiac defects in developing
embryos and/or fetuses. These are listed in Table 4-103. Additionally, a number of avian and
rodent in vivo studies and in vitro assays have examined various aspects of the induction of
cardiac malformations.
Table 4-103. Summary of studies that identified cardiac malformations
associated with TCE exposures
Finding
Cardiac defects
Altered heart rate
Species
Human
Rat
Chicken
Human
References
ATSDR (2008b, 2006a); Yauck et al.
(2004)
Dawson et al. (1993. 1990): Johnson et al.
(2003); Johnson et al. (2005): Johnson et
al. (1998b; 1998a)a: Smith et al. (1989).
(1992)a: Epstein et al. (1992)a
Brass et al. (1983): Boyer et al. (2000):
Loeber et al. (1988): Drake et al. (2006a:
2006b); Mishima et al. (2006); Rufer et
al. (2010: 2008)
Jasinka (1965. translation)
"Metabolites of TCE.
In humans, an increased risk of cardiac defects has been observed after exposure to TCE
in studies reported by ATSDR (2008b, 2006a) and Yauck et al. (2004). although others saw no
significant effect (Bove, 1996: Boveetal.. 1995: Goldberg et al.. 1990: Lagakos et al., 1986).
possibly due to a small number of cases. In addition, altered heart rate was seen in one study
(Jasinska, 1965, translation). A cohort of water contamination in Santa Clara County, California
is often cited as a study of TCE exposure and cardiac defects; however, the chemical of exposure
is in fact trichloroethane, not TCE (Deane et al.. 1989: Swan etal.. 1989).
In laboratory animal models, avian studies were the first to identify adverse effects of
TCE exposure on cardiac development. As described in Section 4.8.3.2.2.1, cardiac
malformations have been reported in chick embryos exposed to TCE (Rufer et al., 2008: Drake
et al.. 2006a: Drake et al.. 2006b: Mishima et al.. 2006: Bover et al.. 2000: Loeber etal.. 1988:
Bross et al., 1983). Additionally, a number of studies were conducted in rodents in which
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cardiac malformations were observed in fetuses following the oral administration of TCE to
maternal animals during gestation (Johnson et al., 2005, 2003; Dawson etal., 1993, 1990) (see
Section 4.8.3.2.1.2). Cardiac defects were also observed in rats following oral gestational
treatment with metabolites of TCE (Johnson et al., 1998b: Johnson et al., 1998a: Epstein et al.,
1992: Smith etal., 1992: Smith etal., 1989).
However, cardiac malformations were not observed in a number of other studies in
laboratory animals in which TCE was administered during the period of cardiac organogenesis
and fetal visceral findings were assessed. These included inhalation studies in rats (Carney et al.,
2006: Healvetal., 1982: Hardinetal., 1981: Dorfmueller et al., 1979: Schwetz et al., 1975) and
rabbits (Hardin etal., 1981), and gavage studies in rats (Fisher et al., 2001: Narotsky and
Kavlock, 1995: Narotsky et al., 1995) and mice (Cosby andDukelow, 1992).
It is generally recognized that response variability among developmental bioassays
conducted with the same chemical agent may be related to factors such as the study design (e.g.,
the species and strain of laboratory animal model used, the day(s) or time of day of dose
administration in relation to critical developmental windows, the route of exposure, the vehicle
used, the day of study termination), or the study methodologies (e.g., how fetuses were
processed, fixed, and examined; what standard procedures were used in the evaluation of
morphological landmarks or anomalies; and whether there was consistency in the fetal
evaluations that were conducted). In the case of studies that addressed cardiac malformations,
there is additional concern as to whether detailed visceral observations were conducted and
whether or not cardiac evaluation was conducted using standardized dissection procedures (e.g.,
with the use of a dissection microscope or including confirmation by histopathological
evaluation, and whether the examinations were conducted by technicians who were trained and
familiar with fetal cardiac anatomy). Furthermore, interpretation of the findings can be
influenced by the analytical approaches applied to the data as well as by biological
considerations such as the historical incidence data for the species and strain of interest. These
issues have been critically examined in the case of the TCE developmental toxicity studies
(Watson et al., 2006: Hardin et al., 2005).
In the available animal developmental studies with TCE, differences were noted in the
procedures used to evaluate fetal cardiac morphology following TCE gestational exposures
across studies, and some of these differences may have resulted in inconsistent fetal outcomes
and/or the inability to detect cardiac malformations. Most of the studies that did not identify
cardiac anomalies used a traditional free-hand sectioning technique (as described in Wilson,
1965) on fixed fetal specimens (Healvetal., 1982: Hardinetal., 1981: Dorfmueller et al., 1979:
Schwetz etal., 1975). Detection of cardiac anomalies can be enhanced through the use of a fresh
dissection technique as described by Staples (1974) and Stuckhardt and Poppe (1984); a
significant increase in treatment-related cardiac heart defects was observed by Dawson et al.
(1990) when this technique was used. Further refinement of this fresh dissection technique was
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employed by Dawson and colleagues at the University of Arizona (UA), resulting in several
additional studies that reported cardiac malformations (Johnson et al., 2005, 2003; Dawson et al.,
1993). However, two studies conducted in an attempt to verify the teratogenic outcomes of the
UA laboratory studies used the same or similar enhanced fresh dissection techniques and were
unable to detect cardiac anomalies (Carney et al., 2006; Fisher etal., 2001). Although the
Carney et al. (2006) study was administered via inhalation (a route that has not previously been
shown to produce positive outcomes), the Fisher et al. (2001) study was administered orally and
included collaboration between industry and UA scientists. It was suggested that the apparent
differences between the results of the Fisher et al. (2001) study and the Dawson et al. (1993) and
Johnson et al. (2003) studies may be related to factors such as differences in purity of test
substances or in the rat strains, or differences in experimental design (e.g., gavage vs. drinking
water, exposure only during the period of organogenesis versus during the entire gestation
period, or the use of a staining procedure).
It is notable that all studies that identified cardiac anomalies following gestational
exposure to TCE or its metabolites were: (1) conducted in rats and (2) dosed by an oral route of
exposure (gavage or drinking water). Cross-species and route-specific differences in fetal
response may be due in part to toxicokinetic factors. Although a strong accumulation and
retention of TCA was found in the amniotic fluid of pregnant mice following inhalation
exposures to TCE (Ghantous et al., 1986), other toxicokinetic factors may be critical. The
consideration of toxicokinetics in determining the relevance of murine developmental data for
human risk assessment is briefly discussed by Watson et al. (2006). There are differences in the
metabolism of TCE between rodent and humans in that TCE is metabolized more efficiently in
rats and mice than humans, and a greater proportion of TCE is metabolized to DCA in rodents
versus to TCA in humans. Studies that examined the induction of cardiac malformations with
gestational exposures of rodents to various metabolites of TCE identified TCA and DCA as
putative cardiac teratogens. Johnson et al. (1998b: 1998a) and Smith et al. (1989) reported
increased incidences of cardiac defects with gestational TCA exposures, while Smith et al.
(1992) and Epstein et al. (1992) reported increased incidences following DCA exposures.
In all studies that observed increased cardiac defects, either TCE or its metabolites were
administered during critical windows of in utero cardiac development, primarily during the entire
duration of gestation, or during the period of major organogenesis (e.g., GDs 6-15 in the rat).
The study by Epstein et al. (1992) used dosing with DCA on discrete days of gestation and had
identified GDs 9 through 12 as a particularly sensitive period for eliciting high interventricular
septal defects associated with exposures to TCE or its metabolites.
In the oral studies that identified increased incidences of cardiac malformations following
gestational exposure to TCE, there was a broad range of administered doses at which effects
were observed. In drinking water studies, Dawson et al. (1993) observed cardiac anomalies at
1.5 and 1,100 ppm (with no NOAEL) and Johnson et al. (2005, 2003) reported effects at 250 ppb
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(with a NOAEL of 2.5 ppb). One concern is the lack of a clear dose-response for the incidence
of any specific cardiac anomaly or combination of anomalies was not identified, a disparity for
which no reasonable explanation for this disparity has been put forth.
The analysis of the incidence data for cardiac defects observed in the Dawson et al.
(1993. 1990) and Johnson et al. (2005. 2003) studies has been critiqued (Watson et al.. 2006).
Issues of concern that have been raised include the statistical analyses of findings on a per-fetus
(rather than the more appropriate per-litter) basis (Benson, 2004). Johnson et al. was further
criticized for the use of nonconcurrent control data in the analysis (Hardin et al., 2004). In
response, the study author has further explained procedures used (Johnson et al., 2004) and has
provided individual litter incidence data to the EPA for independent statistical analysis (P.
Johnson, personal communication, 2008) (see Section 5.1.2.8). In sum, while the studies by
Dawson et al. (1993, 1990) and Johnson et al. (2005, 2003), have significant limitations, there is
insufficient reason to dismiss their findings.
4.8.3.3.2.1. Mode of action for cardiac malformations
A number of in vitro studies have been conducted to further characterize the potential for
alterations in cardiac development that have been attributed to exposures with TCE and/or its
metabolites. It was noted that many of the cardiac defects observed in humans and laboratory
species (primarily rats and chickens) involved septal and valvular structures.
During early cardiac morphogenesis, outflow tract and AV endothelial cells differentiate
into mesenchymal cells. These mesenchymal cells have characteristics of smooth muscle-like
myofibroblasts and form endocardial cushion tissue, which is the primordia of septa and valves
in the adult heart. Events that take place in cardiac valve formation in mammals and birds are
summarized by NRC (2006) and reproduced in Table 4-104.
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Table 4-104. Events in cardiac valve formation in mammals and birds"
Stage and event
Early cardiac development
Epithelial-mesenchymal
cell transformation
Mesenchymal cell
migration and proliferation
Development of septa and
valvular structures
Structural description1"
The heart is a hollow, linear, tube-like structure with two cell layers. The outer surface
is a myocardial cell layer, and the inner luminal surface is an endothial layer.
Extracellular matrix is between the two cell layers.
A subpopulation of endothelial cells lining the AVC detaches from adjacent cells and
invades the underlying extracellular matrix.
Three events occur:
> Endothelial cell activation (avian stage 14)
> Mesenchymal cell formation (avian stage 16)
> Mesenchymal cell migration into the extracellular matrix (avian stages 17 and 18).
Endothelial-derived mesenchymal cells migrate toward the surrounding myocardium
and proliferate to populate the AVC extracellular matrix.
Cardiac mesenchyme provides cellular constituents for:
> Septum intermedium
> Valvular leaflets of the mitral and tricuspid AV valves.
The septum intermedium subsequently contributes to:
> Lower portion of the interatrial septum
> Membranous portion of the interventricular septum.
aAs summarized in NRC (2006).
bMarkwald et al. (1996: 1984): Boyer et al. (2000).
Methods have been developed to extract the chick stage 16 AVC from the embryo and
culture it on a hydrated collagen gel for 24-48 hours, allowing evaluation of the described stages
of cardiac development and their response to chemical treatment. Factors that have been shown
to influence the induction of endocardial cushion tissue include molecular components such as
fibronectin, laminin, and galactosyltransferase (Loeber and Runyan, 1990; Mjaatvedt et al.,
1987), components of the extracellular matrix (Mjaatvedt et al., 1991), and smooth muscle a-
actin and transforming growth factor P3 (Nakajima et al., 1997; Ramsdell and Markwald, 1997).
Boyer et al. (2000) utilized the in vitro chick AVC culture system to examine the
molecular mechanism of TCE effects on cardiac morphogenesis. AVC explants from stage 16
chick embryos (15/treatment level) were placed onto collagen gels and treated with 0, 50, 100,
150, 200, or 250 ppm TCE and incubated for a total of 54 hours. Epithelial-mesenchymal
transformation, endothelial cell density, cell migration, and immunohistochemistry were
evaluated. TCE treatment was found to inhibit endothelial cell activation and normal
mesenchymal cell transformation, endothelial cell-cell separation, and protein marker expression
(i.e., transcription factor Mox-1 and extracellular matrix protein fibrillin 2). Mesenchymal cell
migration was not affected, nor was the expression of smooth muscle a-actin. The study authors
proposed that TCE may cause cardiac valvular and septal malformations by inhibiting
endothelial separation and early events of mesenchymal cell formation. Hoffman et al. (2004)
proposed alternatively that TCE may be affecting the adhesive properties of the endocardial
cells. No experimental data are currently available that address the levels of TCE in cardiac
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tissue in vivo, resulting in some questions (Dugard, 2000) regarding the relevance of these
mechanistic findings to human health risk assessment.
In a study by Mishima et al. (2006), White Leghorn chick whole embryo cultures
(stage 13 and 14) were used to assess the susceptibility of endocardial epithelial-mesenchymal
transformation in the early chick heart to TCE at analytically determined concentrations of 0, 10,
20, 40, or 80 ppm. This methodology maintained the anatomical relationships of developing
tissues and organs, while exposing precisely staged embryos to quantifiable levels of TCE and
facilitating direct monitoring of developmental morphology. Following 24 hours of incubation,
the numbers of mesenchymal cells in the inferior and superior AV cushions were counted. TCE
treatment significantly reduced the number of mesenchymal cells in both the superior and
inferior AV cushions at 80 ppm.
Ou et al. (2003) examined the possible role of endothelial nitric oxide synthase (which
generates nitric oxide that has an important role in normal endothelial cell proliferation and
hence normal blood vessel growth and development) in TCE-mediated toxicity. Cultured
proliferating bovine coronary endothelial cells were treated with TCE at 0-100 uM and
stimulated with a calcium ionophore to determined changes in endothelial cells and the
generation of endothelial nitric oxide synthase, nitric oxide, and superoxide anion. TCE was
shown to alter heat shock protein interactions with endothelial nitric oxide synthase and induce
endothelial nitric oxide synthase to shift nitric oxide to superoxide-anion generation. These
findings provide insight into how TCE impairs endothelial proliferation.
Several studies have also identified a TCE-related perturbation of several proteins
involved in regulation of intracellular Ca2+. After 12 days of maternal exposure to TCE in
drinking water, Serca2a (sarcoendoplasmic reticulum Ca2+ ATPase) mRNA expression was
reduced in rat embryo cardiac tissues (Collier et al., 2003). Selmin et al. (2008) conducted a
microarray analysis of a P19 mouse stem cell line exposed to 1-ppm TCE in vitro, identifying
altered expression ofRyr2 (ryanodine receptor isoform 2), a Ca2+ release channel that is
important in normal rhythmic heart activity (Gyorke and Terentyev, 2008). Alterations in Ca2+
cycling and resulting contractile dysfunction is a recognized pathogenic mechanism of cardiac
arrhythmias and sudden cardiac death (Lehnart et al., 2008; Yano et al., 2008; Leandri et al.,
1995). Caldwell et al. (2008c) used real-time PCR and digital imaging microscopy to
characterize the effects of various doses of TCE on gene expression and Ca2+ response to
vasopressin in rat cardiac myocytes (H9c2) Serca2a and Ryr2 expression were reduced at
12 and 48 hours following exposure to TCE. Additionally, Ca2+ response to vasopressin was
altered following TCE treatment. Makwana et al. (2010) dosed chick embryos in ovo with 8 or
800 ppb TCE; real time-PCR analysis of RNA isolated during specific windows of cardiac
development demonstrated effects on the expression of genes associated with reduced blood
flow. Although it has been hypothesized that TCE might interfere with the folic
acid/methylation pathway in liver and kidney and alter gene regulation by epigenetic
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mechanisms, Caldwell et al. (2010) found that the effects of TCE exposure on normal gene
expression in rat embryonic hearts was not altered by the administration of exogenous folate.
Overall, these data suggest that TCE may disrupt the ability to regulate cellular Ca2+ fluxes,
altering blood flow and leading to morphogenic consequences in the developing heart. This
remains an open area of research.
Thus, in summary, a number of studies have been conducted in an attempt to characterize
the mode of action for TCE-induced cardiac defects. A major research focus has been on
disruptions in cardiac valve formation, using avian in ovo and in vitro studies. These studies
demonstrated treatment-related alterations in endothelial cushion development that could
plausibly be associated with defects involving septal and valvular morphogenesis in rodents and
chickens. However, a broad array of cardiac malformations has been observed in animal models
following TCE exposures (Johnson et al., 2005, 2003; Dawson et al., 1993), and other evidence
of molecular disruption of Ca2+ during cardiac development has been examined (Caldwell et al.,
2008c: Selmin et al., 2008; Collier et al., 2003), suggesting the possible existence of multiple
modes of action. The observation of defective myocardial development in a mouse model
deficient for gp!30, a signal transducer receptor for IL-6 (Yoshida et al., 1996), suggests the
potential involvement of immune-mediated effects.
4.8.3.3.2.2. Association of PPARa with developmental outcomes
The PPARs are ligand activated receptors that belong to the nuclear hormone receptor
family. Three isotypes have been identified (PPARa, PPAR5 [also known as PPARp], and
PPARy). These receptors, upon binding to an activator, stimulate the expression of target genes
implicated in important metabolic pathways. In rodents, all three isotypes show specific time-
and tissue-dependent patterns of expression during fetal development and in adult animals. In
development, they have been especially implicated in several aspects of tissue differentiation
(e.g., of the adipose tissue, brain, placenta, and skin). Epidermal differentiation has been linked
strongly with PPARa and PPAR6 (Michalik et al., 2002). PPARa starts late in development,
with increasing levels in organs such as liver, kidney, intestine, and pancreas; it is also
transiently expressed in fetal epidermis and CNS (Braissant and Wahli, 1998) and has been
linked to phthalate-induced developmental and testicular toxicity (Gorton and Lapinskas, 2005).
Liver, kidney, and heart are the sites of highest PPARa expression (Toth et al., 2007). PPAR5
and PPARy have been linked to placental development and function, with PPARy found to be
crucial for vascularization of the chorioallantoic placenta in rodents (Wendling et al., 1999), and
placental anomalies mediated by PPARy have been linked to rodent cardiac defects (Barak et al.,
2008). While it might be hypothesized that there is some correlation between PPAR signaling,
fetal deaths, and/or cardiac defects observed following TCE exposures in rodents, no definitive
data have been generated that elucidate a possible PPAR-mediated mode of action for these
outcomes.
4-564
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4.8.3.3.2.3. Summary of the weight of evidence on cardiac malformations
The evidence for an association between TCE exposures in the human population and the
occurrence of congenital cardiac defects is not particularly strong. Many of the epidemiological
study designs were not sufficiently robust to detect exposure-related birth defects with a high
degree of confidence. However, two well-conducted studies by ATSDR (2008b, 2006a) clearly
demonstrated an elevation in cardiac defects. It could be surmised that the identified cardiac
defects were detected because they were severe, and that additional cases with less severe
cardiac anomalies may have gone undetected.
The animal data provide strong, but not unequivocal, evidence of the potential for TCE-
induced cardiac malformations following oral exposures during gestation. Strengths of the
evidence are the duplication of the adverse response in several studies from the same laboratory
group, detection of treatment-related cardiac defects in both mammalian and avian species (i.e.,
rat and chicken), general cross-study consistency in the positive association of increased cardiac
malformations with test species (i.e., rat), route of administration (i.e., oral), and the
methodologies used in cardiac morphological evaluation (i.e., fresh dissection of fetal hearts).
Furthermore, when differences in response are observed across studies, they can generally be
attributed to obvious methodological differences, and a number of in ovo and in vitro studies
demonstrate a consistent and biologically plausible mode of action for one type of malformation
observed. Weaknesses in the evidence include lack of a clear dose-related response in the
incidence of cardiac defects, and the broad variety of cardiac defects observed, such that they
cannot all be grouped easily by type or etiology.
Taken together, the epidemiological and animal study evidence raise sufficient concern
regarding the potential for developmental toxicity (increased incidence of cardiac defects) with
in utero TCE exposures.
4.8.3.3.3. Other structural developmental outcomes
A summary of other structural developmental outcomes that have been associated with
TCE exposures is presented in Table 4-105.
4-565
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Table 4-105. Summary of other structural developmental outcomes
associated with TCE exposures
Finding
Eye/ear birth anomalies
Oral cleft defects
Kidney /urinary tract disorders
Musculoskeletal birth anomalies
Anemia/blood disorders
Lung/respiratory tract disorders
Skeletal
Other3
Species
Human
Rat
Human
Human
Human
Human
Human
Mouse
Rat
Human
References
Lagakos et al. (1986)
Narotsky (1995): Narotsky and Kavlock
(1995)
Bove (1996): Bove et al. (1995): Lagakos
et al. (1986): Lorente et al. (2000)
Lagakos et al. (1986)
Lagakos et al. (1986)
Burg and Gist (1999)
Lagakos et al. (1986)
Das and Scott (1994)
Healy et al. (1982)
ATSDR (2001)
aAs reported by the authors.
In humans, a variety of birth defects other than cardiac have been observed. These
include total birth defects (ATSDR. 2001: Bove. 1996: Boveetal., 1995: Flood. 1988) CNS
birth defects (ATSDR. 2001: Bove. 1996: Boveetal., 1995: Lagakos et al.. 1986), eye/ear birth
anomalies (Lagakos et al., 1986): oral cleft defects (Lorente et al., 2000: Bove, 1996: Bove et al.,
1995: Lagakos et al., 1986): kidney/urinary tract disorders (Lagakos et al., 1986):
musculoskeletal birth anomalies (Lagakos et al., 1986): anemia/blood disorders (Burg and Gist,
1999): and lung/respiratory tract disorders (Lagakos et al., 1986). While some of these results
were statistically significant, they have not been reported elsewhere. Occupational cohort
studies, while not reporting positive results, are generally limited by the small number of
observed or expected cases of birth defects (Lorente et al., 2000: Taskinen etal., 1989: Tola et
al., 1980).
In experimental animals, a statistically significant increase in the incidence of fetal eye
defects, primarily micropththalmia and anopththalmia, manifested as reduced or absent eye
bulge, was observed in rats following gavage administration of 1,125 mg/kg-day TCE during the
period of organogenesis (Narotsky and Kavlock, 1995: Narotsky et al., 1995). Dose-related
nonsignificant increases in the incidence of F344 rat pups with eye defects were also observed at
lower dose levels (101, 320, 475, 633, and 844 mg/kg-day) in the Narotsky et al. (1995) study
(also reported in Barton and Das, 1996). However, no other developmental or reproductive
toxicity studies identified abnormalities of eye development following TCE exposures. For
example, in a study reported by Warren et al. (2006), extensive computerized morphometric
ocular evaluation was conducted in Sprague-Dawley rat fetuses that had been examined for
cardiac defects by Fisher et al. (2001): the dams had been administered TCE (500 mg/kg-day),
4-566
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DCA (300 mg/kg-day), or TCA (300 mg/kg-day) during GDs 6-15. No ocular defects were
found with TCE exposures; however, significant reductions in the lens area, globe area, and
interocular distance were observed with DCA exposures, and nonsignificant decreases in these
measures as well as the medial canthus distance were noted with TCA exposures.
Developmental toxicity studies conducted by Smith et al. (1992; 1989) also identified orbital
defects (combined soft tissue and skeletal abnormalities) in Long-Evans rat fetuses following
GD 6-15 exposures with TCA and DCA (statistically or biologically significant at >800 and
>900 mg/kg-day, respectively). Overall, the study evidence indicates that TCE and its oxidative
metabolites can disrupt ocular development in rats. In addition to the evidence of alteration to
the normal development of ocular structure, these findings may also be an indicator of
disruptions to nervous system development. It has been suggested by Warren et al. (2006) and
Williams and DeSesso (2008) that the effects of concern (defined as statistically significant
outcomes) are observed only at high dose levels and are not relevant to risk assessment for
environmental exposures. On the other hand, Barton and Das (1996) point out that BMD
modeling of the quantal eye defect incidence data provides a reasonable approach to the
development of oral toxicity values for TCE human health risk assessment. It is also noted that
concerns may exist not only for risks related to low level environmental exposures, but also for
risks resulting from acute or short-term occupational or accidental exposures, which may be
associated with much higher inadvertent doses.
It was also notable that a study using a single i.p. dose of 3,000 mg/kg TCE to mice
during late gestation (GD 17) identified apparent delays in lung development and increased
neonatal mortality (Das and Scott 1994). No further evaluation of this outcome has been
identified in the literature.
Healy et al. (1982) did not identify any treatment-related fetal malformations following
inhalation exposure of pregnant inbred Wistar rats to 0 or 100 ppm (535 mg/m3) on GDs 8-21.
In this study, significant differences between control and treated litters were observed as an
increased incidence of minor ossification variations (p = 0.003) (absent or bipartite centers of
ossification).
4.8.3.3.4. Developmental neurotoxicity
Studies that address effects of TCE on the developing nervous system are discussed in
detail in Section 4.3, addressed above in the sections on human developmental toxicity (see
Section 4.8.3) and on mammalian studies (see Section 4.8.3.2.1) by route of exposure, and
summarized in Table 4-106. The available data collectively suggest that the developing brain is
susceptible to TCE exposures.
4-567
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Table 4-106. Summary of developmental neurotoxicity associated with TCE
exposures
Positive findings
CNS defects, neural tube defects
Eye defects
Delayed newborn reflexes
Impaired learning or memory
Aggressive behavior
Hearing impairment
Speech impairment
Encephalopathy
Impaired executive function
Impaired motor function
Attention deficit
ASD
Delayed or altered biomarkers of CNS
development
Behavioral alterations
Species
Human
Rat
Human
Human
Human
Rat
Human
Human
Human
Human
Human
Human
Human
Rat
Mice
Rat
References
ATSDR (2001)
Bove (1996); Bove et al. (1995)
Lagakos et al. (1986)
Narotsky (1995): Narotsky and Kavlock
(1995)
Beppu (1968)
Bernad et al. (1987). abstract
White et al. (1997)
Bernad et al. (1987). abstract
Blossom et al. (2008)
ATSDR (2003b); Burg et al. (1995): Burg
and Gist (1999)
Beppu (1968)
ATSDR (2003b): Burg et al. (1995): Burg
and Gist (1999)
White et al. (1997)
White et al. (1997)
White et al. (1997)
White et al. (1997)
Bernad et al. (1987). abstract
Windham et al. (2006)
Isaacson and Taylor (1989): Noland-
Gerbec et al. (1986); Westergren et al.
(1984)
Blossom et al. (2008): Fredriksson et al.
(1993)
George et al. (1986): Taylor et al. (1985)
In humans, CNS birth defects were observed in a few studies (ATSDR, 2001; Bove,
1996; Bove etal., 1995; Lagakos et al., 1986). Postnatally, observed adverse effects in humans
include delayed newborn reflexes following use of TCE during childbirth (Beppu, 1968),
impaired learning or memory (White et al., 1997; Bernad et al., 1987, abstract): aggressive
behavior (Bernad et al., 1987, abstract): hearing impairment (ATSDR, 2003b: Burg and Gist,
1999: Burg etal., 1995: Beppu, 1968): speech impairment (Burg and Gist 1999: White et al.,
1997: Burg etal., 1995): encephalopathy (White etal., 1997): impaired executive and motor
function (White etal., 1997): attention deficit (White etal., 1997: Bernad et al., 1987, abstract),
and ASD (Windham et al., 2006). While there are broad developmental neurotoxic effects that
have been associated with TCE exposure, there are many limitations in the studies.
More compelling evidence for the adverse effect of TCE exposure on the developing
nervous system is found in the animal study data, although a rigorous evaluation of potential
4-568
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outcomes has not been conducted. For example, there has not been an assessment of cognitive
function (i.e., learning and memory) following developmental exposures to TCE, nor have most
of the available studies characterized the pre- or postnatal exposure of the offspring to TCE or its
metabolites. Nevertheless, there is evidence of treatment-related alterations in brain
development and in behavioral parameters (e.g., spontaneous motor activity and social
behaviors) associated with exposures during neurological development. The animal study
database includes the following information: following inhalation exposures of 150 ppm to mice
during mating and gestation, the specific gravity of offspring brains were significantly decreased
at postnatal time points through the age of weaning; however, this effect did not persist to
1 month of age (Westergren et al., 1984). In studies reported by Taylor et al. (1985), Isaacson
and Taylor (1989), and Noland-Gerbec et al. (1986), 312 mg/L exposures in drinking water that
were initiated 2 weeks prior to mating and continued to the end of lactation resulted,
respectively, in: (1) significant increases in exploratory behavior at GDs 60 and 90;
(2) reductions in myelination in the brains of offspring at weaning; and (3) significantly
decreased uptake of 2-deoxyglucose in the neonatal rat brain (suggesting decreased neuronal
activity). Ocular malformations in rats observed by Narotsky (1995) and Narotsky and Kavlock
(1995) following maternal gavage doses of 1,125 mg/kg-day during gestation may also be
indicative of alterations of nervous system development. Gestational exposures to mice
(Fredriksson et al., 1993) resulted in significantly decreased rearing activity on GD 60, and
dietary exposures during the course of a continuous breeding study in rats (George et al., 1986)
found a significant trend toward increased time to cross the first grid in open field testing. In a
study by Blossom et al. (2008), alterations in social behaviors (deficits in nest-building quality
and increased aggression in males) were observed in pubertal-age MRL +/+ mice that had been
exposed to 0.1 mg/mL TCE via drinking water during prenatal and postnatal development (until
PND 42). Dorfmueller et al. (1979) was the only study that assessed neurobehavioral endpoints
following in utero exposure (maternal inhalation exposures of 1,800 ± 200 ppm during gestation)
and found no adverse effects that could be attributed to TCE exposure. Specifically, an
automated assessment of ambulatory response in a novel environment on GDs 10, 20 and 100,
did not identify any effect on general motor activity of offspring.
4.8.3.3.5. Developmental immunotoxicity
Studies that address the developmental immunotoxic effects of TCE are discussed in
detail in Section 4.6, addressed above in the sections on human developmental toxicity (see
Section 4.8.3) and on mammalian studies (see Section 4.8.3.2.1) by route of exposure, and
summarized in Table 4-107.
4-569
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Table 4-107. Summary of developmental immunotoxicity associated with
TCE exposures
Finding
Significant reduction in Thl IL-2 producing cells
Altered immune response
Suppression of PFC responses, increased T-cell
subpopulations, decreased spleen cellularity, and increased
hypersensitivity response
Altered splenic and thymic T-cell subpopulations
Altered thymic T-cell subpopulations; transient increased
proinflammatory cytokine production by T-cells; increased
autoantibody levels and IgG
Increased proinflammatory cytokine production by T-cells
Species (strain)
Human
Human
Mouse (B6C3FO
Mouse (MRL +/+)
Mouse (MRL +/+)
Mouse (MRL +/+)
References
Lehmann et al. (2002)
Byers et al. (1988)
Peden- Adams et al.
(2006)
Peden- Adams et al.
(2008)
Blossom and Doss (2007)
Blossom et al. (2008)
Two epidemiological studies that addressed potential immunological perturbations in
children that were exposed to TCE were reported by Lehmann et al. (2002; 2001). In the 2001
study, no association was observed between TCE and allergic sensitization to egg white and
milk, or to cytokine producing peripheral T-cells, in premature neonates and 36-month-old
neonates that were at risk of atopy. In the 2002 study, there was a significant reduction in Thl
IL-2 producing cells. Another study observed altered immune response in family members of
those diagnosed with childhood leukemia, including 13 siblings under 19 years old at the time of
exposure, but an analysis looking at only these children was not done (Byers etal., 1988).
Several studies were identified (Blossom et al., 2008; Peden-Adams et al., 2008; Blossom
and Doss, 2007; Peden-Adams et al., 2006) that assessed the potential for developmental
immunotoxicity in mice following oral (drinking water) TCE exposures during critical pre- and
postnatal stages of immune system development. Peden-Adams et al. (2006) noted evidence of
immune system perturbation (suppression of PFC responses, increased T-cell subpopulations,
decreased spleen cellularity, and increased hypersensitivity response) in B6C3Fi offspring
following in utero and 8 weeks of postnatal exposures to TCE. Evidence of autoimmune
response was not observed in the offspring of this nonautoimmune-prone strain of mice.
However, in a study by Peden-Adams et al. (2008) MRL +/+ mice, which are autoimmune-
prone, were exposed from conception until 12 months of age. Consistent with the Peden-Adams
et al. (2006) study, no evidence of increased autoantibody levels was observed in the offspring.
In two other studies focused on autoimmune responses following drinking water exposures of
MRL +/+ mice to TCE during in utero development and continuing until the time of sexual
maturation, Blossom and Doss (2007) and Blossom et al. (2008) reported some peripheral blood
changes that were indicative of treatment-related autoimmune responses in offspring. Positive
response levels were 0.5 and 2.5 mg/mL for Blossom and Doss (2007) and 0.1 mg/mL for
Blossom et al. (2008). None of these studies were designed to extensively evaluate recovery,
latent outcomes, or differences in severity of response that might be attributed to the early life
4-570
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exposures. Consistency in response in these animal studies was difficult to ascertain due to the
variations in study design (e.g., animal strain used, duration of exposure, treatment levels
evaluated, timing of assessments, and endpoints evaluated). Likewise, the endpoints assessed in
the few epidemiological studies that evaluated immunological outcomes following
developmental exposures to TCE were dissimilar from those evaluated in the animal models, and
so provided no clear cross-species correlation. The most sensitive immune system response
noted in the studies that exposed developing animals were the decreased PFC and increased
hypersensitivity observed by Peden-Adams et al. (2006): treatment-related outcomes were noted
in mice exposed in the drinking water at a concentration of 1,400 ppb. None of the other studies
that treated mice during immune system development assessed these same endpoints; therefore,
direct confirmation of these findings across studies was not possible. It is noted, however, that
similar responses were not observed in studies in which adult animals were administered TCE
(e.g., Woolhiser et al., 2006), suggesting increased susceptibility in the young. Differential
lifestage-related responses have been observed with other diverse chemicals (e.g.,
diethylstilbestrol; diazepam; lead; 2,3,7,8-tetrachlorobenzo-/? dioxin; and tributyltin oxide) in
which immune system perturbations were observed at lower doses and/or with greater
persistence when tested in developing animals as compared to adults (Luebke et al., 2006).
Thus, such an adverse response with TCE exposure is considered biologically plausible and an
issue of concern for human health risk assessment.
4.8.3.3.6. Childhood cancers
A summary of childhood cancers that have been associated with TCE exposures
discussed above is presented in Table 4-108. A summary of studies that observed childhood
leukemia is also discussed in detail in Sections 4.6.1.2 and 4.8.3.1.2.4 contains details of
epidemiologic studies on childhood brain cancer.
Table 4-108. Summary of childhood cancers associated with TCE exposures
Finding
Leukemia
Neuroblastoma
Species
Human
Human
References
AZ DHS (ADHS. 1990: Flood. 19881
AZ DHS (Kioski et al.. 1990a)
Cohn et al. (1994b)
Cutler et al. (1986): Costas et al. (2002); Lagakos et al. (1986): MDPH
(1997c)
Lowengart et al. (1987)
McKinney et al. (1991)
Shu et al. (1999)
De Roos et al. (2001)
Peters et al. (1985: 1981)
4-571
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A nonsignificant increased risk of leukemia diagnosed during childhood has been
observed in a number of studies examining TCE exposure (Costas et al., 2002; Shu etal., 1999;
MDPH. 1997b: Cohnetal.. 1994b: McKinnev et al.. 1991: APRS. 1990: Kioski etal.. 1990a:
Flood, 1988: Lowengart et al., 1987: Lagakos et al., 1986). However, other studies did not
observed an increased risk for childhood leukemia after TCE exposure (Morgan and Cassady,
2002: Flood, 1997b: Kioski etal., 1990b), possibly due to the limited number of cases or the
analysis based on multiple solvents.
CNS cancers during childhood have been reported on in a few studies. Neuroblastomas
were not statistically elevated in one study observing parental exposure to multiple chemicals,
including TCE (De Roos et al., 2001). Brain tumors were observed in another study, but the OR
could not be determined (Peters et al., 1985: Peters et al., 1981). CNS cancers were not elevated
in other studies (Morgan and Cassady, 2002: Kioski et al., 1990a). Other studies did not see an
excess risk of total childhood cancers (ATSDR, 2006a; Morgan and Cassady, 2002).
A follow-up study of the Camp Lejeune cohort that will examine childhood cancers
(along with birth defects) was initiated in 1999 (ATSDR, 2003a), is expected to be completed
soon (ATSDR. 2009: U.S. GAP, 2007b, a), and may provide additional insight.
No studies of cancers in experimental animals in early lifestages have been identified.
4.9. OTHER SITE-SPECIFIC CANCERS
4.9.1. Esophageal Cancer
Increasing esophageal cancer incidence has been observed in males, but not females in
the United States between 1975 and 2002, a result of increasing incidence of esophageal
adenocarcinoma (Ward et al., 2006). Males also have higher age-adjusted incidence and
mortality rates (incidence, 7.8 per 100,000; mortality, 7.8 per 100,000) than females (incidence,
2.0 per 100,000; mortality, 1.7 per 100,000) (Ries et al.. 2008). Survival for esophageal cancer
remains poor, and age-adjusted mortality rates are just slightly lower than incidence rates. Major
risk factors associated with esophageal cancer are smoking and alcohol for squamous cell
carcinoma, typically found in the upper third of the esophagus, and obesity, gastroesophageal
reflux, and Barrett's esophagus for adenocarcinoma that generally occurs in the lower esophagus
(Ward et al.. 2006).
Seventeen epidemiologic studies on TCE exposure reported RRs for esophageal cancer
(Clapp and Hoffman. 2008: Radican etal.. 2008: Sung et al.. 2007: ATSDR. 2006a: Boice et al..
2006b: Zhao et al.. 2005: ATSDR. 2004a: Raaschou-Nielsen et al.. 2003: Hansen et al.. 2001:
Boice etal.. 1999: Ritz, 1999a: Blair etal.. 1998: Greenland et al.. 1994: Siemiatvcki, 1991:
Blair etal.. 1989: Costa etal.. 1989: Garabrant et al.. 1988). Ten studies had high likelihood of
TCE exposure in individual study subjects and were judged to have met, to a sufficient degree,
the standards of epidemiologic design and analysis (Radican et al., 2008; Boice et al., 2006b;
Zhao et al.. 2005: Raaschou-Nielsen et al.. 2003: Hansen et al.. 2001: Boice etal.. 1999: Ritz.
4-572
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1999a: Blair etal.. 1998: Greenland et al.. 1994: Siemiatvcki, 1991). Four studies with TCE
exposure potential assigned to individual subjects (Blair etal., 1998 [Incidence]: Morgan et al.,
1998: Anttila et al., 1995: Axel son et al., 1994) did not present RR estimates for esophageal
cancer and TCE exposure nor did two other studies, which carry less weight in the analysis
because of design limitations (Henschler et al., 1995: Sinks etal., 1992). Only Raaschou-
Nielsen et al. (2003) examined esophageal cancer histologic type, an important consideration
given differences between suspected risk factors for adenocarcinoma and those for squamous cell
carcinoma. Appendix B identifies these studies' design and exposure assessment characteristics.
Several population case-control studies (Ramanakumar et al., 2008: Santibanez et al.,
2008: Weiderpass etal., 2003: Engel et al., 2002: Parent et al., 2000b: Gustavsson et al., 1998:
Yu et al., 1988) examined esophageal cancer and organic solvents or occupational job titles with
past TCE use documented (Bakke et al., 2007). RR estimates in case-control studies that
examine metal occupations or job titles, or solvent exposures are found in Table 4-109. The lack
of exposure assessment to TCE, low prevalence of exposure to chlorinated hydrocarbon solvents,
or few exposed cases and controls in those studies lowers their sensitivity for informing
evaluations of TCE and esophageal cancer.
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Table 4-109. Selected observations from case-control studies of TCE exposure and esophageal cancer
Study
population
Exposure group
All esophageal cancers
RR (95% CI)
Number of
observable
events
Squamous cell cancer
RR (95% CI)
Number of
observable
events
Adenocarcinoma
RR (95% CI)
Number of
observable
events
Population of regions in Eastern Spain
Metal molders, welders, etc.
Metal-processing plant operators
0.94(0.14,6.16)
1.14(0.29,4.44)
3
5
0.40(0.05,3.18)
1.23(0.23,6.51)
2
4
3.55 (0.28, 44.70)
0.86 (0.08, 8.63)
1
1
Chlorinated hydrocarbon solvents
Low exposure
High exposure
1.05(0.15,7.17)
1.76 (0.40, 7.74)
2
6
2.18(0.41, 11.57)
0
5
4.92 (0.69, 34.66)
3.03 (0.28,32.15)
2
1
Population of Montreal, Canada
Painter, Metal coatings
Any exposure
Substantial exposure
1.3 (0.4, 4.2)
4.2(1.1, 17.0)
6
4
Solvents
Any exposure
Nonsubstantial exposure
Substantial exposure
1.1(0.7, 1.7)
1.0(0.5, 1.9)
1.1(0.6, 1.9)
39
16
39
1.4 (0.8, 2.5)
1.3 (0.6, 2.6)
1.4 (0.8, 2.5)
30
12
30
Population of Sweden
Organic solvents
No exposure
Moderate exposure
High exposure
Test for trend
No exposure
Moderate exposure
High exposure
Test for trend
1.0
0.7 (0.4, 1.5)
1.3 (0.7, 2.3)
p = 0.47
1.0
0.5(0.1, 3.9)a
0.4(0.1, 1.8)a
p = 0.44
145
15
21
1
2
1.0
1.2 (0.6, 2.3)
1.4 (0.7, 2.5)
^ = 0.59
1.0
0.4(0.1, 1.5)a
0.9 (0.5, 1.6)a
^ = 0.36
128
14
18
2
12
Reference
Santibanez et al.
(2008)
Ramanakumar et
al. (2008);
Parent et al.
(2000b)
Janssonetal.,
(2006: 2005)
4-574
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Table 4-109. Selected observations from case-control studies of TCE exposure and esophageal cancer (continued)
Study
population
Exposure group
All esophageal cancers
RR (95% CI)
Number of
observable
events
Squamous cell cancer
RR (95% CI)
Number of
observable
events
Adenocarcinoma
RR (95% CI)
Number of
observable
events
Population of Finland (Females)
Chlorinated hydrocarbon solvents
Low level exposure
High level exposure
0.95 (0.54, 1.66)
0.62(0.34, 1.13)
Not
reported
Not
reported
Population of New Jersey, Connecticut, Washington State
Precision metal workers
Metal product manufacturing
Not reported
Not reported
0.7 (0.3, 1.5)
0.8 (0.3, 1.8)
12
15
1.4(0.8,2.3)
1.3(0.8,2.3)
25
26
Reference
Weiderpass et
al. (2003)
Engel et al.
(2002)
"Jansson et al. (2006) is a registry-based study of the Swedish Construction Worker Cohort. RRs are incidence rate ratios from Cox regression analysis using
calendar time and adjustment for attained age, calendar period at entry into the cohort, tobacco smoking status at entry into the cohort and BMI at entry into the
cohort.
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Table 4-110 presents risk estimates for TCE exposure and esophageal cancer observed in
cohort, PMR, case-control, and geographic-based studies. Ten studies in which there is a high
likelihood of TCE exposure in individual study subjects (e.g., based on JEMs or biomarker
monitoring) reported risk estimates for esophageal cancer (Radican et al., 2008; Boice et al.,
2006b: Zhao et al.. 2005: Raaschou-Nielsen et al.. 2003: Hansen et al.. 2001: Boice etal.. 1999:
Ritz, 1999a: Blair etal.. 1998: Greenland et al.. 1994: Siemiatvcki, 1991). Some evidence for
association with esophageal cancer and overall TCE exposure comes from studies with high
likelihood of TCE exposure (5.6, 95% CI: 0.7, 44.5 (Blair etal.. 1998) and 1.88, 95% CI: 0.61,
5.79 [Radican et al. (2008), which was an update of Blair et al. (1998) with an additional
10 years of follow-up]; 4.2, 95% CI: 1.5, 9.2, (Hansen et al., 2001): 1.2, 95% CI: 0.84, 1.57
(Raaschou-Nielsen et al., 2003)]. Two studies support an association with adenocarcinoma
histologic type of esophageal cancer and TCE exposure [five of the six observed esophageal
cancers were adenocarcinomas [<1 expected; Hansen et al. (2001)1; 1.8, 95% CI: 1.2, 2.7
(Raaschou-Nielsen et al., 2003). Risk estimates in other studies are based on few deaths, low
statistical power to detect a doubling of esophageal cancer risk, and CIs that include a risk
estimate of 1.0 (no increased risk).
4-576
-------
Table 4-110. Summary of human studies on TCE exposure and esophageal
cancer
Exposure group
RR
(95% CI)
Number of
observable
events
Reference
Cohort studies — incidence
Aerospace workers (Rocketdyne)
Any exposure to TCE
Low cumulative TCE score
Med cumulative TCE score
High TCE score
p for trend
Not reported
1.00a
1.66 (0.62, 4.41)b
0.82(0.17, 3. 95)b
p = 0.974
9
8
2
All employees at electronics factory (Taiwan)
Males
Females
Not reported
1.16(0.0.14, 4.20)c
2
Danish blue-collar worker with TCE exposure
Any exposure, all subjects
Any exposure, males
Any exposure, females
Any exposure, males
Any exposure, females
1.2 (0.84, 1.57)
1.1(0.81, 1.53)
2.0(0.54,5.16)
1.8(1. 15, 2.73)d
44
40
4
23
0 (0.4 exp)d
Exposure lag time
20yrs
1.7 (0.8, 3.0)d
10
Employment duration
5yrs
1.7 (0.6, 3.6)d
1.9 (0.9, 3.6)d
1.9 (0.8, 3.7)d
6
9
8
Subcohort with higher exposure
Any TCE exposure
Employment duration
1-4.9 yrs
>5yrs
1.7 (0.9, 2.9)d
1.6 (0.6, 3.4)d
1.9 (0.8, 3.8)d
13
6
7
Zhao et al. (2005)
Sung et al. (2007)
Raaschou-Nielsen et al.
(2003)
4-577
-------
Table 4-110. Summary of human studies on TCE exposure and esophageal
cancer (continued)
Exposure group
Biologically -monitored Danish workers
Any TCE exposure, males
Adenocarcinoma histologic type
Any TCE exposure, females
RR
(95% CI)
4.0 1.5, 8.72)
4.2(1.5,9.2)
3.6(1.2, 8.3)e
Number of
observable
events
6
6
5
O(O.lexp)
Cumulative exposure (Ikeda)
<17 ppm-yr
>17 ppm-yr
6.5(1.3, 19)
4.2(1.5,9.2)
3
3
Mean concentration (Ikeda)
<4ppm
4+ppm
8.0 (2.6, 19)
1.3 (0.02, 7.0)
5
1
Employment duration
<6.25 yr
>6.25 yr
4.4 (0.5, 16)
6.6(1.8, 17)
2
4
Aircraft maintenance workers from Hill Air Force Base
TCE subcohort
Not reported
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0"
Not reported
Not reported
Not reported
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
Not reported
Not reported
Not reported
Biologically -monitored Finnish workers
All subjects
Not reported
Mean air-TCE (Ikeda extrapolation)
<6ppm
6+ppm
Not reported
Not reported
Reference
Hansen et al. (2001)
Blair et al. (1998)
Anttila et al. (1995)
4-578
-------
Table 4-110. Summary of human studies on TCE exposure and esophageal
cancer (continued)
Exposure group
RR
(95% CI)
Number of
observable
events
Cardboard manufacturing workers in Arnsburg, Germany
Exposed workers
Biologically -monitored Swedish workers
Any TCE exposure, males
Any TCE exposure, females
Not reported
Not reported
Not reported
Cardboard manufacturing workers, Atlanta area, Georgia
All subjects
Not reported
Reference
Henschler et al. (1995)
Axelson et al. (1994)
Sinks et al. (1992)
Cohort and PMR studies-mortality
Computer manufacturing workers (IBM), New York
Males
1.12(0.30, 2.86)f
5.24 (0.13, 29.2)f
Aerospace workers (Rocketdyne)
Any TCE (utility /eng flush)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
0.88(0.18,2.58)
Not reported
1.00a
1.40 (0.70, 2.82)b
1.27(0.52, 3. 13)b
;? = 0.535
3
18
15
7
View-Master employees
Males
Females
0.62 (0.02, 3.45)f
1
0 (1.45 exp)f
All employees at electronics factory (Taiwan)
Males
Females
0(3.34exp)
0 (0.83 exp)
United States uranium-processing workers (Fernald)
Any TCE exposure
Light TCE exposure, >2-yr duration
Moderate TCE exposure, >2-yr duration
Not reported
2.61 (0.99, 6.88)8
12
0
Clapp and Hoffman (2008)
Boice et al. (2006b)
Zhao et al. (2005)
ATSDR (2004a)
Chang et al. (2003)
Ritz (1999a)
4-579
-------
Table 4-110. Summary of human studies on TCE exposure and esophageal
cancer (continued)
Exposure group
RR
(95% CI)
Number of
observable
events
Aerospace workers (Lockheed)
Routine exposure
Routine-intermittent3
Duration of exposure
Oyr
5yrs
p for trend
0.83 (0.34, 1.72)
Not presented
1.0a
0.23 (0.05, 0.99)
0.57 (0.20, 1.67)
0.91 (0.38, 2.22)
p > 0.20
7
11
28
2
4
7
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)
High intensity (>50 ppm)
TCE subcohort (Cox Analysis)
Never exposed
Ever exposed
Peak
No/Low
Medium/high
Cumulative
Referent
Low
High
Not reported
Not reported
Not reported
Not reported
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
5.6 (0.7, 44.5)a
10
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
Not reported11
Not reported11
Not reported11
3
2
4
Reference
Boice et al. (1999)
Morgan et al. (1998)
Blair et al. (1998)
4-580
-------
Table 4-110. Summary of human studies on TCE exposure and esophageal
cancer (continued)
Exposure group
RR
(95% CI)
Number of
observable
events
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
3.6 (0.2, 58)
1.88 (0.61, 5.79)
1.66 (0.48, 5.74)
1.0a
1.84(0.48,7.14)
1.33 (0.27, 6.59)
1.67 (0.40, 7.00)
2.81(0.25,31.10)
1.0a
3.99 (0.25, 63.94)
9,.59 (0.60, 154.14)
1
0
0
17
15
7
3
5
2
1
1
0
Cardboard manufacturing workers in Arnsburg, Germany
TCE exposed workers
Unexposed workers
Deaths reported to among GE pension fund
(Pittsfield, Massachusetts)
Cardboard manufacturing workers, Atlanta area,
Georgia
Not reported
Not reported
0.95(0.1,3.17)'
Not reported
13
U.S. Coast Guard employees
Marine inspectors
Noninspectors
0.72 (0.09, 2.62)
0.74 (0.09, 2.68)
2
2
Aircraft manufacturing plant employees (Italy)
All subjects
Rubber Workers
0.21 (0.01, 1.17)
Not reported1
1
Aircraft manufacturing plant employees (San Diego, California)
All subjects
1.14(0.62, 1.92)
14
Reference
Blair et al. (1998)
(continued)
Radican et al. (2008)
Henschler et al. (1995)
Greenland et al. (1994)
Sinks et al. (1992)
Blair et al. (1989)
Costa et al. (1989)
Wilcosky et al. (1984)
Garabrant et al. (1988)
4-581
-------
Table 4-110. Summary of human studies on TCE exposure and esophageal
cancer (continued)
Exposure group
RR
(95% CI)
Number of
observable
events
Reference
Case-control studies
Population of Montreal, Canada
Any TCE exposure
Substantial TCE exposure
0.5(0.1,2.5y
0.8(0.1,4.6)>
1
1
Siemiatycki et al. (1991):
Parent et al. (2000b)
Geographic-based studies
Residents in two study areas in Endicott, New
York
Residents of 13 census tracts in Redlands,
California
0.78 (0.29, 1.70)
Not reported
6
Finnish residents
Residents of Hausjarvi
Residents of Huttula
Not reported
Not reported
ATSDR (2QQ6a)
Morgan and Cassady
(2002)
Vartiainen et al. (1993)
"Internal referents, workers not exposed to TCE.
bRitz (1999a) and Zhao et al. (2005) reported RRs for the combined site of esophagus and stomach.
cSung et al. (2007) and Chang et al. (2005)—SIR for females and reflects a 10-yr lag period.
dSIR for adenocarcinoma of the esophagus.
eThe SIR for adenocarcinoma histologic type cannot be calculated because Hansen et al. (2001) do not present
expected numbers for adenocarcinoma histologic type of esophageal cancer. An approximation of the SIR for
adenocarcinoma histologic type is presented using the expected number of total number of expected esophageal
cancers for males (n = 1.4). The expected numbers of esophageal adenocarcinomas in males will be lower; Hansen
et al. (2001) noted the proportion of adenocarcinomas among the comparable Danish male population during the
later period of the study (1990-1996) as 38%. A rough approximation of the expected number of esophageal
carcinomas would be 0.5 expected cases and an approximated SIR of 9.4 (3.1, 22).
fPMR.
8Adjusted RRs for >2-year exposure duration and 15-year lag from 1st exposure.
hNo esophageal cancer deaths occurred in the referent population in Blair et al. (1998) and RR could not be
calculated for this reason.
'OR from nested case-control analysis.
J90% CI.
Seven other studies (Clapp and Hoffman. 2008: Sung et al.. 2007: ATSDR, 2006a,
2004a: Blair etal.. 1989: Costa etal.. 1989: Garabrant et al.. 1988) with lower likelihood for
TCE exposure, in addition to limited statistical power and other design limitations, observed RR
estimates between 0.21 (95% CI: 0.0.01, 1.17) (Costa etal.. 1989) and 1.14 (95% CI: 0.62, 1.92)
(Garabrant et al., 1988). For these reasons, esophageal cancer observations in these studies are
not inconsistent with Blair et al. (1998) and its update Radican et al. (2008), Hansen et al. (2001),
or Raaschou-Nielsen et al. (2003). No study reported a statistically significant deficit in the
esophageal cancer risk estimate and overall of TCE exposure. Of those studies with exposure-
response analyses, a pattern of increasing esophageal cancer RR with increasing exposure metric
is not generally noted (Radican et al., 2008: Zhao et al., 2005: Boiceetal., 1999: Blair et al.,
1998: Siemiatvcki, 1991) except for Hansen et al. (2001) and Raaschou-Nielsen et al. (2003). In
4-582
-------
these last two studies, esophageal cancer RR estimates associated with long employment
duration were slightly higher [SIR: 6.6, 95% CI: 1.8, 17 (Hansen et al.. 2001): SIR: 1.9, 95% CI:
0.8, 3.7 (Raaschou-Nielsen et al., 2003)1 than those for short employment duration [SIR: 4.4,
95% CI: 0.5, 16 (Hansen et al.. 2001): SIR: 1.7, 95% CI: 0.6, 3.6 (Raaschou-Nielsen et al..
2003)]. Hansen et al. (2001) also reported risk for two other TCE exposure surrogates, average
intensity and cumulative exposure, and in both cases, observed lower risk estimates with the
higher exposure surrogate.
Meta-analysis is not adopted as a tool for examining the body of epidemiologic evidence
on esophageal cancer and TCE exposure given the absence of reported RR estimates in several of
the studies in which there is a high likelihood of TCE exposure in individual study subjects and
which met, to a sufficient degree, the standards of epidemiologic design and analysis in a
systematic review (Morgan et al., 1998; Anttila et al., 1995; Axel son et al., 1994).
Overall, three cohort studies in which there is a high likelihood of TCE exposure in
individual study subjects and which met, to a sufficient degree, the standards of epidemiologic
design and analysis in a systematic review provide some evidence of association for esophageal
cancer and TCE exposure. The finding in two of these studies of esophageal risk estimates
among subjects with long employment duration were higher than those associated with low
employment duration provides additional evidence (Raaschou-Nielsen et al., 2003; Hansen et al.,
2001). The cohort studies are unable to directly examine possible confounding due to suspected
risk factors for esophageal cancer such as smoking, obesity, and alcohol. The use of an internal
referent group, similar in SES status as exposed subjects, is believed to minimize but may not
completely control for possible confounding related to smoking and health status (Blair et al.,
(1998): Radican et al., (2008): Zhao et al., (2005): Boice et al., (2006b). Observation of a higher
risk for adenocarcinoma histologic type than for a combined category of esophageal cancer in
Raaschou-Nielsen et al. (2003) also suggests minimal confounding from smoking. Smoking is
not identified as a possible risk factor for the adenocarcinoma histologic type of esophageal
cancer, but is believed to be a risk factor for squamous cell histologic type. Furthermore, the
magnitude of lung cancer risk in Raaschou-Nielsen et al. (2003) suggests that a high smoking
rate is unlikely. The lack of association with overall TCE exposure and the absence of exposure-
response patterns in the other studies of TCE exposure may reflect limitations in statistical
power, the possibility of exposure misclassification, and differences in measurement methods.
These studies do not provide evidence against an association between TCE exposure and
esophageal cancer.
4.9.2. Bladder Cancer
Twenty-five epidemiologic studies present risk estimates for bladder cancer (Radican et
al.. 2008: Sung et al.. 2007: AT SDR, 2006a; Boice et al.. 2006b: Chang et al.. 2005: Zhao et al..
2005: ATSDR. 2004a. b; Chang et al.. 2003: Raaschou-Nielsen et al.. 2003: Morgan and
4-583
-------
Cassadv, 2002: Hansenetal.. 2001: Pesch et al.. 2000a: Boiceetal.. 1999: Blair etal.. 1998:
Morgan et al.. 1998: Anttila et al.. 1995: Axel son et al.. 1994: Greenland et al.. 1994: Sinks et al..
1992: Siemiatvcki, 1991: Mallin. 1990: Blair etal.. 1989: Costa etal.. 1989: Garabrant et al..
1988: Shannon et al., 1988). Table 4-111 presents risk estimates for TCE exposure and bladder
cancer observed in cohort, case-control, and geographic-based studies. Thirteen studies, all
either cohort or case-control studies, which there is a high likelihood of TCE exposure in
individual study subjects (e.g., based on JEMs or biomarker monitoring) or which met, to a
sufficient degree, the standards of epidemiologic design and analysis in a systematic review,
reported RR estimates for bladder or urothelial cancer between 0.6 (Siemiatycki, 1991) and 1.7
(Boice et al., 2006b) and overall TCE exposure. RR estimates were generally based on small
numbers of cases or deaths, except for one study (Raaschou-Nielsen et al., 2003), with the result
of wide CIs on the estimates. Of these studies, two reported statistically significant elevated
bladder or urothelial cancer risks with the highest cumulative TCE exposure category (2.71, 95%
CI: 1.10, 6.65 (Morgan et al.. 1998): 1.8, 95% CI: 1.2, 2.7 (Pesch et al.. 2000a) and five
presented risk estimates and categories of increasing cumulative TCE exposure (Radican et al.,
2008: Zhao et al., 2005: Pesch et al., 2000a: Blair et al., 1998: Morgan et al., 1998). Risk
estimates in Morgan et al. (1998), Pesch et al. (2000a), and Zhao et al. (2005) appeared to
increase with increasing cumulative TCE exposure with the/>-value for trend of 0.07 in Zhao et
al. (2005), the only study to present a formal statistical test for linear trend. Risk estimates did
not appear to either increase or decrease with increasing cumulative TCE exposure in Blair et al.
(1998) or its update Radican et al. (2008), which added another 10 years of follow-up. Twelve
additional studies were given less weight because of their lesser likelihood of TCE exposure and
other design limitations that would decrease statistical power and study sensitivity (Sung et al.,
2007: ATSDR, 2006a; Chang et al., 2005: ATSDR, 2004a: Chang et al., 2003: Morgan and
Cassadv, 2002: Sinks etal., 1992: Mallin, 1990: Blair etal., 1989: Costa etal., 1989: Garabrant
et al., 1988: Shannon et al., 1988).
4-584
-------
Table 4-111. Summary of human studies on TCE exposure and bladder
cancer
Exposure group
RR
(95% CI)
Number
of
observabl
e events
Reference
Cohort studies — incidence
Aerospace workers (Rocketdyne)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
Not reported
1.00a
1.54 (0.81, 2.92)b
1.98 (0.93, 4.22)b
p = 0.069
20
19
11
TCE, 20-yr exposure lag
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
1.00a
1.76(0.61,5.10)°
3.68 (0.87, 15.5)c
p = 0.064
20
20
10
All employees at electronics factory (Taiwan)
Males
Females
Males
Females
Not reported
0.34 (0.07, 1.00)
1.06 (0.45, 2.08)d
1.09 (0.56, 1.91)d
10
8
12
Danish blue-collar worker with TCE exposure
Any exposure, all subjects
Any exposure, males
Any exposure, females
Biologically -monitored Danish workers
Any TCE exposure, males
Any TCE exposure, females
1.1(0.92, 1.21)
1.0(0.89, 1.18)
1.6 (0.93, 2.57)
1.0 (0.48, 1.86)
1.1(0.50,2.0)
0.5 expected
220
203
17
10
10
0
Aircraft maintenance workers from Hill Air Force Base
TCE subcohort
Not reported
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
1.7(0.6,4.4)
1.7(0.6,4.9)
1.4(0.5,4.1)
13
9
9
Zhao et al. (2005)
Sung et al. (2007)
Chang et al. (2005)
Raaschou-Nielsen et al.
(2001)
Hansen et al. (2001)
Blair et al. (1998)
4-585
-------
Table 4-111. Summary of human studies on TCE exposure and bladder
cancer (continued)
Exposure group
RR
(95% CI)
Number
of
observabl
e events
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
1.1(0.1, 10.8)
1.0(0.1,9.1)
1
0
1
Biologically -monitored Finnish workers
All subjects
0.82 (0.27, 1.90)
5
Biologically -monitored Swedish workers
Any TCE exposure, males
Any TCE exposure, females
1.02 (0.44, 2.00)
Not reported
8
Reference
Blair et al. (1998) (continued)
Anttila et al. (1995)
Axelson et al. (1994)
Cohort and PMR studies-mortality
Aerospace workers (Rocketdyne)
Any TCE (utility /eng flush)
Any exposure to TCE
Low cumulative TCE score
Med cumulative TCE score
High TCE score
p for trend
1.66 (0.54, 3.87)
Not reported
1.00a
1.27 (0.43, 3.73)b
1.15(0.29, 4.51)b
;? = 0.809
5
8
6
3
TCE, 20-yr exposure lag
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
1.00a
0.95(0.15, 6.02)c
1.85(0.12, 27.7)c
;? = 0.533
8
7
2
View-Master employees
Males
Females
1.22(0.15,4.40)
0.78 (0.09, 2.82)
United States uranium-processing workers (Fernald)
Any TCE exposure
Light TCE exposure, >2-yr duration
Moderate TCE exposure, >2-yr duration
Not reported
Not reported
Not reported
Aerospace workers (Lockheed)
Routine exposure
Routine-intermittent3
0.55(0.18, 1.28)
Not reported
5
Boice et al. (2006b)
Zhao et al. (2005)
ATSDR (2004a)
Ritz (1999a)
Boice et al. (1999)
4-586
-------
Table 4-111. Summary of human studies on TCE exposure and bladder
cancer (continued)
Exposure group
RR
(95% CI)
Number
of
observabl
e events
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)
High intensity (>50 ppm)
1.36 (0.59, 2.68)
0.51(0.01,2.83)
1.79 (0.72, 3.69)
8
1
7
TCE subcohort (Cox Analysis)
Never exposed
Ever exposed
1.0a
2.05 (0.86, 4.85)e
8
Peak
No/low
Medium/high
1.0a
1.41(0.52,3.81)
5
Cumulative
Referent
Low
High
1.0a
0.69 (0.09, 5.36)
2.71(1.10,6.65)
1
7
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
1.2(0.5,2.9)a
17
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
1.8(0.5,6.2)
2.1 (0.6,8.0)
1.0(0.2,5.1)
7
5
3
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
0.8(0.1,7.5)
0.80(0.41, 1.58)
1.05 (0.47, 2.35)
1.0a
0.96(0.37,2.51)
1.77 (0.70, 4.52)
0.67(0.15,2.95)
0
0
1
25
24
9
10
5
Reference
Morgan etal. (1998)
Blair et al. (1998)
Radican et al. (2008)
4-587
-------
Table 4-111. Summary of human studies on TCE exposure and bladder
cancer (continued)
Exposure group
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
RR
(95% CI)
0.22 (0.03, 1.83)
1.0a
2.86 (0.27, 29.85)
Number
of
observabl
e events
1
0
1
0
Cardboard manufacturing workers in Arnsburg, Germany
TCE exposed workers
Unexposed workers
Deaths reported to GE pension fund (Pittsfield,
Massachusetts)
Not reported
Not reported
0.85 (0.32, 2.23)f
20
Cardboard manufacturing workers, Atlanta area, Georgia
0.3 (0.0, 1.6)
1
U.S. Coast Guard employees
Marine inspectors
Noninspectors
0.50 (0.06, 1.79)
0.90(0.18,2.62)
2
3
Aircraft manufacturing plant employees (Italy)
All subjects
0.74 (0.30, 1.53)
7
Aircraft manufacturing plant employees (San Diego, California)
All subjects
Lamp manufacturing workers (GE)
1.26 (0.74, 2.03)
0.93(0.19,2.72)
17
3
Reference
Radican et al. (2008)
(continued)
Henschler et al. (1995)
Greenland et al. (1994)
Sinks et al. (1992)
Blair et al. (1989)
Costa et al. (1989)
Garabrant et al. (1988)
Shannon et al. (1988)
Case-control studies
Population of five regions in Germany
Any TCE exposure
Males
Females
Not reported
Not reported
Not reported
Males
Medium
High
Substantial
0.8 (0.6, 1.2)g
1.3 (0.8, 1.7)g
1.8(1.2, 2.7)g
47
74
36
Population of Montreal, Canada
Any TCE exposure
Substantial TCE exposure
0.6 (0.3, 1.2)
0.7 (0.3, 1.6)
8
5
Pesch et al. (2000a)
Siemiatycki (1991);
Siemiatycki et al. (1994)
Geographic-based studies
Residents in two study areas in Endicott, New York
0.71 (0.38, 1.21)
13
Residents of 13 census tracts inRedlands, California
0.98 (0.71, 1.29)h
82
Finnish residents
Residents of Hausjarvi
Not reported
ATSDR (2006a)
Morgan and Cassady (2002)
Vartiainen et al. (1993)
4-588
-------
Table 4-111. Summary of human studies on TCE exposure and bladder
cancer (continued)
Exposure group
Residents of Huttula
RR
(95% CI)
Not reported
Number
of
observabl
e events
Residents of 9 county area in Northwestern Illinois
All zip codes in study area
Males
Females
1.4(1.1,1.9)
1.8(1.2,2.7)
47
21
Cluster community
Males
Females
1.7(1.1,2.6)
2.6(1.2,4.7)
21
10
Adjacent community
Males
Females
1.2(0.6,2.0)
1.6(0.5,3.8)
12
5
Remainder of zip code areas
Males
Females
1.4(0.8,2.2)
1.4(0.5,3.0)
14
6
Reference
Mallin (1990)
Internal referents, workers not exposed to TCE.
bRR estimates for TCE exposure after adjustment for 1st employment, SES status, and age at event.
°RR estimates for TCE exposure after adjustment for 1st employment, SES status, age at event, and all other
carcinogen exposures, including hydrazine.
dChang et al. (2005) and Costa et al. (1989) report estimated risks for a combined site of all urinary organ cancers.
eRisk ratio from Cox Proportional Hazard Analysis, stratified by age, sex and decade (EHS. 1997).
fOR from nested case-control analysis.
8OR for urothelial cancer, a category of bladder, ureter, and renal pelvis cancers) and cumulative TCE exposure, as
assigned using a ITEM approach (Pesch etal.. 2000a).
h99% CI.
Meta-analysis is not adopted as a tool for examining the body of epidemiologic evidence
on bladder cancer and TCE.
Overall, three cohort or case-control studies in which there is a high likelihood of TCE
exposure in individual study subjects and which met, to a sufficient degree, the standards of
epidemiologic design and analysis in a systematic review provide some evidence of association
for bladder or urothelial cancer and high cumulative TCE exposure (Zhao et al., 2005; Pesch et
al., 2000a: Morgan et al., 1998). The case-control study of Pesch et al. (2000a) adjusted for age,
study center, and cigarette smoking, with a finding of a statistically significant risk estimate
between urothelial cancer and the highest TCE exposure category. Cancer cases in this study are
of several sites (bladder, ureter, and renal pelvis), and grouping different site-specific cancers
with possible etiologic heterogeneity may introduce misclassification bias. The cohort studies
are unable to directly examine possible confounding due to suspected risk factors for esophageal
cancer such as smoking, obesity, and alcohol. The use of an internal referent group, similar in
4-589
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SES status as exposed subjects, by Morgan et al. (1998) and Zhao et al. (2005) is believed to
minimize but may not completely control for possible confounding related to smoking and health
status. The lack of association with overall TCE exposure in other studies and the absence of
exposure-response patterns with TCE exposure in Blair et al. (1998) and Radican et al. (2008)
may reflect limitations in statistical power, the possibility of exposure misclassification, and
differences in measurement methods. These studies do not provide evidence against an
association between TCE exposure and bladder cancer.
4.9.3. CNS and Brain Cancers
Brain cancer is examined in most cohort studies and in one case-control study (Clapp and
Hoffman. 2008: Radican et al.. 2008: Sung et al.. 2007: Boice et al.. 2006b: Chang et al.. 2005:
Zhao et al.. 2005: Chang et al.. 2003: Raaschou-Nielsen et al.. 2003: Hansen et al.. 2001: Boice
etal.. 1999: Ritz. 1999a: Blair etal.. 1998: Morgan et al.. 1998: Anttila et al.. 1995: Henschler et
al.. 1995: Greenland et al.. 1994: Heineman et al.. 1994: Blair etal.. 1989: Costa etal.. 1989:
Garabrant et al., 1988). Overall, these epidemiologic studies do not provide strong evidence for
or against association between TCE and brain cancer in adults (see Table 4-112). RR estimates
in well-designed and -conducted cohort studies, Axelson et al. (1994), Anttila et al. (1995), Blair
et al. (1998). its follow-up reported in Radican et al. (2008). Morgan et al. (1998). Boice et al.
(1999), Zhao et al. (2005), and Boice et al. (2006b), are near a risk of 1.0 and imprecise, CIs all
include a risk estimate of 1.0. All studies except Raaschou-Nielsen et al. (2003), observations
are based on few events and lowered statistical power. Bias resulting from exposure
misclassification is likely in these studies, although of a lower magnitude compared to other
cohort studies identified in Table 4-112, and may partly explain observations. Exposure
misclassification is also likely in the case-control study of occupational exposure of Heineman et
al. (1994) who do not report association with TCE exposure.
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Table 4-112. Summary of human studies on TCE exposure and brain cancer
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Cohort studies — incidence
Aerospace workers (Rocketdyne)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
Not reported
1.00a
0.46 (0.09, 2.25)b
0.47 (0.06, 3.95)b
p= 0.382
7
2
1
All employees at electronics factory (Taiwan)
Males
Females
Males
Females
Not reported
1.07 (0.59, 1.80)c
0.40 (0.05, 1.46)
0.97 (0.54, 1.61)
2
15
Danish blue-collar worker with TCE exposure
Any exposure, all subjects
Any exposure, males
Any exposure, females
Biologically -monitored Danish workers
Any TCE exposure, males
Any TCE exposure, females
1.0 (0.84, 1.24)
1.0(0.76, 1.18)
1.1(0.67, 1.74)
0.3 (0.01, 1.86)
0.4(0.01,2.1)
0.5 expected
104
85
19
1
1
0
Aircraft maintenance workers from Hill Air Force Base
TCE subcohort
Not reported
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
2.0 (0.2, 19.7)
3.9 (0.4, 34.9)
0.8(0.1, 13.2)
3
4
1
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
0
0
0
Biologically -monitored Finnish workers
All subjects
1.09 (0.50, 2.07)
9
Mean air-TCE (Ikeda extrapolation)
<6ppm
6+ppm
1.52(0.61,3.13)
0.76 (0.01, 2.74)
7
2
Biologically -monitored Swedish workers
Any TCE exposure, males
Any TCE exposure, females
Not reported
Not reported
Zhao et al. (2005)
Sung et al. (2007)
Chang et al. (2005)
Raaschou-Nielsen et al. (2003)
Hansenetal. (2001)
Blair et al. (1998)
Anttila et al. (1995)
Axelson et al. (1994)
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Table 4-112. Summary of human studies on TCE exposure and brain cancer
(continued)
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Cohort and PMR studies-mortality
Computer manufacturing workers (IBM), New
York
Males
Females
1.90 (0.52, 4.85)
4
0
Aerospace workers (Rocketdyne)
Any TCE (utility /eng flush)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
p for trend
0.81(0.17,2.36)
Not reported
1.00a
0.42(0.12, 1.50)
0.83 (0.23, 3.08)
;? = 0.613
3
12
3
3
View-Master employees
Males
Females
Not reported
Not reported
All employees at electronics factory (Taiwan)
Males
Females
0.96 (0.01, 5.36)
0.96(0.01,5.33)
1
1
United States uranium-processing workers (Fernald)
Any TCE exposure
Light TCE exposure, >2-yr duration, 0 lag
Moderate TCE exposure, >2-yr duration, 0
lag
Light TCE exposure, >5-yr duration, 15-yr
lag
Moderate TCE exposure, >5-yr duration,
15-yr lag
Not reported
1.81 (0.49, 6.71)d
3.26 (0.37, 28.9)d
5.41 (0.87, 33.9)d
14.4 (1.24, 167)d
6
1
3
1
Aerospace workers (Lockheed)
Routine exposure
Routine-intermittent3
0.54(0.15, 1.37)
Not presented
4
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)d
High intensity (>50 ppm)d
0.99 (0.64, 1.47)
0.73 (0.09, 2.64)
0.44 (0.05, 1.58)
4
2
2
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
0.8 (0.2, 2.2)a
11
Clapp and Hoffman (2008)
Boice et al. (2006b)
Zhao et al. (2005)
ATSDR (2004a)
Chang et al. (2003)
Ritz (1999a)
Boice et al. (1999)
Morgan et al. (1998)
Blair etal. (1998)
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Table 4-112. Summary of human studies on TCE exposure and brain cancer
(continued)
Exposure group
RR (95% CI)
Number of
observable
events
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
0.7 (0.7, 3,3)
2.0 (0.5, 8.4)
0.9 (0.2, 4.4)
3
5
2
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
1.0a
1.02 (0.39, 2.67)
1.26 (0.43, 3.75)
1.0a
1.46 (0.44, 4.86)
1.74(0.49,6.16)
0.66(0.15,2.95)
0
0
0
17
17
8
6
3
0
Cardboard manufacturing workers in Arnsburg, Germany
TCE exposed workers
Unexposed workers
Deaths reported to GE pension fund (Pittsfield,
Massachusetts)
3.70 (0.09, 20.64)
9.38(1.93,27.27)
0.93 (0.32, 2.69)e
1
3
16
Cardboard manufacturing workers, Atlanta area, Georgia
Not reported
U.S. Coast Guard employees
Marine inspectors
Noninspectors
1.70 (0.55, 3.95)
1.36(0.44,3.17)
5
5
Aircraft manufacturing plant employees (Italy)
All subjects
0.79(0.16,2.31)
3
Aircraft manufacturing plant employees (San Diego, California)
All subjects
0.78 (0.42, 1.34)
16
Reference
Blair et al. (1998) (continued)
Radican et al. (2008)
Henschler et al. (1995)
Greenland et al. (1994)
Sinks et al. (1992)
Blair etal. (1989)
Costa et al. (1989)
Garabrant et al. (1988)
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Table 4-112. Summary of human studies on TCE exposure and brain cancer
(continued)
Exposure group
RR (95% CI)
Number of
observable
events
Reference
Case-control studies
Children's Cancer Group/Pediatric Oncology Group
Any TCE exposure
1.64 (0.95, 2.84)
37
Neuroblastoma, <15 yrs of age
Paternal TCE exposure
Serf-reported exposure
IH assignment of probable exposure
1.4(0.7,2.9)
0.9(0.3,2.5)
22
9
Population of So. LA, NJ, Philadelphia, PA
Any TCE exposure
Low exposure
Medium exposure
High exposure
p for trend
1.1 (0.8, 1.6)
1.1 (0.7, 1.7)
1.1 (0.6, 1.8)
1.1(0.5,2.8)
0.45
128
27
42
12
De Roos et al. (2001)
Heineman et al. (1994)
Geographic-based studies
Residents in two study areas in Endicott, New York
Brain/CNS, <19 yrs of age
Residents of 13 census tracts in Redlands,
California
Brain/CNS, <15 yrs of age
Not reported
1.05 (0.24, 2.70)f
<6
6
Resident of Tucson Airport Area, Arizona
Brain/CNS, <19 yrs of age
1970-1986
1987-1991
0.84(0.23,2.16)
0.78 (0.26, 2.39)
3
2
ATSDR (2006a)
Morgan and Cassady (2002)
AZ DHS (1995. 1990)
Internal referents, workers not exposed to TCE.
bRRs for TCE exposure after adjustment for 1st employment, SES status, and age at event.
°SIR from analyses lagging exposure 10 years prior to end of follow-up or date of incident cancer.
dRRs for TCE exposure after adjustment for time since 1st hired, external and internal radiation dose, and same
chemical at a different level.
eOR from nested case-control analysis.
f99% CI.
Three geographic-based studies and one case-control study examined childhood brain
cancer (ATSDR, 2006a; Morgan and Cassadv. 2002: De Roos et al.. 2001: APRS. 1995. 1990).
The strongest study, De Roos et al. (2001), a population case-control study that examined
paternal exposure, used expert judgment to evaluate the probably of TCE exposure from self-
reported information in an attempt to reduce exposure misclassification bias. The OR estimate in
this study was 0.9 (95% CI: 0.3, 2.5). Like many population case-control studies, a low
prevalence of TCE exposure was found, and only nine fathers were identified with probable TCE
exposure by the industrial hygiene review, which greatly impacted statistical power. There is
some concern for childhood brain cancer and organic solvent exposure based on Peters et al.
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(1981) whose case-control study of childhood brain cancer reported to the Los Angeles County
Cancer Surveillance Program observed a high OR estimate for paternal employment in the
aircraft industry (OR: oo,/? < 0.001). This study does not present an OR for TCE exposure only
although it did identify two of the 14 case and control fathers with previous employment in the
aircraft industry reported exposure to TCE.
4.10. SUSCEPTIBLE LIFESTAGES AND POPULATIONS
Variation in response among segments of the population may be due to age, genetics, and
ethnicity, as well as to differences in lifestyle, nutrition, and disease status. These could be
potential risk factors that play an important role in determining an individual's susceptibility and
sensitivity to chemical exposures. Available studies on TCE toxicity in relation to some of these
risk factors including lifestage, gender, genetics, race/ethnicity, preexisting health status, and
lifestyle are discussed below. However, there is a general lack of data demonstrating the
modulation of health effects from TCE exposure based on these factors. Additional data
examining these factors would provide further understanding of the populations that may be
more susceptible to the health effects from TCE exposure. Others have also reviewed factors
related to human variability and their potential for susceptibility to TCE (NRC, 2006; Clewell et
al.. 2000: Pasting et al.. 2000: ATSDR. 1998b. 1997c: Barton etal.. 1996: Davidson and Bellies.
1991).
4.10.1. Lifestages
Individuals of different lifestages are physiologically, anatomically, and biochemically
different. Early (infants and children) and later (the elderly) lifestages differ greatly from
adulthood in body composition, organ function, and many other physiological parameters that
can influence the toxicokinetics of chemicals and their metabolites in the body (Guzelian et al.,
1992). The limited data on TCE exposure among these segments of the population—particularly
individuals in early lifestages—suggest they may have greater susceptibility than does the
general population. This section presents and evaluates the pertinent published literature
available to assess how individuals of differing lifestages may respond differently to TCE.
4.10.1.1. Early Lifestages
4.10.1.1.1. Early lifestage-specific exposures
Section 2.4 describes the various exposure pathways of concern for TCE. For all
postnatal lifestages, the primary exposure routes of concern include inhalation and contaminated
drinking water. In addition, there are exposure pathways to TCE are unique to early lifestages.
Fetal and infant exposure to TCE can occur through placental transfer and breast milk
consumption if the mother has been exposed, and could potentially increase overall TCE
exposure. Placental transfer of TCE has been demonstrated in humans (Laham, 1970: Beppu,
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1968). rats (Withev and Karpinski, 19851 mice (Ghantous et al.. 1986). rabbits (Beppu, 1968).
and sheep and goats (Helliwell and Hutton, 1950). Similarly, TCE has been found in breast milk
in humans (Fisher et al., 1997; Pellizzari et al., 1982), goats (Hamada and Tanaka, 1995), and
rats (Fisher et al., 1990). Pellizzari et al. (1982) conducted a survey of environmental
contaminants in human milk, using samples from cities in the northeastern region of the United
States and one in the southern region and detected TCE in 8 milk samples taken from
42 lactating women. No details of when the samples were taken postpartum, milk lipid content,
or TCE concentration in milk or blood were reported. Fisher et al. (1997) predicted that a
nursing infant would consume 0.496 mg TCE during a 24-hour period. In lactating rats exposed
to 600 ppm (3,225 mg/m3) TCE for 4 hours resulted in concentrations of TCE in milk of
110 |ig/mL immediately following the cessation of exposure (Fisher et al., 1990).
Direct childhood exposures to TCE from oral exposures may also occur. A
contamination of infant formula resulted in levels of 13 ppb (Fan, 1988). Children consume high
levels of dairy products, and TCE has been found in butter and cheese (Wu and Schaum, 2000).
In addition, TCE has been found in food and beverages containing fats such as margarine
(Wallace et al., 1984), grains, and peanut butter (Wu and Schaum, 2000), all of which children
consume in high amounts. A number of studies have examined the potential adverse effects of
prenatal or postnatal exposure to drinking water contaminated with TCE (ATSDR, 2001;
Sonnenfeldetal.. 2001: Rodenbeck et al., 2000: Burg and Gist, 1999: ATSDR, 1998b: White et
al., 1997: see Section 4.10.2.1: Bove, 1996: Boveetal., 1995: Goldberg et al., 1990: Bernad et
al., 1987, abstract: Lagakos et al., 1986). TCE in residential water may also be a source of
dermal or inhalation exposure during bathing and showering (Franco et al., 2007: Lee et al.,
2002: Wu and Schaum, 2000: Giardino and Andelman, 1996: Weisel and Jo, 1996: Fan, 1988): it
has been estimated that showering and bathing scenarios in water containing 3 ppm TCE, a child
of 22 kg receives a higher dose (about 1.5 times) on a mg/kg basis than a 70 kg adult (Fan,
1988).
Direct childhood inhalation exposure to TCE have been documented in both urban and
rural settings. A study of VOCs measured personal, indoor, and outdoor TCE in 284 homes,
with 72 children providing personal measures and time-activity diaries (Adgate et al., 2004b).
The intensive-phase of the study found a mean personal level of 0.8 |ig/m3 and mean indoor and
outdoor levels of 0.6 |ig/m3, with urban homes have significantly higher indoor levels of TCE
than nonurban homes (t = 2.3, p = 0.024) (Adgate et al., 2004b). A similar study of personal,
indoor, and outdoor TCE was conducted in two inner-city elementary schools as well as in the
homes of 113 children along with time-activity diaries, and found a median a median personal
level of 0.3 |ig/m3, a median school indoor level of 0.2 |ig/m3, a median home indoor level of
0.3 |ig/m3, and a median outdoor level of 0.3 |ig/m3 in the winter, with slightly lower levels in
the spring (Adgate et al., 2004a). Studies from Leipzig, Germany measured the median air level
of TCE in children's bedrooms to be 0.42 |ig/m3 (Lehmann et al., 2001) and 0.6 |ig/m3
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(Lehmann et al., 2002). A study of VOCs in Hong Kong measured air levels in schools,
including an 8-hour average of 1.28 |ig/m3, which was associated with the lowest risk of cancer
in the study (Guo et al., 2004). Another found air TCE levels to be highest in school/work
settings, followed by outside, in home, in other, and in transit settings (Sexton et al., 2007).
Measured indoor air levels ranged from 0.18 to 140 ug/m3 for children exposed through vapor
intrusion from soil vapor (ATSDR, 2006a). Contaminated soil may be a source of either dermal
or ingestion exposure of TCE for children (Wu and Schaum, 2000).
Additional TCE exposure has also been documented to have occurred during medical
procedures. TCE was used in the past as an anesthetic during childbirth (Phillips and
Macdonald, 1971; Beppu, 1968) and surgery during childhood (Jasinska, 1965). These studies
are discussed in more detail in Section 4.8.3.1.1. In addition, the TCE metabolite, CH, has been
used as an anesthetic for children for CAT scans (Steinberg, 1993).
Dose received per body weight for 3 ppm TCE via oral, dermal, dermal plus inhalation,
and bathing scenarios was estimated for a 10-kg infant, a 22-kg child, and a 70-kg adult (Fan,
1988) (see Table 4-113). For the oral route (drinking water), an infant would receive a higher
daily dose than a child, and the child more than the adult. For the dermal and dermal plus
inhalation route, the child would receive more than the adult. For the bathing scenario, the infant
and child would receive comparable amounts, more than the adult.
Table 4-113. Estimated lifestage-specific daily doses for TCE in water3
Drinking water
Showering — dermal
Showering — dermal and
inhalation
Bathing — 15 min
Bathing — 5 min
Body weight
Infant (10 kg)
0.3 mg/kg
-
-
-
0.08 mg/kg
Child (22 kg)
0.204 mg/kg
0.1 mg/kg
0.129 mg/kg
0.24 mg/kg
0.08 mg/kg
Adult (70 kg)
0.086 mg/kg
0.064 mg/kg
0.083 mg/kg
0.154 mg/kg
0.051 mg/kg
"Adapted from Fan (1988).
4.10.1.1.2. Early lifestage-specific toxicokinetics
Chapter 3 describes the toxicokinetics of TCE. However, toxicokinetics in
developmental lifestages are distinct from toxicokinetics in adults (Benedetti et al., 2007;
Ginsberg et al.. 2004a: Ginsberg et al.. 2004b: Hattis et al.. 2003: Ginsberg et al.. 2002) due to,
for example, altered ventilation rates, percentage of adipose tissue, and metabolic enzyme
expression. Early lifestage-specific information is described below for absorption, distribution,
metabolism, and excretion, followed by available early lifestage-specific PBPK models.
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4.10.1.1.2.1. Absorption
As discussed in Section 3.1, exposure to TCE may occur via inhalation, ingestion, and
dermal absorption. In addition, prenatal exposure may result in absorption via the transplacental
route. Exposure via inhalation is proportional to the ventilation rate, duration of exposure, and
concentration of expired air, and children have increased ventilation rates per kg body weight
compared to adults, with an increased alveolar surface area per kg body weight for the first
2 years (U.S. EPA, 2008c). It is not clear to what extent dermal absorption may be different for
children compared to adults; however, infants have a twofold increase in surface area compared
to adults, although similar permeability (except for premature babies) compared to adults (U.S.
EPA. 2008c).
4.10.1.1.2.2. Distribution
Both human and animal studies provide clear evidence that TCE distributes widely to all
tissues of the body (see Section 3.2). For lipophilic compounds such as TCE, percentage adipose
tissue, which varies with age, will affect absorption and retention of the absorbed dose. Infants
have a lower percentage of adipose tissue per body weight than adults, resulting in a higher
concentration of the lipophilic compound in the fat of the child (NRC, 1993).
During pregnancy of humans and experimental animals, TCE is distributed to the
placenta (Ghantous et al.. 1986: Withev and Karpinski. 1985: Laham. 1970: Beppu. 1968:
Helliwell andHutton, 1950). In humans, TCE has been found in newborn blood after exposure
to TCE during childbirth with ratios of concentrations in fetal:maternal blood ranging from
approximately 0.5 to approximately 2 (Laham, 1970). In childhood, blood level concentrations
of TCE were found to range from 0.01 to 0.02 ng/mL (Sexton et al., 2005). Pregnant rats
exposed to TCE vapors on GD 17 resulted in concentrations of TCE in fetal blood approximately
one-third the concentration in corresponding maternal blood, and was altered based upon the
position along the uterine horn (Withey and Karpinski, 1985). TCE has also been found in the
organs of prenatal rabbits including the brain, liver, kidneys, and heart (Beppu, 1968). Rats
prenatally exposed to TCE had increased levels measured in the brain at PND 10, compared to
rats exposed as adults (Rodriguez et al., 2007). TCE can cross the blood:brain barrier during
both prenatal and postnatal development, and may occur to a greater extent in younger children.
It is also important to note that it has been observed in mice that TCE can cycle from the fetus
into the amniotic fluid and back to the fetus (Ghantous et al., 1986).
Studies have examined the differential distribution by age to a mixture of six VOCs
including TCE to children aged 3-10 years and adults aged 20-82 years (Mahle et al., 2007) and
in rats at PND 10, 2 months (adult), and 2 years (aged) (Mahle et al.. 2007: Rodriguez et al..
2007). In humans, the blood:air partition coefficient for male or female children was
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significantly lower compared to adult males (Mahle et al., 2007). In rats, the difference in
tissue:air partition coefficients increased with age (Mahle et al., 2007). Higher peak
concentrations of TCE in the blood were observed in the PND 10 rat compared to the adult rat
after inhalation exposure, likely due to the lower metabolic capacity of the young rats (Rodriguez
et al.. 2007).
4.10.1.1.2.3. Metabolism
Section 3.3 describes the enzymes involved in the metabolism of TCE, including CYP
and GST. Expression of these enzymes changes during various stages of fetal development
(Shao et al.. 2007: Dome et al.. 2005: Hines and McCarver, 2002: Hakkola et al.. 1998a: 1998b:
van Lieshout et al., 1998: Hakkola et al., 1996a: Hakkola et al., 1996b) and during postnatal
development (Blake et al., 2005: Dome et al., 2005: Tateishi etal., 1997), and may result in
altered susceptibility.
Expression of CYP enzymes have been shown to play a role in decreasing the
metabolism of TCE during pregnancy in rats, although metabolism increased in young (3-week-
old) rats compared to adult (18-week-old) rats (Nakaiima et al., 1992b). For TCE, CYP2E1 is
the main metabolic CYP enzyme, and expression of this enzyme has been observed in humans in
prenatal brain tissue at low levels beginning at 8 weeks of gestation and increasing throughout
gestation (Brzezinski et al., 1999). Very low levels of CYP2E1 have been detected in some
samples of fetal liver during the second trimester (37% of samples) and third trimester (80% of
samples) (Johnsrud et al., 2003: Carpenter et al., 1996), although hepatic expression surges
immediately after birth in most cases (Johnsrud et al., 2003: Vieira et al., 1996) and in most
infants, reaches adult values by 3 months of age (Johnsrud et al., 2003: Vieira et al., 1996).
Although there is some uncertainty as to which GST isoforms mediate TCE conjugation,
it should be noted that their expression changes with fetal development (McCarver and Hines,
2002: Raijmakers et al., 2001: van Lieshout et al., 1998).
4.10.1.1.2.4. Excretion
The major processes of excretion of TCE and its metabolites are discussed in Section 3.4,
yet little is known about whether there are age-related differences in excretion of TCE. The
major pathway for elimination of TCE is via exhalation, and its metabolites via urine and feces,
and it is known that renal processes are not mature until about 6 months of age (NRC, 1993).
Only one case study was identified that measured TCE or its metabolites in exhaled breath and
urine in a 17-year-old who ingested a large quantity of TCE (Bruning et al., 1998). TCE has also
been measured in the breast milk in lactating women (Fisher et al., 1997: Pellizzari et al., 1982),
goats (Hamada and Tanaka, 1995), and rats (Fisher et al., 1990).
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4.10.1.1.2.5. PBPK models
Early lifestage-specific information regarding absorption, distribution, metabolism, and
excretion needs to be considered for a child-specific and chemical-specific PBPK model. To
adequately address the risk to infants and children, age-specific parameters for these values
should be used in PBPK models that can approximate the internal dose an infant or child receives
based on a specific exposure level (see Section 3.5).
Fisher and colleagues developed PBPK models to describe the toxicokinetics of TCE in
the pregnant rat (Fisher et al., 1989), lactating rat and nursing pup (Fisher et al., 1990). The
prenatal study demonstrates that approximately two-thirds of maternal exposure to both TCE and
TCA reached the fetus after maternal inhalation, gavage, or drinking water exposure (Fisher et
al., 1989). After birth, only 2% of maternal exposure to TCE reaches the pup; however, 15 and
30% of maternal TCA reaches the pup after maternal inhalation and drinking water exposure,
respectively (Fisher et al., 1990). One analysis of PBPK models examined the variability in
response to VOCs including TCE between adults and children, and concluded that the
intraspecies uncertainty factor (UF) for pharmacokinetics is sufficient to capture variability
between adults and children (Pelekis etal., 2001).
4.10.1.1.3. Early lifestage-specific effects
Although limited data exist on TCE toxicity as it relates to early lifestages, there is
enough information to discuss the qualitative differences. In addition to the evidence described
below, Section 4.8 contains information on reproductive and developmental toxicity. In
addition, Sections 4.3 on neurotoxicity and Section 4.6 on immunotoxicity characterize a wide
array of postnatal developmental effects.
4.10.1.1.3.1. Differential noncancer outcomes in early lifestages
Some adverse health outcomes, in particular birth defects, are observed only after early
lifestage exposure to TCE. A summary of structural developmental outcomes that have been
associated with TCE exposures is presented in Sections 4.8.3.3.
Cardiac birth defects have been observed after exposure to TCE in humans (ATSDR,
2006a; Yauck et al.. 2004: Goldberg et al.. 1990: Lagakos et al.. 1986), rodents (Johnson et al..
2005. 2003: Johnson et al.. 1998b: Johnson et al.. 1998a: Dawsonetal.. 1993: Smith etal.. 1992:
Dawsonetal.. 1990: Smith etal.. 1989). and chicks (Rufer et al.. 2008: Drake et al.. 2006a:
Drake et al.. 2006b: Mishima et al.. 2006: Bover et al.. 2000: Loeberetal.. 1988: Bross et al..
1983). However, it is notable that cardiac malformations were not observed in a number of other
studies in humans (Taskinen et al., 1989: Lagakos etal., 1986: Tola etal., 1980), rodents
(Carney et al.. 2006: Fisher etal.. 2001: Narotsky and Kavlock. 1995: Narotsky et al.. 1995:
Coberly et al.. 1992: Cosby and Dukelow. 1992: Healvetal.. 1982: Hardinetal.. 1981:
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Dorfmueller et al.. 1979: Schwetz et al.. 1975). and rabbits (Hardin et al.. 1981). See
Section 4.8.3.3.2.3 for further discussion on cardiac malformations.
Structural CNS birth defects were observed in humans (ATSDR, 2001; Bove, 1996; Bove
et al., 1995; Lagakos et al., 1986). In addition, a number of postnatal nonstructural adverse
effects on the CNS system have been observed in humans and experimental animals following
prenatal exposure to TCE. See Sections 4.3.10 and 4.8.3.3.4 for further discussion on
developmental neurotoxicity.
A variety of other birth defects have been observed—including eye/ear birth anomalies in
humans and rats (Narotsky and Kavlock, 1995; Narotsky et al., 1995; Lagakos et al., 1986):
lung/respiratory tract disorders in humans and mice (Das and Scott 1994; Lagakos etal., 1986):
and oral cleft defects (Bove, 1996; Bove et al., 1995; Lagakos et al., 1986), kidney/urinary tract
disorders, musculoskeletal birth anomalies (Lagakos et al., 1986), and anemia/blood disorders
(Burg and Gist, 1999) in humans. See Section 4.8.3.3.3 for further discussion on other structural
developmental outcomes. A current follow-up study of the Camp Lejeune cohort will examine
birth defects and may provide additional insight (ATSDR, 2009: U.S. GAP, 2007b, a; AT SDR,
2003a).
4.10.1.1.3.2. Susceptibility to noncancer outcomes in early lifestages
There are a number of adverse health outcomes observed after exposure to TCE that are
observed in both children and adults. Below is a discussion of differential exposure, incidence,
and/or severity in early lifestages compared to adulthood.
Occupational TCE poisonings via inhalation exposure resulted in an elevated percentage
of cases in the adolescents aged 15-19 years old compared those >20 years old (McCarthy and
Jones, 1983). In addition, there is concern for intentional exposure to TCE during adolescence,
including a series of deaths involving inhaling typewriter correction fluid (King et al., 1985) a
case of glue sniffing likely associated with cerebral infarction in a 12-year-old boy with a 2-year
history of exposure (Parker et al., 1984), and a case of attempted suicide by ingestion of 70 mg
TCE in a 17-year-old boy (Bruning et al.. 1998).
4.10.1.1.3.2.1. Neurotoxicity
Adverse CNS effects observed after early lifestage exposure to TCE in humans include
delayed newborn reflexes (Beppu, 1968): impaired learning or memory (White et al., 1997:
Bernad et al., 1987): aggressive behavior (Blossom et al., 2008: Bernad et al., 1987): hearing
impairment (Burg and Gist, 1999: Burg etal., 1995): speech impairment (Burg and Gist, 1999:
White etal., 1997: Burg etal., 1995): encephalopathy (White etal., 1997): impaired executive
and motor function (White et al., 1997): attention deficit (Bernad et al., 1987) (White et al.,
1997): and ASD (Windham et al., 2006). One analysis observed a trend for increased adversity
during development, with those exposed during childhood demonstrating more deficits than
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those exposed during adulthood (White et al., 1997). In experimental animals, observations
include decreased specific gravity of newborn brains until weaning (Westergren et al., 1984),
reductions in myelination in the brains at weaning, significantly decreased uptake of 2-
deoxyglucose in the neonatal rat brain, significant increase in exploratory behavior (Isaacson and
Taylor, 1989; Noland-Gerbec et al., 1986; Taylor etal., 1985), decreased rearing activity
(Fredriksson et al., 1993), and increased time to cross the first grid in open field testing (George
etal., 1986).
Few studies addressed whether or not children are more susceptible to CNS effects
compared to adults (Burg and Gist, 1999; White et al., 1997; Burg et al., 1995). An analysis of
three residential exposures of TCE observed speech impairments in younger children and not at
any other lifestage (White et al., 1997). A national TCE exposure registry also observed
statistically significant speech impairment and hearing impairment in 0-9 year olds and no other
age group (Burg and Gist, 1999; Burg etal., 1995). However, a follow-up study did not find a
continued association with speech and hearing impairment in these children, although the
absence of acoustic reflexes remained significant (ATSDR, 2002). See Section 4.3 for further
information on CNS toxicity, and Section 4.8.3.3.4 for further information on developmental
neurotoxicity.
4.10.1.1.3.2.2. Liver toxicity
No early lifestage-specific effects were observed after TCE exposure. See Section 4.5 for
further information on liver toxicity.
4.10.1.1.3.2.3. Kidney toxicity
Residents of Woburn, Massachusetts including 4,978 children were surveyed on
residential and medical history to examine an association between observed adverse health
outcomes and wells contaminated with TCE and other chemicals; among these children, an
association was observed for higher cumulative exposure measure and history of kidney and
urinary tract disorders (primarily kidney or urinary tract infections) and with lung and respiratory
disorders (asthma, chronic bronchitis, or pneumonia) (Lagakos et al., 1986). Comparisons were
not made for the adults living in this community. See Section 4.4 for further information on
kidney toxicity.
4.10.1.1.3.2.4. Immunotoxicity
Several studies in exposure to TCE in early lifestages of humans (Lehmann et al., 2002;
Lehmann et al., 2001) and experimental animals (Blossom et al., 2008; Peden-Adams et al.,
2008: Blossom and Doss, 2007: Peden-Adams et al., 2006: Adams et al., 2003) were identified
that assessed the potential for developmental immunotoxicity. While some noted evidence of
immune system perturbation (Blossom et al., 2008: Blossom and Doss, 2007: Peden-Adams et
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al.. 2006: Adams et al.. 2003: Lehmann et al.. 2002). others did not (Peden-Adams et al.. 2008:
Lehmann et al., 2001). However, none of these studies assessed whether exposure during early
life resulted in evidence of increased susceptibility as compared to exposure during adulthood;
this is an area for future research. See Section 4.6 for further information on immunotoxicity,
and Section 4.8.3.3.5 for further discussion on developmental immunotoxicity.
4.10.1.1.3.2.5. Respiratory toxicity
Residents of Woburn, Massachusetts including 4,978 children were surveyed on
residential and medical history to examine an association between observed adverse health
outcomes and wells contaminated with TCE and other chemicals; among these children, an
association was observed for lung and respiratory disorders (asthma, chronic bronchitis, or
pneumonia) (Lagakos et al., 1986). Comparisons were not made for the adults living in this
community. See Section 4.7 for further information on respiratory tract toxicity.
4.10.1.1.3.3. Susceptibility to cancer outcomes in early lifestages
The epidemiologic and experimental animal evidence is limited regarding susceptibility
to cancer from exposure to TCE during early lifestages. The human epidemiological evidence is
summarized above for cancer diagnosed during childhood (see Sections 4.8.2.1 and 4.8.3.3.6),
including a discussion of childhood cancers of the nervous system including neuroblastoma and
the immune system including leukemia (see Section 4.6.1.2). A current follow-up study of the
Camp Lejeune cohort will examine childhood cancers and may provide additional insight
(ATSDR. 2009: U.S. GAP. 2007b. a; ATSDR. 2003a). No studies of cancers in experimental
animals in early lifestages have been observed.
4.10.1.1.3.3.1. Total childhood cancer
Total childhood cancers have been examined in relationship to TCE exposure (ATSDR,
2006a; Morgan and Cassady, 2002). Two studies examining total childhood cancer in relation to
TCE in drinking water did not observe an association. A study in Endicott, New York
contaminated by a number of VOCs, including -thousands of gallons" of TCE observed fewer
than six cases of cancer diagnosed between 1980 and 2001 in children aged 0-19 years, and did
not exceed expected cases or types (ATSDR, 2006a). A California community exposed to TCE
in drinking water from contaminated wells was examined for cancer, with a specific emphasis on
childhood cancer (<15 years old); however, the incidence did not exceed those expected for the
community (Morgan and Cassady, 2002). A third study of childhood cancer in relation to TCE
in drinking water in Camp Lejeune, North Carolina is currently underway (U.S. GAP, 2007b, a).
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4.10.1.1.3.3.2. Childhood leukemia
Childhood leukemia has been examined in relationship to TCE exposure (Costas et al.,
2002: Shu et al.. 1999: Cohn et al.. 1994b: McKinnev et al.. 1991: Lowengart et al.. 1987:
Lagakos et al., 1986). In a study examining drinking water exposure to TCE in 75 New Jersey
towns, childhood leukemia (including ALL) was significantly increased for girls (n = 6)
diagnosed before age 20 years, but this was not observed for boys (Cohn et al., 1994b). A
community in Woburn, Massachusetts with contaminated well water including TCE experienced
20 cases of childhood leukemia, significantly more than expected (Lagakos et al., 1986):
however, the incidence of leukemia among children was not compared to the incidence rate
among adults living in this community. Further analysis by Costas et al. (2002) also observed a
greater than twofold increase over expected cases of childhood leukemia. Cases were more
likely to be male (76%), <9 years old at diagnosis (62%), breast-fed (OR: 10.17, 95% CI: 1.22-
84.50), and exposed during pregnancy (ORadj: 8.33, 95% CI: 0.73-94.67). The highest risk was
observed for exposure during pregnancy compared to preconception or postnatal exposure, and a
dose-response was seen for exposure during pregnancy (Costas et al., 2002). In addition, family
members of those diagnosed with childhood leukemia, including 13 siblings under age 19 at the
time of exposure, had altered immune response, but an analysis looking at only these children
was not done (Byersetal., 1988).
Case-control studies examined children diagnosed with ALL for parental occupational
exposures and found a nonsignificant two- to fourfold increase of childhood leukemia risk for
exposure to TCE during preconception, pregnancy, postnatally, or all developmental periods
combined (Shu et al., 1999: McKinnev et al., 1991: Lowengart et al., 1987). Some studies
showed an elevated risk for maternal (Shu et al., 1999) or paternal exposure (McKinnev et al.,
1991: Lowengart et al., 1987), while others did not show an elevated risk for maternal
(McKinnev et al., 1991) or paternal exposure (Shu et al., 1999), possibly due to the small number
of cases. No variability was observed in the developmental stages in Shu et al. (1999), although
Lowengart et al. (1987) observed the highest risk to be paternal exposure to TCE after birth.
4.10.1.1.3.3.3. CNS tumors
In a case-control study of parental occupational exposures, paternal self-reported
exposure to TCE was not significantly associated with neuroblastoma in the offspring (OR = 1.4,
95% CI: 0.7-2.9) (De Roos et al., 2001). Brain tumors have also been observed in the offspring
of fathers exposed to TCE, but the OR could not be determined (Peters et al., 1985: Peters et al.,
1981).
4.10.1.1.3.3.4. ADAFs
According to EPA's Supplemental Guidance for Assessing Susceptibility from Early-Life
Exposure to Carcinogens (U.S. EPA, 2005e), there may be increased susceptibility to early-life
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exposures for carcinogens with a mutagenic mode of action. Therefore, because the weight of
evidence supports a mutagenic mode of action for TCE carcinogenicity in the kidney (see
Section 4.4.7), the lack of data suggesting an absence of GSTT1 expression in neonates, and in
the absence of chemical-specific data to evaluate differences in susceptibility, early-life
susceptibility should be assumed and the ADAFs should be applied, in accordance with the
Supplemental Guidance.
4.10.1.2. Later Lifestages
Few studies examine the differential effects of TCE exposure for elderly adults
(>65 years old). These limited studies suggest that older adults may experience increased
adverse effects than younger adults. However, there is no further evidence for elderly
individuals exposed to TCE beyond these studies.
Toxicokinetics in later lifestages can be distinct from toxicokinetics in younger adults
(Benedetti et al., 2007; Ginsberg et al., 2005), although there is limited evidence showing a
possible age-related difference in CYP expression (Dome et al., 2005; Parkinson et al., 2004;
George etal., 1995b). GST expression has been observed to decrease with age in human
lymphocytes, with the lowest expression in those aged 60-80 years old (van Lieshout and Peters,
1998).
Studies have examined the age differences in TK after exposure to a mixture of six VOCs
including TCE for humans (Mahle et al., 2007) and rats (Mahle et al., 2007; Rodriguez et al.,
2007). In humans, the blood:air partition coefficient for adult males (20-82 years) was
significantly (p < 0.05) higher (11.7 ± 1.9) compared to male (11.2 ± 1.8) or female (11.0 ± 1.6)
children (3-10 years) (Mahle et al., 2007): when the data was stratified for adults above and
below 55 years of age, there was no significant difference observed between adults (20-55 years)
and aged (56-82) (data not reported). In rats, the difference in tissue:air partition coefficients
also increased from PND 10 to adult (2 months) to aged (2 years) rat (Mahle et al., 2007). TCE
has also been measured in the brain of rats, with an increased level observed in older (2-year-old)
rats compared to adult (2-month-old) rats (Rodriguez et al., 2007). It was also observed that
aged rats reached steady state slower with higher concentrations compared to the adult rat; the
authors suggest that the almost twofold greater percentage of body fat in the elderly is
responsible for this response (Rodriguez et al., 2007).
One cohort of TCE exposed metal degreasers found an increase in psychoorganic
syndrome and increased vibration threshold related to increasing age (Rasmussen et al., 1993b:
Rasmussen et al., 1993c, d), although the age groups were <29, 30-39, and 40+ years, but the
age ranged only from 18 to 68 years and did not examine >65 years as a separate category.
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4.10.2. Other Susceptibility Factors
Aside from age, many other factors may affect susceptibility to TCE toxicity. A partial
list of these factors includes gender, genetic polymorphisms, preexisting disease status,
nutritional status, diet, and previous or concurrent exposures to other chemicals. The toxicity
that results due to changes in multiple factors may be quite variable, depending on the exposed
population and the type of exposure. Qualitatively, the presence of multiple susceptibility
factors will increase the variability that is seen in a population response to TCE toxicity.
4.10.2.1. Gender
Individuals of different genders are physiologically, anatomically, and biochemically
different. Males and females can differ greatly in many physiological parameters such as body
composition, organ function, and ventilation rate, which can influence the toxicokinetics of
chemicals and their metabolites in the body (Gochfeld, 2007; Gandhi et al., 2004).
4.10.2.1.1. Gender-specific toxicokinetics
Chapter 3 describes the toxicokinetics of TCE. Gender-specific information is described
below for absorption, distribution, metabolism, and excretion, followed by available gender-
specific PBPK models.
4.10.2.1.1.1. Absorption
As discussed in Section 3.1, exposure to TCE may occur via inhalation, ingestion, and
skin absorption. Exposure via inhalation is proportional to the ventilation rate, duration of
exposure, and concentration of expired air, and women have increased ventilation rates during
exercise compared to men (Gochfeld, 2007). Percentage of body fat varies with gender
(Gochfeld, 2007), which for lipophilic compounds such as TCE will affect absorption and
retention of the absorbed dose. After experimental exposure to TCE, women were found to
absorb a lower dose due to lower alveolar intake rates compared to men (Sato, 1993; Sato et al.,
1991b).
4.10.2.1.1.2. Distribution
Both human and animal studies provide clear evidence that TCE distributes widely to all
tissues of the body (see Section 3.2). The distribution of TCE to specific organs will depend on
organ blood flow and the lipid and water content of the organ, which may vary between genders
(Gochfeld, 2007). After experimental exposure to humans, higher distribution of TCE into fat
tissue was observed in women leading to a greater blood concentration 16 hours after exposure
compared to men (Sato, 1993; Sato et al., 1991b). In experimental animals, male rats generally
have higher levels of TCE in tissues compared to female rats, likely due to gender differences in
metabolism (Lash et al., 2006). In addition, TCE has been observed in the male reproductive
organs (epididymis, vas deferens, testis, prostate, and seminal vesicle) (Zenick et al., 1984).
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4.10.2.1.1.3. Metabolism
Section 3.3 describes the metabolic processes involved in the metabolism of TCE,
including CYP and GST enzymes. In addition, the role of metabolism in male reproductive
toxicity is discussed in Section 4.8.1.3.2.1. In general, there is some indication that TCE
metabolism is different between males and females, with females more rapidly metabolizing
TCE after oral exposure to rats (Lash et al., 2006), i.p. injections in rats (Verma and Rana, 2003),
and in mouse, rat, and human liver microsomes (Elfarra et al., 1998).
In general, CYP expression may differ between genders (Gochfeld, 2007; Gandhi et al.,
2004; Parkinson et al., 2004), although no gender-related difference in CYP2E1 activity is
observed in the human liver microsomes (Parkinson et al., 2004; George etal., 1995a). After
exposure to TCE, CYP2E1 was detected in the epididymis and testes of mice (Forkert et al.,
2002), and CYP2E1 and GST-alpha has been detected in the ovaries of rats (Wu and Berger,
2008), indicating that metabolism of TCE can occur in both the male and female reproductive
tracts. One study of TCE exposure in mice observed induced CYP2E1 expression in the liver of
males only (Nakajima et al., 2000). Male rats have been shown to have higher levels of TCE
metabolites in the liver (Lash et al., 2006), and lower levels of TCE metabolites in the kidney
(Lash et al., 2006) compared to female rats. However, another study did not observe ant sex-
related differences in the metabolism of TCE in rats (Nakajima et al., 1992b).
Unlike CYP-mediated oxidation, quantitative differences in the polymorphic distribution
or activity levels of GST isoforms in humans are not presently known. However, the available
data (Lash et al., 1999a: Lash et al., 1999b) do suggest that significant variation in
GST-mediated conjugation of TCE exists in humans. One study observed that GSH conjugation
is higher in male rats compared to female rats (Lash et al., 2000b): however, it has also been
speculated that any gender difference may be due to a polymorphism in GSH conjugation of
TCE rather than a true gender difference (Lash etal., 1999b). Also, induction of PPARa
expression in male mice after TCE exposure was greater than that in females (Nakajima et al.,
2000).
4.10.2.1.1.4. Excretion
The major processes of excretion of TCE and its metabolites are discussed in Section 3.4.
Two human voluntary inhalation exposure studies observed the levels of TCE and its metabolites
in exhaled breath and urine (Kimmerle and Eben, 1973a: Nomiyama and Nomiyama, 1971).
Increased levels of TCE in exhaled breath in males were observed in one human voluntary
inhalation exposure study of 250-380 ppm for 160 minutes (Nomiyama and Nomiyama, 1971),
but no difference was observed in another study of 40 ppm for 4 hours or 50 ppm for 4 hours for
5 days (Kimmerle and Eben, 1973a).
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After experimental exposure to TCE, women were generally found to excrete higher
levels of TCE and TCA compared to men (Kimmerle and Eben, 1973a: Nomiyama and
Nomiyama, 1971). However, other studies observed an increase in TCE in the urine of males
(Inoue et al., 1989), an increase in TCA in the urine of males (Sato et al., 1991b), or no
statistically significant (p > 0.10) gender difference for TCA in the urine (Inoue et al., 1989).
Others found that the urinary elimination half-life of TCE metabolites is longer in women
compared to men (Ikeda, 1977; Ikeda and Imamura, 1973).
In addition to excretion pathways that occur in both genders, excretion occurs uniquely in
men and women. In both humans and experimental animals, it has been observed that females
can excrete TCE and metabolites in breast milk (Fisher et al., 1997; Hamada and Tanaka, 1995;
Fisher et al., 1990; Pellizzari et al., 1982), while males can excrete TCE and metabolites in
seminal fluid (Forkert et al., 2003: Zenicketal., 1984).
4.10.2.1.1.5. PBPK models
Gender-specific differences in uptake and metabolism of TCE were incorporated into a
PBPK model using human exposure data (Fisher et al., 1998). The chemical-specific parameters
included cardiac output at rest, ventilation rates, tissue volumes, blood flow, and fat volume.
This model found that gender differences for the toxicokinetics of TCE are minor.
4.10.2.1.2. Gender-specific effects
4.10.2.1.2.1. Gender susceptibility to noncancer outcomes
4.10.2.1.2.1.1. Liver toxicity
No gender susceptibility to noncancerous outcomes in the liver was observed. A detailed
discussion of the studies examining the effects of TCE on the liver can be found in Section 4.5.
4.10.2.1.2.1.2. Kidney toxicity
A detailed discussion of the studies examining the noncancer effects of TCE on the
kidney can be found in Section 4.4. A residential study found that females aged 55-64 years old
had an elevated risk of kidney disease (RR = 4.57, 99% CI: 2.10-9.93) compared to males,
although an elevated risk of urinary tract disorders was reported for both males and females
(Burg et al., 1995). Additionally, a higher rate of diabetes in females compared to males exposed
to TCE was reported in two studies (Davis et al., 2005; Burg et al., 1995). In rodents, however,
kidney weights were increased more in male mice than in females (Kj ell strand et al., 1983a:
Kjellstrand et al., 1983b), and male rats have exhibited increased renal toxicity to TCE compared
to females (Lash et al., 200Ib: Lashetal., 1998a).
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4.10.2.1.2.1.3. Immunotoxicity
A detailed discussion of the studies examining the immunotoxic effects of TCE can be
found in Section 4.6. Most of the immunotoxicity studies present data stratified by sex. The
prevalence of exposure to TCE is generally lower in women compared with men. In men, the
studies generally reported ORs between 2.0 and 8.0, and in women, the ORs were between
1.0 and 2.0 (Cooper et al., 2009). Based on small numbers of cases, an occupational study of
TCE exposure found an increased risk for systemic sclerosis for men (OR: 4.75, 95% CI: 0.99-
21.89) compared to women (OR: 2.10; 95% CI: 0.65-6.75) (Diot et al.. 2002). Another study
found similar results, with an elevated risk for men with a maximum intensity, cumulative
intensity, and maximum probability of exposure to TCE compared to women (Nietert et al.,
1998). These two studies, along with one focused exclusively on the risk of scleroderma to
women (Garabrant et al., 2003), were included in a meta-analysis conducted by the EPA
resulting in a combined estimate for "any" exposure, was OR = 2.5 (95% CI: 1.1, 5.4) for men
and OR = 1.2 (95% CI: 0.58, 2.6) in women.
4.10.2.1.2.1.4. Respiratory toxicity
No gender susceptibility to noncancerous outcomes in the respiratory tract after TCE
exposure was observed. A detailed discussion of the studies examining the respiratory effects of
TCE can be found in Section 4.7.
4.10.2.1.2.1.5. Reproductive toxicity
A detailed discussion of the studies examining the gender-specific noncancer
reproductive effects of TCE can be found in Section 4.8.1.
Studies examining males after exposure to TCE observed altered sperm morphology and
hyperzoospermia (Chia et al., 1996), altered endocrine function (Goh et al., 1998; Chia et al.,
1997), decreased sexual drive and function (Saihan etal., 1978; El Ghawabi et al., 1973;
Bardodej and Vyskocil, 1956), and altered fertility to TCE exposure. Infertility was not
associated with TCE exposure in other studies (Forkert et al., 2003; Sallmen et al., 1998), and
sperm abnormalities were not observed in another study (Rasmussen et al., 1988).
There is more limited evidence for reproductive toxicity in females. There are
epidemiological indicators of a possible effect of TCE exposure on female fertility (Sallmen et
al., 1998), increased rate of miscarriage (ATSDR, 2001), and menstrual cycle disturbance
(ATSDR, 2001; Zielinski, 1973; Bardodej and Vyskocil, 1956). In experimental animals, the
effects on female reproduction include evidence of reduced in vitro oocyte fertilizability in rats
(Wu and Berger, 2008, 2007; Berger and Horner, 2003). However, in other studies that assessed
reproductive outcome in female rodents (Cosby andDukelow, 1992; George et al., 1986; George
etal., 1985; Manson et al., 1984), there was no evidence of adverse effects of TCE exposure on
female reproductive function.
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4.10.2.1.2.1.6. Developmental toxicity
A detailed discussion of the studies examining the gender-specific noncancer
developmental effects of TCE can be found in Section 4.8.3. Only one study of contaminated
drinking water exposure in Camp Lejeune, North Carolina observed a higher risk of SGA in
males compared to females (Sonnenfeldetal.. 2001: ATSDR, 1998a).
4.10.2.1.2.2. Gender susceptibility to cancer outcomes
A detailed discussion of the studies examining the carcinogenic effects of TCE can be
found on the liver in Section 4.5, on the kidney in Section 4.4, in the immune system in
Section 4.6, in the respiratory system in Section 4.7, and on the reproductive system in
Section 4.8.2.
4.10.2.1.2.2.1. Liver cancer
An elevated risk of liver cancer was observed for females compared to males in both
human (Raaschou-Nielsen et al., 2003) and rodent (Elfarra et al., 1998) studies. In addition,
gallbladder cancer was significantly elevated for women compared to men (Raaschou-Nielsen et
al., 2003). A detailed discussion of the studies examining the gender-specific liver cancer effects
of TCE can be found in Section 4.5.
4.10.2.1.2.2.2. Kidney cancer
One study of occupational exposure to TCE observed an increase in RCC for women
compared to men (Dosemeci etal., 1999), but no gender difference was observed in other studies
(Raaschou-Nielsen et al.. 2003: Pesch et al.. 2000b). Blair et al. (1998) and Hansen et al. (2001)
also present some results by sex, but both of these studies have too few cases to be informative
about a sex difference for kidney cancer. Exposure differences between males and females in
Dosemeci et al. (1999) may explain their finding. These studies, however, provide little
information to evaluate susceptibility between sexes because of their lack of quantitative
exposure assessment and lower statistical power. A detailed discussion of the studies examining
the gender-specific kidney cancer effects of TCE can be found in Section 4.4.
4.10.2.1.2.2.3. Cancers of the immune system
Two drinking water studies suggest that there may be an increase of leukemia (Cohn et
al., 1994b: Fagliano et al., 1990) and NHL (Cohn etal., 1994b) among females compared to
males. An occupational study also observed an elevated risk of leukemia in females compared to
males (Raaschou-Nielsen et al., 2003), although a study of contaminated drinking water in
Woburn, Massachusetts observed an increased risk of childhood leukemia in males compared to
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females (Costas et al., 2002). A detailed discussion of the studies examining the gender-specific
cancers of the immune system following TCE exposure can be found in Section 4.6.
4.10.2.1.2.2.4. Respiratory cancers
One study observed significantly elevated risk of lung cancer following occupational
TCE exposure for both men and women, although the risk was found to be higher for women
compared to men (Raaschou-Nielsen et al., 2003). This same study observed a nonsignificant
elevated risk in both men and women for laryngeal cancer, again with an increased risk for
women compared to men (Raaschou-Nielsen et al., 2003). Conversely, a study of Iowa residents
with TCE-contaminated drinking water observed a sevenfold increased annual age-adjusted
incidence for males compared to females (Isacson et al., 1985). However, other studies did not
observe a gender-related difference (ATSDR, 2002: Hansen et al.. 2001: Blair etal.. 1998). A
detailed discussion of the studies examining the gender-specific respiratory cancers following
TCE exposure can be found in Sections 4.7.
4.10.2.1.2.2.5. Reproductive cancers
Breast cancer in females and prostate cancer in males were reported after exposure to
TCE in drinking water (Isacson etal., 1985). A statistically elevated risk for cervical cancer, but
not breast, ovarian, or uterine cancer, was observed in women in another study (Raaschou-
Nielsen et al., 2003). This study also did not observe elevated prostate or testicular cancer
(Raaschou-Nielsen et al., 2003). A detailed discussion of the studies examining the gender-
specific reproductive cancers following TCE exposure can be found in Section 4.8.2.
4.10.2.1.2.2.6. Other Cancers
Bladder and rectal cancer was increased in men compared to women after exposure to
TCE in drinking water, but no gender difference was observed for colon cancer (Isacson et al.,
1985). After occupational TCE exposure, bladder, stomach, colon, and esophageal cancer was
nonsignificantly elevated in women compared to men (Raaschou-Nielsen et al., 2003).
4.10.2.2. Genetic Variability
Section 3.3 describes the metabolic processes involved in the metabolism of TCE.
Human variation in response to TCE exposure may be associated with genetic variation. TCE is
metabolized by both CYP and GST; therefore, it is likely that polymorphisms will alter the
response to exposure (Garte et al., 2001: Nakajima and Aoyama, 2000), as well as exposure to
other chemicals that may alter the metabolism of TCE (Lash et al., 2007) (see Section 4.10.2.6).
It is important to note that even with a given genetic polymorphism, metabolic expression is not
static, and depends on lifestage (see Section 4.10.1), obesity (see Section 4.10.2.4), and alcohol
intake (see Section 4.10.2.5).
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4.10.2.2.1. CYP genotypes
In general, variability in CYP expression occurs within humans (Dome et al., 2005), and
variability in CYP expression has been observed in experimental animals exposed to TCE
(Nakajima et al., 1993). In particular, increased CYP2E1 activity may lead to increased
susceptibility to TCE (Lipscomb et al.. 1997). The CYP2E1*3 allele and the CYP2E1*4 allele
were more common among those who developed scleroderma who were exposed to solvents
including TCE (Povev et al.. 2001). A PBPK model of CYP2E1 expression after TCE exposure
has been developed for rats and humans (Yoon et al., 2007).
In experimental animals, toxicokinetics of TCE differed among CYP2E1 knockout and
wild-type mice (Kim and Ghanayem, 2006). This study found that exhalation was more
prevalent among the knockout mice, whereas urinary excretion was more prevalent among the
wild-type mice. In addition, the dose was found to be retained to a greater degree by the
knockout mice compared to the wild-type mice.
4.10.2.2.2. GST genotype
There is a possibility that GST polymorphisms could play a role in variability in toxic
response to TCE (Caldwell and Keshava, 2006), but this has not been sufficiently tested (NRC,
2006). One study of renal cell cancer in workers exposed to TCE demonstrated a significant
increased for those with GSTM1+ and GSTT1+ polymorphisms, compared to a negative risk for
those with GSTM1- and GSTTl-polymorphisms (Briming et al., 1997a). Another study of
occupational TCE exposure found that RCC was significantly associated with the GSTT+
polymorphism but not with GSTT- (Moore et al., 2010). However, another study did not
confirm this hypothesis, observing no clear relationship between GSTM1 and GSTT1
polymorphisms and RCC among TCE-exposed individuals, although they did see a possible
association with the homozygous wild-type allele GSTP1*A (Wiesenhutter et al., 2007).
Unrelated to TCE exposure, Sweeney et al. (2000) found GSTT1- to be associated with an
increased risk of RCC, but no difference was seen for GSTM1 and GSTP1 alleles. The role of
GST polymorphisms in the development of RCC is an area in need of future research.
4.10.2.2.3. Other genotypes
Other genetic polymorphisms could play a role in variability in toxic response, in
particular TCE-related skin disorders. Studies have found that many TCE-exposed patients
diagnosed with skin conditions exhibited the slow-acetylator NAT2 genotype (Nakajima et al.,
2003; Huang et al., 2002), whereas there was no difference in NAT2 status for those diagnosed
with RCC (Wiesenhutter et al., 2007). Other studies have found that many TCE-exposed
patients diagnosed with skin conditions expressed variant HLA alleles (Li et al., 2007; Yue et al.,
2007), in particular HLA-B*1301, which is more common in Asians compared to whites (Cao et
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al.. 2001: Williams etal.. 2001). or TNF a-308 allele (Dai et al.. 2004). Also, an in vitro study of
human lung adenocarcinoma cells exposed to TCE varied in response based on their p53 status,
with p53-wild-type cells resulting in severe cellular damage, but not the p53-null cells (Chen et
al.. 2002a).
4.10.2.3. Race/Ethnicity
Different racial or ethnic groups may express metabolic enzymes in different ratios and
proportions due to genetic variability (Garte et al., 2001). In particular, ethnic variability in CYP
(Dome et al.. 2005: Parkinson et al.. 2004: McCarver et al.. 1998: Shimada et al.. 1994: Stephens
etal., 1994) and GST (Nelson etal., 1995) expression has been reported.
It has been observed that the metabolic rate for TCE may differ between the Japanese and
Chinese (Inoue et al., 1989). Also, body size varies among ethnic groups, and increased body
size was related to increased absorption of TCE and urinary excretion of TCE metabolites (Sato
etal.. 1991b).
4.10.2.4. Preexisting Health Status
It is known that kidney and liver diseases can affect the clearance of chemicals from the
body, and therefore, poor health may lead to increased half-lives for TCE and its metabolites.
There are some data indicating that obesity/metabolic syndrome, diabetes, and hypertension may
increase susceptibility to TCE exposure through altered toxicokinetics. In addition, some of
these conditions lead to increased risk for adverse effects that have also been associated with
TCE exposure, though the possible interaction between TCE and known risk factors for these
effects is not understood.
4.10.2.4.1. Obesity
TCE is lipophilic and stored in adipose tissue; therefore, obese individuals may
experience altered toxicokinetics of TCE compared to thin individuals. The absorption of TCE
is increased in obese individuals compared to thin individuals (Clewell et al., 2000), as observed
by lower blood concentrations immediately after exposure in obese men compared to thin men
(Sato, 1993: Sato et al., 1991b). Once absorbed, obese individuals have increased storage of
TCE in the adipose tissue compared to thin men (Clewell et al., 2000), which prolongs internal
exposures (Lash et al., 2000b: Davidson and Beliles, 1991). Obesity also likely alters TCE
metabolism, since increased CYP2E1 expression has been observed in obese individuals
compared to thin individuals (McCarver et al., 1998). Finally, delayed excretion has been
observed in obese individuals compared to thin individuals in both exhaled air (Monster, 1979)
and urine (Sato, 1993: Sato et al., 1991b). In sum, obese individuals have altered toxicokinetics
of TCE compared to thin individuals due to increased storage of TCE, increased CYP2E1
metabolism, and a slower rate of elimination.
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In addition, individuals with high BMI are at increased risk of some of the same health
effects associated with TCE exposure. For example, RCC, liver cancer, and prostate cancer may
be positively associated with BMI or obesity (Wigle et al., 2008; El-Serag and Rudolph, 2007;
Benichou et al.. 1998: Asaletal.. 1988a: Asaletal.. 1988b). However, whether and how TCE
interacts with known risk factors for such diseases is unknown, as existing epidemiologic studies
have only examined these factors as possible confounders for effects associated with TCE, or
vice versa (Krishnadasan et al., 2008; Charbotel et al., 2006).
4.10.2.4.2. Diabetes
A higher rate of diabetes in females compared to males exposed to TCE was reported in
two studies (Davis et al., 2005; Burg et al., 1995). Whether the TCE may have caused the
diabetes or the diabetes may have increased susceptibility to TCE is not clear. However, it has
been observed that CYP2E1 expression is increased in obese Type II diabetics (Wang et al.,
2003), and in poorly controlled Type I diabetics (Song et al., 1990), which may consequently
alter the metabolism of TCE.
4.10.2.4.3. Hypertension
One study found no difference in risk for RCC among those diagnosed with hypertension
among those living in an area with high TCE exposure; however, a slightly elevated risk was
seen for those being treated for hypertension (OR: 1.57, 95% CI: 0.90-2.72) (Charbotel et al.,
2006). Unrelated to TCE exposure, hypertension has been associated with increased risk of RCC
in women compared to men (Benichou et al., 1998).
4.10.2.5. Lifestyle Factors and Nutrition Status
4.10.2.5.1. Alcohol intake
A number of studies have examined the interaction between TCE and ethanol exposure in
both humans (McCarver et al., 1998: Sato, 1993: Satoetal., 1991a: Barret etal., 1984: Sato et
al., 1981: 1975: Stewart et al., 1974b: Bardodej and Vyskocil, 1956) and experimental animals
(Kaneko et al., 1994: Nakajima et al., 1992a: Okinoetal., 1991: Nakajima et al., 1990: Larson
and Bull, 1989: Nakaiima et al., 1988: Sato and Nakaiima, 1985: Satoetal., 1983: White and
Carlson, 1981b: Satoetal., 1980).
The co-exposure causes metabolic inhibition of TCE in humans (Windemuller and
Ettema, 1978: Muller et al., 1975), male rats (Kaneko et al., 1994: Okinoetal., 1991: Nakajima
etal., 1990: Larson and Bull, 1989: Nakaiima et al., 1988: Sato and Nakaiima, 1985: Sato et al.,
1981: Nakanishi et al., 1978), and rabbits (White and Carlson, 1981b). Similarly, individuals
exposed to TCE reported an increase in alcohol intolerance (Rasmussen and Sabroe, 1986:
Bardodej and Vyskocil, 1956: Grand]ean et al., 1955). Disulfiram, used to treat alcoholism, has
also been found to decrease the elimination of TCE and TCA (Bartonicek and Teisinger, 1962).
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A —degrasers flush" has been described, reflecting a reddening of the face of those
working with TCE after drinking alcohol, and measured an elevated level of TCE in exhaled
breath compared to nondrinkers exposed to TCE (Stewart et al., 1974a). This may be due to
increased CYP2E1 expression in those that consume alcohol compared to nondrinkers, unrelated
to TCE exposure (Caldwell et al., 2008b: Liangpunsakul et al., 2005; Lieber, 2004; Parkinson et
al.. 2004: McCarver et al.. 1998: Perrot et al.. 1989).
In experimental animals, male rats pretreated with ethanol experienced an induction of
TCE metabolism (Nakajima et al., 1992a), although another study of male rats observed that
pretreatment with ethanol did not decrease CYP activity (Okino et al., 1991). It is important to
note that a further increased response of TCE and ethanol has been reported when also combined
with low-fat or low-carbohydrate diets in male rats (Sato et al., 1983).
Since the liver is a target organ for both TCE and alcohol, decreased metabolism of TCE
could be related to cirrhosis of the liver as a result of alcohol abuse (McCarver et al., 1998), and
an in increase in clinical liver impairment along with degreasers flush has been observed (Barret
etal.. 1984).
The CNS may also be impacted by the co-exposure. Individuals exposed to TCE and
ethanol reported an increase in altered mood states (Reif et al., 2003), decreased mental capacity
as described as small increases in functional load (Windemuller and Ettema, 1978), and those
exposed to TCE and tetrachloroethylene who consumed alcohol had an elevated color confusion
index (Valic et al., 1997).
4.10.2.5.2. Tobacco smoking
Individuals who smoke tobacco may be at increased risk of the health effects from TCE
exposure. One study examining those living in an area with high TCE exposure found an
increasing trend of risk (p = 0.008) for RCC among smokers, with the highest OR among those
with >40 pack-years (OR = 3.27, 95% CI: 1.48-7.19) (Charbotel et al., 2006). It has been shown
that RCC is independently associated with smoking in a dose-response manner (Yuan et al.,
1998), particularly in men (Benichou et al., 1998). While Charbotel et al. (2006) adjusted for
smoking effects in analyses examining TCE exposure and RCC, this study provides no
information on potential effect modification of TCE exposure by smoking.
A number of factors correlated to smoking (e.g., SES status, diet, alcohol consumption)
may positively confound results if greater smoking rates were over-represented, as observed in
an occupational cohort exposed to TCE (Raaschou-Nielsen et al., 2003). Absence of smoking
information, on the other hand, could introduce a negative bias. In a drinking water study with
exposures to TCE and perchlorate, Morgan and Cassidy (2002) noted that the relatively high
education and high income levels as well as high access to health care of subjects in this study
compared to the averages for the county as a whole likely leads to a lower smoking rate.
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4.10.2.5.3. Nutritional status
Malnutrition may also increase susceptibility to TCE. Bioavailability of TCE after oral
and i.v. exposure increased with fasting from approximately 63% in nonfasted rats to >90% in
fasted rats, with blood levels in fasted rats were elevated two- to threefold, and increased half-
life in the blood of fasted rats (D'Souza et al., 1985). Food deprivation (Sato and Nakajima,
1985) and carbohydrate restriction (Sato and Nakajima, 1985; Nakajima et al., 1982) enhanced
metabolism of TCE in male rats, but this was not observed for dietary changes in protein or fat
levels (Nakaiima et al.. 1982).
Vitamin intake may also alter susceptibility to TCE. An in vitro study of cultured normal
human epidermal keratinocyte demonstrated an increased lipid peroxidation in a dose-dependent
manner after exposure to TCE, which were then attenuated by exposure to Vitamin E (Ding et
al.. 2006).
4.10.2.5.4. Physical activity
Increased inhalation during physical activity increases TCE concentrations in the alveoli
when compared to inhalation in a resting state (Astrand, 1975). Studies have examined the time
course of inhaled TCE and metabolites in blood and urine in individuals with different workloads
(Jakubowski and Wieczorek, 1988; Astrand and Ovrum, 1976; Monster et al., 1976; Vesterberg
and Astrand, 1976; Vesterberg et al., 1976). These studies demonstrate that an increase in
pulmonary ventilation increases the amount of TCE taken up during exposure (Sato, 1993;
Jakubowski and Wieczorek, 1988; Astrand and Ovrum, 1976; Monster et al., 1976).
The Rocketdyne aerospace cohort exposed to TCE (and other chemicals) found a
protective effect with high physical activity, but only after controlling for TCE exposure and
SES status (OR = 0.55, 95% CI: 0.32-0.95,p trend = 0.04) (Krishnadasan et al.. 2008). In
general, physical activity may provide a protective effect for prostate cancer (Wigle et al., 2008)
(see Section 4.8.2.1.1).
4.10.2.5.5. SES
SES can be an indicator for a number of co-exposures, such as increased tobacco
smoking, poor diet, education, income, and health care access, which may play a role in the
results observed in the health effects of TCE exposure (Morgan and Cassady, 2002).
Children's exposure to TCE was measured in a low SES community, as characterized by
income, educational level, and receipt of free or reduced cost school meals (Sexton et al., 2005):
however, this study did not compare data to a higher SES community, nor examine health
effects.
An elevated risk of NHL and esophagus/adenocarcinoma after exposure to TCE was
observed for blue-collar workers compared to white collar workers and workers with unknown
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SES (Raaschou-Nielsen et al., 2003). Authors speculate that these results could be confounded
due to other related factors than SES such as smoking.
4.10.2.6. Mixtures
TCE exposure often occurs concurrently with other chemical substances. In general, the
effects of exposures to multiple chemicals is considered by EPA in the Framework for
Cumulative Risk Assessment (U.S. EPA, 2003 a). A summary of the interactive effects of TCE
and other chemical co-exposures is addressed in Caldwell et al. (2008b) and in Chapter 10 of the
NRC's report Assessing the Human Health Risks ofTrichloroethylene: Key Scientific Issues
(NRC, 2006).
Chapter 2 discusses that other parent compounds produce similar metabolites to TCE (see
Table 2-1) or have similar properties or industrial uses (see Tables 2-3 and 2-14). The metabolic
pathway of TCE in discussed in Section 3.3; due its metabolism into multiple compounds,
exposure to TCE itself can be considered as exposure to a mixture (NRC, 2006). Many of the
studies discussed above in Chapter 4 demonstrate that exposure to TCE and other chemical
substances often occur together in both occupational and nonoccupational settings.
Co-exposures to other solvents may induce or saturate toxicokinetic pathways, altering
the way in which TCE is metabolized and cleared from the body. The limited data summarized
by the ATSDRin its interaction profile on TCE, 1,1,1-trichloroethane, 1,1-dichloroethane, and
tetrachloroethylene suggest that additive joint action is plausible (ATSDR, 2004b: Pohl et al.,
2003). Joint exposure to TCE and the fungicide fenarimol has been shown to alter TCE
metabolism and genetic expression in mice (Hrelia et al., 1994). Joint exposure to TCE,
benzene, and methyl mercury has been shown to induce genetic expression in the liver and the
kidney of rats (Hendriksen et al., 2007). Metabolic competition was also observed for TCE and
various agents in another study by Jakobson et al. (1986).
PBPK models have been developed demonstrating the interaction between 1,1-DCE and
TCE (Andersen et al., 1987b) and the interaction between TCE, tetrachloroethylene, and
1,1,1-trichloroethane in rats (Dobrev et al., 2001) and humans (Dobrev et al., 2002). Other
PBPK models also showed metabolic inhibition at higher doses for TCE and toluene (Thrall and
Poet 2000). and for TCE and chloroform (Isaacs et al.. 2004). Another PBPK model of TCE
and multiple VOCs showed metabolic inhibition and induction when exposure occurs
concurrently (Haddad et al.. 2000).
4.10.3. Uncertainty of Database and Research Needs for Susceptible Populations
There is some evidence that certain populations may be more susceptible to exposure to
TCE. These populations include early and later lifestages, gender, genetic polymorphisms,
race/ethnicity, preexisting health status, and lifestyle factors and nutrition status. In general, this
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database would be improved by future epidemiologic and toxicological studies of TCE exposure
that provide data on effect modification, including the factors discussed here.
Although the toxicokinetic variability has been characterized by population PBPK
modeling (see Section 3.5), the available data are limited due to the relative small numbers of
individuals (n < 100), their all being adults, and the fact that subjects were selected nonrandomly
(healthy volunteers).
Although there is more information on early life exposure to TCE than on other
potentially susceptible populations, there remain a number of uncertainties and data gaps
regarding children's susceptibility. Improved PBPK modeling for using childhood parameters
early lifestages as recommended by the NRC (2006), and validation of these models, will aid in
determining how variations in metabolic enzymes affect TCE metabolism. In particular, the
NRC states that it is prudent to assume children need greater protection than adults—unless
sufficient data are available to justify otherwise (NRC, 2006).
More studies specifically designed to evaluate effects in early and later lifestages are
needed in order to more fully characterize potential lifestage-related TCE toxicity. Because the
neurological effects of TCE constitute the most sensitive endpoints of concern for noncancer
effects, it is quite likely that the early lifestages may be more susceptible to these outcomes than
are adults. Lifestage-specific neurotoxic effects, particularly in the developing fetus, need
further evaluation. It is important to consider the use of age-appropriate testing for assessment of
these and other outcomes, both for cancer and noncancer outcomes. Data specific to the
carcinogenic effects of TCE exposure during the critical periods of development of experimental
animals and humans also are sparse.
There is a need to better characterize the implications of TCE exposures to susceptible
populations. There is suggestive evidence that there may be greater susceptibility for exposures
to the elderly. Gender and race/ethnic differences in susceptibility are likely due to variation in
physiology and exposure, and genetic variation likely has an effect on the toxicokinetics of TCE.
In particular, the relationship between genetic variation and generalized hypersensitivity skin
diseases is relevant for future study (see Sections 4.6.1.1.2 and 4.10.2.2). Diminished health
status (e.g., impaired kidney liver or kidney), alcohol consumption, tobacco smoking, and
nutritional status will likely affect an individual's ability to metabolize TCE. In addition, further
evaluation of the effects due to co-exposures to other compounds with similar or different modes
of action need to be evaluated. Future research should better characterize possible susceptibility
for certain lifestages or populations.
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4.11. HAZARD CHARACTERIZATION
4.11.1. Characterization of Noncancer Effects
4.11.1.1. Neurotoxicity
Both human and animal studies have associated TCE exposure with effects on several
neurological domains. The strongest neurological evidence of hazard in humans is for changes
in trigeminal nerve function or morphology and impairment of vestibular function. Fewer and
more limited evidence exists in humans on delayed motor function, and changes in auditory,
visual, and cognitive function or performance. Acute and subchronic animal studies show
morphological changes in the trigeminal nerve, disruption of the peripheral auditory system
leading to permanent function impairments and histopathology, changes in visual evoked
responses to patterns or flash stimulus, and neurochemical and molecular changes. Additional
acute studies reported structural or functional changes in hippocampus, such as decreased
myelination or decreased excitability of hippocampal CA1 neurons, although the relationship of
these effects to overall cognitive function is not established. Some evidence exists for motor-
related changes in rats/mice exposed acutely/subchronically to TCE, but these effects have not
been reported consistently across all studies.
Epidemiologic evidence supports a relationship between TCE exposure and trigeminal
nerve function changes, with multiple studies in different populations reporting abnormalities in
trigeminal nerve function in association with TCE exposure (Mhiri et al., 2004; Kilburn, 2002a:
Kilburn and Warshaw, 1993a: Feldman et al.. 1992: Ruiiten et al.. 1991: Feldman et al.. 1988:
Barret etal.. 1987: Barret etal.. 1984: Barret etal.. 1982). Of these, two well-conducted
occupational cohort studies, each including >100 TCE-exposed workers without apparent
confounding from multiple solvent exposures, additionally reported statistically significant dose-
response trends based on ambient TCE concentrations, duration of exposure, and/or urinary
concentrations of the TCE metabolite TCA (1987: Barret et al., 1984). Limited additional
support is provided by a positive relationship between prevalence of abnormal trigeminal nerve
or sensory function and cumulative exposure to TCE (most subjects) or CFC113 (<25% of
subjects) (Rasmussen et al., 1993a). Test for linear trend in this study was not statistically
significant and may reflect exposure misclassification since some subjects included in this study
did not have TCE exposure. The lack of association between TCE exposure and overall nerve
function in three small studies (trigeminal (El Ghawabi et al., 1973): ulnar and medial (Triebig et
al., 1983: 1982)) does not provide substantial evidence against a causal relationship between
TCE exposure and trigeminal nerve impairment because of limitations in statistical power, the
possibility of exposure misclassification, and differences in measurement methods. Laboratory
animal studies have also shown TCE-induced changes in the trigeminal nerve. Although one
study reported no significant changes in TSEP in rats exposed to TCE for 13 weeks (Albee et al.,
2006), there is evidence of morphological changes in the trigeminal nerve following short-term
exposures in rats (Barret et al., 1992: 1991).
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Human chamber, occupational, geographic-based/drinking water, and laboratory animal
studies clearly established TCE exposure causes transient impairment of vestibular function.
Subjective symptoms such as headaches, dizziness, and nausea resulting from occupational (Liu
et al., 1988; Rasmussen and Sabroe, 1986; Smith, 1970; Grandjean et al., 1955), environmental
(Hirsch etal., 1996), or chamber exposures (Smith, 1970; Stewart et al., 1970) have been
reported extensively. A few laboratory animal studies have investigated vestibular function,
either by promoting nystagmus or by evaluating balance (Umezu et al., 1997; Niklasson et al.,
1993: Thametal., 1984: 1979).
In addition, mood disturbances have been reported in a number of studies, although these
effects also tend to be subjective and difficult to quantify (Gash et al., 2008: Kilburn, 2002b, a;
Kilburn and Warshaw, 1993a: Troster and Ruff, 1990: McCunney, 1988: Rasmussen and Sabroe,
1986: Mitchell and Parsons-Smith, 1969), and a few studies have reported no effects from TCE
on mood (Reif etal., 2003: Triebig etal., 1977a: Triebig etal., 1976). Few comparable mood
studies are available in laboratory animals, although both Moser et al. (2003) and Albee et al.
(2006) reported increases in handling reactivity among rats exposed to TCE. Finally,
significantly increased number of sleep hours was reported by Arito et al. (1994) in rats exposed
via inhalation to 50-300-ppm TCE for 8 hours/day for 6 weeks.
Four epidemiologic studies of chronic exposure to TCE observed disruption of auditory
function. One large occupational cohort study showed a statistically significant difference in
auditory function with cumulative exposure to TCE or CFC113 as compared to control groups
after adjustment for possible confounders, as well as a positive relationship between auditory
function and increasing cumulative exposure (Rasmussen et al., 1993c). Of the three studies
based on populations from ATSDR's TCE Subregistry from the National Exposure Registry,
more limited than Rasmussen et al. (1993c) due to inferior exposure assessment, Burg et al.
(1995) and Burg and Gist (1999) reported a higher prevalence of self-reported hearing
impairments. The third study reported that auditory screening revealed abnormal middle ear
function in children <10 years of age, although a dose-response relationship could not be
established and other tests did not reveal differences in auditory function (ATSDR, 2002).
Further evidence for these effects is provided by numerous laboratory animal studies
demonstrating that high dose subacute and subchronic TCE exposure in rats disrupts the auditory
system leading to permanent functional impairments and histopathology.
Studies in humans exposed under a variety of conditions, both acutely and chronically,
report impaired visual functions such as color discrimination, visuospatial learning tasks, and
visual depth perception in subjects with TCE exposure. Abnormalities in visual depth perception
were observed with a high acute exposure to TCE under controlled conditions (Vernon and
Ferguson, 1969). Studies of lower TCE exposure concentrations also observed visuofunction
effects. One occupational study (Rasmussen et al., 1993c) reported a statistically significant
positive relationship between cumulative exposure to TCE or CFC113 and visual gestalts
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learning and retention among Danish degreasers. Two studies of populations living in a
community with drinking water containing TCE and other solvents furthermore suggested
changes in visual function (Reif etal., 2003; Kilburn, 2002b, a). These studies used more direct
measures of visual function as compared to Rasmussen et al. (1993c), but their exposure
assessment is more limited because TCE exposure is not assigned to individual subjects
(Kilburn 2002a), or because there are questions regarding control selection and exposure to
several solvents (Reif etal.. 2003: Kilburn. 2002b, a).
Additional evidence of effects of TCE exposure on visual function is provided by a
number of laboratory animal studies demonstrating that acute or subchronic TCE exposure
causes changes in visual evoked responses to patterns or flash stimulus (Boyes et al., 2005a:
Boyes et al., 2003; Blain et al., 1994). Animal studies have also reported that the degree of some
effects is correlated with simultaneous brain TCE concentrations (Boyes et al., 2005a: Boyes et
al., 2003) and that, after a recovery period, visual effects return to control levels (Blain et al.,
1994; Rebert et al., 1991). Overall, the human and laboratory animal data together suggest that
TCE exposure can cause impairment of visual function, and some animal studies suggest that
some of these effects may be reversible with termination of exposure.
Studies of human subjects exposed to TCE either acutely in chamber studies or
chronically in occupational settings have observed deficits in cognition. Five chamber studies
reported statistically significant deficits in cognitive performance measures or outcome measures
suggestive of cognitive effects (Triebig et al., 1977a: Gamberale et al., 1976; Triebig et al., 1976;
Stewart et al., 1970). Danish degreasers with high cumulative exposure to TCE or CFC113 had
a high risk (OR = 13.7, 95% CI: 2.0-92.0) for psychoorganic syndrome characterized by
cognitive impairment, personality changes, and reduced motivation, vigilance, and initiative
compared to workers with low cumulative exposure. Studies of populations living in a
community with contaminated groundwater also reported cognitive impairments0020(Kilburn,
2002b, a; Kilburn and Warshaw, 1993a), although these studies carry less weight in the analysis
because TCE exposure is not assigned to individual subjects and their methodological design is
weaker.
Laboratory studies provide some additional evidence for the potential for TCE to affect
cognition, although the predominant effect reported has been changes in the time needed to
complete a task, rather than impairment of actual learning and memory function (Umezu et al.,
1997; Kishi etal., 1993; Kulig, 1987). In addition, in laboratory animals, it can be difficult to
distinguish cognitive changes from motor-related changes. However, several studies have
reported structural or functional changes in the hippocampus, such as decreased myelination
(Isaacson etal., 1990; Isaacson and Taylor, 1989) or decreased excitability of hippocampal CA1
neurons (Ohta et al., 2001), although the relationship of these effects to overall cognitive
function is not established.
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Two studies of TCE exposure, one chamber study of acute exposure duration and one
occupational study of chronic duration, reported changes in psychomotor responses. The
chamber study of Gamberale et al. (1976) reported a dose-related decrease in performance in a
CRT test in healthy volunteers exposed to 100 and 200 ppm TCE for 70 minutes as compared to
the same subjects without exposure. Rasmussen et al. (1993a) reported a statistically significant
association with cumulative exposure to TCE or CFC113 and dyscoordination trend among
Danish degreasers. Observations in a third study (Gunetal., 1978) are difficult to judge given
the author's lack of statistical treatment of data. In addition, Gash et al. (2008) reported that
14 out of 30 TCE-exposed workers exhibited significantly slower fine motor hand movements as
measured through a movement analysis panel test. Studies of populations living in communities
with TCE and other solvents detected in groundwater supplies reported significant delays in
SRTs and CRTs in individuals exposed to TCE in contaminated groundwater as compared to
referent groups (Kilburn, 2002b, a; Kilburn and Thornton, 1996; Kilburn and Warshaw, 1993a).
Observations in these studies are more uncertain given questions of the representativeness of the
referent population, lack of exposure assessment to individual study subjects, and inability to
control for possible confounders including alcohol consumption and motivation. Finally, in a
presentation of two case reports, decrements in motor skills as measured by the grooved
pegboard and finger tapping tests were observed (Troster and Ruff, 1990).
Laboratory animal studies of acute or subchronic exposure to TCE observed psychomotor
effects, such as loss of righting reflex (Shih et al., 2001; Umezu et al., 1997) and decrements in
activity, sensory-motor function, and neuromuscular function (2003; Moser et al., 1995; Kishi et
al., 1993). However, two studies also noted an absence of significant changes in some measures
of psychomotor function (Albee et al., 2006; Kulig, 1987). In addition, less consistent results
have been reported with respect to locomotor activity in rodents. Some studies have reported
increased locomotor activity after an acute i.p. dosage (Wolff and Siegmund, 1978) or decreased
activity after acute or short-term gavage dosing (2003; Moser et al., 1995). No change in activity
was observed following exposure through drinking water (Waseem et al., 2001), inhalation
(Kulig, 1987), or orally during the neurodevelopment period (Fredriksson et al., 1993).
Several neurochemical and molecular changes have been reported in laboratory
investigations of TCE toxicity. Kjellstrand et al. (1987) reported inhibition of sciatic nerve
regeneration in mice and rats exposed continuously to 150 ppm TCE via inhalation for 24 days.
Two studies have reported changes in GAB Aergic and glutamatergic neurons in terms of GAB A
or glutamate uptake (Briving et al., 1986) or response to GABAergic antagonistic drugs (Shih et
al., 2001) as a result of TCE exposure, with the Briving et al. (1986) conducted at 50 ppm for
12 months. Although the functional consequences of these changes is unclear, Tham et al.
(1984; 1979) described central vestibular system impairments as a result of TCE exposure that
may be related to altered GABAergic function. In addition, several in vitro studies have
demonstrated that TCE exposure alters the function of inhibitory ion channels such as receptors
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for GABAA glycine, and serotonin (Lopreato et al., 2003; Beckstead et al., 2000; Krasowski and
Harrison, 2000) or of voltage-sensitive calcium channels (Shafer et al., 2005).
4.11.1.2. Kidney Toxicity
There are few human data pertaining to TCE-related noncancer kidney toxicity.
Observation of elevated excretion of urinary proteins in the available studies (Bolt et al., 2004;
Green et al.. 2004: Bruning et al., 1999a: Bruning et al., 1999b: Rasmussen et al.. 1993b)
indicates the occurrence of a toxic insult among TCE-exposed subjects compared to unexposed
controls. Two studies are of subjects with previously diagnosed kidney cancer (Bolt et al., 2004;
Bruning et al., 1999a), while subjects in the other studies are disease free. Urinary proteins are
considered nonspecific markers of nephrotoxicity and include al-microglobulin, albumin, and
NAG (Lybarger et al., 1999; 1999; Price et al., 1996). Four studies measure al-microglobulin
with elevated excretion observed in the German studies (Bolt et al., 2004; Bruning et al., 1999a:
Bruning et al., 1999b) but not Green et al. (2004). However, Rasmussen et al. (1993b) reported a
positive relationship between increasing urinary NAG, another nonspecific marker of tubular
toxicity, and increasing exposure duration; and Green et al. (2004) found statistically significant
group mean differences in NAG. Observations in Green et al. (2004) provide evidence of
tubular damage among workers exposed to TCE at current occupational levels. Elevated
excretion of NAG has also been observed with acute TCE poisoning (Carrieri et al., 2007).
Some support for TCE nephrotoxicity in humans is provided by a study of ESRD in a cohort of
workers at Hill Air Force Base (Radican et al., 2006), although subjects in this study were
exposed to hydrocarbons, JP-4 gasoline, and solvents in addition to TCE, including 1,1,1-tri-
chloroethane, and a second reporting a twofold elevated risk for progression of
glomerulonephritis to ESRD with TCE exposure (Jacob et al., 2007).
Laboratory animal and in vitro data provide additional support for TCE nephrotoxicity.
Multiple studies with both gavage and inhalation exposure show that TCE causes renal toxicity
in the form of cytomegaly and karyomegaly of the renal tubules in male and female rats and
mice (summarized in Section 4.4.4). Further studies with TCE metabolites have demonstrated a
potential role for DCVC, TCOH, and TCA in TCE-induced nephrotoxicity. Of these, available
data suggest that DCVC-induced renal effects most like those of TCE and is formed in sufficient
amounts following TCE exposure to account for these effects. TCE or DCVC have also been
shown to be cytotoxic to primary cultures of rat and human renal tubular cells (Cummings and
Lash. 2000: Cummings et al.. 2000a: Cummings et al.. 2000c).
Overall, multiple lines of evidence support the conclusion that TCE causes nephrotoxicity
in the form of tubular toxicity, mediated predominantly through the TCE GSH conjugation
product DCVC.
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4.11.1.3. Liver Toxicity
Few studies on liver toxicity and TCE exposure are found in humans. Of these, three
studies reported significant changes in serum liver function tests, widely used in clinical settings
in part to identify patients with liver disease, in metal degreasers whose TCE exposure was
assessed using urinary trichloro-compounds as a biomarker (Xu et al., 2009; Nagayaet al., 1993;
Rasmussen et al., 1993b). Two additional studies reported plasma or serum bile acid changes
(Neghab etal., 1997; Driscoll et al., 1992). One study of subjects from the TCE subregistry of
ATSDR's National Exposure Registry is suggestive of liver disorders but limitations preclude
inferences whether TCE caused these conditions is not possible given the study's limitations
(Davis et al., 2005). Furthermore, a number of case reports exist of liver toxicity including
hepatitis accompanying immune-related generalized skin diseases described as a variation of
erythema multiforme, Stevens-Johnson syndrome, toxic epidermal necrolysis patients, and
hypersensitivity syndrome (Kamijima et al., 2007) in addition to jaundice, hepatomegaly,
hepatosplenomegaly, and liver failure in TCE-exposed workers (Huang et al., 2002; Thiele et al.,
1982). Cohort studies have examined cirrhosis mortality and either TCE exposure (Radican et
al.. 2008: Boice et al.. 2006b: ATSDR, 2004a: Boiceetal.. 1999: Ritz. 1999a: 1998: Morgan et
al.. 1998: Blair etal.. 1989: Garabrant et al.. 1988) or solvent exposure (Leigh and Jiang. 1993).
but are greatly limited by their use of death certificates where there is a high degree (up to 50%)
of underreporting (Blake etal., 1988), so these null findings do not rule out an effect of TCE on
cirrhosis. Overall, while some evidence exists of liver toxicity as assessed from liver function
tests, the data are inadequate for making conclusions regarding causality.
In laboratory animals, TCE exposure is associated with a wide array of hepatotoxic
endpoints. Like humans, laboratory animals exposed to TCE have been observed to have
increased serum bile acids (Neghab etal., 1997: Bai etal., 1992b), although the toxicological
importance of this effect is unclear. Most other effects in laboratory animals have not been
studied in humans, but nonetheless provide evidence that TCE exposure leads to hepatotoxicity.
These effects include increased liver weight, small transient increases in DNA synthesis,
cytomegaly in the form of —swoHn" or enlarged hepatocytes, increased nuclear size probably
reflecting polyploidization, and proliferation of peroxisomes. Liver weight increases
proportional to TCE dose are consistently reported across numerous studies and appear to be
accompanied by periportal hepatocellular hypertrophy (Laughter et al., 2004: Nunes et al., 2001:
Nakaiima et al., 2000: Tao et al., 2000: Berman et al., 1995: Dees and Travis, 1993: Goel et al.,
1992: Merrick et al., 1989: Goldsworthv and Popp, 1987: Melnick et al., 1987: Buben and
O'Flahertv, 1985: Elcombe et al., 1985: Kj ell strand etal., 1983a: Kjellstrand et al., 1983b:
Tucker etal., 1982: Kjellstrand etal., 1981b). There is also evidence of increased DNA
synthesis in a small portion of hepatocytes at around 10 days in vivo exposure (Channel et al.,
1998: Dees and Travis, 1993: Mirsalis et al., 1989: Elcombe et al., 1985). The lack of
correlation of hepatocellular mitotic figures with whole-liver DNA synthesis or DNA synthesis
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observed in individual hepatocytes (Dees and Travis, 1993; Elcombe et al., 1985) supports the
conclusions that cellular proliferation is not the predominant cause of increased DNA synthesis
and that nonparenchymal cells may also contribute to such synthesis. Indeed, nonparenchymal
cell activation or proliferation has been noted in several studies (Goel et al., 1992; Kjellstrand et
al., 1983a). Moreover, the histological descriptions of TCE-exposed livers are consistent with
and, in some cases, specifically note increased polyploidy (Buben and O'Flaherty, 1985).
Interestingly, changes in TCE-induced hepatocellular ploidy, as indicated by histological
changes in nuclei, have been noted to remain after the cessation of exposure (Kjellstrand et al.,
1983a). In regard to apoptosis, TCE has been reported either to have no effect or to cause a
slight increase at high doses (Channel et al., 1998; Dees and Travis, 1993). Some studies have
also noted effects from dosing vehicle alone (such as corn oil, in particular) not only on liver
pathology, but also on DNA synthesis (Channel et al., 1998; Merrick et al., 1989). Available
data also suggest that TCE does not induce substantial cytotoxicity, necrosis, or regenerative
hyperplasia, as only isolated, focal necroses and mild to moderate changes in serum and liver
enzyme toxicity markers have been reported (Channel etal., 1998; Dees and Travis, 1993;
Elcombe et al., 1985). Data on peroxisome proliferation, along with increases in a number of
associated biochemical markers, show effects in both mice and rats (Channel etal., 1998;
Goldsworthy andPopp, 1987; Elcombe etal., 1985). These effects are consistently observed
across rodent species and strains, although the degree of response at a given mg/kg/day dose
appears to be highly variability across strains, with mice on average appearing to be more
sensitive.
While it is likely that oxidative metabolism is necessary for TCE-induced effects in the
liver, the specific metabolite or metabolites responsible is less clear. TCE, TCA, and DCA
exposures have all been associated with induction of changes in liver weight, DNA synthesis,
and peroxisomal enzymes. The available data strongly support TCA not being the sole or
predominant active moiety for TCE-induced liver effects, particularly with respect to
hepatomegaly. In particular, TCE and TCA dose-response relationships are quantitatively
inconsistent, for TCE leads to greater increases in liver/body weight ratios that expected from
predicted rates of TCA production (see analysis in Section 4.5.6.2.1). In fact, above a certain
dose of TCE, liver/body weight ratios are greater than that observed under any conditions studied
so far for TCA. Histological changes and effects on DNA synthesis are generally consistent with
contributions from either TCA or DCA, with a degree of polyploidization, rather than cell
proliferation, likely to be significant for TCE, TCA, and DCA.
Overall, TCE, likely through its oxidative metabolites, clearly leads to liver toxicity in
laboratory animals, with mice appearing to be more sensitive than other laboratory animal
species, but there is only limited epidemiologic evidence of hepatotoxicity being associated with
TCE exposure.
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4.11.1.4. Immunotoxicity
Studies in humans provide evidence of associations between TCE exposure and a number
of immunotoxicological endpoints. The relation between systemic autoimmune diseases, such as
scleroderma, and occupational exposure to TCE has been reported in several recent studies. A
meta-analysis of scleroderma studies (Garabrant et al., 2003; Diot et al., 2002; Nietert et al.,
1998) conducted by the EPA resulted in a statistically significant combined OR for any exposure
in men (OR: 2.5, 95% CI: 1.1, 5.4), with a lower RR seen in women (OR: 1.2, 95% CI: 0.58,
2.6). The incidence of systemic sclerosis among men is very low (approximately 1 per 100,000
per year), and is approximately 10 times lower than the rate seen in women (Cooper and
Stroehla, 2003). Thus, the human data at this time do not allow determination of whether the
difference in effect estimates between men and women reflects the relatively low background
risk of scleroderma in men, gender-related differences in exposure prevalence or in the reliability
of exposure assessment (Messing et al., 2003), a gender-related difference in susceptibility to the
effects of TCE, or chance. Changes in levels of inflammatory cytokines were reported in an
occupational study of degreasers exposed to TCE (lavicoli et al., 2005) and a study of infants
exposed to TCE via indoor air (2002; Lehmann et al., 2001).
Experimental studies provide additional support for these effects. Numerous studies have
demonstrated accelerated autoimmune responses in autoimmune-prone mice (Cai et al., 2008;
Blossom et al.. 2007: Blossom et al.. 2004: Griffin et al.. 2000a: Griffin et al.. 2000b). With
shorter exposure periods, effects include changes in cytokine levels similar to those reported in
human studies. More severe effects, including autoimmune hepatitis, inflammatory skin lesions,
and alopecia, were manifest at longer exposure periods, and interestingly, these effects differ
somewhat from the —norral" expression in these mice. Immunotoxic effects, including increases
in anti-dsDNA antibodies in adult animals, decreased thymus weights, and decreased PFC
response with prenatal and neonatal exposure, have been also reported in B6C3Fi mice, which
do not have a known particular susceptibility to autoimmune disease (Keil et al., 2009; Peden-
Adams et al., 2006; Gilkeson et al., 2004). Recent mechanistic studies have focused on the roles
of various measures of oxidative stress in the induction of these effects by TCE (Wang et al.,
2008: Wang et al., 2007b).
There have been a large number of case reports of a severe hypersensitivity skin disorder,
distinct from contact dermatitis and often accompanied by hepatitis, associated with occupational
exposure to TCE, with prevalences as high as 13% of workers in the same location (2008;
Kamijima et al., 2007). Evidence of a treatment-related increase in delayed hypersensitivity
response accompanied by hepatic damage has been observed in guinea pigs following
intradermal injection (Tang et al., 2008; Tang et al., 2002), and hypersensitivity response was
also seen in mice exposed via drinking water pre- and postnatally (GD 0 through to 8 weeks of
age) (Peden-Adams et al., 2006).
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Human data pertaining to TCE-related immunosuppression resulting in an increased risk
of infectious diseases is limited to the report of an association between reported history of
bacteria of viral infections in Woburn, Massachusetts (Lagakos et al., 1986). Evidence of
localized immunosuppression, as measured by pulmonary response to bacterial challenge (i.e.,
risk of Streptococcal pneumonia-related mortality and clearance ofKlebsiella bacteria) was seen
in an acute exposure study in CD-I mice (Aranyi et al., 1986). A 4-week inhalation exposure in
Sprague-Dawley rats reported a decrease in PFC response at exposures of 1,000 ppm (Woolhiser
et al.. 2006).
Overall, the human and animal studies of TCE and immune-related effects provide strong
evidence for a role of TCE in autoimmune disease and in a specific type of generalized
hypersensitivity syndrome, while there are less data pertaining to immunosuppressive effects.
4.11.1.5. Respiratory Tract Toxicity
There are very limited human data on pulmonary toxicity and TCE exposure. Two recent
reports of a study of gun manufacturing workers reported asthma-related symptoms and lung
function decrements associated with solvent exposure (Saygun et al., 2007; Cakmak et al., 2004),
but these studies are limited by multiple solvent exposures and the significant effect of smoking
on pulmonary function. Laboratory studies in mice and rats have shown toxicity in the bronchial
epithelium, primarily in Clara cells, following acute exposures to TCE by inhalation (see
Section 4.7.2.1.1). A few studies of longer duration have reported more generalized toxicity,
such as pulmonary fibrosis 90 days after a single 2,000 mg/kg i.p. dose in mice and pulmonary
vasculitis after 13-week gavage exposures to 2,000 mg/kg-day in rats (Forkert and Forkert, 1994;
NTP, 1990). However, respiratory tract effects were not reported in other longer-term studies.
Acute pulmonary toxicity appears to be dependent on oxidative metabolism, although the
particular active moiety is not known. While earlier studies implicated chloral produced in situ
by CYP enzymes in respiratory tract tissue was responsible for toxicity (reviewed in Green,
2000), the evidence is inconsistent, and several other possibilities are viable. First, substantial
—acumulation" of chloral is unlikely, as it is likely either to be rapidly converted to TCOH in
respiratory tract tissue or to diffuse rapidly into blood and be converted to TCOH in erythrocytes
or the liver. Conversely, a role for systemically produced oxidative metabolites cannot be
discounted, as CH and TCOH in blood have both been reported following inhalation dosing in
mice. In addition, a recent study reported DCAC protein adducts in the lungs of mice to which
TCE was administered by i.p. injection, suggesting DCAC, which is not believed to be derived
from chloral, may also contribute to TCE respiratory toxicity. Although humans appear to have
lower overall capacity for enzymatic oxidation in the lung relative to mice, CYP enzymes do
reside in human respiratory tract tissue, suggesting that, qualitatively, the respiratory tract
toxicity observed in rodents is biologically plausible in humans. However, quantitative estimates
of differential sensitivity across species due to respiratory metabolism are highly uncertain due to
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limited data. Therefore, overall, data are suggestive of TCE causing respiratory tract toxicity,
based primarily on short-term studies in mice and rats, and no data suggest that such hazards
would be biologically precluded in humans.
4.11.1.6. Reproductive Toxicity
Reproductive toxicity related to TCE exposure has been evaluated in human and
experimental animal studies for effects in males and females. Only a limited number of studies
have examined whether TCE causes female reproductive toxicity. Epidemiologic studies have
identified possible associations of TCE exposure with effects on female fertility (ATSDR, 2001;
Sallmen et al., 1995) and with menstrual cycle disturbances (ATSDR, 2001; Sagawaetal., 1973;
Zielinski, 1973; Bardodej and Vyskocil, 1956). Reduced in vitro oocyte fertilizability has been
reported as a result of TCE exposure in rats (Wu and Berger, 2007; Berger and Horner, 2003),
but a number of other laboratory animal studies did not report adverse effects on female
reproductive function (Cosby and Dukelow, 1992; George et al., 1986; George et al., 1985;
Manson et al., 1984). Overall, there are inadequate data to conclude whether adverse effects on
human female reproduction are caused by TCE.
By contrast, a number of human and laboratory animal studies suggest that TCE exposure
has the potential for male reproductive toxicity. In particular, human studies have reported TCE
exposure to be associated, in several cases statistically-significantly, with increased sperm
density and decreased sperm quality (Chia et al., 1996; Rasmussen et al., 1988), altered sexual
drive or function (Saihan et al., 1978; El Ghawabi et al., 1973; Bardodej and Vyskocil, 1956), or
altered serum endocrine levels (Goh et al., 1998; Chia et al., 1997). In addition, three studies
that reported measures of fertility did not or could not report changes associated with TCE
exposure (Forkertetal., 2003: AT SDR, 2001: Sallmen et al., 1998), although the statistical
power of these studies is quite limited. Further evidence of similar effects is provided by several
laboratory animal studies that reported effects on sperm (Kumar et al., 200lb: Veeramachaneni
etal., 2001: Kumar et al., 2000a: Kumar et al., 2000b: George etal., 1985: Landetal., 1981),
libido/copulatory behavior (Veeramachaneni et al., 2001: George et al., 1986: Zenick et al.,
1984), and serum hormone levels (Veeramachaneni et al., 2001: Kumar et al., 2000a). As with
the human database, some studies that assessed sperm measures did not report treatment-related
alterations (Xu et al., 2004: Cosby and Dukelow, 1992: George etal., 1986: Zenick etal., 1984).
Additional adverse effects on male reproduction have also been reported, including
histopathological lesions in the testes or epididymides (Kan et al., 2007: Forkert et al., 2002:
Kumar etal., 200 lb: Kumar et al., 2000b: George etal., 1986) and altered in vitro sperm-oocyte
binding or in vivo fertilization due to TCE or metabolites (DuTeaux et al., 2004a: Xu et al.,
2004). While reduced fertility in rodents was only observed in one study (George et al., 1986),
this is not surprising given the redundancy and efficiency of rodent reproductive capabilities.
Furthermore, while George et al. (1986) proposed that the adverse male reproductive outcomes
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observed in rats were due to systemic toxicity, the database as a whole suggests that TCE does
induce reproductive toxicity independent of systemic effects. Therefore, overall, the human and
laboratory animal data together support the conclusion that TCE exposure poses a potential
hazard to the male reproductive system.
4.11.1.7. Developmental Toxicity
The relationship between TCE exposure (direct or parental) and adverse developmental
outcomes has been investigated in a number of epidemiologic and laboratory animal studies.
Prenatal effects examined include death (spontaneous abortion, perinatal death, pre- or
postimplantation loss, resorptions), decreased growth (low birth weight, SGA, IUGR, decreased
postnatal growth), and congenital malformations, in particular eye and cardiac defects. Postnatal
developmental outcomes examined include growth and survival, developmental neurotoxicity,
developmental immunotoxicity, and childhood cancers.
A few epidemiological studies have reported associations between parental exposure to
TCE and spontaneous abortion or perinatal death (ATSDR, 2001; Taskinen et al., 1994;
Windham et al., 1991), although other studies reported mixed or null findings (ATSDR, 2008b,
2006a: Bove, 1996: Boveetal.. 1995: Goldberg et al.. 1990: Lindbohm et al.. 1990: Taskinen et
al., 1989: Lagakos et al., 1986). Studies examining associations between TCE exposure and
decreased birth weight or SGA have reported small, often nonstatistically significant, increases
in risk for these effects (ATSDR. 2008b, 2006a: Windham et al.. 1991). However, other studies
observed mixed or no association (Rodenbeck et al., 2000: Bove, 1996: Boveetal., 1995:
Lagakos et al., 1986). While comprising both occupational and environmental exposures, these
studies are overall not highly informative due to their small numbers of cases and limited
exposure characterization or to the fact that exposures to mixed solvents were involved.
However, a number of laboratory animal studies show analogous effects of TCE exposure in
rodents. In particular, pre- or postimplantation losses, increased resorptions, perinatal death, and
decreased birth weight have been reported in multiple well-conducted studies in rats and mice
(Kumar et al.. 2000b: Narotsky andKavlock, 1995: Narotsky et al.. 1995: George etal.. 1986:
George et al., 1985: Healy etal., 1982). Interestingly, the rat studies reporting these effects used
F344 or Wistar rats, while several other studies, all of which used Sprague-Dawley rats, reported
no increased risk in these developmental measures (Carney et al., 2006: Hardin et al., 1981:
Schwetz etal., 1975). Overall, based on weakly suggestive epidemiologic data and fairly
consistent laboratory animal data, it can be concluded that TCE exposure poses a potential
hazard for prenatal losses and decreased growth or birth weight of offspring.
Epidemiologic data provide some support for the possible relationship between maternal
TCE exposure and birth defects in offspring, in particular cardiac defects. Other developmental
outcomes observed in epidemiology and experimental animal studies include an increase in total
birth defects (ATSDR. 2001: Flood 19881 CNS defects (ATSDR. 2001: Bove. 1996: Bove et
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al.. 1995: Lagakos et al.. 1986). oral cleft defects (Lorente et al.. 2000: Bove, 1996: Bove et al..
1995: Lagakos et al., 1986), eye/ear defects (Narotsky and Kavlock, 1995: Narotsky et al., 1995:
Lagakos et al., 1986), kidney/urinary tract disorders (Lagakos et al., 1986), musculoskeletal birth
anomalies (Lagakos et al., 1986), lung/respiratory tract disorders (Das and Scott, 1994: Lagakos
et al., 1986), and skeletal defects (Healy etal., 1982). Occupational cohort studies, while not
consistently reporting positive results, are generally limited by the small number of observed or
expected cases of birth defects (Lorente et al., 2000: Taskinen et al., 1989: Tola etal., 1980).
While only one of the epidemiological studies specifically reported observations of eye
anomalies (Lagakos et al., 1986), studies in rats have identified increases in the incidence of fetal
eye defects following oral exposures during the period of organogenesis with TCE (Narotsky and
Kavlock, 1995: Narotsky et al., 1995) or its oxidative metabolites, DCA and TCA (Warren et al.,
2006: Smith et al., 1992: Smith et al., 1989). No other developmental or reproductive toxicity
studies identified abnormalities of eye development following TCE exposures, which may have
been related to the administered dose or other aspects of study design (e.g., level of detail applied
to fetal ocular evaluation). Overall, the study evidence suggests a potential for the disruption of
ocular development by exposure to TCE and its oxidative metabolites.
The epidemiological studies, while individually limited, as a whole show relatively
consistent elevations, some of which were statistically significant, in the incidence of cardiac
effects in TCE-exposed populations compared to reference groups (ATSDR, 2008b, 2006a:
Yauck et al., 2004: ATSDR, 2001: Bove, 1996: Bove etal., 1995: Goldberg et al., 1990).
Interestingly, Goldberg et al. (1990) noted that the OR for congenital heart disease in offspring
declined from threefold to no difference as compared to controls after TCE-contaminated
drinking water wells were closed, suggestive of a causal relationship. However, this study
reported no significant differences in cardiac lesions between exposed and nonexposed groups
(Goldberg et al., 1990). One additional community study reported that, among the five cases of
cardiovascular anomalies, there was no significant association with TCE (Lagakos et al., 1986),
but due to the small number of cases, this does not support an absence of effect. In laboratory
animal models, avian studies were the first to identify adverse effects of TCE exposure on
cardiac development, and the initial findings have been confirmed multiple times (Rufer et al.,
2008: Drake et al., 2006a: Drake et al., 2006b: Mishima et al., 2006: Bover et al., 2000: Loeber
etal., 1988: Brossetal., 1983). Additionally, administration of TCE and TCE metabolites TCA
and DCA in maternal drinking water during gestation has been reported to induce cardiac
malformations in rat fetuses (Johnson et al., 2005, 2003: Johnson et al., 1998b: Johnson et al.,
1998a: Dawsonetal., 1993: Epstein et al., 1992: Smith etal., 1992: Dawsonetal., 1990: Smith
et al., 1989). However, it is notable that a number of other studies, several of which were well
conducted, did not report induction of cardiac defects in rats or rabbits from TCE administered
by inhalation (Carney et al., 2006: Healvetal., 1982: Hardinetal., 1981: Dorfmueller et al..
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1979; Schwetz et al., 1975) or in rats and mice by gavage (Fisher et al., 2001; Narotsky and
Kavlock, 1995: Narotsky et al.. 1995: Cosby and Dukelow, 1992).
The potential importance of these effects warrants a more detailed discussion of possible
explanations for the apparent inconsistencies in the laboratory animal studies. Many of the
studies that did not identify cardiac anomalies used a traditional free-hand section technique on
fixed fetal specimens (Healy et al., 1982: Hardin et al., 1981: Dorfmueller et al., 1979: Schwetz
et al., 1975). Detection of such anomalies can be enhanced through the use of a fresh dissection
technique as described by Staples (1974) and Stuckhardt and Poppe (1984) and this was the
technique used in the study by Dawson et al. (1990) with further refinement of the technique
used in the positive studies by Dawson et al. (1993) and Johnson et al. (2005, 2003). However,
two studies that used the same or similar fresh dissection technique did not report cardiac
anomalies (Carney et al., 2006: Fisher et al., 2001), although it has been suggested that
differences in experimental design (e.g., inhalation versus gavage versus drinking water route of
administration, exposure during organogenesis versus the entire gestational period, or varied
dissection or evaluation procedures) may have been contributing factors to the differences in
observed response. A number of other limitations in the studies by Dawson et al. (1993) and
Johnson et al. (2005, 2003) have been suggested (Watson et al., 2006: Hardin et al., 2005). One
concern is the lack of clear dose-response relationship for the incidence of any specific cardiac
anomaly or combination of anomalies, a disparity for which no reasonable explanation has been
put forth. In addition, analyses on a fetal- rather than litter-basis and the pooling of data
collected over an extended period, including nonconcurrent controls, have been criticized. With
respect to the first issue, the study authors provided individual litter incidence data to EPA for
analysis (see Chapter 5, Dose-Response Assessment), and, in response to the second issue, the
study authors provided further explanation as to their experimental procedures (Johnson et al.,
2004). In sum, while the studies by Dawson et al. (1993) and Johnson et al. (2005, 2003) have
significant limitations, there is insufficient reason to dismiss their findings.
Finally, mechanistic studies, particularly based on the avian studies mentioned above,
provide additional support for TCE-induced fetal cardiac malformation, particularly with respect
to defects involving septal and valvular morphogenesis. As summarized by NRC (2006), there is
substantial concordance in the stages and events of cardiac valve formation between mammals
and birds. While quantitative extrapolation of findings from avian studies to humans is not
possible without appropriate kinetic data for these experimental systems, the treatment-related
alterations in endothelial cushion development observed in avian in ovo and in vitro studies
(Mishima et al., 2006: Ou et al., 2003: Boyer et al., 2000) provide a plausible mechanistic basis
for defects in septal and valvular morphogenesis observed in rodents, and consequently support
the plausibility of cardiac defects induced by TCE in humans.
Postnatal developmental outcomes examined after TCE prenatal and/or postnatal
exposure in both humans and experimental animals include developmental neurotoxicity,
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developmental immunotoxicity, and childhood cancer. Effects on the developing nervous
system included a broad array of structural and behavioral alterations in humans (Windham et
al.. 2006: Laslo-Baker et al.. 2004: ATSDR. 2002: Till etal.. 2001a: Burg and Gist 1997: White
etal.. 1997. abstract: Burg etal.. 1995: Bernad et al.. 1987: Beppu. 1968) and animals (Blossom
et al.. 2008: Narotsky and Kavlock. 1995: Fredriksson et al.. 1993: Isaacson and Taylor. 1989:
George etal.. 1986: Noland-Gerbec et al.. 1986: Taylor etal.. 1985: Westergren et al.. 1984).
Adverse immunological findings in humans following developmental exposures to TCE were
reported by Lehmann et al. (2002) and Byers et al. (1988). In mice, alterations in T-cell
subpopulations, spleen and/or thymic cellularity, cytokine production, autoantibody levels (in an
autoimmune-prone mouse strain), and/or hypersensitivity response were observed after
exposures during development (Blossom et al., 2008: Peden-Adams et al., 2008: Blossom and
Doss, 2007: Peden-Adams et al., 2006). Childhood cancers included leukemia and NHL (Costas
et al.. 2002: Morgan and Cassadv. 2002: Shu etal.. 1999: Flood. 1997a: MDPH. 1997a: Cohn et
al.. 1994b: McKinney et al.. 1991: APRS. 1990: Kioski etal.. 1990a: Kioski etal.. 1990b:
Flood. 1988: Lowengart et al.. 1987: Cutler etal.. 1986: Lagakos et al.. 1986). CNS tumors
(Morgan and Cassadv. 2002: DeRoos etal.. 2001: Flood. 1997a: APRS. 1990: Kioski et al..
1990a: Flood. 1988: Peters etal.. 1985: Peters etal.. 1981). and total cancers (AT SDR, 2006a;
Flood. 1997a: Porter. 1993: APRS. 1990: Flood. 1988). These outcomes are discussed in the
other relevant sections for neurotoxicity, immunotoxicity, and carcinogenesis.
4.11.2. Characterization of Carcinogenicity
Following EPA (2005b) Guidelines for Carcinogen Risk Assessment, TCE is
characterized as -earcinogenic to humans" by all routes of exposure. This conclusion is based on
convincing evidence of a causal association between TCE exposure in humans and kidney
cancer. The kidney cancer association cannot be reasonably attributed to chance, bias, or
confounding. The human evidence of carcinogenicity from epidemiologic studies of TCE
exposure is strong for NHL but less convincing than for kidney cancer, and more limited for
liver and biliary tract cancer. In addition to the body of evidence pertaining to kidney cancer,
NHL, and liver cancer, the available epidemiologic studies also provide more limited evidence of
an association between TCE exposure and other types of cancer, including bladder, esophageal,
prostate, cervical, breast, and childhood leukemia. Differences between these sets of data and
the data for kidney cancer, NHL, and liver cancer are observations from fewer numbers of
studies, a mixed pattern of observed risk estimates, and the general absence of exposure-response
data from the studies using a quantitative TCE-specific exposure measure.
There are several lines of supporting evidence for TCE carcinogenicity in humans. First,
TCE induces multiple types of cancer in rodents given TCE by gavage and inhalation, including
cancers in the same target tissues identified in the epidemiologic studies - kidney, liver, and
lymphoid tissues. Second, toxicokinetic data indicate that TCE absorption, distribution,
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metabolism, and excretion are qualitatively similar in humans and rodents. Finally, there is
sufficient weight of evidence to conclude that a mutagenic mode of action is operative for TCE-
induced kidney tumors, and this mode of action is clearly relevant to humans. Modes of action
have not been established for other TCE-induced cancers in rodents, and no mechanistic data
indicate that any hypothesized key events are biologically precluded in humans.
4.11.2.1. Summary Evaluation of Epidemiologic Evidence of TCE and Cancer
The available epidemiologic studies provide convincing evidence of a causal association
between TCE exposure and cancer. The strongest epidemiologic evidence consists of reported
increased risks of kidney cancer, with more limited evidence for NHL and liver cancer, in
several well-designed cohort and case-control studies (discussed below). The summary
evaluation below of the evidence for causality is based on guidelines adapted from Hill (1965)
by EPA (2005b), and focuses on evidence related to kidney cancer, NHL, and liver cancer.
4.11.2.1.1. (a) Consistency of observed association
Elevated risks for kidney cancer have been observed across many independent studies.
Twenty-four studies in which there was a high likelihood of TCE exposure in individual study
subjects (e.g., based on JEMs or biomarker monitoring) and which were judged to have met, to a
sufficient degree, the standards of epidemiologic design and analysis were identified in a
systematic review of the epidemiologic literature. Of the 15 of these 24 studies reporting risks of
kidney cancer (Moore et al.. 2010: Radican et al.. 2008: Charbotel et al.. 2006: Zhao et al.. 2005:
Bruning et al., 2003: Raaschou-Nielsen et al.. 2003: Hansen et al.. 2001: Pesch et al.. 2000b:
Boiceetal.. 1999: Dosemeci etal.. 1999: Morgan etal.. 1998: Anttila et al.. 1995: Axel son et al..
1994: Greenland et al., 1994: Siemiatycki, 1991), most estimated RRs between 1.1 and 1.9 for
overall exposure to TCE. Six of these 15 studies reported statistically significant increased risks
either for overall exposure to TCE (Moore etal., 2010: Bruning et al., 2003: Raaschou-Nielsen et
al., 2003: Dosemeci et al., 1999) or for one of the highest TCE exposure groups (Moore et al.,
2010: Charbotel et al., 2006: Zhao et al., 2005: Raaschou-Nielsen et al., 2003). Thirteen other
cohort, case-control, and geographic-based studies were given less weight because of their lesser
likelihood of TCE exposure and other study design limitations that would decrease statistical
power and study sensitivity (see Sections 4.1 and 4.4.2).
The consistency of the association between TCE exposure and kidney cancer is further
supported by the results of the meta-analyses of the 15 cohort and case-control studies of
sufficient quality and with high probability of TCE exposure to individual subjects. These
analyses observed a statistically significant increased RRm estimate for kidney cancer of 1.27
(95% CI: 1.13, 1.43) for overall TCE. The RRms were robust and did not change appreciably
with the removal of any individual study or with the use of alternate RR estimates from
individual studies. In addition, there was no evidence for heterogeneity or publication bias.
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The consistency of increased kidney cancer RR estimates across a large number of
independent studies of different designs and populations from different countries and industries
argues against chance, bias or confounding as the basis for observed associations. This
consistency thus provides substantial support for a causal effect between kidney cancer and TCE
exposure.
Some evidence of consistency is found between TCE exposure and NHL and liver
cancer. In a weight-of-evidence review of the NHL studies, 17 studies in which there was a high
likelihood of TCE exposure in individual study subjects (e.g., based on JEMs or biomarker
monitoring) and which met, to a sufficient degree, the standards of epidemiologic design and
analysis were identified. These studies generally reported excess RR estimates for NHL between
0.8 and 3.1 for overall TCE exposure. Statistically significant elevated RR estimates for overall
exposure were observed in two cohort studies (Raaschou-Nielsen et al., 2003; Hansen et al.,
2001) and one case-control study (Hardell etal., 1994). The other 14 identified studies reported
elevated RR estimates with overall TCE exposure that were not statistically significant (Purdue
etal.. 2011: Cocco et al.. 2010: Wang et al.. 2009: Radican et al.. 2008: Miligi et al.. 2006: Zhao
et al., 2005: Boiceetal., 1999: Persson and Fredrikson, 1999: Morgan et al., 1998: Nordstrom et
al.. 1998: Anttila et al.. 1995: Axel son et al.. 1994: Greenland et al.. 1994: Siemiatvcki, 1991).
Fifteen additional studies were given less weight because of their lesser likelihood of TCE
exposure and other design limitations that would decrease study power and sensitivity (Sinks et
al., 1992)(see Sections 4.1 and 4.6.1.2). The observed lack of association with NHL in these
studies likely reflects study design and exposure assessment limitations and is not considered
inconsistent with the overall evidence on TCE and NHL.
Consistency of the association between TCE exposure and NHL is further supported by
the results of meta-analyses. These meta-analyses found a statistically significant increased
RRm estimate for NHL of 1.23 (95% CI: 1.07, 1.42) for overall TCE exposure. This result and
its statistical significance were not overly influenced by most individual studies. Some
heterogeneity was observed across the 17 studies of overall exposure, although it was not
statistically significant (p = 0.16). Analyzing the cohort and case-control studies separately
resolved most of the heterogeneity, but the result for the summary case-control studies was only
about a 7% increased RR estimate and was not statistically significant. The sources of
heterogeneity are uncertain but may be the result of some bias associated with exposure
assessment and/or disease classification, or from differences between cohort and case-control
studies in average TCE exposure. In addition, there is some evidence of potential publication
bias in this data set; however, it is uncertain that this is actually publication bias rather than an
association between SE and effect size resulting for some other reason (e.g., a difference in study
populations or protocols in the smaller studies). Furthermore, if there is publication bias in this
data set, it does not appear to account completely for the finding of an increased NHL risk.
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There are fewer studies on liver cancer than for kidney cancer and NHL. Of nine studies,
all of them cohort studies, in which there was a high likelihood of TCE exposure in individual
study subjects (e.g., based on JEMs or biomarker monitoring) and which met, to a sufficient
degree, the standards of epidemiologic design and analysis in a systematic review (Radican et al.,
2008: Boice et al.. 2006b: Raaschou-Nielsen et al.. 2003: Hansen et al.. 2001: Boiceetal.. 1999:
Morgan et al.. 1998: Anttila et al.. 1995: Axel son et al.. 1994: Greenland et al.. 1994). most
reported RR estimates for liver and gallbladder cancer between 0.5 and 2.0 for overall exposure
to TCE. RR estimates were generally based on small numbers of cases or deaths, with the result
of wide CIs on the estimates, except for one study (Raaschou-Nielsen et al., 2003). This study
reported almost 6 times more cancer cases than the next largest study and observed a statistically
significant elevated liver and gallbladder cancer risk with overall TCE exposure (RR =1.35
[95% CI: 1.03, 1.77]). Ten additional studies were given less weight because of their lesser
likelihood of TCE exposure and other design limitations that would decrease statistical power
and study sensitivity (see Sections 4.1 and 4.5.2).
Consistency of the association between TCE exposure and liver cancer is further
supported by the results of meta-analyses. These meta-analyses found a statistically significant
increased RRm estimate for liver and biliary tract cancer of 1.29 (95% CI: 1.07, 1. 56) with
overall TCE exposure. Although there was no evidence of heterogeneity or publication bias and
the summary estimate was fairly insensitive to the use of alternative RR estimates, the statistical
significance of the summary estimate depends heavily on the one large study by Raaschou-
Nielsen et al. (2003). However, there were fewer adequate studies available for meta-analysis of
liver cancer (9 vs. 17 for NHL and 15 for kidney), leading to lower statistical power, even with
pooling. Moreover, liver cancer is comparatively rarer, with age-adjusted incidences roughly
half or less those for kidney cancer or NHL; thus, fewer liver cancer cases are generally observed
in individual cohort studies.
4.11.2.1.2. (b) Strength of the observed association
In general, the observed associations between TCE exposure and cancer are modest, with
RRs or ORs for overall TCE exposure generally <2.0 and higher RRs or ORs for high exposure
categories. Among the highest statistically significant RRs were those reported for kidney
cancer in the studies by Henschler et al. (1995) (7.97 [95% CI: 2.59, 8.59]) and Vamvakas et al.
(1998) (10.80 [95% CI: 3.36, 34.75]). As discussed in Section 4.4.2.2.1, risk magnitude in both
studies is highly uncertain due, in part, to possible selection biases, and neither was included in
the meta-analyses. However, the findings of these studies were corroborated, though with lower
reported RRs, by later studies, which overcame many of their deficiencies, such as B riming et al.
(2003) (2.47 [95% CI: 1.36, 4.49]), Charbotel et al. (2006) (2.16 [95% CI: 1.02, 4.60] for the
high cumulative exposure group), and Moore et al. (2010) (2.05 [95% CI: 1.13, 3.73] for high
confidence assessment of TCE). In addition, the very high apparent exposure in the subjects of
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Henschler et al. (1995) and Vamvakas et al. (1998) may have contributed to their reported RRs
being higher than those in other studies. Exposures in most population case-control studies are
of lower overall TCE intensity compared to exposures in B riming et al. (2003) and Charbotel et
al. (2006), and, as would be expected, observed RR estimates are lower (1.24 [95% CI: 1.03,
1.49]), Pesch et al. (2000b): 1.30 [95% CI: 0.9, 1.9], Dosemeci et al. (1999)). A few high-quality
cohort and case-control studies reported statistically significant RRs of approximately 2.0 with
highest exposure, including Zhao et al. (2005) (4.9 [95% CI: 1.23, 19.6] for high TCE score),
Raaschou-Nielsen et al. (2003) (1.7 [95% CI: 1.1, 2.4] for >5 year exposure duration, subcohort
with higher exposure]), Charbotel et al. (2006) (2.16 [95% CI: 1.02, 4.60] for high cumulative
exposure and 2.73 [95% CI: 1.06, 7.07] for high cumulative exposure plus peaks) and Moore et
al. (2010) (2.23 [95% CI: 1.07, 4.64] for high cumulative exposure and 2.41 [95% CI: 1.05, 5.56]
for high average intensity TCE exposure).
Among the highest statistically significant RRs reported for NHL were those of Hansen
et al. (2001) (3.1 [95% CI: 1.3, 6.1]), Hardell et al. (1994) (7.2 [95% CI: 1.3, 42]), the latter a
case-control study whose magnitude of risk is uncertain because of self-reported occupational
TCE exposure. A similar magnitude of risk was reported in Purdue et al. (2011) for highest
exposure (3.3 [95% CI: 1.1, 10.1], >234,000 ppm-hour, and 7.9 [95% CI: 1.8, 34.3], >360 ppm-
hour/week). Observed RR estimates for liver cancer and overall TCE exposure are generally
more modest.
The strength of association between TCE exposure and cancer is modest with overall
TCE exposure. Large RR estimates are considered strong evidence of causality; however, a
modest risk does not preclude a causal association and may reflect a lower level of exposure, an
agent of lower potency, or a common disease with a high background level (U.S. EPA, 2005b).
Modest RR estimates have been observed with several well-established human carcinogens such
as benzene and secondhand smoke. Chance cannot explain the observed association between
TCE and cancer; statistically significant associations were found in a number of the studies that
contribute greater weight to the overall evidence, given their design and statistical analysis
approaches. In addition, other known or suspected risk factors cannot fully explain the observed
elevations in kidney cancer RRs. All kidney cancer case-control studies included adjustment for
possible confounding effects of smoking, and some studies included BMI, hypertension, and co-
exposure to other occupational agents such as cutting or petroleum oils. Cutting and petroleum
oils, known as metalworking fluids, have not been associated with kidney cancer (Mirer, 2010;
NIOSH, 1998), and potential confounding by this occupational co-exposure is unable to explain
the observed association with TCE. Additionally, the associations between kidney cancer and
TCE exposure remained in these studies after statistical adjustment for possible known and
suspected confounders. Charbotel et al. (2006) observed a nonstatistically significantly kidney
cancer risk with exposure to TCE adjusted for cutting or petroleum oil exposures (1.96 [95% CI:
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0.71, 5.37] for the high-cumulative exposure group and 2.63 [95% CI: 0.79, 8,83] for high-
exposure group with peaks).
All kidney cancer case-control studies adjusted for smoking except the Moore et al.
(2010) study, which reported that smoking did not significantly change the overall association
with TCE exposure. Although direct examination of smoking and other suspected kidney cancer
risk factors is usually not possible in cohort studies, confounding is less likely in Zhao et al.
(2005), given their use of an internal referent group and adjustment for SES status, an indirect
surrogate for smoking, and other occupational exposures. In addition, the magnitude of the lung
cancer risk in Raaschou-Nielsen et al. (2003) suggests that a high smoking rate is unlikely and
cannot explain their finding on kidney cancer. Last, a meta-analysis of the nine cohort studies
that reported kidney cancer risks found an RRm estimate for lung cancer of 0.96 (95% CI: 0.76,
1.21) for overall TCE exposure and 0.96 (95% CI: 0.72, 1.27) for the highest exposure group.
These observations suggest that confounding by smoking is not an alternative explanation for the
kidney cancer meta-analysis results.
Few risk factors are recognized for NHL, with the exception of viruses and suspected
factors such as immunosuppression or smoking, which are associated with specific NHL
subtypes. Associations between NHL and TCE exposure are based on groupings of several NHL
subtypes. Three of the seven NHL case-control studies adjusted for age, sex, and smoking in
statistical analyses (Wang et al., 2009; Miligi et al., 2006) two others adjusted for age, sex, and
education (Purdue et al., 2011; Cocco et al., 2010), and the other three case-control studies
adjusted for age only or age and sex (Persson and Fredrikson, 1999; Nordstrom et al., 1998;
Hardell et al., 1994). Like for kidney cancer, direct examination of possible confounding in
cohort studies is not possible. The use of internal controls in some of the cohort studies is
intended to reduce possible confounding related to lifestyle differences, including smoking
habits, between exposed and referent subjects.
Heavy alcohol use and viral hepatitis are established risk factors for liver cancer, with
severe obesity and diabetes characterized as a metabolic syndrome associated with liver cancer.
Only cohort studies for liver cancer are available, and they were not able to consider these
possible risk factors.
4.11.2.1.3. (c) Specificity of the observed association
Specificity is generally not as relevant as other aspects for judging causality. As stated in
the EPA Guidelines for Carcinogen Risk Assessment (2005b), based on our current
understanding that many agents cause cancer at multiple sites and that cancers have multiple
causes, the absence of specificity does not detract from evidence for a causal effect. Evidence
for specificity could be provided by a biological marker in tumors that was specific to TCE
exposure. There is some evidence suggesting that particular VHL mutations in kidney tumors
may be caused by TCE, but uncertainties in these data preclude a definitive conclusion.
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4.11.2.1.4. (d) Temporal relationship of the observed association
Each cohort study was evaluated for the adequacy of the follow-up period to account for
the latency of cancer development. The studies with the greatest weight based on study design
characteristics (e.g., those used in the meta-analysis) all had adequate follow-up to assess
associations between TCE exposure and cancer. Therefore, the findings of those studies are
consistent with a temporal relationship.
4.11.2.1.5. (e) Biological gradient (exposure-response relationship)
Exposure-response relationships are examined in the TCE epidemiologic studies only to a
limited extent. Many studies examined only overall "exposed" vs. "unexposed" groups and did
not provide exposure information by level of exposure. Others do not have adequate exposure
assessments to confidently distinguish between levels of exposure. For example, many studies
used duration of employment as an exposure surrogate; however, this is a poor exposure metric
given subjects may have differing exposure intensity with similar exposure duration (NRC,
2006).
Three studies of kidney cancer reported a statistically significant trend of increasing risk
with increasing TCE exposure, Zhao et al. (2005) (p = 0.023 for trend with TCE score),
Charbotel et al. (2006) (p = 0.04 for trend with cumulative TCE exposure) and Moore et al.
(2010) (p = 0.02 for trend with cumulative TCE exposure). Charbotel et al. (2006) was
specifically designed to examine TCE exposure and had a high-quality exposure assessment, and
the Moore et al. (2010) exposure assessment considered detailed information on jobs using
solvents. Zhao et al. (2005) also had a relatively well-designed exposure assessment. A positive
trend was also observed in one other study (Raaschou-Nielsen et al. (2003), with employment
duration).
Biological gradient is further supported by meta-analyses for kidney cancer using only
the highest exposure groups and accounting for possible reporting bias, which yielded a higher
RRm estimate (1.58 [95% CI: 1.28, 1.96]) than for overall TCE exposure (1.27 [95% CI: 1.13,
1.43]). Although this analysis uses a subset of studies in the overall TCE exposure analysis, the
finding of higher risk in the highest exposure groups, where such groups were available, is
consistent with a trend of increased risk with increased exposure.
The NHL case-control study of Purdue et al. (2011) reported a statistically significant
trend with TCE exposure (p = 0.02 for trend with average-weekly TCE exposure), and NHL risk
in Boice et al. (1999) appeared to increase with increasing exposure duration (p = 0.20 for
routine-intermittent exposed subjects). The borderline trend with TCE intensity in the case-
control studies of Wang et al. (2009) (p = 0.06) and Purdue et al. (2011) (p = 0.08 for trend with
cumulative TCE exposure) is consistent with their findings for average weekly TCE exposure.
As with kidney cancer, further support was provided by meta-analyses using only the highest
exposure groups, which yielded a higher RRm estimate (1.43 [95% CI: 1.13, 1.82]) than for
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overall TCE exposure (1.23 [95% CI: 1.07, 1.42]). For liver cancer, the meta-analyses using
only the highest exposure groups yielded a lower, and nonstatistically significant, RRm estimate
(1.28 [95% CI: 0.93, 1.77]) than for overall TCE exposure (1.29 [95% CI: 1.07, 1.56]). There
were no case-control studies on liver cancer and TCE, and the cohort studies generally had few
liver cancer cases, making it more difficult to assess exposure-response relationships. The one
large study (Raaschou-Nielsen et al., 2003) used only duration of employment, which is an
inferior exposure metric.
4.11.2.1.6. (f) Biological plausibility
TCE metabolism is similar in humans, rats, and mice and results in reactive metabolites.
TCE is metabolized in multiple organs and metabolites are systemically distributed. Several
oxidative metabolites produced primarily in the liver, including CH, TCA and DC A, are rodent
hepatocarcinogens. Two other metabolites, DCVC and DCVG, which can be produced and
cleared by the kidney, have shown genotoxic activity, suggesting the potential for
carcinogenicity. Kidney cancer, NHL, and liver cancer have all been observed in rodent
bioassays (see below). The laboratory animal data for liver and kidney cancer are the most
robust, and are corroborated in multiple studies, sexes, and strains, although each has only been
reported in a single species and the incidences of kidney cancer are quite low. Lymphomas were
only reported to be statistically significantly elevated in a single study in mice, but one additional
mouse study reported elevated lymphoma incidence and one rat study reported elevated leukemia
incidence. In addition, there is some evidence both in humans and laboratory animals for kidney,
liver and immune system noncancer toxicity from TCE exposure. Several hypothesized modes
of action have been presented for the rodent tumor findings, and the available evidence does not
preclude the relevance of the hypothesized modes of action to humans. Activation of
macrophages, NK cells, and cytokine production (e.g., tumor necrosis factor) may also play an
etiologic role in carcinogenesis, and thus, the immune-related effects of TCE should also be
considered. In addition, the decreased in lymphocyte counts and subsets, including CD4+ T
cells, and decreased lymphocyte activation seen in TCE-exposed workers (Lan et al., 2010) also
support the biological plausibility of a role of TCE exposure in NHL.
4.11.2.1.7. (g) Coherence
Coherence is defined as consistency with the known biology. As discussed under
biological plausibility, the observance of kidney and liver cancer and NHL in humans is
consistent with the biological processing and toxicity of TCE.
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4.11.2.1.8. (h) Experimental evidence (from human populations)
Few experimental data from human populations are available on the relationship between
TCE exposure and cancer. The only study of a —natial experiment" (i.e., observations of a
temporal change in cancer incidence in relation to a specific event) notes that childhood
leukemia cases appeared to be more evenly distributed throughout Woburn, Massachusetts, after
closure of the two wells contaminated with TCE and other organic solvents (MDPH, 1997c).
4.11.2.1.9. (i) Analogy
Exposure to structurally related chlorinated solvents such as tetrachloroethylene and
dichloromethane have also been associated with kidney, lymphoid, and liver tumors in human,
although the evidence for TCE is considered stronger.
4.11.2.1.10. Conclusion
In conclusion, based on the weight-of-evidence analysis for kidney cancer and in
accordance with EPA guidelines, TCE is characterized as —arcinogenic to humans." This
hazard descriptor is used when there is convincing epidemiologic evidence of a causal
association between human exposure and cancer. Convincing evidence is found in the
consistency of the kidney cancer findings. The consistency of increased kidney cancer relative
risk estimates across a large number of independent studies of different designs and populations
from different countries and industries provides compelling evidence given the difficulty, a
priori, in detecting effects in epidemiologic studies when the relative risks are modest, the
cancers are relatively rare, and therefore, individual studies have limited statistical power. This
strong consistency argues against chance, bias, and confounding as explanations for the elevated
kidney cancer risks. In addition, statistically significant exposure-response trends are observed
in high-quality studies. These studies were designed to examine kidney cancer in populations
with high TCE exposure intensity. These studies addressed important potential confounders and
biases, further supporting the observed associations with kidney cancer as causal. In a meta-
analysis of the 15 studies that met the inclusion criteria, a statistically significant RRm estimate
was observed for overall TCE exposure (RRm: 1.27 [95% CI: 1.13, 1.43]). The RRm estimate
was greater for the highest TCE exposure groups (RRm: 1.58 [95% CI: 1.28, 1.96]; n = 13
studies). Meta-analyses investigating the influence of individual studies and the sensitivity of the
results to alternate risk estimate selections found the RRm estimates to be highly robust.
Furthermore, there was no indication of publication bias or significant heterogeneity. It would
require a substantial amount of negative data from informative studies (i.e., studies having a high
likelihood of TCE exposure in individual study subjects and which meet, to a sufficient degree,
the standards of epidemiologic design and analysis in a systematic review) to contradict this
observed association.
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The evidence is less convincing for NHL and liver cancer. While the evidence is strong
for NHL, issues of (nonstatistically significant) study heterogeneity, potential publication bias,
and weaker exposure-response results contribute greater uncertainty. The evidence is more
limited for liver cancer mainly because only cohort studies are available and most of these
studies have small numbers of cases. In addition to the body of evidence described above
pertaining to kidney cancer, NHL, and liver cancer, the available epidemiologic studies also
provide suggestive evidence of an association between TCE exposure and other types of cancer,
including bladder, esophageal, prostate, cervical, breast, and childhood leukemia, breast.
Differences between these sets of data and the data for kidney cancer, NHL, and liver cancer are
observations are from fewer numbers of studies, a mixed pattern of observed risk estimates and
the general absence of exposure-response data from the studies using a quantitative TCE-specific
cumulative exposure measure.
4.11.2.2. Summary of Evidence for TCE Carcinogenicity in Rodents
Additional evidence of TCE carcinogenicity consists of increased incidences of tumors
reported in multiple chronic bioassays in rats and mice. In total, this database identifies some of
the same target tissues of TCE carcinogenicity also seen in epidemiological studies, including the
kidney, liver, and lymphoid tissues.
Of particular note is the site-concordant finding of TCE-induced kidney cancer in rats. In
particular, low, but biologically and sometimes statistically significant, increases in the incidence
of kidney tumors were observed in multiple strains of rats treated with TCE by either inhalation
or corn oil gavage (NTP. 1990: Maltoni et al.. 1988: NTP, 1988: Maltoni et al.. 1986). For
instance, Maltoni et al. (1986) reported that although only 4/130 renal adenocarcinomas were
noted in rats in the highest dose group, these tumors had never been observed in over 50,000
Sprague-Dawley rats (untreated, vehicle-treated, or treated with different chemicals) examined in
previous experiments in the same laboratory. In addition, the gavage study by NCI (1976) and
two inhalation studies by Henschler et al. (1980), and Fukuda et al. (1983) each observed one
renal adenoma or adenocarcinoma in some dose groups and none in controls. The largest (but
still small) incidences were observed in treated male rats, only in the highest dose groups.
However, given the small numbers, an effect in females cannot be ruled out. Several studies in
rats were limited by excessive toxicity, accidental deaths, or deficiencies in reporting (NTP,
1990, 1988: NCI, 1976). Individually, therefore, these studies provide only suggestive evidence
of renal carcinogenicity. Overall, given the rarity of these types of tumors in the rat strains tested
and the repeated similar results across experiments and strains, these studies taken together
support the conclusion that TCE is a kidney carcinogen in rats, with males being more sensitive
than females. No other tested laboratory species (i.e., mice and hamsters) have exhibited
increased kidney tumors, although high incidences of kidney toxicity have been reported in mice
(NTP, 1990: Maltoni et al., 1988: Maltoni et al., 1986: NCI, 1976). The GSH-conjugation-
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derived metabolites suspected of mediating TCE-induced kidney carcinogenesis have not been
tested in a standard 2-year bioassay, so their role cannot be confirmed definitively. However, it
is clear that GSH conjugation of TCE occurs in humans and that the human kidney contains the
appropriate enzymes for bioactivation of GSH conjugates. Therefore, the production of the
active metabolites thought to be responsible for kidney tumor induction in rats likely occurs in
humans.
Statistically significant increases in TCE-induced liver tumors have been reported in
multiple inhalation and gavage studies with male Swiss mice and B6C3Fi mice of both sexes
(Bull et al.. 2002: Annaetal.. 1994: NTP, 1990: Maltoni et al.. 1988: Herren-Freund et al.. 1987:
Maltoni etal.. 1986: NCI, 1976). On the other hand, in female Swiss mice, Fukuda et al. (1983)
(CD-I [ICR, Swiss-derived] mice) and Maltoni et al. (1988: 1986) both reported small,
nonsignificant increases at the highest dose by inhalation. Henschler et al. (1984: 1980) reported
no increases in either sex of Han:NMRI (also Swiss-derived) mice exposed by inhalation and
ICR/HA (Swiss) mice exposed by gavage. However, the inhalation study (Henschler et al.,
1980) had only 30 mice per dose group and the gavage study (Henschler et al., 1984) had dosing
interrupted due to toxicity. Studies in rats (NTP, 1990: Maltoni et al.. 1988: NTP, 1988: Maltoni
etal.. 1986: Henschler et al.. 1980: NCL 1976) and hamsters (Henschler et al.. 1980) did not
report statistically significant increases in liver tumor induction with TCE treatment. However,
several studies in rats were limited by excessive toxicity or accidental deaths (NTP, 1990, 1988:
NCI, 1976), and the study in hamsters only had 30 animals per dose group. These data are
inadequate for concluding that TCE lacks hepatocarcinogenicity in rats and hamsters, but are
indicative of a lower potency in these species. Moreover, it is notable that a few studies in rats
reported low incidences (too few for statistical significance) of very rare biliary- or endothelial-
derived tumors in the livers of some treated animals (Maltoni etal., 1988: Maltoni etal., 1986:
Fukuda et al., 1983: Henschler et al., 1980). Further evidence for the hepatocarcinogenicity of
TCE is derived from chronic bioassays of the TCE oxidative metabolites CH, TCA, and DCA in
mice (e.g., DeAngelo et al., 2008: Leakey et al., 2003a: Leakey et al., 2003b: George et al., 2000:
DeAngelo et al., 1999: DeAngelo et al., 1996: Bull etal., 1990), all of which reported
hepatocarcinogenicity. Very limited testing of these TCE metabolites has been done in rats, with
a single experiment reported in both Richmond et al. (1995) and DeAngelo et al. (1996) finding
statistically significant DCA-induced hepatocarcinogenicity. With respect to TCA, DeAngelo et
al. (1997), often cited as demonstrating lack of hepatocarcinogenicity in rats, actually reported
elevated adenoma multiplicity and carcinoma incidence from TCA treatment. However,
statistically, the role of chance could not be confidently excluded because of the low number of
animals per dose group (20-24 per treatment group at final sacrifice). Overall, TCE and its
oxidative metabolites are clearly carcinogenic in mice, with males more sensitive than females
and the B6C3Fi strain appearing to be more sensitive than the Swiss strain. Such strain and sex
differences are not unexpected, as they appear to parallel, qualitatively, differences in
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background tumor incidence. Data in other laboratory animal species are limited. Thus, except
for DCA, which is carcinogenic in rats, inadequate evidence exists to evaluate the
hepatocarcinogenicity of these compounds in rats or hamsters. However, to the extent that there
is hepatocarcinogenic potential in rats, TCE is clearly less potent in the strains tested in this
species than in B6C3Fi and Swiss mice.
Additionally, there is more limited evidence for TCE-induced lymphohematopoietic
cancers in rats and mice, lung tumors in mice, and testicular tumors in rats. With respect to
lymphomas, Henschler et al. (1980) reported statistically significant increases in lymphomas in
female Han:NMRI mice treated via inhalation. While Henschler et al. (1980) suggested that
these lymphomas were of viral origin specific to this strain, subsequent studies reported
increased lymphomas in female B6C3Fi mice treated via corn oil gavage (NTP, 1990) and
leukemias in male Sprague-Dawley and female August rats (Maltoni et al., 1988; NTP, 1988;
Maltoni etal., 1986). However, these tumors had relatively modest increases in incidence with
treatment, and were not reported to be increased in other studies. With respect to lung tumors,
rodent bioassays have demonstrated a statistically significant increase in pulmonary tumors in
mice following chronic inhalation exposure to TCE (Maltoni et al., 1988; Maltoni et al., 1986;
Fukudaetal., 1983). Pulmonary tumors were not reported in other species tested (i.e., rats and
hamsters; (Maltoni etal.. 1988: Maltoni et al.. 1986: Fukudaetal.. 1983: Henschler et al..
1980)). Chronic oral exposure to TCE led to a nonstatistically significant increase in pulmonary
tumors in mice but, again, not in rats or hamsters (NTP. 1990: Maltoni et al.. 1988: NTP. 1988:
Maltoni et al.. 1986: Henschler et al.. 1984: Van Duuren et al.. 1979: NCL 1976). A lower
response via oral exposure would be consistent with a role of respiratory metabolism in
pulmonary carcinogenicity. Finally, increased testicular (interstitial cell and Leydig cell) tumors
have been observed in rats exposed by inhalation and gavage (NTP, 1990, 1988: Maltoni et al.,
1986). Statistically significant increases were reported in Sprague-Dawley rats exposed via
inhalation (Maltoni etal., 1988: Maltoni et al., 1986) and Marshall rats exposed via gavage
(NTP, 1988). In three rat strains, ACT, August, and F344/N, a high (>75%) control rate of
testicular tumors was observed, limiting the ability to detect a treatment effect (NTP, 1990,
1988).
In summary, there is clear evidence for TCE carcinogenicity in rats and mice, with
multiple studies showing TCE to cause different kinds of cancers. The apparent lack of site
concordance across laboratory animal species may be due to limitations in design or conduct in a
number of rat bioassays and/or genuine interspecies differences in sensitivity. Nonetheless, these
studies have shown carcinogenic effects across different strains, sexes, and routes of exposure,
and site-concordance is not necessarily expected for carcinogens. Of greater import is the
finding that there is site-concordance between the main cancers observed in TCE-exposed
humans and those observed in rodent studies—in particular, cancers of the kidney, liver, and
lymphoid tissues.
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4.11.2.3. Summary of Additional Evidence on Biological Plausibility
Additional evidence from toxicokinetic, toxicity, and mechanistic studies supports the
biological plausibility of TCE carcinogenicity in humans.
4.11.2.3.1. Toxicokinetics
As described in Chapter 3, there is no evidence of major qualitative differences across
species in TCE absorption, distribution, metabolism, and excretion. In particular, available
evidence is consistent with TCE being readily absorbed via oral, dermal, and inhalation
exposures, and rapidly distributed to tissues via systemic circulation. Extensive in vivo and in
vitro data show that mice, rats, and humans all metabolize TCE via two primary pathways:
oxidation by CYPs and conjugation with GSH via GSTs. Several metabolites and excretion
products from both pathways, including TCA, DCA, TCOH, TCOG, NAcDCVC, and DCVG,
have been detected in blood and urine from exposed humans was well as from at least one rodent
species. In addition, the subsequent distribution, metabolism, and excretion of TCE metabolites
are qualitatively similar among species. Therefore, humans possess the metabolic pathways that
produce the TCE metabolites thought to be involved in the induction of rat kidney and mouse
liver tumors, and internal target tissues of both humans and rodents experience a similar mix of
TCE and metabolites.
As addressed in further detail elsewhere (see Chapters 3 and 5), examples of quantitative
interspecies differences in toxicokinetics include differences in partition coefficients, metabolic
capacity and affinity in various tissues, and plasma binding of the metabolite TCA. These and
other differences are addressed through PBPK modeling, which also incorporates physiological
differences among species (see Section 3.5), and are accounted for in the PBPK model-based
dose-response analyses (see Chapter 5). Importantly, these quantitative differences affect only
interspecies extrapolations of carcinogenic potency, and do not affect inferences as to the
carcinogenic hazard for TCE. In addition, available data on toxicokinetic differences do not
appear sufficient to explain interspecies differences in target sites of TCE carcinogenicity
(discussed further in Chapter 5: Dose-Response Assessment).
4.11.2.3.2. Toxicity and mode of action
Many different modes of action have been proposed for TCE-induced carcinogenesis.
With respect to genotoxicity, although it appears unlikely that TCE, as a pure compound, causes
point mutations, there is evidence for TCE genotoxicity with respect to other genetic endpoints,
such as micronucleus formation (see Section 4.2.1.4.4). In addition, as discussed further below,
several TCE metabolites have tested positive in genotoxicity assays. The mode-of-action
conclusions for specific target organs in laboratory animals are summarized below. Only in the
case of the kidney is it concluded that the data are sufficient to support a particular mode of
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action being operative. However, the available evidence do not indicate that qualitative
differences between humans and test animals would preclude any of the hypothesized key events
in rodents from occurring in humans.
For the kidney, the predominance of positive genotoxicity data in the database of
available studies of TCE metabolites derived from GSH conjugation (in particular DCVC, see
Section 4.2.5), together with toxicokinetic data consistent with their systemic delivery to and in
situ formation in the kidney, supports the conclusion that a mutagenic mode of action is
operative in TCE-induced kidney tumors (see Section 4.4.7.1). Relevant data include
demonstration of genotoxicity in available in vitro assays of GSH conjugation metabolites and
reported kidney-specific genotoxicity after in vivo administration of TCE or DCVC.
Mutagenicity is a well-established cause of carcinogenicity. While supporting the biological
plausibility of this hypothesized mode of action, available data on the VHL gene in humans or
transgenic animals do not conclusively elucidate the role of VHL mutation in TCE-induced renal
carcinogenesis. Cytotoxicity and compensatory cell proliferation, also presumed to be mediated
through metabolites formed after GSH-conjugation of TCE, have also been suggested to play a
role in the mode of action for renal carcinogenesis, as high incidences of nephrotoxicity have
been observed in animals at doses that also induce kidney tumors. Human studies have reported
markers for nephrotoxicity at current occupational exposures, although data are lacking at lower
exposures. Toxicity is observed in both mice and rats, in some cases with nearly 100% incidence
in all dose groups, but kidney tumors are only observed at low incidences in rats at the highest
tested doses. Therefore, nephrotoxicity alone appears to be insufficient, or at least not rate-
limiting, for rodent renal carcinogenesis, since maximal levels of toxicity are reached before the
onset of tumors. In addition, nephrotoxicity has not been shown to be necessary for kidney
tumor induction by TCE in rodents. In particular, there is a lack of experimental support for
causal links, such as compensatory cellular proliferation or clonal expansion of initiated cells,
between nephrotoxicity and kidney tumors induced by TCE. Furthermore, it is not clear if
nephrotoxicity is one of several key events in a mode of action, if it is a marker for an
—upsfeam" key event (such as oxidative stress) that may contribute independently to both
nephrotoxicity and renal carcinogenesis, or if it is incidental to kidney tumor induction.
Therefore, although the data are consistent with the hypothesis that cytotoxicity and regenerative
proliferation contribute to TCE-induced kidney tumors, the weight of evidence is not as strong as
the support for a mutagenic mode of action. Moreover, while toxicokinetic differences in the
GSH conjugation pathway, along with their uncertainty, are addressed through PBPK modeling,
no data suggest that any of the proposed key events for TCE-induced kidney tumors rats are
precluded in humans. Therefore, TCE-induced rat kidney tumors provide additional support for
the convincing human evidence of TCE-induced kidney cancer, with mechanistic data supportive
of a mutagenic mode of action.
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The strongest data supporting the hypothesis of a mutagenic mode of action in either the
lung or the liver are those demonstrating the genotoxicity of CH (see Section 4.2.4), which is
produced in these target organs as a result of oxidative metabolism of TCE. It has been
suggested that CH mutagenicity is unlikely to be the cause of TCE hepatocarcinogenicity
because the concentrations required to elicit these responses are several orders of magnitude
higher that achieved in vivo (Moore and Harrington-Brock, 2000). However, it is not clear how
much of a correspondence is to be expected from concentrations in genotoxicity assays in vitro
and concentrations in vivo, as reported in vivo CH concentrations are in whole liver homogenate
while in vitro concentrations are in culture media. The use of i.p. administration, which leads to
an inflammatory response, in many other in vivo genotoxicity assays in the liver and lung
complicates the comparison with carcinogenicity data. Also, it is difficult with the available data
to assess the contributions from genotoxic effects of CH along with those from the genotoxic and
nongenotoxic effects of other oxidative metabolites (e.g., DCA and TCA). Therefore, while data
are insufficient to conclude that a mutagenic mode of action mediated by CH is operant, a
mutagenic mode of action in the liver or lung, either mediated by CH or by some other oxidative
metabolite of TCE, cannot be ruled out.
A second mode-of-action hypothesis for TCE-induced liver tumors involves activation of
the PPARa receptor. Clearly, in vivo administration of TCE leads to activation of PPARa in
rodents and likely does so in humans as well (based on in vitro data for TCE and its oxidative
metabolites). However, the evidence as a whole does not support the view that PPARa is the
sole operant mode of action mediating TCE hepatocarcinogenesis. Although metabolites of TCE
activate PPARa, the data on the subsequent elements in the hypothesized mode of action (e.g.,
gene regulation, cell proliferation, apoptosis, and selective clonal expansion), while limited,
indicate significant differences between PPARa agonists such as Wy-14643 and TCE or its
metabolites. For example, compared with other agonists, TCE induces transient as opposed to
persistent increases in DNA synthesis; increases (or is without effect on), as opposed to
decreases, apoptosis; and induces a different H-ras mutation frequency or spectrum. These data
support the view that mechanisms other than PPARa activation may contribute to these effects;
besides PPARa activation, the other hypothesized key events are nonspecific, and available data
(e.g., using knockout mice) do not indicate that they are solely or predominantly dependent on
PPARa. A second consideration is whether certain TCE metabolites (e.g., TCA) that activate
PPARa are the sole contributors to its carcinogenicity. As summarized above (see Section
4.11.1.3), TCA is not the only metabolite contributing to the observed noncancer effects of TCE
in the liver. Other data also suggest that multiple metabolites may also contribute to the hepatic
carcinogenicity of TCE. Liver phenotype experiments, particularly those utilizing
immunostaining for c-Jun, support a role for both DCA and TCA in TCE-induced tumors, with
strong evidence that TCA cannot solely account for the characteristics of TCE-induced tumors
(e.g.. Bull et al., 2002). In addition, H-ras mutation frequency and spectrum of TCE-induced
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tumors more closely resembles that of spontaneous tumors or of those induced by DC A, and
were less similar in comparison to that of TCA-induced tumors. The heterogeneity of
TCE-induced tumors is similar to that observed to be induced by a diversity carcinogens
including those that do not activate PPARa, and to that observed in human liver cancer. Taken
together, the available data indicate that, rather than being solely dependent on a single
metabolite (TCA) and/or molecular target (PPARa), multiple TCE metabolites and multiple
toxicity pathways contribute to TCE-induced liver tumors.
Other considerations as well as new data published since the NRC (2006) review are also
pertinent to the liver tumor mode of action conclusions. It is generally acknowledged that,
qualitatively, there are no data to support the conclusion that effects mediated by the PPARa
receptor that contribute to hepatocarcinogenesis would be biologically precluded in humans
(NRC, 2006; Klaunig et al., 2003). It has, on the other hand, been argued that due to quantitative
toxicokinetic and toxicodynamic differences, the hepatocarcinogenic effects of chemicals
activating this receptor are —unlik^T to occur in humans (NRC, 2006; Klaunig et al., 2003):
however, several lines of evidence strongly undermine the confidence in this assertion. With
respect to toxicokinetics, as discussed above, quantitative differences in oxidative metabolism
are accounted for in PBPK modeling of available in vivo data, and do not support interspecies
differences of a magnitude that would preclude hepatocarcinogenic effects based on
toxicokinetics alone. With respect to the mode of action proposed by Klaunig et al. (2003),
recent experiments have demonstrated that PPARa activation and the sequence of key events in
the hypothesized mode of action are not sufficient to induce hepatocarcinogenesis (Yang et al.,
2007). Moreover, the demonstration that the PPARa agonist DEHP induces tumors in PPARa-
null mice supports the view that the events comprising the hypothesized mode of action are not
necessary for liver tumor induction in mice by this PPARa agonist (Ito et al., 2007). Therefore,
several lines of evidence, including experiments published since the NRC (2006) review, call
into question the scientific validity of using the PPARa mode-of-action hypothesis as the basis
for evaluating the relevance to human carcinogenesis of rodent liver tumors (Guvton et al.,
2009).
In summary, available data support the conclusion that the mode of action for
TCE-induced liver tumors in laboratory animals is not known. However, a number of qualitative
similarities exist between observations in TCE-exposed mice and what is known about the
etiology and induction of human HCCs. Polyploidization, changes in glycogen storage,
inhibition of GST-zeta, and aberrant DNA methylation status, which have been observed in
studies of mice exposed to TCE or its oxidative metabolites, are all either clearly related to
human carcinogenesis or are areas of active research as to their potential roles (PPARa activation
is discussed below). The mechanisms by which TCE exposure may interact with known risk
factors for human HCCs are not known. However, available data do not suggest that TCE
exposure to mice results in liver tumors that are substantially different in terms of their
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phenotypic characteristics either from human HCCs or from rodent liver tumors induced by other
chemicals.
Comparing various other, albeit relatively nonspecific, tumor characteristics between
rodent species and humans provides additional support to the biologic plausibility of TCE
carcinogenicity. For example, in the kidney and the liver, the higher incidences of background
and TCE-induced tumors in male rats and mice, respectively, as compared to females parallels
the observed higher human incidences in males for these cancers (Ries et al., 2008). For the
liver, while there is a lower background incidence of liver tumors in humans than in rodents, in
the United States, there is an increasing occurrence of liver cancer associated with several
factors, including viral hepatitis, higher survival rates for cirrhosis, and possibly diabetes
(reviewed in El-Serag, 2007). In addition, Leakey et al. (2003a) reported that increased body
weight in B6C3Fi mice is strongly associated with increased background liver tumor incidences,
although the mechanistic basis for this risk factor in mice has not been established. Nonetheless,
it is interesting that recent epidemiologic studies have suggested obesity, in addition to
associated disorders such as diabetes and metabolic syndrome, as a risk factor for human liver
cancer (El-Serag, 2007; El-Serag and Rudolph, 2007). Furthermore, the phenotypic and
morphologic heterogeneity of tumors seen in the human liver is qualitatively similar to
descriptions of mouse liver tumors induced by TCE exposure, as well as those observed from
exposure to a variety of other chemical carcinogens. These parallels suggest similar pathways
(e.g., for cell signaling) of carcinogenesis may be active in mice and humans and support the
qualitative relevance of mouse models of liver to human liver cancer.
For mouse lung tumors, mode-of-action hypotheses have centered on TCE metabolites
produced via oxidative metabolism in situ. As discussed above, the hypothesis that the
mutagenicity of reactive intermediates or metabolites (e.g., CH) generated during CYP
metabolism contributes to lung tumors cannot be ruled out, although available data are
inadequate to conclusively support this mode of action. An alternative mode of action has been
posited involving other effects of such oxidative metabolites, particularly CH, including
cytotoxicity and regenerative cell proliferation. Experimental support for this alternative
hypothesis remains limited, with no data on proposed key events in experiments >2 weeks in
duration. While the data are inadequate to support this mode-of-action hypothesis, the data also
do not suggest that any proposed key events would be biologically plausible in humans.
Furthermore, the focus of the existing mode-of-action hypothesis involving cytotoxicity has been
CH, and, as summarized above (see Section 4.11.1.5), other metabolites may contribute to
respiratory tract noncancer toxicity or carcinogenicity. In sum, the mode of action for mouse
lung tumors induced by TCE is not known.
A mode of action subsequent to in situ oxidative metabolism, whether involving
mutagenicity, cytotoxicity, or other key events, may also be relevant to other tissues where TCE
would undergo CYP metabolism. For instance, CYP2E1, oxidative metabolites, and protein
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adducts have been reported in the testes of rats exposed to TCE, and, in some rat bioassays, TCE
exposure increased the incidence of rat testicular tumors. However, inadequate data exist to
adequately define a mode-of-action hypothesis for this tumor site.
4.11.3. Characterization of Factors Impacting Susceptibility
As discussed in more detail in Section 4.10, there is some evidence that certain
populations may be more susceptible to exposure to TCE. Factors affecting susceptibility
examined include lifestage, gender, genetic polymorphisms, race/ethnicity, preexisting health
status, and lifestyle factors and nutrition status.
Examination of early lifestages includes exposures such as transplacental transfer
(Ghantous et al.. 1986: Withev and Karpinski, 1985: Laham, 1970: Beppu, 1968: Helliwell and
Hutton, 1950) and breast milk ingestion (Fisher et al., 1997: Hamada and Tanaka, 1995: Fisher et
al., 1990: Pellizzari etal., 1982), early lifestage-specific toxicokinetics, PBPK models (Fisher et
al., 1990, 1989), and differential outcomes in early lifestages such as developmental cardiac
defects. Although there is more information on susceptibility to TCE during early lifestages than
on susceptibility during later lifestages or for other populations with potentially increased
susceptibility, there remain a number of uncertainties and data gaps regarding children's
susceptibility. Improved PBPK modeling for using childhood parameters for early lifestages as
recommended by the NRC (2006), and validation of these models will aid in determining how
variations in metabolic enzymes affect TCE metabolism. In particular, the NRC states that it is
prudent to assume children need greater protection than adults, unless sufficient data are
available to justify otherwise (NRC, 2006). Because the weight of evidence supports a
mutagenic mode of action for TCE carcinogenicity in the kidney (see Section 4.4.7), and there is
an absence of chemical-specific data to evaluate differences in carcinogenic susceptibility, early-
life susceptibility should be assumed and the ADAFs should be applied, in accordance with the
Supplemental Guidance (discussed further in Chapter 5).
Fewer data are available on later lifestages, although there is suggestive evidence to
indicate that older adults may experience increased adverse effects than younger adults (Mahle et
al., 2007: Rodriguez et al., 2007). In general, more studies specifically designed to evaluate
effects in early and later lifestages are needed in order to more fully characterize potential
lifestage-related TCE toxicity.
Examination of gender-specific susceptibility includes toxicokinetics, PBPK models
(Fisher et al., 1998), and differential outcomes. Gender differences observed are likely due to
variation in physiology and exposure.
Genetic variation likely has an effect on the toxicokinetics of TCE. In particular,
differences in CYP2E1 activity may affect susceptibility of TCE due to effects on production of
toxic metabolites (Yoon et al., 2007: Kim and Ghanayem, 2006: Povey etal., 2001: Lipscomb et
al., 1997). GST polymorphisms could also play a role in variability in toxic response
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(Wiesenhiitter et al., 2007; Briining et al., 1997a), as well as other genotypes, but these have not
been sufficiently tested. Differences in genetic polymorphisms related to the metabolism of TCE
have also been observed among various race/ethnic groups (Sato etal., 1991b: Inoue et al.,
1989).
Preexisting diminished health status may alter the response to TCE exposure. Individuals
with increased body mass may have an altered toxicokinetic response (Clewell et al., 2000; Lash
et al.. 2000a: McCarver et al.. 1998: Sato. 1993: Davidson and Bellies. 1991: Sato etal.. 1991b:
Monster et al., 1979a), resulting in changes the internal concentrations of TCE or in the
production of toxic metabolites. Other conditions, including diabetes and hypertension, are risk
factors for some of the same health effects that have been associated with TCE exposure, such as
RCC. However, the interaction between TCE and known risk factors for human diseases is not
known, and further evaluation of the effects due to these factors is needed.
Lifestyle and nutrition factors examined include alcohol consumption, tobacco smoking,
nutritional status, physical activity, and SES status. In particular, alcohol intake has been
associated with metabolic inhibition (altered CYP2E1 expression) of TCE in both humans and
experimental animals (McCarver et al., 1998: Kanekoet al., 1994: Sato, 1993: Nakajima et al.,
1992a: Okinoetal., 1991: Sato etal., 1991a: Nakaiima et al., 1990: Larson and Bull, 1989:
Nakajima et al., 1988: Sato and Nakajima, 1985: Barret etal., 1984: Sato etal., 1983, 1981:
White and Carlson, 198 la: Sato etal., 1980: Muller et al., 1975: Stewart et al., 1974a: Bardodei
and Vyskocil, 1956). In addition, such factors have been associated with increased baseline risks
for health effects associated with TCE, such as kidney cancer (e.g., smoking) and liver cancer
(e.g., alcohol consumption). However, the interaction between TCE and known risk factors for
human diseases is not known, and further evaluation of the effects due to these factors is needed.
In sum, there is some evidence that certain populations may be more susceptible to
exposure to TCE. Factors affecting susceptibility examined include lifestage, gender, genetic
polymorphisms, race/ethnicity, preexisting health status, and lifestyle factors and nutrition status.
However, except in the case of toxicokinetic variability characterized using the PBPK model
described in Section 3.5, there are inadequate chemical-specific data to quantify the degree of
differential susceptibility due to such factors.
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5. DOSE-RESPONSE ASSESSMENT
5.1. DOSE-RESPONSE ANALYSES FOR NONCANCER ENDPOINTS
Because of the large number of noncancer health effects associated with TCE exposure and
the large number of studies reporting on these effects, a screening process, described below, was
used to reduce the number of endpoints and studies to those that would best inform the selection
of the critical effects for the inhalation RfC and oral RfD.16 The screening process helped
identify the more sensitive endpoints for different types of effects within each health effect
domain (e.g., different target systems) and provided information on the exposure levels that
could contribute to the most sensitive effects, used for the RfC and RfD, as well as to additional
noncancer effects as exposure increases. These more sensitive endpoints were also used to
investigate the impacts of pharmacokinetic uncertainly and variability.
The general process used to derive the RfD and RfC was as follows (see Figure 5-1):
(1) Consider all studies described in Chapter 4 that reported adverse noncancer health effects
or markers for such effects and provide quantitative dose-response data17.
(2) Consider for each study/endpoint possible points of departure (PODs) on the basis of
applied dose, with the order of preference being first a BMD18 derived from empirical
modeling of the dose-response data, then aNOAEL, and lastly a LOAEL.
(3) Adjust each POD by endpoint/study-specific -tmcertainty factors" (UFs), accounting for
uncertainties and adjustments in the extrapolation from the study conditions to conditions
of human exposure, to derive candidate RfCs (cRfCs) or RfDs (cRfDs) intended to be
protective for each endpoint (individually) on the basis of applied dose.
(4) Array the cRfCs and cRfDs across the following health effect domains: (1) neurotoxic
effects; (2) systemic (body weight) and organ toxicity (kidney, liver) effects;
(3) immunotoxic effects; (4) reproductive effects; and (5) developmental effects.
(5) Select as candidate critical effects those endpoints with the lowest cRfCs or cRfDs for
each species (where appropriate), within each of these effect domains, taking into account
the confidence in each estimate. When there are alternative estimates available for a
particular endpoint, preference is given to studies whose design characteristics (e.g.,
species, statistical power, exposure level(s) and duration, endpoint measures) are better
suited for determining the most sensitive human health effects of chronic TCE exposure.
16In U.S. EPA noncancer health assessments, the RfC (RfD) is an estimate (with uncertainty spanning perhaps an
order of magnitude) of a continuous inhalation (daily oral) exposure to the human population (including sensitive
subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. It can be derived
from a NOAEL, LOAEL, or benchmark concentration (dose), with uncertainty factors generally applied to reflect
limitations of the data used.
"Adequate dose-response data comprise, at a minimum, one exposure group and an appropriate control group, from
which one can derive a LOAEL (or a NOAEL, if evidence of the effect is available from some other comparable
study).
18More precisely, it is the BMDL, i.e., the (one-sided) 95% lower confidence bound on the dose corresponding to the
benchmark response for the effect that is used as the POD.
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(6) For each candidate critical effect selected in step 5, use, to the extent possible, the PBPK
model developed in Section 3.5 to calculate an internal dose POD (idPOD) for plausible
internal dose-metrics that were selected on the basis of what is understood about the role
of different TCE metabolites in toxicity and the mode of action for toxicity. Effects
within the same health effect domain were generally assumed to have the same relevant
internal dose-metrics; thus, screening for the effects with the lowest cRfCs and cRfDs for
each species within health effect domains on the basis of applied dose should capture the
same endpoints which would have the lowest candidate reference values on the basis of
an appropriate dose-metric.
(7) For each idPOD for each candidate critical effect, use the PBPK model to estimate
interspecies and within-human pharmacokinetic variability (or just within-human
variability for human-based PODs). The results of this calculation are 99th percentile
estimates of the human equivalent concentration and human equivalent dose (FtECgg and
for each candidate critical effect.19
(8) Adjust each HECgg or HED99 by endpoint-/study-specific UFs (which, due to the use of
the PBPK model, may differ from the UFs used in step 3) to derive a PBPK model-based
candidate RfCs (p-cRfC) and RfD (p-cRfD) for each candidate critical effect.
(9) Characterize the uncertainties in the cRfCs, cRfDs, p-cRfCs, and p-cRfDs, with the
inclusion of quantitative uncertainty analyses of pharmacokinetic uncertainty and
variability as derived from the Bayesian population analysis using the PBPK model.
(10) Evaluate the most sensitive cRfCs, p-cRfCs, cRfDs, and p-cRfDs, taking into account the
confidence in the estimates, to arrive at an RfC and RfD for TCE. Except for candidate
critical effects for which the PBPK model could not be used, the candidate reference
values considered in the final selection process were those based on the most plausible
internal dose-metric on the basis of the metabolism and mode-of-action considerations
for each candidate critical effect.
19The choice of the 99th percentile is discussed in Section 5.1.3.2.
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^^
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2)
X"
candidate RfDs
(cRfDs)
[applied dose]
^ K^f
"^ Lowest \J
values within
each domain /
V
J Candidate — Cg^A PBPK-based
critical effects/ Apply PBPK \ candidate RfCs
^turiiP* model; / (p-cRfDs)&
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rRfPt; A rRfnt; / cancnaate KTUS,
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-------
the application of these approaches to the studies and endpoints for each health effect domain
follows in the next section (see Section 5.1.2).
Standard adjustments20 were made to the applied doses to obtain continuous inhalation
exposures and daily average oral doses over the study exposure period (see Appendix F for
details), except for effects for which there was sufficient evidence that the effect was more
closely associated with administered exposure level (e.g., changes in visual function). The PODs
based on applied dose in the following sections and in Appendix F are presented in terms of the
adjusted doses (except where noted).
As described above, wherever possible,21 BMD modeling was conducted to obtain
benchmark dose lower bounds (BMDLs) to serve as PODs for the cRfCs and cRfDs. Note that
not all quantitative dose-response data are amenable to BMD modeling. For example, while
nonnumerical data (e.g., data presented in line or bar graphs rather than in tabular form) were
considered for developing LOAELs or NOAELs, they were not used for BMD modeling. In
addition, sometimes, the available models used do not provide an adequate fit to the data. For
the BMD modeling for this assessment, the EPA's BenchMark Dose Software (BMDS), which is
freely available at www.epa.gov/ncea/bmds, was used. For dichotomous responses, the log-
logistic, multistage, and Weibull models were fitted. This subset of BMDS dichotomous models
was used to reduce modeling demands, and these particular models were selected because, as a
group, they have been found to be capable of describing the great majority of dose-response data
sets, and specifically for some TCE data sets (Filipsson and Victorin, 2003). For continuous
responses, the distinct models available in BMDS—the power, polynomial, and Hill models—
were fitted. For some reproductive and developmental data sets, two nested models (the nested
logistic and the Rai and Van Ryzin models in BMDS22) were fitted to examine and account for
potential intralitter correlations. Models with unconstrained power parameters <1 were
considered when the dose-response relationship appeared supralinear, but these models often
yield very low BMDL estimates and there was no situation in which an unconstrained model
with a power parameter <1 was selected for the data sets modeled here. In most cases, a
constrained model or the Hill model provided an adequate fit to such a dose-response
relationship. In a few cases, the highest dose group was dropped to obtain an improved fit to the
lower dose groups. See Appendix F for further details on model fitting and parameter
constraints.
Discontinuous exposures (e.g., gavage exposures once a day, 5 days/week, or inhalation exposures for
5 days/week, 6 hours/day) were adjusted to the continuous exposure yielding the same cumulative exposure. For
inhalation studies, these adjustments are equivalent to those recommended by U.S. EPA (1994a) for deriving a
human equivalent concentration for a Category 3 gas for which the blood:air partition coefficient in laboratory
animals is greater than that in humans (The posterior population median estimate for the TCE blood:air partition
coefficient was 14 in the mouse [Table 3-37], 19 in the rat [Table 3-38], and 9.2 in the human [Table 3-39]).
21 An exception was for the systemic effect of decreased body weight, which was observed in multiple chronic
studies. Dose-response data were available, but the resources were not invested into modeling these data because
the endpoint appeared a priori to be less sensitive than others and was not expected to be a critical effect.
22The BMDS vl.4 module for the National Center for Toxicological Research model failed with the TCE data sets.
5-4
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After fitting these models to the data sets, the following procedure for model selection
was applied. First, models were rejected if the/?-value for goodness of fit was <0.10.23 Second,
models were rejected if they did not appear to adequately fit the low-dose region of the dose-
response relationship, based on an examination of graphical displays of the data and scaled
residuals. If the BMDL estimates from the remaining models were —suffiiently close" (with a
criterion of within twofold for —isfficiently close"), then the model with the lowest Akaike's
Information Criteria (AIC) was selected.24 If the BMDL estimates from the remaining models
are not sufficiently close, some model dependence is assumed. With no clear biological or
statistical basis to choose among them, the lowest BMDL was chosen as a reasonable
conservative estimate, unless the lowest BMDL appeared to be an outlier, in which case, further
judgments were made. Additionally, for continuous models, constant variance models were used
for model parsimony unless the/>-value for the test of homogenous variance was <0.10, in which
case the modeled variance models were considered.
For BMR selection, statistical and biological considerations were taken into account. For
dichotomous responses, our general approach was to use 10% extra risk as the BMR for
borderline or minimally adverse effects and either 5 or 1% extra risk for adverse effects, with 1%
reserved for the most severe effects. For continuous responses, the preferred approach for
defining the BMR is to use a preestablished cut-point for the minimal level of change in the
endpoint at which the effect is generally considered to become biologically significant (e.g.,
there is substantial precedence for using a 10% change in weight for organ and body weights and
a 5% change in weight for fetal weight). In the absence of a well-established cut-point, a BMR
of 1 (control) SD change from the control mean, or 0.5 SD for effects considered to be more
serious, was generally selected. For one neurological effect (traverse time), a doubling (i.e.,
twofold change) was selected because the control SD appeared unusually small.
After the PODs were determined for each study/endpoint, UFs were applied to obtain the
cRfCs and cRfDs. UFs are used to address differences between study conditions and conditions
of human environmental exposure (U.S. EPA, 2002b). These include:
(a) Extrapolating from laboratory animals to humans: If a POD is derived from
experimental animal data, it is divided by an UF to reflect pharmacokinetic and
pharmacodynamic differences that may make humans more sensitive than laboratory
animals. For oral exposures, the standard value for the interspecies UF is 10, which
breaks down (approximately) to a factor of 3 for pharmacokinetic differences (which
is removed if the PBPK model is used) and a factor of 3 for pharmacodynamic
23In a few cases in which none of the models fit the data with/) > 0.10, linear models were selected on the basis of
an adequate visual fit overall.
24Akaike's Information Criteria—a measure of information loss from a dose-response model that can be used to
compare a set of models. Among a specified set of models, the model with the lowest AIC is considered the —bst."
If two or more models share the lowest AIC, an average of the BMDLs could be used, but averaging was not used in
this assessment because for the one occasion in which models shared the lowest AIC, a selection was made based on
visual fit.
5-5
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differences. For inhalation exposures, ppm equivalence across species is generally
assumed or other cross-species scaling is performed, in accordance with U.S. EPA
(1994a) inhalation dosimetry guidance, in which case, residual pharmacokinetic
differences are considered to be negligible, and the standard value used for the
interspecies UF is 3, which is ascribed to pharmacodynamic differences. These
standard values were used for all of the cRfCs and cRfDs based on laboratory animal
data in this assessment.
(b) Human (intraspecies) variability: RfCs and RfDs apply to the human population,
including sensitive subgroups, but studies rarely examine sensitive humans. Sensitive
humans could be adversely affected at lower exposures than a general study
population; consequently, PODs from general-population studies are divided by an
UF to address sensitive humans. Similarly, the animals used in most laboratory
animal studies are considered to be —Apical" or —averge" responders, and the human
(intraspecies) variability UF is also applied to PODs from such studies to address
sensitive subgroups. The standard value for the human variability UF is 10, which
breaks down (approximately) to a factor of 3 for pharmacokinetic variability (which
is removed if the PBPK model is used) and a factor of 3 for pharmacodynamic
variability. This standard value was used for all of the PODs in this assessment with
the exception of the PODs for a few immunological effects that were based on data
from a sensitive (autoimmune-prone) mouse strain; for those PODs, an UF of 3 was
used for human variability.
(c) Uncertainty in extrapolating from subchronic to chronic exposures:25 RfCs and RfDs
apply to lifetime exposure, but sometimes the best (or only) available data come from
less-than-lifetime studies. Lifetime exposure can induce effects that may not be
apparent or as large in magnitude in a shorter study; consequently, a dose that elicits a
specific level of response from a lifetime exposure may be less than the dose eliciting
the same level of response from a shorter exposure period. Thus, PODs based on
subchronic exposure data are generally divided by a subchronic-to-chronic UF, which
has a standard value of 10. If there is evidence suggesting that exposure for longer
time periods does not increase the magnitude of an effect, a lower value of 3 or one
might be used. For some reproductive and developmental effects, chronic exposure is
that which covers a specific window of exposure that is relevant for eliciting the
effect, and subchronic exposure would correspond to an exposure that is notably less
than the full window of exposure.
(d) Uncertainty in extrapolating from LOAELs to NOAELs: PODs are intended to be
estimates of exposure levels without appreciable risk under the study conditions so
that, after the application of appropriate UFs for interspecies extrapolation, human
variability, and/or duration extrapolation, the absence of appreciable risk is conveyed
to the RfC or RfD exposure level to address sensitive humans with lifetime exposure.
Under the NOAEL/LOAEL approach to determining a POD, however, adverse
effects are sometimes observed at all study doses. If the POD is a LOAEL, then it is
divided by an UF to better estimate a NOAEL. The standard value for the LOAEL-
to-NOAEL UF is 10, although a value of 3 is sometimes used if the effect is
considered minimally adverse at the response level observed at the LOAEL or is an
25Rodent studies exceeding 90 days of exposure are considered chronic, and rodent studies with 4 weeks to 90 days
of exposure are considered subchronic (see http://www.epa.gov/iris/help gloss.htm).
5-6
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early marker for an adverse effect. For one POD in this assessment, a value of 30
was used for the LOAEL-to-NOAEL UF because the incidence rate for the adverse
effect was >90% at the LOAEL.
(e) Additional database uncertainties: A database UF of 1, 3, or 10 is used to reflect the
potential for deriving an underprotective toxicity value as a result of an incomplete
characterization of the chemical's toxicity. No database UF was used in this
assessment. See Section 5.1.4.1 for additional discussion of the uncertainties
associated with the overall database for TCE.
(Note that UF values of "3" actually represent VlO, and, when 2 such values are multiplied
together, the result is 10 rather than 9.)
5.1.2. Candidate Critical Effects by Effect Domain
A large number of endpoints and studies were considered within each of the five health
effect domains. A comprehensive list of all endpoints/studies that were considered for
developing cRfCs and cRfDs is shown in Tables 5-1-5-5. These tables also summarize the
PODs for the various study endpoints, the UFs applied, and the resulting cRfCs or cRfDs.
Inhalation and oral studies are presented together so that the extent of the available data, as well
as concordance, or lack thereof, in the responses across routes of exposure, is evident. In
addition, the PBPK model developed in Section 3.5 will be applied to each candidate critical
effect to develop an idPOD; and subsequent extrapolation of the idPOD to pharmacokinetically
sensitive humans is performed for both inhalation and oral human exposures, regardless of the
route of exposure in the original study.
The sections below discuss the cRfCs and cRfDs developed from the effects and studies
identified in the hazard characterization (see Chapter 4) that were suitable for the derivation of
reference values (i.e., that provided quantitative dose-response data). Because the general
approach for applying UFs was discussed above, the sections below only discuss the selection of
particular UFs when there are study characteristics that require additional judgment as to the
appropriate UF values and possible deviations from the standard values usually assigned.
5.1.2.1. Candidate Critical Neurological Effects on the Basis of Applied Dose
As summarized in Section 4.11.1.1, both human and experimental animal studies have
associated TCE exposure with effects on several neurological domains. The strongest
neurological evidence of hazard is for changes in trigeminal nerve function or morphology and
impairment of vestibular function. There is also evidence for effects on motor function; changes
in auditory, visual, and cognitive function or performance; structural or functional changes in the
brain; and neurochemical and molecular changes. Studies with numerical dose-response
information are summarized in Table 5-1, with their corresponding cRfCs or cRfDs shown in
Table 5-2. Because impairment of vestibular function occurs at higher exposures, such changes
were not considered candidate critical effects; however, the other neurological effect domains are
5-7
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represented. For trigeminal nerve effects, cRfC estimates based on two human studies are in a
similar range of 0.4-0.5 ppm (Mhiri et al., 2004; Ruijten et al., 1991). There remains some
uncertainty as to the exposure characterization, as shown by the use of an alternative POD for
Mhiri et al. (2004) based on urinary TCA resulting in a fivefold smaller cRfC. However, the
overall confidence in these estimates is increased by the fact that they are based on humans
exposed under chronic or nearly chronic conditions. Other human studies (e.g.. Barret et al.,
1984), while indicative of hazard, did not have adequate exposure information for quantitative
estimates of an inhalation POD. A cRfD of 0.2 mg/kg/day was developed from the only oral
study demonstrating trigeminal nerve changes, a subchronic study in rats (Barret et al., 1992).
This estimate required multiple extrapolations with a composite UF of 10,000.26
For auditory effects, a high confidence cRfC of about 0.7 ppm was developed based on
BMD modeling of data from Crofton and Zhao (1997): and cRfCs developed from two other
auditory studies (Albee et al., 2006; Rebert etal., 1991) were within about fourfold. No oral data
were available for auditory effects. For psychomotor effects, the available human studies (e.g.,
Rasmussen et al., 1993a: Rasmussen et al., 1993b: Rasmussen et al., 1993d) did not have
adequate exposure information for quantitative estimates of an inhalation POD. However, a
relatively high confidence cRfC of 0.5 ppm was developed from a study in rats (Waseem et al.,
2001). Two cRfDs within a narrow range of 0.7-1.7 mg/kg/day were developed based on two
oral studies reporting psychomotor effects (Nunes et al., 2001; Moser et al., 1995), although
varying in degree of confidence.
26U.S. EPA's report on the RfC and RfD processes (U.S. EPA. 2002b') recommends not deriving reference values
with a composite UF of >3,000; however, composite UFs exceeding 3,000 are considered here because the
derivation of the cRfCs and cRfDs is part of a screening process and the subsequent application of the PBPK model
for candidate critical effects will reduce the values of some of the individual UFs.
5-8
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Table 5-1. Summary of studies of neurological effects suitable for dose-response assessment
Effect type
Study reference
Trigeminal nerve effects
Mhiri et al. (2004)
Ruijten et al. (1991)
Barret et al. (1992)
Auditory effects
Rebert et al. (1991)
Albee et al. (2006)
Crofton and Zhao (1997)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Human phosphate
industry workers
(23 exposed, 23 controls)
Human mail printing
workers (3 1 exposed, 28
controls)
Rat, Sprague-Dawley,
female, 7/group
Rat, Long-Evans, male,
10/group
Rat, F344, male and
female, 10/sex/group
Rat, Long-Evans, male,
8-10/group
Exposure(s) used for
dose-response
assessment
Inhalation: Exposure
ranged from 50 to
150 ppm, for 6 hrs/d for
at least 2 yrs
Inhalation: Mean
cumulative exposure:
704 ppm x yrs; mean
exposure duration: 16 yrs
Oral: 0 and 2,500 mg/kg;
1 dose/d, 5 d/wk, 10 wks
Inhalation: 0, 1,600, and
3,200 ppm; 12 hrs/d,
12 wks
Inhalation: 0, 250, 800,
and 2,500 ppm; 6 hrs/d,
5 d/wk, 13 wks
Inhalation: 0, 800, 1,600,
2,400, and 3,200 ppm;
6 hrs/d, 5 d/wk, 13 wks
Endpoint(s) used for dose-response
assessment
Increased TSEP latency.
Increased latency in masseter reflex.
Increased internode length and fiber
diameter in class A fibers of the
trigeminal nerve observed with TCE
treatment; changes in fatty acid
composition.
Significant decreases in B AER amplitude
and an increase in latency of appearance
of the initial peak (PI).
Mild frequency specific hearing deficits;
focal loss of cochlear hair cells.
Increased auditory thresholds as
measured by BAERs for the 16 kHz tone.
Chapter 4
Section/Table
Section 4.3.1
Table 4-20
Table 4-20
Table 4-21
Section 4.3.2
Table 4-23
Table 4-23
Table 4-23
5-9
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Table. 5-1 Summary of studies of neurological effects suitable for dose-response assessment (continued)
Effect type
Study reference
Psychomotor effects
Waseem et al. (2001)
Nunes et al. (2001)
Moser et al. (1995)
Visual function effects
Blain et al. (1994)
Cognitive effects
Kulig et al. (1987)
Isaacson et al. (1990)
Mood and sleep disorders
Albee et al. (2006)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Rat, Wistar, male,
8/group
Rat, Sprague-Dawley,
male, 10/group
Rat, F344, female, 8/dose
Rabbit, New Zealand
albino, male, 6-8/group
Rat, Wistar, male, 8/dose
Rat, Sprague-Dawley,
male weanlings, 12/dose
Rat, F344, male and
female, 10/sex/group
Exposure(s) used for
dose-response
assessment
Inhalation: 0 and 376 ppm
for up to 180d;4hrs/d,
5d/wk
Oral: 0 and 2,000
mg/kg/d; 7 d
Oral: 0, 150, 500, 1,500,
and 5,000 mg/kg, 1 dose
0, 50, 150, 500, and
1,500 mg/kg/d, 14 d
Inhalation: 0, 350,
700 ppm; 4 hrs/d, 4 d/wk,
12 wks
Inhalation: 0, 500, 1,000,
and 1,500 ppm; 16 hrs/d,
5 d/wk, 18 wks
Oral: (1) 0 mg/kg/d,
8 wks
(2) 47 mg/kg/d, 4 wks +
0 mg/kg/d, 4 wks
(3) 47 mg/kg/d, 4 wks +
0 mg/kg/d, 2 wks +
24 mg/kg/d, 2 wks
Inhalation: 0, 250, 800,
and 2,500 ppm; 6 hrs/d,
5 d/wk, 13 wks
Endpoint(s) used for dose-response
assessment
Changes in locomotor activity.
Increased foot splay.
Neuro-muscular impairment.
Increased rearing activity.
Weekly ERGs and OPs.
Increased time in two-choice visual
discrimination test.
Demyelination of hippocampus
Increased handling reactivity.
Chapter 4
Section/Table
Section 4.3.6
Table 4-31
Table 4-30
Table 4-30
Table 4-30
Section 4.3.4
Table 4-26
Sections 4.3.5 and
4.3.6
Table 4-31
Table 4-28
Section 4.3.7
Table 4-33
5-10
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Table. 5-1 Summary of studies of neurological effects suitable for dose-response assessment (continued)
Effect type
Study reference
Arito et al. (1994)
Other neurological effects
Kjellstrand et al. (1987)
Gash et al. (2008)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Rat, Wistar, male,
5/group
Rat, Sprague-Dawley,
female
Mouse, NMRI, male
Rat, F344, male, 9/group
Exposure(s) used for
dose-response
assessment
Inhalation: 0, 50, 100, and
300 ppm; 8 hrs/d, 5 d/wk,
for 6 wks
0 and 300 ppm, 24 hrs/d,
24 d
0, 150, or 300 ppm,
24 hrs/d, 24 d
Oral: 0 and 1,000 mg/kg;
5 d/wk, 6 wks
Endpoint(s) used for dose-response
assessment
Significant decreases in wakefulness.
Sciatic nerve regeneration was inhibited.
Sciatic nerve regeneration was inhibited.
Degeneration of dopamine-containing
neurons in substantia nigra.
Chapter 4
Section/Table
Table 4-33
Section 4.3.9
Table 4-36
Table 4-36
Table 4-35
5-11
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Table 5-2. Neurological effects in studies suitable for dose-response assessment, and corresponding cRfCs and
cRfDs
Effect type
Supporting studies3
Species
POD
type
PODb
UFS
UFA
UFH
UFL
UFD
UFC
cRfC
(ppm)
cRfD
(mg/kg/d)
Effect; comments
Trigeminal nerve effects
Mhiri et al. (2004)
Ruii ten etal. (1991)
Barret et al. (1992)
Human
Human
Human
Rat
LOAEL
LOAEL
LOAEL
LOAEL
40
6
14
1,800
1
1
1
10
1
1
1
10
10
10
10
10
10
10
3
10
1
1
1
1
100
100
30
10,000d
0.40
0.06
0.47
0.18
Abnormal TSEPs; preferred POD based
on middle of reported range of 50-
150 ppm.
Alternate POD based on U-TCA and
Ikeda etal. (1972).
Trigeminal nerve effects; POD based on
mean cumulative exposure and mean
duration, UFL = 3 due to early marker
effect and minimal degree of change.
Morphological changes; uncertain
adversity; some effects consistent with
demyelination.
Auditory effects
Rebert et al. (1991)
Albee et al. (2006)
Crofton and Zhao
(1997)
Rat
Rat
Rat
NOAEL
NOAEL
BMDL
800
140
274
10
10
10
3
3
3
10
10
10
1
1
1
1
1
1
300
300
300
2.7
0.47
0.91
Preferred, due to better dose-response
data, amenable to BMD modeling.
BMR = 10 dB absolute change.
Psychomotor effects
Waseem et al. (2001)
Nunes et al. (2001)
Rat
Rat
LOAEL
LOAEL
45
2,000
1
10
3
10
10
10
3
3
1
1
3,000
0.45
0.67
Changes in locomotor activity; transient,
minimal degree of adversity; no effect
reported in same study for oral exposures
(210 mg/kg/d).
t Foot splaying; minimal adversity.
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Table 5-2. Neurological effects in studies suitable for dose-response assessment, and corresponding cRfCs and
cRfDs (continued)
Effect type
Supporting studies3
Species
POD
type
PODb
UFS
UFA
UFH
UFL
UFD
UFC
cRfC
(ppm)
cRfD
(mg/kg/d)
Effect; comments
Psychomotor effects (continued)
Moser et al. (1995)
Rat
Rat
BMDL
NOAEL
248
500
3
3
10
10
10
10
1
1
1
1
300
300
0.83
1.7
t # rears (standing on hindlimbs);
BMR = 1 SD change.
t Severity score for neuromuscular
changes.
Visual function effects
Blain et al. (1994)
Rabbit
LOAEL
350
10
3
10
10
1
3,000
0.12
POD not adjusted to continuous exposure
because visual effects more closely
associated with administered exposure.
Cognitive effects
Kulig et al. (19871
Isaacson et al. (1990)
Rat
Rat
NOAEL
LOAEL
500
47
1
10
3
10
10
10
1
10
1
1
30
10,000d
17
0.0047
1 time in 2-choice visual discrimination
test; test involves multiple systems but
largely visual so not adjusted to
continuous exposure.
Demyelination in hippocampus.
Mood and sleep disorders
Albee et al. (2006)
Arito et al. (1994)
Rat
Rat
NOAEL
LOAEL
140
12
10
3
3
3
10
10
1
10
1
1
300
1,000
0.47
0.012
Hyperactivity.
Changes in wakefulness.
Other neurological effects
Kjellstrand et al.
(1987)
Gash etal. (2008)
Rat
Mouse
Rat
LOAEL
LOAEL
LOAEL
300
150
710
10
10
10
3
3
10
10
10
10
10
10
10
1
1
1
3,000
3,000
10,000d
0.10
0.050
0.071
J, regeneration of sciatic nerve.
| regeneration of sciatic nerve.
Degeneration of dopaminergic neurons.
aShaded studies/endpoints were selected as candidate critical effects/studies.
bAdjusted to continuous exposure unless otherwise noted. For inhalation studies, adjustments yield a POD that is a HEC as recommended for a Category 3 gas in
U.S. EPA (1994a) in the absence of PBPK modeling. Same units as cRfC (ppm) or cRfD (mg/kg/day).
'Product of individual UFs.
dEPA's report on the RfC and RfD processes (U.S. EPA. 2002b) recommends not deriving reference values with a composite UF of >3,000; however, composite
UFs exceeding 3,000 are considered here because the derivation of the cRfCs and cRfDs is part of a screening process and the subsequent application of the
PBPK model for candidate critical effects will reduce the values of some of the individual UFs.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-13
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For the other neurological effects, the estimated cRfCs and cRfDs were more uncertain,
as there were fewer studies available for any particular endpoint, and the PODs from several
studies required more adjustment to arrive at a cRfC or cRfD. However, the endpoints in these
studies also tended to be indicative of more sensitive effects and, therefore, they need to be
considered. The lower cRfCs fall in the range 0.01-0.1 ppm and were based on effects on visual
function in rabbits (Blain et al., 1994), wakefulness in rats (Arito et al., 1994), and regeneration
of the sciatic nerve in mice and rats (Kj ell strand etal., 1987). Of these, altered wakefulness
(Arito et al., 1994) has both the lowest POD and the lowest cRfC. There is relatively high
confidence in this study, as it shows a clear dose-response trend, with effects persisting
postexposure. For the subchronic-to-chronic UF, a value of 3 was used because, even though it
was just a 6-week study, there was no evidence of a greater impact on wakefulness following
6 weeks of exposure than there was following 2 weeks of exposure at the LOAEL, although
there was an effect of repeated exposure on the postexposure period impacts of higher exposure
levels. The cRfDs, in the range 0.005-0.07, were based on demyelination in the hippocampus
(Isaacson et al., 1990) and degeneration of dopaminergic neurons (Gash et al., 2008), both in
rats. In both of these cases, adjusting for study design characteristics led to a composite
uncertainty factor of 10,000,27 so the confidence in these cRfDs is lower. However, no other
studies of these effects are available.
In summary, although there is high confidence both in the hazard and in the cRfCs and
cRfDs for trigeminal nerve, auditory, or psychomotor effects, the available data suggest that the
more sensitive indicators of TCE neurotoxicity are changes in wakefulness, regeneration of the
sciatic nerve, demyelination in the hippocampus, and degeneration of dopaminergic neurons.
Therefore, these more sensitive effects are considered the candidate critical effects for
neurotoxicity, albeit with more uncertainty in the corresponding cRfCs and cRfDs. Of these
more sensitive effects, for the reasons discussed above, there is greater confidence in the changes
in wakefulness reported by Arito et al. (1994). In addition, trigeminal nerve effects are
considered a candidate critical effect because this is the only type of neurological effect for
which human data are available, and the POD for this effect is similar to that from the most
sensitive rodent study (Arito et al., 1994, for changes in wakefulness). Between the two human
studies of trigeminal nerve effects, Ruijten et al. (1991) is preferred for deriving noncancer
reference values because its exposure characterization is considered more reliable.
5.1.2.2. Candidate Critical Kidney Effects on the Basis of Applied Dose
As summarized in Section 4.11.1.2, multiple lines of evidence support TCE
nephrotoxicity in the form of tubular toxicity, mediated predominantly through the GSH
27U.S. EPA's report on the RfC and RfD processes (U.S. EPA. 2002b') recommends not deriving reference values
with a composite UF of >3,000; however, composite UFs exceeding 3,000 are considered here because the
derivation of the cRfCs and cRfDs is part of a screening process and the subsequent application of the PBPK model
for candidate critical effects will reduce the values of some of the individual UFs.
5-14
-------
conjugation product DCVC. Available human studies, while providing evidence of hazard, did
not have adequate exposure information for quantitative estimates of PODs. Several studies in
rodents, some of chronic duration, have shown histological changes, nephropathy, or increased
kidney/body weight ratios. Studies with numerical dose-response information are summarized in
Table 5-3, with their corresponding cRfCs or cRfDs shown in Table 5-4.
The cRfCs developed from three suitable inhalation studies, one reporting
meganucleocytosis in rats (Maltoni etal., 1986), and two others reporting increased kidney
weights in mice (Kjellstrand et al., 1983a) and rats (Woolhiser et al., 2006),28 are in a narrow
range of 0.5-1.3 ppm. All three utilized BMD modeling and, thus, take into account statistical
limitations of the Woolhiser et al. (2006) and Kjellstrand et al. (1983a) studies, such as
variability in responses or the use of low numbers of animals in the experiment. The response
used for kidney weight increases was the organ weight as a percentage of body weight, to
account for any commensurate decreases in body weight, although the results did not generally
differ much when absolute weights were used instead. Although the two studies reporting
kidney weight changes were subchronic, longer-term experiments by Kjellstrand et al. (1983a)
did not report increased severity, so no subchronic-to-chronic UF was used in the derivation of
the cRfC. The high response level of 73% at the lowest dose for meganucleocytosis in the
chronic study of Maltoni et al. (1986) implies more uncertainty in the low-dose extrapolation.
However, it is the only inhalation study that includes histopathological analysis, and it uses
relatively high numbers of animals per dose group.
28Woolhiser et al. (2006) is an Organisation for Economic Co-operation and Development guideline immunotoxicity
study performed by the Dow Chemical Company, certified by Dow as conforming to Good Laboratory Practices as
published by the U.S. EPA for the Toxic Substances Control Act.
5-15
-------
Table 5-3. Summary of studies of kidney, liver, and body weight effects suitable for dose-response assessment
Effect type
Study reference
Histological changes in kidney
Maltoni et al. (1986)
NTP (1990)
NCI (1976)
NTP (1988)
| kidney/body weight ratio
Kjellstrand et al. (1983a)
Woolhiser et al. (2006)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Rat, Sprague-Dawley, M,
116-124/group
Rat, F34W, male and
female, 48-50/group
Mouse, B6C3FJ, female,
20-50/group
Rat, Marshall, F, 44-
50/group
Mouse, NMRI, M, 10-
20/group
Rat, Sprague-Dawley, F,
16/group
Exposure(s) used for
dose-response
assessment
Inhalation: 0, 100, 300,
and 600 ppm, 7 hrs/d,
5 d/wk, 104 wks
exposure, observed for
lifespan
Oral: 0, 500, and
1,000 mg/kg/d, 5 d/wk,
103 wks
Oral: 0, 869, and
1,739 mg/kg/d, 5 d/wk,
TWA during exposure
period (78 wks), observed
for 90 wks
Oral: 0, 500, and
1,000 mg/kg/d, 5 d/wk,
104 wks
Inhalation: 0 (air), 37, 75,
150, 225, 300, 450, 900,
1,800, and 3,600 ppm;
continuous and
intermittent exposures for
30-120 d
Inhalation: 0, 100, 300,
and 1,000 ppm, 6 hr/d,
5 d/wk, for 4 wks
Endpoint(s) used for dose-response
assessment
Meganucleocytosis
Cytomegaly and karyomegaly
Toxic nephrosis
Toxic nephropathy
Increased kidney /body weight ratio
Increased kidney /body weight ratio
Chapter 4
Section/Table
Section 4.4.4
Table 4-49, Table 4-
43
Table 4-45, Table 4-
44
Table 4-46, Table 4-
44
Table 4-47, Table 4-
44
Section 4.4.4
Table 4-43
Table 4-43
5-16
-------
Table 5-3. Summary of studies of kidney, liver, and body weight effects suitable for dose-response assessment
(continued)
Effect type
Study reference
| liver/body weight ratio
Kjellstrand et al. (1983a)
Woolhiser et al. (2006)
Buben and O'Flaherty (1985)
Decreased body weight
NTP (1990)
NCI (1976)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Mouse, NMRI, M, 10-
20/group
Rat, Sprague-Dawley, F,
16/group
Mouse, Swiss-Cox, 12-
15/group
Mouse, B6C3Fb M, 48-
50/group
Rat, Osborne-Mendel, M
and F, 20-50/group
Exposure(s) used for
dose-response
assessment
Inhalation: 0 (air), 37, 75,
150, 225, 300, 450, 900,
1,800, and 3,600 ppm;
continuous and
intermittent exposures for
30-120 d
Inhalation: 0, 100, 300,
and 1,000 ppm, 6 hr/d,
5 d/wk, for 4 wks
Oral: 0, 100, 200, 400,
800, 1,600, 2,400, and
3,200 mg/kg/d, 5 d/wk for
6 wks
Oral: 0 and 1,000
mg/kg/d, 5 d/wk, 103 wks
Oral: 0, 549, and
1,097 mg/kg/d, 5 d/wk,
TWA during exposure
period (78 wks), observed
at 110 wks
Endpoint(s) used for dose-response
assessment
Increased liver/body weight ratio
Increased liver/body weight ratio
Increased liver/body weight ratio
Decreased body weight.
Decreased body weight.
Chapter 4
Section/Table
Section 4.5.4.1
Table 4-59
Table 4-59
Table 4-58
NA
NA
5-17
-------
Table 5-4. Kidney, liver, and body weight effects in studies suitable for dose-response assessment, and
corresponding cRfCs and cRfDs
Effect type
Supporting studies3
Species
POD
type
PODb
UFS
UFA
UFH
UFL
UFD
UFC
cRfC
(ppm)
cRfD
(mg/kg/d)
Effect; comments
Histological changes in kidney
Maltoni (1986)
Maltoni (1986)
NTP (1990)
NCI (1976)
NTP (1988)
Rat
Rat
Rat
Mouse
Rat
BMDL
BMDL
LOAEL
LOAEL
BMDL
40.2
34
360
620
9.45
1
1
1
1
1
o
J
10
10
10
10
10
10
10
10
10
1
1
10
30
1
1
1
1
1
1
30
100
1,000
3,000
100
1.3
0.34
0.36
0.21
0.0945
meganucleocytosis; BMR = 10% extra
risk
meganucleocytosis; BMR = 10% extra
risk
cytomegaly and karyomegaly; considered
minimally adverse, but UFL = 10 due to
high response rate (>98%) at LOAEL;
also in mice, but use NCI (1976) for that
species
toxic nephrosis; UFL = 30 due to >90%
response at LOAEL for severe effect
toxic nephropathy; female Marshall (most
sensitive sex/strain); BMR = 5% extra
risk
| kidney/body weight ratio
Kjellstrand et al.
(1983a)
WoolMser et al. (2006)
Mouse
Rat
BMDL
BMDL
34.7
15.7
1
1
o
J
3
10
10
1
1
1
1
30
30
1.2
0.52
BMR = 10% increase; 30 d, but 120 d @
120 ppm not more severe so UFS = 1;
results are for males, which were slightly
more sensitive, and yielded better fit to
variance model
BMR = 10% increase; UFS = 1 based on
Kjellstrand et al. (1983a) result
| liver/body weight ratio
Kjellstrand et al.
(I983a)
Woolhiser et al. (2006)
Buben and O'Flaherty
(1985)
Mouse
Rat
Mouse
BMDL
BMDL
BMDL
21.6
25.2
81.5
1
1
1
o
j
-3
j
10
10
10
10
1
1
1
1
1
1
30
30
100
0.72
0.84
0.82
BMR = 10% increase; UFS = 1 based on
not more severe at 4 months
BMR = 10% increase; UFS = 1 based on
Kjellstrand et al. (1983a) result
BMR = 10% increase; UFS = 1 based on
Kjellstrand et al. (1983a) result
5-18
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Table 5-4. Kidney, liver, and body weight effects in studies suitable for dose-response assessment, and
corresponding cRfCs and cRfDs (continued)
Effect type
Supporting studies
Species
POD
type
PODa
UFS
UFA
UFH
UFL
UFD
UFb
cRfC
(ppm)
cRfD
(mg/kg/d)
Effect; comments
Histological changes in kidney
NTP (1990)
NCI (19761
Mouse
Rat
LOAEL
LOAEL
710
360
1
1
10
10
10
10
10
10
1
1
1,000
1,000
0.71
0.36
Reflects several, but not all, strains/sexes.
"Shaded studies/endpoints were selected as candidate critical effects/studies.
bAdjusted to continuous exposure unless otherwise noted. For inhalation studies, adjustments yield a POD that is a HEC as recommended for a Category 3 gas in
U.S. EPA (1994a) in the absence of PBPK modeling. Same units as cRfC (ppm) or cRfD (mg/kg/day).
'Product of individual UFs.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-19
-------
The suitable oral studies give cRfDs within a narrow range of 0.09-0.4 mg/kg/day, as
shown in Table 5-4, although the degree of confidence in the cRfDs varies considerably. For
cRfDs based on NTP (NTP, 1990) and NCI (NCI, 1976) chronic studies in rodents, extremely
high response rates of >90% precluded BMD modeling. An UF of 10 was applied for
extrapolation from a LOAEL to a NOAEL in the NTP (1990) study because the effect
(cytomegaly and karyomegaly), although minimally adverse, was observed at such a high
incidence. An UF of 30 was applied for extrapolation from a LOAEL to a NOAEL in the NCI
(1976) study because of the high incidence of a clearly adverse effect (toxic nephrosis). There is
more confidence in the cRfDs based on meganucleocytosis reported in Maltoni et al. (1986) and
toxic nephropathy NTP (1988), as BMD modeling was used to estimate BMDLs. Because these
two oral studies measured somewhat different endpoints, but both were sensitive markers of
nephrotoxic responses, they were considered to have similarly strong weight from a hazard
perspective. For meganucleocytosis, a BMR of 10% extra risk was selected because the effect
was considered to be minimally adverse. For toxic nephropathy, a BMR of 5% extra risk was
used because toxic nephropathy is a severe toxic effect. This BMR required substantial
extrapolation below the observed responses (about 60%); however, the response level seemed
warranted for this type of effect and the ratio of the BMD to the BMDL was not large (1.56).
Thus, from a dose-response extrapolation perspective, there is more confidence in Maltoni et al.
(1986). However, the effect observed in NTP (1988) is more severe and therefore also merits
consideration.
In summary, there is high confidence in the hazard and moderate confidence in the cRfCs
and cRfDs for histopathological and weight changes in the kidney. These effects are considered
to be candidate critical effects for several reasons. First, they appear to be the most sensitive
indicators of toxicity that are available for the kidney. In addition, as discussed in Section 3.5,
some pharmacokinetic data indicate substantially more production of GSH-conjugates thought to
mediate TCE kidney effects in humans relative to rats and mice, although there is uncertainty in
these data due to possible analytic errors. As discussed above, several studies are considered
reliable for developing cRfCs and cRfDs for these endpoints. For histopathological changes, in
general, the most sensitive were selected as candidate critical studies. These include the only
available inhalation study (Maltoni et al.. 1986). the Maltoni et al. (1986) and NTP (1988) oral
studies in rats, and the NCI (1976) oral study in mice. For oral studies in rats, Maltoni et al.
(1986) was considered in addition to NTP (1988), despite its having a higher cRfD, because of
the much greater degree of low-dose extrapolation necessary for NTP (1988) and the excessive
mortality present in that study. While the NCI (1976) study has even greater uncertainty, as
discussed above, with a high response incidence at the POD that necessitates greater low-dose
extrapolation, it is included to add a second species to the set of candidate critical effects. For
kidney weight changes, both available studies were chosen as candidate critical studies.
5-20
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5.1.2.3. Candidate Critical Liver Effects on the Basis of Applied Dose
As summarized in Section 4.11.1.3, while there is only limited epidemiologic evidence of
TCE hepatotoxicity, TCE clearly leads to liver toxicity in laboratory animals, likely through its
oxidative metabolites. Available human studies contribute to the overall weight of evidence of
hazard, but did not have adequate exposure information for quantitative estimates of PODs. In
rodent studies, TCE causes a wide array of hepatotoxic endpoints: increased liver weight, small
transient increases in DNA synthesis, changes in ploidy, cytomegaly, increased nuclear size, and
proliferation of peroxisomes. Increased liver weight (hepatomegaly, or specifically increased
liver/body weight ratio) has been the most studied endpoint across a range of studies in both
sexes of rats and mice, with a variety of exposure routes and durations. Hepatomegaly was
selected as the critical liver effect for multiple reasons. First, it has been consistently reported in
multiple studies in rats and mice following both inhalation and oral routes of exposure. In
addition, it appears to accompany the other hepatic effects at the doses tested, and hence
constitutes a hepatotoxicity marker of similar sensitivity to the other effects. Finally, in several
studies, there are good dose-response data for BMD modeling.
As shown in Table 5-4, cRfCs for hepatomegaly developed from the two most suitable
subchronic inhalation studies (Woolhiser et al., 2006; Kjellstrand et al., 1983a), while in different
species (rats and mice, respectively), are both based on similar PODs derived from BMD
modeling, have the same composite UF of 30, and result in similar cRfC estimates of about
0.8 ppm. The cRfD for hepatomegaly developed from the oral study of Buben and O'Flaherty
(1985) in mice also was based on a POD derived from BMD modeling and resulted in a cRfD
estimate of 0.8 mg/kg/day. Among the studies reporting liver weight changes (reviewed in
Section 4.5 and Appendix E), this study had by far the most extensive dose-response data. The
response used in each case was the liver weight as a percentage of body weight, to account for
any commensurate decreases in body weight, although the results did not generally differ much
when absolute weights were used instead.
There is high confidence in all of these candidate reference values. BMD modeling takes
into account statistical limitations such as variability in response or low numbers of animals and
standardizes the response rate at the POD. Although the studies were subchronic, hepatomegaly
occurs rapidly with TCE exposure, and the degree of hepatomegaly does not increase with
chronic exposure (Kjellstrand et al., 1983a), so no subchronic-to-chronic UF was used.
In summary, there is high confidence both in the hazard and the cRfCs and cRfDs for
hepatomegaly. Hepatomegaly also appears to be the most sensitive indicator of toxicity that is
available for the liver and is therefore considered a candidate critical effect. As discussed above,
several studies are considered reliable for developing cRfCs and cRfDs for this endpoint, and,
since they all indicated similar sensitivity but represented different species and/or routes of
exposure, they were all considered candidate critical studies.
5-21
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5.1.2.4. Candidate Critical Body Weight Effects on the Basis of Applied Dose
The chronic oral bioassays, NCI (1976) and NTP (1990), reported decreased body weight
with TCE exposure, as shown in Table 5-4. However, the lowest doses in these studies were
quite high, even on an adjusted basis (see PODs in Table 5-4). These were not considered
critical effects because they are not likely to be the most sensitive noncancer endpoints, and were
not considered candidate critical effects.
5.1.2.5. Candidate Critical Immunological Effects on the Basis of Applied Dose
As summarized in Section 4.11.1.4, the human and experimental animal studies of TCE
and immune-related effects provide strong evidence for a role of TCE in autoimmune disease
and in a specific type of generalized hypersensitivity syndrome, while there are fewer data
pertaining to immunosuppressive effects. Available human studies, while providing evidence of
hazard, did not have adequate exposure information for quantitative estimates of PODs. Several
studies in rodents were available on autoimmune and immunosuppressive effects that were
adequate for deriving cRfCs and cRfDs. Studies with numerical dose-response information are
summarized in Table 5-5, with their corresponding cRfCs or cRfDs summarized in Table 5-6.
For decreased thymus weights, a cRfD from the only suitable study (Keil et al., 2009) is
0.00035 mg/kg/day based on results from nonautoimmune-prone B6C3Fi mice, with a composite
UF of 1,000 for a POD that is a LOAEL (the dose-response relationship is sufficiently
supralinear that attempts at BMD modeling did not result in adequate fits to these data). Thymus
weights were not significantly affected in autoimmune prone mice in the same study, consistent
with the results reported by Kaneko et al. (2000) in autoimmune-prone mice. In addition, Keil et
al. (2009) and Peden-Adams et al. (2008) reported that for several immunotoxicity endpoints
associated with TCE, the autoimmune-prone strain appeared to be less sensitive than the
nonautoimmune prone B6C3Fi strain. In rats, Woolhiser et al. (2006) reported no significant
change in thymus weights in the Sprague-Dawley strain. These data are consistent with normal
mice being sensitive to this effect as compared to autoimmune-prone mice or Sprague-Dawley
rats, so the results of Keil et al. (2009) are not necessarily discordant with the other studies.
5-22
-------
Table 5-5. Summary of studies of immunological effects suitable for dose-response assessment
Effect type
Study reference
J, thymus weight
Keil et al. (2009)
Autoimmunity
Kaneko et al. (2000)
Keil et al. (2009)
Griffin etal.QQQOb)
Cai et al. (2008)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Mouse, B6C3F!, Female,
10/group
5/group
Mouse, B6C3F!, Female,
10/group
Mouse, MRL +/+,
Female, 8/group
Mouse, MRL +/+,
Female, 5/group
Exposure(s) used for
dose-response
assessment
Oral: 0, 1,400, or
14,000 ppb TCE (0, 0.35,
or 3.5 mg/kg/d), 27 wks
Inhalation: 0, 500, 1,000,
or 2,000 ppm TCE, 4
hrs/d, 6 d/wk, 8 wks
Oral: 0, 1,400, or
14,000 ppb TCE (0, 0.35,
or 3.5 mg/kg/d), 27 wks
Oral: 0, 21, 100, or
400 mg/kg/d, 32 wks
Oral: 0 or 60 mg/kg/d,
48 wks
Endpoint(s) used for dose-response
assessment
Decreased thymus weights; decrease in
thymus cellularity
Liver inflammation, splenomegaly and
hyperplasia of lymphatic follicles
Increased anti-dsDNA and anti-ssDNA
antibodies
Various signs of autoimmune hepatitis
(serology, ex vivo assays of cultured
splenocytes, clinical and histopathologic
findings)
Hepatic necrosis; hepatocyte
proliferation; leukocyte infiltrate in the
liver, lungs, and kidneys
Chapter 4
Section/Table
Section 4.6.2.3
Table 4-78
Section 4.6.2.3
Table 4-78
Table 4-78
Table 4-78
Table 4-78
5-23
-------
Table 5-5. Summary of studies of immunological effects suitable for dose-response assessment (continued)
Effect type
Study reference
Immunosuppression
Woolhiser et al. (2006)
Sanders et al. (1982b)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Rat, Sprague-Dawley,
female, 16/group
Mouse, CD-I, Female, 7-
25/group
Exposure(s) used for
dose-response
assessment
Inhalation: 0, 100, 300, or
1,000 ppm, 6 hrs/d,
5 d/wk, 4 wks
Oral: 0,0.1, 1.0, 2.5, or
5.0mg/mL(0, 18,217,
393,or660mg/kg/d,
from Tucker et al., 1982),
4 or 6 mo
Endpoint(s) used for dose-response
assessment
Decreased PFC assay response
Decreased humoral immunity, cell-
mediated immunity, and bone marrow
stem cell colonization
Chapter 4
Section/Table
Section 4.6.2.1
Table 4-76
Table 4-76
5-24
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Table 5-6. Immunological effects in studies suitable for dose-response assessment, and corresponding cRfCs and
cRfDs
Effect type
Supporting studies3
Species
POD
type
PODb
UFS
UFA
UFH
UFL
UFD
UFC
cRfC
(ppm)
cRfD
(mg/kg/d)
Effect; comments
1 thvmus weight
Keiletal. (2009)
Mouse
LOAEL
0.35
1
10
10
10
1
1,000
0.00035
1 thymus weight; corresponding decrease in total
thymic cellularily reported at 10 x higher dose
Autoimmunity
Kaneko et al., (20001
Keiletal. (2009)
Griffin etal. QQQQb)
Cai et al. (2008)
Mouse
(MRL-
Ipr/lpr)
Mouse
Mouse
(MRL+/+)
Mouse
(MRL+/+)
LOAEL
LOAEL
BMDL
LOAEL
70
0.35
13.4
60
10
1
1
1
3
10
10
10
3
10
3
3
10
3
1
10
1
1
1
1
1,000
300
30
300
0.070
0.0012
0.45
0.20
Changes inimmunoreactive organs — liver (incl.
sporadic necrosis in hepatic lobules), spleen;
UFH = 3 due to autoimmune-prone mouse
t anti-dsDNA and anti-ssDNA Abs (early
markers for autoimmune disease) (B6C3Fi
mouse); UFL = 3 due to early marker
Various signs of autoimmune hepatitis;
BMR = 10% extra risk for > minimal effects
Inflammation in liver, kidney, lungs, and
pancreas indicative of autoimmune disease;
hepatic necrosis; UFH = 3 due to autoimmune-
prone mouse
Immunosuppression
Woolhiser etal. (2006)
Sanders et al. Q982b)
Sanders et al. (1982b)
Rat
Mouse
Mouse
BMDL
NOAEL
LOAEL
31.2
190
18
10
1
1
3
10
10
10
10
10
1
1
10
1
1
1
300
100
1,000
0.10
1.9
0.018
| PFC response; BMR = 1 SD change
I humoral response to SRBC; largely transient
during exposure
J, cell-mediated response to SRBC (largely
transient during exposure) and j stem cell bone
marrow recolonization (sustained); females more
sensitive; UFL = 10 since multiple
immunotoxicity effects were observed
"Shaded studies/endpoints were selected as candidate critical effects/studies.
bAdjusted to continuous exposure unless otherwise noted. For inhalation studies, adjustments yield a POD that is a HEC as recommended for a Category 3 gas in
U.S. EPA (1994a) in the absence of PBPK modeling. Same units as cRfC (ppm) or cRfD (mg/kg/day).
'Product of individual UFs.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-25
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For autoimmune effects, the cRfC from the only suitable inhalation study (Kaneko et al.,
2000) is 0.07 ppm. This study reported changes in immunoreactive organs (i.e., liver and spleen)
in autoimmune-prone mice. BMD modeling was not feasible, so a LOAEL was used as the
POD. The standard value of 10 was used for the LOAEL-to-NOAEL UF because the
inflammation was reported to include sporadic necrosis in the hepatic lobules at the LOAEL, so
this was considered an adverse effect. A value of 3 was used for the human (intraspecies)
variability UF because the effect was induced in autoimmune-prone mice, a sensitive mouse
strain for such an effect. The cRfDs from the oral studies (Keil et al., 2009; Cai et al., 2008;
Griffin et al., 2000b) spanned over a 100-fold range from 0.001 to 0.5 mg/kg/day. Each of the
studies used different markers for autoimmune effects, which may explain the over 100-fold
range of PODs (0.4-60 mg/kg/day). The most sensitive endpoint, reported by Keil et al. (2009),
was increases in anti-dsDNA and anti-ssDNA antibodies in B6C3Fimice exposed to the lowest
tested dose of 0.35 mg/kg/day. These markers of autoimmune responsiveness were not
accompanied by evidence of inflammation or kidney disease in a similar dose- and time-
dependent manner. In accordance with the interpretation of these measures as an early,
subclinical or pre-clinical marker of disease, a LOAEL-to-NOAEL UF of 3 was used, and the
resulting cRfD was 0.001 mg/kg/day. The results of Keil et al. (2009) are not discordant with the
higher PODs and cRfDs derived from the other oral studies that examined leukocyte infiltration
and tissue damage in autoimmune-prone mice (Cai et al., 2008; Griffin et al., 2000a). Cai et al.
(2008) noted that the autoimmune nephritis together with multi-organ involvement and an
increased level of antinuclear antibodies observed in their study suggested the induction of
autoimmune disease.
For immunosuppressive effects, the only suitable inhalation study (Woolhiser et al.,
2006) gave a cRfC of 0.08 ppm. The cRfDs from the only suitable oral study (Sanders et al.,
1982b) ranged from 0.06 to 2 mg/kg/day, based on different markers for immunosuppression.
Woolhiser et al. (2006) reported decreased PFC response in rats. Data from Woolhiser et al.
(2006) were amenable to BMD modeling, but there is notable uncertainty in the modeling. First,
it is unclear what should constitute the cut-point for characterizing the change as minimally
biologically significant, so a BMR of 1 control SD change was used. In addition, the dose-
response relationship is supralinear, and the highest exposure group was dropped to improve the
fit to the low-dose data points. Nonetheless, the uncertainty in the BMD modeling is no greater
than the uncertainty inherent in the use of a LOAEL or NOAEL. The more sensitive endpoints
reported by Sanders et al. (1982b), both of which were in female mice exposed to a LOAEL of
18 mg/kg/day TCE in drinking water for 4 months, were decreased cell-mediated response to
SRBC and decreased stem cell bone recolonization, a sign of impaired bone marrow function.
The cRfD based on these endpoints is 0.02 mg/kg/day, with a LOAEL-to-NOAEL UF of 10 for
the multiple effects of decreased cell-mediated response to SRBC and decreased stem cell bone
recolonization.
5-26
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In summary, there is high qualitative confidence for TCE immunotoxicity and moderate
confidence in the cRfCs and cRfDs that can be derived from the available studies. Decreased
thymus weight reported at relatively low exposures in nonautoimmune-prone mice is a clear
indicator of immunotoxicity (Keil et al., 2009), and is therefore considered a candidate critical
effect. A number of studies have also reported changes in markers of immunotoxicity at
relatively low exposures. Therefore, among markers for autoimmune effects, the more sensitive
measures of autoimmune changes in liver and spleen (Kaneko et al., 2000) and increased anti-
dsDNA and anti-ssDNA antibodies (Keil et al., 2009) are considered the candidate critical
effects. Similarly, for markers of immunosuppression, the more sensitive measures of decreased
PFC response (Woolhiser et al., 2006), decreased stem cell bone marrow recolonization, and
decreased cell-mediated response to SRBC [both from Sanders et al. (1982b)] are considered the
candidate critical effects.
5.1.2.6. Candidate Critical Respiratory Tract Effects on the Basis of Applied Dose
As summarized in Section 4.11.1.5, available data are suggestive of TCE causing
respiratory tract toxicity, based primarily on short-term studies in mice and rats. However, these
studies are generally at high inhalation exposures and over durations of <2 weeks. Thus, these
were not considered critical effects because such data are not necessarily indicators of longer-
term effects at lower exposure and are not likely to be the most sensitive noncancer endpoints for
chronic exposures. Therefore, cRfCs and cRfDs were not developed for them.
5.1.2.7. Candidate Critical Reproductive Effects on the Basis of Applied Dose
As summarized in Section 4.11.1.6, both human and experimental animal studies have
associated TCE exposure with adverse reproductive effects. The strongest evidence of hazard is
for effects on sperm and male reproductive outcomes, with evidence from multiple human
studies and several experimental animal studies. There is also substantial evidence for effects on
the male reproductive tract and male serum hormone levels, as well as evidence for effects on
male reproductive behavior. There are fewer data and more limited support for effects on female
reproduction. Studies with numerical dose-response information are summarized in Table 5-7,
with their corresponding cRfCs or cRfDs summarized in Table 5-8.
5-27
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Table 5-7. Summary of studies of reproductive effects suitable for dose-response assessment
Effect type
Study reference
Effects on sperm, male reproductive
outcomes
Chia et al. (1996)
Land et al. (1981)
Kan et al. (2007)
Xu et al. (2004)
Kumar et al. (2000b)
Kumar et al. (200 Ib)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Human, 85 men (37 low
exposure, 48 high
exposure)
Mouse, C57BlxC3H (Fl),
M, 5 or 10/group
Mouse, CD-I, male,
4/group
Mouse, CD-I, male, 4-
27/group
Rat, Wistar, male, 12-
13/group
Rat, Wistar, male,
6/group
Exposure(s) used for
dose-response
assessment
Inhalation: Mean personal
air TCE: 29.6 ppm; Mean
U-TCA: 22.4 mg/g
creatinine
Inhalation: 0, 200, 2,000
ppm, 4 hrs/d, 5 d
exposure, 23 d rest
Inhalation: 0 or 1,000
ppm, 6 hrs/d,5 d/wk,
4 wks
Inhalation: 0 or
1,000 ppm, 6 hrs/d,
5 d/wk, 6 wks
Inhalation: 0 or 376 ppm,
4 hrs/d, 5 d/wk, 2-10 wks
exposed, 2-8 wks
unexposed.
Inhalation: 0 or 376 ppm,
4 hrs/d, 5 d/wk, 12 and
24 wks
Endpoint(s) used for dose-response
assessment
Decreased normal sperm morphology
and hyperzoospermia.
Increased percent morphologically
abnormal epididymal sperm.
Abnormalities of the head and tail in
sperm located in the epididymal lumen.
Decreased in vitro sperm-oocyte binding
and in vivo fertilization.
Multiple sperm effects; pre- and
postimplantation losses.
Multiple sperm effects, increasing
severity from 12 to 24 wks exposure.
Chapter 4
Section/Table
Sections 4.8.1.1-
4.8.1.2
Table 4-85
Table 4-86
Table 4-86
Table 4-86
Table 4-86
Table 4-86
5-28
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Table 5-7. Summary of studies of reproductive effects suitable for dose-response assessment (continued)
Effect type
Study reference
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Exposure(s) used for
dose-response
assessment
Endpoint(s) used for dose-response
assessment
Chapter 4
Section/Table
George et al. (1985)
Mouse, CD-I, male and
female, 20 pairs/treatment
group; 40 controls/sex
Oral: 0, 173, 362, or
737 mg/kg/d, Breeders
exposed 1 wk premating,
then for 13 wks; pregnant
females exposed
throughout gestation (i.e.,
18 wks total)
Decreased sperm motility in FO and Fl
males.
Table 4-87
DuTeaux et al. (2004a)
Rat, Sprague-Dawley,
male, 3/group, or
Simonson albino (UC
Davis), male, 3/group
Oral: 0, 143, or
270 mg/kg/d, 14 d
Decreased ability of sperm to fertilize
oocytes collected from untreated females.
Oxidative damage to sperm membrane in
head and mid-piece.
Table 4-87
Male reproductive tract effects
Section 4.8.1.2
Forkert et al. (2002)
Mouse, CD-I, male,
6/group
Inhalation: 0 or
1,000 ppm, 6 hrs/d,
5 d/wk, 19 d over 4 wks
Sloughing of epididymal epithelial cells.
Table 4-86
Kan et al. (2007)
Mouse, CD-I, male,
4/group
Inhalation: 0 or
1,000 ppm, 6 hrs/d,
5 d/wk, 1-4 wks
Degeneration and sloughing of
epididymal epithelial cells (more severe
by 4 wks). Vesiculation in cytoplasm,
disintegration of basolateral cell
membranes, sloughing of epithelial cells.
Table 4-86
Kumar et al. (2000b)
Rat, Wistar, male, 12-
13/group
Inhalation: 0 or 376 ppm,
4 hrs/d, 5 d/wk, 2-10 wks
exposed, 2-8 wks
unexposed
Smaller, necrotic spermatogenic tubules.
Table 4-86
Kumar et al. (200 Ib)
Rat, Wistar, male,
6/group
Inhalation: 0 or 376 ppm,
4 hrs/d, 5 d/wk, 12 and
24 wks
Decreased testes weight, numbers of
spermatogenic cells and spermatids,
testes atrophy, smaller tubules devoid of
spermatocytes and spermatids,
hyperplastic Leydig cells, altered
testicular enzyme markers. Increasing
severity from 12 to 24 wks of exposure.
Table 4-86
5-29
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Table 5-7. Summary of studies of reproductive effects suitable for dose-response assessment (continued)
Effect type
Study reference
George et al. (1985)
George et al. (1986)
Female maternal weight gain
Carney et al. (2006)
Schwetz et al. (1975)
Narotsky et al. (1995)
Manson et al. (1984)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Mouse, CD-I, male and
female, 20 pairs/treatment
group; 40 controls/sex
Rat, F334, males and
female, 20 pairs/treatment
group, 40 controls/sex
Rat, Sprague-Dawley,
females, 27 dams/group
Rat, Sprague-Dawley,
female, 20-35/group
Rat, F344, females, 8-
12 dams/group
Rat, Long-Evans, female,
23-25/group
Exposure(s) used for
dose-response
assessment
Oral: 0, 173, 362, or
737 mg/kg/d, Breeders
exposed 1 wk premating,
then for 13 wks; pregnant
females exposed
throughout gestation (i.e.,
18 wks total)
Oral: 0, 72, 186, or
389 mg/kg/d (estimated),
Breeders exposed 1 wk
premating, then for
13 wks; pregnant females
exposed throughout
gestation (i.e., 18 wks
total)
Inhalation: 0, 50, 150, or
600 ppm, 6 hrs/d; CDs 6-
20
Inhalation: 0 or 300 ppm,
7 hrs/d; CDs 6-15
Oral:0, 10.1,32,101,
320, 475, 633, 844, or
1,125 mg/kg/d, CDs 6-15
Oral: 0, 10, 100, or
1,000 mg/kg/d, 6 wks:
2 wks premating, 1 wk
mating period, GDs 1-21
Endpoint(s) used for dose-response
assessment
Decreased testes and seminal vesicle
weights inFO.
Increased testes and epididymis weights
inFO.
Decreased body weight gain on GDs 6-9.
Decreased body weight gain on GDs 6-9.
Decreased body weight gain on GDs 6-8
and 6-20.
Decreased gestation body weight gain.
Chapter 4
Section/Table
Table 4-87
Table 4-87
Section 4.8.3.2
Table 4-96
Table 4-96
Table 4-98
Table 4-87
5-30
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Table 5-7. Summary of studies of reproductive effects suitable for dose-response assessment (continued)
Effect type
Study reference
George et al. (1986)
Female reproductive outcomes
Narotsky et al. (1995)
Reproductive behavior
Zenick et al. (1984)
George et al. (1986)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Rat, F334, males and
female, 20 pairs/treatment
group, 40 controls/sex
Rat, F344, females, 8-12
dams/group
Rat, Long-Evans, male,
10/group
Rat, F334, males and
female, 20 pairs/treatment
group, 40 controls/sex
Exposure(s) used for
dose-response
assessment
Oral: 0, 72, 186, or
389 mg/kg/d (estimated),
Breeders exposed 1 wk
premating, then for
13 wks; pregnant females
exposed throughout
gestation (i.e., 18 wks
total)
Oral:0, 10.1,32,101,
320, 475, 633, 844, or
1,125 mg/kg/d, CDs 6-15
Oral: 0, 10, 100, or
1,000 mg/kg/d, 5 d/wk,
6 wks exposure, 4 wks
recovery
Oral: 0, 72, 186, or
389 mg/kg/d (estimated),
Breeders exposed 1 wk
premating, then for
13 wks; pregnant females
exposed throughout
gestation (i.e., 18 wks
total)
Endpoint(s) used for dose-response
assessment
Decreased term and postpartum dam
body weight in FO and F 1 .
Delayed parturition.
Impaired copulatory performance.
Decreased FO mating in cross-over
mating trials.
Chapter 4
Section/Table
Table 4-87
Section 4.8.3.2
Table 4-98
Section 4.8.1.2
Table 4-87
Table 4-87
5-31
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Table 5-7. Summary of studies of reproductive effects suitable for dose-response assessment (continued)
Effect type
Study reference
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Exposure(s) used for
dose-response
assessment
Endpoint(s) used for dose-response
assessment
Chapter 4
Section/Table
Reproductive effects from exposure to
both sexes
Section 4.8.1.2
George et al.
Rat, F334, males and
female, 20 pairs/treatment
group, 40 controls/sex
Oral: 0, 72, 186, or
389 mg/kg/d (estimated),
Breeders exposed 1 wk
premating, then for
13 wks; pregnant females
exposed throughout
gestation (i.e., 18 wks
total)
Decreased FO litters/pair and live Fl
pups/litter.
Table 4-87
5-32
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Table 5-8. Reproductive effects in studies suitable for dose-response assessment, and corresponding cRfCs and
cRfDs
Effect type
Supporting studies3
Species
POD
type
PODb
UFS
UFA
UFH
UFL
UFD
UFC
cRfC
(ppm)
cRfD
(mg/kg/d)
Effect; comments
Effects on sperm, male reproductive outcomes
Chiaetal. (1996)
Land et al. (1981)
Kan et al. (2007)
Xu et al. (2004)
Kumar et al. (200 Ib:
2000b)
Kumar et al. QOOOb)
George et al. (1985)
DuTeaux et al., (2004a)
Human
Mouse
Mouse
Mouse
Rat
Rat
Mouse
Rat
BMDL
BMDL
LOAEL
LOAEL
LOAEL
LOAEL
NOAEL
LOAEL
1.43
46.9
180
180
45
45
362
141
10
10
10
10
10
1
1
10
1
3
3
3
3
3
10
10
10
10
10
10
10
10
10
10
1
1
10
10
10
10
1
10
1
1
1
1
1
1
1
1
100
300
3,000
3,000
3,000
300
100
10,000d
0.014
0.16
0.060
0.060
0.015
0.15
3.6
0.014
Hyperzoospermia; exposure estimates
based on U-TCA from Ikeda et al.
(1972); BMR = 10% extra risk
t abnormal sperm; BMR = 0.5 SD
t abnormal sperm; Land et al. (1981)
cRfC preferred due to BMD modeling
| fertilization
Multiple sperm effects, increasing
severity from 12 to 24 wks
Pre- and postimplantation losses;
UFS = 1 due to exposure covered time
period for sperm development; higher
response for preimplantation losses
J, sperm motility
| ability of sperm to fertilize in vitro
Male reproductive tract effects
Forkert et al. (2002).
Kan etal. (2007)
Kumar etal. (200 Ib:
2000b)
George et al. (1985)
George et al. (1986)
Mouse
Rat
Mouse
Rat
LOAEL
LOAEL
NOAEL
NOAEL
180
45
362
186
10
10
1
1
3
3
10
10
10
10
10
10
10
10
1
1
1
1
1
1
3,000
3,000
100
100
0.060
0.015
3.6
1.9
Effects on epididymis epithelium
Testes effects, altered testicular
enzyme markers, increasing severity
from 12 to 24 wks
J, testis/seminal vesicle weights
t testis/epididymis weights
5-33
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Table 5-8. Reproductive effects in studies suitable for dose-response assessment, and corresponding cRfCs and
cRfDs (continued)
Effect type
Supporting studies3
Species
POD
type
PODb
UFS
UFA
UFH
UFL
UFD
UFC
cRfC
(ppm)
cRfD
(mg/kg/d)
Effect; comments
Female maternal weight gain
Carney et al. (2006)
Schwetz et al. (1975)
Narotsky et al. (1995)
Manson et al. (1984)
George et al. (1986)
Rat
Rat
Rat
Rat
Rat
BMDL
LOAEL
BMDL
NOAEL
NOAEL
10.5
88
108
100
186
1
1
1
1
1
3
3
10
10
10
10
10
10
10
10
1
10
1
1
1
1
1
1
1
1
30
300
100
100
100
0.35
0.29
1.1
1.0
1.9
| Body weight gain; BMR = 10%
decrease
J, maternal body weight; Carney et al.
(2006) cRfC preferred due to BMD
modeling
| Body weight gain; BMR = 10%
decrease
J, Body weight gain; Narotsky et al.
(1995) preferred due to BMD
modeling (different strain)
J, postpartum body weight; Narotsky et
al. (1995) cRfD preferred due to BMD
modeling
Female reproductive outcomes
Narotsky etal. (1995)
Rat
LOAEL
475
1
10
10
10
1
1,000
0.48
Delayed parturition
Reproductive behavior
Zenick et al. (1984)
George et al. (1986)
Rat
Rat
NOAEL
LOAEL
100
389
1
1
10
10
10
10
1
10
1
1
100
1,000
1.0
0.39
J, copulatory performance in males
J, mating (both sexes exposed)
Reproductive effects from exposure to both sexes
George et al. (1986)
Rat
Rat
BMDL
BMDL
179
152
1
1
10
10
10
10
1
1
1
1
100
100
1.8
1.5
I number of litters/pair; BMR =
0.5 SD
| live pups/litter; BMR = 0.5 SD
aShaded studies/endpoints were selected as candidate critical effects/studies.
bAdjusted to continuous exposure unless otherwise noted. For inhalation studies, adjustments yield a POD that is a HEC as recommended for a Category 3 gas in
U.S. EPA (1994a) in the absence of PBPK modeling. Same units as cRfC (ppm) or cRfD (mg/kg/day).
'Product of individual UFs.
dEPA's report on the RfC and RfD processes (U.S. EPA. 2002b) recommends not deriving reference values with a composite UF of >3,000; however, composite
UFs exceeding 3,000 are considered here because the derivation of the cRfCs and cRfDs is part of a screening process and the subsequent application of the
PBPK model for candidate critical effects will reduce the values of some of the individual UFs.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-34
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5.1.2.7.1. Male reproductive effects (effects on sperm and reproductive tract)
A number of available studies have reported functional and structural changes in sperm
and male reproductive organs and effects on male reproductive outcomes following TCE
exposure (see Table 5-8). A cRfC of 0.014 ppm was derived based on hyperzoospermia reported
in the available human study (Chiaet al., 1996), but there is substantial uncertainty in this
estimate due to multiple issues.29 Among the rodent inhalation studies, the cRfC of 0.2 ppm
based on increased abnormal sperm in the mouse reported by Land et al. (1981) is considered
relatively reliable because it is based on BMD modeling rather than a LOAEL or NOAEL.
However, increased sperm abnormalities do not appear to be the most sensitive effect, as
Kumar et al. (2001b; 2000b) reported a similar POD to be a LOAEL for reported multiple effects
on sperm and testes, as well as altered testicular enzyme markers, in the rat. Although there are
greater uncertainties associated with the cRfC of 0.02 ppm for this effect and a composite UF of
3,000 was applied to the POD, the uncertainties are generally typical of those encountered in
RfC derivations.
Standard values of 3, 10, and 10 were used for the interspecies UF, the human variability
UF, and the LOAEL-to-NOAEL UF, respectively. In addition, although the study would have
qualified as a chronic exposure study based on its duration of 24 weeks (i.e., >10% of lifetime),
statistically significant decreases in testicular weight and in sperm count and motility were
already observed from subchronic exposure (12 weeks) to the same TCE exposure concentration
and these effects became more severe after 24 weeks of exposure. Moreover, several testicular
enzyme markers associated with spermatogenesis and germ cell maturation had significantly
altered activities after 12 weeks of exposure, with more severe alterations at 24 weeks, and
histological changes were also observed in the testes at 12 weeks, with the testes being severely
deteriorated by 24 weeks. Thus, since the single exposure level used was already a LOAEL from
subchronic exposure, and the testes were even more seriously affected by longer exposures, a
subchronic-to-chronic UF of 10 was applied.30 Note that for the cRfC derived for pre- and
postimplantation losses reported by Kumar et al. (2000b), the subchronic-to-chronic UF was not
applied because the exposure covered the time period for sperm development. This cRfC was
29Mean exposure estimates for the exposure groups were limited because they were defined in terms of ranges and
because they were based on mean urinary TCA (mg/g creatinine). There is substantial uncertainty in the conversion
of urinary TCA to TCE exposure level (see discussion of Mhiri et al. (2004), for neurotoxicity, above). In addition,
there was uncertainty about the adversity of the effect being measured. While rodent evidence supports effects of
TCE on sperm, and hyperzoospermia has reportedly been associated with infertility, the adversity of the
hyperzoospermia (i.e., high sperm density) outcome measured in the Chia et al. (1996) study is unclear.
Furthermore, the cut-point used to define hyperzoospermia in this study (i.e., >120 million sperm per mL ejaculate)
is lower than some other reported cut-points, such as 200 and 250 million sperm/mL. A BMR of 10% extra risk was
used on the assumption that this is a minimally adverse effect, but biological significance of this effect level is
unclear.
^Alternatively, the value of the LOAEL-to-NOAEL UF could have been increased above 10 to reflect the extreme
severity of the effects at the LOAEL after 24 weeks; however, the comparison of the 12- and 24-week results gives
such a clear depiction of the progression of the effects, it was more compelling to frame the issue as a subchronic-to-
chronic extrapolation issue.
5-35
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0.2 ppm, similar to that derived from Land et al. (1981) based on BMD modeling of increases in
abnormal sperm.
At a higher inhalation POD, Xu et al. (2004) reported decreased fertilization following
exposure in male mice, and Forkert et al. (2002) and Kan et al. (2007) reported effects on the
epididymal epithelium in male mice. Kan et al. (2007) reported degenerative effects on the
epididymis as early as 1 week into exposure that became more severe at 4 weeks of exposure
when the study ended; increases in abnormal sperm were also observed. As with the cRfC
developed from the Kumar et al. (200lb: 2000b) studies, a composite UF of 3,000 was applied to
these data, but the uncertainties are again typical of those encountered in RfC derivations.
Standard values of 3 for the interspecies UF, 10 for the human variability UF, 10 for the
LOAEL-to-NOAEL UF, and 10 for the subchronic-to-chronic UF were applied to each of the
study PODs.
Among the oral studies, cRfDs derived for decreased sperm motility and changes in
reproductive organ weights in rodents reported by George et al. (1986; 1985) were relatively
high (2-4 mg/kg/day), and these effects were not considered candidate critical effects. The
remaining available oral study of male reproductive effects is DuTeaux et al. (2004a), which
reported decreased ability of sperm from TCE-exposed rats to fertilize eggs in vitro. This effect
occurred in the absence of changes in combined testes/epididymes weight, sperm concentration
or motility, or histological changes in the testes or epididymes. DuTeaux et al. (2004a)
hypothesized that the effect is due to oxidative damage to the sperm. A LOAEL was used as the
POD, and the standard UF values of 10 were used for each of the UFs, i.e., the subchronic-to-
chronic UF (14-day study; substantially less than the 70-day time period for sperm
development), the interspecies UF for oral exposures, the human variability UF, and the
LOAEL-to-NOAEL UF. The resulting composite UF was 10,000,31 and this yielded a cRfD of
0.01 mg/kg/day. The excessive magnitude of the composite UF, however, highlights the
uncertainty in this estimate.
In summary, there is high qualitative confidence for TCE male reproductive tract toxicity
and lower confidence in the cRfCs and cRfDs that can be derived from the available studies.
Relatively high PODs are derived from several studies reporting less sensitive endpoints (George
et al., 1986; George et al., 1985; 1981), and correspondingly higher cRfCs and cRfDs suggest
that they are not likely to be critical effects. The studies reporting more sensitive endpoints also
tend to have greater uncertainty. For the human study by Chia et al. (1996), as discussed above,
there are uncertainties in the characterization of exposure and the adversity of the effect
measured in the study. For the Kumar et al. (2001b: 2000a: 2000b), Forkert et al. (2002), and
Kan et al. (2007) studies, the severity of the sperm and testes effects appears to be continuing to
31U.S. EPA's report on the RfC and RfD processes (U.S. EPA. 2002b') recommends not deriving reference values
with a composite UF of >3,000; however, composite UFs exceeding 3,000 are considered here because the
derivation of the cRfCs and cRfDs is part of a screening process and the subsequent application of the PBPK model
for candidate critical effects will reduce the values of some of the individual UFs.
5-36
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increase with duration even at the end of the study, so it is plausible that a lower exposure for a
longer duration may elicit similar effects. For the DuTeaux et al. (2004a) study, there is also
duration- and low-dose extrapolation uncertainty due to the short duration of the study in
comparison to the time period for sperm development as well as the lack of a NOAEL at the
tested doses. Overall, even though there are limitations in the quantitative assessment, there
remains sufficient evidence to consider these to be candidate critical effects.
5.1.2.7.2. Other reproductive effects
With respect to female reproductive effects, several studies reporting decreased maternal
weight gain were suitable for deriving candidate reference values (see Table 5-8). The cRfCs
from the two inhalation studies (Carney et al., 2006; Schwetz et al., 1975) yielded virtually the
same estimate (0.3-0.4 ppm), although the Carney et al. (2006) result is preferred due to the use
of BMD modeling, which obviates the need for the 10-fold LOAEL-to-NOAEL UF used for
Schwetz et al. (1975) (the other UFs, with a product of 30, were the same). The cRfDs for this
endpoint from the three oral studies were within twofold of each other (1.1-1.9 mg/kg/day), with
the same composite UFs of 100. The most sensitive estimate of Narotsky et al. (1995) is
preferred due to the use of BMD modeling and the apparent greater sensitivity of the rat strain
used.
With respect to other reproductive effects, the most reliable cRfD estimates of about
2 mg/kg/day, derived from BMD modeling with composite UFs of 100, are based on decreased
litters/pair and decreased live pups/litter in rats reported in the continuous breeding study of
George et al. (1986). Both of these effects were considered severe adverse effects, so a BMR of
a 0.5 control SD shift from the control mean was used. Somewhat lower cRfDs of 0.4-
1 mg/kg/day were derived based on delayed parturition in females (Narotsky et al., 1995),
decreased copulatory performance in males (Zenick et al., 1984), and decreased mating for both
exposed males and females in cross-over mating trials (George etal., 1986), all with composite
UFs of 100 or 1,000, depending on whether a LOAEL or NOAEL was used.
In summary, there is moderate confidence both in the hazard and the cRfCs and cRfDs
for reproductive effects other than the male reproductive effects discussed previously. While
there are multiple studies suggesting decreased maternal body weight with TCE exposure, this
systemic change may not be indicative of more sensitive reproductive effects. None of the
estimates developed from other reproductive effects is particularly uncertain or unreliable.
Therefore, delayed parturition (Narotsky et al., 1995) and decreased mating (George et al.,
1986), which yielded the lowest cRfDs, were considered candidate critical effects. These effects
were also included so that candidate critical reproductive effects from oral studies would not
include only that reported by DuTeaux et al. (2004a), from which deriving the cRfD entailed a
higher degree of uncertainty.
5-37
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5.1.2.8. Candidate Critical Developmental Effects on the Basis of Applied Dose
As summarized in Section 4.11.1.7, both human and experimental animal studies have
associated TCE exposure with adverse developmental effects. Weakly suggestive epidemiologic
data and fairly consistent experimental animal data support TCE exposure posing a hazard for
increased prenatal or postnatal mortality and decreased pre- or postnatal growth. In addition,
congenital malformations following maternal TCE exposure have been reported in a number of
epidemiologic and experimental animal studies. There is also some support for TCE effects on
neurological and immunological development. Available human studies, while indicative of
hazard, did not have adequate exposure information for quantitative estimates of PODs, so only
experimental animal studies are considered here. Studies with numerical dose-response
information are summarized in Table 5-9, with their corresponding cRfCs or cRfDs summarized
in Table 5-10.
For pre- and postnatal mortality and growth, a cRfC of 0.06 ppm for resorptions,
decreased fetal weight, and variations in skeletal development indicative of delays in ossification
was developed based on the single available (rat) inhalation study considered (Healy et al., 1982)
and utilizing the composite UF of 300 for an inhalation POD that is a LOAEL. The cRfDs for
pre- and postnatal mortality derived from oral studies were within about a 10-fold range of 0.4-
5 mg/kg/day, depending on the study and specific endpoint assessed. Of these, the estimate
based on Narotsky et al. (1995) rat data was both the most sensitive and most reliable cRfD. The
dose response for increased full-litter resorptions from this study is based on BMD modeling.
Because of the severe nature of this effect, a BMR of 1% extra risk was used. The ratio of the
resulting BMD to the BMDL was 5.7, which is on the high side, but given the severity of the
effect and the low background response, a judgment was made to use 1% extra risk.
Alternatively, a 10% extra risk could have been used, in which case the POD would have been
considered more analogous to a LOAEL than a NOAEL, and a LOAEL-to-NOAEL UF of 10
would have been applied, ultimately resulting in the same cRfD estimate. The cRfDs for altered
pre- and postnatal growth developed from the oral studies ranged about 10-fold from 0.8 to
8 mg/kg/day, all utilizing the composite UFs for the corresponding type of POD. The cRfDs for
decreased fetal weight, both of which were based on NOAELs, were consistent, being about
twofold apart (Narotsky et al., 1995; George et al., 1985). The cRfD based on postnatal growth
at 21 days, reported in George et al. (1986), was lower and is preferred because it was based on
BMD modeling. A BMR of 5% decrease in weight was used for postnatal growth at 21 days
because decreases in weight gain so early in life were considered similar to effects on fetal
weight.
5-38
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Table 5-9. Summary of studies of developmental effects suitable for dose-response assessment
Effect type
Study reference
Pre- and postnatal mortality
George et al. (1985)
Narotsky et al. (1995)
Manson et al. (1984)
Healy et al. (1982)
Pre- and postnatal growth
Healy et al. (1982)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Mouse, CD-I, male and
female, 20 pairs/treatment
group; 40 controls/sex
Rat, F344, females, 8-
12 dams/group
Rat, Long-Evans, female,
23-25/group
Rat, Wistar, females, 3 1-
32 dams/group
Rat, Wistar, females, 3 1-
32 dams/group
Exposure(s) used for
dose-response
assessment
Oral: 0, 173, 362, or
737 mg/kg/d, Breeders
exposed 1 wk premating,
then for 13 wks; pregnant
females exposed
throughout gestation (i.e.,
18 wks total)
Oral:0, 10.1,32,101,
320, 475, 633, 844, or
1,125 mg/kg/d, CDs 6-15
Oral: 0, 10, 100, or
1,000 mg/kg/d, 6 wks:
2 wks premating, 1 wk
mating period, GDs 1-21
Inhalation: 0 or 100 ppm,
4 hrs/d; GDs 8-21
Inhalation: 0 or 100 ppm,
4 hrs/d; GDs 8-21
Endpoint(s) used for dose-response
assessment
Increase perinatal mortality (PNDs 0-21)
Increased resorptions, prenatal loss, and
postnatal mortality
Increased neonatal deaths on PNDs 1, 10,
and 14.
Increased resorptions.
Decreased fetal weight, increased
bipartite, or absent skeletal ossification
centers
Chapter 4
Section/Table
Section 4.8.1.2 and
4.8.3.2
Table 4-87
Table 4-98
Table 4-87
Table 4-96
Section 4.8.3.2
Table 4-96
5-39
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Table 5-9. Summary of studies of developmental effects suitable for dose-response assessment (continued)
Effect type
Study reference
Narotsky et al. (1995)
George et al. (1985)
George et al. (1986)
Congenital defects
Narotsky et al. (1995)
Johnson et al. (2003)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Rat, F344, females, 8-
12 dams/group
Mouse, CD-I, male and
female, 20 pairs/treatment
group; 40 controls/sex
Rat, F334, males and
female, 20 pairs/treatment
group, 40 controls/sex
Rat, F344, females, 8-
12 dams/group
Rat, Sprague-Dawley,
female, 9-13/group, 55 in
control group
Exposure(s) used for
dose-response
assessment
Oral:0, 10.1,32,101,
320, 475, 633, 844, or
1, 125 mg/kg/d, CDs 6-15
Oral: 0, 173, 362, or
737 mg/kg/d, Breeders
exposed 1 wk premating,
then for 13 wks; pregnant
females exposed
throughout gestation (i.e.,
18 wks total)
Oral: 0, 72, 186, or
389 mg/kg/d (estimated),
Breeders exposed 1 wk
premating, then for
13 wks; pregnant females
exposed throughout
gestation (i.e., 18 wks
total)
Oral:0, 10.1,32,101,
320, 475, 633, 844, or
1,125 mg/kg/d, CDs 6-15
Oral: 0, 0.00045, 0.048,
0.218, or 129 mg/kg/d),
CDs 0-22
Endpoint(s) used for dose-response
assessment
Decreased pup body weight on PNDs 1
and 6.
Decreased live birth weights, PND 4 pup
body weights.
Decreased Fl body weight on PNDs 4-
80.
Increased incidence of eye defects.
Increased percentage of abnormal hearts;
increased percentage of litters with
abnormal hearts.
Chapter 4
Section/Table
Table 4-98
Table 4-87
Table 4-87
Section 4.8.3.2
Table 4-98
Table 4-98
5-40
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Table 5-9. Summary of studies of developmental effects suitable for dose-response assessment (continued)
Effect type
Study reference
Developmental neurotoxicity
George et al. (1986)
Fredriksson et al. (1993)
Taylor et al. (1985)
Isaacson and Taylor (1989)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Rat, F334, males and
female, 20 pairs/treatment
group, 40 controls/sex
Mouse, NMRI, male
pups, 12 pups from 3 to
4 different litters/group
Rat, Sprague-Dawley,
females, no. dams/group
not reported
Rat, Sprague-Dawley,
females, 6 dams/group
Exposure(s) used for
dose-response
assessment
Oral: 0, 72, 186, or
389 mg/kg/d (estimated),
Breeders exposed 1 wk
premating, then for
13 wks; pregnant females
exposed throughout
gestation (i.e., 18 wks
total)
Oral: 0, 50, or 290
mg/kg/d, PNDs 10-16
Oral:0, 312,625, or
1,250 mg/L (0, 45, 80, or
140 mg/kg/d estimated),
dams (and pups) exposed
from 14 d prior to mating
until end of lactation
Oral:0, 4.0, or8.1mg/d
(0, 15, or 32 mg/kg/d
estimated)3, dams (and
pups) exposed from 14 d
prior to mating until end
of lactation.
Endpoint(s) used for dose-response
assessment
Decreased locomotor, as assessed by
increased time required for pups to cross
the first grid in open-field testing.
Decreased rearing activity on PND 60.
Increased exploratory behavior in 60- and
90-d-old male rats (offspring).
Decreased myelinated fibers in the
stratum lacunosum-moleculare of pups;
decreased myelin in the hippocampus.
Chapter 4
Section/Table
Sections 4.3.8.2 and
4.8.3.2
Tables 4-34 and 4-98
Tables 4-34 and 4-98
Tables 4-34 and 4-98
Tables 4-34 and 4-98
5-41
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Table 5-9. Summary of studies of developmental effects suitable for dose-response assessment (continued)
Effect type
Study reference
Developmental immunotoxicity
Peden-Adams et al. (2006)
Species, strain (if
applicable), sex, number
used for dose-response
assessment
Mouse, B6C3F!, dams
and both sexes offspring,
5 dams/group; 5-
7 pups/group at 3 wks; 4-
5 pups/sex/group at 8 wks
Exposure(s) used for
dose-response
assessment
Oral: 0, 1,400, or
14,000 ppb in water (0,
0.37, or3.7mg/kg/d
estimated), parental mice
and/or offspring exposed
during mating, and from
CDs 0 through 3 or 8 wks
of age
Endpoint(s) used for dose-response
assessment
Suppressed PFC responses in males and
in females. Delayed hypersensitivity
response increased at 8 wks of age in
females. Splenic cell population
decreased in 3-wk-old pups. Increased
thymic T-cells at 8 wks of age. Delayed
hypersensitivity response increased at
8 wks of age in males and females
Chapter 4
Section/Table
Section 4.8.3.2
Table 4-98
The Isaacson and Taylor (1989) and Taylor et al. (1985) studies report different doses despite identical study designs and administered concentrations, both
studies taking TCE degradation into account. Taylor et al. (1985) report total consumption of 646, 1,102, and 1,991 mg TCE for rats exposed to 312, 625, and
1,250 mgTCE/L drinking water, respectively. Dividing by the 56 days of exposure and the average 250 g per rat for female Sprague-Dawley rats of those ages
yields estimated doses of roughly 45, 80, and 140 mg/kg/day, respectively. Isaacson and Taylor (1989) report average doses of TCE of 4.0 and 8.1 mg/day
corresponding to exposures of 312 and 625 mg TCE/L drinking water, respectively. Dividing by the average 250 g per rat yields estimated doses of 16 and
32 mg/kg/day, respectively. Thus, the estimated doses for Taylor et al. (1985) are nearly 3 times higher than those for Isaacson and Taylor (1989). for reasons
unknown.
5-42
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Table 5-10. Developmental effects in studies suitable for dose-response assessment, and corresponding cRfCs
and cRfDs
Effect type
Supporting studies3
Species
POD
type
PODb
UFS
UFA
UFH
UFL
UFD
UFC
cRfC
(ppm)
cRfD
(mg/kg/d)
Effect; comments
Pre- and postnatal mortality
George et al. (1985)
Narotsky et al. (1995)
Manson et al. (1984)
Healy et al. (1982)
Narotsky et al. (1995)
Narotsky etal. (1995)
Mouse
Rat
Rat
Rat
Rat
Rat
NOAEL
LOAEL
NOAEL
LOAEL
BMDL
BMDL
362
475
100
17
469
32.2
1
1
1
1
1
1
10
10
10
3
10
10
10
10
10
10
10
10
1
10
1
10
1
1
1
1
1
1
1
1
100
1,000
100
300
100
100
0.057
3.6
0.48
1.0
4.7
0.32
t perinatal mortality
Postnatal mortality; Manson et al.
(1984) cRfD preferred for same
endpoint due to NOAEL vs. LOAEL
t neonatal death
Resorptions
Prenatal loss; BMR = 1% extra risk
Resorptions; BMR = 1% extra risk
Pre- and postnatal growth
Healy et al. (1982)
Narotsky et al. (1995)
George et al. (1985)
George et al. (1986)
Rat
Rat
Mouse
Rat
LOAEL
NOAEL
NOAEL
BMDL
17
844
362
79.7
1
1
1
1
3
10
10
10
10
10
10
10
10
1
1
1
1
1
1
1
300
100
100
100
0.057
8.4
3.6
0.80
I fetal weight; skeletal effects
I fetal weight
I fetal weight
| Body weight at d21; BMR = 5%
decrease
Congenital defects
Narotsky et al. (1995)
Johnson et al. (2003)
Johnson et al. (2003)
Rat
Rat
Rat
BMDL
BMDL
BMDL
60
0.0146
0.0207
1
1
1
10
10
10
10
10
10
1
1
1
1
1
1
100
100
100
0.60
0.00015
0.00021
Eye defects; low BMR (1%), but
severe effect and low background, rate
(<1%)
Heart malformations (litters);
BMR = 10% extra risk (only -1/10
from each litter affected); highest-dose
group (1,000-fold higher than next
highest) dropped for model fit.
Heart malformations (pups);
BMR = 1% extra risk; preferred due to
accounting for intralitter effects via
nested model and pups being the unit
of measure; highest-dose group
(1,000-fold higher than next highest)
dropped for model fit
5-43
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Table 5-10. Developmental effects in studies suitable for dose-response assessment, and corresponding cRfCs
and cRfDs (continued)
Effect type
Supporting studies3
Species
POD
type
PODb
UFS
UFA
UFH
UFL
UFD
UFC
cRfC
(ppm)
cRfD
(mg/kg/d)
Effect; comments
Developmental neurotoxicity
George et al. (1986)
Fredriksson et al.
(1993)
Taylor et al. (1985)
Isaacson and Taylor
(1989)
Rat
Mouse
Rat
Rat
BMDL
LOAEL
LOAEL
LOAEL
72.6
50
45
16
1
3
1
1
10
10
10
10
10
10
10
10
1
10
10
10
1
1
1
1
100
3,000
1,000
1,000
0.73
0.017
0.045
0.016
1 locomotor activity; BMR = doubling
of traverse time; results from females
(males similar with BMDL = 92)
J, rearing postexposure; pup gavage
dose; no effect at tested doses on
locomotion behavior; UFS = 3 because
exposure only during PNDs 10-16
t exploration postexposure; estimated
dam dose; less sensitive than Isaacson
and Taylor (1989), but included
because exposure is preweaning, so
can utilize PBPK model
I myelination in hippocampus;
estimated dam dose
Developmental immunotoxicity
Peden-Adams et al.
(2006)
Mouse
LOAEL
0.37
1
10
10
10
1
1,000
0.00037
i PFC, f DTH; POD is estimated dam
dose (exposure throughout gestation
and lactation + to 3 or 8 wks of age);
UF LOAEL = 10 since multiple
immunotoxicity effects
aShaded studies/endpoints were selected as candidate critical effects/studies.
bAdjusted to continuous exposure unless otherwise noted. For inhalation studies, adjustments yield a POD that is a HEC as recommended for a Category 3 gas in
U.S. EPA (1994a) in the absence of PBPK modeling. Same units as cRfC (ppm) or cRfD (mg/kg/day).
'Product of individual UFs.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-44
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For congenital defects, there is relatively high confidence in the cRfD for eye defects in
rats reported in Narotsky et al. (1995), derived using a composite UF of 100 for BMD modeling
in a study of duration that encompasses the full window of eye development. However, the most
sensitive developmental effect by far was heart malformations in the rat reported by
Johnson et al. (2003), yielding a cRfD estimate of 0.0002 mg/kg/day, also with a composite UF
of 100. As discussed in detail in Section 4.8 and summarized in Section 4.11.1.7, although this
study has important limitations, the overall weight of evidence supports an effect of TCE on
cardiac development, and this is the only study of heart malformations available for conducting
dose-response analysis. Individual data were kindly provided by Dr. Johnson (personal
communication from Paula Johnson, University of Arizona, to Susan Makris, EPA, 25 August
2008), and, for analyses for which the pup was the unit of measure, BMD modeling was done
using nested models because accounting for the intralitter correlation improved model fit. For
these latter analyses, a 1% extra risk of a pup having a heart malformation was used as the BMR
because of the severity of the effect, since, for example, some of the types of malformations
observed could have been fatal. The ratio of the resulting BMD to the BMDL was about three.
For developmental neurotoxicity, the cRfD estimates based on the four oral studies span a
wide range from 0.02 to 0.8 mg/kg/day. The most reliable estimate, with a composite UF of 100,
is based on BMD modeling of decreased locomotor activity in rats reported in George et al.
(1986), although a nonstandard BMR of a twofold change was selected because the control SD
appeared unusually small. The cRfDs developed for decreased rearing postexposure in mice
(Fredriksson et al., 1993), increased exploration postexposure in rats (Taylor et al., 1985), and
decreased myelination in the hippocampus of rats (Isaacson and Taylor, 1989), while being
>10-fold lower, are all within a 3-fold range of 0.02-0.05 mg/kg/day. Importantly, there is some
evidence from adult neurotoxicity studies of TCE causing demyelination, so there is additional
biological support for the latter effect. There is greater uncertainty in the Fredriksson et al.
(1993), the cRfD for which utilized a subchronic-to-chronic UF of 3 rather than 1, because
exposure during PND 10-16 does not cover the full developmental window (Rice and Bar one,
2000). The cRfDs derived from Taylor et al. (1985) and (Isaacson and Taylor, 1989) used the
composite UF of 1,000 for a POD that is a LOAEL. While there is greater uncertainty in these
endpoints, none of the uncertainties is particularly high, and they also appear to be more
sensitive indicators of developmental neurotoxicity than that from George et al. (1986).
A cRfD of 0.0004 mg/kg/day was developed from the study (Peden-Adams et al., 2006)
that reported developmental immunotoxicity. The main effects observed were significantly
decreased PFC response and increased delayed-type hypersensitivity. The data on these effects
were kindly provided by Dr. Peden-Adams (personal communication from Margie Peden-
Adams, Medical University of South Carolina, to Jennifer Jinot, EPA, 26 August 2008):
however, the dose-response relationships were sufficiently supralinear that attempts at BMD
modeling did not result in adequate fits to these data. Thus, the LOAEL was used as the POD.
5-45
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A LOAEL-to-NOAEL UF of 10 was used for the multiple effects of decreased PFC response and
increased delayed-type hypersensitivity at the same dose. While there is uncertainty in this
estimate, it is notable that decreased PFC response was also observed in an immunotoxicity
study in adult animals (Woolhiser et al., 2006), lending biological plausibility to the effect.
In summary, there is moderate-to-high confidence both in the hazard and the cRfCs and
cRfDs for developmental effects of TCE. It is also noteworthy that the PODs for the more
sensitive developmental effects were similar to or, in most cases, lower than the PODs for the
more sensitive reproductive effects, suggesting that developmental effects are not a result of
paternal or maternal toxicity. Among inhalation studies, cRfCs were only developed for effects
in rats reported in Healy et al. (1982), so the effects of resorptions, decreased fetal weight, and
delayed skeletal ossification were considered candidate critical developmental effects. Because
resorptions were also reported in oral studies, the most sensitive (rat) oral study (and most
reliable for dose-response analysis) of Narotsky et al. (1995) was also selected as a candidate
critical study for this effect. The confidence in the oral studies and candidate reference values
developed for more sensitive endpoints is more moderate, but still sufficient for consideration as
candidate critical effects. The most sensitive endpoints by far are the increased fetal heart
malformations in rats reported by Johnson et al. (2003) and the developmental immunotoxicity in
mice reported by Peden-Adams et al. (2006), and these are both considered candidate critical
effects. Neurodevelopmental effects are a distinct type among developmental effects. Thus, the
next most sensitive endpoints of decreased rearing postexposure in mice (Fredriksson et al.,
1993), increased exploration postexposure in rats (Taylor et al., 1985), and decreased
myelination in the hippocampus of rats (Isaacson and Taylor, 1989) are also considered
candidate critical effects.
5.1.2.9. Summary of cRfCs, cRfDs, and Candidate Critical Effects
An overall summary of the cRfCs, cRfDs, and candidate critical effects across the health
effect domains is shown in Tables 5-11 and 5-12. These tables present, for each type of
noncancer effect, the relative ranges of the cRfC and cRfD developed for the different endpoints.
The candidate critical effects selected above for each effect domain are shown in bold. As
discussed above, these effects were generally selected to represent the most sensitive endpoints,
across species where possible. From these candidate critical effects, candidate reference values
based on internal dose-metrics from the PBPK model (p-cRfCs and p-cRfDs) were developed
where possible. Effects within the same health effect domain were generally assumed to have
the same relevant internal dose-metrics; thus, screening for the effects with the lowest cRfCs and
cRfDs for each species within health effect domains on the basis of applied dose should capture
the same endpoints which would have the lowest candidate reference values on the basis of an
appropriate dose-metric. Application of the PBPK model is discussed in the next section.
5-46
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Table 5-11. Ranges of cRfCs based on applied dose for various noncancer effects associated with inhalation
TCE exposure"
cRfC range
(ppm)
10-100
1-10
0.1-1
0.01-0.1
Neurological
Impaired visual discrimination
(rat)
Ototoxicity (rat)
Hyperactivity (rat)
Changes in locomotor activity
(rat)
Trigeminal nerve effects
(human)
Impaired visual function
(rabbit)
J, regeneration of sciatic
nerve (rat)
J, regeneration of sciatic
nerve (mouse)
Disturbed wakefulness (rat)
Systemic/organ-specific
Kidney
meganucleocytosis (rat)
| kidney weight (mouse)
| liver weight (rat)
| liver weight (mouse)
| kidney weight (rat)
Immunological
I PFC response (rat)
Autoimmune changes
(MRL — Ipr/lpr mouse)
Reproductive
I maternal body weight gain
(rat)
t abnormal sperm (mouse)
pre/postimplantation losses
(male rat exp)
Effects on epididymis
epithelium (mouse)
J, fertilization (male mouse
exp)
Testes and sperm effects (rat)
Hyperzoospermia (human)
Developmental
Resorptions (female rat)
J, fetal weight (rat)
Skeletal effects (rat)
aEndpoints in bold were selected as candidate critical effects (see Sections 5.1.2.1-5.1.2.8).
5-47
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Table 5-12. Ranges of cRfDs based on applied dose for various noncancer effects associated with oral TCE
exposure3
cRfD range
(mg/kg/d)
1-10
0.1-1
0.01-0.1
0.001-0.01
io-4-o.ooi
Neurological
t neuromuscular changes
(rat)
t number rears (rat)
t foot splaying (rat)
Trigeminal nerve effect
(rat)
Degeneration of
dopaminergic neurons
(rat)
Demyelination in
hippocampus (rat)
Systemic/organ-specific
I Body weight (mouse)
t liver weight (mouse)
J, Body weight (mouse)
J, Body weight (rat)
Toxic nephropathy (other
rat strains/sexes and mouse)
Meganucleocytosis (male
Sprague-Dawley rat)
Toxic nephropathy (female
Marshall rat)
Immunological
I humoral response to
SRBC (mouse)
Signs of autoimmune
hepatitis (MRL +/+ mouse)
Inflammation in various
tissues (MRL +/+ mouse)
J, cell-mediated response
to SRBC (mouse)
J, stem cell bone marrow
recolonization (mouse)
t anti-dsDNA and anti-
ssDNA Abs (early marker
for autoimmune disease)
(mouse)
J, thymus weight (mouse)
Reproductive
I testis/seminal vesicle
weight (mouse)
1 sperm motility (mouse)
t testis/epididymis weight
(rat)
1 litters/pair (rat)
J, live pups/litter (rat)
J, Body weight gain (rat)
J, copulatory performance
(rat)
Delayed parturition (rat)
J, mating (rat)
J, ability of sperm to
fertilize (rat)
Developmental
I fetal weight (rat)
Prenatal loss (rat)
I fetal weight (mouse)
t neonatal mortality (mouse,
rat)
| Body weight at PND 21
(rat)
J, locomotor activity (rat)
Eye defects (rat)
Resorptions (rat)
t exploration
(postexposure) (rat)
J, rearing (postexposure)
(mouse)
J, myelination in
hippocampus (rat)
Immunotoxicity (J, PFC,
t DTK) (B6C3F! mouse)
Heart malformations (rat)
aEndpoints in bold were selected as candidate critical effects (see Sections 5.1.2.1-5.1.2.8).
5-48
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5.1.3. Application of PBPK Model to Inter- and Intraspecies Extrapolation for Candidate
Critical Effects
For the candidate critical effects, the use of PBPK modeling of internal doses could
justify, where appropriate, replacement of the UFs for pharmacokinetic inter- and intraspecies
extrapolation. For more details on PBPK modeling used to estimate levels of dose-metrics
corresponding to different exposure scenarios in rodents and humans, as well as a qualitative
discussion of the uncertainties and limitations of the model, see Section 3.5.
Quantitative analyses of the PBPK modeling uncertainties and their implications for
dose-response assessment, utilizing the results of the Bayesian analysis of the PBPK model, are
discussed separately in Section 5.1.4.
5.1.3.1. Selection of Dose-metrics for Different Endpoints
One area of scientific uncertainty in noncancer dose-response assessment is the
appropriate scaling between rodent and human doses for equivalent responses. As discussed
above, the interspecies UF of 10 is usually thought of as a product of two factors of
(approximately) three each for pharmacokinetics (UFA-Pk) and pharmacodynamics (UFA-Pd). In
this assessment, EPA's cross-species scaling methodology, grounded in general principles of
allometric variation of biologic processes, is used for describing pharmacokinetic equivalence
(U.S. EPA, 1992. 201 la. 2005b: Allen and Fisher. 1993: Crump etal.. 1989: Allen etal.. 1987).
Briefly, in the absence of adequate information to the contrary, the methodology determines
pharmacokinetic equivalence across species through equal average lifetime concentrations or
AUCs of the toxicant. Thus, in cases where the PBPK model can predict internal concentrations
of the active moiety, equivalent daily AUCs are assumed to address cross-species
pharmacokinetics, and the interspecies UF is reduced to 3 to account for the remaining
pharmacodynamic factor.
In the absence of directly estimated AUCs, the cross-species scaling methodology
assumes that, unless there is evidence to the contrary (U.S. EPA, 1992, 201 la, 2005b):
(1) The production of the active moiety(ies) is proportional to dose
(2) The clearance of the active moiety(ies) scales allometrically by body weight to the
3/4 power; and
(3) The tissue distribution is equal across species.
Under these assumptions, for oral exposures, pharmacokinetic equivalence of AUCs
between animals to humans is expressed on the basis of mg/kg Vday, not mg/kg/day (—body
weight scaling"). For inhalation exposures, pharmacokinetic equivalence would be on the basis
of equivalent air concentrations, since the alveolar ventilation rate (which determines dose, for a
5-49
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constant air concentration) scales approximately by body weight to the % power, cancelling out
the assumed scaling dependence of clearance.
However, when one or more metabolites are thought to be the lexicologically active
compound(s), it is often the case that a PBPK model can predict the rate of production of the
active moiety(ies) (i.e., the rate of metabolism) but cannot predict AUCs due to lack of data to
inform clearance. In this case, assumption (1) above can be replaced by the PBPK model, while
the other two cross-species scaling methodology assumptions are retained. The resulting
pharmacokinetic equivalence can therefore be expressed on the basis of rate of
metabolism/kg Vday.32 Thus, in cases where the PBPK model can predict the rate of production
of the active metabolite(s), equivalent daily amounts metabolized through the appropriate
pathway per unit body weight to the % power are assumed to address cross-species
pharmacokinetics, and the interspecies UF is reduced to 3 to account for the remaining
pharmacodynamic factor.
In addition, in some cases when AUCs cannot be estimated, there are data to replace
assumption (2), above, that the clearance of the active moiety(ies) scales allometrically by body
weight to the 3/4 power. Often, this is considered for toxicity associated with local (in situ)
production of —reative" metabolites whose concentrations cannot be directly measured in the
target tissue. In such a case, an alternative approach of scaling the rate of local metabolism by
target tissue mass, rather than body weight to the % power, is appropriate if the metabolites are
sufficiently reactive and are cleared by —spoiatneous" deactivation (i.e., changes in chemical
structure without the need of biological influences). In particular, use of this alternative scaling
approach requires evidence that: (1) the active moiety or moieties do not leave the target tissue
in appreciable quantities (i.e., are cleared primarily by in situ transformation to other chemical
species and/or binding to/reactions with cellular components), and (2) the clearance of the active
moieties from the target tissue is governed by biochemical reactions whose rates are independent
of body weight (e.g., purely chemical reactions). If these conditions are met, equivalent daily
amounts metabolized through the appropriate pathway per unit target tissue mass are assumed to
address cross-species pharmacokinetics, and the interspecies UF is reduced to 3 to account for
the remaining pharmacodynamic factor.
32Consider a circulating stable metabolite X. Under a one-compartment model, at steady-state, the production of X
will be equal to the clearance of X, so that
where Rmet = rate of production of X (mg/time), Vd = fractional volume of distribution, BW = body weight,
Cx = concentration of X and kd = clearance of X in units of I/time. Then, for the concentration Cx to be equivalent
between experimental animals (A) and humans (H):
Cx = [RmeJBW x kcl *Vd}H = [RmeJBW x kd x Vd}A.
Under the cross-species scaling methodology, it is assumed that Vd is the same across species, so
[RmetIBW x kci]H = [RmejBW x kd]A. Next, under the cross-species scaling methodology, kd (with units of I/time) is
assumed to scale according to BW1M (U.S. EPA, 2005b: U.S. EPA, 201 la), leading to:
D /DTJ/ 3/4 _ n /DTJ/ 3/4
f^met (H/-^ YY H ~ ^met (A)'™ ** A
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Finally, there is the case where local metabolism, rather than systemically delivered
metabolite(s), is thought to be involved in toxicity, but there are inadequate data to determine
either the rate of local metabolism or its clearance. In this case, assumption (1) above can be
replaced by the assumption that local metabolism will be proportional to blood concentration.
Because tissue blood flow approximately scales allometrically by body weight to the 3/4 power,
combining this with assumptions (2) and (3) above will lead to the AUC of the parent compound
in blood as an appropriate surrogate for local metabolism. Thus, in this case, equivalent daily
AUCs of the parent compound are assumed to address cross-species pharmacokinetics, and the
interspecies UF is reduced to 3 to account for the remaining pharmacodynamic factor.
To summarize, the internal dose-metric for addressing cross-species pharmacokinetics is
based on the Agency's cross-species scaling methodology. The preferred dose-metric under this
methodology is equivalent daily AUC of the active moiety (parent compound or metabolite).
For metabolites, in cases where the rate of production, but not the rate of clearance, of the active
moiety can be estimated, the preferred dose-metric is the rate of metabolism (through the
appropriate pathway) scaled by body weight to the 3/4 power. If there are sufficient data to
consider the active metabolite moiety(ies) reactive" and cleared through nonbiological
processes, then the preferred dose-metric is the rate of metabolism (through the appropriate
pathway) scaled by the tissue mass. Finally, if local metabolism is thought to be involved, but
cannot be estimated with the available data, then the AUC of the parent compound in blood is
considered an appropriate surrogate and thus the preferred dose-metric.
These dose-metrics were then also used in addressing the pharmacokinetic component,
UFn-pk, of the UF for human (intraspecies) variability. Because all of the dose-metrics used for
TCE were for adults, and the dose-metrics are not very sensitive to the plausible range of adult
body weight, for convenience the body weight 3/4 scaling used for interspecies extrapolation was
retained for characterization of human variability. However, it should be emphasized that this
intraspecies characterization is of pharmacokinetics only, and not pharmacodynamics.
In general, an attempt was made to use tissue-specific dose-metrics representing
particular pathways or metabolites identified from available data on the role of metabolism in
toxicity for each endpoint (discussed in more detail below). The selection was limited to dose-
metrics for which uncertainty and variability could be adequately characterized by the PBPK
model (see Section 3.5). For most endpoints, sufficient information on the role of metabolites or
mode of action was not available to identify likely relevant dose-metrics, and more —upsfeam"
metrics representing either parent compound or total metabolism had to be used. The —pmary"
or -preferred" dose-metric referred to in subsequent tables has the greater biological support for
its involvement in toxicity, whereas -alternative" dose-metrics are those that may also be
plausibly involved (discussed further below). A discussion of the dose-metrics selected for
particular noncancer endpoints follows.
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5.1.3.1.1. Kidney toxicity (meganucleocytosis, increased kidney weight, toxic
nephropathy)
As discussed in Sections 4.4.6-4.4.7, there is sufficient evidence to conclude that
TCE-induced kidney toxicity is caused predominantly by GSH conjugation metabolites either
produced in situ in or delivered systemically to the kidney. As discussed in Section 3.3.3.2,
bioactivation of DCVG, DCVC, and 7V-acetyl-S-(l,2-dichlrovinyl)-L-cysteine (NAcDCVC)
within the kidney, either by beta-lyase, flavin mono-oxygenase (FMO), or CYP, produces
reactive species, any or all of which may cause nephrotoxicity. Therefore, multiple lines of
evidence support the conclusion that renal bioactivation of DCVC is the preferred basis for
internal dose extrapolations for TCE-induced kidney toxicity. However, uncertainties remain as
to the relative contribution from each bioactivation pathway; and quantitative clearance data
necessary to calculate the concentration of each species are lacking. Moreover, the estimates of
the amount bioactivated are indirect, derived from the difference between overall GSH
conjugation flux and NAcDCVC excretion (see Section 3.5.7.3.1).
Under the cross-species scaling methodology, the rate of renal bioactivation of DCVC
would be scaled by body weight to the 3/4 power. However, it is necessary to consider whether
there are adequate data to support use of the alternative scaling by target tissue mass. For the
beta-lyase pathway, Dekant et al. (1988) reported in trapping experiments that the postulated
reactive metabolites decompose to stable (unreactive) metabolites in the presence of water.
Moreover, the necessity of a chemical trapping mechanism to detect the reactive metabolites
suggests a very rapid reaction such that it is unlikely that the reactive metabolites leave the site
of production. Therefore, these data support the conclusion that, for this bioactivation pathway,
clearance is chemical in nature and hence species-independent. If this were the only
bioactivation pathway, then scaling by kidney weight would be supported. With respect to the
FMO bioactivation pathway, Sausen and Elfarra (1991) reported that after direct dosing of the
postulated reactive sulfoxide (DCVC sulfoxide), the sulfoxide was detected as an excretion
product in bile. These data suggest that reactivity in the tissue to which the sulfoxide was
delivered (the liver, in this case) is insufficient to rule out a significant role for enzymatic or
other biologically mediated systemic clearance. Therefore, according to the criteria outlined
above, for this bioactivation pathway, the data support scaling the rate of metabolism by body
weight to the % power. For P450-mediated bioactivation producing NAcDCVC sulfoxide, the
only relevant data on clearance are from a study of the structural analogue to DCVC,
fluoromethyl-2,2-difluoro-l-(trifluoromethyl)vinyl ether (FDVE) (Sheffels et al.. 20041 which
reported that the postulated reactive sulfoxide was detected in urine. This suggests that the
sulfoxide is sufficiently stable to be excreted by the kidney and supports the scaling of the rate of
metabolism by body weight to the % power.
Therefore, because the contributions to TCE-induced nephrotoxicity from each possible
bioactivation pathway are not clear, and the scaling by body weight to the % power is supported
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for two of the identified three bioactivation pathways, it is decided here to scale the DCVC
bioactivation rate by body weight to the 3/4 power. The primary internal dose-metric for
TCE-induced kidney toxicity is thus, the weekly rate of DCVC bioactivation per unit body
weight to the % power (ABioactDCVCBW34 [mg/kgy7week]). However, it should be noted
that due to the larger relative kidney weight in rats as compared to humans, scaling by kidney
weight instead of body weight to the % power would only change the quantitative interspecies
extrapolation by about twofold,33 so the sensitivity of the results to the scaling choice is relatively
small. In addition, quantitative estimates for this dose-metric are only available in rats and
humans, and not in mice. Accordingly, this metric was only used for extrapolating results from
rat toxicity studies.
An alternative dose-metric that also involves the GSH conjugation pathway is the amount
of GSH conjugation scaled by the % power of body weight (AMetGSHBW34 [mg/kgy7week]).
This dose-metric uses the total flux of GSH conjugation as the toxicologically-relevant dose,
and, thus, incorporates any direct contributions from DCVG and DCVC, which are not addressed
in the DCVC bioactivation metric. The rationale for scaling by body weight to the 3/4 power
rather than target tissue mass is the same as above. Because of the lack of availability of the
DCVC bioactivation dose-metric in mice, the GSH conjugation metric is used as the primary
dose-metric for the nephrotoxicity endpoint in studies of mice.
Another alternative dose-metric is the total amount of TCE metabolism (oxidation and
GSH conjugation together) scaled by the % power of body weight (TotMetabBW34
[mg/kgy7week]). This dose-metric uses the total flux of TCE metabolism as the lexicologically
relevant dose, and, thus, incorporates the possible involvement of oxidative metabolites, acting
either additively or interactively, in addition to GSH conjugation metabolites in nephrotoxicity
(see Section 4.4.6). However, this dose-metric is given less weight than those involving GSH
conjugation because, as discussed in Sections 4.4.6, the weight of evidence supports the
conclusion that GSH conjugation metabolites play a predominant role in nephrotoxicity. The
rationale for scaling by body weight to the 3/4 power rather than target tissue mass is the same as
above.
5.1.3.1.2. Liver weight increases (hepatomegaly)
As discussed in Section 4.5.6, there is substantial evidence that oxidative metabolism is
involved in TCE hepatotoxicity, based primarily on similarities in noncancer effects with a
number of oxidative metabolites of TCE (e.g., CH, TCA, and DCA). While TCA is a stable,
circulating metabolite, CH and DCA are relatively short-lived, although enzymatically cleared
(see Section 3.3.3.1). As discussed in Section 4.5.6.2.1, there is substantial evidence that TCA
33The range of the difference is 2.1-2.4-fold using the posterior medians for the relative kidney weight in rats and
humans from the PBPK model described in Section 3.5 (see Table 3-38), and body weights of 0.3-0.4 kg for rats
and 60-70 kg for humans.
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alone does not adequately account for the hepatomegaly induced by TCE; therefore, unlike in
previous dose-response analyses (Clewell and Andersen, 2004; Barton and Clewell 2000), the
AUC of TCA in plasma or in liver were not considered as dose-metrics. However, there are
inadequate data across species to quantify the dosimetry of CH and DC A, and other
intermediates of oxidative metabolism (such as TCE-oxide or dichloroacetylchloride) may be
involved in hepatomegaly. Thus, due to uncertainties as to the active moiety(ies), but given the
strong evidence associating TCE liver effects with oxidative metabolism in the liver, hepatic
oxidative metabolism is the preferred basis for internal dose extrapolations of TCE-induced liver
weight increases.
Under the cross-species scaling methodology, the rate of hepatic oxidative metabolism
would be scaled by body weight to the % power. However, it is necessary to consider whether
there are adequate data to support use of the alternative scaling by target tissue mass. Several of
the oxidative metabolites are stable and systemically available, and several of those that are
cleared rapidly are metabolized enzymatically, so, according to the criteria discussed above,
there are insufficient data to support the conclusions that the active moiety or moieties do not
leave the target tissue in appreciable quantities and are cleared by mechanisms whose rates are
independent of body weight.
Therefore, the primary internal dose-metric for TCE-induced liver weight changes is
selected to be the weekly rate of hepatic oxidation per unit body weight to the % power
(AMetLivlBW34 [mg/kg Vweek]). The use of this dose-metric is also supported by the analysis
in Section 4.5.6.2.1 showing much more consistency in the dose-response relationships for
TCE-induced hepatomegaly across studies and routes of exposure using this metric and the total
oxidative metabolism dose-metric (discussed below) as compared to the AUC of TCE in blood.
It should be noted that due to the larger relative liver weight in mice as compared to humans,
scaling by liver weight instead of body weight to the % power would only change the
quantitative interspecies extrapolation by about fourfold,34 so the sensitivity of the results to the
scaling choice is relatively modest.
It is also known that the lung has substantial capacity for oxidative metabolism, with
some proportion of the oxidative metabolites produced there entering systemic circulation. Thus,
it is possible that extrahepatic oxidative metabolism can contribute to TCE-induced
hepatomegaly. Therefore, the total amount of oxidative metabolism of TCE scaled by the
3/4 power of body weight (TotOxMetabBW34 [mg/kgy7week]) was selected as an alternative
dose-metric (the rationale for the body weight to the % power scaling is analogous to that for
hepatic oxidative metabolism, above).
34The range of the difference is 3.5-3.9-fold using the posterior medians for the relative liver weight in mice and
humans from the PBPK model described in Section 3.5 (see Table 3 -37), and body weights of 0.03-0.04 kg for mice
and 60-70 kg for humans.
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5.1.3.1.3. Developmental toxicity—heart malformations
As discussed in Section 4.8.3.2.1, several studies have reported that the prenatal exposure
to TCE oxidative metabolites TCA or DCA also induces heart malformations, suggesting that
oxidative metabolism is involved in TCE-induced heart malformations. However, there are
inadequate data across species to quantify the dosimetry of DCA, and it is unclear if other
products of TCE oxidative metabolism are involved. Therefore, the total amount of oxidative
metabolism of TCE scaled by the 3/4 power of body weight (TotOxMetabBW34 [mg/kg3/4/week])
was selected as the primary dose-metric. The rationale for the scaling by body weight to the
% power is analogous to that for hepatic oxidative metabolism, above.
An alternative dose-metric that is considered here is the AUC of TCE in (maternal) blood
(AUCCBld [mg-hour/L/day]). The placenta is a highly perfused tissue, and TCE is known to
cross the placenta to the fetus, with rats showing similar (within twofold) maternal and fetal
blood TCE concentrations (see Section 3.2). This dose-metric accounts for the possible roles
either of local metabolism or of TCE itself.
5.1.3.1.4. Reproductive toxicity—decreased ability of sperm to fertilize oocytes
The decreased ability of sperm to fertilize oocytes observed by DuTeaux et al. (2004a)
occurred in the absence of changes in combined testes/epididymes weight, sperm concentration
or motility, or histological changes in the testes or epididymes. However, there was evidence of
oxidative damage to the sperm, and DuTeaux et al. (2003) previously reported the ability of the
rat epididymis and efferent ducts to metabolize TCE oxidatively. Based on this evidence,
DuTeaux et al. (2004a) hypothesized that the decreased ability to fertilize is due to oxidative
damage to the sperm from local metabolism. Thus, the primary dose-metric for this endpoint is
selected to be the AUC of TCE in blood (AUCCBld [mg-hour/L/day]), based on the assumption
that in situ oxidation of systemically-delivered TCE (the flow rate of which scales as body
weight to the 3/4 power) is the determinant of toxicity.
Because metabolites causing oxidative damage may be delivered systemically to the
target tissue, an alternative dose-metric that is considered here is total oxidative metabolism of
TCE scaled by the 3/4 power of body weight (TotOxMetabBW34 [mg/kg3/4/day]). The rationale
for the scaling by body weight to the % power is analogous to that for hepatic oxidative
metabolism, above. Because oxidative metabolites make up the majority of TCE metabolism,
total metabolism gives very similar results (within 1.2-fold) to total oxidative metabolism and is
therefore not included as a dose-metric.
5.1.3.1.5. Other reproductive and developmental effects and neurological effects and
immunologic effects
For all other candidate critical endpoints listed in Tables 5-11 and 5-12, including
developmental effects other than heart malformations and reproductive effects other than
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decreased ability of sperm to fertilize, there is insufficient information for site-specific
determinations of an appropriate dose-metric. While TCE metabolites and/or metabolizing
enzymes have been reported in some of these tissues (e.g., male reproductive tract), their general
roles in toxicity in the respective tissues have not been established. The choice of total
metabolism as the primary dose-metric is based on the observation that, in general, TCE toxicity
is associated with metabolism rather than the parent compound. It is acknowledged that there is
no compelling evidence that definitively establishes one metric as more plausible than the other
in any particular case. Nonetheless, as a general inference in the absence of specific data, total
metabolism is viewed as more likely to be involved in toxicity than the concentration of TCE
itself.
Therefore, given that the majority of the toxic and carcinogenic responses in many tissues
to TCE appears to be associated with metabolism, the primary dose-metric is selected to be total
metabolism of TCE scaled by the 3/4 power of body weight (TotMetabBW34 [mg/kg3/4/day]). The
rationale for the scaling by body weight to the % power is analogous to that for the other
metabolism dose-metrics, above. Because oxidative metabolites make up the majority of TCE
metabolism, total oxidative metabolism gives very similar results (within 1.2-fold) to total
metabolism and is therefore not included as a dose-metric.
An alternative dose-metric that is considered here is the AUC of TCE in blood
(AUCCBld [mg-hour/L/day]). This dose-metric would account for the possible role of local
metabolism, which is determined by TCE delivered in blood via systemic circulation to the target
tissue (the flow rate of which scales as body weight to the 3/4 power), and the possible role of
TCE itself. This dose-metric would also be most applicable to tissues that have similar
tissue:blood partition coefficients across and within species.
Because the PBPK model described in Section 3.5 did not include a fetal compartment,
the maternal internal dose-metric is taken as a surrogate for developmental effects in which
exposure was before or during pregnancy (Johnson et al., 2003; Narotsky et al., 1995;
Fredriksson et al., 1993; Taylor etal., 1985). This was considered reasonable because TCE and
the major circulating metabolites (TCA and TCOH) appear to cross the placenta (see Sections
3.2, 3.3, and 4.10 (Fisher et al.. 1989: Ghantous et al.. 1986V). and maternal metabolizing
capacity is generally greater than that of the fetus (see Section 4.10). In the cases where
exposure continues after birth (Peden-Adams et al., 2006; Isaacson and Taylor, 1989), no PBPK
model-based internal dose was used. Because of the complicated fetus/neonate dosing that
includes transplacental, lactational, and direct (if dosing continues postweaning) exposure, the
maternal internal dose is no more accurate a surrogate than applied dose in this case.
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5.1.3.2. Methods for Inter- and Intraspecies Extrapolation Using Internal Doses35
As shown in Figures 5-2 and 5-3, the general approach taken to use the internal dose-
metrics in deriving HECs and HEDs was to first apply the rodent PBPK model to get rodent
values for the dose-metrics corresponding to the applied doses in a study reporting noncancer
effects. The idPOD is then obtained either directly from the internal dose corresponding to the
applied dose LOAEL or NOAEL, or by dose-response modeling of responses with respect to the
internal doses to derive a BMDL in terms of internal dose. Separately, the human PBPK model
is run for a range of continuous exposures from 10"1 to 2 x 103 ppm or mg/kg/day to obtain the
relationship between human exposure and internal dose for the same dose-metric used for the
rodent. The human equivalent exposure (HEC or HED) corresponding to the idPOD is derived
by interpolation. It should be noted that median values of dose-metrics were used for rodents,
whereas both median and 99th percentile values were used for humans. As discussed in
Section 3.5, the rodent population model characterizes study-to-study variation, while, within a
study, animals with the same sex/species/strain combination were assumed to be identical
pharmacokinetically and represented by the group average (typically the only data reported).
Therefore, use of median dose-metric values can be interpreted as assuming that the animals in
the noncancer toxicity study were all —typial" animals and the idPOD is for a rodent that is
pharmacokinetically —jtpical." In practice, the use of median or mean internal doses for rodents
did not make much difference except when the uncertainty in the rodent dose-metric was high.
The impact of the uncertainty in the rodent PBPK dose-metrics is analyzed quantitatively in
Section 5.1.4.2.
35An alternative approach (e.g.. Clewell et al. 1995) applies the UFs to the internal dose prior to using the human
PBPK model to derive a human exposure level. As noted by Barton and Clewell (2000) for previous TCE PBPK
models, because the human PBPK model for TCE is linear for all the dose metrics over very broad dose and
concentration ranges, essentially identical results would be obtained using this alternative approach. Specifically,
for all the primary dose metrics, the difference in the two approaches is less than two-fold, with the results from the
critical studies differing by <0.1%. For some studies using AUCBld as an alternative dose metric, the difference
ranged from three- to -sevenfold. Overall, use of the alternative approach would not significantly change the
noncancer dose-response assessment of TCE, and the derived RfC and RfD would be identical.
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istribution
Rodent
model
parameters
istribution
[distribution (combined
ncertainty and variability)
fixed
Ipnedian
Dose-Response Model
or
Human
model
parameters
Human
internal
dose as
function of
applied do/se
distribution (separate
ncertainty and variability)
LU/^tfV/VU/ltL
v
t
idPOD (internal
dose unit) =
BMDLor
LOAELor
NOAEL
/
invert functions of dose
or concentration
Overall /
median^''
"Typical"
human internal
dose as
function
of applied
dose
t
"Typical"
human
equivalent
dose or
concentration
HEC50 or
HED50
(replaces
POD/UFA.pk)
\ Overall
s%^99th percentile
"Sensitive"
human internal
dose as
function
of applied
dose
"Sensitive"
human
equivalent
dose or
concentration
HECgg or
HEDgg
[replaces
POD/(UFA-pk*UFH-pk)]
Square nodes indicate point values, circle nodes indicate distributions, and the
inverted triangle indicates a (deterministic) functional relationship.
Figure 5-2. Flow-chart for dose-response analyses of rodent noncancer
effects using PBPK model-based dose-metrics.
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Rodent internal
dose
Human internal
dose
Uncertainty &
variability
distribution
Human inhalation
exposure (ppm)
/ Study
dose groups
Human internal
dose
Uncertainty &
variability
ribution
Human oral exposure
(mg/kg/d)
, Lower 99th
percentile
In the case where BMD modeling is performed, the applied dose values are
replaced by the corresponding median internal dose estimate, and the idPOD is
the modeled BMDL in internal dose units.
Figure 5-3. Schematic of combined interspecies, intraspecies, and route-to-
route extrapolation from a rodent study LOAEL or NOAEL.
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The human population model characterizes individual-to-individual variation, in addition
to its uncertainty. The —radian" value for the HEC or HED was calculated as a point of
comparison but was not actually used for derivation of candidate reference values. Because the
RfC and RfD are intended to characterize the dose below which a sensitive individual would
likely not experience adverse effects, the overall 99th percentile of the combined uncertainty and
variability distribution was used for deriving the HEC and HED (denoted HEC99 and HED99)
from each idPOD.36 As shown in Figures 5-2 and 5-3, the HEC99 or HED99 replaces the quantity
POD/(UFA-Pk x UFn-pk) in the calculation of the RfC or RfD (i.e., the pharmacokinetic
components of the UFs representing interspecies extrapolation and human interindividual
variability).
As calculated, the extrapolated HEC99 and HED99 can be interpreted as being the dose or
exposure for which there is 99% likelihood that a randomly selected individual will have an
internal dose less than or equal to the idPOD derived from the rodent study. By contrast, the
HECso and HED50 can be interpreted as being the dose or exposure for which there is 50%
likelihood that a randomly selected individual will have an internal dose less than or equal to the
idPOD derived from the rodent study. Values of HEC99 or HED99 are shown for each study and
dose-metric considered in Tables 5-13 through 5-18. In addition, values of HECso or HED50 are
shown for comparison, to give a sense of the difference between the median and the 99%
confidence bound for combined uncertainty and variability. The separate contributions of
uncertainty and variability in the human PBPK model are analyzed quantitatively, along with the
uncertainty in the rodent PBPK dose-metrics as mentioned above, in Section 5.1.4.2.
36While for uncertainty, a 95th percentile is often selected by convention, there is no explicit guidance on the
selection of the percentile for human toxicokinetic variability. Ideally, all sources of uncertainty and variability
would be included, and percentile selected that is more in line with the levels of risk at which cancer dose-response
is typically characterized (e.g., 10~6 to 10~4) along with a level of confidence. However, only toxicokinetic
uncertainty and variability is assessed quantitatively. Because the distribution here incorporates both uncertainty
and variability simultaneously, a percentile higher than the 95th (a conventional choice for uncertainty only) was
selected. However, percentiles greater than the 99th percentile are likely to be progressively less reliable due to the
unknown shape of the tail of the input uncertainty and variability distributions for the PBPK model parameters
(which were largely assumed to be normal or lognormal), and the fact that only 42 individuals were incorporated in
the PBPK model for characterization of uncertainty and inter-individual variability (see Section 3.5). This concern
is somewhat ameliorated because the candidate reference values also incorporate use of UFs to account for inter-
and intraspecies toxicodynamic sensitivity.
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Table 5-13. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical neurological effects
Effect type
Candidate critical studies3
Species
POD
type
HEC50
or
HED50
POD,
HEC99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect;
comments [dose-metric]
Trigeminal nerve effects
Ruijten et al. (1991)
Human
LOAEL
HEC
HEC
HED
HED
14
14
7.4
59
14
5.3
8.3
7.3
14
1
1
1
1
1
1
1
1
1
1
10
3
3
3
3
3
3
3
3
3
1
1
1
1
1
30
10
10
10
10
0.47
0.53
0.83
0.73
1.4
Trigeminal nerve effects
[TotMetabBW34]
[AUCCBld]
TotMetabBW34] (route-to-route)
AUCCBld] (route-to-route)
Cognitive effects
Isaacson et al. (1990)
Rat
LOAEL
HED
HED
HEC
HEC
9.4
31
18
3.8
47
9.2
4.3
7.1
2.3
10
10
10
10
10
10
3
3
3
3
10
3
3
3
3
10
10
10
10
10
1
1
1
1
1
10,000d
1,000
1,000
1,000
1,000
0.0071
0.0023
0.0047
0.0092
0.0043
demyelination in hippocampus
[TotMetabBW34]
[AUCCBld]
TotMetabBW34] (route-to-route)
AUCCBld] (route-to-route)
Mood and sleep disorders
Arito et al. (1994)
Rat
LOAEL
HEC
HEC
HED
HED
13
15
6.6
65
12
4.8
9.0
6.5
15
3
3
3
3
3
3
3
3
3
3
10
3
3
3
3
10
10
10
10
10
1
1
1
1
1
1,000
300
300
300
300
0.012
0.016
0.030
0.022
0.051
Changes in wakefulness
[TotMetabBW34]
[AUCCBld]
TotMetabBW34] (route-to-route)
AUCCBld] (route-to-route)
5-61
-------
Table 5-13. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical neurological effects (continued)
Effect type
Candidate critical studies3
Species
POD
type
HEC50
or
HED50
POD,
HEC99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect; comments
[dose-metric]
Other neurological effects
Kjellstrand et al. (1987)
Gash et al. (2008)
Rat
Mouse
Rat
LOAEL
HEC
HEC
HED
HED
LOAEL
HEC
HEC
HED
HED
LOAEL
HED
HED
HEC
HEC
274
487
110
436
378
198
145
237
56
571
126
679
300
93
257
97
142
150
120
108
120
76
710
53
192
47
363
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
3
3
3
3
3
3
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3,000
1,000
1,000
1,000
1,000
3,000
1,000
1,000
1,000
1,000
10,000d
1,000
1,000
1,000
1,000
0.10
0.093
0.26
0.050
0.12
0.11
0.047
0.36
0.097
0.14
0.12
0.076
0.071
0.053
0.19
I regeneration of sciatic nerve
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
I regeneration of sciatic nerve
[TotMetabBW34]
[AUCCBld]
TotMetabBW34] (route-to-route)
AUCCBld] (route-to-route)
degeneration of dopaminergic neurons
[TotMetabBW34]
[AUCCBld]
TotMetabBW34] (route-to-route)
AUCCBld] (route-to-route)
aShaded rows represent the p-cRfC or p-cRfD using the preferred PBPK model dose-metric.
Applied dose POD adjusted to continuous exposure unless otherwise noted. POD, HEC99, and HED99 have same units as cRfC (ppm) or cRfD (mg/kg/day).
'Product of individual UFs, rounded to 3, 10, 30, 100, 300, 1,000, 3,000, or 10,000 [see Footnote d below].
dEPA's report on the RfC and RfD processes (U.S. EPA, 2002b) recommends not deriving reference values with a composite UF of >3,000; however, composite UFs
exceeding 3,000 are considered here because the derivation of the cRfCs and cRfDs is part of a screening process and the application of the PBPK model for candidate
critical effects reduces the values of some of the individual UFs for the p-cRfCs and p-cRfDs.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-62
-------
Table 5-14. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical kidney effects
Effect type
Candidate critical
studies3
Species
POD
type
J7LlLC_.5o
or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect;
comments [dose-metric]
Histological changes in kidney
Maltoni (1986)
(inhalation)
NCI (1976)
Rat
Mouse
BMDL
HEC
HEC
HEC
HED
HED
HED
LOAEL
HED
HED
HEC
HEC
0.28
0.45
39
0.22
0.35
19
2.9
51
3.9
113
40.2
0.038
0.058
15.3
0.023
0.036
19
620
0.30
48
0.50
42
1
1
1
1
1
1
1
1
1
1
1
1
3
3
3
3
3
3
3
10
3
3
3
3
10
3
3
3
3
3
3
10
3
3
3
3
1
1
1
1
1
1
1
30
30
30
30
30
1
1
1
1
1
1
1
1
1
1
1
1
30
10
10
10
10
10
10
3,000
300
300
300
300
1.3
0.0038
0.0058
1.5
0.00165
0.140
0.0023
0.0036
1.9
0.21
0.00101
0.160
meganucleocytosis; BMR = 10%
[ABioactDCVCBW34]
[AMetGSHBW34]
[TotMetabBW34]
[ABioactDCVCBW34] (route-to-
route)
[AMetGSHBW34] (route-to-route)
[TotMetabBW34] (route-to-route)
toxic nephrosis
[AMetGSHBW34]
[TotMetabBW34]
[AMetGSHBW34] (route-to-route)
[TotMetabBW34] (route-to-route)
5-63
-------
Table 5-14. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical kidney effects (continued)
Effect type
Candidate critical
studies3
Species
POD
type
J7LlLC_.5o
or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect;
comments [dose-metric]
Histological changes in kidney
NTPQ988)
Maltom (1986) (oral)
Rat
Rat
BMDL
HED
HED
HED
HEC
HEC
HEC
BMDL
HED
HED
HED
HEC
HEC
HEC
0.033
0.053
0.75
0.042
0.067
1.4
0.15
0.25
11
0.19
0.31
22
9.45
0.0034
0.0053
0.74
0.0056
0.0087
0.51
34
0.015
0.025
11
0.025
0.041
8.5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
10
3
3
3
3
3
3
10
3
3
3
3
3
3
10
3
3
3
3
3
3
10
3
3
3
3
3
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
100
10
10
10
10
10
10
100
10
10
10
10
10
10
0.00056
0.00087
0.051
0.0025
0.0041
0.85
0.0945
0.00034
0.00053
0.074
0.34
0.0015
0.0025
0.11
Toxic nephropathy; BMR = 5%;
female Marshall (most sensitive
sex/strain)
[ABioactDCVCBW34]
[AMetGSHBW34]
[TotMetabBW34]
[ABioactDCVCBW34] (route-to-
route)
[AMetGSHBW34] (route-to-route)
[TotMetabBW34] (route-to-route)
meganucleocytosis; BMR = 10%
[ABioactDCVCBW34]
[AMetGSHBW34]
[TotMetabBW34]
[ABioactDCVCBW34] (route-to-
route)
[AMetGSHBW34] (route-to-route)
[TotMetabBW34] (route-to-route)
5-64
-------
Table 5-14. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical kidney effects (continued)
Effect type
Candidate critical
studies3
Species
POD
type
J7LlLC_.5o
or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect;
comments [dose-metric]
| Kidney/body weight ratio
Kjellstrand et al.
(1983V)
Woolhiser et al.
(2006)
Mouse
Rat
BMDL
HEC
HEC
HED
HED
BMDL
HEC
HEC
HEC
HED
HED
HED
0.88
52
0.69
25
0.099
0.17
29
0.078
0.13
14
34.7
0.12
21
0.070
25
15.7
0.013
0.022
11
0.0079
0.013
14
1
1
1
1
1
1
1
1
1
1
1
1
3
3
3
3
3
3
3
3
3
3
3
3
10
3
3
3
3
10
3
3
3
3
3
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
30
10
10
10
10
30
10
10
10
10
10
10
1.2
0.012
2.1
0.52
0.0013
0.0022
1.1
0.0070
2.5
0.00079
0.0013
1.4
BMR = 10%
[AMetGSHBW34]
[TotMetabBW34]
[AMetGSHBW34] (route-to-route)
[TotMetabBW34] (route-to-route)
BMR = 10%
[ABioactDCVCBW34]
[AMetGSHBW34]
[TotMetabBW34]
[ABioactDCVCBW34] (route-to-
route)
[AMetGSHBW34] (route-to-route)
[TotMetabBW34] (route-to-route)
aShaded rows represent the p-cRfC or p-cRfD using the preferred PBPK model dose-metric.
bApplied dose POD adjusted to continuous exposure unless otherwise noted. POD, HEC99, and HED99 have same units as cRfC or cRfD.
'Product of individual UFs, rounded to 3, 10, 30, 100, 300, 1,000, or 3,000.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-65
-------
Table 5-15. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical liver effects
Effect type
Candidate critical
studies3
Species
POD
type
HEC50
or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect; comments
[dose-metric]
| Liver/body weight ratio
Kjellstrand et al.
(1983V)
Woolhiser et al.
(2006)
Buben and O'Flaherty
(1985)
Mouse
Rat
Mouse
BMDL
HEC
HEC
HED
HED
BMDL
HEC
HEC
HED
HED
BMDL
HED
HED
HEC
HEC
25
75
9,0
32
53
46
19
20
12
15
32
34
21.6
9.1
24.9
7.9
25.7
25
19
16
16
17
82
10
13
11
11
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
3
3
3
3
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
3
3
3
3
1
1
1
1
13
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
30
10
10
10
10
30
10
10
10
10
100
10
10
10
10
0.72
0.91
2.5
0.83
1.9
1.6
1.1
1.1
0.79
2.6
1.6
1.7
0.82
1.0
1.3
BMR= 10% increase
[AMetLivlBW34]
[TotOxMetabBW34]
[AMetLivlBW34] (route-to-route)
[TotOxMetabBW34] (route-to-route)
BMR= 10% increase
[AMetLivlBW34]
[TotOxMetabBW34]
[AMetLivlBW34] (route-to-route)
[TotOxMetabBW34] (route-to-route)
BMR= 10% increase
[AMetLivlBW34]
[TotOxMetabBW34]
[AMetLivlBW34] (route-to-route)
[TotOxMetabBW34] (route-to-route)
aShaded rows represent the p-cRfC or p-cRfD using the preferred PBPK model dose-metric.
Applied dose POD adjusted to continuous exposure unless otherwise noted. POD, HEC99, and HED99 have same units as cRfC (ppm) or cRfD (mg/kg/day).
"Product of individual UFs, rounded to 3, 10, 30, 100, 300, 1,000, or 3,000.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-66
-------
Table 5-16. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical immunological effects
Effect type
Candidate critical
studies3
Species
POD
type
HEC50
or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect; comments
[dose-metric]
J, Thymus weight
Keil et al. (2009)
Mouse
LOAEL
HED
HED
HEC
HEC
0.049
0.20
0.092
0.014
0.35
0.048
0.016
0.033
0.0082
1
1
1
1
1
10
3
3
3
3
10
3
3
3
3
10
10
10
10
10
1
1
1
1
1
1,000
100
100
100
100
0.00033
0.000082
0.00035
0.00048
0.00016
I thymus weight
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
Autoimmunity
Kaneko et al. (2000)
Keil et al. (2009)
Mouse
Mouse
LOAEL
HEC
HEC
HED
HED
LOAEL
HED
HED
HEC
HEC
97
121
44
181
0.049
0.20
0.092
0.014
70
37
69
42
57
0.35
0.048
0.016
0.033
0.0082
10
10
10
10
10
1
1
1
1
1
3
3
3
3
3
10
3
3
3
3
3
1
1
1
1
10
3
3
3
3
10
10
10
10
10
3
3
3
3
3
1
1
1
1
1
1
1
1
1
1
1,000
300
300
300
300
300
30
30
30
30
0.070
0.12
0.23
0.0011
0.00027
0.14
0.19
0.0012
0.0016
0.00053
Changes in immunoreactive organs -
liver (including sporadic necrosis in
hepatic lobules), spleen; UFH = 3 due
to autoimmune-prone mouse
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
t anti-dsDNA and anti-ssDNA Abs
(early markers for autoimmune
disease)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
5-67
-------
Table 5-16. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical immunological effects (continued)
Effect type
Candidate critical
studies3
Species
POD
type
HEC50
or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect; comments
[dose-metric]
Immunosuppression
Woolhiser et al.
(2006)
Sanders et al.
(1982b)
Rat
Mouse
BMDL
HEC
HEC
HED
HED
LOAEL
HED
HED
HEC
HEC
29
263
14
282
2.5
8.8
4.8
0.73
24.9
11
140
14
91
18
2.5
0.84
1.7
0.43
10
10
10
10
10
1
1
1
1
1
3
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
3
3
3
3
1
1
1
1
1
10
10
10
10
10
1
1
1
1
1
1
1
1
1
1
300
100
100
100
100
1000
100
100
100
100
0.083
0.11
1.4
0.017
0.0043
0.14
0.91
0.018
0.025
0.0084
1 PFC response; BMR = 1 SD change;
dropped highest dose
[TotMetabBW34]; all does groups
[AUCCBld]; all does groups
[TotMetabBW34] (route-to-route); all
does groups
[AUCCBld] (route-to-route); all does
groups
J, stem cell bone marrow
recolonization (sustained); J, cell-
mediated response to SRBC (largely
transient during exposure); females
more sensitive
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
aShaded rows represent the p-cRfC or p-cRfD using the preferred PBPK model dose-metric.
bApplied ose POD adjusted to continuous exposure unless otherwise noted. POD, HEC99, and HED99 have same units as cRfC (ppm) or cRfD (mg/kg/day).
"Product of individual UFs, rounded to 3, 10, 30, 100, 300, 1,000, or 3,000.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-68
-------
Table 5-17. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical reproductive effects
Effect type
Candidate critical
studies3
Species
POD
type
HEC50
or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect; comments
[dose-metric]
Effects on sperm, male reproductive outcomes
Chia et al. (1996)
Xu et al. (2004)
Kumar et al. (2QQOb);
(200 Ib)
Human
Mouse
Rat
BMDL
HEC
HEC
HED
HED
LOAEL
HEC
HEC
HED
HED
LOAEL
HEC
HEC
HED
HED
1.4
1.4
0.74
15
190
321
80
324
32
91
16
157
1.4
0.50
0.83
0.73
1.6
180
67
170
73
104
45
13
53
16
49
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
1
1
1
1
1
3
3
3
3
3
3
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
3
3
3
3
1
1
1
1
1
10
10
10
10
10
10
10
10
10
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
100
30
30
30
30
3,000
1,000
1,000
1,000
1,000
3,000
1,000
1,000
1,000
1,000
0.014
0.0017
0.0028
0.060
0.067
0.17
0.015
0.013
0.053
0.024
0.053
0.073
0.10
0.016
0.049
Hyperzoospermia; BMR = 10% extra
risk
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
I fertilization
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
Multiple sperm effects, increasing
severity from 12 to 24 wks
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
5-69
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Table 5-17. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical reproductive effects (continued)
Effect type
Candidate critical
studies8
DuTeaux et al.
(2004a)
Species
Rat
POD
type
LOAEL
HED
HED
HEC
HEC
HEC50
or
HED50
66
65
16
160
POD,
HEC99,
or
HED99b
141
16
42
9.3
43
UFS
10
10
10
10
10
UFA
10
3
3
3
3
UFH
10
3
3
3
3
UFL
10
10
10
10
10
UFD
1
1
1
1
1
UFC
10,000d
1,000
1,000
1,000
1,000
cRfC or
p-cRfC
(ppm)
0.0093
0.043
cRfDor
p-cRfD
(mg/kg/d)
0.014
0.016
0.042
Candidate critical effect; comments
[dose-metric]
J, ability of sperm to fertilize in vitro
[AUCCBld]
[TotOxMetabBW34]
[AUCCBld] (route-to-route)
[TotOxMetabBW34] (route-to-route)
Male reproductive tract effects
Forkert et al. (2002);
Kan et al. (2007)
Kumar et al. (2000b.
200 Ib)
Mouse
Rat
LOAEL
HEC
HEC
HED
HED
LOAEL
HEC
HEC
HED
HED
190
321
80
324
32
91
16
157
180
67
170
73
104
45
13
53
16
49
10
10
10
10
10
10
10
10
10
10
3
3
3
3
3
3
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
10
10
10
10
10
10
10
10
10
1
1
1
1
1
1
1
1
1
1
3,000
1,000
1,000
1,000
1,000
3,000
1,000
1,000
1,000
1,000
0.060
0.067
0.17
0.015
0.013
0.053
0.073
0.10
0.016
0.049
Effects on epididymis epithelium
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
Testes effects, testicular enzyme
markers, increasing severity from
12 to 24 wks
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
Female reproductive outcomes
Narotsky et al. (1995)
Rat
LOAEL
HED
HED
HEC
HEC
47
350
98
363
475
44
114
37
190
1
1
1
1
1
10
3
3
3
3
10
3
3
3
3
10
10
10
10
10
1
1
1
1
1
1,000
100
100
100
100
0.37
1.9
0.48
0.44
1.1
Delayed parturition
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
5-70
-------
Table 5-17. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical reproductive effects (continued)
Effect type
Candidate critical
studies8
Species
POD
type
HEC50
or
HED50
POD,
HEC99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect; comments
[dose-metric]
Reproductive behavior
George et al. (1986)
Rat
LOAEL
HED
HED
HEC
HEC
85
167
204
103
389
77
52
71
60
1
1
1
1
1
10
3
3
3
3
10
3
3
3
3
10
10
10
10
10
1
1
1
1
1
1,000
100
100
100
100
0.71
0.60
0.39
0.77
0.52
J, mating (both sexes exposed)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
aShaded rows represent the p-cRfC or p-cRfD using the preferred PBPK model dose-metric.
Applied dose POD adjusted to continuous exposure unless otherwise noted. POD, HEC99, and HED99 have same units as cRfC (ppm) or cRfD (mg/kg/day).
"Product of individual UFs, rounded to 3, 10, 30, 100, 300, 1,000, 3,000, or 10,000 (see footnote [d] below).
dEPA's report on the RfC and RfD processes (U.S. EPA, 2002b) recommends not deriving reference values with a composite UF of >3,000; however, composite UFs
exceeding 3,000 are considered here because the derivation of the cRfCs and cRfDs is part of a screening process and the application of the PBPK model for candidate
critical effects reduces the values of some of the individual UFs for the p-cRfCs and p-cRfDs.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-71
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Table 5-18. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical developmental effects
Effect type
Candidate critical
studies*
Species
POD
type
HEC50 or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect;
comments [dose-metric]
Pre- and postnatal mortality
Healy et al. (1982)
Narotsky et al. (1995)
Rat
Rat
LOAEL
HEC
HEC
HED
HED
BMDL
HED
HED
HEC
HEC
16
23
8.7
73
29
95
57
40
17
6.2
14
8.5
20
32.2
28
29
23
24
1
1
1
1
1
1
1
1
1
1
3
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
3
3
3
3
10
10
10
10
10
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
300
100
100
100
100
100
10
10
10
10
0.057
0.062
0.14
2.3
2.4
0.085
0.20
0.32
2.8
2.9
Resorptions
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
Resorptions; BMR = 1% extra
risk
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
Pre- and postnatal growth
Healy et al. (1982)
Rat
LOAEL
HEC
HEC
HED
HED
16
23
8.7
73
17
6.2
14
8.5
20
1
1
1
1
1
3
3
3
3
3
10
3
3
3
3
10
10
10
10
10
1
1
1
1
1
300
100
100
100
100
0.057
0.062
0.14
0.085
0.20
I fetal weight; skeletal effects
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
5-72
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Table 5-18. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical developmental effects (continued)
Effect type
Candidate critical
studies*
Species
POD
type
HEC50 or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect;
comments [dose-metric]
Congenital defects
Johnson et al. (2003)
Rat
BMDL
HED
HED
HEC
HEC
0.0058
0.019
0.012
0.0016
0.0207
0.0052
0.0017
0.0037
0.00093
1
1
1
1
1
10
3
3
3
3
10
3
3
3
3
1
1
1
1
1
1
1
1
1
1
100
10
10
10
10
0.00037
0.000093
0.00021
0.00052
0.00017
Heart malformations (pups);
BMR = 1% extra risk; highest-
dose group (1,000-fold higher
than next highest) dropped to
improve model fit
[TotOxMetabBW34]
[AUCCBld]
[TotOxMetabBW34] (route-to-
route)
[AUCCBld] (route-to-route)
Developmental neurotoxicity
Fredriksson et al.
(1993)
Taylor et al. (1985)
Isaacson and Taylor
(1989)
Mouse
Rat
Rat
LOAEL
HED
HED
HEC
HEC
LOAEL
HED
HED
HEC
HEC
LOAEL
4,2
27
8.0
3.1
11
30
22
3.7
50
4.1
3.5
3.0
1.8
45
11
4.1
8.4
2.2
16
3
3
3
3
3
1
1
1
1
1
1
10
3
3
3
3
10
3
3
3
3
10
10
3
3
3
3
10
3
3
3
3
10
10
10
10
10
10
10
10
10
10
10
10
1
1
1
1
1
1
1
1
1
1
1
3,000
300
300
300
300
1,000
100
100
100
100
1,000
0.010
0.0061
0.084
0.022
0.017
0.014
0.012
0.045
0.11
0.041
0.016
I rearing postexposure; pup
gavage dose
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
1 exploration postexposure;
estimated dam dose
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (route-to-route)
[AUCCBld] (route-to-route)
J, myelination in hippocampus;
estimated dam dose
5-73
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Table 5-18. cRfCs and cRfDs (based on applied dose) and p-cRfCs and p-cRfDs (based on PBPK modeled
internal dose-metrics) for candidate critical developmental effects (continued)
Effect type
Candidate critical
studies*
Species
POD
type
HEC50 or
HED50
POD,
JrLiL(^99,
or
HED99b
UFS
UFA
UFH
UFL
UFD
UFC
cRfC or
p-cRfC
(ppm)
cRfDor
p-cRfD
(mg/kg/d)
Candidate critical effect;
comments [dose-metric]
Developmental immunotoxicity
Peden-Adams et al.
(2006)
Mouse
LOAEL
0.37
1
10
10
10
1
1,000
0.00037
I PFC, f DTH; POD is estimated
dam dose (exposure throughout
gestation and lactation + to 3 or 8
wks of age)
aShaded rows represent the p-cRfC or p-cRfD using the preferred PBPK model dose-metric or, in the cases where the PBPK model was not used, the cRfD or cRfC based
on applied dose.
bApplied dose POD adjusted to continuous exposure unless otherwise noted. POD, HEC99, and HED99 have same units as cRfC (ppm) or cRfD (mg/kg/day).
'Product of individual UFs, rounded to 3, 10, 30, 100, 300, 1,000, or 3,000.
UFS = subchronic-to-chronic UF; UFA = interspecies UF; UFH = human variability UF; UFL = LOAEL-to-NOAEL UF; UFD = database UF
5-74
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Because they are derived from rodent internal dose estimates, the HEC and HED are
derived in the same manner independent of the route of administration of the original rodent
study. Therefore, a route-to-route extrapolation from an oral (inhalation) study in rodents to a
HEC (HED) in humans is straight-forward. As shown in Tables 5-13-5-18, route-to-route
extrapolation was performed for a number of endpoints with low cRfCs and cRfDs to derive
p-cRfDs and p-cRfCs.
5.1.3.3. Results and Discussion of p-RfCs and p-RfDs for Candidate Critical Effects
Tables 5-13-5-18 present the p-cRfCs and p-cRfDs developed using the PBPK internal
dose-metrics, along with the cRfCs and cRfDs based on applied dose for comparison, for each
health effect domain.
The greatest impact of using the PBPK model was, as expected, for kidney effects, since
as discussed in Sections 3.3 and 3.5, some toxicokinetic data indicate substantially more GSH
conjugation of TCE and subsequent bioactivation of GSH-conjugates in humans relative to rats
or mice. In addition, as discussed in Sections 3.3 and 3.5, the available in vivo data indicate high
interindividual variability in the amount of TCE conjugated with GSH. The overall impact is
that the p-cRfCs and p-cRfDs based on the preferred dose-metric of bioactivated DCVC are 300-
400-fold lower than the corresponding cRfCs and cRfDs based on applied dose. As shown in
Figure 3-20 in Section 3.5, for this dose-metric there is about a 30-100-fold difference
(depending on exposure route and level) between rats and humans in the —centrafestimates" of
interspecies differences for the fraction of TCE that is bioactivated as DCVC. The uncertainty in
the human central estimate is only on the order of 2-fold (in either direction), while that in the rat
central estimate is substantially greater, about 10-fold (in either direction). In addition, the
interindividual variability about the human median estimate is on the order of 10-fold (in either
direction). However, as noted in Section 3.3.3.2, there are a number of discrepancies in
estimates for the extent of GSH conjugation that may be related to different analytical methods,
and it is possible that GSH conjugation data to which the PBPK model was calibrated
overestimated the extent of DCVG formation by a substantial amount. Thus, there remain
significant uncertainties in the human estimates of GSH conjugation derived from the PBPK
model. Moreover, the estimates of the amount bioactivated are indirect, derived from the
difference between overall GSH conjugation flux and NAcDCVC excretion (see
Section 3.5.7.3.1). Therefore, while there is a high degree of confidence in the nephrotoxic
hazard posed by TCE, there is less confidence in the p-cRfCs and p-RfDs derived using GSH
conjugation dose-metrics for these effects.
In addition, in two cases in which BMD modeling was employed, using internal dose-
metrics led to a sufficiently different dose-response shape so as to change the resulting reference
value by greater than fivefold. For the Woolhiser et al. (2006) decreased PFC response, this
occurred with the AUC of TCE in blood dose-metric, leading to a p-cRfC 17-fold higher than the
5-75
-------
cRfC based on applied dose. However, the model fit for this effect using this metric was
substantially worse than the fit using the preferred metric of Total oxidative metabolism.
Moreover, whereas an adequate fit was obtained with applied dose only with the highest-dose
group dropped, all of the dose groups were included when the total oxidative metabolism dose-
metric was used while still resulting in a good model fit. Therefore, it appears that using this
metric resolves some of the low-dose supralinearity in the dose-response curve. Nonetheless, the
overall impact of the preferred metric was minimal, as the p-cRfC based on the Total oxidative
metabolism metric was less than 1.4-fold larger than the cRfC based on applied dose. The
second case in which BMD modeling based on internal doses changed the candidate reference
value by more than fivefold was for resorptions reported by Narotsky et al. (1995). Here, the
p-cRfDs were seven- to eightfold larger than the corresponding cRfD based on applied dose.
However, for applied dose, there is substantial uncertainty in the low-dose curvature of the dose-
response curve. This uncertainty persisted with the use of internal dose-metrics, so the BMD
remains somewhat uncertain (see figures in Appendix F).In the remaining cases, which generally
involved the "generic" dose-metrics of total metabolism and AUC of TCE in blood, the p-cRfCs
and p-cRfDs were within fivefold of the corresponding cRfC or cRfD based on applied dose,
with the vast majority within threefold. This suggests that the standard UFs for inter- and
intraspecies pharmacokinetic variability are fairly accurate in capturing these differences for
these TCE studies.
5.1.4. Uncertainties in cRfCs and cRfDs
5.1.4.1. Qualitative Uncertainties
An underlying assumption in deriving a reference value for a noncancer effect is that the
dose-response relationship has a threshold. Thus, a fundamental uncertainty is the validity of
that assumption. For some effects, in particular effects on very sensitive processes (e.g.,
developmental processes) or effects for which there is a nontrivial background level and even
small exposures may contribute to background disease processes in more susceptible people, a
practical threshold (i.e., a threshold within the range of environmental exposure levels of
regulatory concern) may not exist.
Nonetheless, under the assumption of a threshold, the desired exposure level to have as a
reference value is the maximum level at which there is no appreciable risk for an adverse effect
in (nonnegligible) sensitive subgroups (of humans). However, because it is not possible to know
what this level is, —unceatinty factors" are used to attempt to address quantitatively various
aspects, depending on the data set, of qualitative uncertainty.
First there is uncertainty about the POD for the application of UFs. Conceptually, the
POD should represent the maximum exposure level at which there is no appreciable risk for an
adverse effect in the study population under study conditions (i.e., the threshold in the dose-
response relationship). Then, the application of the relevant UFs is intended to convey that
5-76
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exposure level to the corresponding exposure level for sensitive human subgroups exposed
continuously for a lifetime. In fact, it is again not possible to know that exposure level even for a
laboratory study because of experimental limitations (e.g., the power to detect an effect, dose
spacing, measurement errors, etc.), and crude approximations like the NOAEL or a BMDL are
used. If a LOAEL is used as the POD, then the LOAEL-to-NOAEL UF is applied as an
adjustment factor to get a better approximation of the desired exposure level (threshold), but the
necessary extent of adjustment is unknown.
If a BMDL is used as the POD, there are uncertainties regarding the appropriate dose-
response model to apply to the data, but these should be minimal if the modeling is in the
observable range of the data. There are also uncertainties about what BMR to use to best
approximate the desired exposure level (threshold, see above). For continuous endpoints, in
particular, it is often difficult to identify the level of change that constitutes the —wt-point" for an
adverse effect. Sometimes, to better approximate the desired exposure level, a BMR somewhat
below the observable range of the data is selected. In such cases, the model uncertainty is
increased, but this is a trade-off to reduce the uncertainty about the POD not being a good
approximation for the desired exposure level.
For each of these types of PODs, there are additional uncertainties pertaining to
adjustments to the administered exposures (doses). Typically, administered exposures (doses)
are converted to equivalent continuous exposures (daily doses) over the study exposure period
under the assumption that the effects are related to concentration x time, independent of the daily
(or weekly) exposure regimen (i.e., a daily exposure of 6 hours to 4 ppm is considered equivalent
to 24 hours of exposure to 1 ppm). However, the validity of this assumption is generally
unknown, and, if there are dose-rate effects, the assumption of C x t equivalence would tend to
bias the POD downwards. Where there is evidence that administered exposure better correlates
to the effect than equivalent continuous exposure averaged over the study exposure period (e.g.,
visual effects), administered exposure was not adjusted. For the PBPK analyses in this
assessment, the actual administered exposures are taken into account in the PBPK modeling, and
equivalent daily values (averaged over the study exposure period) for the dose-metrics are
obtained (see above, Section 5.1.3.2). Additional uncertainties about the PBPK-based estimates
include uncertainties about the appropriate dose-metric for each effect, although for some effects
there was better information about relevant dose-metrics than for others (see Section 5.1.3.1).
Furthermore, as discussed in Section 3.3.3.2, there remains substantial uncertainty in the
extrapolation of GSH conjugation from rodents to humans due to limitations in the available
data.
Second, there is uncertainty about the UFs. The human variability UF is to some extent
an adjustment factor because, for more sensitive people, the dose-response relationship shifts to
lower exposures. However, there is uncertainty about the extent of the adjustment required (i.e.,
about the distribution of human susceptibility). Therefore, in the absence of data on a more
5-77
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sensitive population(s) or on the distribution of susceptibility in the general population, an UF of
10 is generally used, in part for pharmacokinetic variability and in part for pharmacodynamic
variability. The PBPK analyses in this assessment attempt to account for the pharmacokinetic
portion of human variability using human data on pharmacokinetic variability. A quantitative
uncertainty analysis of the PBPK-derived dose-metrics used in the assessment is presented in
Section 5.1.4.2. There is still uncertainty regarding the susceptible subgroups for TCE exposure
and the extent of pharmacodynamic variability.
If the data used to determine a particular POD are from laboratory animals, an
interspecies extrapolation UF is used. This UF is also to some extent an adjustment factor for the
expected scaling for toxicologically-equivalent doses across species (i.e., according to body
weight to the % power for oral exposure). However, there is also uncertainty about the true
extent of interspecies differences for specific noncancer effects from specific chemical
exposures. Often, the —adjutsnent" component of this UF has been attributed to
pharmacokinetics, while the "uncertainty" component has been attributed to pharmacodynamics,
but as discussed above in Section 5.1.3.1, this is not the only interpretation supported. For oral
exposures, the standard value for the interspecies UF is 10, which can be viewed as breaking
down (approximately) to a factor of three for the —adjstment" (nominally pharmacokinetics) and
a factor of three for the —uncstainty" (nominally pharmacodynamics). For inhalation exposures,
no adjustment across species is generally assumed for fixed air concentrations (ppm
equivalence), and the standard value for the interspecies UF is 3, reflecting only —uncetainty"
(nominally pharmacodynamics). The PBPK analyses in this assessment attempt to account for
the —ajiistment" portion of interspecies extrapolation using rodent pharmacokinetic data to
estimate internal doses for various dose-metrics. With respect to the —uoertainty" component,
quantitative uncertainty analyses of the PBPK-derived dose-metrics used in the assessment are
presented in Section 5.1.4.2. However, these only address the pharmacokinetic uncertainties in a
particular dose-metric, and there is still uncertainty regarding the true dose-metrics. Nor do the
PBPK analyses address the uncertainty in either cross-species pharmacodynamic differences
(i.e., about the assumption that equal doses of the appropriate dose-metric convey equivalent risk
across species for a particular endpoint from a specific chemical exposure) or in cross-species
pharmacokinetic differences not accounted for by the PBPK model dose-metrics (e.g., departures
from the assumed interspecies scaling of clearance of the active moiety, in the cases where only
its production is estimated). A value of 3 is typically used for the "uncertainty" about cross-
species differences, and this generally represents true uncertainty because it is usually unknown,
even after adjustments have been made to account for the expected interspecies differences,
whether humans have more or less susceptibility, and to what degree, than the laboratory species
in question.
If only subchronic data are available, the subchronic-to-chronic UF is to some extent an
adjustment factor because, if the effect becomes more severe with increasing exposure, then
5-78
-------
chronic exposure would shift the dose-response relationship to lower exposures. However, the
true extent of the shift is unknown.
Sometimes a database UF is also applied to address limitations or uncertainties in the
database. The overall database for TCE is quite extensive, with studies for many different types
of effects, including two-generation reproductive studies, as well as neurological,
immunological, and developmental immunological studies. In addition, there were sufficient
data to develop a reliable PBPK model to estimate route-to-route extrapolated doses for some
candidate critical effects for which data were only available for one route of exposure. Thus,
there is a high degree of confidence that the TCE database was sufficient to identify sensitive
endpoints.
5.1.4.2. Quantitative Uncertainty Analysis of PBPK Model-Based Dose-metrics for
LOAEL- or NOAEL-Based PODs
The Bayesian analysis of the PBPK model for TCE generates distributions of uncertainty
and variability in the internal dose-metrics that can be readily used for characterizing the
uncertainty and variability in the PBPK model-based derivations of the HEC and HED.
However, in the primary analysis, a number of simplifications are made including: (1) use of
median estimates for rodent internal doses and (2) expressing the —sssitive human" HEC and
HED in terms of combined uncertainty and variability. Therefore, a 2-dimensional quantitative
uncertainty and variability analysis is performed, the objective of which is to characterize the
impact of these assumptions.
As shown in Figure 5-4, the overall approach taken for the uncertainty analysis is similar
to that used for the point estimates except for the carrying through of separate uncertainty and
variability distributions throughout the analysis. In particular, to address simplification
(1), above, the distribution of rodent internal dose estimates is carried through; and to address
simplification (2), above, uncertainty and variability distributions in human internal dose
estimates are kept distinct.
5-79
-------
[distribution (combined
ncertainty and variability)
Dose-Response Model
or
LOAELSNOAEL
istribution
[distribution (separate
ncertainty and variability)
idPOD=
LOAEL,
orNOAEL
(internal /nvert functions of dose
dose unit)/ or concentration
loistribution of functions
iof dose or concentration
Human
dose or
concentratior
Uncertainty distributionX at jdPOD /Uncertainty distribution
of population median /s^ ^\ of population 99th
k percentile
Typical
human
equivalent
Sensitive
Human
equivalent
distribution
distribution
Square nodes indicate point values, circle nodes indicate distributions, and the
inverted triangle indicates a (deterministic) functional relationship.
Figure 5-4. Flow-chart for uncertainty analysis of HECs and HEDs derived
using PBPK model-based dose-metrics.
5-80
-------
Because of a lack of tested software and limitations of time and resources, this analysis
was not performed for idPODs based on BMD modeling, and was only performed for idPODs
derived from a LOAEL or NOAEL. However, for those endpoints for which BMD modeling
was performed, for the purposes of this uncertainly analysis, an alternative idPOD was used
based on the study LOAEL or NOAEL.
In brief, the methodology involves an iterative process of sampling from three separate
distributions—the uncertainty distribution of rodent PBPK model parameters, the uncertainty
distribution of human population PBPK parameters, and the variability distribution of human
individual PBPK model parameters—the latter two of which are related hierarchically. For a
sample from the rodent parameter distribution, the corresponding idPOD is calculated. Then, an
individual is sampled from a human population distribution, which itself is sampled from the
uncertainty distribution of population parameters. For this individual, a human equivalent
exposure (HEC or FED) corresponding to the idPOD is derived by interpolation. Taking
multiple individuals from this population, a FtEC or FLED corresponding to the median and
99th percentile individuals is then derived. Repeating this process (starting again with a sample
from the rodent distribution) results in two distributions (both reflecting uncertainty): one of
—jtpical" individuals represented by the distribution of population medians, and one of
—sesitive" individuals represented by the distribution of an upper percentile of the population
(e.g., 99th percentile). This uncertainty reflects both uncertainty in the rodent internal dose and
uncertainty in the human population parameters. Thus, for selected quantiles of the population
and level of confidence (e.g., Xth percentile individual at Yth% confidence), the interpretation is
that at the resulting FtEC or HED, there is Y% confidence that X% of the population has an
internal dose less than that of the rodent in the toxicity study.
As shown in Tables 5-19-5-23, the FLECgg and FtEDgg derived using the rodent median
dose-metrics and the combined uncertainty and variability in human dose-metrics is generally
near (within 1.3-fold of) the median confidence level estimate of the FtEC and FLED for the
99th percentile individual. Therefore, the interpretation is that there is about 50% confidence that
human exposure at the FIECgg or FIEDgg will, in 99% of the human population, lead to an internal
dose less than or equal to that in the subjects (rodent or human) exposed at the POD in the
corresponding study.
5-81
-------
Table 5-19. Comparison of—snsitive individual" HECs or HEDs for
neurological effects based on PBPK modeled internal dose-metrics at
different levels of confidence and sensitivity, at the NOAEL or LOAEL
Candidate critical effect
Candidate critical study3 (species)
POD
type
Ratio
HEC/DSO:
HEC/D99
HECj^orHED/
X=99
A: =99,
median
A: =99,
951cb
[Dose-metric]
Neurological
Trigeminal nerve effects
Ruijten et al. (1991) (human)
Demyelination in hippocampus
Isaacson et al. (1990) (rat)
Changes in wakefulness
Arito et al. (1994) (rat)
J, Regeneration of sciatic nerve
Kjellstrand et al. (1987) (rat)
J, Regeneration of sciatic nerve
Kjellstrand et al. (1987) (mouse)
Degeneration of dopaminergic
neurons
Gash et al. (2008) (rat)
HEC
HEC
HED
HED
HED
HED
HEC
HEC
HEC
HEC
HED
HED
HEC
HEC
HED
HED
HEC
HEC
HED
HED
HED
HED
HEC
2.62
1.68
1.02
4.31
1.02
7.20
2.59
1.68
2.65
1.67
1.02
4.25
2.94
1.90
1.13
3.08
3.16
1.84
1.21
2.13
1.06
2.98
2.70
5.4
8.3
7.3
14
9.21
4.29
7.09
2.29
4.79
9
6.46
15.2
93.1
257
97.1
142
120
108
120
75.8
53
192
46.8
5.4
8.3
7.2
16
9.20
5.28
6.77
2.42
4.86
9.10
6.50
18.0
93.6
266
96,8
147
125
111
121
79.1
53.8
199
47.9
2,6
4.9
3.8
8.0
7.39
2.52
4.94
0.606
2.37
4.63
3.39
8.33
38.6
114
43.4
78.0
48.8
59.7
57.0
53.4
17.1
94.7
14.2
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
aShaded rows denote results for the primary dose-metric.
bHEC99 = the 99th percentile of the combined human uncertainty and variability distribution of continuous exposure
concentrations that lead to the (fixed) median estimate of the rodent internal dose at the POD; HEC99)median (or
HEC99]95icb) = the median (or 95th percentile lower confidence bound) estimate of the uncertainty distribution of
continuous exposure concentrations for which the 99th percentile individual has an internal dose less than the
(uncertain) rodent internal dose at the POD.
rtr = route-to-route extrapolation using PBPK model and the specified dose-metric
5-82
-------
Table 5-20. Comparison of —snsitive individual" HECs or HEDs for kidney
and liver effects based on PBPK modeled internal dose-metrics at different
levels of confidence and sensitivity, at the NOAEL or LOAEL
Candidate critical effect
Candidate critical study3
(species)
POD
type
Ratio
HEC/D50:
HEC/D99
HEC* or HED/
X=99
A: =99,
median
A: =99,
951cb
[Dose-metric]
Kidney
Meganucleocytosis [NOAEL]0
Maltoni et al. (1986) (rat
inhalation)
Toxic nephrosis
NCI (1976) (mouse)
Toxic nephropathy [LOAEL]0
NTP (1988) (rat)
Meganucleocytosis [NOAEL]0
Maltoni et al. (1986) (rat oral)
t Kidney /body weight ratio
[NOAEL]0
Kjellstrand et al. (1983a)
(mouse)
HEC
HEC
HEC
HED
HED
HED
HED
HED
HEC
HEC
HED
HED
HED
HEC
HEC
HEC
HED
HED
HED
HEC
HEC
HEC
HEC
HEC
HED
HED
7.53
7.70
2.57
9.86
9.83
1.02
9.51
1.05
7.78
2.67
9.75
9.64
1.03
7.55
7.75
2.59
9.85
9.86
1.02
7.55
7.71
2.60
7.69
2.63
9.78
1.03
0.0233
0.0364
8.31
0.0140
0.0223
10.6
0.30
48
0.50
42
0.121
0.193
33.1
0.201
0.314
28.2
0.0133
0.0214
8.7
0.022
0.0349
6.66
0.111
34.5
0.068
39.9
0.0260
0.0411
7.97
0.0156
0.0242
10.7
0.32
48.9
0.514
43.5
0.126
0.210
33.1
0.204
0.353
27.2
0.0145
0.0249
8.57
0.0249
0.0424
6.31
0.103
33.7
0.00641
39.2
0.00366
0.00992
4.03
0.00216
0.00597
5.75
0.044
16.2
0.0703
13.7
0.0177
0.0379
11.1
0.0269
0.0676
8.77
0.00158
0.00366
4.95
0.00256
0.00615
3.70
0.00809
13.5
0.00497
17.9
[ABioactDCVCBW34]
[AMetGSHBW34]
[TotMetabBW34]
[ABioactDCVCBW34]
(rtr)
[AMetGSHBW34] (rtr)
[TotMetabBW34] (rtr)
[AMetGSHBW34]
[TotMetabBW34]
[AMetGSHBW34] (rtr)
[TotMetabBW34] (rtr)
[ABioactDCVCBW34]
[AMetGSHBW34]
[TotMetabBW34]
[ABioactDCVCBW34]
(rtr)
[AMetGSHBW34] (rtr)
[TotMetabBW34] (rtr)
[ABioactDCVCBW34]
[AMetGSHBW34]
[TotMetabBW34]
[ABioactDCVCBW34]
(rtr)
[AMetGSHBW34] (rtr)
[TotMetabBW34] (rtr)
[AMetGSHBW34]
[TotMetabBW34]
[AMetGSHBW34] (rtr)
[TotMetabBW34] (rtr)
5-83
-------
Table 5-20. Comparison of —snsitive individual" HECs or HEDs for kidney
and liver effects based on PBPK modeled internal dose-metrics at different
levels of confidence and sensitivity, at the NOAEL or LOAEL (continued)
Candidate critical effect
Candidate critical study
(species)
t Kidney /body weight ratio
[NOAEL]C
Woolhiser et al. (2006) (rat)
POD
type
HEC
HEC
HEC
HED
HED
HED
Ratio
HEC/DSO:
HEC/D99
7.53
7.70
2.54
9.84
9.81
1.02
HEC^orHED^
X=99
0.0438
0.0724
16.1
0.0264
0.0444
19.5
A: =99,
median
0.0481
0.0827
15.2
0.0282
0.0488
19.2
A: =99,
951cb
0.00737
0.0179
7.56
0.00447
0.0111
10.5
[Dose-metric]
[ABioactDCVCBW34]
[AMetGSHBW34]
[TotMetabBW34]
[ABioactDCVCBW34]
(rtr)
[AMetGSHBW34] (rtr)
[TotMetabBW34] (rtr)
Liver
t Liver/body weight ratio
[LOAEL]C
Kjellstrand et al. (1983a)
(mouse)
t Liver/body weight ratio
[NOAEL]C
Woolhiser et al. (2006) (rat)
t Liver/body weight ratio
[LOAEL]C
Buben and O'Flaherty (1985)
(mouse)
HEC
HEC
HED
HED
HEC
HEC
HED
HED
HED
HED
HEC
HEC
2.85
3.63
1.16
1.53
2.86
2.94
1.20
1.21
1.14
1.14
2.80
3.13
16.2
40.9
14.1
40.1
20.7
18.2
17.8
19.6
8.82
9.64
10.1
7.83
16.3
38.1
14.1
39.4
21.0
17.1
17.7
19.3
8.95
9.78
9.97
7.65
6.92
15.0
5.85
17.9
11.0
8.20
9.94
10.5
4.17
5.28
4.83
4.23
[AMetLivlBW34]
[TotOxMetabBW34]
[AMetLivlBW34] (rtr)
[TotOxMetabBW34] (rtr)
[AMetLivlBW34]
[TotOxMetabBW34]
[AMetLivlBW34] (rtr)
[TotOxMetabBW34] (rtr)
[AMetLivlBW34]
[TotOxMetabBW34]
[AMetLivlBW34] (rtr)
[TotOxMetabBW34] (rtr)
"Shaded rows denote results for the primary dose-metric.
bHEC99 = the 99th percentile of the combined human uncertainty and variability distribution of continuous exposure
concentrations that lead to the (fixed) median estimate of the rodent internal dose at the POD; HEC99!median (or
HEC99)95icb) = the median (or 95th percentile lower confidence bound) estimate of the uncertainty distribution of
continuous exposure concentrations for which the 99th percentile individual has an internal dose less than the
(uncertain) rodent internal dose at the POD.
°BMDL used for p-cRfC or p-cRfD, but LOAEL or NOAEL (as noted) used for uncertainty analysis.
rtr = route-to-route extrapolation using PBPK model and the specified dose-metric
5-84
-------
Table 5-21. Comparison of—snsitive individual" HECs or HEDs for
immunological effects based on PBPK modeled internal dose-metrics at
different levels of confidence and sensitivity, at the NOAEL or LOAEL
Candidate critical effect
Candidate critical study3
(species)
POD
type
Ratio
HEC/D50:
HEC/D99
HECjfOrHED/
X=99
A: =99,
median
A: =99,
951cb
[Dose-metric]
Immunological
Changes in immunoreactive
organs — liver (including
sporadic necrosis in hepatic
lobules), spleen
Kaneko et al. (2000) (mouse)
t Anti-dsDNA and anti-ssDNA
Abs (early markers for auto-
immune disease); J, thymus
weight
Keil et al. (2009) (mouse)
| PFC response [NOAEL]C
Woolhiser et al. (2006) (rat)
J, Stem cell bone marrow
recolonization; J, cell-mediated
response to SRBC
Sanders et al. (1982b)
(mouse)
HEC
HEC
HED
HED
HED
HED
HEC
HEC
HEC
HEC
HED
HED
HED
HED
HEC
HEC
2.65
1.75
1,04
3.21
1,02
12.1
2.77
1.69
2.54
1.73
1,02
3.21
1.02
10.5
2.77
1.68
36.7
68.9
42.3
56.5
0.0482
0.0161
0.0332
0.00821
16.1
59.6
19.5
52
2.48
0.838
1.72
0.43
38.3
70.0
43.3
59.0
0.0483
0.0189
0.0337
0.00787
15.2
60.1
19.2
55.9
2.48
0.967
1.75
0.412
16.0
37.1
21.3
39.8
0.0380
0.00363
0.0246
0.00199
7.56
26.2
10.5
33.0
1.94
0.187
1.28
0.103
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
aShaded rows denote results for the primary dose-metric.
bHEC99 = the 99th percentile of the combined human uncertainty and variability distribution of continuous exposure
concentrations that lead to the (fixed) median estimate of the rodent internal dose at the POD; HEC99)median (or
HEC99j95icb) = the median (or 95th percentile lower confidence bound) estimate of the uncertainty distribution of
continuous exposure concentrations for which the 99th percentile individual has an internal dose less than the
(uncertain) rodent internal dose at the POD.
°BMDL used for p-cRfC or p-cRfD, but LOAEL or NOAEL (as noted) used for uncertainty analysis.
rtr = route-to-route extrapolation using PBPK model and the specified dose-metric
5-85
-------
Table 5-22. Comparison of —snsitive individual" HECs or HEDs for
reproductive effects based on PBPK modeled internal dose-metrics at
different levels of confidence and sensitivity, at the NOAEL or LOAEL
Candidate critical effect
Candidate critical study3
(species)
POD
type
Ratio
HEC/D50:
HEC/D99
HECjfOrHED/
X=99
A: =99,
median
A: =99,
951cb
[Dose-metric]
Reproductive
Hyperzoospermia
Chia et al. (1996) (human)
I Fertilization
Xu et al. (2004) (mouse)
Multiple sperm effects,
testicular enzyme markers
Kumar et al. (200 Ib; 2000b)
(rat)
J, Ability of sperm to fertilize in
vitro
DuTeaux et al. (2004a) (rat)
Effects on epididymis
epithelium
Forkert et al. (2002): Kan et
al. (2007) (mouse)
Testes effects
Kumar et al. (200 Ib; 2000b)
(rat)
Delayed parturition
Narotsky et al. (1995) (rat)
HEC
HEC
HED
HED
HEC
HEC
HED
HED
HEC
HEC
HED
HED
HED
HED
HEC
HEC
HEC
HEC
HED
HED
HEC
HEC
HED
HED
HED
HED
HEC
HEC
2.78
1.68
1,02
9.69
2.85
1.89
1,09
3.11
2.53
1.72
1.02
3.21
4.20
1.57
1,67
3.75
2.85
1.89
1,09
3.11
2.53
1.72
1,02
3.21
1,06
3.07
2.66
1.91
0.50
0.83
0.73
1.6
66,6
170
73.3
104
12.8
53.2
15.8
48.8
15.6
41.7
9.3
42.5
66,6
170
73.3
104
12.8
53.2
15.8
48.8
44.3
114
36.9
190
0.53
0.83
0.71
2.0
72.3
171
76.9
109
12.2
54.4
15.7
52.6
18.1
41.9
10.1
55.6
72.3
171
76.9
109
12.2
54.4
15.7
52.6
43.9
119
35.3
197
0.25
0.49
0.37
0.92
26.6
97.1
32.9
67.9
6.20
23.2
8.60
30.6
4.07
32.0
2.09
39.1
26.6
97.1
32.9
67.9
6.20
23.2
8.60
30.6
15,1
47.7
11.6
48.1
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[AUCCBld]
[TotOxMetabBW34]
[AUCCBld] (rtr)
[TotOxMetabBW34] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
5-86
-------
Table 5-22. Comparison of —snsitive individual" HECs or HEDs for
reproductive effects based on PBPK modeled internal dose-metrics at
different levels of confidence and sensitivity, at the NOAEL or LOAEL
(continued)
Candidate critical effect
Candidate critical study
(species)
I Mating (both sexes exposed)
George et al. (1986) (rat)
POD
type
HED
HED
HEC
HEC
Ratio
HEC/DSO:
HEC/D99
1,10
3.21
2.86
1.73
HEC^orHED^
X=99
77.4
51.9
71.1
59.5
A: =99,
median
77.1
55.8
70.0
63.3
A: =99,
951cb
34.2
14.7
29.5
8.14
[Dose-metric]
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
"Shaded rows denote results for the primary dose-metric.
bHEC99 = the 99th percentile of the combined human uncertainty and variability distribution of continuous exposure
concentrations that lead to the (fixed) median estimate of the rodent internal dose at the POD; HEC99median (or
HEC99)95icb) = the median (or 95th percentile lower confidence bound) estimate of the uncertainty distribution of
continuous exposure concentrations for which the 99th percentile individual has an internal dose less than the
(uncertain) rodent internal dose at the POD.
rtr = route-to-route extrapolation using PBPK model and the specified dose-metric
5-87
-------
Table 5-23. Comparison of —snsitive individual" HECs or HEDs for
developmental effects based on PBPK modeled internal dose-metrics at
different levels of confidence and sensitivity, at the NOAEL or LOAEL
Candidate critical effect
Candidate critical study3
(species)
POD
type
Ratio
HEC/DSO:
HEC/D99
HEC* or HED/
X=99
A: =95,
median
A: =95,
951cb
[Dose-metric]
Developmental
Resorptions
Healy et al. (19821 (rat)
Resorptions [LOAEL]0
Narotsky et al. (1995) (rat)
I Fetal weight; skeletal
effects
Healy et al. (1982) (rat)
Heart malformations (pups)
[LOAELf
Johnson et al. (2003) (rat)
I Rearing postexposure
Fredriksson et al. (1993)
(mouse)
t Exploration postexposure
Taylor et al. (1985) (rat)
HEC
HEC
HED
HED
HED
HED
HEC
HEC
HEC
HEC
HED
HED
HED
HED
HEC
HEC
HED
HED
HEC
HEC
HED
HED
HEC
HEC
2.58
1.69
1.02
3.68
1.06
3.07
2.66
1.91
2.58
1.69
1.02
3.68
1.02
11.6
2.75
1.70
1.02
7.69
2.71
1.68
1.02
7.29
2.57
1.68
6.19
13.7
8.5
19.7
44.3
114
36.9
190
6.19
13.7
8.5
19.7
0.012
0.00382
0.00848
0.00216
4.13
3.46
2.96
1.84
10.7
4.11
8.36
2.19
6.02
13.9
8.50
22.4
43,9
119
35,3
197
6.02
13.9
8.50
22.4
0.012
0.00476
0.00866
0.00221
4.19
4.21
2.96
1.81
10.7
5.08
7.94
2.31
3.13
7.27
4.61
11.5
15.1
47.7
11.6
48.1
3.13
7.27
4.61
11.5
0.0102
0.00112
0.00632
0.000578
2.22
0.592
1.48
0.302
8.86
1.16
5.95
0.580
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotOxMetabBW34]
[AUCCBld]
[TotOxMetabBW34]
(rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
[TotMetabBW34]
[AUCCBld]
[TotMetabBW34] (rtr)
[AUCCBld] (rtr)
aShaded rows denote results for the primary dose-metric.
bHEC99 = the 99th percentile of the combined human uncertainty and variability distribution of continuous exposure
concentrations that lead to the (fixed) median estimate of the rodent internal dose at the POD; HEC99)median (or
HEC99j95icb) = the median (or 95th percentile lower confidence bound) estimate of the uncertainty distribution of
continuous exposure concentrations for which the 99th percentile individual has an internal dose less than the
(uncertain) rodent internal dose at the POD.
°BMDL used for p-cRfC or p-cRfD, but LOAEL or NOAEL (as noted) used for uncertainty analysis.
rtr = route-to-route extrapolation using PBPK model and the specified dose-metric
-------
In several cases, the uncertainty, as reflected in the ratio between the 95 and 50%
confidence bounds on the 99th percentile individual, was rather high (e.g., >5-fold), and reflected
primarily uncertainty in the rodent internal dose estimates, discussed previously in Section 3.5.7.
The largest uncertainties (ratios between 95 to 50% confidence bounds of 8-10-fold) were for
kidney effects in mice using the AMetGSHBW34 dose-metric (Kj ell strand et al., 1983a: NCI,
1976). More moderate uncertainties (ratios between 95 to 50% confidence bounds of five- to
eightfold) were evident in some oral studies using the AUCCBld dose-metric (Keil et al., 2009;
Fredriksson et al., 1993; George et al., 1986; Sanders et al., 1982b), as well as in studies
reporting kidney effects in rats in which the ABioactDCVCBW34 or AMetGSHBW34 dose-
metrics were used (Woolhiser et al.. 2006: NTP, 1988: Maltoni et al.. 1986). Therefore, in these
cases, a POD that is protective of the 99th percentile individual at a confidence level higher than
50% could be as much as an order of magnitude lower.
For comparison, Tables 5-19 and 5-23 also show the ratios of the overall 50th percentile
to the overall 99th percentile HECs and HEDs, reflecting combined human uncertainty and
variability at the median study/endpoint idPOD. The smallest ratios (up to 1.2-fold) are for total,
oxidative, and hepatic oxidative metabolism dose-metrics from oral exposures, due to the large
hepatic first-pass effect resulting in virtually all of the oral intake being metabolized before
systemic circulation. Conversely, the large hepatic first-pass results in high variability in the
blood concentration of TCE following oral exposures, with ratios up to 12-fold at low exposures
(e.g., 90 vs. 99% first-pass would result in amounts metabolized differing by about 10% but TCE
blood concentrations differing by about 10-fold). From inhalation exposures, there is moderate
variability in these metrics, about two- to threefold. For GSH conjugation and bioactivated
DCVC, however, variability is high (8-10-fold) for both exposure routes, which follows from the
incorporation in the PBPK model analysis of the data from Lash et al. (1999b) showing
substantial interindividual variability in GSH conjugation in humans.
Finally, it is important to emphasize that this analysis only addresses pharmacokinetic
uncertainty and variability, so other aspects of extrapolation addressed in the UFs (e.g., LOAEL
to NOAEL, subchronic to chronic, and pharmacodynamic differences), discussed above, are not
included in the level of confidence.
5.1.5. Summary of Noncancer Reference Values
5.1.5.1. Preferred Candidate Reference Values (cRfCs, cRfD, p-cRfCs, and p-cRfDs)
for Candidate Critical Effects
The candidate critical effects that yielded the lowest p-cRfC or p-cRfD for each type of
effect, based on the primary dose-metric, are summarized in Tables 5-24 (p-cRfCs) and 5-25
(p-cRfDs). These results are extracted from Tables 5-13 to 5-18. In cases where a route-to-route
extrapolated p-cRfC (p-cRfD) is lower than the lowest p-cRfC (p-cRfD) from an inhalation
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(oral) study, both values are presented in the table. In addition, if there is greater than usual
uncertainty associated with the lowest p-cRfC or p-cRfD for a type of effect, then the endpoint
with the next lowest value is also presented. Furthermore, given those selections, the same sets
of critical effects and studies are displayed across both tables, with the exception of two oral
studies for which route-to-route extrapolation was not performed. Tables 5-24 and 5-25 are
further summarized in Tables 5-26 and 5-27 to present the overall preferred p-cRfC and p-cRfD
for each type of noncancer effect. The purpose of these summary tables is to show the most
sensitive endpoints for each type of effect and the apparent relative sensitivities (based on
reference value estimates) of the different types of effects.
Table 5-24. Lowest p-cRfCs or cRfCs for different effect domains
Effect domain
Effect type
Candidate critical effect
(species/critical study)
p-cRfC or cRfC in ppm
(composite UF)
Preferred
dose-metric"
Default
methodology
Alternative dose-
metrics/studies
(Tables 5-13-5-18)
Neurologic
Trigeminal nerve
effects
Cognitive effects
Mood/sleep changes
Trigeminal nerve effects
(human/RuijtenetaL 1991)
Demyelination in hippocampus
(rat/Isaacson etal.. 1990)
Changes in wakefulness
(rat/Arito et al.. 1994)
0.54
(10)
0.0071
(1,000)
0.016
(300)
0.47
(30)
[rtr]
0.012
(1,000)
0.83
(10)
0.0023
(1,000)
0.030
(300)
Kidney
Histological
changes
t Kidney weight
Toxic nephropathy
(rat/NTP, 1988)
Toxic nephrosis
(mouse/NCI. 1976)
Meganucleocytosis
(rat/Maltoni et al.. 1986)
t kidney weight
(rat/Woolhiser et al.. 2006)
0.00056
(10)
0.0017
(300)
0.0025
(10)
0.0013
(10)
[rtr]
[rtr]
[rtr]
0.52
(30)
0.00087-1.3
(10-300)
0.0022-2.1
(10-30)
Liver
t Liver weight
t liver weight
(mouse/Kiellstrandetal., 1983a)
0.91
(10)
0.72
(30)
0.83-2.5
(10-30)
Immunologic
J, Thymus weight
Immuno-
suppression
Autoimmunity
J, thymus weight
(mouse/Keil et al., 2009)
J, cell-mediated response to SRBC
J, stem cell recolonization
(mouse/Sanders et al.. 1982b)
Decreased PFC response
(rat/Woolhiser et al.. 2006)
t anti-dsDNA and anti-ssDNA Abs
(mouse/Keil et al.. 2009)
Autoimmune organ changes
(mouse/Kaneko et al.. 2000)
0.00033
(100)
0.017
(100)
0.11
(100)
0.0011
(30)
0.12
(300)
[rtr]
[rtr]
0.083
(300)
[rtr]
0.070
(1,000)
0.000082
(100)
0.0043-1.4
(100)
0.00027-0.23
(30-300)
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Table 5-24. Lowest p-cRfCs or cRfCs for different effect domains
(continued)
Effect domain
Effect type
Candidate critical effect
(species/critical study)
p-cRfC or cRfC in ppm
(composite UF)
Preferred
dose-metric"
Default
methodology
Alternative dose-
metrics/studies
(Tables 5-13-5-18)
Reproductive
Effects on sperm
and tester
I ability of sperm to fertilize
(rat/DuTeaux et al. 2004a)
Multiple effects
(rat/Kumar et al.. 200 Ib, 2000b)
Hyperzoospermia
(human/Chia et al.. 1996)b
0.0093
(1,000)
0.013
(1,000)
0.017
(30)
[rtr]
0.015
(3,000)
0.014
(100)
0.028-0.17
(30-1,000)
Developmental
Congenital defects
Developmental
neurotoxicity
Pre/postnatal
mortality/growth
Heart malformations
(rat/Johnson et al., 2003)
I rearing postexposure
(rat/Fredriksson et al.. 1993)
Resorptions/J, fetal weight/
skeletal effects
(rat/HealvetaL 1982)
0.00037
(10)
0.028
(300)
0.062
(100)
[rtr]
[rtr]
0.057
(300)
0.000093
(10)
0.0077-0.084
(100-300)
0.14-2.4
(10-100)
aThe critical effects/studies and p-cRfCs used to derive the RfC are in bold; supporting effects/studies and p-cRfCs
in italics.
bGreater than usual degree of uncertainty (see Section 5.1.2).
rtr = route-to-route extrapolated result
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Table 5-25. Lowest p-cRfDs or cRfDs for different effect domains
Effect domain
Effect type
Candidate critical effect
(species/critical study)
p-cRfD or cRfD in mg/kg/d
(composite UF)
Preferred
dose-metric"
Default
methodology
Alternative dose-
metrics/studies
(Tables 5-13-5-18)
Neurologic
Trigeminal nerve
effects
Cognitive effects
Mood/sleep
changes
Trigeminal nerve effects
(human/Ruijten et al.. 1991)
Demyelination in hippocampus
(rat/Isaacson et al.. 1990)
Changes in wakefulness
(rat/Aritoetal., 1994)
0.73
(10)
0.0092
(1,000)
0.022
(300)
[rtr]
0.0047
(10,000b)
[rtr]
1.4
(10)
0.0043
(1,000)
0.051
(300)
Kidney
Histological
changes
t Kidney weight
Toxic nephropathy
(rat/NTP, 1988)
Toxic nephrosis
(mouse/NCI, 1976)
Meganucleocytosis
(rat/Maltonietal., 1986)
1 kidney weight
(rat/Woolhiser et al, 2006)
0.00034
(10)
0.0010
(300)
0.0015
(10)
0.00079
(10)
0.0945
(100)
0.34
(100)
[rtr]
0.00053-1.9
(10-300)
0.0013-2.5
(10)
Liver
t Liver weight
1 liver weight
(mouse/Kiellstrandetal., 1983a)
0.79
(10)
[rtr]
0.82-2.6
(10-100)
Immunologic
J, Thymus weight
Immuno-
suppression
Autoimmunity
J, thymus weight
(mouse/Keil et al., 2009)
J, cell-mediated response to SRBC
J, stem cell recolonization
(mouse/Sanders et al., 1982b)
Decreased PFC response
(ratAVoolhiser et al.. 2006)
t anti-dsDNA and anti-ssDNA Abs
(mouse/Keil et al., 2009)
Autoimmune organ changes
(mouse/Kaneko et al., 2000)
0.00048
(100)
0.025
(100)
0.14
(100)
0.0016
(30)
0.14
(300)
0.00035
(1,000)
0.018
(1000)
[rtr]
0.0012
(300)
[rtr]
0.00016
(100)
0.0084-0.91
(100)
0.00053-0.19
(30-300)
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Table 5-25. Lowest p-cRfDs or cRfDs for different effect domains (continued)
Effect domain
Effect type
Candidate critical effect
(species/critical study)
p-cRfD or cRfD in mg/kg/d
(composite UF)
Preferred
dose-metric"
Default
methodology
Alternative dose-
metrics/studies
(Tables 5-13-5-18)
Reproductive
Effects on sperm
and testes
J, Ability of sperm to fertilize
(rat/DuTeaux et al.. 2004a)
Multiple effects
(rat/Kumar et al.. 200 Ib, 2000b)
Hyperzoospermia
(human/Chia et al.. 1996V1
0.016
(1,000)
0.016
(1,000)
0.024
(30)
0.014
(10,000b)
[rtr]
[rtr]
0.042-0.10
(30-1,000)
Developmental
Develop.
immunotox.
Congenital defects
Develop, neurotox.
Pre/postnatal
mortality/growth
J, PFC, t DTK
(rat/Peden-Adams et al., 2006)d
Heart malformations
(rat/Johnson et al., 2003)
I Rearing postexposure
(rat/Fredriksson et al.. 1993)d
Resorptions/J, fetal weight/
skeletal effects
(rat/Healv et al.. 1982)
0.00037
(1,000)
0.00052
(10)
0.016
(1,000)
0.085
(100)
Same as
preferred
0.00021
(100)
Same as
preferred
[rtr]
-
0.00017
(10)
0.017-0.11
(100-3,000)
0.70-2.9
(10-100)
"The critical effects/studies and p-cRfDs or cRfDs used to derive the RfD are in bold; supporting effects/studies and
p-cRfDs in italics.
bEPA's report on the RfC and RfD processes (U.S. EPA. 2002b) recommends not deriving reference values with a
composite UF of >3,000; however, composite UFs exceeding 3,000 are considered here because the derivation of
the cRfCs and cRfDs is part of a screening process and the application of the PBPK model for candidate critical
effects reduces the values of some of the individual UFs for the p-cRfCs and p-cRfDs.
'Greater than usual degree of uncertainty (see Section 5.1.2).
dNo PBPK model based analyses were done, so cRfD on the basis of applied dose only.
rtr = route-to-route extrapolated result (no value for default methodology)
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Table 5-26. Lowest p-cRfCs for candidate critical effects for different types
of effect based on primary dose-metric
Type of effect
Neurological
Kidney
Liver
Immunological
Reproductive
Developmental
Effect
(primary dose-metric)
Demyelination in hippocampus in rats
(TotMetabBW34)
Toxic nephropathy in rats
(ABioactDCVCBW34)
Increased liver weight in mice
(AMetLivlBW34)
Decreased thymus weight in mice
(TotMetabBW34)
Decreased ability of rat sperm to fertilize
(AUCCBld)
Heart malformations in rats
(TotOxMetabBW34)
p-cRfC (ppm)
0.007 (rtr)
0.0006 (rtr)
0.9
0.0003 (rtr)
0.009 (rtr)a
0.0004 (rtr)
aThis value is supported by the p-cRfC value of 0.01 ppm for multiple testes and sperm effects from an inhalation
study in rats.
rtr = route-to-route extrapolated result
Table 5-27. Lowest p-cRfDs for candidate critical effects for different types
of effect based on primary dose-metric
Type of effect
Neurological
Kidney
Liver
Immunological
Reproductive
Developmental
Effect
(primary dose-metric)
Demyelination in hippocampus in rats
(TotMetabBW34)
Toxic nephropathy in rats
(ABioactDCVCBW34)
Increased liver weight in mice
(AMetLivlBW34)
Decreased thymus weight in mice
(TotMetabBW34)
Decreased ability of rat sperm to fertilize (AUCCBld) and multiple
testes and sperm effects (TotMetabBW34)a
Heart malformations in rats
(TotOxMetabBW34)
p-cRfD (mg/kg/d)
0.009
0.0003
0.8 (rtr)
0.0005
0.02
0.0005b
aEndpoints from two different studies yielded the same p-cRfD value.
bThis value is supported by the cRfD value of 0.0004 mg/kg/day derived for developmental immunotoxicity effects
in mice (Peden-Adams et al.. 2006): however, no PBPK analyses were done for this latter effect, so the value of
0.0004 mg/kg/day is based on applied dose.
rtr = route-to-route extrapolated result
For neurological, kidney, immunological, and developmental effects, the lowest p-cRfCs
were derived from oral studies by route-to-route extrapolation. This appears to be a function of
the lack of comparable inhalation studies for many effects studied via the oral exposure route, for
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which there is a larger database of studies. For the liver and reproductive effects, inhalation
studies yielded a p-cRfC lower than the lowest route-to-route extrapolated p-cRfC for that type
of effect. Conversely, the lowest p-cRfDs were derived from oral studies with the exception of
reproductive effects, for which route-to-route extrapolation from an inhalation study in humans
also yielded among the lowest p-cRfDs. The only effect for which there were comparable
studies for comparing a p-cRfC from an inhalation study with a p-cRfC estimated by route-to-
route extrapolation from an oral study was increased liver weight in the mouse. The primary
dose-metric of amount of TCE oxidized in the liver yielded similar p-cRfCs of 1.0 and 1.1 ppm
for the inhalation result and the route-to-route extrapolated result, respectively (see Table 5-15).
As can be seen in these tables, the most sensitive types of effects (the types with the
lowest p-cRfCs and p-cRfDs) appear to be developmental, kidney, and immunological (adult and
developmental) effects, and then neurological and reproductive effects, in that order. Lastly, the
liver effects have p-cRfC and p-cRfD values that are about 3.5 orders of magnitude higher than
those for developmental, kidney, and immunological effects.
5.1.5.2. RfC
The goal is to select an overall RfC that is well supported by the available data (i.e.,
without excessive uncertainty given the extensive database) and protective for all of the
candidate critical effects, recognizing that individual candidate RfC values are by nature
somewhat imprecise. The lowest candidate RfC values within each health effect category span a
3,000-fold range from 0.0003 to 0.9 ppm (see Table 5-26). One approach to selecting an RfC
would be to select the lowest calculated value of 0.0003 ppm for decreased thymus weight in
mice. However, as can be seen in Table 5-24, three p-cRfCs are in the relatively narrow range of
0.0003-0.0006 ppm at the low end of the overall range. Given the somewhat imprecise nature of
the individual candidate RfC values, and the fact that multiple effects/studies lead to similar
candidate RfC values, the approach taken in this assessment is to select an RfC supported by
multiple effects/studies. The advantages of this approach, which is only possible when there is a
relatively large database of studies/effects and when multiple candidate values happen to fall
within a narrow range at the low end of the overall range, are that it leads to a more robust RfC
(less sensitive to limitations of individual studies) and that it provides the important
characterization that the RfC exposure level is similar for multiple noncancer effects rather than
being based on a sole explicit critical effect.
Tables 5-28 and 5-29 summarize the PODs and UFs for the two critical and one
supporting studies/effects, respectively, corresponding to the p-cRfCs that have been chosen as
the basis of the RfC for TCE noncancer effects. Each of these lowest candidate p-cRfCs, ranging
from 0.0003 to 0.0006 ppm, for developmental, immunologic, and kidney effects, are values
derived from route-to-route extrapolation using the PBPK model. The lowest p-cRfC estimate
(for a primary dose-metric) from an inhalation study is 0.001 ppm for kidney effects, which is
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higher than the route-to-route extrapolated p-cRfC from the most sensitive oral study. For each
of the candidate RfCs, the PBPK model was used for inter- and intraspecies extrapolation, based
on the preferred dose-metric for each endpoint.
Table 5-28. Summary of critical studies, effects, PODs, and UFs used to
derive the RfC
For the database, UFo = 1 because there is minimal potential for deriving an underprotective
toxicity value as a result of an incomplete characterization of TCE toxicity.
Keil et al. (2009)—Decreased thymus weight in female B6C3FJ mice exposed for 30 wks by drinking water.
• idPOD = 0.139 mg TCE metabolized/kgyVd, which is the PBPK model-predicted internal dose at the
applied dose LOAEL of 0.35 mg/kg/d (continuous) (no BMD modeling due to inadequate model fit caused
by supralinear dose-response shape) (see Appendix F, Section F.6.3).
• HEC99 = 0.033 ppm (lifetime continuous exposure) derived from combined interspecies, intraspecies, and
route-to-route extrapolation using PBPK model.
• UFL = 10 because POD is a LOAEL for an adverse effect.
• UFA = 3 because the PBPK model was used for interspecies extrapolation.
• UFH = 3 because the PBPK model was used to characterize human toxicokinetic variability.
• p-cRfC = 0.033/100 = 0.00033 ppm (2 ug/m3).
Johnson et al. (2003)—Fetal heart malformations in Sprague-Dawley rats exposed on GDs 1-22 by drinking water.
• idPOD = 0.0142 mg TCE metabolized by oxidation/kg/7d, which is the BMDL from BMD modeling using
PBPK model-predicted internal doses, with highest dose group (1,000-fold higher than next highest dose
group) dropped, pup as unit of analysis, BMR = 1% (due to severity of defects, some of which could have
been fatal), and a nested Log-logistic model to account for intralitter correlation (see Appendix F,
Section F.6.4).
• HEC99 = 0.0037 ppm (lifetime continuous exposure) derived from combined interspecies, intraspecies, and
route-to-route extrapolation using PBPK model.
• UFA = 3 because the PBPK model was used for interspecies extrapolation.
• UFH = 3 because the PBPK model was used to characterize human toxicokinetic variability.
• p-cRfC = 0.0037/10 = 0.00037 ppm (2 ug/m3).
Table 5-29. Summary of supporting studies, effects, PODs, and UFs for the
RfC
For the database, UFo = 1 because there is minimal potential for deriving an underprotective
toxicity value as a result of an incomplete characterization of TCE toxicity.
NTP (1988)—Toxic nephropathy in female Marshall rats exposed for 104 wks by gavage (5 d/wk).
• idPOD = 0.0132 mg DCVC bioactivated/kgy7d, which is the BMDL from BMD modeling using PBPK
model-predicted internal doses, BMR = 5% (clearly toxic effect), and log-logistic model (see Appendix F,
Section F.6.1).
• HEC99 = 0.0056 ppm (lifetime continuous exposure) derived from combined interspecies, intraspecies, and
route-to-route extrapolation using PBPK model.
• UFA = 3 because the PBPK model was used for interspecies extrapolation.
• UFH = 3 because the PBPK model was used to characterize human toxicokinetic variability.
• p-cRfC = 0.0056/10 = 0.00056 ppm (3 ug/m3).
There is moderate confidence in the lowest p-cRfC for developmental effects (heart
malformations) (see Section 5.1.2.8) and the lowest p-cRfC estimate for immunological effects
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(see Section 5.1.2.5), and these are considered the critical effects used for deriving the RfC. For
developmental effects, although the available study has important limitations, the overall weight
of evidence supports an effect of TCE on cardiac development. For immunological effects, there
is high confidence in the evidence for an immunotoxic hazard from TCE, but the available dose-
response data preclude application of BMD modeling.
For kidney effects (see Section 5.1.2.2), there is high confidence in the evidence for a
nephrotoxic hazard from TCE. Moreover, the lowest p-cRfC for kidney effects (toxic
nephropathy) is derived from a chronic study and is based on BMD modeling. However, as
discussed in Section 3.3.3.2, there remains substantial uncertainty in the extrapolation of GSH
conjugation from rodents to humans due to limitations in the available data. In addition, the
p-cRfC for toxic nephropathy had greater dose-response uncertainty since the estimation of its
POD involved extrapolation from high response rates (>60%). Therefore, toxic nephropathy is
considered supportive but is not used as a primary basis for the RfC. The other sensitive
p-cRfCs for kidney effects in Table 5-19 were all within a factor of 5 of that for toxic
nephropathy; however, these values similarly relied on the uncertain interspecies extrapolation of
GSH conjugation.
As a whole, the estimates support an RfC of 0.0004 ppm (0.4 ppb or 2 ug/m3). This
value essentially reflects the midpoint between the similar p-cRfC estimates for the two critical
effects (0.00033 ppm for decreased thymus weight in mice and 0.00037 ppm for heart
malformations in rats), rounded to one significant figure. This value is also within a factor of 2
of the p-cRfC estimate of 0.0006 ppm for the supporting effect of toxic nephropathy in rats.
Thus, there is robust support for an RfC of 0.0004 ppm provided by estimates for multiple effects
from multiple studies. The estimates are based on PBPK model-based estimates of internal dose
for interspecies, intraspecies, and route-to-route extrapolation, and there is sufficient confidence
in the PBPK model and support from mechanistic data for one of the dose-metrics
(TotOxMetabBW34 for the heart malformations). There is high confidence that
ABioactDCVCBW34 and AMetGSHBW34 would be appropriate dose-metrics for kidney
effects, but there is substantial uncertainty in the PBPK model predictions for these dose-metrics
in humans (see Section 5.1.3.1). Note that there is some human evidence of developmental heart
defects from TCE exposure in community studies (see Section 4.8.3.1.1) and of kidney toxicity
in TCE-exposed workers (see Section 4.4.1).
In summary, the RfC is 0.0004 ppm (0.4 ppb or 2 ug/m3) based on route-to-route
extrapolated results from oral studies for the critical effects of heart malformations (rats) and
immunotoxicity (mice). This RfC value is further supported by route-to-route extrapolated
results from an oral study of toxic nephropathy (rats).
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5.1.5.3. RfD
As with the RfC determination above, the goal is to select an overall RfD that is well
supported by the available data (i.e., without excessive uncertainty given the extensive database)
and protective for all of the candidate critical effects, recognizing that individual candidate RfD
values are by nature somewhat imprecise. The lowest candidate RfD values within each health
effect category span a nearly 3,000-fold range from 0.0003 to 0.8 mg/kg/day (see Table 5-26).
One approach to selecting an RfC would be to select the lowest calculated value of 0.0003 ppm
for toxic nephropathy in rats. However, as can be seen in Table 5-25, multiple p-cRfDs or cRfDs
from oral studies are in the relatively narrow range of 0.0003-0.0008 mg/kg/day at the low end
of the overall range. Given the somewhat imprecise nature of the individual candidate RfD
values, and the fact that multiple effects/studies lead to similar candidate RfD values, the
approach taken in this assessment is to select an RfD supported by multiple effects/studies. The
advantages of this approach, which is only possible when there is a relatively large database of
studies/effects and when multiple candidate values happen to fall within a narrow range at the
low end of the overall range, are that it leads to a more robust RfD (less sensitive to limitations
of individual studies) and that it provides the important characterization that the RfD exposure
level is similar for multiple noncancer effects rather than being based on a sole explicit critical
effect.
Tables 5-30 and 5-31 summarize the PODs and UFs for the three critical and
two supporting studies/effects, respectively, corresponding to the p-cRfDs or cRfDs that have
been chosen as the basis of the RfD for TCE noncancer effects. Two of the lowest p-cRfDs for
the primary dose-metrics—0.0008 mg/kg/day for increased kidney weight in rats and
0.0005 mg/kg/day for both heart malformations in rats and decreased thymus weights in mice—
are derived using the PBPK model for inter- and intraspecies extrapolation, and a third—
0.0003 mg/kg/day for increased toxic nephropathy in rats—is derived using the PBPK model for
inter- and intraspecies extrapolation as well as route-to-route extrapolation from an inhalation
study. The other of these lowest values—0.0004 mg/kg/day for developmental immunotoxicity
(decreased PFC response and increased delayed-type hypersensitivity) in mice—is based on
applied dose.
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Table 5-30. Summary of critical studies, effects, PODs, and UFs used to
derive the RfD
For the database, UFD = 1 because there is minimal potential for deriving an underprotective
toxicity value as a result of an incomplete characterization of TCE toxicity.
Keil et al. (2009)—Decreased thymus weight in female B6C3FJ mice exposed for 30 wks by drinking water.
• idPOD = 0.139 mg TCE metabolized/kgyVd, which is the PBPK model-predicted internal dose at the
applied dose LOAEL of 0.35 mg/kg/d (continuous) (no BMD modeling due to inadequate model fit caused
by supralinear dose-response shape) (see Appendix F, Section F.6.3).
• HED99 = 0.048 mg/kg/d (lifetime continuous exposure) derived from combined interspecies and
intraspecies extrapolation using PBPK model.
• UFL = 10 because POD is a LOAEL for an adverse effect.
• UFA = 3 because the PBPK model was used for interspecies extrapolation.
• UFH = 3 because the PBPK model was used to characterize human toxicokinetic variability.
• p-cRfD = 0.048/100 = 0.00048 mg/kg/d.
Peden-Adams et al. (2006)—Decreased PFC response (3 and 8 wks), and increased delayed-type hypersensitivity
(8 wks) in pups exposed from GDs 0-3- or 8 wks of age through drinking water (placenta! and lactational transfer,
and pup ingestion).
• POD = 0.37 mg/kg/d is the applied dose LOAEL (estimated daily dam dose) (no BMD modeling due to
inadequate model fit caused by supralinear dose-response shape). No PBPK modeling was attempted due
to lack of appropriate models/parameters to account for complicated fetal/pup exposure pattern (see
Appendix F, Section F.6.5).
• UFL = 10 because POD is a LOAEL for multiple adverse effects.
• UFA = 10 for interspecies extrapolation because PBPK model was not used.
• UFH = 10 for human variability because PBPK model was not used.
• cRfD = 0.37/1,000 = 0.00037 mg/kg/d.
Johnson et al. (2003)—Fetal heart malformations in Sprague-Dawley rats exposed on GDs 1-22 by drinking water.
• idPOD = 0.0142 mg TCE metabolized by oxidation/kgyVd, which is the BMDL from BMD modeling using
PBPK model-predicted internal doses, with highest dose group (1,000-fold higher than next highest dose
group) dropped, pup as unit of analysis, BMR = 1% (due to severity of defects, some of which could have
been fatal), and a nested Log-logistic model to account for intralitter correlation (see Appendix F,
Section F.6.4).
• HED99 = 0.0051 mg/kg/d (lifetime continuous exposure) derived from combined interspecies and
intraspecies extrapolation using PBPK model.
• UFA = 3 because the PBPK model was used for interspecies extrapolation.
• UFH = 3 because the PBPK model was used to characterize human toxicokinetic variability.
• p-cRfD = 0.0051/10 = 0.00051 mg/kg/d.
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Table 5-31. Summary of supporting studies, effects, PODs, and UFs for the
RfD
For the database, UFD = 1 because there is minimal potential for deriving an underprotective
toxicity value as a result of an incomplete characterization of TCE toxicity.
NTP (1988)—Toxic nephropathy in female Marshall rats exposed for 104 wks by gavage (5 d/wk).
• idPOD = 0.0132 mg DCVC bioactivated/kgy60%). Therefore, kidney effects are considered supportive but are not used as a primary
basis for the RfD.
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As a whole, the estimates support an RfD of 0.0005 mg/kg/day. This value is within 20%
of the estimates for the critical effects—0.0004 mg/kg/day for developmental immunotoxicity
(decreased PFC and increased delayed-type hypersensitivity) in mice, and 0.0005 mg/kg/day for
both heart malformations in rats and decreased thymus weights in mice. This value is also
within approximately a factor of 2 of the supporting effect estimates of 0.0003 mg/kg/day for
toxic nephropathy in rats and 0.0008 mg/kg/day for increased kidney weight in rats. Thus, there
is strong, robust support for an RfD of 0.0005 mg/kg/day provided by the concordance of
estimates derived from multiple effects from multiple studies. The estimates for kidney effects,
thymus effects, and developmental heart malformations are based on PBPK model-based
estimates of internal dose for interspecies and intraspecies extrapolation, and there is sufficient
confidence in the PBPK model and support from mechanistic data for one of the dose-metrics
(TotOxMetabBW34 for the heart malformations). There is high confidence that
ABioactDCVCBW34 would be an appropriate dose-metric for kidney effects, but there is
substantial uncertainty in the PBPK model predictions for this dose-metric in humans (see
Section 5.1.3.1). Note that there is some human evidence of developmental heart defects from
TCE exposure in community studies (see Section 4.8.3.1.1) and of kidney toxicity in
TCE-exposed workers (see Section 4.4.1).
In summary, the RfD is 0.0005 mg/kg/day based on the critical effects of heart
malformations (rats), adult immunological effects (mice), and developmental immunotoxicity
(mice), all from oral studies. This RfD value is further supported by results from an oral study
for the effect of toxic nephropathy (rats) and route-to-route extrapolated results from an
inhalation study for the effect of increased kidney weight (rats).
5.2. DOSE-RESPONSE ANALYSIS FOR CANCER ENDPOINTS
This section describes the dose-response analysis for cancer endpoints. Section 5.2.1
discusses the analyses of data from chronic rodent bioassays. Section 5.2.2 discusses the
analyses of human epidemiologic data. Section 5.2.3 discusses the choice of the preferred
inhalation unit risk and oral slope factor estimates, as well as the application of ADAFs to the
slope factor and unit risk estimates.
5.2.1. Dose-Response Analyses: Rodent Bioassays
This section describes the calculation of cancer slope factor and unit risk estimates based
on rodent bioassays. First, all of the available studies (i.e., chronic rodent bioassays) were
considered, and those suitable for dose-response modeling were selected for analysis (see
Section 5.2.1.1). Then dose-response modeling using the linearized multistage model was
performed using applied doses (default dosimetry) as well as PBPK model-based internal doses
(see Section 5.2.1.2). Bioassays for which time-to-tumor data were available were analyzed
using poly-3 adjustment techniques and using a Multistage Weibull model. In addition, a cancer
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potency estimate for different cancer types combined was derived from bioassays in which there
was more than one type of tumor response in the same sex and species. Slope factor and unit
risk estimates based on PBPK model-estimated internal doses were then extrapolated to human
population slope factor and unit risk estimates using the human PBPK model. From these results
(see Section 5.2.1.3), estimates from the most sensitive bioassay (i.e., that with the greatest slope
factor or unit risk estimate) for each combination of administration route, sex, and species, based
on the PBPK model-estimated internal doses, were considered as candidate slope factor or unit
risk estimates for TCE. Uncertainties in the rodent-based dose-response analyses are described
in Section 5.2.1.4.
5.2.1.1. Rodent Dose-Response Analyses: Studies and Modeling Approaches
The rodent cancer bioassays that were identified for consideration for dose-response
analysis are listed in Tables 5-32 (inhalation bioassays) and 5-33 (oral bioassays) for each
sex/species combination. The bioassays selected for dose-response analysis are marked with an
asterisk; rationales for rejecting the bioassays that were not selected are provided in the
—Cmments" columns of the tables. For the selected bioassays, the tissues/organs that exhibited
a TCE-associated carcinogenic response and for which dose-response modeling was performed
are listed in the —Tissu$)rgan" columns.
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Table 5-32. Inhalation bioassays
Study
Strain
Tissue/organ
Comments
Female mice
Fukuda et al. (19831"
Henschler et al. (1980V
Maltoni et al. (1986V
Maltoni et al. (1986)
Crj:CD-l (ICR)
Han:NMRI
B6C3FJ
Swiss
Lung
Lymphoma
Liver, Lung
-
No dose-response
Male mice
Henschler et al. (1980)
Maltoni et al. (1986)
Maltoni et al. (1986)
Maltoni et al. (1986)a
Han:NMRI
B6C3FJ
B6C3FJ
Swiss
-
Liver
Liver
Liver
No dose-response
Exp #BT306: excessive fighting
Exp #BT306bis. Results similar to
Swiss mice
Female rats
Fukuda et al. (1983)
Henschler et al. (1980)
Maltoni etal. (1986)
Sprague-Dawley
Wistar
Sprague-Dawley
-
-
-
No dose-response
No dose-response
No dose-response
Male rats
Henschler et al. (1980)
Maltoni et al. (1986)a
Wistar
Sprague-Dawley
-
Kidney, Leydig cell,
Leukemia
No dose-response
aSelected for dose-response analysis.
—No doscresponse" = no tumor incidence data suitable for dose-response modeling
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Table 5-33. Oral bioassays
Study
Strain
Tissue/organ
Comments
Female mice
Henschler et al. (1984)
NCI (1976)"
NTP (19901
Van Duuren et al. (1979)
Han:NMRI
B6C3FJ
B6C3FJ
Swiss
-
Liver, lung, sarcomas and
lymphomas
Liver, lung, lymphomas
Liver
Toxicity, no dose-response
Single dose
Single dose, no dose-response
Male mice
Anna et al. (1994)
Bull et al. (20021
Henschler et al. (1984)
NCI (19761"
NTP (1990)
Van Duuren et al. (1979)
B6C3FJ
B6C3FJ
Han:NMRI
B6C3FJ
B6C3FJ
Swiss
Liver
Liver
-
Liver
Liver
-
Single dose
Single dose
Toxicity, no dose-response
Single dose
Single dose, no dose-response
Female rats
NCI (1976)
NTP (1988)
NTP (1988)a
NTP (1988)
NTP (1988)
NTP (1990)
Osborne-Mendel
ACI
August
Marshall
Osborne-Mendel
F344/JV
-
-
Leukemia
-
Adrenal cortex
-
Toxicity, no dose-response
No dose-response
No dose-response
Adenomas only
No dose-response
Male rats
NCI (1976)
NTP (1988)
NTP (1988)a
NTP (1988)a
NTP (1988)a
NTP (1990)a
Osborne-Mendel
ACI
August
Marshall
Osborne-Mendel
F344/N
-
-
Subcutaneous tissue
sarcomas
Testes
Kidney
Kidney
Toxicity, no dose-response
No dose-response
"Selected for dose-response analysis.
—No doscresponse" = no tumor incidence data suitable for dose-response modeling
The general approach used was to model each sex/species/bioassay tumor response to
determine the most sensitive bioassay response (in terms of HEC or HED) for each sex/species
combination. The various modeling approaches, model selection, and slope factor and unit risk
derivation are discussed below. Modeling was done using the applied dose or exposure (default
dosimetry) and several internal dose-metrics. The dose-metrics used in the dose-response
modeling are discussed in Section 5.2.1.2. Because of the large volume of analyses and results,
detailed discussions about how the data were modeled using the various dosimetry and modeling
approaches and results for individual data sets are provided in Appendix G. The overall results
are summarized and discussed in Section 5.2.1.3.
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Most tumor responses were modeled using the multistage model in EPA's BMDS
(www.epa.gov/ncea/bmds). The multistage model is a flexible model, capable of fitting most
cancer bioassay data, and it is EPA's long-standing model for the modeling of such cancer data.
The multistage model has the general form
P(d)= 1 - exp[-(q0+qid + q2d2+ ... + qkdk]~\
where P(d) represents the lifetime risk (probability) of cancer at dose d, and parameters qt > 0,
for /' = 0, 1, ..., k. For each data set, the multistage model was evaluated for one stage and (n - 1)
stages, where n is the number of dose groups in the bioassay. A detailed description of how the
data were modeled, as well as tables of the dose-response input data and figures of the multistage
modeling results, is provided in Appendix G.
Only models with acceptable fit (p > 0.05) were considered.37 If 1-parameter and
2-parameter models were both acceptable (in no case was there a 3-parameter model), then the
more parsimonious model (i.e., the 1-parameter model) was selected unless the inclusion of the
2nd parameter resulted in a statistically significant38 improvement in fit. If two different
1-parameter models were available (e.g., a 1-stage model and a 3-stage model with fii and $2
both equal to 0), then the one with the best fit, as indicated by the lowest AIC value, was
selected. If the AIC values were the same (to three significant figures), then the lower-stage
model was selected. Visual fit and scaled %2 residuals were also considered for confirmation in
model selection. For two data sets, the highest-dose group was dropped to improve the fit in the
lower dose range.
From the selected model for each data set, the maximum likelihood estimate (MLE) for
the dose corresponding to a specified level of risk (i.e., the BMD) and its 95% lower confidence
bound (BMDL) were estimated.39 In most cases, the risk level, or BMR, was 10% extra risk;40
however, in a few cases with low response rates, a BMR of 5%, or even 1%, extra risk was used
to avoid extrapolation above the range of the data. As discussed in Section 4.4, there is sufficient
evidence to conclude that a mutagenic mode of action is operative for TCE-induced kidney
tumors, so linear extrapolation from the BMDL to the origin was used to derive slope factor and
unit risk estimates for this site. The weight of evidence also supports involvement of processes
of cytotoxicity and regenerative proliferation in the carcinogen!city of TCE, although not with
the extent of support as for a mutagenic mode of action. In particular, data linking TCE-induced
37 When considering multiple types of model for noncancer effects, p > 0.10 is used. For cancer, there is a prior
preference for the multistage model, thus the p > 0.05 (which increases the probability of accepting the preferred
model).
38Using a standard criterion for nested models, that the difference in -2 x log-likelihood exceeds 3.84 (the
95th percentile of j2 [1]).
39BMDS estimates confidence intervals using the profile likelihood method.
40Extra risk over the background tumor rate is defined as [P(d) - P(0)] / [1 - P(0)], where P(d) represents the
lifetime risk (probability) of cancer at dose d.
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proliferation to increased mutation or clonal expansion are lacking, as are data informing the
quantitative contribution of cytotoxicity. Moreover, it is unlikely that any contribution from
cytotoxicity leads to a non-linear dose-response relationship near the POD for rodent kidney
tumors, since maximal levels of toxicity are reached before the onset of tumors. Finally, because
any possible involvement of a cytotoxicity mode of action would be additional to mutagenicity,
the dose-response relationship would nonetheless be expected to be linear at low doses.
Therefore, the additional involvement of a cytotoxicity mode of action does not provide evidence
against the use of linear extrapolation from the POD.
For all other cancer types, the available evidence supports the conclusion that the mode(s)
of action for TCE-induced rodent tumors is unknown, as discussed in Sections 4.5-4.10 and
summarized in Section 4.11.2.3. Therefore, linear extrapolation was also used based on the
general principles outlined in EPA's Guidelines for Carcinogen Risk Assessment (U.S. EPA,
2005b) and reviewed below in Section 5.2.1.4.1. Thus, for all TCE-associated rodent tumors,
slope factor and unit risk estimates are equal to BMR/BMDL (e.g., 0.10/BMDLio for a BMR of
10%). See Section 5.2.1.3 for a summary of the slope factor and unit risk estimates for each
sex/species/bioassay/tumor type.
Some of the bioassays exhibited differential early mortality across the dose groups, and,
for three such male rat studies (identified with checkmarks in the —Tim-to-tumor" column of
Table 5-34), analyses that take individual animal survival times into account were performed.
(For bioassays with differential early mortality occurring primarily before the time of the
1st tumor [or 52 weeks, whichever came first], the effects of early mortality were largely
accounted for by adjusting the tumor incidence for animals at risk, as described in Appendix G,
and the dose-response data were modeled using the regular multistage model, as discussed
above, rather than approaches that account for individual animal survival times.)
Two approaches were used to take individual survival times into account. First, EPA's
Multistage Weibull (MSW) software41 was used for time-to-tumor modeling. The Multistage
Weibull time-to-tumor model has the general form:
P(d,t)= 1 - exp-(q0+qld + q2d2 + ... + qkdk)
41This software is available on U.S. EPA's BMDS Web site (www.epa.gov/ncea/bmds).
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Table 5-34. Specific dose-response analyses performed and dose-metrics used
Bioassay
Strain
Endpoint
Applied
dose
PBPK-based — primary
dose-metric"
PBPK-based—
alternative dose-
met ric(s)a
Time-to-
tumor
INHALATION
Female mice
Fukuda et al. (1983)
Henschler et al. (1980)
Maltoni et al. (1986)
Crj:CD-l (ICR)
Han:NMRI
B6C3FJ
Lung adenomas and carcinomas
Lymphoma
Liver hepatomas
Lung adenomas and carcinomas
Combined risk
A/
A/
A/
A/
A/
AMetLngBW34
TotMetabBW34
AMetLivlBW34
AMetLngBW34
TotOxMetabBW34
AUCCBld
AUCCBld
TotOxMetabBW34
TotOxMetabBW34
AUCCBld
Male mice
Maltoni et al. (1986)
Swiss
Liver hepatomas
A/
AMetLivlBW34
TotOxMetabBW34
Female rats
None selected
Male rats
Maltoni et al. (1986)
Sprague-Dawley
Kidney adenomas and carcinomas
Leydig cell tumors
Leukemias
Combined risk
A/
A/
A/
A/
ABioactDCVCBW34
TotMetabBW34
TotMetabBW34
AMetGSHBW34
TotMetabBW34
AUCCBld
AUCCBld
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Table 5-34. Specific dose-response analyses performed and dose-metrics used (continued)
Bioassay
Strain
Endpoint
Applied
dose
PBPK-based — primary
dose-metric
PBPK-based—
alternative dose-
met ric(s)
Time-to-
tumor
ORAL
Female mice
NCI (19761
B6C3FJ
Liver carcinomas
Lung adenomas and carcinomas
Multiple sarcomas/lymphomas
Combined risk
A/
A/
A/
A/
AMetLivlBW34
AMetLngBW34
TotMetabBW34
TotOxMetabBW34
TotOxMetabBW34
AUCCBld
AUCCBld
Male mice
NCI (19761
B6C3FJ
Liver carcinomas
A/
AMetLivlBW34
TotOxMetabBW34
Female rats
NTP (1988)
August
Leukemia
A/
TotMetabBW34
AUCCBld
Male rats
NTP (1988)
NTP (1988)
NTP (1988)
NTP (1990)
August
Marshall
Osborne-Mendel
F34W
Subcutaneous tissue sarcomas
Testicular interstitial cell tumors
Kidney adenomas and carcinomas
Kidney adenomas and carcinomas
A/
A/
A/
A/
TotMetabBW34
TotMetabBW34
ABioactDCVCBW34
ABioactDCVCBW34
AUCCBld
AUCCBld
AMetGSHBW34
TotMetabBW34
AMetGSHBW34
TotMetabBW34
A/
A/
A/
"PBPK-based dose-metric abbreviations:
ABioactDCVCB W34 = Amount of DCVC bioactivated in the kidney per unit body weight'7' (mg DCVC/kgy
-------
where P(d,t) represents the probability of a tumor by age t for dose d, and parameters z > 1,
t0 > 0, and qt > 0 for / = 0,1,...,&, where A: = the number of dose groups; the parameter ^represents
the time between when a potentially fatal tumor becomes observable and when it causes death.
(All of our analyses used the model for incidental tumors, which has no to term.) Although the
fit of the MSW model can be assessed visually using the plot feature of the MSW software,
because there is no applicable goodness-of-fit statistic with a well-defined asymptotic
distribution, an alternative survival-adjustment technique, -poly-3 adjustment," was also applied
(Portier and Bailer, 1989). This technique was used to adjust the tumor incidence denominators
based on the individual animal survival times.42 The adjusted incidence data then served as
inputs for EPA's BMDS multistage model, and model (i.e., stage) selection was conducted as
already described above. Under both survival-adjustment approaches, BMDs and BMDLs were
obtained and slope factor and unit risks were derived as discussed above for the standard
multistage model approach. See Appendix G for a more detailed description of the MSW
modeling and for the results of both the MSW and poly-3 approaches for the individual data sets.
A comparison of the results for the three different data sets and the various dose-metrics used is
presented in Section 5.2.1.3.
For bioassays that exhibited more than one type of tumor response in the same sex and
species (these studies have a row for -combined risk" in the -Endpoint" column of Table 5-34),
the cancer potency for the different cancer types combined was estimated, in accordance with
EPA's Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005b). The combined tumor
risk estimate describes the risk of developing tumors for any (not all together) of the cancer types
that exhibited a TCE-associated tumor response; this estimate then represents the total excess
cancer risk. The model for the combined tumor risk is also multistage, with the sum of the stage-
specific multistage coefficients from the individual tumor models serving as the stage-specific
coefficients for the combined risk model (i.e., for each qf, qi[COmbined\ = qu + qt2 + ... + qtk, where
the q$ are the coefficients for the powers of dose and k is the number of cancer types being
combined) (NRC, 1994; Bogen, 1990). This model assumes that the occurrences of two or more
cancer types are independent. Although the resulting model equation can be readily solved for a
given BMR to obtain an MLE (BMD) for the combined risk, the confidence bounds for the
combined risk estimate were not calculated by modeling software available during the
development of this assessment. Therefore, the confidence bounds on the combined BMD were
estimated using a Bayesian approach, computed using Markov chain Monte Carlo techniques
and implemented using the freely available WinBugs software (Spiegelhalter et al., 2003). Use
of WinBugs for derivation of a distribution of BMDs for a single multistage model has been
demonstrated by Kopylev et al. (2007), and this approach can be straightforwardly generalized to
42Each tumorless animal is weighted by its fractional survival time (number of days on study divided by 728 days,
the typical number of days in a 2-year bioassay) raised to the power of 3 to reflect the fact that animals are at greater
risk of cancer at older ages. Animals with tumors are given a weight of 1. The sum of the weights of all of the
animals in an exposure group yields the effective survival-adjusted denominator.
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derive the distribution of BMDs for the combined tumor load. For further details on the
implementation of this approach and for the results of the analyses, see Appendix G.
5.2.1.2. Rodent Dose-Response Analyses: Dosimetry
In modeling the applied doses (or exposures), default dosimetry procedures were applied
to convert applied rodent doses to HEDs. Essentially, for inhalation exposures, -ppm
equivalence" across species was assumed, consistent with the recommendations of U.S. EPA
(1994a) for deriving a human equivalent concentration for a Category 3 gas for which the
blood:air partition coefficient in laboratory animals is greater than that in humans (e.g., the
posterior population median estimate for the TCE blood:air partition coefficient was 14 in the
mouse [Table 3-37], 19 in the rat [Table 3-38], and 9.2 in the human [Table 3-39]). For oral
doses, 3/4-power body-weight scaling was used, with a default average human body weight of 70
kg. See Appendix G for more details on the default dosimetry procedures.
In addition to applied doses, several internal dose-metrics were used in the dose-response
modeling for each tumor type. Use of internal dose-metrics in dose-response modeling is
described here briefly. For more details on the PBPK modeling used to estimate the levels of the
dose-metrics corresponding to different exposure scenarios in rodents and humans, as well as a
qualitative discussion of the uncertainties and limitations of the model, see Section 3.5; for a
more detailed discussion of how the dose-metrics were used in dose-response modeling, see
Appendix G. Quantitative analyses of the uncertainties and their implications for dose-response
assessment, utilizing the results of the Bayesian analysis of the PBPK model, are discussed
separately in Section 5.2.1.4.2.
5.2.1.2.1. Selection of dose-metrics for different cancer types
One area of scientific uncertainty in cancer dose-response assessment is the appropriate
scaling between rodent and human doses for equivalent responses. As discussed above, for
applied dose, the standard dosimetry assumptions for equal lifetime carcinogenic risk are, for
inhalation exposure, the same lifetime exposure concentration in air, and, for oral exposure, the
same lifetime daily dose scaled by body weight to the 3/4 power. In this assessment, the cross-
species scaling methodology, grounded in the principles of allometric variation of biologic
processes, is used for describing pharmacokinetic equivalence (U.S. EPA, 1992, 201 la, 2005b:
Allen and Fisher. 1993: Crump etal.. 1989: Allen etal.. 1987). Briefly, in the absence of
adequate information to the contrary, the methodology determines pharmacokinetic equivalence
across species through equal average lifetime concentrations or AUCs of the toxicant. Thus, in
cases where the PBPK model can predict internal concentrations of the active moiety, equivalent
daily AUCs are assumed to address cross-species pharmacokinetics. For cancer assessments,
there is currently no adjustment for pharmacodynamic differences.
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More detailed discussion of the cross-species scaling methodology, and its implications
for dose-metric selection, was presented for the noncancer dose-response analyses in
Section 5.1.3.1, and those details are not repeated here.
To summarize, the preferred dose-metric under this methodology is equivalent daily
AUC of the active moiety (parent compound or metabolite). For metabolites, in cases where the
rate of production, but not the rate of clearance, of the active moiety can be estimated, the
preferred dose-metric is the rate of metabolism (through the appropriate pathway) scaled by body
weight to the 3/4 power. If there are sufficient data to consider the active metabolite moiety(ies)
—aactive" and cleared through nonbiological processes, then the preferred dose-metric is the rate
of metabolism (through the appropriate pathway) scaled by the tissue mass. Finally, if local
metabolism is thought to be involved but cannot be estimated with the available data, then the
AUC of the parent compound in blood is considered an appropriate surrogate and thus the
preferred dose-metric.
Generally, an attempt was made to use tissue-specific dose-metrics representing
particular pathways or metabolites identified from available data as having a likely role in the
induction of a tissue-specific cancer. Where insufficient information was available to establish
particular metabolites or pathways of likely relevance to a tissue-specific cancer, more general
—upsfeam" metrics representing either parent compound or total metabolism had to be used. In
addition, the selection of dose-metrics was limited to metrics that could be adequately estimated
by the PBPK model (see Section 3.5). The (PBPK-based) dose-metrics used for the different
cancer types are listed in Table 5-34. For each tumor type, the —pmary" dose-metric referred to
in Table 5-34 is the metric representing the particular metabolite or pathway whose involvement
in carcinogenicity has the greatest biological support, whereas —kernative" dose-metrics
represent upstream metabolic pathways (or TCE distribution, in the case of AUCCBld) that may
be more generally involved.
5.2.1.2.1.1. Kidney
As discussed in Sections 4.4.6-4.4.7, there is sufficient evidence to conclude that
TCE-induced kidney tumors in rats are primarily caused by GSH-conjugation metabolites either
produced in situ in or delivered systemically to the kidney. As discussed in Section 3.3.3.2,
bioactivation of these metabolites within the kidney, either by beta-lyase, FMO, or P450s,
produces reactive species. Therefore, multiple lines of evidence support the conclusion that
renal bioactivation of DCVC is the preferred basis for internal dose extrapolations of TCE-
induced kidney tumors. However, uncertainties remain as to the relative contributions from each
bioactivation pathway, and quantitative clearance data necessary to calculate the concentration of
each species are lacking. Moreover, the estimates of the amount bioactivated are indirect,
derived from the difference between overall GSH conjugation flux and NAcDCVC excretion
(see Section 3.5.7.3.1).
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The rationales for the dose-metrics for kidney tumors are the same as for kidney
noncancer toxicity, discussed above in Section 5.1.3.1.1, and not repeated here. The primary
internal dose-metric for TCE-induced kidney tumors is the weekly rate of DCVC bioactivation
per unit body weight to the % power (ABioactDCVCBW34 [mg/kgy7week]). Due to the larger
relative kidney weight in rats as compared to humans, using the alternative scaling by kidney
weight instead of body weight to the % power would only change the quantitative interspecies
extrapolation by about twofold,43 so the sensitivity of the results to the scaling choice is relatively
small. An alternative dose-metric that also involves the GSH conjugation pathway is the amount
of GSH conjugation scaled by the % power of body weight (AMetGSHBW34 [mg/kgy7week]).
This dose-metric uses the total flux of GSH conjugation as the toxicologically-relevant dose,
and, thus, incorporates any direct contributions from DCVG and DCVC, which are not addressed
in the DCVC bioactivation metric. Another alternative dose-metric is the total amount of TCE
metabolism (oxidation and GSH conjugation together) scaled by the 3/4 power of body weight
(TotMetabBW34 [mg/kgy7week]). This dose-metric uses the total flux of TCE metabolism as
the lexicologically relevant dose, and, thus, incorporates the possible involvement of oxidative
metabolites, acting either additively or interactively, in addition to GSH conjugation metabolites
in nephrocarcinogenicity (see Section 4.4.6). While there is no evidence that TCE oxidative
metabolites can on their own induce kidney cancer, some nephrotoxic effects attributable to
oxidative metabolites (e.g., peroxisome proliferation) may modulate the nephrocarcinogenic
potency of GSH metabolites. However, this dose-metric is given less weight than those
involving GSH conjugation because, as discussed in Sections 4.4.6 and 4.4.7, the weight of
evidence supports the conclusion that GSH conjugation metabolites play a predominant role in
nephrocarcinogenicity.
5.2.1.2.1.2. Liver
As discussed in Section 4.5.6, there is substantial evidence that oxidative metabolism is
involved in TCE hepatocarcinogenicity, based primarily on noncancer and cancer effects similar
to those observed with TCE being observed with a number of oxidative metabolites of TCE (e.g.,
CH, TCA, and DCA). While TCA is a stable, circulating metabolite, CH and DCA are relatively
short-lived, although enzymatically cleared (see Section 3.3.3.1). As discussed in Sections 4.5.6
and 4.5.7, there is now substantial evidence that TCA does not adequately account for the
hepatocarcinogenicity of TCE; therefore, unlike in previous dose-response analyses (Clewell and
Andersen, 2004; Rhomberg, 2000), the AUCs of TCA in plasma and in liver were not considered
as dose-metrics. However, there are inadequate data across species to quantify the dosimetry of
CH and DCA, and other intermediates of oxidative metabolism (such as TCE-oxide or
43The range of the difference is 2.1-2.4-fold using the posterior medians for the relative kidney weight in rats and
humans from the PBPK model described in Section 3.5 (see Table 3-38) and body weights of 0.3-0.4 kg for rats and
60-70 kg for humans.
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dichloroacetylchloride) also may be involved in carcinogenicity. Thus, due to uncertainties as to
the active moiety(ies), but the strong evidence associating TCE liver effects with oxidative
metabolism in the liver, hepatic oxidative metabolism is the preferred basis for internal dose
extrapolations of TCE-induced liver tumors.
The rationales for the dose-metrics for liver tumors are the same as for liver noncancer
toxicity, discussed above in Section 5.1.3.1.2, and not repeated here. The primary internal dose-
metric for TCE-induced liver tumors is selected to be the weekly rate of hepatic oxidation per
unit body weight to the % power (AMetLivlBW34 [mg/kgy7week]). Due to the larger relative
liver weight in mice as compared to humans, scaling by liver weight instead of body weight to
the 3/4 power would only change the quantitative interspecies extrapolation by about fourfold,44 so
the sensitivity of the results to the scaling choice is relatively modest. The total amount of
oxidative metabolism of TCE scaled by the % power of body weight (TotOxMetabBW34
[mg/kgy7week]) was selected as an alternative dose-metric (the justification for the body weight
to the % power scaling is analogous to that for hepatic oxidative metabolism, above). This dose-
metric accounts for the possible additional contributions of systemically delivered products of
lung oxidation.
5.2.1.2.1.3. Lung
As discussed in Section 4.7.3, in situ oxidative metabolism in the respiratory tract may be
more important to lung toxicity than systemically delivered metabolites, at least as evidenced by
acute pulmonary toxicity. While chloral was originally implicated as the active metabolite,
based on either acute toxicity or mutagenicity of chloral and/or CH, more recent evidence
suggests that other oxidative metabolites may also contribute to lung toxicity. These data
include the identification of dichloroacetyl lysine adducts in Clara cells (Forkert et al., 2006),
and the induction of pulmonary toxicity by TCE in CYP2El-null mice, which may generate a
different spectrum of oxidative metabolites as compared to wild-type mice (respiratory tract
tissue also contains P450s from the CYP2F family). Overall, the weight of evidence supports the
selection of respiratory tract oxidation of TCE as the preferred basis for internal dose
extrapolations of TCE-induced lung tumors. However, uncertainties remain as to the relative
contributions from different oxidative metabolites, and quantitative clearance data necessary to
calculate the concentration of each species are lacking.
Under the cross-species scaling methodology, the rate of respiratory tract oxidation
would be scaled by body weight to the % power. For chloral, as discussed in Section 4.7.3, the
reporting of substantial TCOH but no detectable CH in blood following TCE exposure from
experiments in isolated, perfused lungs (Dalbey and Bingham, 1978) support the conclusion that
44The range of the difference is 3.5-3.9-fold using the posterior medians for the relative liver weight in mice and
humans from the PBPK model described in Section 3.5 (see Table 3 -37) and body weights of 0.03-0.04 kg for mice
and 60-70 kg for humans.
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chloral does not leave the target tissue in substantial quantities, but that there is substantial
clearance by enzyme-mediated biotransformation. DCAC is a relatively-short-lived intermediate
from aqueous (nonenzymatic) decomposition of TCE-oxide that can be trapped with lysine or
degrade further to form DC A, among other products (Cai and Guengerich, 1999). Cai and
Guengerich (1999) reported a half-life of TCE-oxide under aqueous conditions of 12 s at 23°C, a
time-scale that would be shorter at physiological conditions (37°C) and that includes formation
of DCAC as well as its decomposition. Therefore, evidence for this metabolite suggests that its
clearance both is sufficiently rapid so that it would remain at the site of formation and is
nonenzymatically mediated so that its rate would be independent of body weight. Other
oxidative metabolites may also play a role, but, because they have not been identified, no
inferences can be made as to their clearance.
Therefore, because it is not clear what the contributions to TCE-induced lung tumors are
from different oxidative metabolites produced in situ and the scaling by body weight to the
% power is supported for at least one of the possible active moieties, it was decided here to scale
the rate of respiratory tract tissue oxidation of TCE by body weight to the 3/4 power. The primary
internal dose-metric for TCE-induced lung tumors is, thus, the weekly rate of respiratory tract
oxidation per unit body weight to the % power (AMetLngBW34 [mg/kgy7week]). It should be
noted that, due to the larger relative respiratory tract tissue weight in mice as compared to
humans, scaling by tissue weight instead of body weight to the % power would change the
quantitative interspecies extrapolation by less than twofold,45 so the sensitivity of the results to
the scaling choice is relatively small.
While there is substantial evidence that acute pulmonary toxicity is related to pulmonary
oxidative metabolism, for carcinogenicity, it is possible that, in addition to locally produced
metabolites, systemically-delivered oxidative metabolites also play a role. Therefore, total
oxidative metabolism scaled by the % power of body weight (TotOxMetabBW34
[mg/kgy7week]) was selected as an alternative dose-metric (the justification for the body weight
to the 3/4 power scaling is analogous to that for respiratory tract oxidative metabolism, above).
Another alternative dose-metric considered here is the AUC of TCE in blood (AUCCBld
[mg-hour/L/week]). This dose-metric would account for the possibility that local metabolism is
determined primarily by TCE delivered in blood via systemic circulation to pulmonary tissue
(the flow rate of which scales as body weight to the % power), as assumed in previous PBPK
models, rather than TCE delivered in air via diffusion to the respiratory tract, as is assumed in
the PBPK model described in Section 3.5. However, as discussed in Section 3.5 and
Appendix A, the available pharmacokinetic data provide greater support for the updated model
structure. This dose-metric also accounts for the possible role of TCE itself in pulmonary
45The range of the difference is 1.6-1.8-fold using the posterior medians for the relative respiratory tract tissue
weight in mice and humans from the PBPK model described in Section 3.5 (see Table 3-37), and body weights of
0.03-0.04 kg for mice and 60-70 kg for humans.
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carcinogenicity (consistent with the assumption that the same average concentration of TCE in
blood will lead to a similar lifetime cancer risk across species).
5.2.1.2.1.4. Other sites
For all other sites listed in Table 5-34, there is insufficient information for site-specific
determinations of appropriate dose-metrics. While TCE metabolites and/or metabolizing
enzymes have been reported in some of these tissues (e.g., male reproductive tract), their roles in
carcinogenicity for these specific sites have not been established. Although —pmary" and
—aernative" dose-metrics are defined, they do not differ appreciably in their degrees of
plausibility.
Given that the majority of the toxic and carcinogenic responses to TCE appear to be
associated with metabolism, total metabolism of TCE scaled by the % power of body weight was
selected as the primary dose-metric (TotMetabBW34 [mg/kgy7week]). This dose-metric uses
the total flux of TCE metabolism as the toxicologically-relevant dose, and, thus, incorporates the
possible involvement of any TCE metabolite in carcinogenicity.
An alternative dose-metric considered here is the AUC of TCE in blood. This dose-
metric would account for the possibility that the determinant of carcinogenicity is local
metabolism, governed primarily by TCE delivered in blood via systemic circulation to the target
tissue (the flow rate of which scales as body weight to the 3/4 power). This dose-metric also
accounts for the possible role of TCE itself in carcinogenicity (consistent with the assumption
that the same average concentration of TCE in blood will lead to a similar lifetime cancer risk
across species).
5.2.1.2.2. Methods for dose-response analyses using internal dose-metrics
As shown in Figure 5-5, the general approach taken for the use of internal dose-metrics in
dose-response modeling was to first apply the rodent PBPK model to obtain rodent values for the
dose-metrics corresponding to the applied doses in a bioassay. Then, dose-response modeling
for a tumor response was performed using the internal dose-metrics and the multistage model or
the survival-adjusted modeling approaches described above to obtain a BMD and BMDL in
terms of the dose-metric. On an internal dose basis, humans and rodents are presumed to have
similar lifetime cancer risks, and the relationship between human internal and external doses is
essentially linear at low doses up to 0.1 mg/kg/day or 0.1 ppm, and nearly linear up to
10 mg/kg/day or 10 ppm. Therefore, the BMD and BMDL were then converted HEDs (or
exposures) using conversion ratios estimated from the human PBPK model at 0.001 mg/kg/day
or 0.001 ppm (see Table 5-35). Because the male and female conversions differed by <11%, the
human BMDLs were derived using the mean of the sex-specific conversion factors (except for
testicular tumors, for which only male conversion factors were used). Finally, a slope factor or
unit risk estimate for that tumor response was derived from the human "BMDLs" as described
5-115
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above (i.e., BMR/BMDL). Note that the converted — BMW and "BMDLs" are not actually
human equivalent BMDs and BMDLs corresponding to the BMR because the conversion was
not made in the dose range of the BMD; the converted BMDs and BMDLs are merely
intermediaries to obtain a converted slope factor or unit risk estimate. In addition, it should be
noted that median values of dose-metrics were used for rodents, whereas mean values were used
for humans. Because the rodent population model characterizes study-to-study variation,
animals of the same sex/species/strain combination within a study were assumed to be identical.
Therefore, use of median dose-metric values for rodents can be interpreted as assuming that the
animals in the bioassay were all "typical" animals and the dose-response model is estimating a
—if,k to the typical rodent." In practice, the use of median or mean internal doses for rodents did
not make much difference except when the uncertainty in the dose-metric was high (e.g.,
AMetLungBW34 dose-metric in mice). A quantitative analysis of the impact of the uncertainty
in the rodent PBPK dose-metrics is included in Section 5.2.1.4.2. On the other hand, the human
population model characterizes individual-to-individual variation. Because the quantity of
interest is the human population mean risk, the expected value (averaging over the uncertainty)
of the population mean (averaging over the variability) dose-metric was used for the conversion
to human slope factor or unit risks. Therefore, the extrapolated slope factor or unit risk estimates
can be interpreted as the expected —asrage risk" across the population based on rodent
bioassays.
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istribution
[distribution (combined
incertainty and variability)
[distribution
[distribution (separate
incertainty and variability)
[population
Irnean
ean
Site-specific
cancer unit
risk= BMR/
BMDL
(per internal
dose unit)
Human site-
specific cancer
unit risk
(perppm or
permg/kg/d)
Square nodes indicate point values, circular nodes indicate distributions, and the
inverted triangles indicate a (deterministic) functional relationship.
Figure 5-5. Flow-chart for dose-response analyses of rodent bioassays using
PBPK model-based dose-metrics.
5-117
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Table 5-35. Mean PBPK model predictions for weekly internal dose in
humans exposed continuously to low levels of TCE via inhalation (ppm) or
orally (mg/kg/day)
Dose-metric"
ABioactDCVCBW34
AMetGSHBW34
AMetLivlBW34
AMetLngBW34
AUCCBld
TotMetabBW34
TotOxMetabBW34
0.001 ppm
Female
0.00324
0.00200
0.00703
0.00281
0.00288
0.0118
0.00984
Male
0.00324
0.00200
0.00683
0.00287
0.00298
0.0117
0.00970
0.001 mg/kg/d
Female
0.00493
0.00304
0.0157
6.60 x 10'5
0.000411
0.0188
0.0157
Male
0.00515
0.00318
0.0164
6.08 x 10'5
0.000372
0.0196
0.0164
aSee note to Table 5-34 for dose-metric abbreviations. Values represent the mean of the (uncertainty) distribution of
population means for each sex and exposure scenario, generated from Monte Carlo simulation of 500 populations of
500 individuals each.
5.2.1.3. Rodent Dose-Response Analyses: Results
A summary of the PODs and slope factor and unit risk estimates for each sex/species/
bioassay/tumor type is presented in Tables 5-36 (inhalation studies) and 5-37 (oral studies). The
PODs for individual cancer types were extracted from the modeling results in the figures in
Appendix G. For the applied dose (default dosimetry) analyses, the POD is the BMDL from the
male human (-M") BMDL entry at the top of the figure for the selected model; male results were
extracted because the default weight for males in the PBPK modeling is 70 kg, which is the
overall human weight in EPA's default dosimetry methods (for inhalation, male and female
results are identical). As described in Section 5.2.1.2, for internal dose-metrics, male and female
results were averaged, and the converted human "BMDLs" are not true BMDLs because they
were converted outside the linear range of the PBPK models. It can be seen in Appendix G that
the male and female results were similar for all of the dose-metrics.
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Table 5-36. Summary of PODs and unit risk estimates for each sex/species/bioassay/tumor type (inhalation)
Study
Tumor type
BMR
PODs (ppm, in HECs)a
Applied
dose
AUC
CBld
TotMetab
BW34
TotOxMetab
BW34
AMetLng
BW34
AMetLivl
BW34
AMetGSH
BW34
ABioact
DCVCBW34
Female mouse
Fukuda et
al. (1983)
Henschler et
al. (19801
Maltoni et
al. (19861
Lung adenoma +
carcinoma
Lymphoma
Lung adenoma +
carcinoma
Liver
Combined
0.1
0.1
0.1
0.05
0.05
26.3
11. Ob
44.6
37.1
15.7
55.5
b
96.6
9.84
31.3
51.4
45.8
20.7
38.8
55.7
41.9
Male mouse
Maltoni et
al. (1986)
Liver
0.1
34.3
51
37.9
Male rat
Maltoni et
al. (1986)
Study
Leukemia
Kidney adenoma +
carcinoma
Leydig cell
Combined
Tumor type
0.05
0.01
0.1
0.01
28.2C
22.7
18.6C
1.44
b
d
28.3
13.7
18.1
1.37
0.197
0.121
Unit risk estimate (ppm V
Applied dose
AUC
CBld
TotMetab
BW34
TotOxMetab
BW34
AMetLng
BW34
AMetLivl
BW34
AMetGSH
BW34
ABioact
DCVCBW34
Female mouse
Fukuda et
al. (1983)
Henschler et
al. (1980)
Maltoni et
al. (1986)
Lung adenoma +
carcinoma
Lymphoma
Lung adenoma +
carcinoma
Liver
Combined
3.8 x 1Q-3
9.1 x 10-3
2.2 x 1Q-3
1.3 x 1Q-3
3.2 x 1Q-3
1.8 x 1Q-3
1.0 x 1Q-3
1.0 x 10 2
3.2 x 1Q-3
1.9 x 1Q-3
1.1 x 1Q-3
2.4 x 10 3
2.6 x 10 3
1.8 x 10 3
1.2 x 10 3
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Table 5-36. Summary of PODs and unit risk estimates for each sex/species/bioassay/tumor type (inhalation)
(continued)
Study
Tumor type
Unit risk estimate (ppm 1)e
Applied dose
AUC
CBld
TotMetab
BW34
TotOxMetab
BW34
AMetLng
BW34
AMetLivl
BW34
AMetGSH
BW34
ABioact
DCVCBW34
Male mouse
Maltoni et
al. (1986)
Liver
2.9 x 1(T3
2.0 x 10'3
2.6 x 10 3
Male rat
Maltoni et
al. (1986)
Leukemia
Kidney adenoma +
carcinoma
Leydig cell
Combined
1.8 x lO'3
4.4 x lO'4
5.4 x lO'3
7.0 x lO'3
1.8 x 10 3
7.3 x lO'4
5.5 x 10 3
7.3 x lO'3
5.1 x lO'2
8.3 x 10 2
Tor the applied doses, the PODs are BMDLs. However, for the internal dose-metrics, the PODs are not actually human equivalent BMDLs corresponding to the
BMR because the interspecies conversion does not apply to the dose range of the BMDL; the converted BMDLs are merely intermediaries to obtain a converted
unit risk estimate. The calculation that was done is equivalent to using linear extrapolation from the BMDLs in terms of the internal dose-metric to get a unit risk
estimate for low-dose risk in terms of the internal dose-metric and then converting that estimate to a unit risk estimate in terms of human equivalent exposures.
The PODs reported here are what one would get if one then used the unit risk estimate to calculate the human exposure level corresponding to a 10% extra risk,
but the unit risk estimate is not intended to be extrapolated upward out of the low-dose range, e.g., above 10"4 risk. In addition, for the internal dose-metrics, the
PODs are the average of the male and female human —MDL" results presented in Appendix G.
blnadequate fit to control group, but the primary metric, TotMetabBW34, fits adequately.
Dropped highest-dose group to improve model fit.
Inadequate overall fit.
eUnit risk estimate = BMR/POD. Results for the primary dose-metric are in bold.
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Table 5-37. Summary of PODs and slope factor estimates for each sex/species/bioassay/tumor type (oral)
Study
Tumor type
BMR
PODs (mg/kg/d, in HEDs)a
Applied
dose
AUC
CBld
TotMetab
BW34
TotOxMetab
BW34
AMetLng
BW34
AMetLivl
BW34
AMetGSH
BW34
ABioact
DCVCBW34
Female mouse
NCI (19761
Liver carcinoma
Lung adenoma +
carcinoma
Leukemias + sarcomas
Combined
0.1
0.1
0.1
0.05
26.5
41.1
43.1
7.43
682
733
20.6
17.6
24.7
5.38
757
14.1
Male mouse
NCI (19761
Liver carcinoma
0.1
8.23
4.34
3.45
Female rat
NTP (19881
Leukemia
0.05
72.3
3,220
21.7
Male rat
NTP (19901°
NTP (19881
Marshall"1
August
Osborne-Mendel0
Kidney adenoma +
carcinoma
Testicular
Subcutaneous sarcoma
Kidney adenoma +
carcinoma
0.1
0.1
0.05
0.1
32
3.95
60.2
41.5
167
2,560
11.5
1.41
21.5
14.3
0.471
0.648
0.292
0.402
Female mouse
NCI (19761
Liver carcinoma
Lung adenoma +
carcinoma
Leukemias + sarcomas
Combined
3.8 x 10'3
2.4 x 10'3
2.3 x 10'3
6.7 x 10'3
1.5 x 10'4
1.4 x 10'4
4.9 x 10 3
5.7 x 10'3
4.0 x 10'3
9.3 x 10 3
1.3 x 10 4
7.1 x 10 3
5-121
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Table 5-37. Summary of PODs and slope factor estimates for each sex/species/bioassay/tumor type (oral)
(continued)
Study
Tumor type
Slope factor estimate (mg/kg/d) * b
Applied dose
AUC
CBld
TotMetab
BW34
TotOxMetab
BW34
AMetLng
BW34
AMetLivl
BW34
AMetGSH
BW34
ABioact
DCVCBW34
Male mouse
NCI (1976)
Liver carcinoma
1.2 x lO'2
2.3 x lO'2
2.9 x 10 2
Female rat
NTP (1988)
Leukemia
6.9 x lO'4
1.6 x lO'5
2.3 x 10 3
Male rat
NTP (1990)°
NTP (19881
Marshall11
August
Osborne-Mendel0
Kidney adenoma +
carcinoma
Testicular
Subcutaneous sarcoma
Kidney adenoma +
carcinoma
1.6 x ID'3
2.5 x ID'2
8.3 x ID'4
2.4 x ID'3
6.0 x ID'4
2.0 x ID'5
4.3 x ID'3
7.1 x 10 2
2.3 x 10 3
7.0 x ID'3
1.1 x ID'1
1.5 x ID'1
1.7 x lO'1
2.5 x 1Q-1
aFor the applied doses, the PODs are BMDLs. However, for the internal dose-metrics, the PODs are not actually human equivalent BMDLs corresponding to the
BMR because the interspecies conversion does not apply to the dose range of the BMDL; the converted BMDLs are merely intermediaries to obtain a converted
slope factor estimate. The calculation that was done is equivalent to using linear extrapolation from the BMDLs in terms of the internal dose-metric to get a
slope factor estimate for low-dose risk in terms of the internal dose-metric and then converting that estimate to a slope factor estimate in terms of HEDs. The
PODs reported here are what one would get if one then used the slope factor estimate to calculate the human dose level corresponding to a 10% extra risk, but the
slope factor estimate is not intended to be extrapolated upward out of the low-dose range, e.g., above 10~4 risk. In addition, for the internal dose-metrics, the
PODs are the average of the male and female human —MDL" results presented in Appendix G.
bSlope factor estimate = BMR/POD. Results for the primary dose-metric are in bold.
°Using MSW adjusted incidences (see text and Table 5-38).
dUsing poly-3 adjusted incidences (see text and Table 5-38).
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For two data sets, the highest dose (exposure) group was dropped to get a better fit when
using applied doses. This technique can improve the fit when the response tends to plateau with
increasing dose. Plateauing typically occurs when metabolic saturation alters the pattern of
metabolite formation or when survival is impacted at higher doses, and it is assumed that these
high-dose responses are less relevant to low-dose risk. The highest-dose group was not dropped
to improve the fit for any of the internal dose-metrics because it was felt that if the dose-metric
was an appropriate reflection of internal dose of the reactive metabolite(s), then use of the dose-
metric should have ameliorated the plateauing in the dose-response relationship (note that
survival-impacted data sets were addressed using survival adjustment techniques). For a 3rd data
set (Henschler lymphomas), it might have helped to drop the highest exposure group, but there
were only two exposure groups, so this was not done. As a result, the selected model, although it
had an adequate fit overall, did not fit the control group very well (the model estimated a higher
background response than was observed); thus, the BMD and BMDL were likely overestimated
and the risk underestimated. The estimates from the NCI (1976) oral male mouse liver cancer
data set are also somewhat more uncertain because the response rate was extrapolated down from
a response rate of about 50% extra risk to the BMR of 10% extra risk.
Some general patterns can be observed in Tables 5-36 and 5-37. For inhalation, the unit
risk estimates for different dose-metrics were generally similar (within about 2.5-fold) for most
cancer types. The exception was for kidney cancer, where the estimates varied by over 2 orders
of magnitude, with the AMetGSHBW34 and ABioactDCVCBW34 metrics yielding the highest
estimates. This occurs because pharmacokinetic data indicate, and the PBPK model predicts,
substantially more GSH conjugation (as a fraction of intake), and hence subsequent
bioactivation, in humans relative to rats. The range of the risk estimates for individual cancer
types overall (across cancer types and dose-metrics) was encompassed by the range of estimates
across the dose-metrics for kidney cancer in the male rat, which was from 4.4 x 10"4 per ppm
(applied dose) to 8.3 x 10'2 per ppm (ABioactDCVCBW34).
For oral exposure, the slope factor estimates are more variable across dose-metrics
because of first-pass effects in the liver (median estimates for the fraction of TCE metabolized in
one pass through the liver in mice, rats, and humans are >0.8). Here, the exception is for the risk
estimates for cancer of the liver itself, which are also within about a 2.5-fold range, because the
liver gets the full dose of all of the metrics during that —fist pass." For the other cancer types,
the range of estimates across dose-metrics varies from about 30-fold to over 2 orders of
magnitude, with the estimates based on AUCCBld and AMetLngBW34 being at the low end and
those based on AMetGSHBW34 and ABioactDCVCBW34 again being at the high end. For
AUCCBld, the PBPK model predicted the blood concentrations to scale more closely to body
weight rather than the % power of body weight, so the extrapolated human unit risks using this
dose-metric are smaller than those obtained by applied dose or other dose-metrics that included
% power body weight scaling. For AMetLngBW34, pharmacokinetic data indicate, and the
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PBPK model predicts, that the human respiratory tract metabolizes a lower fraction of total TCE
intake than the mouse respiratory tract, so the extrapolated risk to humans based on this metric is
lower than that obtained using applied dose or other dose-metrics. Overall, the oral slope factor
estimates for individual cancer types ranged from 1.6 x 10"5 per mg/kg/day (female rat leukemia,
AUCCBld) to 2.5 x 10"1 per mg/kg/day (male Osborne-Mendel rat kidney,
ABioactDCVCBW34), a range of over 4 orders of magnitude. It must be recognized, however,
that not all dose-metrics are equally credible, and, as will be presented below, the slope factor
estimates for total cancer risk for the most sensitive bioassay response for each sex/species
combination using the primary (preferred) dose-metrics fall within a very narrow range.
Results for survival-adjusted analyses are summarized in Table 5-38. For the time-
independent (BMDS) multistage model, the risk estimates using poly-3 adjustment are higher
than those without poly-3 adjustment. This is to be expected because the poly-3 adjustment
decreases denominators when accounting for early mortality, and, for these data sets, the higher-
dose groups had greater early mortality. The difference was fairly modest for the kidney cancer
data sets (about 30% higher) but somewhat larger for the testicular cancer data set (about 150%
higher).
Table 5-38. Comparison of survival-adjusted results for three oral male rat
data sets3
Dose-metric
Adjustment method
BMR
POD
(mg/kg/d)
BMD:BMDL
Slope factor estimate
(per mg/kg/d)
NTP (1990) F344 rat kidney adenoma + carcinoma
Applied dose
TotMetabBW34
AMetGSHBW34
ABioactDCVCBW34
unadj BMDS
poly-3 BMDS
MSW
unadj BMDS
poly-3 BMDS
MSW
unadj BMDS
poly-3 BMDS
MSW
unadj BMDS
poly-3 BMDS
MSW
0.05
0.1
0.05
0.05
0.1
0.05
0.05
0.1
0.05
0.05
0.1
0.05
56.9
89.2
32.0
20.2
31.8
11.5
0.841
1.32
0.471
0.522
0.817
0.292
1.9
1.9
2.6
2.1
1.7
3.1
1.9
1.9
2.4
1.9
1.9
2.4
8.8 x 1Q-4
1.1 x KT3
1.6 x 1Q-3
2.5 x 1Q-3
3.1 x lO'3
4.3 x 10'3
5.9 x 10'2
7.6 x 10'2
1.1 x 10'1
9.6 x 10'2
1.2 x 1Q-1
1.7 x 10 *
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Table 5-38. Comparison of survival-adjusted results for three oral male rat
data sets3
Dose-metric
Adjustment method
BMR
POD
(mg/kg/d)
BMD:BMDL
Slope factor estimate
(per mg/kg/d)
NTP (1988) Osborne-Mendel rat kidney adenoma + carcinoma
Applied dose
TotMetabBW34
AMetGSHBW34
ABioactDCVCBW34
unadj BMDS
poly-3 BMDS
MSW
unadj BMDS
poly-3 BMDS
MSW
unadj BMDS
poly-3 BMDS
MSW
unadj BMDS
poly-3 BMDS
MSW
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
86.6
65.9
41.5
30.4
23.1
14.3
1.35
1.03
0.648
0.835
0.636
0.402
1.7
1.7
2.0
1.7
1.7
2.0
1.7
1.7
2.0
1.7
1.7
2.0
1.2 x 1Q-3
1.5 x 1Q-3
2.4 x lO'3
3.3 x 10'3
4.3 x 10'3
7.0 x 10'3
7.4 x 10'2
9.7 x 1Q-2
1.5 x 1Q-1
1.2 x 1Q-1
1.6 x 1Q-1
2.5 x 10 *
NTP (1988) Marshall rat testicular tumors
Applied dose
AUCCBld
TotMetabBW34
unadj BMDS
poly-3 BMDS
MSW
unadj BMDS
poly-3 BMDS
MSW
unadj BMDS
poly-3 BMDS
MSW
0.
0.
0.
0.
0.
0.
0.
0.
0.
9.94
3.95
1.64
427
167
60.4
3.53
1.41
0.73
1.4
1.5
5.2
1.4
1.6
2.6
4.3
1.5
9.4
1.0 x lO'2
2.5 x lO'2
6.1 x 10'2
2.3 x 10'4
6.0 x 10'4
1.7 x 10'3
2.8 x 1Q-2
7.1 x 10 2
1.4 x 1Q-1
aFor the applied doses, the PODs are BMDLs. However, for the internal dose-metrics, the PODs are not actually
human equivalent BMDLs corresponding to the BMR because the interspecies conversion does not apply to the dose
range of the BMDL; the converted BMDLs are merely intermediaries to obtain a converted slope factor estimate.
Results for the primary dose-metric are in bold.
In addition, the MSW time-to-tumor model generated higher risk estimates than the poly-
3 adjustment technique. The MSW results were about 40% higher for the NTP F344 rat kidney
cancer data sets and about 60% higher for the NTP Osborne-Mendel rat kidney cancer data sets.
For the NTP Marshall rat testicular cancer data set, the discrepancies were greater; the results
ranged from about 100 to 180% higher for the different dose-metrics. As discussed in
Section 5.2.1.1, these two approaches differ in the way they take early mortality into account.
The poly-3 technique merely adjusts the tumor incidence denominators, using a constant power
3 of time, to reflect the fact that animals are at greater risk of cancer at older ages. The MSW
model estimates risk as a function of time (and dose), and it estimates the power (of time)
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parameter for each data set.46 For the NTP F344 rat kidney cancer and NTP Marshall rat
testicular cancer data sets, the estimated power parameter was close to 3 in each case, ranging
from 3.0 to 3.7; for the NTP Osborne-Mendel rat kidney cancer data sets, however, the estimated
power parameter was about 10 for each of the dose-metrics, presumably reflecting the fact that
these were late-occurring tumors (the earliest occurred at 92 weeks). Using a higher power
parameter than 3 in the poly-3 adjustment would give even less weight to nontumor-bearing
animals that die early and would, thus, increase the adjusted incidence even more in the highest-
dose groups where the early mortality is most pronounced, increasing the slope factor estimate.
Nonetheless, as noted above, the MSW results were only about 60% higher for the NTP
Osborne-Mendel rat kidney cancer data sets for which MSW estimated a power parameter of
about 10.
In general, the risk estimates from the MSW model would be preferred because, as
discussed above, this model incorporates more information (e.g., tumor context) and estimates
the power parameter rather than using a constant value of three. From Table 5-38, it can be seen
that the results from MSW yielded higher BMD:BMDL ratios than the results from the poly-
3 technique. These ratios were only slightly higher and not unusually large for MSW model
analyses of the NTP (1990, 1988) kidney tumor estimates, and this, along with the adequate fit
(assessed visually) of the MSW model, supports using the slope factor estimates from the MSW
modeling of rat kidney tumor incidence. On the other hand, the BMD:BMDL ratio was
relatively large for the applied dose analysis and, in particular, for the preferred dose-metric
analysis (9.4-fold) of the NTP Marshall rat testicular tumor data set. Therefore, for this
endpoint, the poly-3-adjusted results were used, although they may underestimate risk somewhat
as compared to the MSW model.
In addition to the results from dose-response modeling of individual cancer types, the
results of the combined tumor risk analyses for the three bioassays in which the rodents exhibited
increased risks at multiple sites are also presented in Tables 5-36 and 5-37, in the rows labeled
—cothined" under the column heading —Tumr Type." These results were extracted from the
detailed results in Appendix G. Note that, because of the computational complexity of the
combined tumor analyses, dose-response modeling was only done using applied dose and a
common upstream internal dose-metric, rather than using the different preferred dose-metrics for
each tumor type within a combined tumor analysis.
For the Maltoni et al. (1986) female mouse inhalation bioassay, the combined tumor risk
estimates are bounded by the highest individual tumor risk estimates and the sums of the
individual tumor risks estimates (the risk estimates are upper bounds, so the combined risk
estimate (i.e., the upper bound on the sum of the individual central tendency estimates) should be
46Conceptually, the approaches differ most when different tumor contexts (incidental or fatal) are considered,
because the poly-3 technique only accounts for time of death, while the MSW model can account for the tumor
context and attempt to estimate an induction time (tO), although this was not done for any of the data sets in this
assessment.
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less than the sum of the individual upper bound estimates), as one would expect. The common
upstream internal dose-metric used for the combined analysis was TotOxMetabBW34, which is
not the primary metric for either of the individual cancer types. For the liver tumors, the primary
metric was AMetLivlBW34, but as can be seen in Table 5-36, it yields results similar to those
for TotOxMetabBW34. Likewise, for the lung tumors, the primary metric was AMetLngBW34,
which yields a unit risk estimate slightly smaller than for TotOxMetabBW34. Thus, the results
of the combined analysis using TotOxMetabBW34 as a common metric is not likely to
substantially over- or underestimate the combined risk based on preferred metrics for each of the
cancer types.
For the Maltoni et al. (1986) male rat inhalation bioassay, the combined risk estimates are
also reasonably bounded, as expected. The common upstream internal dose-metric used for the
combined analysis was TotMetabBW34, which is the primary metric for two of the
three individual cancer types. However, as can be seen in Table 5-36, the risk estimate for the
preferred dose-metric for the third tumor type, ABioactDCVCBW34 for the kidney tumors, is
substantially higher than the risk estimates for the primary dose-metrics for the other two cancer
types and would dominate a combined tumor risk estimate across primary dose-metrics; thus, the
ABioactDCVCBW34-based kidney tumor risk estimate alone can reasonably be used to
represent the total cancer risk for the bioassay using preferred internal dose-metrics, although it
would underestimate the combined risk to some extent (e.g., the kidney-based estimate is
8.3 x 10"2 per ppm; the combined estimate would be about 9 x 10"2 per ppm, rounded to
one significant figure).
For the third bioassay [NCI (1976) female mouse oral bioassay], the combined tumor risk
estimates are once again reasonably bounded. The common upstream internal dose-metric used
for the combined analysis was TotOxMetabBW34, which is not the primary metric for any of the
three individual cancer types but was considered to be the most suitable metric to apply as a
basis for combining risk across these different cancer types. The slope factor estimate for the
lung based on the primary dose-metric for that site becomes negligible compared to the estimates
for the other two cancer types (see Table 5-37). However, the slope factor estimates for the
remaining two cancer types are both somewhat underestimated using the TotOxMetabBW34
metric rather than the primary metrics for those tumors (the TotOxMetabBW34-based estimate
for leukemias + sarcomas, which is not presented in Table 5-30 because, in the absence of better
mechanistic information, more upstream metrics were used for that individual tumor type, is
4.1 x 10"3 per mg/kg/day). Thus, overall, the combined estimate based on TotOxMetabBW34 is
probably a reasonable estimate for the total tumor risk in this bioassay, although it might
overestimate risk slightly.
The most sensitive sex/species results are extracted from Tables 5-29 and 5-30 and
presented in Tables 5-39 (inhalation) and 5-40 (oral). The BMD:BMDL ratios for all of the
results corresponding to the slope factor and unit risk estimates based on the preferred dose-
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metrics ranged from 1.3 to 2.1. For inhalation, the most sensitive bioassay responses based on
the preferred dose-metrics ranged from 2.6 x 10"3 to 8.3 x 10"2 per ppm across the sex/species
combinations (with the exception of the female rat, which exhibited no apparent TCE-associated
response in the 3 available bioassays). For oral exposure, the most sensitive bioassay responses
based on the preferred dose-metrics ranged from 2.3 x 10"3 to 2.5 x 10"1 per mg/kg/day across the
sex/species combinations. For both routes of exposure, the most sensitive sex/species response
was (or was dominated by, in the case of the combined tumors in the male rat by inhalation)
male rat kidney cancer based on the preferred dose-metric of ABioactDCVCBW34.
Table 5-39. Inhalation: most sensitive bioassay for each sex/species
combination3
Sex/species
Female mouse
Male mouse
Female rat
Male rat
Endpoint
(study)
Lymphoma
(Henschler et al.. 1980)
Liver hepatoma
(Maltoni et al.. 1986)
-
Leukemia+
Kidney adenoma and
carcinoma+
Leydig cell tumors
(Maltoni et al.. 1986)
Unit risk per ppm
Preferred dose-
metric
1.0 x 1Q-2
2.6 x ID'3
-
8.3 x ID'2
Default
methodology
9.1 x ID'3
2.9 x ID'3
-
7.0 x ID'3
Alternative dose-
metrics, studies, or
endpoints
1 x 1(T3~4 x ID'3
2 x ID'3
-
4 x ID'4 ~ 5 x ID'2
[individual site results]
aResults extracted from Table 5-36.
Table 5-40. Oral: most sensitive bioassay for each sex/species combination"
Sex/species
Female mouse
Male mouse
Female rat
Male rat
Endpoint
(study)
Liver carcinoma+
lung adenoma and
carcinoma+
sarcomas + leukemias
(NCI, 1976)
Liver carcinoma
(NCI, 1976)
Leukemia
(NTP, 1988)
Kidney adenoma + carcinoma
(NTP, 1988, Osborne-
Mendel)
Unit risk per mg/kg/d
Preferred dose-
metric
9.3 x ID'3
2.9 x lO'2
2.3 x 10'3
2.5 x 10'1
Default
methodology
6.7 x ID'3
1.2 x lO'2
6.9 x 10'4
2.4 x 10'3b
Alternative dose-
metrics, studies, or
endpoints
1 x I(r4~7 x ID'3
[individual site results]
2 x lO'2
2 x 10'5
2 x 10'5~2 x 10'1
aResults extracted from Table 5-37.
bMost sensitive male rat result using default methodology is 2.5 x 10~2 per mg/kg/day for NTP (1988) Marshall rat
testicular tumors.
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5.2.1.4. Uncertainties in Dose-Response Analyses of Rodent Bioassays
5.2.1.4.1. Qualitative discussion of uncertainties
All risk assessments involve uncertainty, as study data are extrapolated to make
inferences about potential effects in humans from environmental exposure. The largest sources
of uncertainty in the TCE rodent-based cancer risk estimates are interspecies extrapolation and
low-dose extrapolation. Some limited human (occupational) data from which to estimate human
cancer risk are available, and cancer risk estimates based on these data are developed in
Section 5.2.2 below. In addition, some quantitative uncertainty analyses of the interspecies
differences in pharmacokinetics were conducted and are presented in Section 5.2.1.4.2.
The rodent bioassay data offer conclusive evidence of carcinogenicity in both rats and
mice, and the available epidemiologic and mechanistic data support the relevance to humans of
the TCE-induced carcinogenicity observed in rodents. The epidemiologic data provide sufficient
evidence that TCE is -earcinogenic to humans" (see Section 4.11). There is even some evidence
of site concordance with the rodent findings, although site concordance is not essential to human
relevance and, in fact, is not observed across TCE-exposed rats and mice. The strongest
evidence in humans is for TCE-induced kidney tumors, with fairly strong evidence for
lymphomas and some lesser support for liver tumors; each of these cancer types has also been
observed in TCE rodent bioassays. Furthermore, the mechanistic data are supportive of human
relevance because, while the exact reactive species associated with TCE-induced cancers are not
known, the metabolic pathways for TCE are qualitatively similar for rats, mice, and humans (see
Section 3.3). The impact of uncertainties with respect to quantitative differences in TCE
metabolism is discussed in Section 5.2.1.4.2.
Typically, the cancer risk estimated is for the total cancer burden from all sites that
demonstrate an increased tumor incidence for the most sensitive experimental species and sex. It
is expected that this approach is protective of the human population, which is more diverse but is
exposed to lower exposure levels.
For the inhalation unit risk estimates, the preferred estimate from the most sensitive
species and sex was the estimate of 8.3 x 10"2 per ppm for the male rat, which was based on
multiple tumors observed in this sex/species but was dominated by the kidney tumor risk
estimated with the dose-metric for bioactivated DCVC. This estimate was the high end of the
range of estimates (see Table 5-39) but was within an order of magnitude of other estimates,
such as the preferred estimate for the female mouse and the male rat kidney estimate based on
the GSH conjugation dose-metric, which provide additional support for an estimate of this
magnitude. The preferred estimate for the male mouse was about an order of magnitude and a
half lower. The female rat showed no apparent TCE-associated tumor response in the three
available inhalation bioassays; however, this apparent absence of response is inconsistent with
the observations of increased cancer risk in occupationally exposed humans and in female rats in
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oral bioassays. In Section 5.2.2.2, an inhalation unit risk estimate based on the human data is
derived and can be compared to the rodent-based estimate.
For the oral slope factor estimate, the preferred estimate from the most sensitive species
and sex was the estimate of 2.5 x 10"1 per mg/kg/day, again for the male rat, based on the kidney
tumor risk estimated with the dose-metric for bioactivated DCVC. This estimate was at the high
end of the range of estimates (see Table 5-40) but was within an order of magnitude of other
estimates, such as the preferred male mouse estimate and the male rat kidney estimate based on
the GSH conjugation dose-metric, which provide additional support for an estimate of this
magnitude. The preferred estimates for the female mouse and the female rat were about another
order of magnitude lower. Some of the oral slope factor estimates based on the alternative dose-
metric of AUC for TCE in the blood were as much as three orders of magnitude lower, but these
estimates were considered less credible than those based on the preferred dose-metrics. In
Section 5.2.2.3, an oral slope factor estimate based on the human (inhalation) data is derived
using the PBPK model for route-to-route extrapolation; this estimate can be compared to the
rodent-based estimate.
Furthermore, the male rat kidney tumor estimates from the inhalation (Maltoni et al.,
1986) and oral (NTP, 1988) studies were consistent on the basis of internal dose using the dose-
metric for bioactivated DCVC. In particular, the linearly extrapolated slope (i.e., the
BMR/BMDL) per unit of internal dose derived from Maltoni et al. (1986) male rat kidney tumor
data was 2.4 x 10"1 per weekly mg DCVC bioactivated per unit body weight34, while the
analogous slope derived from NTP (1988) male rat kidney tumor data was 9.3 x 10"2 per weekly
mg DCVC bioactivated per unit body weight4 (MSW-modeled results), a difference of less than
threefold.47 These results also suggest that differences between routes of administration are
adequately accounted for by the PBPK model using this dose-metric.
Regarding low-dose extrapolation, a key consideration in determining what extrapolation
approach to use is the mode(s) of action. However, mode-of-action data are lacking or limited
for each of the cancer responses associated with TCE exposure, with the exception of the kidney
tumors (see Section 4.11). For the kidney tumors, the weight of the available evidence supports
the conclusion that a mutagenic mode of action is operative (see Section 4.4); this mode of action
supports linear low-dose extrapolation. The weight of evidence also supports involvement of
processes of cytotoxicity and regenerative proliferation in the carcinogen!city of TCE, although
not with the extent of support as for a mutagenic mode of action. In particular, data linking
47For the Maltoni et al. (1986) male rat kidney tumors, the unit risk estimate of 8.3 x 10~2 per ppm using the
ABioactDCVCBW34 dose metric, from Table 5-36, is divided by the average male and female internal doses at
0.001 ppm (0.0034/0.001) from Table 5-35, to yield a unit risk in internal dose units of 2.4 x 10"2. For the
NTP (1988) male rat kidney tumors, the unit risk estimate of 2.5 x 10"1 per mg/kg/day using the
ABioactDCVCBW34 dose metric, from Table 5-37, is divided by the average male and female internal doses at
0.001 mg/kg/day (0.0027/0.001) from Table 5-35, to yield a unit risk in internal dose units of 9.3 x 10"2. Note that
the original BMDLs and unit risks from BMD modeling were in internal dose units that were then converted to
applied dose units using the values in Table 5-35, so this calculation reverses that conversion.
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TCE-induced proliferation to increased mutation or clonal expansion are lacking, as are data
informing the quantitative contribution of cytotoxicity. Moreover, it is unlikely that any
contribution from cytotoxicity leads to a non-linear dose-response relationship near the POD for
rodent kidney tumors, since maximal levels of toxicity are reached before the onset of tumors.
Finally, because any possible involvement of a cytotoxicity mode of action would be additional
to mutagenicity, the dose-response relationship would nonetheless be expected to be linear at low
doses. Therefore, the additional involvement of a cytotoxicity mode of action does not provide
evidence against the use of linear extrapolation from the POD.
For the other TCE-induced cancers, the mode(s) of action is unknown. When the
mode(s) of action cannot be clearly defined, EPA generally uses a linear approach to estimate
low-dose risk (U.S. EPA, 2005b), based on the following general principles:
• A chemical's carcinogenic effects may act additively to ongoing biological processes,
given that diverse human populations are already exposed to other agents and have
substantial background incidences of various cancers.
• A broadening of the dose-response curve (i.e., less rapid fall-off of response with
decreasing dose) in diverse human populations and, accordingly, a greater potential for
risks from low-dose exposures (Lutz et al., 2005; Zeise et al., 1987) is expected for
two reasons: First, even if there is a -threshold" concentration for effects at the cellular
level, that threshold is expected to differ across individuals. Second, greater variability in
response to exposures would be anticipated in heterogeneous populations than in inbred
laboratory species under controlled conditions (due to, e.g., genetic variability, disease
status, age, nutrition, and smoking status).
• The general use of linear extrapolation provides reasonable upper-bound estimates that
are believed to be health-protective (U.S. EPA, 2005b) and also provides consistency
across assessments.
Additional uncertainties arise from the specific dosimetry assumptions, the model
structures and parameter estimates in the PBPK models, the dose-response modeling of data in
the observable range, and the application of the results to potentially sensitive human
populations. As discussed in Section 5.2.1.2.1, one uncertainty in the tissue-specific dose-
metrics used here is whether to scale the rate of metabolism by tissue mass or body weight to the
% in the absence of specific data on clearance; however, in the cases where this is an issue (the
lung, liver, and kidney), the impact of this choice is relatively modest (less than twofold to about
fourfold). An additional dosimetry assumption inherent in this analysis is that equal
concentrations of the active moiety over a lifetime yield equivalent lifetime risk of cancer across
species, and the extent to which this is true for TCE is unknown. Furthermore, it should be noted
that use of tissue-specific dosimetry inherently presumes site concordance of tumors across
species.
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With respect to uncertainties in the estimates of internal dose themselves, a quantitative
analysis of the uncertainty and variability in the PBPK model-predicted dose-metric estimates
and their impacts on cancer risk estimates is presented in Section 5.2.1.4.2. Additional
uncertainties in the PBPK model were discussed in Section 3.5. Furthermore, this assessment
examined a variety of dose-metrics for the different cancer types using PBPK models for rats,
mice, and humans, so the impact of dose-metric selection can be assessed. As discussed in
Section 5.2.1.2.1, there is strong support for the primary dose-metrics selected for kidney, liver,
and, to a lesser extent, lung. For the other tumor sites, there is more uncertainty about dose-
metric selection. The cancer slope factor and unit risk estimates obtained using the preferred
dose-metrics were generally similar (within about threefold) to those derived using default
dosimetry assumptions (e.g., equal risks result from equal cumulative equivalent exposures or
doses), with the exception of the bioactivated DCVC dose-metric for rat kidney tumors and the
metric for the amount of TCE oxidized in the respiratory tract for mouse lung tumors occurring
from oral exposure (see Tables 5-39 and 5-40). The higher risk estimates for kidney tumors
based on the bioactivated DCVC dose-metric are to be expected because pharmacokinetic data
indicate, and the PBPK model predicts, substantially more GSH conjugation (as a fraction of
intake), and hence subsequent bioactivation, in humans relative to rats. Nonetheless, there is
substantial uncertainty in the quantitative extrapolation of GSH conjugation from rodents to
humans due to limitations in the available data. The lower risk estimates for lung tumors from
oral TCE exposure based on the metric for the amount of TCE oxidized in the respiratory tract
are because there is a greater first-pass effect in human liver relative to mouse liver following
oral exposure and because the gavage dosing used in rodent studies leads to a large bolus dose
that potentially overwhelms liver metabolism to a greater extent than a more graded oral
exposure. Both of these effects result in relatively more TCE being available for metabolism in
the lung for mice than for humans. In addition, mice have greater respiratory metabolism
relative to humans. However, because oxidative metabolites produced in the liver may
contribute to respiratory tract effects, using respiratory tract metabolism alone as a dose-metric
may underestimate lung tumor risk. The slope factor or unit risk estimates obtained using the
alternative dose-metrics were also generally similar to those derived using default dosimetry
assumptions, with the exception of the metric for the amount of TCE conjugated with GSH for
rat kidney tumors, again because humans have greater GSH conjugation, and the AUC of TCE in
blood for all of the cancer types resulting from oral exposure, again because of first-pass effects.
With respect to uncertainties in the dose-response modeling, the two-step approach of
modeling only in the observable range, as put forth in EPA's cancer assessment guidelines (U.S.
EPA, 2005b), is designed in part to minimize model dependence. The ratios of the BMDs to the
BMDLs give some indication of the statistical uncertainties in the dose-response modeling.
These ratios did not exceed a value of 2.5 for any of the primary analyses used in this
assessment. Thus, overall, modeling uncertainties in the observable range are considered to be
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minimal. Some additional uncertainty is conveyed by uncertainties in the survival adjustments
made to some of the bioassay data; however, their impact is also believed to be minimal relative
to the uncertainties already discussed (i.e., interspecies and low-dose extrapolations).
Regarding the cancer risks to potentially sensitive human populations or lifestages,
pharmacokinetic data on 42 individuals were used in the Bayesian population analysis of the
PBPK model discussed in Section 3.5. The impacts of these data on the predicted population
mean are incorporated in the quantitative uncertainty analyses presented in Section 5.2.1.4.2.
These data do not, however, reflect the full range of metabolic variability in the human
population (they are all from healthy, mostly male, volunteers) and do not address specific
potentially sensitive subgroups (see Section 4.10). Moreover, there is inadequate information
about disease status, co-exposures, and other factors that make humans vary in their responses to
TCE. It will be a challenge for future research to quantify the differential risk indicated by
different risk factors or exposure scenarios.
5.2.1.4.2. Quantitative uncertainty analysis of PBPK model-based dose-metrics
The Bayesian analysis of the PBPK model for TCE generates distributions of uncertainty
and variability in the internal dose-metrics than can be readily fed into dose-response analysis.
As shown in Figure 5-6, the overall approach taken for the uncertainty analysis is similar to that
used for the point estimates except that distributions are carried through the analysis rather than
median or expected values. In particular, the PBPK model-based rodent internal doses are
carried through to a distribution of BMDs (which also includes sampling variance from the
number of responding and at risk animals in the bioassay). This distribution of BMDs generates
a distribution of cancer slope factors based on internal dose, which then is combined with the
(uncertainty) distribution of the human population mean conversion to applied dose or exposure.
The resulting distribution for the human population mean risk per unit dose or exposure accounts
for uncertainty in the PBPK model parameters (rodent and human) and the binomial sampling
error in the bioassays. These distributions can then be compared with the point estimates, based
on median rodent dose-metrics and mean human population dose-metrics, reported in
Tables 5-36 and 5-37. Details of the implementation of this uncertainty analysis, which used the
WinBugs software in conjugation with the R statistical package, are reported in Appendix G.
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Cancer slope
facto r =
BMR/BMD
(per internal
dose unit))
y
[distribution (combined
iuncertainty and variability)
^distribution
[distribution (separate
iuncertainty and variability)
[Uncertainty
[distribution of
[population mean
Human cancer
slope factor
(perppm or
permg/kg/d)
Population
mean human
internal dose
perppm or
permg/kg/d
distribution
Square nodes indicate point values, circular nodes indicate distributions, and the
inverted triangles indicate a (deterministic) functional relationship.
Figure 5-6. Flow-chart for uncertainty analysis of dose-response analyses of
rodent bioassays using PBPK model-based dose-metrics.
Overall, as shown in Tables 5-41 and 5-42, the 95% confidence upper bound of the
distributions for the linearly extrapolated risk per unit dose or exposure ranged from one- to
eightfold higher than the point slope factors and unit risks derived using the BMDLs reported in
Tables 5-36 and 5-37. The largest differences, up to fourfold, for rat kidney tumors and
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eightfold for mouse lung tumors, primarily reflect the substantial uncertainty in the internal dose-
metrics for rat kidney DCVC and GSH conjugation and for mouse lung oxidation (see Section
3.5). Additionally, despite the differences in the degree of uncertainty due to the PBPK model
across endpoints and dose-metrics, the only case where the choice of the most sensitive bioassay
for each sex/species combination would change based on the 95% confidence upper bounds
reported in Tables 5-41 and 5-42 would be for female mouse inhalation bioassays. Even in this
case, the difference between slope factor or unit risk estimate for the most sensitive and next
most sensitive study/endpoint was only twofold.
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Table 5-41. Summary of PBPK model-based uncertainty analysis of unit risk estimates for each
sex/species/bioassay/tumor type (inhalation)
Study
Tumor type
BMR
Dose-metric
Unit risk estimates (ppm) *)
From
Table
5-36
Summary statistics of unit risk distribution
Mean
5% lower
bound
Median
95% upper
bound
Female mouse
Fukuda et al.
(1983)
Henschler et
al. (1980)
Maltoni et al.
(1986)
Lung adenoma +
carcinoma3
Lymphomab
Lung adenoma +
carcinoma3
Liver
0.1
0.1
0.1
0.05
AMetLngBW34
TotOxMetabBW34
AUCCBld
TotMetabBW34
AMetLngBW34
TotOxMetabBW34
AUCCBld
AMetLivlBW34
TotOxMetabBW34
2.6 x 10 3
3.2 x lO'3
1.8 x lO'3
1.0 x 10 2
1.8 x 10 3
1.9 x 10'3
1.0 x 10'3
1.2 x 10 3
1.1 x 10'3
5.65 x lO'3
1.88 x lO'3
1.01 x lO'3
4.38 x 10'3
3.88 x 10'3
1.10 x 10'3
5.25 x 10'4
6.27 x 10'4
5.98 x 10'4
2.34 x lO'4
3.27 x lO'4
1.54 x lO'4
6.06 x 10'4
1.48 x 10'4
3.73 x 10'4
1.63 x 10'4
2.18 x 10'4
1.81 x 10'4
1.49 x lO'3
1.52 x lO'3
8.36 x lO'4
3.49 x 10'3
1.04 x 10'3
9.52 x 10'4
4.64 x 10'4
5.39 x 10'4
5.07 x 10'4
2.18 x lO'2
4.59 x lO'3
2.44 x lO'3
1.11 x 10'2
1.52 x 10'2
2.32 x 10'3
1.10 x 10'3
1.32 x 10'3
1.31 x 10'3
Male mouse
Maltoni et al.
(1986)
Liver
0.1
AMetLivlBW34
TotOxMetabBW34
2.6 x 10 3
2.0 x 1Q-3
1.35 x 1Q-3
1.23 x 1Q-3
4.28 x 1Q-4
4.24 x 1Q-4
1.16 x 1Q-3
1.06 x 1Q-3
2.93 x 1Q-3
2.60 x 1Q-3
Male rat
Maltoni et al.
(1986)
Leukemiab
Kidney adenoma +
carcinoma
Leydig cellb
0.05
0.01
0.1
TotMetabBW34
ABioactDCVCBW34
AMetGSHBW34
TotMetabBW34
TotMetabBW34
1.8 x 10 3
8.3 x 10 2
5.1 x 1Q-2
7.3 x 1Q-4
5.5 x 10 3
9.38 x 1Q-4
9.07 x 1Q-2
3.90 x 1Q-2
3.94 x 1Q-4
4.34 x 1Q-3
1.26 x 1Q-4
3.66 x 1Q-3
2.71 x 1Q-3
8.74 x 1Q-5
1.99 x 1Q-3
7.86 x 1Q-4
3.64 x 1Q-2
2.20 x 1Q-2
3.42 x 1Q-4
3.98 x 1Q-3
2.25 x 1Q-3
3.21 x 1Q-1
1.30 x 1Q-1
8.74 x 1Q-4
7.87 x 10'3
aWinBUGS dose-response analyses did not adequately converge for the AMetLngBW34 dose-metric using the 3rd-order multistage model (used for results in
Table 5-36), but did converge when the 2nd-order model was used. Summary statistics reflect results of 2nd-order model calculations.
bPoor dose-response fits in point estimates for AUCCBld, so not included in uncertainty analysis.
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Table 5-42. Summary of PBPK model-based uncertainty analysis of slope factor estimates for each
sex/species/bioassay/tumor type (oral)
Study
Tumor type
BMR
Dose-metric
Slope factor estimates (mg/kg/d) -1)
From
Table 5-37
or 5-38
Summary statistics of slope factor distribution
Mean
5% lower
bound
Median
95% upper
bound
Female mouse
NCI (1976)
Liver carcinoma
Lung adenoma +
carcinoma3
Leukemias + sarcomas
0.1
0.1
0.1
AMetLivlBW34
TotOxMetabBW34
AMetLngBW34
TotOxMetabBW34
AUCCBld
TotMetabBW34
AUCCBld
7.1 x 10 3
5.7 x lO'3
1.3 x 10 4
4.0 x lO'3
1.5 x 1Q-4
4.9 x 10 3
1.4 x 1Q-4
3.26 x 10'3
2.63 x lO'3
1.28 x 10"4
1.84 x lO'3
7.16 x 1Q-5
1.60 x 1Q-3
6.36 x 1Q-5
9.35 x 10'4
8.76 x lO'4
6.73 x lO'6
5.29 x lO'4
4.40 x 1Q-6
1.42 x ID'4
3.10 x 1Q-6
2.44 x 10'3
2.01 x lO'3
4.12 x lO'5
1.39 x lO'3
3.39 x ID'5
1.13 x ID'3
2.90 x ID'5
8.35 x 10'3
6.60 x lO'3
4.62 x lO'4
4.73 x lO'3
2.18 x ID'4
4.65 x ID'3
1.94 x ID'4
Male mouse
NCI (19761
Liver carcinoma
0.1
AMetLivlBW34
TotOxMetabBW34
2.9 x 10 2
2.3 x 1Q-2
1.65 x 1Q-2
1.32 x 1Q-2
4.70 x ID'3
4.41 x ID'3
1.25 x ID'2
1.01 x ID'2
4.25 x ID'2
3.29 x ID'2
Female rat
NTP (19881
Leukemia
0.05
TotMetabBW34
AUCCBld
2.3 x 10 3
1.6 x 1Q-5
1.89 x 1Q-3
1.56 x 1Q-5
5.09 x ID'4
3.39 x 1Q-6
1.43 x ID'3
1.07 x ID'5
4.69 x ID'3
3.98 x ID'5
Male rat
NTP (1990)
Kidney adenoma +
carcinomab
0.1
ABioactDCVCBW34
AMetGSHBW34
TotMetabBW34
1.2 x 10 *
7.6 x lO-2
3.1 x 1Q-3
1.40 x 1Q-1
6.18 x 1Q-2
2.49 x 1Q-3
5.69 x ID'3
4.00 x 1Q-3
7.14 x ID'4
5.24 x ID'2
3.27 x ID'2
1.96 x ID'3
5.18 x lO'1
2.11 x ID'1
5.96 x ID'3
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Table 5-42. Summary of PBPK model-based uncertainty analysis of slope factor estimates for each
sex/species/bioassay/tumor type (oral) (continued)
Study
Tumor type
BMR
Dose-metric
Slope factor estimates (mg/kg/d) -1)
From
Table 5-37
or 5-38
Summary statistics of slope factor distribution
Mean
5% lower
bound
Median
95% upper
bound
NTP (1988)
Marshall
August
Osborne-Mendel
Testicularb
Subcut sarcoma
Kidney adenoma +
carcinoma13
0.1
0.05
0.1
TotMetabBW34
AUCCBld
TotMetabBW34
AUCCBld
ABioactDCVCBW34
AMetGSHBW34
TotMetabBW34
7.1 x 10 2
6.0 x 10"4
2.3 x 10 3
2.0 x ID'5
1.6 x K)-1
9.7 x ID'2
4.3 x ID'3
6.18 x lO'2
5.45 x lO'4
1.65 x lO'3
1.35 x ID'5
1.61 x ID'1
7.47 x ID'2
2.73 x ID'3
1.92 x lO'2
1.18 x lO'4
4.58 x lO'4
1.53 x ID'6
5.45 x ID'3
3.90 x ID'3
5.40 x ID'4
4.89 x lO'2
3.70 x lO'4
1.27 x lO'3
8.34 x ID'6
6.35 x ID'2
3.85 x ID'2
2.10 x ID'3
1.45 x 10'1
1.44 x lO'3
4.04 x lO'3
3.73 x ID'5
6.02 x ID'1
2.54 x ID'1
6.89 x ID'3
"WinBUGS dose-response analyses did not adequately converge for AMetLngBW34 dose-metric using the 3rd-order multistage model (used for results in
Table 5-37), but did converge when the 2nd-order model was used. Summary statistics reflect results of 2nd-order model calculations.
bUsing poly-3 adjusted incidences from Table 5-38 (software for WinBUGS-based analyses using the MSW model was not developed).
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5.2.2. Dose-Response Analyses: Human Epidemiologic Data
Of the epidemiological studies of TCE and cancer, only two had sufficient exposure-
response information for potential dose-response analysis. The two studies, Charbotel et al.
(2006) and Moore et al. (2010), were both case-control studies of TCE and kidney cancer, and
both had quantitative cumulative exposure estimates for the individual subjects. In the study by
Moore et al. (2010), however, the cumulative exposure estimates were assessed by experts based
on categorical metrics for frequency and intensity of exposure and not continuous measures.
Moore et al. (2010) also used a categorical confidence-of-exposure metric to classify different
jobs because of the potential for exposure misclassification from this approach. While the
detailed approach used by Moore et al. (2010) should be fairly reliable for general rankings, the
resulting estimates are not expected to be as quantitatively accurate as those in the Charbotel
et al. (2006) study, which relied on a task-exposure matrix based on decades of measurements
from the Arve Valley workshops (Fevotte et al., 2006; see also Section 4.4 for more discussion
of the exposure assessments). Thus, the Charbotel et al. (2006) study was selected as the sole
basis for the derivation of an inhalation unit risk estimate for kidney cancer (see Section 5.2.2.1).
Other epidemiological studies were used in Section 5.2.2.2 below to provide information for a
comparison of RR estimates across cancer types. These epidemiologic data were used to derive
an adjusted inhalation unit risk estimate for the combined risk of developing kidney cancer,
NHL, or liver cancer. The human PBPK model was then used to perform route-to-route
extrapolation to derive an oral slope factor estimate for the combined risk of kidney cancer,
NHL, or liver cancer (see Section 5.2.2.3).
5.2.2.1. Inhalation Unit Risk Estimate for RCC Derived from Charbotel et al. (2006)
Data
The Charbotel et al. (2006) case-control study of 86 incident RCC cases and 316 age-and
sex-matched controls, with individual cumulative exposure estimates for TCE for each subject,
provides a sufficient human data set for deriving quantitative cancer risk estimates for RCC in
humans. The study is a high-quality study that used a detailed exposure assessment (Fevotte et
al., 2006) and took numerous potential confounding factors, including exposure to other
chemicals, into account (see Section 4.4). A significant dose-response relationship was reported
for cumulative TCE exposure and RCC (Charbotel et al., 2006).
The derivation of an inhalation unit risk estimate, defined as the plausible upper bound
lifetime risk of cancer from chronic inhalation of TCE per unit of air concentration, for RCC
incidence in the U.S. population, based on results of the Charbotel et al. (2006) case-control
study, is presented in the following subsections.
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5.2.2.1.1. RCC results from the Charbotel et al. (2006) study
Charbotel et al. (2006) analyzed their data using conditional logistic regression, matching
on sex and age, and reported results (ORs) for cumulative TCE exposure categories, adjusted for
tobacco smoking and BMI (Charbotel et al., 2006, Table 6). The exposure categories were
constructed as tertiles based on the cumulative exposure levels in the exposed control subjects.
The results are summarized in Table 5-43, with mean exposure levels kindly provided by Dr.
Charbotel (2008).
For additional details and discussion of the Charbotel et al. (2006) study, see Section 4.4
and Appendix B.
Table 5-43. Results from Charbotel et al. (2006) on relationship between
TCE exposure and RCC
Cumulative exposure category
Nonexposed
Low
Medium
High
Mean cumulative exposure
(ppm x yrs)
62.4
253.2
925.0
Adjusted OR
(95% CI)
1
1.62 (0.75, 3.47)
1.15(0.47,2.77)
2.16(1.02,4.60)
5.2.2.1.2. Prediction of lifetime extra risk of RCC incidence from TCE exposure
The categorical results summarized in Table 5-43 were used for predicting the extra risk
of RCC incidence from continuous environmental exposure to TCE. Extra risk is defined as:
Extra risk = (Rx - Ro)l(l - Ro\
where Rx is the lifetime risk in the exposed population and Ro is the lifetime risk in an
unexposed population (i.e., the background risk). Because kidney cancer is a rare event, the ORs
in Table 5-43 can be used as estimates of the RR ratio = RxIRo (Rothman and Greenland, 1998).
A weighted linear regression model was used to model the dose-response data in Table 5-43 to
obtain a slope estimate (regression coefficient) for RR of RCC versus cumulative exposure,
under the commonly employed assumption that exposure was measured without error. Use of a
linear model in the observable range of the data is often a good general approach for
epidemiological data because such data are frequently too limited (i.e., imprecise), as is the case
here, to clearly identify an alternate model (U.S. EPA, 2005b). This linear dose-response
function was then used to calculate lifetime extra risks in an actuarial program (life-table
analysis) that accounts for age-specific rates of death and background disease, under the common
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assumption that the RR is independent of age.48 In addition, it is generally assumed that RR
estimates transfer across populations, independent of background disease rates—in this case, the
RR estimates based on the Charbotel et al. (2006) study, which was conducted in France, are
assumed to apply to the U.S. population.49
For the weighted linear regression, the weights used for the RR estimates were the
inverses of the variances, which were calculated from the CIs. Using this approach,50 a linear
regression coefficient of 0.001205 per ppm x year (SE = 0.0008195 per ppm x year) was
obtained from the categorical results.
For the life-table analysis, U.S. age-specific all-cause mortality rates for 2004 for both
sexes and all race groups combined (CDC, 2007) were used to specify the all-cause background
mortality rates in the actuarial program. Because the goal is to estimate the unit risk for extra
risk of cancer incidence, not mortality, and because the Charbotel et al. (2006) data are incidence
data, RCC incidence rates were used for the cause-specific background -^Mortality" rates in the
life-table analysis.51 SEER 2001-2005 cause-specific background incidence rates for RCC were
obtained from the SEER public-use database.52 SEER collects good-quality cancer incidence
data from a variety of geographical areas in the United States. The incidence data used here are
from SEER 17, a registry of 17 states, cities, or regions covering about 26% of the United States
population (http://seer.cancer.gov). The risks were computed up to age 85 years for continuous
exposures to TCE.53 Conversions between occupational TCE exposures and continuous
environmental exposures were made to account for differences in the number of days exposed
per year (240 vs. 365 days) and in the amount of air inhaled per day (10 vs. 20 m3; U.S. EPA,
). The SE for the regression coefficient from the weighted linear regression calculation
described above was used to compute the 95% upper confidence limit (UCL) for the slope
estimate, and this value was used to derive 95% UCLs for risk estimates (or 95% lower
48This program is an adaptation of the approach previously used by the Committee on the Biological Effects of
Ionizing Radiation (BEIR. 1988). The same methodology was also used in U.S. EPA's 1,3-butadiene health risk
assessment (U.S. EPA. 2002d). A spreadsheet illustrating the extra risk calculation for the derivation of the LECM
for RCC incidence is presented in Appendix H.
49In any event, background kidney cancer rates between the United States and France are similar, with estimated
age-adjusted incidence rates of 14.1 per 100,000 in the United States (Surveillance, Epidemiology, and End Results:
http://seer.cancer.gov/statfacts/htmp/kidrp.html) and 10.4 per 100,000 in France (European Cancer Observatory:
http://eu-cancer.iarc.fr/cancer-19-kidney.html.en).
50Equations for this weighted linear regression approach are presented in Rothman (1986) and summarized in
Appendix H.
51No adjustment was made for using RCC incidence rates rather than mortality rates to represent cause-specific
mortality in the actuarial program because the RCC incidence rates are negligible in comparison to the all-cause
mortality rates. Otherwise, all-cause mortality rates for each age interval would have been adjusted to reflect people
dying of a cause other than RCC or being diagnosed with RCC.
52In accordance with the —SER Program Coding and Staging Manual 2007"
(http://seer.cancer.gov/manuals/2007/SPCSM 2007 AppendixC_p6.pdf). pages C-831 to C-833, RRC was
specified as ICD-0-3 histological types coded 8312, 8260, 8310, 8316-8320, 8510, 8959, and 8255 (mixed types).
53Rates above age 85 years are not included because cause-specific disease rates are less stable for those ages. Note
that 85 years is not employed here as an average lifespan but, rather, as a cut-off point for the life-table analysis,
which uses actual age-specific mortality rates.
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confidence limits [LCLs] for corresponding exposure estimates), based on a normal
approximation.
Point estimates and one-sided 95% UCLs for the extra risk of RCC incidence associated
with varying levels of environmental exposure to TCE based on linear regression of the
Charbotel et al. (2006) categorical results were determined by the actuarial program; the results
are presented in Section 5.2.1.3. The models based on cumulative exposure yield extra risk
estimates that are fairly linear for exposures up to approximately 1 ppm.
Consistent with EPA's Guidelines for Carcinogen Risk Assessment (U.S. EPA, 2005b),
the same data and methodology were also used to estimate the exposure level (ECX: — dfective
concentration corresponding to an extra risk of x%") and the associated 95% lower confidence
limit of the effective concentration corresponding to an extra risk of 1% (LECX [lowest effective
concentration], x = 0.01). A 1% extra risk level is commonly used for the determination of the
POD for epidemiological data. Use of a 1% extra risk level for these data is supported by the
fact that, based on the actuarial program, the risk ratio (i.e., Rx/Ro) for an extra risk of 1% for
RCC incidence is 1.9, which is in the range of the ORs reported by Charbotel et al. (see
Table 5-43). Thus, 1% extra risk was selected for determination of the POD, and, consistent
with the Guidelines for Carcinogen Risk Assessment, the LEG value corresponding to that risk
level was used as the actual POD. For the linear model that was selected, the unit risk is
independent of the benchmark risk level used to determine the POD (at low exposures/risk
levels; see Table 5-44); however, selection of a benchmark risk level is generally useful for
comparisons across models.
Table 5-44. Extra risk estimates for RCC incidence from various levels of
lifetime exposure to TCE, using linear cumulative exposure model
Exposure concentration (ppm)
0.001
0.01
0.1
1.0
10.0
MLE of extra risk
2.603 x 1Q-6
2.603 x 1Q-5
2.602 x 1Q-4
2.598 x 1Q-3
2.562 x 1Q-2
95% UCL on extra risk
5.514 x 1Q-6
5.514 x 1Q-5
5.512 x 1Q-4
5.496 x 10-3
5.333 x ID'2
As discussed in Section 4.4, there is sufficient evidence to conclude that a mutagenic
mode of action is operative for TCE-induced kidney tumors, which supports the use of linear
low-dose extrapolation from the POD. The ECoi, LECoi, and inhalation unit risk estimates for
RCC incidence using the linear cumulative exposure model are presented in Table 5-45.
Converting the units, 5.49 x 10"3 per ppm corresponds to a unit risk of 1.02 x 10"6 per ug/m3 for
RCC incidence.
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Table 5-45. ECoi, LECoi, and unit risk estimates for RCC incidence, using
linear cumulative exposure model
ECoi (ppm)
3.87
LECoi (ppm)
1.82
unit risk (per ppm)a
5.49 x 10'3
aUnitrisk = 0.01/LEC0i.
5.2.2.1.3. Uncertainties in the RCC unit risk estimate
The two major sources of uncertainty in quantitative cancer risk estimates are generally
interspecies extrapolation and high-dose to low-dose extrapolation. The unit risk estimate for
RCC incidence derived from the Charbotel et al. (2006) results is not subject to interspecies
uncertainty because it is based on human data. A major uncertainty remains in the extrapolation
from occupational exposures to lower environmental exposures. There was some evidence of a
contribution to increased RCC risk from peak exposures; however, there remained an apparent
dose-response relationship for RCC risk with increasing cumulative exposure without peaks, and
the OR for exposure with peaks compared to exposure without peaks was not significantly
elevated (Charbotel et al., 2006). Although the actual exposure-response relationship at low
exposure levels is unknown, the conclusion that a mutagenic mode of action is operative for
TCE-induced kidney tumors supports the linear low-dose extrapolation that was used (U.S. EPA,
2005b). The weight of evidence also supports involvement of processes of cytotoxicity and
regenerative proliferation in the carcinogen!city of TCE, although not with the extent of support
as for a mutagenic mode of action. In particular, data linking TCE-induced proliferation to
increased mutation or clonal expansion are lacking, as are data informing the quantitative
contribution of cytotoxicity. Because any possible involvement of a cytotoxicity mode of action
would be additional to mutagenicity, the dose-response relationship would nonetheless be
expected to be linear at low doses. Therefore, the additional involvement of a cytotoxicity mode
of action does not provide evidence against the use of linear extrapolation from the POD.
Another notable source of uncertainty in the cancer unit risk estimate is the dose-response
model used to model the study data to estimate the POD. A weighted linear regression across the
categorical ORs was used to obtain a slope estimate; use of a linear model in the observable
range of the data is often a good general approach for human data because epidemiological data
are frequently too limited (i.e., imprecise) to clearly identify an alternate model (U.S. EPA,
2005b). The Charbotel et al. (2006) study is a relatively small case-control study, with only
86 RCC cases, 37 of which had TCE exposure; thus, the dose-response data upon which to
specify a model are indeed limited.
In accordance with EPA's Guidelines for Carcinogen Risk Assessment., the lower bound
on the ECoi is used as the POD; this acknowledges some of the uncertainty in estimating the
POD from the available dose-response data. In this case, the statistical uncertainty associated
with the ECoi is relatively small, as the ratio between the ECoi and the LECoi is about twofold.
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The inhalation unit risk estimate of 5.49 x 10"3 per ppm presented above, which is calculated
based on a linear extrapolation from the POD (LECoi), is expected to provide an upper bound on
the risk of cancer incidence. However, for certain applications, such as benefit-cost analyses,
estimates of—centratendency" for the risk below the POD are desired. Because a linear dose-
response model was used in the observable range of the human data and the POD was within the
low-dose linear range for extra risk as a function of exposure, linear extrapolation below the
LECoi has virtually the same slope as the 95% UCL on the actual (linear) dose-response model
in the low-dose range (i.e., below the POD). This is illustrated in Table 5-44, where the 95%
UCL on extra risk for RCC incidence predicted by the dose-response model is about 5.51 x
10"3 per ppm for exposures at or below about 0.1 ppm, which is virtually equivalent to the unit
risk estimate of 5.49 x 10"3 per ppm derived from the LEC0i (see Table 5-45). The same holds
for the central tendency (weighted least squares) estimates of the extra risk from the (linear)
dose-response model (i.e., the dose-response model prediction of 2.60 x io~3 per ppm from
Table 5-44 is virtually identical to the value of 2.58 x 10"3 per ppm obtained from linear
extrapolation below the EC0i, i.e., by dividing 0.01 extra risk by the EC0i of 3.87 from
Table 5-45). In other words, because the dose-response model that was used to model the data in
the observable range is already low-dose linear near the POD, if one assumes that the same linear
model is valid for the low-dose range, one can use the central tendency (weighted least squares)
estimate from the model to derive a statistical —best estitate" of the slope rather than relying on
an extrapolated risk estimate (0.01/ECoi). (The extrapolated risk estimates are not generally
central tendency estimates in any statistical sense because once risk is extrapolated below the
ECoi using the formulation 0.01/ECoi, it is no longer a function of the original model that
generated the EC0i and LECoi estimates.)
An important source of uncertainty in the underlying Charbotel et al. (2006) study is the
retrospective estimation of TCE exposures in the study subjects. This case-control study was
conducted in the Arve Valley in France, a region with a high concentration of workshops
devoted to screw cutting, which involves the use of TCE and other degreasing agents. Since the
1960s, occupational physicians of the region have collected a large quantity of well-documented
measurements, including TCE air concentrations and urinary metabolite levels (Fevotte et al.,
2006). The study investigators conducted a comprehensive exposure assessment to estimate
cumulative TCE exposures for the individual study subjects, using a detailed occupational
questionnaire with a customized task-exposure matrix for the screw-cutting workers and a more
general occupational questionnaire for workers exposed to TCE in other industries (Fevotte et
al., 2006). The exposure assessment even attempted to take dermal exposure from hand-dipping
practices into account by equating it with an equivalent airborne concentration based on
biological monitoring data. Despite the appreciable effort of the investigators, considerable
uncertainty associated with any retrospective exposure assessment is inevitable, and some
exposure misclassification is unavoidable. Such exposure misclassification was most likely for
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the 19 deceased cases and their matched controls, for which proxy respondents were used, and
for exposures outside the screw-cutting industry (295 of 1,486 identified job periods involved
TCE exposure; 120 of these were not in the screw-cutting industry).
Although the exposure estimates from Moore et al. (2010) were not considered to be as
quantitatively accurate as those of Charbotel et al. (2006), as discussed at the beginning of
Section 5.2.2, it is worth noting, in the context of uncertainty in the exposure assessment, that the
exposure estimates in Moore et al. (2010) are substantially lower than those of Charbotel et al.
(2006) for comparable OR estimates. For example, for all subjects and high-confidence
assessments only, respectively, Moore et al. (2010) reported OR estimates of 1.19 and 1.77 for
cumulative exposures <1.58 ppm x years and 2.02 and 2.23 for cumulative exposures
>1.58 ppm x years. Charbotel et al. (2006), on the other hand, reported OR estimates for all
subjects of 1.62, 1.15, and 2.16 for mean cumulative exposures of 62.4, 253.2, and 925.0 ppm x
years, respectively. If the exposure estimates for Charbotel et al. (2006) are overestimated, as
suggested by the exposure estimates from Moore et al. (2010), then the slope of the linear
regression model, and hence the unit risk estimate, would be correspondingly underestimated.
Another noteworthy source of uncertainty in the Charbotel et al. (2006) study is the
possible influence of potential confounding or modifying factors. This study population, with a
high prevalence of metal-working, also had relatively high prevalences of exposure to petroleum
oils, cadmium, petroleum solvents, welding fumes, and asbestos (Fevotte et al., 2006). Other
exposures assessed included other solvents (including other chlorinated solvents), lead, and
ionizing radiation. None of these exposures was found to be significantly associated with RCC
at ap = 0.05 significance level. Cutting fluids and other petroleum oils were associated with
RCC at ap = 0.1 significance level; however, further modeling suggested no association with
RCC when other significant factors were taken into account (Charbotel et al., 2006). Moreover,
a review of other studies suggested that potential confounding from cutting fluids and other
petroleum oils is of minimal concern (see Section 4.4.2.3). Nonetheless, a sensitivity analysis
was conducted using the OR estimates further adjusted for cutting fluids and other petroleum oils
from the unpublished report by Charbotel et al. (2005), and an essentially identical unit risk
estimate of 5.46 x 10"3 per ppm was obtained.54 In addition, the medical questionnaire included
familial kidney disease and medical history, such as kidney stones, infection, chronic dialysis,
hypertension, and use of antihypertensive drugs, diuretics, and analgesics. BMI was also
calculated, and lifestyle information such as smoking habits and coffee consumption was
collected. Univariate analyses found high levels of smoking and BMI to be associated with
54The OR estimates further adjusted for cutting fluids and other petroleum oils were 1.52 (95% CI: 0.66, 3.49), 1.07
(0.39, 2.88), and 1.96 (0.71, 5.37) for the low, medium, and high cumulative exposure groups, respectively
(Charbotel et al.. 2005). For the linear regression model, these OR estimates yielded a shallower slope estimate of
0.0009475 per ppm x year but a larger SE of 0.0009709 per ppm x year. In the lifetable analysis, these latter
estimates in turn yielded a slightly higher EC0i estimate (4.92 versus 3.87 ppm), because of the shallower slope
estimate, but an essentially identical LEC0i, because of the larger SE.
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increased odds of RCC, and these two variables were included in the conditional logistic
regressions. Thus, although impacts of other factors are possible, this study took great pains to
attempt to account for potential confounding or modifying factors.
Some other sources of uncertainty associated with the epidemiological data are the dose-
metric and lag period. As discussed above, there was some evidence of a contribution to
increased RCC risk from peak TCE exposures; however, there appeared to be an independent
effect of cumulative exposure without peaks. Cumulative exposure is considered a good
measure of total exposure because it integrates exposure (levels) over time. If there is a
contributing effect of peak exposures, not already taken into account in the cumulative exposure
metric, the linear slope may be overestimated to some extent. Sometimes, cancer data are
modeled with the inclusion of a lag period to discount more recent exposures not likely to have
contributed to the onset of cancer. In an unpublished report, Charbotel et al. (2005) also present
the results of a conditional logistic regression with a 10-year lag period, and these results are
very similar to the unlagged results reported in their published paper, suggesting that the lag
period might not be an important factor in this study.
Some additional sources of uncertainty are not so much inherent in the exposure-response
modeling or in the epidemiologic data themselves but, rather, arise in the process of obtaining
more general Agency risk estimates from the epidemiologic results. EPA cancer risk estimates
are typically derived to represent an upper bound on increased risk of cancer incidence for all
sites affected by an agent for the general population. From experimental animal studies, this is
accomplished by using tumor incidence data and summing across all of the tumor sites that
demonstrate significantly increased incidences, customarily for the most sensitive sex and
species, to attempt to be protective of the general human population. However, in estimating
comparable risks from the Charbotel et al. (2006) epidemiologic data, certain limitations are
encountered. For one thing, these epidemiology data represent a geographically limited (Arve
Valley, France), and likely not very diverse, population of working adults. Thus, there is
uncertainty about the applicability of the results to a more diverse general population.
Additionally, the Charbotel et al. (2006) study was a study of RCC only, and so the risk estimate
derived from it does not represent all of the tumor sites that may be affected by TCE. The issue
of cancer risk at other sites is addressed in the next section (see Section 5.2.2.2).
5.2.2.1.4. Conclusions regarding the RCC unit risk estimate
An ECoi of 3.9 ppm was calculated using a life-table analysis and linear modeling of the
categorical conditional logistic regression results for RCC incidence reported in a high-quality
case-control study. Linear low-dose extrapolation from the LEC0i yielded a lifetime extra RCC
incidence unit risk estimate of 5.5 x 10"3 per ppm (1.0 x 10"6 per |ig/m3) of continuous TCE
exposure. The assumption of low-dose linearity is supported by the conclusion that a mutagenic
mode of action is operative for TCE-induced kidney tumors.
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The inhalation unit risk estimate is expected to provide an upper bound on the risk of
RCC incidence; however, this is just the risk estimate for RCC. A risk estimate for total cancer
risk to humans would need to include the risk for other potential TCE-associated cancers.
5.2.2.2. Adjustment of the Inhalation Unit Risk Estimate for Multiple Sites
Human data on TCE exposure and cancer risk sufficient for dose-response modeling are
only available for RCC, yet human and rodent data suggest that TCE exposure increases the risk
of other cancers as well. In particular, there is evidence from human (and rodent) studies for
increased risks of NHL and liver cancer (see Section 4.11). Therefore, the inhalation unit risk
estimate derived from human data for RCC incidence was adjusted to account for potential
increased risk of those cancer types. To make this adjustment, a factor accounting for the
relative contributions to the extra risk for cancer incidence from TCE exposure for these three
cancer types combined versus the extra risk for RCC alone was estimated, and this factor was
applied to the unit risk estimate for RCC to obtain a unit risk estimate for the three cancer types
combined (i.e., lifetime extra risk for developing any of the three types of cancer). This estimate
is considered a better estimate of total cancer risk from TCE exposure than the estimate for RCC
alone.
Although only the Charbotel et al. (2006) study was found adequate for direct estimation
of inhalation unit risks, the available epidemiologic data provide sufficient information for
estimating the relative potency of TCE across tumor sites. In particular, the relative
contributions to extra risk (for cancer incidence) were calculated from two different data sets to
derive the adjustment factor for adjusting the unit risk estimate for RCC to a unit risk estimate
for the three types of cancers (RCC, NHL, and liver) combined. The first calculation is based on
the results of the meta-analyses of human epidemiologic data for the three cancer types (see
Appendix C); the second calculation is based on the results of the Raaschou-Nielsen et al. (2003)
study, the largest single human epidemiologic study by far with RR estimates for all three cancer
types. The approach for each calculation was to use the RR estimates and estimates of the
lifetime background risk in an unexposed population, Ro, to calculate the lifetime risk in the
exposed population, Rx, where Rx = RR x Ro, for each tumor type. Then, the extra risk from
TCE exposure for each tumor type could be calculated using the equation in Section 5.2.2.1.2.
Finally, the extra risks were summed across the three cancer types and the ratio of the sum of the
extra risks to the extra risk for RCC was derived. For the first calculation, the RRm estimates
from the meta-analyses for NHL, kidney cancer, and liver (and biliary) cancer were used as the
RR estimates. For the second calculation, the SIR estimates from the Raaschou-Nielsen et al.
(2003) study were used. For both calculations, Ro for RCC was taken from the life-table
analysis described in Section 5.2.2.1.2 and presented in Appendix H, which estimated a lifetime
risk for RCC incidence up to age 85 years. For Ro values for the other two sites, SEER statistics
for the lifetime risk of developing cancer were used
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(http://seer.cancer.gov/statfacts/httnl/nhl.httnl and
http: //seer. cancer. gov/statfacts/html/li vib d. html).
In both cases, an underlying assumption in deriving the relative potencies is that the
relative values of the age-specific background incidence risks for the person-years from the
epidemiologic studies for each tumor type approximate the relative values of the lifetime
background incidence risks for those cancer types. In other words, at least on a proportional
basis, the lifetime background incidence risks (for the U.S. population) for each site approximate
the age-specific background incidence risks for the study populations. A further assumption is
that the lifetime risk of RCC up to 85 years is an adequate approximation to the full lifetime risk,
which is what was used for the other two cancer types. The first calculation, based on the results
of the meta-analyses for the three cancer types, has the advantage of being based on a large data
set, incorporating data from many different studies. However, this calculation relies on a number
of additional assumptions. First, it is assumed that the RRm estimates from the meta-analyses,
which are based on different groups of studies, reflect similar overall TCE exposures (i.e., that
the overall TCE exposures are similar across the different groups of studies that went into the
different meta-analyses for the three cancer types). Second, it is assumed that the RRm
estimates, which incorporate RR estimates for both mortality and incidence, represent good
estimates for cancer incidence risk from TCE exposure. In addition, it is assumed that the RRm
for kidney cancer, for which RCC estimates from individual studies were used when available, is
a good estimate for the overall RR for RCC and that the RRm estimate for NHL, for which
different studies used different classification schemes, is a good estimate for the overall RR for
NHL. The second calculation, based on the results of the Raaschou-Nielsen et al. (2003) study,
the largest single study with RR estimates for all three cancer types, has the advantage of having
RR estimates that are directly comparable. In addition, the Raaschou-Nielsen et al. study
provided data for the precise cancer types of interest for the calculation (i.e., RCC, NHL, and
liver [and biliary] cancer).
The input data and results of the calculations are presented in Table 5-46. The value for
the ratio of the sum of the extra risks to the extra risk for RCC alone was 3.28 in calculation #1
and 4.36 in calculation #2, which together suggest that 4 is a reasonable factor to use to adjust
the inhalation unit risk estimate based on RCC for multiple sites to obtain a total cancer unit risk
estimate.55 Using this factor to adjust the unit risk estimate based on RCCs entails the further
fundamental assumption that the dose-response relationships for the other two cancer types
(NHL and liver cancer) are similarly linear (i.e., that the relative potencies are roughly
maintained at lower exposure levels). This assumption is consistent with EPA's Guidelines for
Carcinogen Risk Assessment (U.S. EPA, 2005b), which recommends low-dose linear
extrapolation in the absence of sufficient evidence to support a nonlinear mode of action.
55Both the geometric and arithmetic means of the two values for the ratio are 3.8, which rounds to 4, in keeping with
the imprecise nature of the adjustment factor. The factor of 4 is within 25% of either calculated ratio.
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Table 5-46. Relative contributions to extra risk for cancer incidence from
TCE exposure for multiple cancer types
RR
Ro
Rx
Extra risk
Ratio to
kidney value
Calculation #1: using RR estimates from the meta-analyses
Kidney (RCC)
NHL
Liver (and biliary) cancer
Kidney + NHL only
1.27
1.23
1.29
0.0107
0.0202
0.0066
0.01359
0.02485
0.008514
sum
sum
0.002920
0.004742
0.001927
0.009589
0.007662
1
1.62
0.66
3.28
2.62
Calculation #2: using RR estimates from Rasschou-Nielsen et al. (2003)
Kidney (RCC)
NHL
Liver (and biliary) cancer
Kidney + NHL only
1.20
1.24
1.35
0.0107
0.0202
0.0066
0.01284
0.02505
0.008910
sum
sum
0.002163
0.004948
0.002325
0.009436
0.007111
1
2.29
1.07
4.36
3.29
Applying the factor of 4 to the lifetime extra RCC incidence unit risk estimate of
5.49 x 10"3 per ppm (1.0 x 10"6 per |ig/m3) of continuous TCE exposure yields a cancer unit risk
estimate of 2.2 x 10"2 per ppm (4.1 x 10"6 per |ig/m3). Table 5-46 also presents calculations for
just kidney and NHL extra risks combined, because the strongest human evidence is for those
two cancer types. For those two cancer types, the calculations support a factor of 3.56 Applying
this factor to the RCC unit risk estimate yields an estimate of 1.6 x 10"2 per ppm, which results in
the same estimate as for the three cancer types combined when finally rounded to one significant
figure (i.e., 2 x 10"2 per ppm [or 3 x 10"6 per |ig/m3, which is still similar to the three-tumor-type
estimate in those units]).
In addition to the uncertainties in the underlying RCC estimate, there are uncertainties
related to the assumptions inherent in these calculations for adjusting to multiple sites, as
detailed above. Nonetheless, the fact that the calculations based on two different data sets
yielded comparable values for the adjustment factor (both within 25% of the selected factor of 4)
provides more robust support for the use of the factor of 4. Additional uncertainties pertain to
the weight of evidence supporting the association of TCE exposure with increased risk of cancer
for the three cancer types. As discussed in Section 4.11.2, it was found that the weight of
evidence for kidney cancer was sufficient to classify TCE as -carcinogenic to humans." It was
also concluded that there was strong evidence that TCE causes NHL as well, although the
evidence for liver cancer was more limited. In addition, the rodent studies demonstrate clear
56The geometric and mean of the two values for the ratio, 2.62 and 3.29, is 2.96, and the arithmetic mean is 2.94,
which both round to 3, in keeping with the imprecise nature of the adjustment factor. The factor of 3 is within 15%
of either calculated ratio.
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evidence of multisite carcinogen!city, with cancer types including those for which associations
with TCE exposure are observed in human studies (i.e., liver and kidney cancers and NHLs).
Overall, the evidence was found to be sufficiently persuasive to support the use of the adjustment
factor of 4 based on these three cancer types, resulting in a cancer inhalation unit risk estimate of
2.2 x 10"2 per ppm (4.1 x 10"6 per |ig/m3). Alternatively, if one were to use the factor based only
on the two cancer types with the strongest human evidence, the cancer inhalation unit risk
estimate would be only slightly reduced (25%).
5.2.2.3. Route-to-Route Extrapolation Using PBPK Model
Route-to-route extrapolation of the inhalation unit risk estimate was performed using the
PBPK model described in Section 3.5. The (partial) unit risk estimates for NHL and liver cancer
were derived as for the total cancer inhalation unit risk estimate in Section 5.2.2.2, except that
the ratios of extra risk for the individual cancer types relative to kidney cancer were used as
adjustment factors rather than the ratio of the sum. As presented in Table 5-46, for NHL, the
ratios from the two different calculations were 1.62 and 2.29, so a factor of 2 was used; for liver
cancer, the ratios were 0.66 and 1.07, so a factor of 1 was used. (With the ratio of 1 for kidney
cancer itself, the combined adjustment factor is 4, reproducing the factor of 4 used to estimate
the total cancer unit risk from the multiple sites in Section 5.2.2.2)
Because different internal dose-metrics are preferred for each target tissue site, a separate
route-to-route extrapolation was performed for each site-specific unit risk estimate calculated in
Sections 5.2.2.1 and 5.2.2.2. As shown in Figure 5-7, the approach taken to apply the human
PBPK model in the low-dose range where external and internal doses are linearly related to
derive a conversion that is the ratio of internal dose per mg/kg/day to internal dose per ppm. The
expected value of the population mean for this conversion factor (in ppm per mg/kg/day) was
used to extrapolate each inhalation unit risk in units of risk per ppm to an oral slope factor in
units of risk per mg/kg/day. Note that this conversion is the mean of the ratio of internal dose
predictions, and is not the same as taking the ratio of the mean of internal dose predictions in
Table 5-35.57
57For route-to-route extrapolation based on dose-response analysis performed on internal dose, as is the case for
rodent bioassays, it would be appropriate to use the values in Table 5-35 to first -H-nconvert" the unit risk based on
one route, and then recover! to a unit risk based on the other route.
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istribution
[internal dose
permg/kg/d]/
[internal dose
perppm]
[distribution (separate
^uncertainty and variability)
fixed \.
population
jnean
Irnean
Square nodes indicate point values, circle nodes indicate distributions, and the
inverted triangle indicates a (deterministic) functional relationship.
Figure 5-7. Flow-chart for route-to-route extrapolation of human site-
specific cancer inhalation unit risks to oral slope factors.
Table 5-47 shows the results of this route-to-route extrapolation for the —pmary" and
—aernative" dose-metrics. For reference, route-to-route extrapolation based on total intake (i.e.,
ventilation rate * air concentration = oral dose x body weight) using the parameters in the PBPK
model would yield an expected population average conversion of 0.95 ppm per mg/kg/day. For
TotMetabBW34, TotOxMetabBW34, and AMetLivlBW34, the conversion is 2.0-2.8 ppm per
mg/kg/day, greater than that based on intake. This is because of the greater metabolic first pass
in the liver, which leads to a higher percentage of intake being metabolized via oral exposure
relative to inhalation exposure for the same intake. Conversely, for the AUC in blood, the
conversion is 0.14 ppm per mg/kg/day, less than that based on intake—the greater first pass in
the liver means lower blood levels of parent compound via oral exposure relative to inhalation
for the same intake. The conversion for the primary dose-metric for the kidney,
ABioactDCVCBW34, is 1.7 ppm per mg/kg/day, less than that for total, oxidative, or liver
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oxidative metabolism. This is because the majority of metabolism in first pass through the liver
is via oxidation, whereas with inhalation exposure, more parent compound reaches the kidney, in
which metabolism is via GSH conjugation.
Table 5-47. Route-to-route extrapolation of site-specific inhalation unit risks
to oral slope factors
Inhalation unit risk
(risk per ppm)
Primary dose-metric
ppm per mg/kg/db
Oral slope factor
(risk per mg/kg/d)
Alternative dose-metric
ppm per mg/kg/db
Oral slope factor
(risk per mg/kg/d)
Kidney
5.49 x ID'3
ABioactDCVCBW34a
1.70
9.33 x ID'3
TotMetabBW34
1.97
1.08 x ID'2
NHL
1.10 x 1Q-2
TotMetabBW34
1.97
2.16 x ID'2
AUCCBld
0.137
1.50 x 1Q-3
Liver
5.49 x 1Q-3
AMetLivlBW34
2.82
1.55 x 1Q-2
TotOxMetabBW34
2.04
1.12 x 1Q-2
aThe AMetGSHBW34 dose-metric gives the same route-to-route conversion because there is no route dependence in
the pathway between GSH conjugation and DCVC bioactivation.
bAverage of expected population mean of males and females. Male and female estimates differed by <1% for
ABioactDCVCBW34; TotMetabBW34, AMetLivlBW34, and TotOxMetabBW34, and <15% for AUCCBld.
Uncertainty on the population mean route-to-route conversion, expressed as the ratio between the 97.5% quantile the
2.5% quantile, is about 2.6-fold for ABioactDCVCBW34, 1.5-fold for TotMetabBW34, AMetLivlBW34, and
TotOxMetabBW34, and about 3.4-fold for AUCCBld.
When one sums the oral slope factor estimates based on the primary (preferred) dose-
metrics for the three individual cancer types shown in Table 5-47, the resulting total cancer oral
slope factor estimate is 4.64 x io~2 per mg/kg/day. In the case of the oral route-extrapolated
results, the ratio of the risk estimate for the three cancer types combined to the risk estimate for
kidney cancer alone is 5.0. This value differs from the factor of 4 used for the total cancer
inhalation unit risk estimate because of the different dose-metrics used for the different cancer
types when the route-to-route extrapolation is performed. If only the kidney cancer and NHL
results, for which the evidence is strongest, were combined, the resulting total cancer oral slope
factor estimate would be 3.09 x 10"2 per mg/kg/day, and the ratio of this risk estimate to that for
kidney cancer alone would be 3.3.
If one were to use some of the risk estimates based on alternative dose-metrics in
Table 5-40, the total cancer risk estimate would vary depending on for which tumor type(s) an
alternative metric was used. The most extreme difference would occur when the alternative
metric is used for NHL and liver tumors; in that case, the resulting total cancer oral slope factor
estimate would be 2.20 x 10"2 per mg/kg/day, and the ratio of this risk estimate to that for kidney
cancer alone (based on the primary dose-metric of ABioactDCVCBW34) would be 2.4.
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The uncertainties in these conversions are relatively modest. As discussed in the note to
Table 5-47, the 95% confidence range for the route-to-route conversions at its greatest spans 3.4-
fold. The greatest uncertainty is in the selection of the dose-metric for NHL, since the use of the
alternative dose-metric of AUCCBld yields a converted oral slope factor that is 14-fold lower
than that using the primary dose-metric of TotMetabBW34. However, for the other two tumor
sites, the range of conversions is tighter, and lies within threefold of the conversion based solely
on intake.
5.2.3. Summary of Unit Risk Estimates
5.2.3.1. Inhalation Unit Risk Estimate
The inhalation unit risk for TCE is defined as a plausible upper bound lifetime extra risk
of cancer from chronic inhalation of TCE per unit of air concentration. The preferred estimate of
the inhalation unit risk for TCE is 2.20 x 10"2 per ppm (2 x 10~2 per ppm [4 x 10~6 per ug/m3]
rounded to one significant figure), based on human kidney cancer risks reported by Charbotel
et al. (2006) and adjusted for potential risk for NHL and liver cancer. This estimate is based on
good-quality human data, thus avoiding the uncertainties inherent in interspecies extrapolation.
This value is supported by inhalation unit risk estimates from multiple rodent bioassays,
the most sensitive of which range from 1 x 10~2 to 2 x 10"1 per ppm [2 x 10~6 to 3 x 10~5 per
ug/m3]. From the inhalation bioassays selected for analysis in Section 5.2.1.1, and using the
preferred PBPK model-based dose-metrics, the inhalation unit risk estimate for the most
sensitive sex/species is 8 x 10"2 per ppm [2 x 10"5 per |ig/m3], based on kidney adenomas and
carcinomas reported by Maltoni et al. (1986) for male Sprague-Dawley rats. Leukemias and
Leydig cell tumors were also increased in these rats, and, although a combined analysis for these
cancer types that incorporated the different site-specific preferred dose-metrics was not
performed, the result of such an analysis is expected to be similar, about 9 x 10"2 per ppm
[2 x 10"5 per |ig/m3]. The next most sensitive sex/species from the inhalation bioassays is the
female mouse, for which lymphomas were reported by Henschler et al. (1980): these data yield a
unit risk estimate of 1.0 x 10"2 per ppm [2 x 10"6 per |ig/m3]. In addition, the 90% CIs reported
in Table 5-41 for male rat kidney tumors from Maltoni et al. (1986) and female mouse
lymphomas from Henschler et al. (1980), derived from the quantitative analysis of PBPK model
uncertainty, both included the estimate based on human data of 2 x 10"2 per ppm. Furthermore,
PBPK model-based route-to-route extrapolation of the results for the most sensitive sex/species
from the oral bioassays, kidney tumors in male Osborne-Mendel rats and testicular tumors in
Marshall rats (NTP, 1988), leads to inhalation unit risk estimates of 2 x 10"1 per ppm [3 x
10"5 per |ig/m3] and 4 x 10"2 per ppm [8 x 10"6 per |ig/m3], respectively, with the preferred
estimate based on human data falling within the route-to-route extrapolation of the 90%
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CIs reported in Table 5-42.58 Finally, for all of these estimates, the ratios of BMDs to the
BMDLs did not exceed a value of 3, indicating that the uncertainties in the dose-response
modeling for determining the POD in the observable range are small.
Although there are uncertainties in these various estimates, as discussed in
Sections 5.2.1.4, 5.2.2.1.3, and 5.2.2.2, confidence in the proposed inhalation unit risk estimate
of 2 x 10"2 per ppm [4 x 10"6 per |ig/m3], based on human kidney cancer risks reported by
Charbotel et al. (2006) and adjusted for potential risk for NHL and liver cancer (as discussed in
Section 5.2.2.2), is further increased by the similarity of this estimate to estimates based on
multiple rodent data sets.
5.2.3.2. Oral Slope Factor Estimate
The oral slope factor for TCE is defined as a plausible upper bound lifetime extra risk of
cancer from chronic ingestion of TCE per mg/kg/day oral dose. The preferred estimate of the
oral slope factor is 4.64 x 10"2 per mg/kg/day (5 x 10~2 per mg/kg/day rounded to one significant
figure), resulting from PBPK model-based route-to-route extrapolation of the inhalation unit risk
estimate based on the human kidney cancer risks reported in Charbotel et al. (2006) and adjusted
for potential risk for NHL and liver cancer. This estimate is based on good-quality human data,
thus avoiding uncertainties inherent in interspecies extrapolation. In addition, uncertainty in the
PBPK model-based route-to-route extrapolation is relatively low (Chiu, 2006; Chiu and White,
2006). In this particular case, extrapolation using different dose-metrics yielded expected
population mean risks within about a twofold range, and, for any particular dose-metric, the 95%
CI for the extrapolated population mean risks for each site spanned a range of no more than
about threefold.
This value is supported by oral slope factor estimates from multiple rodent bioassays, the
most sensitive of which range from 3 x 10~2 to 3 x 10"1 per mg/kg/day. From the oral bioassays
selected for analysis in Section 5.2.1.1, and using the preferred PBPK model-based dose-metrics,
the oral slope factor estimate for the most sensitive sex/species is 3 x 10"1 per mg/kg/day, based
on kidney tumors in male Osborne-Mendel rats (NTP, 1988). The oral slope factor estimate for
testicular tumors in male Marshall rats (NTP, 1988) is somewhat lower at 7 x 10"2 per
mg/kg/day. The next most sensitive sex/species result from the oral studies is for male mouse
liver tumors (NCI, 1976), with an oral slope factor estimate of 3 x 10"2 per mg/kg/day. In
addition, the 90% CIs reported in Table 5-42 for male Osborne-Mendel rat kidney tumors (NTP,
58For oral-to-inhalation extrapolation of NTP (1988) male rat kidney tumors, the unit risk estimate of 2.5 x 10-1 per
mg/kg/day using the ABioactDCVCBW34 dose metric, from Table 5-37, is divided by the average male and female
internal doses at 0.001 mg/kg/day, (0.00504/0.001), and then multiplied by the average male and female internal
doses at 0.001 ppm (0.00324/0.001), both from Table 5-35, to yield a unit risk of 1.6 x 10-1 [3.0 x 10-5 per ug/m3].
For oral-to-inhalation extrapolation of NTP (1988) male rat testicular tumors, the unit risk estimate of 7.1 x 10"2 per
mg/kg/day using the TotMetabBW34 dose metric, from Table 5-37, is divided by the male internal dose at
0.001 mg/kg/day, (0.0192/0.001), and then multiplied by the male internal doses at 0.001 ppm (0.0118/0.001), both
from Table 5-35, to yield a unit risk of 4.4 x 10'2 [8.1 x 10'6 per ug/m3].
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1988), male F344 rat kidney tumors (NTP, 1990). and male Marshall rat testicular tumors (NTP,
1988), derived from the quantitative analysis of PBPK model uncertainty, all included the
estimate based on human data of 5 x 10"2 per mg/kg/day, while the upper 95% confidence bound
for male mouse liver tumors from NCI (1976) was slightly below this value at 4 x 10"2 per
mg/kg/day. Furthermore, PBPK model-based route-to-route extrapolation of the most sensitive
endpoint from the inhalation bioassays, male rat kidney tumors from Maltoni et al. (1986), leads
to an oral slope factor estimate of 1 x 10"1 per mg/kg/day, with the preferred estimate based on
human data falling within the route-to-route extrapolation of the 90% CI reported in Table 5-
41.59 Finally, for all of these estimates, the ratios of BMDs to the BMDLs did not exceed a value
of 3, indicating that the uncertainties in the dose-response modeling for determining the POD in
the observable range are small.
Although there are uncertainties in these various estimates, as discussed in
Sections 5.2.1.4, 5.2.2.1.3, 5.2.2.2, and 5.2.2.3, confidence in the proposed oral slope factor
estimate of 5 x 10"2 per mg/kg/day, resulting from PBPK model-based route-to-route
extrapolation of the inhalation unit risk estimate based on the human kidney cancer risks reported
in Charbotel et al. (2006) and adjusted for potential risk for NHL and liver cancer (as discussed
in Section 5.2.2.2), is further increased by the similarity of this estimate to estimates based on
multiple rodent data sets.
5.2.3.3. Application of ADAFs
When there is sufficient weight of evidence to conclude that a carcinogen operates
through a mutagenic mode of action, and in the absence of chemical-specific data on age-specific
susceptibility, EPA's Supplemental Guidance for Assessing Susceptibility from Early-Life
Exposure to Carcinogens (U.S. EPA, 2005e) advises that increased early-life susceptibility be
assumed and recommends that default ADAFs be applied to adjust for this potential increased
susceptibility from early-life exposure. As discussed in Section 4.4, there is sufficient evidence
to conclude that a mutagenic mode of action is operative for TCE-induced kidney tumors. The
weight of evidence also supports involvement of processes of cytotoxicity and regenerative
proliferation in the carcinogen!city of TCE, although not with the extent of support as for a
mutagenic mode of action. In particular, data linking TCE-induced proliferation to increased
mutation or clonal expansion are lacking, as are data informing the quantitative contribution of
cytotoxicity. Because any possible involvement of a cytotoxicity mode of action would be
additional to mutagenicity, the mutagenic mode of action would be expected to dominate at low
doses. Therefore, the additional involvement of a cytotoxicity mode of action does not provide
evidence against the application of ADAFs. In addition, as described in Section 4.10, TCE-
59For the Maltoni et al. (1986) male rat kidney tumors, the unit risk estimate of 8.3 * 10~2 per ppm using the
ABioactDCVCBW34 dose metric, from Table 5-36, is divided by the average male and female internal doses at
0.001 ppm (0.00324/0.001) and then multiplied by the average male and female internal doses at 0.001 mg/kg/day,
(0.00504/0.001), both from Table 5-35, to yield a unit risk of 1.3 x 10'1 per mg/kg/day.
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specific data are inadequate for quantification of early-life susceptibility to TCE carcinogenicity.
Therefore, as recommended in the Supplemental Guidance, the default ADAFs are applied.
See the Supplemental Guidance for detailed information on the general application of
these adjustment factors. In brief, the Supplemental Guidance establishes ADAFs for
three specific age groups. The current ADAFs and their age groupings are 10 for <2 years, 3 for
2-<16 years, and 1 for >16 years (U.S. EPA, 2005e). For risk assessments based on specific
exposure assessments, the 10- and 3-fold adjustments to the slope factor or unit risk estimates are
to be combined with age-specific exposure estimates when estimating cancer risks from early-
life (<16-years-of-age) exposure. Currently, due to lack of appropriate data, no ADAFs are used
for other lifestages, such as the elderly. However, the ADAFs and their age groups may be
revised over time. The most current information on the application of ADAFs for cancer risk
assessment can be found at www.epa.gov/cancerguidelines.
In the case of TCE, the inhalation unit risk and oral slope factor estimates reflect lifetime
risk for cancer at multiple sites, and a mutagenic mode of action has been established for one of
these sites, the kidney. The following subsections illustrate how one might apply the default
ADAFs to the kidney-cancer component of the inhalation unit risk and oral slope factor estimates
for TCE. These are sample calculations, and individual risk assessors should use exposure-
related parameters (e.g., age-specific water ingestion rates) that are appropriate for their
particular risk assessment applications.
In addition to the uncertainties discussed above for the inhalation and oral total cancer
unit risk or slope factor estimates, there are uncertainties in the application of ADAFs to adjust
for potential increased early-life susceptibility. For one thing, the adjustment is made only for
the kidney cancer component of total cancer risk because that is the tumor type for which the
weight of evidence was sufficient to conclude that TCE-induced carcinogenesis operates through
a mutagenic mode of action. However, it may be that TCE operates through a mutagenic mode
of action for other cancer types as well or that it operates through other modes of action that
might also convey increased early-life susceptibility. Additionally, the ADAFs are general
default factors, and it is uncertain to what extent they reflect increased early-life susceptibility
for exposure to TCE, if increased early-life susceptibility occurs.
Furthermore, the assumption of increased early-life susceptibility, invoked by the finding
of a mutagenic mode of action for kidney cancer, is in contradiction to the assumption that RR is
independent of age that was used to derive the unit risk estimates in the life-table analysis. In
some other assessments faced with a similar situation, a small modification has been made to the
derivation of the unit risk estimate to avoid the contradictory assumptions (by calculating an
adult-exposure-only unit risk estimate for the application of ADAFs). This has the effect of
slightly reducing the unit risk estimate to which the ADAFs are applied. Because there are
multiple cancer types for TCE but the finding of a mutagenic mode of action applies to only one
of them, and because under these circumstances application of the ADAFs already has a minimal
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impact on the total risk for most exposure scenarios, as discussed with respect to the examples in
Sections 5.2.3.3.1 and 5.2.3.3.2 below, no attempt was made to modify the kidney cancer unit
risk estimate for this assessment. Such a modification would have substantially increased the
complexity of the calculations, which are already more elaborate than the standard ADAF
applications, without having much quantitative impact on the final risk estimates.
5.2.3.3.1. Example application of ADAFs for inhalation exposures.
A calculation template for application of the ADAFs is provided in Table 5-48, with an
Excel spreadsheet version available on the HERO database (U.S. EPA, 20 lie). In the example
provided, it is assumed that an individual is exposed to 1 |ig/m3 in air from birth through age 70
years. Using the template, risk estimates for different exposure scenarios can be obtained by
changing the exposure concentrations (including possibly zero for some age groups). The steps
in the calculation are as follows:
(1) Separate the kidney cancer contribution from the NHL + liver cancer contribution to the
inhalation unit risk estimate. From Section 5.2.2.1.4, the kidney lifetime unit risk is
1.0 x 10"6 per |ig/m3 in air. Subtracting this from the total lifetime unit risk of 4.1 x
10"6 per |ig/m3 from Section 5.2.2.2 results in the estimated contribution of NHL + liver
cancer being 3.1 x 10"6 per |ig/m3.
(2) Assign a lifetime unit risk estimate for each age group. The template shows the
recommended age groupings from U.S. EPA (2005c) in Column A (augmented by
additional age groups from U.S. EPA, 2008c, and for assessing 30 year exposures), along
with the age group duration (Column D), and the fraction of lifetime each age group
represents (Column E; used as a duration adjustment). For each age group, the
(unadjusted) lifetime unit risk estimates for kidney cancer, total cancer, and NHL + liver
cancer are shown in Column F, I, and J, respectively.
(3) For each age group, the kidney cancer inhalation unit risk estimate (Column F) is
multiplied by the risk per |ig/m3 equivalence (Column B), the exposure concentration
(Column C), the duration adjustment (Column E), and the ADAF (Column G), to obtain
the partial risk from exposure during those ages (Column H). For inhalation exposures, a
-HEk per |ig/m3 equivalence" of 1 is assumed across age groups (i.e., equivalent risk from
equivalent exposure levels in air, independent of body size), as shown in Column B. In
this calculation, a unit lifetime exposure of 1 |ig/m3 is assumed, as shown in Column C.
(4) For each age group, the NHL + liver cancer unit risk estimate (Column J) is multiplied by
the risk per |ig/m3 equivalence (Column B), the exposure concentration (Column C), and
the duration adjustment (Column E), to obtain the partial risk from exposure during those
ages (Column K).
(5) For each age group, the ADAF-adjusted partial risk for kidney cancer (Column H) is
added to the partial risk for NHL + liver cancer (Column K), resulting in the total partial
risk (Column L).
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(6) The age-group-specific partial risks are added together to obtain the estimated total
lifetime risk (bottom of Column L).
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Table 5-48. Sample calculation for total lifetime cancer risk based on the kidney unit risk estimate, potential
risk for NHL and liver cancer, and potential increased early-life susceptibility, assuming a constant lifetime
exposure to 1 ug/m3 of TCE in air
Column A
Units:
Age group
Birth to <1 mo
l-<3mo
3-<6 mo
6-<12 mo
l-<2 yrs
2-<3yrs
3-<6 yrs
6-
-------
From the example calculation, based on continuous exposure to 1 ug/m3 from birth to age
70, the estimated total lifetime risk is 4.8 x 10"6, which corresponds to a lifetime unit risk
estimate of 4.8 x 10"6 per ug/m3. The risk-specific air concentrations at risk levels of 10"6, 10"5,
and 10"4 are 0.21, 2.1, and 21 ug/m3, respectively.
This total cancer unit risk estimate of 4.8 x 10"6 per ug/m3 (2.6 x 10"2 per ppm), adjusted
for potential increased early-life susceptibility, is only minimally (17.5%) increased over the
unadjusted total cancer unit risk estimate because the kidney cancer risk estimate that gets
adjusted for potential increased early-life susceptibility is only part of the total cancer risk
estimate. Thus, foregoing the ADAF adjustment in the case of full lifetime calculations will not
seriously impact the resulting risk estimate. For less-than-lifetime exposure calculations, the
impact of applying the ADAFs will increase as the proportion of time at older ages decreases.
The maximum impact will be when exposure is for only the first 2 years of life, in which case,
the partial lifetime total cancer risk estimate for exposure to 1 ug/m3 adjusted for potential
increased early-life susceptibility is 10 x (1 ug/m3) x (1.0 x 10"6 per ug/m3) x (2 / 70) for the
kidney cancer risk + (1 ug/m3) x (3.1 x 10"6 per ug/m3) x (2 / 70) for the NHL and liver cancer,
or 3.7 x 10"7, which is over 3 times greater than the unadjusted partial lifetime total cancer risk
estimate for exposure to 1 ug/m3 of (1 ug/m3) x (4.1 x ICT6 per ug/m3) x (2 / 70), or 1.2 x 10"7.
5.2.3.3.2. Example application of ADAFs for oral drinking water exposures
For oral exposures, the calculation of risk estimates adjusted for potential increased early-
life susceptibility is complicated by the fact that for a constant exposure level (e.g., a constant
concentration of TCE in drinking water) doses will vary by age because of different age-specific
uptake rates (e.g., drinking water consumption rates). Different EPA Program or Regional
Offices may have different default age-specific uptake rates that they use for risk assessments for
specific exposure scenarios, and the calculations presented below are merely to illustrate the
general approach to applying ADAFs for oral TCE exposures, using exposure to 1 ug/L of TCE
in drinking water from birth through age 70 years as an example. Using the template, risk
estimates for different exposure scenarios can be obtained by changing the intake rates and
exposure concentrations (including possibly zero for some age groups). The steps in the
calculation, illustrated in the template in Table 5-49 (available as an Excel spreadsheet version
on the HERO database, U.S. EPA. 2011 e). are as follows:
(1) Separate the kidney cancer contribution from the NHL + liver cancer contribution to the
oral slope factor estimate. From Section 5.2.2.3, the kidney lifetime oral slope factor is
9.3 x 10"3 per mg/kg/day. Subtracting this from the total lifetime oral slope factor of
4.6 x 10"2 per mg/kg/day from Section 5.2.2.3 results in an estimated contribution from
NHL + liver cancer of 3.7 x 10"2 per mg/kg/day.
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(2) Assign a lifetime oral slope factor estimate for each age group. The template shows the
recommended age groupings from U.S. EPA (2005c) in Column A (augmented by
additional age groups from U.S. EPA, 2008c, and for assessing 30 year exposures), along
with the age group duration (Column D), and the fraction of lifetime each age group
represents (Column E; used as a duration adjustment). For each age group, the
(unadjusted) lifetime oral slope factor estimates for kidney cancer, total cancer, and NHL
+ liver cancer are shown in Columns F, I, and J, respectively.
(3) For each age group, the kidney cancer oral slope factor estimate (Column F) is multiplied
by the drinking water ingestion rate (Column B), the exposure concentration (Column C),
the duration adjustment (Column E), and the ADAF (Column G), to obtain the partial risk
from exposure during those ages (Column H). Age-specific water ingestion rates in
L/kg/day, taken from the EPA Office of Water Policy Document Age Dependent
Adjustment Factor (ADAF) Application are shown in Column B.60 In this calculation, a
lifetime unit exposure of 1 |ig/L is assumed, as shown in Column C.
(4) For each age group, the NHL + liver cancer oral slope factor estimate (Column J) is
multiplied by the drinking water ingestion rate (Column B), the exposure concentration
(Column C), and the duration adjustment (Column E), to obtain the partial risk from
exposure during those ages (Column K).
(5) For each age group, the ADAF-adjusted partial risk for kidney cancer (Column H) is
added to the partial risk for NHL + liver cancer (Column K), resulting in the total partial
risk (Column L).
(6) The age-group-specific partial risks are added together to obtain the estimated total
lifetime risk (bottom of Column L).
60Values for the 90th percentile were taken from Table 3-19 of U.S. EPA (2008a) (consumers-only estimates of
combined direct and indirect water ingestion from community water) and U.S. EPA (2004) (Table Al). The 90th
percentile was based on the policy in the U.S. EPA Office of Water for determining risk through direct and indirect
consumption of drinking water (U.S. EPA. 201 If). Community water was used in the illustration because U.S. EPA
only regulates community water sources and not private wells and cisterns or bottled water. Data for —msumers
only" (i.e., excluding individuals who did not ingest community water) were used because formula-fed infants (as
opposed to breast-fed infants, who consume very little community water), children, and young adolescents are often
the population of concern with respect to water consumption.
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Table 5-49. Sample calculation for total lifetime cancer risk based on the kidney cancer slope factor estimate,
potential risk for NHL and liver cancer, and potential increased early-life susceptibility, assuming a constant
lifetime exposure to 1 ug/L of TCE in drinking water
Column A
Units:
Age group
Birth to <1 mo
l-<3 mo
3-<6 mo
6-<12 mo
l-<2 yrs
2-<3yrs
3-<6 yrs
6-
-------
Because the TCE intake is not constant across age groups, one does not calculate a
lifetime unit risk estimate in terms of risk per mg/kg/day adjusted for potential increased early-
life susceptibility. One could calculate a unit risk estimate for TCE in drinking water in terms of
ug/L from the result in Table 5-49, but this is dependent on the water ingestion rates used. Based
on the example calculation assuming continuous exposure to 1 ug/L of TCE in drinking water
from birth to age 70 years and using the drinking water intake rates shown, estimated total
lifetime risk is 2.0 x 10"6, which corresponds to a lifetime drinking water unit risk estimate of
2.0 x 10"6 per ug/L. The corresponding risk-specific drinking water concentrations at risk levels
of 10"6, 10"5, and 10"4 are 0.51, 5.1, and 51 ug/L, respectively. For different exposure and intake
parameters, the risk-specific drinking water concentrations would need to be recalculated.
As with the adjusted inhalation risk estimate in Section 5.2.3.3.1, the lifetime total cancer
risk estimate of 2.0 x 10"6 calculated for lifetime exposure to 1 ug/L of TCE in drinking water
adjusted for potential increased early-life susceptibility is only minimally (25%) increased over
the unadjusted total cancer unit risk estimate. (This calculation is not shown, but if one omits the
ADAFs for each of the age groups in Table 5-49, the resulting total lifetime risk estimate is
1.6 x 10"6.) Unlike with inhalation exposure under the assumption of ppm equivalence, which is
generally assumed to extend across age groups as well as species, the oral intake rates are higher
in the potentially more susceptible younger age groups. This would tend to yield a larger relative
impact of adjusting for potential increased early-life susceptibility for oral risk estimates
compared to inhalation risk estimates. In the case of TCE, however, this impact is partially
offset by the lesser proportion of the total oral cancer risk that is accounted for by the kidney
cancer risk, which is the component of total risk that is being adjusted for potential increased
early-life susceptibility, based on the primary dose-metrics (1/5 vs. 1/4 for inhalation). Thus, as
with lifetime inhalation risk, foregoing the ADAF adjustment in the case of full lifetime
calculations will not seriously impact the resulting risk estimate. For less-than-lifetime exposure
calculations, the impact of applying the ADAFs will increase as the proportion of time at older
ages decreases. The maximum impact will be when exposure is for only the first 2 years of life,
in which case the partial lifetime total cancer risk estimate for exposure to 1 ug/L adjusted for
potential increased early-life susceptibility is 3.8 x 10"7 (adding partial risks from Table 5-49 for
the appropriate ages groups), which is almost 3 times greater than the unadjusted partial lifetime
total cancer risk estimate for exposure to 1 ug/L of 5 x (0.001 mg/L) x (0.103 L/kg/day) x (9.33
x 10"3 per mg/kg/day) x (2/70), or 1.4 x 10"7, where 5 is the factor for the multiple cancer types
for oral exposure, 0.103 L/kg/day is the time-weighted ingestion rate for the 1st two years of life
using the rates in Table 5-49, 9.33 x 10"3 per mg/kg/day is the unadjusted oral slope factor
estimate for kidney cancer, and 2/70 is the duration adjustment.
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5.3. KEY RESEARCH NEEDS FOR TCE DOSE-RESPONSE ANALYSES
For noncancer dose-response assessment, key research that would substantially improve
the accuracy or utility of TCE noncancer risk estimates includes:
• Research to obtain toxicokinetic data to better quantify the amount of bioactivation of
DCVC to toxic moiety(ies) in rats and humans, including data on human variability in
DCVC bioactivation.
• Research to obtain mechanistic data that would identify the active moiety(ies) for
TCE-induced immunological effects and developmental cardiac defects. As a
corollary, data on human variability pharmacokinetics of the active moiety after TCE
exposure would also be informative.
• Research to obtain mechanistic data that would quantitatively inform the
pharmacodynamic factors that would make individuals more or less susceptible to
kidney, immunological, and developmental cardiac defects induced by TCE.
• Research to obtain TCE dose-response data on kidney effects, immunological effects,
and developmental cardiac defects at a larger number of doses at and below the
current LOAELs, so as to better describe the dose-response shape at low effect levels.
Ideally, studies would be based on human epidemiologic data with good quantitative
exposure assessment. Studies in laboratory animals would need to address the
limitations in the currently available studies. For example, studies of cardiac defects
would need to address limitations of the Johnson et al. (2003) study described in
Section 4.8.3.3.2.
• Development of a probabilistic approach to noncancer dose-response analysis that
would enable calculation of a risk-specific dose for noncancer effects, while capturing
uncertainty and variability quantitatively.
For cancer dose-response assessment, key research that would substantially improve the
accuracy or utility of TCE cancer risk estimates includes:
• Research to obtain toxicokinetic data to better quantify the amount of bioactivation of
DCVC to toxic moiety(ies) in humans, including data on human variability in DCVC
bioactivation.
• Research to obtain mechanistic data that would identify the active moiety(ies) for
TCE-induced liver tumors and NHL. As a corollary, data on human variability
pharmacokinetics of the active moiety after TCE exposure would also be informative.
• Research to obtain mechanistic data that would quantitatively inform the
pharmacodynamic factors that would make individuals more or less susceptible to
kidney tumors, liver tumors, and NHL induced by TCE. This includes data on life-
stage-specific susceptibility that would replace the default ADAFs for kidney tumors
and the assumption of no life-stage-specific susceptibility for liver tumors and NHL.
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• Research to obtain human epidemiologic dose-response data on TCE-induced kidney
tumors, liver tumors, and NHL with good quantitative exposure assessment.
• Research to obtain additional human epidemiologic data on TCE exposure and other
tumors, so as to better estimate the total risk of cancer from TCE exposure.
• Development of a probabilistic approach to cancer dose-response analysis that would
enable calculation of a differential susceptibility to carcinogenic effects, while
capturing uncertainty and variability quantitatively.
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6. MAJOR CONCLUSIONS IN THE CHARACTERIZATION OF
HAZARD AND DOSE RESPONSE
6.1. HUMAN HAZARD POTENTIAL
This section summarizes the human hazard potential for TCE. For extensive discussions
and references, see Chapter 2 for exposure information, Chapter 3 for toxicokinetics and PBPK
modeling, and Sections 4.1-4.9 for the epidemiologic and experimental studies of TCE
noncancer and cancer toxicity. Section 4.10 summarizes information on susceptibility, and
Section 4.11 provides a more detailed summary and references for noncancer toxicity and
carcinogenicity.
6.1.1. Exposure (see Chapter 2)
TCE is a volatile compound with moderate water solubility. Most TCE produced today
is used for metal degreasing. The highest environmental releases are to the air. Ambient air
monitoring data suggest that mean levels have remained fairly constant since 1999 at about
0.3 ug/m3 (0.06 ppb). As discussed in Chapter 2, in 2006, ambient air monitors (n = 258) had
annual means ranging from 0.03 to 7.73 ug/m3 with a median of 0.13 ug/m3 and an overall
average of 0.23 ug/m3. Indoor levels are commonly >3 times higher than outdoor levels due to
releases from building materials and consumer products. Vapor intrusion is a likely significant
source in situations where residences are located near soils or groundwater with high
contamination levels and sparse indoor air sampling had detected TCE levels ranging from 1 to
140 ug/m3. TCE is among the most common groundwater contaminants and the one present in
the highest concentration in a summary of groundwater analyses reported in 1982. The median
level of TCE in groundwater, based on a large survey by the USGS for 1985-2001, is 0.15 ug/L.
It has also been detected in a wide variety of foods in the 1-100 ug/kg range. None of the
environmental sampling has been done using statistically based national surveys. However, a
substantial amount of air and groundwater data have been collected allowing reasonably well-
supported estimates of typical daily intakes by the general population: inhalation—13 ug/day and
water ingestion—0.2 ug/day. The limited food data suggest an intake of about 5 ug/day, but this
must be considered preliminary. Higher exposures have occurred to various occupational
groups, particularly with vapor degreasing that has the highest potential for exposure because
vapors can escape into the work place. For example, past studies of aircraft workers have shown
short-term peak exposures in the hundreds of ppm (>500,000 ug/m3) and long-term exposures in
the low tens of ppm (>50,000 ug/m3). Occupational exposures have likely decreased in recent
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years due to better release controls, improvements in worker protection, and substituting other
solvents for TCE.
Exposure to a variety of TCE-related compounds, which include metabolites of TCE and
other parent compounds that produce similar metabolites, can alter or enhance TCE metabolism
and toxicity by generating higher internal metabolite concentrations than would result from TCE
exposure by itself. Available estimates suggest that exposures to most of these TCE-related
compounds are comparable to or greater than TCE itself.
6.1.2. Toxicokinetics and PBPK Modeling (see Chapter 3 and Appendix A)
TCE is a lipophilic compound that readily crosses biological membranes. Exposures may
occur via the oral, dermal, and inhalation routes, with evidence for systemic availability from
each route. TCE can also be transferred transplacentally and through breast milk ingestion. TCE
is rapidly and nearly completely absorbed from the gut following oral administration, and animal
studies indicate that exposure vehicle may impact the time course of absorption: oily vehicles
may delay absorption, whereas aqueous vehicles result in a more rapid increase in blood
concentrations. See Section 3.1 for additional discussion of TCE absorption.
Following absorption to the systemic circulation, TCE distributes from blood to solid tissues by
each organ's solubility. This process is mainly determined by the blood:tissue partition
coefficients, which are largely determined by tissue lipid content. Adipose partitioning is high,
so adipose tissue may serve as a reservoir for TCE, and accumulation into adipose tissue may
prolong internal exposures. TCE attains high concentrations relative to blood in the brain,
kidney, and liver—all of which are important target organs of toxicity. TCE is cleared via
metabolism mainly in three organs: the kidney, liver, and lungs. See Section 3.2 for additional
discussion of TCE distribution.
The metabolism of TCE is an important determinant of its toxicity. Metabolites are
generally thought to be responsible for toxicity-especially for the liver and kidney. Initially,
TCE may be oxidized via CYP isoforms or conjugated with GSH by GST enzymes. While
CYP2E1 is generally accepted to be the CYP isoform most responsible for TCE oxidation, others
forms may also contribute. There are conflicting data as to which GST isoforms are responsible
for TCE conjugation, with one rat study indicating alpha-class GSTs and another rat study
indicating mu and pi-class GST. The balance between oxidative and conjugative metabolites
generally favors the oxidative pathway, especially at lower concentrations, and inhibition of
CYP-dependent oxidation in vitro increases GSH conjugation in renal preparations. However,
different investigators have reported considerably different rates for TCE conjugation in human
liver and kidney cell fractions, perhaps due to different analytical methods. The inferred flux
through the GSH pathway differs by >4 orders of magnitude across data sets. While the
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available data are consistent with the higher values being overestimates, the degree of
overestimation is unclear, and differing results may be attributable to true interindividual
variation. Overall, there remains significant uncertainty in the quantitative estimation of TCE
GSH conjugation. See Section 3.3 for additional discussion of TCE metabolism.
Once absorbed, TCE is excreted primarily either in breath as unchanged TCE or carbon
dioxide [CO2], or in urine as metabolites. Minor pathways of elimination include excretion of
metabolites in saliva, sweat, and feces. Following oral administration or upon cessation of
inhalation exposure, exhalation of unmetabolized TCE is a major elimination pathway. Initially,
elimination of TCE upon cessation of inhalation exposure demonstrates a steep concentration-
time profile: TCE is rapidly eliminated in the minutes and hours postexposure, and then the rate
of elimination via exhalation decreases. Following oral or inhalation exposure, urinary
elimination of parent TCE is minimal, with urinary elimination of the metabolites, TCA and
TCOH, accounting for the bulk of the absorbed dose of TCE. See Section 3.4 for additional
discussion of TCE excretion.
As part of this assessment, a comprehensive Bayesian PBPK model-based analysis of the
population toxicokinetics of TCE and its metabolites was developed in mice, rats, and humans
(also reported in Chiu et al., 2009). This analysis considered a wider range of physiological,
chemical, in vitro, and in vivo data than any previously published analysis of TCE. The
toxicokinetics of the -population average," its population variability, and their uncertainties are
characterized and estimates of experimental variability and uncertainty are included in this
analysis. The experimental database included separate sets for model calibration and evaluation
for rats and humans; fewer data were available in mice, and were all used for model calibration.
Local sensitivity analyses confirm that the calibration data inform the value of most model
parameters, with the remaining parameters either informed by substantial prior information or
having little sensitivity with respect to dose metric predictions. The total combination of these
approaches and PBPK analysis substantially supports the model predictions. In addition, the
approach employed yields an accurate characterization of the uncertainty in metabolic pathways
for which available data were sparse or relatively indirect, such as GSH conjugation and
respiratory tract metabolism. Key conclusions from the model predictions include: (1) as
expected, TCE is substantially metabolized, primarily by oxidation at doses below saturation; (2)
GSH conjugation and subsequent bioactivation in humans appear to be 10-100-fold greater than
previously estimated; and (3) mice had the greatest rate of respiratory tract oxidative metabolism
compared to rats and humans. However, there are uncertainties as to the accuracy of the
analytical method used for some of the available in vivo data on GSH conjugation. Because
these data are highly influential, the PBPK modeling results for the flux of GSH conjugation
should be interpreted with caution. Thus, there is lower confidence in the accuracy of GSH
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conjugation predictions as compared to other dose-metrics, such as those related to the parent
compound, total metabolism, or oxidative metabolites. The predictions of the PBPK model are
subsequently used in noncancer and cancer dose-response analyses for inter- and intraspecies
extrapolation of toxicokinetics (see Section 6.2, below). See Section 3.5 and Appendix A for
additional discussion of and details about PBPK modeling of TCE and metabolites.
6.1.3. Noncancer Toxicity
This section summarizes the weight of evidence for TCE noncancer toxicity. Based on
the available human epidemiologic data and experimental and mechanistic studies, it is
concluded that TCE poses a potential human health hazard for noncancer toxicity to the CNS,
kidney, liver, immune system, male reproductive system, and developing fetus. The evidence is
more limited for TCE toxicity to the respiratory tract and female reproductive system. The
conclusions pertaining to specific endpoints within these tissues and systems are summarized
below.
6.1.3.1. Neurological Effects (see Sections 4.3 and 4.11.1.1 and Appendix D)
Both human and animal studies have associated TCE exposure with effects on several
neurological domains. Multiple epidemiologic studies in different populations have reported
abnormalities in trigeminal nerve function in association with TCE exposure. Two small studies
did not report an association between TCE exposure and trigeminal nerve function. However,
statistical power was limited, exposure misclassification was possible, and, in one case, methods
for assessing trigeminal nerve function were not available. As a result, these studies do not
provide substantial evidence against a causal relationship between TCE exposure and trigeminal
nerve impairment. Laboratory animal studies have also demonstrated TCE-induced changes in
the morphology of the trigeminal nerve following short-term exposures in rats. However, one
study reported no significant changes in TSEP in rats exposed to TCE for 13 weeks. See
Section 4.3.1 for additional discussion of studies of alterations in nerve conduction and
trigeminal nerve effects. Human chamber, occupational, and geographic-based/drinking water
studies have consistently reported subjective symptoms such as headaches, dizziness, and
nausea, which are suggestive of vestibular system impairments. One study reported changes in
nystagmus threshold (a measure of vestibular system function) following an acute TCE
exposure. There are only a few laboratory animal studies relevant to this neurological domain,
with reports of changes in nystagmus, balance, and handling reactivity. See Section 4.3.3 for
additional discussion of TCE effects on vestibular function. Fewer and more limited
epidemiologic studies are suggestive of TCE exposure being associated with delayed motor
function, and changes in auditory, visual, and cognitive function or performance (see
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Sections 4.3.2, 4.3.4, 4.3.5, and 4.3.6). Acute and subchronic animal studies show disruption of
the auditory system, changes in visual evoked responses to patterns or flash stimulus, and
neurochemical and molecular changes. Animal studies suggest that while the effects on the
auditory system lead to permanent function impairments and histopathology, effects on the
visual system may be reversible with termination of exposure. Additional acute studies reported
structural or functional changes in hippocampus, such as decreased myelination or decreased
excitability of hippocampal CA1 neurons, although the relationship of these effects to overall
cognitive function is not established (see Section 4.3.9). An association between TCE exposure
and sleep changes has also been demonstrated in rats (see Section 4.3.7). Some evidence exists
for motor-related changes in rats/mice exposed acutely/subchronically to TCE, but these effects
have not been reported consistently across all studies (see Section 4.3.6). Gestational exposure
to TCE in humans has been reported to be associated with neurodevelopmental abnormalities
including neural tube defects, encephalopathy, impaired cognition, aggressive behavior, and
speech and hearing impairment. Developmental neurotoxicological changes have also been
observed in animals including aggressive behaviors following an in utero exposure to TCE and a
suggestion of impaired cognition as noted by decreased myelination in the CA1 hippocampal
region of the brain. See Section 4.3.8 for additional discussion of developmental neurological
effects of TCE. Therefore, overall, the strongest neurological evidence of human toxicological
hazard is for changes in trigeminal nerve function or morphology and impairment of vestibular
function, based on both human and experimental studies, while fewer and more limited evidence
exists for delayed motor function, changes in auditory, visual, and cognitive function or
performance, and neurodevelopmental outcomes.
6.1.3.2. Kidney Effects (see Sections 4.4.1, 4.4.4, 4.4.6, and 4.11.1.2)
Kidney toxicity has also been associated with TCE exposure in both human and animal
studies. There are few human data pertaining to TCE-related noncancer kidney toxicity;
however, several available studies reported elevated excretion of urinary proteins, considered
nonspecific markers of nephrotoxicity, among TCE-exposed subjects compared to unexposed
controls. While some of these studies include subjects previously diagnosed with kidney cancer,
other studies report similar results in subjects who are disease free. Some additional support for
TCE nephrotoxicity in humans is provided by two studies of ESRD; a study reporting a greater
incidence of ESRD in TCE-exposed workers as compared to unexposed controls and a second
study reporting a greater risk for progression from IgA or membranous nephropathy
glomerulonephritis to ESRD and TCE-exposure. See Section 4.4.1 for additional discussion of
human data on the noncancer kidney effects of TCE. Laboratory animal and in vitro data
provide additional support for TCE nephrotoxicity. TCE causes renal toxicity in the form of
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cytomegaly and karyomegaly of the renal tubules in male and female rats and mice following
either oral or inhalation exposure. In rats, the pathology of TCE-induced nephrotoxicity appears
distinct from age-related nephropathy. Increased kidney weights have also been reported in
some rodent studies. See Section 4.4.4 for additional discussion of laboratory animal data on the
noncancer kidney effects of TCE. Further studies with TCE metabolites have demonstrated a
potential role for DCVC, TCOH, and TCA in TCE-induced nephrotoxicity. Of these, available
data suggest that DCVC-induced renal effects are most similar to those of TCE and that DCVC
is formed in sufficient amounts following TCE exposure to account for these effects. TCE or
DCVC have also been shown to be cytotoxic to primary cultures of rat and human renal tubular
cells. See Section 4.4.6 for additional discussion on the role of metabolism in the noncancer
kidney effects of TCE. Overall, multiple lines of evidence support the conclusion that TCE
causes nephrotoxicity in the form of tubular toxicity, mediated predominantly through the TCE
GSH conjugation product DCVC.
6.1.3.3. Liver Effects (see Sections 4.5.1, 4.5.3, 4.5.4, 4.5.6, and 4.11.1.3, and
Appendix E)
Liver toxicity has also been associated with TCE exposure in both human and animal
studies. Although there are few human studies on liver toxicity and TCE exposure, several
available studies have reported TCE exposure to be associated with significant changes in serum
liver function tests, widely used in clinical settings in part to identify patients with liver disease,
or changes in plasma or serum bile acids. Additional, more limited human evidence for TCE
induced liver toxicity includes reports suggesting an association between TCE exposure and liver
disorders, and case reports of liver toxicity including hepatitis accompanying immune-related
generalized skin diseases, jaundice, hepatomegaly, hepatosplenomegaly, and liver failure in
TCE-exposed workers. Cohort studies examining cirrhosis mortality and either TCE exposure or
solvent exposure are generally null, but these studies cannot rule out an association with TCE
because of their use of death certificates where there is a high degree (up to 50%) of
underreporting. Overall, while some evidence exists of liver toxicity as assessed from liver
function tests, the data are inadequate for making conclusions regarding causality. See
Section 4.5.1 for additional discussion of human data on the noncancer liver effects of TCE. In
rats and mice, TCE exposure causes hepatomegaly without concurrent cytotoxicity. Like
humans, laboratory animals exposed to TCE have been observed to have increased serum bile
acids, although the toxicological importance of this effect is unclear. Other effects in the rodent
liver include small transient increases in DNA synthesis, cytomegaly in the form of—wollen" or
enlarged hepatocytes, increased nuclear size probably reflecting polyploidization, and
proliferation of peroxisomes. Available data also suggest that TCE does not induce substantial
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cytotoxicity, necrosis, or regenerative hyperplasia, since only isolated, focal necroses and mild to
moderate changes in serum and liver enzyme toxicity markers have been reported. These effects
are consistently observed across rodent species and strains, although the degree of response at a
given mg/kg/day dose appears to be highly variable across strains, with mice on average
appearing to be more sensitive. See Sections 4.5.3 and 4.5.4 for additional discussion of
laboratory animal data on the noncancer liver effects of TCE. While it is likely that oxidative
metabolism is necessary for TCE-induced effects in the liver, the specific metabolite or
metabolites responsible is less clear. However, the available data are strongly inconsistent with
TCA being the sole or predominant active moiety for TCE-induced liver effects, particularly
with respect to hepatomegaly. See Section 4.5.6 for additional discussion on the role of
metabolism in the noncancer liver effects of TCE. Overall, TCE, likely through its oxidative
metabolites, clearly leads to liver toxicity in laboratory animals, with mice appearing to be more
sensitive than other laboratory animal species, but there is only limited epidemiologic evidence
of hepatotoxicity being associated with TCE exposure.
6.1.3.4. Immunological Effects (see Sections 4.6.1.1, 4.6.2, and 4.11.1.4)
Effects related the immune system have also been associated with TCE exposure in both
human and animal studies. A relationship between systemic autoimmune diseases, such as
scleroderma, and occupational exposure to TCE has been reported in several recent studies, and a
meta-analysis of scleroderma studies resulted in a statistically significant combined OR for any
exposure in men (OR [OR]: 2.5, 95% CI: 1.1, 5.4), with a lower RR seen in women (OR: 1.2,
95% CI: 0.58, 2.6). The human data at this time do not allow a determination of whether the
difference in effect estimates between men and women reflects the relatively low background
risk of scleroderma in men, gender-related differences in exposure prevalence or in the reliability
of exposure assessment, a gender-related difference in susceptibility to the effects of TCE, or
chance. Additional human evidence for the immunological effects of TCE includes studies
reporting TCE-associated changes in levels of inflammatory cytokines in occupationally-exposed
workers and infants exposed via indoor air at air concentrations typical of such exposure
scenarios (see Section 6.1.1); a large number of case reports (mentioned above) of a severe
hypersensitivity skin disorder, distinct from contact dermatitis and often accompanied by
hepatitis; and a reported association between increased history of infections and exposure to TCE
contaminated drinking water. See Section 4.6.1.1 for additional discussion of human data on the
immunological effects of TCE. Immunotoxicity has also been reported in experimental rodent
studies of TCE. Numerous studies have demonstrated accelerated autoimmune responses in
autoimmune-prone mice, including changes in cytokine levels similar to those reported in human
studies, with more severe effects, including autoimmune hepatitis, inflammatory skin lesions,
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and alopecia, manifesting at longer exposure periods. Immunotoxic effects have been also
reported in B6C3Fi mice, which do not have a known particular susceptibility to autoimmune
disease. Developmental immunotoxicity in the form of hypersensitivity responses have been
reported in TCE-treated guinea pigs and mice via drinking water pre- and postnatally. Evidence
of localized immunosuppression has also been reported in mice and rats. See Section 4.6.2 for
additional discussion of laboratory animal data on the immunological effects of TCE. Overall,
the human and animal studies of TCE and immune-related effects provide strong evidence for a
role of TCE in autoimmune disease and in a specific type of generalized hypersensitivity
syndrome, while there are less data pertaining to immunosuppressive effects.
6.1.3.5. Respiratory Tract Effects (see Sections 4.7.1.1, 4.7.2.1, 4.7.3, and 4.11.1.5)
The very few human data on TCE and pulmonary toxicity are too limited for drawing
conclusions (see Section 4.7.1.1), but laboratory studies in mice and rats have shown toxicity in
the bronchial epithelium, primarily in Clara cells, following acute exposures to TCE (see
Section 4.7.2.1). A few studies of longer duration have reported more generalized toxicity, such
as pulmonary fibrosis in mice and pulmonary vasculitis in rats. However, respiratory tract
effects were not reported in other longer-term studies. Acute pulmonary toxicity appears to be
dependent on oxidative metabolism, although the particular active moiety is not known. While
earlier studies implicated chloral produced in situ by CYP enzymes in respiratory tract tissue in
toxicity, the evidence is inconsistent and several other possibilities are viable. Although humans
appear to have lower overall capacity for enzymatic oxidation in the lung relative to mice, CYP
enzymes do reside in human respiratory tract tissue, suggesting that, qualitatively, the respiratory
tract toxicity observed in rodents is biologically plausible in humans. See Section 4.7.3 for
additional discussion of the role of metabolism in the noncancer respiratory tract toxicity of
TCE. Therefore, overall, data are suggestive of TCE causing respiratory tract toxicity, based
primarily on short-term studies in mice and rats, with available human data too few and limited
to add to the weight of evidence for pulmonary toxicity.
6.1.3.6. Reproductive Effects (see Sections 4.8.1 and 4.11.1.6)
A number of human and laboratory animal studies suggest that TCE exposure has the
potential for male reproductive toxicity, with a more limited number of studies examining female
reproductive toxicity. Human studies have reported TCE exposure to be associated (in all but
one case statistically-significantly) with increased sperm density and decreased sperm quality,
altered sexual drive or function, or altered serum endocrine levels. Measures of male fertility,
however, were either not reported or were reported to be unchanged with TCE exposure, though
the statistical power of the available studies is quite limited. Epidemiologic studies have
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identified possible associations of TCE exposure with effects on female fertility and with
menstrual cycle disturbances, but these data are fewer than those available for male reproductive
toxicity. See Section 4.8.1.1 for additional discussion of human data on the reproductive effects
of TCE. Evidence of similar effects, particularly for male reproductive toxicity, is provided by
several laboratory animal studies that reported effects on sperm, libido/copulatory behavior, and
serum hormone levels, although some studies that assessed sperm measures did not report
treatment-related alterations. Additional adverse effects on male reproduction have also been
reported, including histopathological lesions in the testes or epididymides and altered in vitro
sperm-oocyte binding or in vivo fertilization due to TCE or metabolites. While reduced fertility
in rodents was only observed in one study, this is not surprising given the redundancy and
efficiency of rodent reproductive capabilities. In addition, although the reduced fertility
observed in the rodent study was originally attributed to systemic toxicity, the database as a
whole suggests that TCE does induce reproductive toxicity independent of systemic effects.
Fewer data are available in rodents on female reproductive toxicity. While in vitro oocyte
fertilizability has been reported to be reduced as a result of TCE exposure in rats, a number of
other laboratory animal studies did not report adverse effects on female reproductive function.
See Section 4.8.1.2 for additional discussion of laboratory animal data on the reproductive
effects of TCE. Very limited data are available to elucidate the mode of action for these effects,
though some aspects of a putative mode of action (e.g., perturbations in testosterone
biosynthesis) appear to have some commonalities between humans and animals (see
Section 4.8.1.3.2). Together, the human and laboratory animal data support the conclusion that
TCE exposure poses a potential hazard to the male reproductive system, but are more limited
with regard to the potential hazard to the female reproductive system.
6.1.3.7. Developmental Effects (see Sections 4.8.3 and 4.11.1.7)
The relationship between TCE exposure (direct or parental) and developmental toxicity
has been investigated in a number of epidemiologic and laboratory animal studies. Postnatal
developmental outcomes examined include developmental neurotoxicity (addressed above with
neurotoxicity), developmental immunotoxicity (addressed above with immunotoxicity), and
childhood cancers. Prenatal effects examined include death (spontaneous abortion, perinatal
death, pre- or postimplantation loss, resorptions), decreased growth (low birth weight, SGA,
IUGR, decreased postnatal growth), and congenital malformations, in particular cardiac defects.
Some epidemiological studies have reported associations between parental exposure to TCE and
spontaneous abortion or perinatal death, and decreased birth weight or SGA, although other
studies reported mixed or null findings. While comprising both occupational and environmental
exposures, these studies are overall not highly informative due to the small numbers of cases and
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limited exposure characterization or to the fact that exposures were to a mixture of solvents. See
Section 4.8.3.1 for additional discussion of human data on the developmental effects of TCE.
However, multiple well-conducted studies in rats and mice show analogous effects of TCE
exposure: pre- or postimplantation losses, increased resorptions, perinatal death, and decreased
birth weight. Interestingly, the rat studies reporting these effects used F344 or Wistar rats, while
several other studies, all of which used Sprague-Dawley rats, reported no increased risk in these
developmental measures, suggesting a strain difference in susceptibility. See Section 4.8.3.2 for
additional discussion of laboratory animal data on the developmental effects of TCE. Therefore,
overall, based on weakly suggestive epidemiologic data and fairly consistent laboratory animal
data, it can be concluded that TCE exposure poses a potential hazard for prenatal losses and
decreased growth or birth weight of offspring.
With respect to congenital malformations, epidemiology and experimental animal studies
of TCE have reported increases in total birth defects, CNS defects, oral cleft defects, eye/ear
defects, kidney/urinary tract disorders, musculoskeletal birth anomalies, lung/respiratory tract
disorders, skeletal defects, and cardiac defects. Human occupational cohort studies, while not
consistently reporting positive results, are generally limited by the small number of observed or
expected cases of birth defects. While only one of the epidemiological studies specifically
reported observations of eye anomalies, studies in rats have identified increases in the incidence
of fetal eye defects following oral exposures during the period of organogenesis with TCE or its
oxidative metabolites, DCA and TCA. The epidemiological studies, while individually limited,
as a whole show relatively consistent elevations, some of which were statistically significant, in
the incidence of cardiac defects in TCE-exposed populations compared to reference groups. In
laboratory animal models, avian studies were the first to identify adverse effects of TCE
exposure on cardiac development, and the initial findings have been confirmed multiple times.
Additionally, administration of TCE and its metabolites, TCA and DCA, in maternal drinking
water during gestation has been reported to induce cardiac malformations in rat fetuses. It is
notable that a number of other studies, several of which were well-conducted, did not report
induction of cardiac defects in rats, mice, or rabbits in which TCE was administered by
inhalation or gavage. However, many of these studies used a traditional free-hand section
technique on fixed fetal specimens, and a fresh dissection technique that can enhance detection
of anomalies was used in the positive studies by Dawson et al. (1993) and Johnson et al. (2005,
2003). Nonetheless, two studies that used the same or similar fresh dissection technique did not
report cardiac anomalies. Differences in other aspects of experimental design may have been
contributing factors to the differences in observed response. In addition, mechanistic studies,
such as the treatment-related alterations in endothelial cushion development observed in avian in
ovo and in vitro studies, provide a plausible mechanistic basis for defects in septal and valvular
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morphogenesis observed in rodents, and consequently support the plausibility of cardiac defects
induced by TCE in humans. Therefore, while the studies by Dawson et al. (1993) and Johnson et
al. (2003) 2005) have significant limitations, including the lack of clear dose-response
relationship for the incidence of any specific cardiac anomaly and the pooling of data collected
over an extended period, there is insufficient reason to dismiss their findings. See
Section 4.8.3.3.2 for additional discussion of the conclusions with respect to TCE-induced
cardiac malformations. Therefore, overall, based on weakly suggestive, but overall consistent,
epidemiologic data, in combination with evidence from experimental animal and mechanistic
studies, it can be concluded that TCE exposure poses a potential hazard for congenital
malformations, including cardiac defects, in offspring.
6.1.4. Carcinogenicity (see Sections 4.1, 4.2, 4.4.2, 4.4.5, 4.4.7, 4.5.2, 4.5.5, 4.5.6, 4.5.7,
4.6.1.2, 4.6.2.4, 4.7.1.2, 4.7.2.2, 4.7.4, 4.8.2, 4.9, and 4.11.2, and Appendices B and C)
Following EPA (2005b) Guidelines for Carcinogen Risk Assessment, based on the
available data as of 2010, TCE is characterized as —carcinogenic to huians" by all routes of
exposure. This conclusion is based on convincing evidence of a causal association between TCE
exposure in humans and kidney cancer. The consistency of increased kidney cancer RR
estimates across a large number of independent studies of different designs and populations from
different countries and industries provides compelling evidence given the difficulty, a priori, in
detecting effects in epidemiologic studies when the RRs are modest and the cancers are relatively
rare, and therefore, individual studies have limited statistical power. This strong consistency of
the epidemiologic data on TCE and kidney cancer argues against chance, bias, and confounding
as explanations for the elevated kidney cancer risks. In addition, statistically significant
exposure-response trends were observed in high-quality studies. These studies were conducted
in populations with high TCE exposure intensity or had the ability to identify TCE-exposed
subjects with high confidence. These studies addressed important potential confounders and
biases, further supporting the observed associations with kidney cancer as causal. See
Section 4.4.2 for additional discussion of the human epidemiologic data on TCE exposure and
kidney cancer. In a meta-analysis of 15 studies with high exposure potential, a statistically
significant RRm estimate was observed for overall TCE exposure (RRm: 1.27 [95% CI: 1.13,
1.43]). The RRm estimate was greater for the highest TCE exposure groups (RRm: 1.58 [95%
CI: 1.28, 1.96]; n = 13 studies). Meta-analyses investigating the influence of individual studies
and the sensitivity of the results to alternate RR estimate selections found the RRm estimates to
be highly robust. Furthermore, there was no indication of publication bias or significant
heterogeneity across the 15 studies. It would require a substantial amount of negative data from
informative studies (i.e., studies having a high likelihood of TCE exposure in individual study
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subjects and which meet, to a sufficient degree, the standards of epidemiologic design and
analysis in a systematic review) to contradict this observed association. See Section 4.4.2.5 and
Appendix C for additional discussion of the kidney cancer meta-analysis.
The human evidence of carcinogenicity from epidemiologic studies of TCE exposure is
strong for NHL but less convincing than for kidney cancer. Studies with high exposure potential
generally reported excess RR estimates, with statistically significant increases in three studies
with overall TCE exposure, and a statistically significant increase in the high TCE exposure
group and statistically significant trend in a fourth study (see Section 4.6.1.2). The consistency
of the association between TCE exposure and NHL is further supported by the results of meta-
analyses (see Section 4.6.1.2.2 and Appendix C). A statistically significant RRm estimate was
observed for overall TCE exposure (RRm: 1.23 [95% CI: 1.07, 1.42]; n = 17 studies), and, as
with kidney cancer, the RRm estimate was greater for the highest TCE exposure groups
(RRm: 1.43 [95% CI: 1.13, 1.82]; n = 13 studies) than for overall TCE exposure. Sensitivity
analyses indicated that these results and their statistical significance were not overly influenced
by any single study or choice of individual (study-specific) risk estimates, and in all of the
influence and sensitivity analyses, the RRm estimate was statistically significantly increased.
Some heterogeneity was observed, particularly between cohort and case-control studies, but it
was not statistically significant. In addition, there was some evidence of potential publication
bias. Thus, while the evidence is strong for NHL, issues of study heterogeneity, potential
publication bias, and weaker exposure-response results contribute greater uncertainty.
The evidence is more limited for liver and biliary tract cancer mainly because only cohort
studies are available and most of these studies have small numbers of cases due the comparative
rarity of liver and biliary tract cancer. While most studies with high exposure potential reported
excess RR estimates, they were generally based on small numbers of cases or deaths, with the
result of wide CIs on the estimates. The low number of liver cancer cases in the available studies
made assessing exposure-response relationships difficult. See Section 4.5.2 for additional
discussion of the human epidemiologic data on TCE exposure and liver cancer. Consistency of
the association between TCE exposure and liver cancer is supported by the results of meta-
analyses (see Section 4.5.2 and Appendix C). These meta-analyses found a statistically
significant increased RRm estimate for liver and biliary tract cancer of 1.29 (95% CI: 1.07, 1.56;
n = 9 studies) with overall TCE exposure; but the meta-analyses using only the highest exposure
groups yielded a lower, and nonstatistically significant, summary estimate for primary liver
cancer (1.28 [95% CI: 0.93, 1.77], n = 8 studies). Although there was no evidence of
heterogeneity or publication bias and the summary estimates were fairly insensitive to the use of
alternative RR estimates, the statistical significance of the summary estimates depends heavily
on the one large study by Raaschou-Nielsen et al. (2003). There were fewer adequate studies
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with high exposure potential available for meta-analysis of liver cancer (9 vs. 17 for NHL and
15 for kidney), leading to lower statistical power, even with pooling. Thus, while there is
epidemiologic evidence of an association between TCE exposure and liver cancer, the much
more limited database, both in terms of number of available studies and number of cases within
studies, contributes to greater uncertainty as compared to the evidence for kidney cancer or NHL.
In addition to the body of evidence pertaining to kidney cancer, NHL, and liver cancer,
the available epidemiologic studies also provide more limited evidence of an association between
TCE exposure and other types of cancer, including bladder, esophageal, prostate, cervical, breast,
and childhood leukemia. Differences between these sets of data and the data for kidney cancer,
NHL, and liver cancer are observations from fewer numbers of studies, a mixed pattern of
observed risk estimates, and the general absence of exposure-response data from the studies
using a quantitative TCE-specific exposure measure.
There are several other lines of supporting evidence for TCE carcinogenicity in humans
by all routes of exposure. First, multiple chronic bioassays in rats and mice have reported
increased incidences of tumors with TCE treatment via inhalation and gavage, including tumors
in the kidney, liver, and lymphoid tissues - target tissues of TCE carcinogenicity also seen in
epidemiological studies. Of particular note is the site-concordant finding of low, but biologically
and sometimes statistically significant, increases in the incidence of kidney tumors in multiple
strains of rats treated with TCE by either inhalation or corn oil gavage (see Section 4.4.5). The
increased incidences were only detected at the highest tested doses, and were greater in male
than female rats; although, notably, pooled incidences in females from five rat strains tested by
NTP (NTP, 1990, 1988) resulted in a statistically significant trend. Although these studies have
shown limited increases in kidney tumors, and several individual studies have a number of
limitations, given the rarity of these tumors as assessed by historical controls and the
repeatability of this result across studies and strains, these are considered biologically significant.
Therefore, while individual studies provide only suggestive evidence of renal carcinogenicity,
the database as a whole supports the conclusion that TCE is a kidney carcinogen in rats, with
males being more sensitive than females. No other tested laboratory species (i.e., mice and
hamsters) have exhibited increased kidney tumors, with no adequate explanation for these
species differences (particularly with mice, which have been extensively tested). With respect to
the liver, TCE and its oxidative metabolites CH, TCA, and DCA are clearly carcinogenic in
mice, with strain and sex differences in potency that appear to parallel, qualitatively, differences
in background tumor incidence. Data in other laboratory animal species are limited; thus, except
for DCA which is carcinogenic in rats, inadequate evidence exists to evaluate the
hepatocarcinogenicity of these compounds in rats or hamsters. However, to the extent that there
is hepatocarcinogenic potential in rats, TCE is clearly less potent in the strains tested in this
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species than in B6C3Fi and Swiss mice. See Section 4.5.5 for additional discussion of
laboratory animal data on TCE-induced liver tumors. Additionally, there is more limited
evidence for TCE-induced lymphohematopoetic cancers in rats and mice, lung tumors in mice,
and testicular tumors in rats. With respect to the lymphohematopoietic cancers, two studies in
mice reported increased incidences of lymphomas in females of two different strains, and two
studies in rats reported leukemias in males of one strain and females of another. However, these
tumors had relatively modest increases in incidence with treatment, and were not reported to be
increased in other studies. See Section 4.6.2.4 for additional discussion of laboratory animal data
on TCE-induced lymphohematopoetic tumors. With respect to lung tumors, rodent bioassays
have demonstrated a statistically significant increase in pulmonary tumors in mice following
chronic inhalation exposure to TCE, and nonstatistically significant increases in mice exposed
orally; but pulmonary tumors were not reported in other species tested (i.e., rats and hamsters)
(see Section 4.7.2.2). Finally, increased testicular (interstitial or Leydig cell) tumors have been
observed in multiple studies of rats exposed by inhalation and gavage, although in some cases,
high (> 75%) control rates of testicular tumors in rats limited the ability to detect a treatment
effect. See Section 4.8.2.2 for additional discussion of laboratory animal data on TCE-induced
tumors of the reproductive system. Overall, TCE is clearly carcinogenic in rats and mice. The
apparent lack of site concordance across laboratory animal studies may be due to limitations in
design or conduct in a number of rat bioassays and/or genuine interspecies differences in
qualitative or quantitative sensitivity (i.e., potency). Nonetheless, these studies have shown
carcinogenic effects across different strains, sexes, and routes of exposure, and site-concordance
is not necessarily expected for carcinogens. Of greater import is the finding that there is site-
concordance between the main cancers observed in TCE-exposed humans and those observed in
rodent studies—in particular, cancers of the kidney, liver, and lymphoid tissues.
A second line of supporting evidence for TCE carcinogenicity in humans consists of
toxicokinetic data indicating that TCE is well absorbed by all routes of exposure, and that TCE
absorption, distribution, metabolism, and excretion are qualitatively similar in humans and
rodents. As summarized above, there is evidence that TCE is systemically available, distributes
to organs and tissues, and undergoes systemic metabolism from all routes of exposure.
Therefore, although the strongest evidence from epidemiologic studies largely involves
inhalation exposures, the evidence supports TCE carcinogenicity being applicable to all routes of
exposure. In addition, there is no evidence of major qualitative differences across species in
TCE absorption, distribution, metabolism, and excretion. Extensive in vivo and in vitro data
show that mice, rats, and humans all metabolize TCE via two primary pathways: oxidation by
CYPs and conjugation with GSH via GSTs. Several metabolites and excretion products from
both pathways have been detected in blood and urine from exposed humans as well as from at
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least one rodent species. In addition, the subsequent distribution, metabolism, and excretion of
TCE metabolites are qualitatively similar among species. Therefore, humans possess the
metabolic pathways that produce the TCE metabolites thought to be involved in the induction of
rat kidney and mouse liver tumors, and internal target tissues of both humans and rodents
experience a similar mix of TCE and metabolites. See Sections 3.1-3.4 for additional discussion
of TCE toxicokinetics. Quantitative interspecies differences in toxicokinetics do exist, and are
addressed through PBPK modeling (see Section 3.5 and Appendix A). Importantly, these
quantitative differences affect only interspecies extrapolations of carcinogenic potency, and do
not affect inferences as to the carcinogenic hazard for TCE.
Finally, available mechanistic data do not suggest a lack of human carcinogenic hazard
from TCE exposure. In particular, these data do not suggest qualitative differences between
humans and test animals that would preclude any of the hypothesized key events in the
carcinogenic mode of action in rodents from occurring in humans. For the kidney, the
predominance of positive genotoxicity data in the database of available studies of TCE
metabolites derived from GSH conjugation (in particular DCVC), together with toxicokinetic
data consistent with their systemic delivery to and in situ formation in the kidney, supports the
conclusion that a mutagenic mode of action is operative in TCE-induced kidney tumors. While
supporting the biological plausibility of this hypothesized mode of action, available data on the
VHL gene in humans or transgenic animals do not conclusively elucidate the role of VHL
mutation in TCE-induced renal carcinogenesis. Cytotoxicity and compensatory cell
proliferation, similarly presumed to be mediated through metabolites formed after GSH-
conjugation of TCE, have also been suggested to play a role in the mode of action for renal
carcinogenesis, as high incidences of nephrotoxicity have been observed in animals at doses that
induce kidney tumors. Human studies have reported markers for nephrotoxicity at current
occupational exposures, although data are lacking at lower exposures. Nephrotoxicity is
observed in both mice and rats, in some cases with nearly 100% incidence in all dose groups, but
kidney tumors are only observed at low incidences in rats at the highest tested doses. Therefore,
nephrotoxicity alone appears to be insufficient, or at least not rate-limiting, for rodent renal
carcinogenesis, since maximal levels of toxicity are reached before the onset of tumors. In
addition, nephrotoxicity has not been shown to be necessary for kidney tumor induction by TCE
in rodents. In particular, there is a lack of experimental support for causal links, such as
compensatory cellular proliferation or clonal expansion of initiated cells, between nephrotoxicity
and kidney tumors induced by TCE. Furthermore, it is not clear if nephrotoxicity is one of
several key events in a mode of action, if it is a marker for an —upsfeam" key event (such as
oxidative stress) that may contribute independently to both nephrotoxicity and renal
carcinogenesis, or if it is incidental to kidney tumor induction. Therefore, although the data are
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consistent with the hypothesis that cytotoxicity and regenerative proliferation contribute to TCE-
induced kidney tumors, the weight of evidence is not as strong as the support for a mutagenic
mode of action. Moreover, while toxicokinetic differences in the GSH conjugation pathway
along with their uncertainty are addressed through PBPK modeling, no data suggest that any of
the proposed key events for TCE-induced kidney tumors in rats are precluded in humans. See
Section 4.4.7 for additional discussion of the mode of action for TCE-induced kidney tumors.
Therefore, TCE-induced rat kidney tumors provide additional support for the convincing human
evidence of TCE-induced kidney cancer, with mechanistic data supportive of a mutagenic mode
of action.
With respect to other tumor sites, data are insufficient to conclude that any of the other
hypothesized modes of action are operant. In the liver, a mutagenic mode of action mediated by
CH, which has evidence for genotoxic effects, or some other oxidative metabolite of TCE cannot
be ruled out, but data are insufficient to conclude it is operant. A second mode-of-action
hypothesis for TCE-induced liver tumors involves activation of the PPARa receptor. Clearly, in
vivo administration of TCE leads to activation of PPARa in rodents and likely does so in humans
as well. However, the evidence as a whole does not support the view that PPARa is the sole
operant mode of action mediating TCE hepatocarcinogenesis. Rather, there is evidential support
for multiple TCE metabolites and multiple toxicity pathways contributing to TCE-induced liver
tumors. Furthermore, recent experiments have demonstrated that PPARa activation and the
sequence of key events in the hypothesized mode of action are not sufficient to induce
hepatocarcinogenesis (Yang et al., 2007). Moreover, the demonstration that the PPARa agonist
di(2-ethylhexyl) phthalate induces tumors in PPARa-null mice supports the view that the events
comprising the hypothesized PPARa activation mode of action are not necessary for liver tumor
induction in mice by this PPARa agonist (Ito et al., 2007). See Section 4.5.7 for additional
discussion of the mode of action for TCE-induced liver tumors. For mouse lung tumors, as with
the liver, a mutagenic mode of action involving CH has also been hypothesized, but there are
insufficient data to conclude that it is operant. A second mode-of-action hypothesis for mouse
lung tumors has been posited involving other effects of oxidative metabolites including
cytotoxicity and regenerative cell proliferation, but experimental support remains limited, with
no data on proposed key events in experiments of duration two weeks or longer. See
Section 4.7.4 for additional discussion of the mode of action for TCE-induced lung tumors. A
mode of action subsequent to in situ oxidative metabolism, whether involving mutagenicity,
cytotoxicity, or other key events, may also be relevant to other tissues where TCE would
undergo CYP metabolism. For instance, CYP2E1, oxidative metabolites, and protein adducts
have been reported in the testes of rats exposed to TCE, and, in some rat bioassays, TCE
exposure increased the incidence of rat testicular tumors. However, inadequate data exist to
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adequately define a mode-of-action hypothesis for this tumor site (see Section 4.8.2.3 for
additional discussion of the mode of action for TCE-induced testicular tumors).
6.1.5. Susceptibility (see Sections 4.10 and 4.11.3)
There is some evidence that certain populations may be more susceptible to exposure to
TCE. Factors affecting susceptibility examined include lifestage, gender, genetic
polymorphisms, race/ethnicity, preexisting health status, and lifestyle factors and nutrition status.
Factors that affect early lifestage susceptibility include exposures such as transplacental transfer
and breast milk ingestion, early lifestage-specific toxicokinetics, and differential outcomes in
early lifestages such as developmental cardiac defects (see Section 4.10.1). Because the weight
of evidence supports a mutagenic mode of action being operative for TCE carcinogenicity in the
kidney (see Section 4.4.7), and there is an absence of chemical-specific data to evaluate
differences in carcinogenic susceptibility, early-life susceptibility should be assumed and the
ADAFs should be applied, in accordance with the Supplemental Guidance (see summary below
in Section 6.2.2.5). Fewer data are available on later lifestages, although there is suggestive
evidence to indicate that older adults may experience increased adverse effects than younger
adults due to greater tissue distribution of TCE. In general, more studies specifically designed to
evaluate effects in early and later lifestages are needed in order to more fully characterize
potential lifestage-related TCE toxicity. Gender-specific (see Section 4.10.2.1) differences also
exist in toxicokinetics (e.g., cardiac outputs, percent body fat, expression of metabolizing
enzymes) and susceptibility to toxic endpoints (e.g., gender-specific effects on the reproductive
system, gender differences in baseline risks to endpoints such as scleroderma or liver cancer).
Genetic variation (see Section 4.10.2.2) likely has an effect on the toxicokinetics of TCE.
Increased CYP2E1 activity and GST polymorphisms may influence susceptibility of TCE due to
effects on production of toxic metabolites or may play a role in variability in toxic response.
Differences in genetic polymorphisms related to the metabolism of TCE have also been observed
among various race/ethnic groups (see Section 4.10.2.3). Preexisting diminished health status
(see Section 4.10.2.4) may alter the response to TCE exposure. Individuals with increased body
mass may have an altered toxicokinetic response due to the increased uptake of TCE into fat.
Other conditions that may alter the response to TCE exposure include diabetes and hypertension,
and lifestyle and nutrition factors (see Section 4.10.2.5) such as alcohol consumption, tobacco
smoking, nutritional status, physical activity, and SES status. Alcohol intake has been associated
with inhibition of TCE metabolism in both humans and experimental animals. In addition, such
conditions have been associated with increased baseline risks for health effects also associated
with TCE, such as kidney cancer and liver cancer. However, the interaction between TCE and
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known risk factors for human diseases is not known, and further evaluation of the effects due to
these factors is needed.
In sum, there is some evidence that certain populations may be more susceptible to
exposure to TCE. Factors affecting susceptibility examined include lifestage, gender, genetic
polymorphisms, race/ethnicity, preexisting health status, and lifestyle factors and nutrition status.
However, except in the case of toxicokinetic variability characterized using the PBPK model
described in Section 3.5, there are inadequate chemical-specific data to quantify the degree of
differential susceptibility due to such factors.
6.2. DOSE-RESPONSE ASSESSMENT
This section summarizes the major conclusions of the dose-response analysis for TCE
noncancer effects and carcinogenicity, with more detailed discussions in Chapter 5.
6.2.1. Noncancer Effects (see Section 5.1)
6.2.1.1. Background and Methods
As summarized above, based on the available human epidemiologic data and
experimental and mechanistic studies, it is concluded that TCE poses a potential human health
hazard for noncancer toxicity to the CNS, kidney, liver, immune system, male reproductive
system, and developing fetus. The evidence is more limited for TCE toxicity to the respiratory
tract and female reproductive system.
Dose-response analysis for a noncancer endpoint generally involves two steps: (1) the
determination of a POD derived from a BMD,61 a NOAEL, or a LOAEL, and (2) adjustment of
the POD by endpoint/study-specific —uncstainty factors" (UFs), accounting for adjustments and
uncertainties in the extrapolation from the study conditions to conditions of human exposure.
Because of the large number of noncancer health effects associated with TCE exposure
and the large number of studies reporting on these effects, in contrast to toxicological reviews for
chemicals with smaller databases of studies, a formal, quantitative screening process (see
Section 5.1) was used to reduce the number of endpoints and studies to those that would best
inform the selection of the critical effects for the inhalation RfC and oral RfD.62 As described in
Section 5.1, for all studies described in Chapter 4 which reported adverse noncancer health
effects and provided quantitative dose-response data, PODs on the basis of applied dose,
61More precisely, it is the benchmark dose lower bound (BMDL), i.e., the (one-sided) 95% lower confidence bound
on the dose corresponding to the benchmark response (BMR) for the effect, that is used as the POD.
62In EPA noncancer health assessments, the RfC [RfD] is an estimate (with uncertainty spanning perhaps an order of
magnitude) of a continuous inhalation [daily oral] exposure to the human population (including sensitive subgroups)
that is likely to be without an appreciable risk of deleterious effects during a lifetime. It can be derived from a
NOAEL, LOAEL, or benchmark concentration [dose], with uncertainty factors generally applied to reflect
limitations of the data used.
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adjusted by endpoint/study-specific UFs, were used to develop candidate RfCs (cRfCs) and
candidate RfDs (cRfDs) intended to be protective for each endpoint individually. Candidate
critical effects - those with the lowest cRfCs and cRfDs taking into account the confidence in
each estimate - were selected within each of the following health effect domains: (1)
neurological, (2) kidney; (3) liver; (4) immunological; (5) reproductive; and (6) developmental.
For each of these candidate critical effects, the PBPK model developed in Section 3.5 was used
for interspecies, intraspecies, and route-to-route extrapolation on the basis of internal dose to
develop PBPK model-based PODs. Plausible internal dose-metrics were selected based on what
is understood about the role of different TCE metabolites in toxicity and the mode of action for
toxicity. These PODs were then adjusted by endpoint/study-specific UFs, taking into account
the use of the PBPK model, to develop PBPK model-based candidate RfCs (p-cRfCs) and
candidate RfDs (p-cRfDs). The most sensitive cRfCs, p-cRfCs, cRfDs, and p-cRfDs were then
evaluated, taking into account the confidence in each estimate, to arrive at overall candidate
RfCs and RfDs for each health effect type. Then, the RfC and RfD for TCE were selected so as
to be protective of the most sensitive effects. In contrast to the approach used in most previous
assessments, in which the RfC and RfD are each based on a single critical effect, the final RfC
and RfD for TCE were based on multiple critical effects that resulted in very similar candidate
RfC and RfD values at the low end of the full range of values. This approach was taken here
because it provides robust estimates of the RfC and RfD and because it highlights the multiple
effects that are all yielding very similar candidate values.
6.2.1.2. Uncertainties and Application of UFs (see Sections 5.1.1 and 5.1.4)
An underlying assumption in deriving a reference value for a noncancer effect is that the
dose-response relationship has a threshold. Thus, a fundamental uncertainty is the validity of
that assumption. For some effects, in particular effects on very sensitive processes (e.g.,
developmental processes) or effects for which there is a nontrivial background level and even
small exposures may contribute to background disease processes in more susceptible people, a
practical threshold (i.e., a threshold within the range of environmental exposure levels of
regulatory concern) may not exist.
Nonetheless, under the assumption of a threshold, the desired exposure level to have as a
reference value is the maximum level at which there is no appreciable risk for an adverse effect
in sensitive subgroups (of humans). However, because it is not possible to know what this level
is, UFs are used to attempt to address quantitatively various aspects, depending on the data set,
of qualitative uncertainty.
First there is uncertainty about the POD for the application of UFs. Conceptually, the
POD should represent the maximum exposure level at which there is no appreciable risk for an
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adverse effect in the study population under study conditions (i.e., the threshold in the dose-
response relationship). Then, the application of the relevant UFs is intended to convey that
exposure level to the corresponding exposure level for sensitive human subgroups exposed
continuously for a lifetime. In fact, it is again not possible to know that exposure level even for a
laboratory study because of experimental limitations (e.g., the power to detect an effect, dose
spacing, measurement errors, etc.), and crude approximations like the NOAEL or a BMDL are
used. If a LOAEL is used as the POD, then the LOAEL-to-NOAEL UF is applied as an
adjustment factor to better approximate the desired exposure level (threshold), although the
necessary extent of adjustment is unknown. The standard value for the LOAEL-to-NOAEL UF
is 10, although sometimes a value of 3 is used if the effect is considered minimally adverse at the
response level observed at the LOAEL or is an early marker for an adverse effect. For one POD
in this assessment, a value of 30 was used for the LOAEL-to-NOAEL UF because the incidence
rate for the adverse effect was >90% at the LOAEL.
If a BMDL is used as the POD, then there are uncertainties regarding the appropriate
dose-response model to apply to the data, but these should be minimal if the modeling is in the
observable range of the data. There are also uncertainties about what BMR to use to best
approximate the desired exposure level (threshold, see above). For continuous endpoints, in
particular, it is often difficult to identify the level of change that constitutes the —wt-point" for an
adverse effect. Sometimes, to better approximate the desired exposure level, a BMR somewhat
below the observable range of the data is selected. In such cases, the model uncertainty is
increased, but this is a trade-off to reduce the uncertainty about the POD not being a good
approximation for the desired exposure level.
For each of these types of PODs, there are additional uncertainties pertaining to
adjustments to the administered exposures (doses). Typically, administered exposures (doses)
are converted to equivalent continuous exposures (daily doses) over the study exposure period
under the assumption that the effects are related to concentration x time, independent of the daily
(or weekly) exposure regimen (i.e., a daily exposure of 6 hours to 4 ppm is considered equivalent
to 24 hours of exposure to 1 ppm). However, the validity of this assumption is generally
unknown, and, if there are dose-rate effects, the assumption of concentration times time (C x f)
equivalence would tend to bias the POD downwards. Where there is evidence that administered
exposure better correlates to the effect than equivalent continuous exposure averaged over the
study exposure period (e.g., visual effects), administered exposure was not adjusted. For the
PBPK analyses in this assessment, the actual administered exposures are taken into account in
the PBPK modeling, and equivalent daily values (averaged over the study exposure period) for
the dose-metrics are obtained (see above, Section 5.1.3.2). Additional uncertainties about the
PBPK-based estimates include uncertainties about the appropriate dose-metric for each effect,
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although, for some effects, there was better information about relevant dose-metrics than for
others, and uncertainties in the PBPK model predictions for the dose-metrics in humans,
particularly for GSH conjugation (see Section 5.1.3.1).
There is also uncertainty about the other UFs. The human variability UF is, to some
extent, an adjustment factor because, for more sensitive people, the dose-response relationship
shifts to lower exposures. But there is uncertainty about the extent of the adjustment required
(i.e., about the distribution of human susceptibility). Therefore, in the absence of data on a
susceptible population(s) or on the distribution of susceptibility in the general population, an UF
of 10 is generally used, which breaks down (approximately) to a factor of 3 for pharmacokinetic
variability and a factor of 3 for pharmacodynamic variability. This standard value was used for
all of the PODs based on applied dose in this assessment with the exception of the PODs for a
few immunological effects that were based on data from a sensitive (autoimmune-prone) mouse
strain. For those PODs, an UF of 3 (reflecting pharmacokinetics only) was used for human
variability. The PBPK analyses in this assessment attempt to account for the pharmacokinetic
portion of human variability using human data on pharmacokinetic variability. For PBPK
model-based candidate reference values, the pharmacokinetic component of this UF was omitted.
A quantitative uncertainty analysis of the PBPK derived dose-metrics used in the assessment is
presented in Section 5.1.4.2. There is still uncertainty regarding the susceptible subgroups for
TCE exposure and the extent of pharmacodynamic variability.
If the data used to determine a particular POD are from laboratory animals, an
interspecies extrapolation UF is used. This UF is also, to some extent, an adjustment factor for
the expected scaling for lexicologically equivalent doses across species (i.e., according to body
weight to the 3/4 power for oral exposures). However, there is also uncertainty about the true
extent of interspecies differences for specific noncancer effects from specific chemical
exposures. For oral exposures, the standard value for the interspecies UF is 10, which can be
viewed as breaking down (approximately) to a factor of 3 for the —djustment" (nominally
pharmacokinetics) and a factor of 3 for the —uoertainty" (nominally pharmacodynamics). For
inhalation exposures for systemic toxicants, such as TCE, for which the blood:air partition
coefficient in laboratory animals is greater than that in humans, no adjustment across species is
generally assumed for fixed air concentrations (ppm equivalence; U.S. EPA, 1994a), and the
standard value for the interspecies UF is 3, reflecting only —ncertainty" (nominally
pharmacodynamics). The PBPK analyses in this assessment attempt to account for the
—adjstment" portion of interspecies extrapolation using rodent pharmacokinetic data to estimate
internal doses for various dose-metrics. Equal doses of these dose-metrics, appropriately scaled,
are then assumed to convey equivalent risk across species. For PBPK model-based candidate
reference values, the —ajiistment" component of this UF was omitted. With respect to the
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—unoetainty" component, quantitative uncertainty analyses of the PBPK-derived dose-metrics
used in the assessment are presented in Section 5.1.4.2. However, these only address the
pharmacokinetic uncertainties in a particular dose-metric, and there is still uncertainty regarding
the true dose-metrics. Nor do the PBPK analyses address the uncertainty in either cross-species
pharmacodynamic differences (i.e., about the assumption that equal doses of the appropriate
dose-metric convey equivalent risk across species for a particular endpoint from a specific
chemical exposure) or in cross-species pharmacokinetic differences not accounted for by the
PBPK model dose-metrics (e.g., departures from the assumed interspecies scaling of clearance of
the active moiety, in the cases where only its production is estimated). A value of 3 is typically
used for the —uncstainty" about cross-species differences, and this generally represents true
uncertainty because it is usually unknown, even after adjustments have been made to account for
the expected interspecies differences, whether humans have more or less susceptibility, and to
what degree, than the laboratory species in question.
RfCs and RfDs apply to lifetime exposure, but sometimes the best (or only) available
data come from less-than-lifetime studies. Lifetime exposure can induce effects that may not be
apparent or as large in magnitude in a shorter study; consequently, a dose that elicits a specific
level of response from a lifetime exposure may be less than the dose eliciting the same level of
response from a shorter exposure period. If the effect becomes more severe with increasing
exposure, then chronic exposure would shift the dose-response relationship to lower exposures,
although the true extent of the shift is unknown. PODs based on subchronic exposure data are
generally divided by a subchronic-to-chronic UF, which has a standard value of 10. If there is
evidence suggesting that exposure for longer time periods does not increase the magnitude of an
effect, a lower value of 3 or 1 might be used. For some reproductive and developmental effects,
chronic exposure is that which covers a specific window of exposure that is relevant for eliciting
the effect, and subchronic exposure would correspond to an exposure that is notably less than the
full window of exposure.
Sometimes a database UF is also applied to address limitations or uncertainties in the
database. The overall database for TCE is quite extensive, with studies for many different types
of effects, including two-generation reproductive studies, as well as neurological and
immunological studies. In addition, there were sufficient data to develop a reliable PBPK model
to estimate route-to-route extrapolated doses for some candidate critical effects for which data
were only available for one route of exposure. Thus, there is a high degree of confidence that the
TCE database was sufficient to identify sensitive endpoints, and no database UF was used in this
assessment.
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6.2.1.2.1. Candidate Critical Effects and Reference Values (see Sections 5.1.2 and 5.1.3)
A large number of endpoints and studies were considered within each health effect
domain. Chapter 5 contains a comprehensive discussion of all endpoints/studies that were
considered for developing candidate reference values (cRfCs, cRfDs, p-cRfCs, and p-cRfDs),
their PODs, and the UFs applied. The summary below reviews the selection of candidate critical
effects for each health effect domain, the confidence in the reference values, the selection of
PBPK model-based dose-metrics, and the impact of PBPK modeling on the candidate reference
values.
6.2.1.2.2. Neurological effects
Candidate reference values were developed for several neurological domains for which
there was evidence of hazard (see Tables 5-2 and 5-13). There is higher confidence in the
candidate reference values for trigeminal nerve, auditory, or psychomotor effects, but the
available data suggest that the more sensitive indicators of TCE neurotoxicity are changes in
wakefulness, regeneration of the sciatic nerve, demyelination in the hippocampus, and
degeneration of dopaminergic neurons. Therefore, these more sensitive effects are considered
the candidate critical effects for neurotoxicity, albeit with more uncertainty in the corresponding
candidate reference values. Of these more sensitive effects, there is greater confidence in the
changes in wakefulness reported by Arito et al. (1994). In addition, trigeminal nerve effects are
considered a candidate critical effect because this is the only type of neurological effect for
which human data are available, and the POD for this effect is similar to that from the most
sensitive rodent study (Arito et al., 1994, for changes in wakefulness). Between the two human
studies of trigeminal nerve effects, Ruijten et al. (1991) is preferred for deriving noncancer
reference values because its exposure characterization is considered more reliable.
Because of the lack of specific data as to the metabolites involved and the mode of action
for the candidate critical neurologic effects, PBPK model predictions of total metabolism (scaled
by body weight to the % power) were selected as the preferred dose-metric based on the general
observation that TCE toxicity is associated with metabolism. The AUC of TCE in blood was
used as an alternative dose-metric. With these dose-metrics, the candidate reference values
derived using the PBPK model were only modestly (-threefold or less) different than those
derived on the basis of applied dose.
6.2.1.2.3. Kidney effects
Candidate reference values were developed for histopathological and weight changes in
the kidney (see Tables 5-4 and 5-15), and these are considered to be candidate critical effects for
several reasons. First, they appear to be the most sensitive indicators of toxicity that are
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available for the kidney. In addition, as discussed in Sections 3.3 and 3.5, both in vitro and in
vivo pharmacokinetic data indicate substantially more production of GSH-conjugates thought to
mediate TCE kidney effects in humans relative to rats and mice. Several studies are considered
reliable for developing candidate reference values for these endpoints. For histopathological
changes, these were the only available inhalation study (the rat study of Maltoni et al., 1986), the
NTP (1988) study in rats, and the NCI (NCI, 1976) study in mice. For kidney weight changes,
both available studies (Woolhiser et al., 2006; Kj ell strand et al., 1983a) were chosen as candidate
critical studies.
Due to the substantial evidence supporting the role of GSH conjugation metabolites in
TCE-induced nephrotoxicity, the preferred PBPK model dose-metrics for kidney effects were the
amount of DCVC bioactivated in the kidney for rat studies and the amount of GSH conjugation
(both scaled by body weight to the 3/4 power) for mouse studies (inadequate toxicokinetic data are
available in mice for predicting the amount of DCVC bioactivation). With these dose-metrics,
the candidate reference values derived using the PBPK model were 300-400-fold lower than
those derived on the basis of applied dose. As discussed above and in Chapter 3, this is due to
the available in vivo and in vitro data supporting not only substantially more GSH conjugation in
humans than in rodents, but also substantial interindividual toxicokinetic variability. Overall,
there is high confidence in the nephrotoxic hazard from TCE exposure and in the appropriateness
of the dose-metrics discussed above; however, there is substantial uncertainty in the
extrapolation of GSH conjugation from rodents to humans due to limitations in the available data
(see Section 3.3.3.2).
6.2.1.2.4. Liver effects
Hepatomegaly appears to be the most sensitive indicator of toxicity that is available for
the liver and is therefore considered a candidate critical effect. Several studies are considered
reliable for developing high-confidence candidate reference values for this endpoint. Since they
all indicated similar sensitivity but represented different species and/or routes of exposure, they
were all considered candidate critical studies (see Tables 5-4 and 5-14).
Due to the substantial evidence supporting the role of oxidative metabolism in TCE-
induced hepatomegaly (and evidence against TC A being the sole mediator of TCE-induced
hepatomegaly (Evans et al., 2009)), the preferred PBPK model dose-metric for liver effects was
the amount of hepatic oxidative metabolism (scaled by body weight to the % power). Total
(hepatic and extrahepatic) oxidative metabolism (scaled by body weight to the 3/4 power) was
used as an alternative dose-metric. With these dose-metrics, the candidate reference values
derived using the PBPK model were only modestly (-threefold or less) different than those
derived on the basis of applied dose.
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6.2.1.2.5. Immunological effects
There is high qualitative confidence for TCE immunotoxicity and moderate confidence in
the candidate reference values that can be derived from the available studies (see Tables 5-6
and 5-16). Decreased thymus weight reported at relatively low exposures in nonautoimmune-
prone mice is a clear indicator of immunotoxicity (Keil et al., 2009), and is therefore considered
a candidate critical effect. A number of studies have also reported changes in markers of
immunotoxicity at relatively low exposures. Among markers for autoimmune effects, the more
sensitive measures of autoimmune changes in liver and spleen (Kaneko et al., 2000) and
increased anti-dsDNA and anti-ssDNA antibodies (early markers for autoimmune disease) (Keil
et al., 2009) are considered the candidate critical effects. For markers of immunosuppression,
the more sensitive measures of decreased PFC response (Woolhiser et al., 2006), decreased stem
cell bone marrow recolonization, and decreased cell-mediated response to sRBC (both from
Sanders etal., 1982b) are considered the candidate critical effects. Developmental
immunological effects are discussed below as part of the summary of developmental effects.
Because of the lack of specific data as to the metabolites involved and the mode of action
for the candidate critical immunologic effects, PBPK model predictions of total metabolism
(scaled by body weight to the % power) was selected as the preferred dose-metric based on the
general observation that TCE toxicity is associated with metabolism. The AUC of TCE in blood
was used as an alternative dose-metric. With these dose-metrics, the candidate reference values
derived using the PBPK model were, with one exception, only modestly (-threefold or less)
different than those derived on the basis of applied dose. For the Woolhiser et al. (2006)
decreased PFC response, with the alternative dose-metric of AUC of TCE in blood, BMD
modeling based on internal doses changed the candidate reference value by 17-fold higher than
the cRfC based on applied dose. However, the dose-response model fit for this effect using this
metric was substantially worse than the fit using the preferred metric of total oxidative
metabolism, with which the change in candidate reference value was only 1.3-fold.
6.2.1.2.6. Reproductive effects
While there is high qualitative confidence in the male reproductive hazard posed by TCE,
there is lower confidence in the reference values that can be derived from the available studies of
these effects (see Tables 5-8 and 5-17). Relatively high PODs are derived from several studies
reporting less sensitive endpoints (George etal., 1986; George etal., 1985; Land etal., 1981),
and correspondingly higher cRfCs and cRfDs suggest that they are not likely to be critical
effects. The studies reporting more sensitive endpoints also tend to have greater uncertainty.
For the human study by Chia et al. (1996), there are uncertainties in the characterization of
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exposure and the adversity of the effect measured in the study. For the Kumar et al. (200 lb:
2000a: 2000b\ Forkert et al. (2002). and Kan et al. (2007) studies, the severity of the sperm and
testes effects appears to be continuing to increase with duration even at the end of the study, so it
is plausible that a lower exposure for a longer duration may elicit similar effects. For the
DuTeaux et al. (2004a) study, there is also duration- and low-dose extrapolation uncertainty due
to the short duration of the study in comparison to the time period for sperm development as well
as the lack of a NOAEL at the tested doses. Overall, even though there are limitations in the
quantitative assessment, there remains sufficient evidence to consider these to be candidate
critical effects.
There is moderate confidence both in the hazard and the candidate reference values for
reproductive effects other than male reproductive effects. While there are multiple studies
suggesting decreased maternal body weight with TCE exposure, this systemic change may not be
indicative of more sensitive reproductive effects. None of the estimates developed from other
reproductive effects is particularly uncertain or unreliable. Therefore, delayed parturition
(Narotsky et al., 1995) and decreased mating (George et al., 1986), which yielded the lowest
cRfDs, were considered candidate critical effects. These effects were also included so that
candidate critical reproductive effects from oral studies would not include only that reported by
DuTeaux et al. (2004a), from which deriving the cRfD entailed a higher degree of uncertainty.
Because of the general lack of specific data as to the metabolites involved and the mode
of action for the candidate critical developmental effects, PBPK model predictions of total
metabolism (scaled by body weight to the % power) was selected as the preferred dose-metric
based on the general observation that TCE toxicity is associated with metabolism. The AUC of
TCE in blood was used as an alternative dose-metric. The only exception to this was for the
DuTeaux et al. (2004a) study, which suggested that local oxidative metabolism of TCE in the
male reproductive tract was involved in the effects reported. Therefore, in this case, AUC of
TCE in blood was considered the preferred dose-metric, while total oxidative metabolism (scaled
by body weight to the % power) was considered the alternative metric. With these dose-metrics,
the candidate reference values derived using the PBPK model were only modestly (~3.5-fold or
less) different than those derived on the basis of applied dose.
6.2.1.2.7. Developmental effects
There is moderate-to-high confidence both in the hazard and the candidate reference
values for developmental effects of TCE (see Tables 5-10 and 5-18). It is also noteworthy that
the PODs for the more sensitive developmental effects were similar to or, in most cases, lower
than the PODs for the more sensitive reproductive effects, suggesting that developmental effects
are not a result of paternal or maternal toxicity. Among inhalation studies, candidate reference
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values were only developed for effects in rats reported in Healy et al. (1982), of resorptions,
decreased fetal weight, and delayed skeletal ossification. These were all considered candidate
critical developmental effects. Because resorptions were also reported in oral studies, the most
sensitive (rat) oral study for this effect (and most reliable for dose-response analysis) of Narotsky
et al. (1995) was also selected as a candidate critical study. The confidence in the oral studies
and candidate reference values developed for more sensitive endpoints is more moderate, but still
sufficient for consideration as candidate critical effects. The most sensitive endpoints by far are
the increased fetal heart malformations in rats reported by Johnson et al. (2003) and the
developmental immunotoxicity in mice reported by Peden-Adams et al. (2006), and these are
both considered candidate critical effects. Neurodevelopmental effects are a distinct type among
developmental effects. Thus, the next most sensitive endpoints of decreased rearing
postexposure in mice (Fredriksson et al., 1993), increased exploration postexposure in rats
(Taylor et al., 1985), and decreased myelination in the hippocampus of rats (Isaacson and Taylor,
1989) are also considered candidate critical effects.
Because of the general lack of specific data as to the metabolites involved and the mode
of action for the candidate critical developmental effects, PBPK model predictions of total
metabolism (scaled by body weight to the 3/4 power) was selected as the preferred dose-metric
based on the general observation that TCE toxicity is associated with metabolism. The AUC of
TCE in blood was used as an alternative dose-metric. The only exception to this was for the
Johnson et al. (2003) study, which suggested that oxidative metabolites were involved in the
effects reported based on similar effects being reported from TCA and DCA exposure.
Therefore, in this case, total oxidative metabolism (scaled by body weight to the 3/4 power) was
considered the preferred dose-metric, while AUC of TCE in blood was considered the alternative
metric. With these dose-metrics, the candidate reference values derived using the PBPK model
were, with one exception, only modestly (-threefold or less) different than those derived on the
basis of applied dose. For resorptions reported by Narotsky et al. (1995), BMD modeling based
on internal doses changed the candidate reference value by seven to eightfold larger than the
corresponding cRfD based on applied dose. However, there is substantial uncertainty in the low-
dose curvature of the dose-response curve for modeling both with applied and internal dose, so
the BMD remains somewhat uncertain for this endpoint/study. Finally, for two studies (Peden-
Adams et al., 2006; Isaacson and Taylor, 1989), PBPK modeling of internal doses was not
performed due to the inability to model the complicated exposure pattern (in utero, followed by
lactational transfer, followed by drinking water postweaning).
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6.2.1.2.8. Summary of most sensitive candidate reference values
As shown in Sections 5.1.3 and 5.1.5, the most sensitive candidate reference values are
for the developmental effect of heart malformations in rats (candidate RfC of 0.0004 ppm and
candidate RfD of 0.0005 mg/kg/day), developmental immunotoxicity in mice exposed pre- and
postnatally (candidate RfD of 0.0004 mg/kg/day), immunological effects in mice (lowest
candidate RfCs of 0.0003-0.003 ppm and lowest candidate RfDs of 0.0005-0.005 mg/kg/day),
and kidney effects in rats and mice (candidate RfCs of 0.0006-0.002 ppm and candidate RfDs of
0.0003-0.001 mg/kg/day). The most sensitive candidate reference values also generally have
low composite UFs (with the exception of some mouse immunological and kidney effects), so
they are expected to be reflective of the most sensitive effects as well. Thus, the most sensitive
candidate references values for multiple effects span about an order of magnitude for both
inhalation (0.0003-0.003 ppm [0.002-0.02 mg/m3]) and oral (0.0004-0.005 mg/kg/day)
exposures. The most sensitive candidate references values for neurological and reproductive
effects are about an order of magnitude higher (lowest candidate RfCs of 0.007-0.02 ppm [0.04-
0.1 mg/m3] and lowest candidate RfDs of 0.009-0.02 mg/kg/day). Lastly, the liver effects have
candidate reference values that are another two orders of magnitude higher (candidate RfCs of 1-
2 ppm [6-10 mg/m3] and candidate RfDs of 0.9-2 mg/kg/day).
6.2.1.3. Noncancer Reference Values (see Section 5.1.5)
6.2.1.3.1. RfC
The goal is to select an overall RfC that is well supported by the available data (i.e.,
without excessive uncertainty given the extensive database) and protective for all of the
candidate critical effects, recognizing that individual candidate RfC values are by nature
somewhat imprecise. As discussed in Section 5.1, the lowest candidate RfC values within each
health effect category span a 3,000-fold range from 0.0003 to 0.9 ppm (see Table 5-26). One
approach to selecting an RfC would be to select the lowest calculated value of 0.0003 ppm for
decreased thymus weight in mice. However, three candidate RfCs (cRfCs and p-cRfCs) are in
the relatively narrow range of 0.0003-0.0006 ppm at the low end of the overall range (see
Table 5-24). Given the somewhat imprecise nature of the individual candidate RfC values, and
the fact that multiple effects/studies lead to similar candidate RfC values, the approach taken in
this assessment is to select an RfC supported by multiple effects/studies. The advantages of this
approach, which is only possible when there is a relatively large database of studies/effects and
when multiple candidate values happen to fall within a narrow range at the low end of the overall
range, are that it leads to a more robust RfC (less sensitive to limitations of individual studies)
and that it provides the important characterization that the RfC exposure level is similar for
multiple noncancer effects rather than being based on a sole explicit critical effect.
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Therefore, two critical and one supporting studies/effects were chosen as the basis of the
RfC for TCE noncancer effects (see Tables 5-28 and 5-29). These lowest candidate RfCs,
ranging from 0.0003 to 0.0006 ppm for developmental, kidney, and immunologic effects, are
values derived from route-to-route extrapolation using the PBPK model. The lowest candidate
RfC estimate from an inhalation study is 0.001 ppm for kidney effects, which is higher than the
route-to-route extrapolated candidate RfC estimate from the most sensitive oral study. For all of
the candidate RfCs, the PBPK model was used for inter- and intraspecies extrapolation, based on
the preferred dose-metric for each endpoint. There is moderate-to-high confidence in the lowest
candidate RfC for immunological effects (see Section 5.1.2.5), and moderate confidence in the
lowest candidate RfC for developmental effects (heart malformations) (see Section 5.1.2.8);
these are considered the critical effects for deriving the RfC. For kidney effects (toxic
nephropathy), there is high confidence in the nephrotoxic hazard from TCE exposure and in the
appropriateness of the selected dose-metric; however, as discussed in Section 3.3.3.2, there
remains substantial uncertainty in the extrapolation of GSH conjugation from rodents to humans
due to limitations in the available data, and thus toxic nephropathy is considered a supporting
effect.
As a whole, the estimates support an RfC of 0.0004 ppm (0.4 ppb or 2 ug/m3). This value
essentially reflects the midpoint between the similar candidate RfC estimates for the two critical
effects (0.00033 ppm for decreased thymus weight in mice and 0.00037 ppm for heart
malformations in rats), rounded to one significant figure. This value is also within a factor of 2
of the candidate RfC estimate of 0.0006 ppm for the supporting effect of toxic nephropathy in
rats. Thus, this assessment does not rely on a single estimate alone; rather, each estimate is
supported by estimates of similar magnitude from other effects. In other words, there is robust
support for an RfC of 0.0004 ppm provided by estimates for multiple effects from multiple
studies. The estimates are based on PBPK model-based estimates of internal dose for
interspecies, intraspecies, and route-to-route extrapolation, and there is sufficient confidence in
the PBPK model and support from mechanistic data for one of the dose-metrics (total oxidative
metabolism for the heart malformations). There is high confidence that bioactivation of DCVC
and total GSH metabolism would be appropriate dose-metrics for toxic nephropathy, but there is
substantial uncertainty in the PBPK model predictions for these dose-metrics in humans (see
Section 5.1.3.1). Note that there is some human evidence of developmental heart defects from
TCE exposure in community studies (see Section 4.8.3.1.1) and of kidney toxicity in
TCE-exposed workers (see Section 4.4.1).
In summary, the RfC is 0.0004 ppm (0.4 ppb or 2 ug/m3) based on route-to-route
extrapolated results from oral studies for the critical effects of heart malformations (rats) and
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immunotoxicity (mice). This RfC value is further supported by route-to-route extrapolated
results from an oral study of toxic nephropathy (rats).
6.2.1.3.2. RfD
As with the RfC determination above, the goal is to select an overall RfD that is well-
supported by the available data (i.e., without excessive uncertainty given the extensive database)
and protective for all of the candidate critical effects, recognizing that individual candidate RfD
values are by nature somewhat imprecise. As discussed in Section 5.1, the lowest candidate RfD
values (cRfDs and p-cRfDs) within each health effect category span a nearly 3,000-fold range
from 0.0003 to 0.8 mg/kg/day (see Table 5-26). However, multiple candidate RfDs are in the
relatively narrow range of 0.0003-0.0008 mg/kg/day at the low end of the overall range. Given
the somewhat imprecise nature of the individual candidate RfD values, and the fact that multiple
effects/studies lead to similar candidate RfD values, the approach taken in this assessment is to
select an RfD supported by multiple effects/studies. The advantages of this approach, which is
only possible when there is a relatively large database of studies/effects and when multiple
candidate values happen to fall within a narrow range at the low end of the overall range, are that
it leads to a more robust RfD (less sensitive to limitations of individual studies) and that it
provides the important characterization that the RfD exposure level is similar for multiple
noncancer effects rather than being based on a sole explicit critical effect.
Therefore, three critical and two supporting studies/effects were chosen as the basis of the
RfD for TCE noncancer effects (see Tables 5-30 and 5-31). All but one of the lowest candidate
RfD values—0.0008 mg/kg/day for increased kidney weight in rats, 0.0005 mg/kg/day for both
heart malformations in rats and decreased thymus weights in mice, and 0.0003 mg/kg/day for
increased toxic nephropathy in rats—are derived using the PBPK model for inter- and
intraspecies extrapolation, based on the preferred dose-metric for each endpoint, and the latter
value is derived also using the PBPK model for route-to-route extrapolation from an inhalation
study. The other of these lowest candidate RfDs—0.0004 mg/kg/day for developmental
immunotoxicity (decreased PFC response and increased delayed-type hypersensitivity) in
mice—is based on applied dose. There is moderate-to-high confidence in the candidate RfDs for
decreased thymus weights (see Section 5.1.2.5) and developmental immunological effects, and
moderate confidence in that for heart malformations (see Section 5.1.2.8); these are considered
the critical effects for deriving the RfC. For kidney effects, there is high confidence in the
nephrotoxic hazard from TCE exposure and in the appropriateness of the selected dose-metric;
however, as discussed in Section 3.3.3.2, there remains substantial uncertainty in the
extrapolation of GSH conjugation from rodents to humans due to limitations in the available
data, and thus these effects are considered supporting effects.
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As a whole, the estimates support an RfD of 0.0005 mg/kg/day. This value is within 20%
of the estimates for the critical effects—0.0004 mg/kg/day for developmental immunotoxicity
(decreased PFC and increased delayed-type hypersensitivity) in mice and 0.0005 mg/kg/day for
both heart malformations in rats and decreased thymus weights in mice. This value is also
within approximately a factor of 2 of the supporting effect estimates of 0.0003 mg/kg/day for
toxic nephropathy in rats and 0.0008 mg/kg/day for increased kidney weight in rats. Thus, this
assessment does not rely on any single estimate alone; rather, each estimate is supported by
estimates of similar magnitude from other effects. In other words, there is strong, robust support
for an RfD of 0.0005 mg/kg/day provided by the concordance of estimates derived from multiple
effects from multiple studies. The estimates for kidney effects, thymus effects, and
developmental heart malformations are based on PBPK model-based estimates of internal dose
for interspecies and intraspecies extrapolation, and there is sufficient confidence in the PBPK
model and support from mechanistic data for one of the dose-metrics (total oxidative metabolism
for the heart malformations). There is high confidence that bioactivation of DCVC would be an
appropriate dose-metric for toxic nephropathy, but there is substantial uncertainty in the PBPK
model predictions for this dose-metric in humans (see Section 5.1.3.1). Note that there is some
human evidence of developmental heart defects from TCE exposure in community studies (see
Section 4.8.3.1.1) and of kidney toxicity in TCE-exposed workers (see Section 4.4.1).
In summary, the RfD is 0.0005 mg/kg/day based on the critical effects of heart
malformations (rats), adult immunological effects (mice), and developmental immunotoxicity
(mice), and toxic nephropathy (rats), all from oral studies. This RfD value is further supported
by results from an oral study for the effect of toxic nephropathy (rats) and route-to-route
extrapolated results from an inhalation study for the effect of increased kidney weight (rats).
6.2.2. Cancer (see Section 5.2)
6.2.2.1. Background and Methods (rodent: see Section 5.2.1.1; human: see
Section 5.2.2.1)
As summarized above, following EPA (2005b) Guidelines for Carcinogen Risk
Assessment, TCE is characterized as —cscinogenic to humans" by all routes of exposure, based
on convincing evidence of a causal association between TCE exposure in humans and kidney
cancer, but there is also human evidence of TCE carcinogenicity in the liver and lymphoid
tissues. This conclusion is further supported by rodent bioassay data indicating carcinogenicity
of TCE in rats and mice at tumor sites that include those identified in human epidemiologic
studies. Therefore, both human epidemiologic studies as well as rodent bioassays were
considered for deriving PODs for dose-response assessment of cancer endpoints. For PODs
derived from rodent bioassays, default dosimetry procedures were applied to convert applied
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rodent doses to HEDs. Essentially, for inhalation exposures, -ppm equivalence" across species
was assumed, as recommended by U.S. EPA (1994a) for Category 3 gases for which the
blood:air partition coefficient in laboratory animals is greater than that in humans. For oral
doses, 3/4-power body-weight scaling was used, with a default average human body weight of
70 kg. In addition to applied doses, several internal dose-metrics estimated using a PBPK model
for TCE and its metabolites were used in the dose-response modeling for each tumor type. In
general, an attempt was made to use tissue-specific dose-metrics representing particular
pathways or metabolites identified from available data as having a likely role in the induction of
a tissue-specific cancer. Where insufficient information was available to establish particular
metabolites or pathways of likely relevance to a tissue-specific cancer, more general —upstom"
metrics had to be used. In addition, the selection of dose-metrics was limited to metrics that
could be adequately estimated by the PBPK model.
Regarding low-dose extrapolation, a key consideration in determining what extrapolation
approach to use is the mode(s) of action. However, mode-of-action data are lacking or limited
for each of the cancer responses associated with TCE exposure, with the exception of the kidney
tumors. For the kidney tumors, the weight of the available evidence supports the conclusion that
a mutagenic mode of action is operative; this mode of action supports linear low-dose
extrapolation. The weight of evidence also supports involvement of processes of cytotoxicity
and regenerative proliferation in the carcinogenicity of TCE, although not with the extent of
support as for a mutagenic mode of action. In particular, data linking TCE-induced proliferation
to increased mutation or clonal expansion are lacking, as are data informing the quantitative
contribution of cytotoxicity. Moreover, it is unlikely that any contribution from cytotoxicity
leads to a non-linear dose-response relationship near the PODs. In the case of the rodent
bioassays, maximal levels of toxicity are reached before the onset of tumors. Finally, because
any possible involvement of a cytotoxicity mode of action would be additional to mutagenicity,
the dose-response relationship would nonetheless be expected to be linear at low doses.
Therefore, the additional involvement of a cytotoxicity mode of action does not provide evidence
against the use of linear extrapolation from the POD. For the other TCE-induced cancers, the
mode(s) of action is unknown. When the mode(s) of action cannot be clearly defined, EPA
generally uses a linear approach to estimate low-dose risk (2005b), based on the following
general principles:
• A chemical's carcinogenic effects may act additively to ongoing biological processes,
given that diverse human populations are already exposed to other agents and have
substantial background incidences of various cancers.
• A broadening of the dose-response curve (i.e., less rapid fall-off of response with
decreasing dose) in diverse human populations and, accordingly, a greater potential for
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risks from low-dose exposures (Lutz et al., 2005; Zeise et al., 1987) is expected for two
reasons. First, even if there is a —tteshold" concentration for effects at the cellular level,
that threshold is expected to differ across individuals. Second, greater variability in
response to exposures would be anticipated in heterogeneous populations than in inbred
laboratory species under controlled conditions (due to, e.g., genetic variability, disease
status, age, nutrition, and smoking status).
• The general use of linear extrapolation provides reasonable upper-bound estimates that
are believed to be health-protective (U.S. EPA, 2005b) and also provides consistency
across assessments.
6.2.2.2. Inhalation Unit Risk Estimate (rodent: see Section 5.2.1.3; human: see
Sections 5.2.2.1 and 5.2.2.2)
The inhalation unit risk for TCE is defined as a plausible upper bound lifetime extra risk
of cancer from chronic inhalation of TCE per unit of air concentration. The inhalation unit risk
for TCE is 2.20 x 10"2 per ppm (2 x 10 2 per ppm [4 x 10 6 per ug/m3] rounded to one
significant figure), based on human kidney cancer risks reported by Charbotel et al. (2006) and
adjusted for potential risk for NHL and liver cancer. This estimate is based on good-quality
human data, thus avoiding the uncertainties inherent in interspecies extrapolation. The Charbotel
et al. (2006) case-control study of 86 incident RCC cases and 316 age- and sex-matched controls,
with individual cumulative exposure estimates for TCE inhalation for each subject, provides a
sufficient human data set for deriving quantitative cancer risk estimates for RCC in humans. The
study is a high-quality study that used a detailed exposure assessment (Fevotte et al., 2006) and
took numerous potential confounding factors, including exposure to other chemicals, into
account. A significant dose-response relationship was reported for cumulative TCE exposure
and RCC (Charbotel et al., 2006). Human data on TCE exposure and cancer risk sufficient for
dose-response modeling are only available for RCC, yet human and rodent data suggest that TCE
exposure increases the risk of other cancers as well. In particular, there is evidence from human
(and rodent) studies for increased risks of lymphoma and liver cancer. Therefore, the inhalation
unit risk estimate derived from human data for RCC incidence was adjusted to account for
potential increased risk of those cancer types. To make this adjustment, a factor accounting for
the relative contributions to the extra risk for cancer incidence from TCE exposure for these
three cancer types combined versus the extra risk for RCC alone was estimated, and this factor
was applied to the unit risk estimate for RCC to obtain a unit risk estimate for the three cancer
types combined (i.e., lifetime extra risk for developing any of the three types of cancer). This
estimate is considered a better estimate of total cancer risk from TCE exposure than the estimate
for RCC alone. Although only the Charbotel et al. (2006) study was found adequate for direct
estimation of inhalation unit risks, the available epidemiologic data provide sufficient
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information for estimating the relative potency of TCE across tumor sites. In particular, the
relative contributions to extra risk (for cancer incidence) were calculated from two different data
sets to derive the adjustment factor for adjusting the unit risk estimate for RCC to a unit risk
estimate for the three types of cancers (RCC, NHL, and liver) combined. The first calculation is
based on the results of the meta-analyses of human epidemiologic data for the three cancer types;
the second calculation is based on the results of the Raaschou-Nielsen et al. (2003) study, the
largest single human epidemiologic study by far with RR estimates for all three cancer types.
These calculations support an adjustment factor of 4.
The inhalation unit risk based on human epidemiologic data is supported by inhalation
unit risk estimates from multiple rodent bioassays, the most sensitive of which range from 1 x
10 2 to 2 x 10"1 per ppm [2 x 10~6 to 3 x 10~5 per jig/m3]. From the inhalation bioassays
selected for analysis in Section 5.2.1.1, and using the preferred PBPK model-based dose-metrics,
the inhalation unit risk estimate for the most sensitive sex/species is 8 x 10"2 per ppm [2 x
10"5 per ug/m3], based on kidney adenomas and carcinomas reported by Maltoni et al. (1986) for
male Sprague-Dawley rats. Leukemias and Leydig cell tumors were also increased in these rats,
and, although a combined analysis for these cancer types which incorporated the different site-
specific preferred dose-metrics was not performed, the result of such an analysis is expected to
be similar, about 9 x 10"2 per ppm [2 x 10"5 per ug/m3]. The next most sensitive sex/species
from the inhalation bioassays is the female mouse, for which lymphomas were reported by
Henschler et al. (1980): these data yield a unit risk estimate of 1.0 x 10"2 per ppm [2 x 10"6 per
ug/m3]. In addition, the 90% CIs (i.e., 5-95% bounds) reported in Table 5-41 for male rat
kidney tumors from Maltoni et al. (1986) and female mouse lymphomas from Henschler et al.
(1980), derived from the quantitative analysis of PBPK model uncertainty, both included the
estimate based on human data of 2 x 10"2 per ppm. Furthermore, PBPK model-based route-to-
route extrapolation of the results for the most sensitive sex/species from the oral bioassays,
kidney tumors in male Osborne-Mendel rats and testicular tumors in Marshall rats (NTP, 1988),
leads to inhalation unit risk estimates of 2 x 10"1 per ppm [3 x 10"5 per ug/m3] and 4 x 10"2 per
ppm [8 x 10"6 per ug/m3], respectively, with the preferred estimate based on human data falling
within the route-to-route extrapolation of the 90% CIs reported in Table 5-42. Finally, for all of
these estimates, the ratios of BMDs to the BMDLs did not exceed a value of 3, indicating that the
uncertainties in the dose-response modeling for determining the POD in the observable range are
small.
Although there are uncertainties in these various estimates, confidence in the proposed
inhalation unit risk estimate of 2 x 10"2 per ppm [4 x 10"6 per ug/m3], based on human kidney
cancer risks reported by Charbotel et al. (2006) and adjusted for potential risk for NHL and liver
cancer (as summarized in Section 6.1.4), is further increased by the similarity of this estimate to
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estimates based on multiple rodent data sets. Application of the ADAFs for the kidney cancer
risks, due to the weight of evidence supporting a mutagenic mode of action for this endpoint, is
summarized in Section 6.2.2.5.
6.2.2.3. Oral Slope Factor Estimate (rodent: see Section 5.2.1.3; human: see
Section 5.2.2.3)
The oral slope factor for TCE is defined as a plausible upper bound lifetime extra risk of
cancer from chronic ingestion of TCE per mg/kg/day oral dose. The oral slope factor is 4.64 x
10"2 per mg/kg/day (5 x 10~2 per mg/kg/day rounded to one significant figure), resulting from
PBPK model-based route-to-route extrapolation of the inhalation unit risk estimate based on the
human kidney cancer risks reported in Charbotel et al. (2006) and adjusted for potential risk for
NHL and liver cancer. This estimate is based on good-quality human data, thus avoiding
uncertainties inherent in interspecies extrapolation. In addition, uncertainty in the PBPK model-
based route-to-route extrapolation is relatively low (Chiu, 2006; Chiu and White, 2006). In this
particular case, extrapolation using different dose-metrics yielded expected population mean
risks within about a twofold range, and, for any particular dose-metric, the 95% CI for the
extrapolated population mean risks for each site spanned a range of no more than about
threefold.
This value is supported by oral slope factor estimates from multiple rodent bioassays, the
most sensitive of which range from 3 x 10~2 to 3 x 10"1 per mg/kg/day. From the oral bioassays
selected for analysis in Section 5.2.1.1, and using the preferred PBPK model-based dose-metrics,
the oral slope factor estimate for the most sensitive sex/species is 3 x 10"1 per mg/kg/day, based
on kidney tumors in male Osborne-Mendel rats (NTP, 1988). The oral slope factor estimate for
testicular tumors in male Marshall rats (NTP, 1988) is somewhat lower at 7 x 10"2 per
mg/kg/day. The next most sensitive sex/species result from the oral studies is for male mouse
liver tumors (NCI, 1976), with an oral slope factor estimate of 3 x 10"2 per mg/kg/day. In
addition, the 90% CIs reported in Table 5-42 for male Osborne-Mendel rat kidney tumors (NTP,
1988), male F344 rat kidney tumors (NTP, 1990), and male Marshall rat testicular tumors (NTP,
1988), derived from the quantitative analysis of PBPK model uncertainty, all included the
estimate based on human data of 5 x 10"2 per mg/kg/day, while the upper 95% confidence bound
for male mouse liver tumors from NCI (1976) was slightly below this value at 4 x 10"2 per
mg/kg/day. Furthermore, PBPK model-based route-to-route extrapolation of the most sensitive
endpoint from the inhalation bioassays, male rat kidney tumors from Maltoni et al. (1986), leads
to an oral slope factor estimate of 1 x 10"1 per mg/kg/day, with the preferred estimate based on
human data falling within the route-to-route extrapolation of the 90% CI reported in Table 5-41.
Finally, for all of these estimates, the ratios of BMDs to the BMDLs did not exceed a value of 3,
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indicating that the uncertainties in the dose-response modeling for determining the POD in the
observable range are small.
Although there are uncertainties in these various estimates, confidence in the proposed
oral slope factor estimate of 5 x 10"2 per mg/kg/day, resulting from PBPK model-based route-to-
route extrapolation of the inhalation unit risk estimate based on the human kidney cancer risks
reported in Charbotel et al. (2006) and adjusted for potential risk for NHL and liver cancer (as
summarized above for the inhalation unit risk estimate, but with an adjustment factor of 5 for
oral exposure because of the differences in the relative values of the dose-metrics), is further
increased by the similarity of this estimate to estimates based on multiple rodent data sets.
Application of the ADAFs for the kidney cancer risks, due to the weight of evidence supporting
a mutagenic mode of action for this endpoint, is summarized below in Section 6.2.2.5.
6.2.2.4. Uncertainties in Cancer Dose-Response Assessment
6.2.2.4.1. Uncertainties in estimates based on human epidemiologic data (see
Section 5.2.2.1.3)
All risk assessments involve uncertainty, as study data are extrapolated to make general
inferences about potential effects in humans from environmental exposure. The values for the
slope factor and unit risk estimates are based on good quality human data, which avoids
interspecies extrapolation, one of the major sources of uncertainty in quantitative cancer risk
estimates.
A remaining major uncertainty in the unit risk estimate for RCC incidence derived from
the Charbotel et al. (2006) study is the extrapolation from occupational exposures to lower
environmental exposures. There was some evidence of a contribution to increased RCC risk
from peak exposures; however, there remained an apparent dose-response relationship for RCC
risk with increasing cumulative exposure without peaks, and the OR for exposure with peaks
compared to exposure without peaks was not significantly elevated (Charbotel et al., 2006).
Although the actual exposure-response relationship at low exposure levels is unknown, the
conclusion that a mutagenic mode of action is operative for TCE-induced kidney tumors
supports the linear low-dose extrapolation that was used (U.S. EPA, 2005b). Additional support
for use of linear extrapolation is discussed above in Section 6.2.2.1.
Another source of uncertainty is the dose-response model used to model the study data to
estimate the POD. A weighted linear regression across the categorical ORs was used to obtain a
slope estimate; use of a linear model in the observable range of the data is often a good general
approach for human data because epidemiological data are frequently too limited (the Charbotel
et al. [(2006)1 study had 86 RCC cases, 37 of which had TCE exposure) to clearly identify an
alternate model (U.S. EPA, 2005b). The ratio of the maximum likelihood estimate of the
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effective concentration for a 1% response (ECoi) to the LECoi, which gives some indication of
the statistical uncertainties in the dose-response modeling, was about a factor of 2.
A further source of uncertainty is the retrospective estimation of TCE exposures in the
Charbotel et al. (2006) study. This case-control study was conducted in the Arve Valley in
France, a region with a high concentration of screw cutting workshops using TCE and other
degreasing agents. Since the 1960s, occupational physicians of the region have collected a large
quantity of well-documented measurements, including TCE air concentrations and urinary
metabolite levels (Fevotte et al., 2006). The study investigators conducted a comprehensive
exposure assessment to estimate cumulative TCE exposures for the individual study subjects,
using a detailed occupational questionnaire with a customized task-exposure matrix for the
screw-cutting workers and a more general occupational questionnaire for workers exposed to
TCE in other industries (Fevotte et al., 2006). The exposure assessment also attempted to take
dermal exposure from hand-dipping practices into account by equating it with an equivalent
airborne concentration based on biological monitoring data. Despite the appreciable effort of the
investigators, considerable uncertainty associated with any retrospective exposure assessment is
inevitable, and some exposure misclassification is unavoidable. Such exposure misclassification
was most likely for the 19 deceased cases and their matched controls, for which proxy
respondents were used, and for exposures outside the screw-cutting industry. The exposure
estimates from the RCC study of Moore et al. (2010) were not considered to be as quantitatively
accurate as those of Charbotel et al. (2006) and so were not used for derivation of a unit risk
estimate (see Section 5.2.2); nonetheless, it should be noted that these exposure estimates are
substantially lower than those of Charbotel et al. (2006) for comparable OR estimates. If the
exposure estimates for Charbotel et al. (2006) are overestimated, as suggested by the exposure
estimates from Moore et al. (2010), the slope of the linear regression model, and hence the unit
risk estimate, would be correspondingly underestimated.
Another source of uncertainty in the Charbotel et al. (2006) study is the possible
influence of potential confounding or modifying factors. This study population, with a high
prevalence of metal-working, also had relatively high prevalences of exposure to petroleum oils,
cadmium, petroleum solvents, welding fumes, and asbestos (Fevotte et al., 2006). Other
exposures assessed included other solvents (including other chlorinated solvents), lead, and
ionizing radiation. None of these exposures was found to be significantly associated with RCC
at ap = 0.05 significance level. Cutting fluids and other petroleum oils were associated with
RCC at ap = 0.1 significance level; however, further modeling suggested no association with
RCC when other significant factors were taken into account (Charbotel et al., 2006). Moreover,
a review of other studies suggested that potential confounding from cutting fluids and other
petroleum oils is of minimal concern (see Section 4.4.2.3). Nonetheless, a sensitivity analysis
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was conducted using the OR estimates further adjusted for cutting fluids and other petroleum oils
from the unpublished report by Charbotel et al. (2005), and an essentially identical unit risk
estimate of 5.46 x 10"3 per ppm was obtained. In addition, the medical questionnaire included
familial kidney disease and medical history, such as kidney stones, infection, chronic dialysis,
hypertension, and use of antihypertensive drugs, diuretics, and analgesics. BMI was also
calculated, and lifestyle information such as smoking habits and coffee consumption was
collected. Univariate analyses found high levels of smoking and BMI to be associated with
increased odds of RCC, and these two variables were included in the conditional logistic
regressions. Thus, although impacts of other factors are possible, this study took great pains to
attempt to account for potential confounding or modifying factors.
Some other sources of uncertainty associated with the epidemiological data are the dose-
metric and lag period. As discussed above, there was some evidence of a contribution to
increased RCC risk from peak TCE exposures; however, there appeared to be an independent
effect of cumulative exposure without peaks. Cumulative exposure is considered a good
measure of total exposure because it integrates exposure (levels) over time. If there is a
contributing effect of peak exposures, not already taken into account in the cumulative exposure
metric, the linear slope may be overestimated to some extent. Sometimes, cancer data are
modeled with the inclusion of a lag period to discount more recent exposures not likely to have
contributed to the onset of cancer. In an unpublished report, Charbotel et al. (2005) also
presented the results of a conditional logistic regression with a 10-year lag period, and these
results are very similar to the unlagged results reported in their published paper, suggesting that
the lag period might not be an important factor in this study.
Some additional sources of uncertainty are not so much inherent in the exposure-response
modeling or in the epidemiologic data themselves but, rather, arise in the process of obtaining
more general Agency risk estimates from the epidemiologic results. EPA cancer risk estimates
are typically derived to represent an upper bound on increased risk of cancer incidence for all
sites affected by an agent for the general population. From experimental animal studies, this is
accomplished by using tumor incidence data and summing across all of the tumor sites that
demonstrate significantly increased incidences, customarily for the most sensitive sex and
species, to attempt to be protective of the general human population. However, in estimating
comparable risks from the Charbotel et al. (2006) epidemiologic data, certain limitations are
encountered. For one thing, these epidemiology data represent a geographically limited (Arve
Valley, France) and likely not very diverse population of working adults. Thus, there is
uncertainty about the applicability of the results to a more diverse general population.
Additionally, the Charbotel et al. (2006) study was a study of RCC only, and so the risk
estimate derived from it does not represent all of the tumor sites that may be affected by TCE.
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This uncertainty was addressed by adjusting the RCC estimate to multiple sites, but there are also
uncertainties related to the assumptions inherent in the calculations for this adjustment. As
discussed in Section 5.2.2.2, adequate quantitative dose-response data were only available for
one cancer type in humans, so other human data were used to adjust the estimate derived for
RCC to include risk for other cancers with substantial human evidence of hazard (NHL and liver
cancer). The relative contributions to extra risk (for cancer incidence) were calculated from two
different data sets to derive an adjustment factor. The first calculation is based on the results of
the meta-analyses for the three cancer types; the second calculation is based on the results of the
Raaschou-Nielsen et al. (2003) study, the largest single study by far with RR estimates for all
three cancer types. The fact that the calculations based on two different data sets yielded
comparable values for the adjustment factor (both within 25% of the selected factor of 4)
provides more robust support for the use of the factor of 4. Additional uncertainties pertain to
the weight of evidence supporting the association of TCE exposure with increased risk of cancer
for the three cancer types. As discussed in Section 4.11.2, it is concluded that the weight of
evidence for kidney cancer is sufficient to classify TCE as —arcinogenic to humans." It is also
concluded that there is strong evidence that TCE causes NHL as well, although the evidence for
liver cancer was more limited. In addition, the rodent studies demonstrate clear evidence of
multisite carcinogenicity, with cancer types including those for which associations with TCE
exposure are observed in human studies (i.e., liver and kidney cancers and lymphomas). Overall,
the evidence is sufficiently persuasive to support the use of the adjustment factor of 4 based on
these three cancer types. Alternatively, if one were to use the factor based only on the two
cancer types with the strongest human evidence, the cancer inhalation unit risk estimate would
be only slightly reduced (25%).
Finally, the value for the oral slope factor estimate was based on route-to-route
extrapolation of the inhalation unit risk based on human data using predictions from the PBPK
model. Because different internal dose-metrics are preferred for each target tissue site, a separate
route-to-route extrapolation was performed for each site-specific slope factor estimate. As
discussed above, uncertainty in the PBPK model-based route-to-route extrapolation is relatively
low (Chiu, 2006; Chiu and White, 2006). In this particular case, extrapolation using different
dose-metrics yielded expected population mean risks within about a twofold range, and, for any
particular dose-metric, the 95% CI for the extrapolated population mean risks for each site
spanned a range of no more than about threefold.
6.2.2.4.2. Uncertainties in estimates based on rodent bioassays (see Section 5.2.1.4)
With respect to rodent-based cancer risk estimates, the cancer risk is typically estimated
from the total cancer burden from all sites that demonstrate an increased tumor incidence for the
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most sensitive experimental species and sex. It is expected that this approach is protective of the
human population, which is more diverse but is exposed to lower exposure levels. In the case of
TCE, the impact of selection of the bioassay is limited, since, as discussed in Sections 5.2.1.3
and 5.2.3, estimates based on the two or three most sensitive bioassays are within an order of
magnitude of each other, and are consistent across routes of exposure when extrapolated using
the PBPK model.
Another source of uncertainty in the TCE rodent-based cancer risk estimates is
interspecies extrapolation. Several plausible PBPK model-based dose-metrics were used for
extrapolation of toxicokinetics, but the cancer slope factor and unit risk estimates obtained using
the preferred dose-metrics were generally similar (within about threefold) to those derived using
default dosimetry assumptions, with the exception of the bioactivated DCVC dose-metric for rat
kidney tumors and the metric for the amount of TCE oxidized in the respiratory tract for mouse
lung tumors occurring from oral exposure. However, there is greater biological support for these
selected dose-metrics. The uncertainty in the PBPK model predictions themselves was analyzed
quantitatively through an analysis of the impact of parameter uncertainties in the PBPK model.
The 95% lower bounds on the BMD including parameter uncertainties in the PBPK model were
no more than fourfold lower than those based on central estimates of the PBPK model
predictions. The greatest uncertainty was for slope factors and unit risks derived from rat kidney
tumors, primarily reflecting the substantial uncertainty in the rat internal dose and in the
extrapolation of GSH conjugation from rodents to humans.
Regarding low-dose extrapolation, a key consideration in determining what extrapolation
approach to use is the mode(s) of action. However, mode-of-action data are lacking or limited
for each of the cancer responses associated with TCE exposure, with the exception of the kidney
tumors. For the kidney tumors, the weight of the available evidence supports the conclusion that
a mutagenic mode of action is operative; this mode of action supports linear low-dose
extrapolation. For the other TCE-induced cancers, the data either support a complex mode of
action or are inadequate to specify the key events and modes of action involved. When the
mode(s) of action cannot be clearly defined, EPA generally uses a linear approach to estimate
low-dose risk (U.S. EPA, 2005b), based on the general principles discussed above.
With respect to uncertainties in the dose-response modeling, the two-step approach of
modeling only in the observable range, as put forth in EPA's Guidelines for Carcinogen Risk
Assessment (U.S. EPA, 2005b), is designed in part to minimize model dependence. The ratios of
the BMDs to the BMDLs, which give some indication of the statistical uncertainties in the dose-
response modeling, did not exceed a value of 2.5 for all of the primary analyses used in this
assessment. Thus, overall, modeling uncertainties in the observable range are considered to be
minimal. Some additional uncertainty is conveyed by uncertainties in the survival adjustments
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made to some of the bioassay data; however, a comparison of the results of two different survival
adjustment methods suggest that their impact is minimal relative to the uncertainties already
discussed.
6.2.2.5. Application of ADAFs (see Section 5.2.3.3)
When there is sufficient weight of evidence to conclude that a carcinogen operates
through a mutagenic mode of action, and in the absence of chemical-specific data on age-specific
susceptibility, EPA's Supplemental Guidance for Assessing Susceptibility from Early-Life
Exposure to Carcinogens (U.S. EPA, 2005b) recommends the application of default ADAFs to
adjust for potential increased susceptibility from early-life exposure. See the Supplemental
Guidance for detailed information on the general application of these adjustment factors. In
brief, the Supplemental Guidance establishes ADAFs for three specific age groups. The current
ADAFs and their age groupings are 10 for <2 years, 3 for 2-<16 years, and 1 for >16 years (U.S.
EPA, 2005b). For risk assessments based on specific exposure assessments, the 10- and 3-fold
adjustments to the slope factor or unit risk estimates are to be combined with age-specific
exposure estimates when estimating cancer risks from early-life (<16 years age) exposure.
In the case of TCE, the inhalation unit risk and oral slope factor estimates reflect lifetime
risk for cancer at multiple sites, and a mutagenic mode of action has been established for one of
these sites, the kidney. The weight of evidence also supports involvement of processes of
cytotoxicity and regenerative proliferation in the carcinogen!city of TCE, although not with the
extent of support as for a mutagenic mode of action. In particular, data linking TCE-induced
proliferation to increased mutation or clonal expansion are lacking, as are data informing the
quantitative contribution of cytotoxicity. Because any possible involvement of a cytotoxicity
mode of action would be additional to mutagenicity, the mutagenic mode of action would be
expected to dominate at low doses. Therefore, the additional involvement of a cytotoxicity mode
of action does not provide evidence against application of ADAFs. In addition, as discussed in
Section 4.10, inadequate TCE-specific data exists to quantify early-life susceptibility to TCE
carcinogenicity; therefore, as recommended in the Supplemental Guidance, the default ADAFs
are used. As illustrated in the example calculations in Sections 5.2.3.3.1 and 5.2.3.3.2,
application of the default ADAFs to the kidney cancer inhalation unit risk and oral slope factor
estimates for TCE is likely to have minimal impact on the total cancer risk except when exposure
is primarily during early life.
In addition to the uncertainties discussed above for the inhalation and oral total cancer
unit risk and slope factor estimates, there are uncertainties in the application of ADAFs to adjust
for potential increased early-life susceptibility. The adjustment is made only for the kidney
cancer component of total cancer risk because that is the tumor type for which the weight of
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evidence was sufficient to conclude that TCE-induced carcinogenesis operates through a
mutagenic mode of action. However, it may be that TCE operates through a mutagenic mode of
action for other cancer types as well or that it operates through other modes of action that might
also convey increased early-life susceptibility. Additionally, the ADAFs from the 2005
Supplemental Guidance are not specific to TCE, and it is uncertain to what extent they reflect
increased early-life susceptibility to kidney cancer from exposure to TCE, if increased early-life
susceptibility occurs.
6.3. OVERALL CHARACTERIZATION OF TCE HAZARD AND DOSE RESPONSE
There is substantial potential for human exposure to TCE, as it has a widespread presence
in ambient air, indoor air, soil, and groundwater. At the same time, humans are likely to be
exposed to a variety of compounds that are either metabolites of TCE or have common
metabolites or targets of toxicity. Once exposed, humans, as well as laboratory animal species,
rapidly absorb TCE, which is then distributed to tissues via systemic circulation, extensively
metabolized, and then excreted primarily in breath as unchanged TCE or CO2, or in urine as
metabolites.
Based on the available human epidemiologic data and experimental and mechanistic
studies, it is concluded that TCE poses a potential human health hazard for noncancer toxicity to
the CNS, the kidney, the liver, the immune system, the male reproductive system, and the
developing fetus. The evidence is more limited for TCE toxicity to the respiratory tract and
female reproductive system. Following EPA (2005b) Guidelines for Carcinogen Risk
Assessment, TCE is characterized as —carcinogenic to huians" by all routes of exposure. This
conclusion is based on convincing evidence of a causal association between TCE exposure in
humans and kidney cancer. The human evidence of carcinogenicity from epidemiologic studies
of TCE exposure is strong for NHL, but less convincing than for kidney cancer, and more limited
for liver and biliary tract cancer. Less human evidence is found for an association between TCE
exposure and other types of cancer, including bladder, esophageal, prostate, cervical, breast, and
childhood leukemia. Further support for the characterization of TCE as —carinogenic to
humans" by all routes of exposure is derived from positive results in multiple rodent cancer
bioassays in rats and mice of both sexes, similar toxicokinetics between rodents and humans,
mechanistic data supporting a mutagenic mode of action for kidney tumors, and the lack of
mechanistic data supporting the conclusion that any of the mode(s) of action for TCE-induced
rodent tumors are irrelevant to humans.
As TCE toxicity and carcinogenicity are generally associated with TCE metabolism,
susceptibility to TCE health effects may be modulated by factors affecting toxicokinetics,
including lifestage, gender, genetic polymorphisms, race/ethnicity, preexisting health status,
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lifestyle, and nutrition status. In addition, while some of these factors are known risk factors for
effects associated with TCE exposure, it is not known how TCE interacts with known risk factors
for human diseases.
For noncancer effects, the most sensitive types of effects, based either on HECs/HEDs or
on candidate RfCs/RfDs, appear to be developmental, kidney, and immunological (adult and
developmental) effects. The neurological and reproductive effects appear to be about an order of
magnitude less sensitive, with liver effects another 2 orders of magnitude less sensitive. The
RfC of 0.0004 ppm (0.4 ppb or 2 ug/m3) is based on route-to-route extrapolated results from oral
studies for the critical effects of heart malformations (rats) and immunotoxicity (mice). This
RfC value is further supported by route-to-route extrapolated results from an oral study of toxic
nephropathy (rats). Similarly, the RfD for noncancer effects of 0.0005 mg/kg/day is based on
the critical effects of heart malformations (rats), adult immunological effects (mice), and
developmental immunotoxicity (mice), all from oral studies. This RfD value is further supported
by results from an oral study for the effect of toxic nephropathy (rats) and route-to-route
extrapolated results from an inhalation study for the effect of increased kidney weight (rats).
There is high confidence in these noncancer reference values, as they are supported by moderate-
to-high confidence estimates for multiple effects from multiple studies.
For cancer, the inhalation unit risk is 2 x 10"2 per ppm [4 x 10"6 per jig/m3], based on
human kidney cancer risks reported by Charbotel et al. (2006) and adjusted, using human
epidemiologic data, for potential risk for NHL and liver cancer. The oral slope factor for cancer
is 5 x 10~2 per mg/kg/day, resulting from PBPK model-based route-to-route extrapolation of the
inhalation unit risk estimate based on the human kidney cancer risks reported in Charbotel et al.
(2006) and adjusted, using human epidemiologic data, for potential risk for NHL and liver
cancer. There is high confidence in these unit risks for cancer, as they are based on good-quality
human data, as well as being similar to unit risk estimates based on multiple rodent bioassays.
There is both sufficient weight of evidence to conclude that TCE operates through a mutagenic
mode of action for kidney tumors and a lack of TCE-specific quantitative data on early-life
susceptibility. Generally, the application of ADAFs is recommended when assessing cancer
risks for a carcinogen with a mutagenic mode of action. However, because the ADAF
adjustment applies only to the kidney cancer component of the total risk estimate, it is likely to
have a minimal impact on the total cancer risk except when exposures are primarily during early
life.
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EPA/635/R-09/011F
www.epa.gov/iris
r/EPA
TOXICOLOGICAL REVIEW
OF
TRICHLOROETHYLENE
APPENDICES
(CAS No. 79-01-6)
In Support of Summary Information on the
Integrated Risk Information System (IRIS)
September 2011
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CONTENTS of TOXICOLOGICAL REVIEW for TRICHLOROETHYLENE
Appendices
(CAS No. 79-01-6)
TOXICOLOGICAL REVIEW OF TRICHLOROETHYLENE APPENDICES i
CONTENTS of TOXICOLOGICAL REVIEW for TRICHLOROETHYLENE
Appendices ii
LIST OF TABLES xiii
LIST OF FIGURES xviii
A. PBPK MODELING OF TCE AND METABOLITES—DETAILED METHODS
AND RESULTS A-l
A. 1. THE HIERARCHICAL BAYESIAN APPROACH TO CHARACTERIZING
PBPK MODEL UNCERTAINTY AND VARIABILITY A-l
A.2. EVALUATION OF THE HACK ET AL. (2006) PBPK MODEL A-4
A.2.1. Convergence A-4
A.2.2. Evaluation of Posterior Distributions for Population Parameters A-5
A.2.3. Comparison of Model Predictions With Data A-7
A.2.3.1. Mouse Model A-8
A.2.3.2. Rat Model A-15
A.2.3.3. Human Model A-23
A.3. PRELIMINARY ANALYSIS OF MOUSE GAS UPTAKE DATA:
MOTIVATION FOR MODIFICATION OF RESPIRATORY
METABOLISM A-32
A.4. DETAILS OF THE UPDATED PBPK MODEL FOR TCE AND ITS
METABOLITES A-38
A.4.1. PBPKModel Structure and Equations A-38
A.4.1.1. TCE Submodel A-50
A.4.1.2. TCOH Submodel A-55
A.4.1.3. TCOG Submodel A-57
A.4.1.4. TCA Submodel A-59
A.4.1.5. GSH Conjugation Submodel A-63
A.4.2. Model Parameters and Baseline Values A-64
A.4.3. Statistical Distributions for Parameter Uncertainty and Variability A-64
A.4.3.1. Initial Prior Uncertainty in Population Mean Parameters A-64
A.4.3.2. Interspecies Scaling to Update Selected Prior Distributions in
the Rat and Human A-64
A.4.3.3. Population Variance: Prior Central Estimates and Uncertainty... A-75
A.4.3.4. Likelihood Function and Prior distributions for Residual
Error Estimates A-78
A.4.4. Summary of Bayesian Posted or Distribution Function A-81
A.5. RESULTS OF UPDATED PBPK MODEL A-82
A.5.1. Convergence and Posterior Distributions of Sampled Parameters A-82
A. 5.2. Comparison of Model Predictions with Data A-l 15
A.5.2.1. Mouse Data and Model Predictions A-l 16
A.5.2.2. Rat Data and Model Predictions A-126
11
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A.5.2.3. Human Data and Model Predictions A-140
A.6. EVALUATION OF RECENTLY PUBLISHED TOXICOKINETIC DATA A-174
A.6.1. TCE Metabolite Toxicokinetics in Mice: Kim et al. (2009) A-175
A.6.2. TCE Toxicokinetics in Rats: Liu et al. (2009) A-179
A.6.3. TCA Toxicokinetics in Mice and Rats: Mahle et al. (1999) and Green
(2003a, 2003b) A-180
A.6.3.1. Analysis Using Evans et al. (2009) and Chiu et al. (2009)
PBPK Model A-181
A.6.3.2. Summary of Results From Chiu of Bayesian Updating of
Evans et al. (2009) and Chiu et al. (2009) Model Using TCA
Drinking Water Data A-182
A.7. UPDATED PBPK MODEL CODE A-190
B. SYSTEMATIC REVIEW OF EPIDEMIOLOGIC STUDIES ON CANCER AND
TCE EXPOSURE B-l
B.I. INTRODUCTION B-l
B.2. METHODOLOGIC REVIEW OF EPIDEMIOLOGIC STUDIES ON
CANCER AND TCE B-l
B.2.1. Study Designs and Characteristics B-25
B.2.2. Outcomes Assessed in TCE Epidemiologic Studies B-27
B.2.3. Disease Classifications Adopted in TCE Epidemiologic Studies B-28
B.2.4. Exposure Classification B-30
B.2.5. Follow-up in TCE Cohort Studies B-32
B.2.6. Interview Approaches in Case-Control Studies of Cancer and TCE
Exposure B-33
B.2.7. Sample Size and Approximate Statistical Power B-35
B.2.8. Statistical Analysis and Result Documentation B-42
B.2.9. Systematic Review for Identifying Cancer Hazards and TCE Exposure ....B-45
B.2.9.1. Cohort Studies B-46
B.2.9.2. Case-Control Studies B-49
B.2.9.3. Geographic-Based Studies B-53
B.2.9.4. Recommendation of Studies for Treatment Using Meta-
Analysis Approaches B-53
B.3. INDIVIDUAL STUDY REVIEWS AND ABSTRACTS B-56
B.3.1. Cohort Studies B-56
B.3.1.1. Studies of Aerospace Workers B-56
B.3.1.2. Cancer Incidence Studies Using Biological Monitoring
Databases B-93
B.3.1.3. Studies in the Taoyuan Region of Taiwan B-104
B.3.1.4. Studies of Other Cohorts B-l 16
B.3.2. Case-Control Studies B-160
B.3.2.1. Bladder Cancer Case-Control Studies B-160
B.3.2.2. CNS Cancers Case-Control Studies B-168
B.3.2.3. Colon and Rectal Cancers Case-Control Studies B-176
B.3.2.4. Esophageal Cancer Case-Control Studies B-188
B.3.2.5. Liver Cancer Case-Control Studies B-192
in
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B.3.2.6. Lymphoma Case-Control Studies B-196
B.3.2.7. Childhood Leukemia B-235
B.3.2.8. Melanoma Case-Control Studies B-250
B.3.2.9. Pancreatic Cancer Case-Control Studies B-254
B.3.2.10.Prostatic Cancer Case-Control Studies B-258
B.3.2.11.RCC Case-Control Studies—Arnsberg Region of Germany B-262
B.3.2.12.RCC Case-Control Studies—Arve Valley Region of France B-274
B.3.2.13.RCC Case-Control Studies in Other Regions B-282
B.3.2.14.Other Cancer Site Case-Control Studies B-296
B.3.3. Geographic-Based Studies B-300
B.3.3.1. Coyle et al. (2005) B-300
B.3.3.2. Morgan and Cassady (2002) B-304
B.3.3.3. Cohnetal. (1994b) B-308
B.3.3.4. Vartiainen et al. (1993) B-312
B.3.3.5. Mallin(1990) B-315
B.3.3.6. Isacsonetal. (1985) B-319
B.3.3.7. Studies in the Endicott Area of New York B-323
B.3.3.8. Studies in Arizona B-333
C. META-ANALYSIS OF CANCER RESULTS FROM EPIDEMIOLOGICAL
STUDIES C-l
C.I. METHODOLOGY C-l
C.2. META-ANALYSIS FOR NHL C-5
C.2.1. Overall Effect of TCE Exposure C-5
C.2.1.1. Select!on of RR Estimates C-5
C.2.1.2. Results of Meta-Analyses C-10
C.2.2. NHL Effect in the Highest Exposure Groups C-17
C.2.2.1. Select!on of RR Estimates C-17
C.2.2.2. Results of Meta-Analyses C-22
C.2.3. Discussion of NHL Meta-Analysis Results C-25
C.3. META-ANALYSIS FOR KIDNEY CANCER C-28
C.3.1. Overall Effect of TCE Exposure C-28
C.3.1.1. Select!on of RREstimates C-28
C.3.1.2. Results of Meta-Analyses C-34
C.3.2. Kidney Cancer Effect in the Highest Exposure Groups C-38
C.3.2.1. Select!on of RREstimates C-38
C.3.2.2. Results of Meta-Analyses C-43
C.3.3. Discussion of Kidney Cancer Meta-Analysis Results C-46
C.4. META-ANALYSIS FOR LIVER CANCER C-49
C.4.1. Overall Effect of TCE Exposure C-49
C.4.1.1. Select!on of RR Estimates C-49
C.4.1.2. Results of Meta-Analyses C-53
C.4.2. Liver Cancer Effect in the Highest Exposure Groups C-57
C.4.2.1. Select!on of RR Estimates C-57
C.4.2.2. Results of Meta-Analyses C-60
C.4.3. Discussion of Liver Cancer Meta-Analysis Results C-62
C.5. META-ANALYSIS FOR LUNG CANCER C-63
iv
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C.5.1. Overall Effect of TCE Exposure C-63
C.5.1.1. Select!on of RR Estimates C-63
C.5.1.2. Results of Meta-Analyses C-66
C.5.2. Lung Cancer Effect in the Highest Exposure Groups C-70
C.5.2.1. Select!on of RR Estimates C-70
C.5.2.2. Results of Meta-Analyses C-73
C.5.3. Discussion of Lung Cancer Meta-Analysis Results C-74
C.6. DISCUSSION OF STRENGTHS, LIMITATIONS, AND UNCERTAINTIES
IN THE META-ANALYSES C-76
C.7. CONCLUSIONS C-78
D. NEUROLOGICAL EFFECTS OF TCE D-l
D.I. HUMAN STUDIES ON THE NEUROLOGICAL EFFECTS OF TCE D-l
D.I.I. Changes in Nerve Conduction D-l
D. 1.1.1. Blink Reflex and Masseter Reflex Studies—Trigeminal
Nerve D-l
D.I.1.2. TSEP Studies—Trigeminal Nerve D-6
D.I.1.3. Nerve Conduction Velocity Studies D-8
D.1.2. Auditory Effects D-9
D.1.3. VestibularEffects D-12
D.1.4. Visual Effects D-15
D.I.5. Cognition D-17
D.1.6. Psychomotor Effects D-21
D.l.6.1. RT D-21
D.I.6.2. MuscularDyscoordination D-25
D.I.7. Summary Tables D-27
D.2. CNS TOXICITY IN ANIMAL STUDIES FOLLOWING TCE EXPOSURE D-84
D.2.1. Alterations in Nerve Conduction D-84
D.2.2. Auditory Effects D-85
D.2.2.1. Inhalation D-85
D.2.2.2. Oral and Injection Studies D-89
D.2.3. Vestibular System Studies D-89
D.2.4. Visual Effects D-90
D.2.5. Cognitive Function D-91
D.2.6. Psychomotor Effects D-92
D.2.6.1. Loss of Righting Reflex D-93
D.2.6.2. FOB and Locomotor Activity Studies D-93
D.2.6.3. Locomotor Activity D-96
D.2.7. Sleep and Mood Disorders D-97
D.2.7.1. Effects on Mood: Laboratory Animal Findings D-97
D.2.7.2. Sleep Disturbances D-97
D.2.8. Mechanistic Studies D-97
D.2.8.1. Dopaminergic Neurons D-97
D.2.8.2. GABA and Glutamatergic Neurons D-98
D.2.8.3. Demyelination Following TCE Exposure D-100
D.2.9. Summary Tables D-102
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E. ANALYSIS OF LIVER AND CO-EXPOSURE ISSUES FOR THE TCE
TOXICOLOGICAL REVIEW E-l
E. 1. BASIC PHYSIOLOGY AND FUNCTION OF THE LIVER—A STORY OF
HETEROGENEITY E-l
E. 1.1. Heterogeneity of Hepatocytes and Zonal Differences in Function and
Ploidy E-l
E.I.2. Effects of Environment and Age: Variability of Response E-7
E.2. CHARACTERIZATION OF HAZARD FROM TCE STUDIES E-10
E.2.1. Acute Toxicity Studies E-10
E.2.1.1. Sonietal. (1998) E-ll
E.2.1.2. Sonietal. (1999) E-13
E.2.1.3. Okinoetal. (1991) E-14
E.2.1.4. Nunesetal. (2001) E-15
E.2.1.5. Tao et al. (2000) E-16
E.2.1.6. Tucker etal. (1982) E-16
E.2.1.7. Goldsworthy and Popp (1987) E-18
E.2.1.8. Elcombe etal. (1985) E-19
E.2.1.9. Dees and Travis (1993) E-31
E.2.1.10.Nakajimaetal. (2000) E-35
E.2.1. ll.Berman etal. (1995) E-38
E.2.1.12. Melnick etal. (1987) E-39
E.2.1.13. Laughter etal. (2004) E-42
E.2.1.14.Ramdhan etal. (2008) E-46
E.2.1.15.Ramdhan etal. (2010) E-53
E.2.2. Subchronic and Chronic Studies of TCE E-57
E.2.2.1. Merrick etal. (1989) E-58
E.2.2.2. Goel etal. (1992) E-62
E.2.2.3. Kjellstrandetal. (1981b) E-63
E.2.2.4. Woolhiser et al. (2006) E-67
E.2.2.5. Kjellstrandetal. (1983b) E-68
E.2.2.6. Kjellstrandetal. (1983a) E-72
E.2.2.7. Buben and O'Flaherty (1985) E-76
E.2.2.8. Channel et al. (1998) E-79
E.2.2.9. Dorfmueller etal. (1979) E-82
E.2.2.10. Kumar etal. (2001a) E-83
E.2.2.11. Kawamoto etal. (1988b) E-84
E.2.2.12. NTP (1990) E-85
E.2.2.13.NTP(1988) E-89
E.2.2.14. Fukuda etal. (1983) E-91
E.2.2.15. Henschler etal. (1980) E-92
E.2.2.16.Maltoni etal. (1986) E-94
E.2.2.17.Maltoni etal. (1988) E-98
E.2.2.18. Van Duuren etal. (1979) E-98
E.2.2.19.NCI(1976) E-99
E.2.2.20. Herren-Freund etal. (1987) E-103
E.2.2.21. Anna etal. (1994) E-104
E.2.2.22.Bull etal. (2002) E-106
VI
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E.2.3. Mode of Action: Relative Contribution of TCE Metabolites E-108
E.2.3.1. Acute studies of DCA/TCA E-108
E.2.3.2. Subchronic and Chronic Studies of DCA and TCA E-133
E.2.4. Summaries and Comparisons Between TCE, DCA, and TCA Studies E-191
E.2.4.1. Summary of Results For Short-term Effects of TCE E-192
E.2.4.2. Summary of Results For Short-Term Effects of DCA and
TCA: Comparisons With TCE E-199
E.2.4.3. Summary of TCE Subchronic and Chronic Studies E-221
E.2.4.4. Summary of Results for Subchronic and Chronic Effects of
DCA and TCA: Comparisons With TCE E-232
E.2.5. Studies of CH E-254
E.2.6. Serum Bile Acid Assays E-259
E.3. STATE OF SCIENCE OF LIVER CANCER MODES OF ACTION E-261
E.3.1. State of Science for Cancer and Specifically Human Liver Cancer E-263
E.3.1.1. Epigenetics and Disease States (Transgenerational Effects,
Effects of Aging, and Background Changes) E-263
E.3.1.2. Emerging Technologies, DNA and siRNA, miRNA
Microarrays—Promise and Limitations for Modes of Action.... E-270
E.3.1.3. Etiology, Incidence, and Risk Factors for HCC E-279
E.3.1.4. Issues Associated with Target Cell Identification E-282
E.3.1.5. Status ofMechanism of Action for Human HCC E-286
E.3.1.6. Pathway and Genetic Disruption Associated with HCC and
Relationship to Other Forms of Neoplasia E-288
E.3.1.7. Epigenetic Alterations in HCC E-290
E.3.1.8. Heterogeneity of Preneoplastic and HCC Phenotypes E-292
E.3.2. Animal Models of Liver Cancer E-299
E.3.2.1. Similarities with Human and Animal Transgenic Models E-302
E.3.3. Hypothesized Key Events in HCC Using Animal Models E-306
E.3.3.1. Changes in Ploidy E-306
E.3.3.2. Hepatocellular Proliferation and Increased DNA Synthesis E-312
E.3.3.3. Nonparenchymal Cell Involvement in Disease States
Including Cancer E-315
E.3.3.4. Gender Influences on Susceptibility E-322
E.3.3.5. Epigenomic Modification E-324
E.3.4. Specific Hypothesis for Mode of Action of TCE
Hepatocarcinogenicity in Rodents E-326
E.3.4.1. PPARa Agonism as the Mode of Action for Liver Tumor
Induction—The State of the Hypothesis E-326
E.3.4.2. Other TCE Metabolite Effects That May Contribute to its
Hepatocarcinogenicity E-357
E.4. EFFECTS OF CO-EXPOSURES ON MODE OF ACTION—INTERNAL
AND EXTERNAL EXPOSURES TO MIXTURES INCLUDING ALCOHOL..E-367
E.4.1. Internal Co-exposures to TCE Metabolites: Modulation of Toxicity
and Implications for TCE Mode of Action E-369
E.4.2. Initiation Studies as Co-exposures E-369
E.4.2.1. Herren-Freund et al. (1987) E-370
E.4.2.2. Parnell et al. (1986) E-371
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E.4.2.3. Pereira and Phelps (1996) E-372
E.4.2.4. Tao et al. (2000) E-376
E.4.2.5. Latendresse and Pereira (1997) E-377
E.4.2.6. Pereira etal. (1997) E-379
E.4.2.7. Tao etal. (1998) E-381
E.4.2.8. Stauber etal. (1998) E-382
E.4.3. Co-exposures of Haloacetates and Other Solvents E-384
E.4.3.1. Carbon tetrachloride, DCA, TCA: Implications for Mode of
Action from Co-exposures E-384
E.4.3.2. Chloroform, DCA, and TCA Coexposures: Changes in
Methylation Status E-386
E.4.3.3. Co-exposures to Brominated Haloacetates: Implications for
Common Modes of Action and Background Additivity to
Toxicity E-388
E.4.3.4. Co-exposures to Ethanol: Common Targets and Modes of
Action E-391
E.4.3.5. Co-exposure Effects on Pharmacokinetics: Predictions Using
PBPK Models E-392
E.5. POTENTIALLY SUSCEPTIBLE LIFE STAGES AND CONDITIONS
THAT MAY ALTER RISK OF LIVER TOXICITY AND CANCER E-395
E.6. UNCERTAINTY AND VARIABILITY E-396
F.NONCANCER DOSE-RESPONSE ANALYSES F-l
F.I. DATA SOURCES F-l
F.2. DOSIMETRY F-l
F.2.1. Estimates of TCE in Air From Urinary Metabolite Data Using Ikeda et
al. (1972) F-l
F.2.1.1. Results for Chia et al. (1996) F-l
F.2.1.2. Results for Mhiri etal. (2004) F-4
F.2.2. Dose Adjustments to Applied Doses for Intermittent Exposure F-4
F.2.3. Estimation of the Applied Doses for the Oral Exposures via Drinking
Water and Feed F-5
F.2.4. PBPK Model-Based Internal Dose-Metrics F-6
F.3. DOSE-RESPONSE MODELING PROCEDURES F-6
F.3.1. Models for Dichotomous Response Data F-6
F.3.1.1. Quantal Models F-6
F.3.1.2. Nested Dichotomous Models F-7
F.3.2. Models for Continuous Response Data F-7
F.3.3. Model Selection F-7
F.3.4. Additional Adjustments for Selected Data Sets F-8
F.4. DOSE-RESPONSE MODELING RESULTS F-8
F.4.1. Quantal Dichotomous and Continuous Modeling Results F-8
F.4.2. Nested Dichotomous Modeling Results F-9
F.4.2.1. Johnson etal. (2003) Fetal Cardiac Defects F-9
F.4.2.2. Narotsky etal. (1995) F-15
F.4.3. Model Selections and Results F-19
viii
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F.5. DERIVATION OF POINTS OF DEPARTURE F-26
F.5.1. Applied Dose Points of Departure F-26
F.5.2. PBPK Model-Based Human Points of Departure F-26
F.6. SUMMARY OF POINTS OF DEPARTURE (PODs) FOR STUDIES AND
EFFECTS SUPPORTING THE INHALATION RfC AND ORAL RfD F-27
F.6.1. NTP (NTP, 1988)—BMD Modeling of Toxic Nephropathy in Rats F-27
F.6.1.1. Dosimetry and BMD Modeling F-28
F.6.1.2. Derivation of HEC99 and HED99 F-28
F.6.2. Woolhiser et al. (2006)—BMD Modeling of Increased Kidney Weight
in Rats F-30
F.6.2.1. Dosimetry and BMD Modeling F-30
F.6.2.2. Derivation of HEC99 and HED99 F-32
F.6.3. Keil et al. (2009)—LOAEL for Decreased Thymus Weight in Mice F-32
F.6.3.1. Dosimetry F-33
F.6.3.2. Derivation of HEC99 and HED99 F-33
F.6.4. Johnson et al. (2003)—BMD Modeling of Fetal Heart Malformations
in Rats F-34
F.6.4.1. Dosimetry and BMD Modeling F-34
F.6.4.2. Derivation of HEC99 and HED99 F-34
F.6.5. Peden-Adams et al. (2006)—LOAEL for Decreased PFC Response
and Increased Delayed-Type Hypersensitivity in Mice F-3 5
G. TCE CANCER DOSE-RESPONSE ANALYSES WITH RODENT CANCER
BIOASSAYDATA G-l
Gl. DATA SOURCES G-l
G.I.I. Numbers at Risk G-l
G.I.2. Cumulative Incidence G-2
G.2. INTERNAL DOSE-METRICS AND DOSE ADJUSTMENTS G-2
G.3. DOSE ADJUSTMENTS FOR INTERMITTENT EXPOSURE G-3
G.4. RODENT TO HUMAN DOSE EXTRAPOLATION G-4
G.5. COMBINING DATA FROM RELATED EXPERIMENTS IN MALTONI ET
AL. (1986) G-5
G.6. DOSE-RESPONSE MODELING RESULTS G-10
G.7. MODELING TO ACCOUNT FOR DOSE GROUPS DIFFERING IN
SURVIVAL TIMES G-ll
G.7.1. Time-to-TumorModeling G-ll
G.I2. Poly-3 Calculation of Adjusted Number at Risk G-12
G.8. COMBINED RISK FROM MULTIPLE TUMOR SITES G-13
G.8.1. Methods G-13
G.8.1.1. Single Tumor Sites G-13
G.8.1.2. Combined Risk From Multiple Tumor Sites G-14
G.8.2. Results G-15
G.9. PBPK-MODEL UNCERTAINTY ANALYSIS OF UNIT RISK ESTIMATES... G-34
IX
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H. LIFETABLE ANALYSIS AND WEIGHTED LINEAR REGRESSION BASED ON
RESULTS FROM CHARBOTEL ET AL. (2006) H-l
H.I. LIFETABLE ANALYSIS H-l
H.2. EQUATIONS USED FOR WEIGHTED LINEAR REGRESSION OF
RESULTS FROM CHARBOTEL ET AL. (2006) [source: Rothman (1986), p.
343-344] H-l
I. EPA Response to Major Peer Review and Public Comments 1-1
I.I. PBPK Modeling (SAB Report Section 1): Comments and EPA Response 1-1
1.1.1. SAB Overall Comments: 1-1
1.1.2. Major SAB Recommendations and EPA Response: 1-1
1.1.2.1. PBPK Model Structure (SAB Report Section la) 1-1
1.1.2.2. Bayesian Statistical Approach (SAB Report Section Ib) 1-2
1.1.2.3. Parameter Calibration (SAB Report Section Ic) 1-2
1.1.2.4. Model Fit Assessment and Dose-Metric Projections (SAB
Report Section Id) 1-3
1.1.2.5. Lack of Adequate Sensitivity Analysis (SAB Report Section
le) 1-4
1.1.3. Summary of Major Public Comments and EPA Responses: 1-4
1.2. Meta-Analyses of Cancer Epidemiology (SAB Report Section 2): Comments
and EPA Response 1-5
1.2.1. SAB Overall Comments: 1-5
1.2.2. Major SAB Recommendations and EPA Response: 1-5
1.2.3. Summary of Major Public Comments and EPA Responses: 1-7
1.3. NonCancer Hazard Assessment (SAB Report Section 3): Comments and EPA
Response 1-8
1.3.1. SAB Overall Comments: 1-8
1.3.2. Major SAB Recommendations and EPA Response: 1-8
1.3.3. Summary of Major Public Comments and EPA Responses: 1-9
1.4. Carcinogenic Weight of Evidence (SAB Report Section 4): Comments and
EPA Response 1-9
1.4.1. SAB Overall Comments: 1-9
1.4.2. Major SAB Recommendations and EPA Response: 1-10
1.4.3. Summary of Major Public Comments and EPA Responses: 1-11
1.5. Role of Metabolism (SAB Report Section 5): Comments and EPA Response 1-11
1.5.1. SAB Overall Comments: 1-11
1.5.2. Major SAB Recommendations and EPA Response: 1-12
1.5.2.1. Mediation of TCE-Induced Liver Effects by Oxidative
Metabolism (SAB Report Section 5a) 1-12
1.5.2.2. Contribution of TCA to Adverse effects on the Liver (SAB
Report Section 5b) 1-12
1.5.2.3. Role of GSH-Conjugation Pathway on TCE-Induced Kidney
Effects (SAB Report Section 5c) 1-13
1.5.3. Summary of Major Public Comments and EPA Responses: 1-14
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1.6. Mode of Action (SAB Report Section 6): Comments and EPA Response 1-14
1.6.1. SAB Overall Comments: 1-14
1.6.2. Major SAB Recommendations and EPA Response: 1-15
1.6.2.1. Hazard Assessment and Mode of Action (SAB Report
Section 6a) 1-15
1.6.2.2. Mode of Action for TCE-Induced Kidney Tumors (SAB
Report Section 6b) 1-16
1.6.2.3. Inadequate Support for PPARa Agonism and its Sequellae
Being Key Events in TCE-Induced Liver Carcinogenesis
(SAB Report Section 6c) 1-16
1.6.2.4. Inadequate Data to specify Key Events and Modes of Action
Involved in Other TCE-Induced Cancer and Noncancer
Effects (SAB Report Section 6d) 1-16
1.6.2.5. Human Relevance of TCE-Induced Cancer and Noncancer
Effects in Rodents (SAB Report Section 6e) 1-16
1.6.3. Summary of Major Public Comments and EPA Responses: 1-17
1.7. Susceptible Populations (SAB Report Section 7): Comments and EPA
Response 1-17
1.7.1. SAB Overall Comment: 1-17
1.7.2. Major SAB Recommendations and EPA Response: 1-18
1.7.3. Summary of Major Public Comments and EPA Responses: 1-19
1.8. NonCancer Dose-Response Assessment (SAB Report Section 8): Comments
and EPA Response 1-20
1.8.1. SAB Overall Comments 1-20
1.8.1.1. Select!on of Critical Studies and Effects 1-20
1.8.1.2. Derivation of RfD and RfC 1-20
1.8.1.3. UFs 1-21
1.8.2. Major SAB Recommendations and EPA Response: 1-21
1.8.2.1. The Screening, Evaluation, and Selection of Candidate
Critical Studies and Effects (SAB Report Section 8a) 1-21
1.8.2.2. The PODs, Including those Derived from BMD Modeling
(e.g., Selection of Dose-Response Models, BMR Levels)
(SAB Report Section 8b) 1-22
1.8.2.3. The Selected PBPK-Based Dose-Metrics for Inter-Species,
Intra-Species, and Route-to-Route Extrapolation, Including
the Use of Body Weight to the 3A Power Scaling for Some
Dose-Metrics (SAB Report Section 8c) 1-22
1.8.2.4. UFs (SAB Report Section 8d) 1-23
1.8.2.5. The Equivalent Doses and Concentrations for Sensitive
Humans Developed from PBPK Modeling to Replace
Standard Ufs for Inter- and Intra-Species Toxicokinetics,
Including Selection of the 99th Percentile for Overall
Uncertainty and Variability to Represent the
Toxicokinetically-Sensitive Individual (SAB Report Section
8e) 1-24
1.8.2.6. The Qualitative and Quantitative Characterization of
Uncertainty and Variability (SAB Report Section 8f) 1-24
XI
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1.8.2.7. The Selection of NTP (1988) [Toxic Nephropathy], NCI
(1976) [Toxic Nephrosis], Woolhiser et al. (2006) [Increased
Kidney Weights], Keil et al. (2009) [Decreased Thymus
Weights and Increased Anti-dsDNA and Anti-ssDNA
Antibodies], Peden-Adams et al. (2008) [Developmental
Immunotoxicity], and Johnson et al. (2003) [Fetal Heart
Malformations] as the Critical Studies and Effects for
Noncancer Dose-Response Assessment (SAB Report Section
8g) 1-25
1.8.2.8. The Selection of the Draft RfC and RfD on the Basis of
Multiple Critical Effects for Which Candidate Reference
Values are in a Narrow Range at the Low End of the Full
Range of Candidate Critical Effects, Rather than on the Basis
of the Single Most Sensitive Critical Effect (SAB Report
Section 8h) 1-25
1.8.3. Summary of Major Public Comments and EPA Responses: 1-25
1.9. Cancer Dose-Response Assessment (Inhalation Unit Risk and Oral Unit Risk)
(SAB Report Section 9): Comments and EPA Response 1-27
1.9.1. SAB Overall Comment: 1-27
1.9.2. Major SAB Recommendations and EPA Response: 1-27
1.9.2.1. Estimation of Unit Risks forRCC (SAB Report Section 9a) 1-27
1.9.2.2. Adjustment of RCC Unit Risks (SAB Report Section 9b) 1-28
1.9.2.3. Estimation of Human Unit Risks from Rodent Bioassays
(SAB Report Section 9c) 1-28
1.9.2.4. Use of Linear Extrapolation for Cancer Dose-Response
Assessment (SAB Report Section 9d) 1-29
1.9.2.5. Application of PBPK Modeling (SAB Report Section 9e) 1-29
1.9.2.6. Qualitative and Quantitative Characterization of Uncertainty
and Variability (SAB Report Section 9f) 1-29
1.9.2.7. Conclusion on the Consistency of Unit Risk Estimates Based
on Human Epidemiologic Data and Rodent Bioassay Data
(SAB Report Section 9g) 1-29
1.9.2.8. Preference for the Unit Risk Estimates based on Human
Epidemiologic Data (SAB Report Section 9h) 1-29
1.9.3. Summary of Major Public Comments and EPA Responses: 1-29
1.10. ADAFs (SAB Report Section 10): Comments and EPA Response 1-30
1.10.1. SAB Overall Comment: 1-30
1.10.2. Major SAB Recommendations and EPA Response: 1-30
1.10.3. Summary of Major Public Comments and EPA Responses: 1-31
1.11. Additional key studies (SAB Report Section 11) and editorial comments:
Comments and EPA Response 1-31
xn
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LIST OF TABLES
Table A-l. Evaluation of Hack et al. (2006) PBPK model predictions for in vivo data in mice A-
9
Table A-2. Evaluation of Hack et al. (2006) PBPK model predictions for in vivo data in rats .. A-
16
Table A-3. Evaluation of Hack et al. (2006) PBPK model predictions for in vivo data in humans
A-24
Table A-4. PBPK model parameters, baseline values, and scaling relationships A-39
Table A-5. Uncertainty distributions for the population mean of the PBPK model parameters . A-
66
Table A-6. Updated prior distributions for selected parameters in the rat and human A-71
Table A-7. Uncertainty distributions for the population variance of the PBPK model parameters
A-75
Table A-8. Measurements used for calibration A-79
Table A-9. Posterior distributions for mouse PBPK model population parameters A-84
Table A-10. Posterior distributions for mouse residual errors A-86
Table A-11. Posterior correlations for mouse population mean parameters A-87
Table A-12. Posterior distributions for rat PBPK model population parameters A-94
Table A-13. Posterior distributions for rat residual errors A-96
Table A-14. Posterior correlations for rat population mean parameters A-98
Table A-15. Posterior distributions for human PBPK model population parameters A-105
Table A-16. Posterior distributions for human residual errors A-107
Table A-17. Posterior correlations for human population mean parameters A-108
Table A-l 8. Summary characteristics of model runs A-185
Table B-l. Description of epidemiologic cohort and PMR studies assessing cancer and TCE
exposure B-3
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure B-9
Table B-3. Geographic-based studies assessing cancer and TCE exposure B-20
Table B-4. Approximate statistical power (%) in cohort and geographic-based studies to detect
anRR = 2 B-37
Table B-5. Summary of rationale for study selection for meta-analysis B-54
Table B-6. Characteristics of epidemiologic investigations of Rocketdyne workers B-71
Table C-l. Selected RR estimates for NHL associated with TCE exposure (overall effect) from
cohort studies C-6
Table C-2. Selected RR estimates for NHL associated with TCE exposure from case-control
studies'1 C-7
Xlll
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Table C-3. Summary of some meta-analysis results for TCE (overall) and NHL C-13
Table C-4. Selected RR estimates for NHL risk in highest TCE exposure groups C-18
Table C-5. Summary of some meta-analysis results for TCE (highest exposure groups) and NHL
C-23
Table C-6. Selected RR estimates for kidney cancer associated with TCE exposure (overall
effect) from cohort studies C-29
Table C-7. Selected RR estimates for RCC associated with TCE exposure from case-control
studies'1 C-30
Table C-8. Summary of some meta-analysis results for TCE (overall) and kidney cancer C-35
Table C-9. Selected RR estimates for kidney cancer risk in highest TCE exposure groups... C-39
Table C-10. Summary of some meta-analysis results for TCE (highest exposure groups) and
kidney cancer C-45
Table C-l 1. Selected RR estimates for liver cancer associated with TCE exposure (overall
effect) from cohort studies C-50
Table C-12. Summary of some meta-analysis results for TCE and liver cancer C-54
Table C-13. Selected RR estimates for liver cancer risk in highest TCE exposure groups C-58
Table C-14. Selected RR estimates for lung (& bronchus) cancer associated with TCE exposure
(overall effect) from cohort studies C-64
Table C-l5. Summary of some meta-analysis results for TCE and lung cancer C-69
Table C-l6. Selected RR estimates for lung cancer risk in highest TCE exposure groups C-72
Table C-l7. Summary of some meta-analysis results for TCE (highest exposure groups) and
lung cancer C-75
Table D-l. Epidemiological studies: Neurological effects of TCE D-28
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents D-64
Table D-3. Literature review of studies of TCE and domains assessed with
neurobehavioral/neurological methods D-82
Table D-4. Summary of mammalian in vivo trigeminal nerve studies D-103
Table D-5. Summary of mammalian in vivo ototoxicity studies D-104
Table D-6. Summary of mammalian sensory studies—vestibular and visual systems D-l06
Table D-7. Summary of mammalian cognition studies D-107
Table D-8. Summary of mammalian psychomotor function, locomotor activity, and RT studies
D-108
Table D-9. Summary of mammalian in vivo dopamine neuronal studies D-l 10
Table D-10. Summary of neurochemical effects with TCE exposure D-l 11
Table D-ll. Summary of in vitro ion channel effects with TCE exposure D-l 13
Table D-12. Summary of mammalian in vivo developmental neurotoxicity studies—oral
exposures D-l 14
Table E-l. Mice data for 13 weeks: mean body and liver weights E-85
xiv
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Table E-2. Prevalence and multiplicity data from DeAngelo et al. (1999) E-146
Table E-3. Difference in pathology by inclusion of unscheduled deaths from DeAngelo et al.
(1999) E-146
Table E-4. Comparison of data from Carter et al. (2003) and DeAngelo et al. (1999) E-151
Table E-5. Prevalence of foci and tumors in mice administered NaCl, DCA, or TCA from
Pereira(1996) E-156
Table E-6. Multiplicity of foci and tumors in mice administered NaCl, DCA, or TCA from
Pereira(1996) E-157
Table E-7. Phenotype of foci reported in mice exposed to NaCl, DCA, or TCA by Pereira (1996)
E-158
Table E-8. Phenotype of tumors reported in mice exposed NaCl, DCA, or TCA by Pereira
(1996) E-158
Table E-9. Multiplicity and incidence data (31 week treatment) from Pereira and Phelps (1996)
E-159
Table E-10. Comparison of descriptions of control data between George et al. (2000) and
DeAngelo et al. (2008) E-175
Table E-l 1. TCA-induced increases in liver tumor occurrence and other parameter over control
after 60 weeks (Study #1) E-182
Table E-12. TCA-induced increases in liver tumor occurrence after 104 weeks (Studies #2 and
#3) E-185
Table E-13. Comparison of liver effects from TCE, TCA, and DCA (10-day exposures in mice)
E-202
Table E-14. Liver weight induction as percent liver/body weight fold-of-control in male B6C3Fi
mice from DCA or TCA drinking water studies E-205
Table E-l5. Liver weight induction as percent liver/body weight fold-of-control in male B6C3Fi
or Swiss mice from TCE gavage studies E-206
Table E-16. B6C3Fi and Swiss (data sets combined) E-207
Table E-l7. Power calculations21 for experimental design described in text, using Pereira and
colleagues (1996) as an example E-242
Table E-18. Comparison between results for Yang et al. (2007) and Cheung et al. (2004)a..E-350
Table F-l. Dose-response data from Chia et al. (1996) F-l
Table F-2. Data on TCE in air (ppm) and urinary metabolite concentrations in workers reported
bylkedaetal. (1972) F-2
Table F-3. Estimated urinary metabolite and TCE air concentrations in dose groups from Chia et
al. (1996) F-4
Table F-4. Data on fetuses and litters with abnormal hearts from Johnson et al. (2003) F-9
Table F-5. Comparison of observed and predicted numbers of fetuses with abnormal hearts from
Johnson et al. (2003), with and without the high-dose group, using a nested model ....F-10
xv
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Table F-6. Results of nested log-logistic model for fetal cardiac anomalies from Johnson et al.
(2003) without the high-dose group, on the basis of applied dose (mg/kg/day in drinking
water) F-10
Table F-7. Results of nested log-logistic model for fetal cardiac anomalies from Johnson et al.
(2003) without the high-dose group, using the TotOxMetabBW34 dose-metric F-12
Table F-8. Results of nested log-logistic model for fetal cardiac anomalies from Johnson et al.
(2003) without the high-dose group, using the AUCCBld dose-metric F-14
Table F-9. Analysis of LSCs with respect to dose from Narotsky etal. (1995) F-15
Table F-10. Results of nested log-logistic and Rai-VanRyzin model for fetal eye defects from
Narotsky et al. (1995), on the basis of applied dose (mg/kg/day in drinking water) F-16
Table F-l 1. Comparison of results of nested log-logistic (without LSC or 1C) and quantal
log-logistic model for fetal eye defects from Narotsky et al. (1995) F-18
Table F-12. Results of nested log-logistic and Rai-VanRyzin model for prenatal loss from
Narotsky et al. (1995), on the basis of applied dose (mg/kg/day in drinking water) F-19
Table F-13. Model selections and results for noncancer dose-response analyses F-22
Table G-l. Internal dose-metrics used in dose-response analyses, identified by "X" G-2
Table G-2. Experiments BT304 and BT304bis, female Sprague-Dawley rats, Maltoni et al.
(1986). Number alive is reported for week of first tumor observation in either males or
females.a These data were not used for dose-response modeling because there is no
consistent trend (for the combined data, there is no significant trend by the
Cochran-Armitage test, and no significant differences between control and dose groups
by Fisher's exact test) G-6
Table G-3. Experiments BT304 and BT304bis, male Sprague-Dawley rats, Maltoni et al. (1986):
leukemias. Number alive is reported for week of first tumor observation in either males
or females.a G-7
Table G-4. Experiments BT304 and BT304bis, male Sprague-Dawley rats, Maltoni et al. (1986):
kidney adenomas + carcinomas. Number alive is reported for week of first tumor
observation in either males or females.a G-8
Table G-5. Experiments BT304 and BT304bis, male Sprague-Dawley rats, Maltoni et al. (1986):
testis, Ley dig cell tumors. Number alive is reported for week of first tumor observation.21
G-9
Table G-6. Rodent to human conversions for internal dose-metric TotOxMetabBW34 G-14
Table G-7. Rodent to human conversions for internal dose-metric TotMetabBW34 G-14
Table G-8. Female B6C3Fi mice—applied doses: data G-l6
Table G-9. Female B6C3Fi mice—applied doses: model selection comparison of model fit
statistics for multistage models of increasing order G-16
Table G-10. Female B6C3Fi mice—applied doses: BMD and risk estimates (inferences for
BMRofO.05 extra risk at 95% confidence level) G-17
xvi
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Table G-ll. B6C3Fi female mice inhalation exposure—applied doses G-19
Table G-12. B6C3Fi female mice—applied doses: model selection comparison of model fit
statistics for multistage models of increasing order G-19
Table G-13. B6C3Fi female mice inhalation exposure—applied doses (inferences for 0.05 extra
risk at 95% confidence level) G-20
Table G-14. Maltoni Sprague-Dawley male rats—applied doses G-22
Table G-15. Maltoni Sprague-Dawley male rats—applied doses: model selection comparison of
model fit statistics for multistage models of increasing order G-22
Table G-16. Maltoni Sprague-Dawley male rats—applied doses G-23
Table G-17. Female B6C3Fi mice—internal dose-metric (total oxidative metabolism): dataG-25
Table G-18. Female B6C3Fi mice—internal dose: model selection comparison of model fit
statistics for multistage models of increasing order G-25
Table G-19. Female B6C3Fi mice—internal dose-metric (total oxidative metabolism): BMD
and risk estimates (values rounded to 4 significant figures) (inferences for BMR of 0.05
extra risk at 95% confidence level) G-26
Table G-20. B6C3Fi female mice inhalation exposure—internal dose-metric (total oxidative
metabolism) G-28
Table G-21. B6C3Fi female mice—internal dose: model selection comparison of model fit
statistics for multistage models of increasing order G-28
Table G-22. B6C3Fi female mice inhalation exposure—internal dose-metric (total oxidative
metabolism) (inferences for 0.05 extra risk at 95% confidence level) G-29
Table G-23. Maltoni Sprague-Dawley male rats—internal dose-metric (total metabolism)... G-31
Table G-24. Maltoni Sprague-Dawley male rats—internal dose model selection comparison of
model fit statistics for multistage models of increasing order G-31
Table G-25. Maltoni Sprague-Dawley male rats—internal dose-metric (total metabolism)
(inferences for 0.01 extra risk at 95% confidence level) G-32
xvn
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LIST OF FIGURES
Figure A-l. Hierarchical population statistical model for PBPK model parameter uncertainty
and variability A-2
Figure A-2. Schematic of how posterior predictions were generated for comparison with
experimental data A-8
Figure A-3. Limited optimization results for male closed-chamber data from Fisher et al. (1991)
without (top) and with (bottom) respiratory metabolism A-35
Figure A-4. Limited optimization results for female closed-chamber data from Fisher et al.
(1991) without (top) and with (bottom) respiratory metabolism A-36
Figure A-5. Respiratory metabolism model for updated PBPK model A-37
Figure A-6. Submodel for TCE gas exchange, respiratory metabolism, and arterial blood
concentration A-50
Figure A-7 Submodel for TCE oral absorption, tissue distribution, and metabolism A-51
Figure A-8. Submodel for TCOH A-55
Figure A-9. Submodel for TCOG A-58
Figure A-10. Submodel for TCA A-60
Figure A-l 1. Submodel for TCE GSH conjugation metabolites A-63
Figure A-12. Updated hierarchical structure for rat and human models A-81
Figure A-13. Prior and posterior mouse population mean parameters (Part 1) A-88
Figure A-14. Prior and posterior mouse population mean parameters (Part 2) A-89
Figure A-l5. Prior and posterior mouse population mean parameters (Part 3) A-90
Figure A-16. Prior and posterior mouse population variance parameters (Part 1) A-91
Figure A-17. Prior and posterior mouse population variance parameters (Part 2) A-92
Figure A-18. Prior and posterior mouse population variance parameters (Part 3) A-93
Figure A-19. Prior and posterior rat population mean parameters (Part 1) A-99
Figure A-20. Prior and posterior rat population mean parameters (Part 2) A-100
Figure A-21. Prior and posterior rat population mean parameters (Part 3) A-101
Figure A-22. Prior and posterior rat population variance parameters (Part 1) A-102
Figure A-23. Prior and posterior rat population variance parameters (Part 2) A-103
Figure A-24. Prior and posterior rat population variance parameters (Part 3) A-104
Figure A-25. Prior and posterior human population mean parameters (Part 1) A-109
Figure A-26. Prior and posterior human population mean parameters (Part 2) A-110
Figure A-27. Prior and posterior human population mean parameters (Part 3) A-111
Figure A-28. Prior and posterior human population variance parameters (Part 1) A-l 12
Figure A-29. Prior and posterior human population variance parameters (Part 2) A-113
Figure A-30. Prior and posterior human population variance parameters (Part 3) A-l 14
xvin
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Figure A-31. Comparison of mouse calibration data (boxes) and PBPK model predictions (red
line: using the posterior mean of the subject-specific parameters; + with error bars: single
data points; or shaded regions: 2.5, 25, 50, 75, and 97.5% population-based predictions).
A-116
Figure A-32. Comparison of rat calibration data (boxes) and PBPK model predictions (red line:
using the posterior mean of the subject-specific parameters; + with error bars: single data
points; or shaded regions: 2.5, 25, 50, 75, and 97.5% population-based predictions) A-
126
Figure A-33. Comparison of rat evaluation data (boxes) and PBPK model predictions (+ with
error bars: single data points or shaded regions: 2.5, 25, 50, 75, and
97.5% population-based predictions) A-137
Figure A-34. Comparison of human calibration data (boxes) and PBPK model predictions (red
line: using the posterior mean of the subject-specific parameters; + with error bars: single
data points; or shaded regions: 2.5, 25, 50, 75, and 97.5% population-based predictions).
A-140
Figure A-3 5. Comparison of human evaluation data (boxes) and PBPK model predictions (+
with error bars: single data points or shaded regions: 2.5, 25, 50, 75, and
97.5% population-based predictions) A-165
Figure A-36. Comparison of Kim et al. (2009) mouse data (boxes) and PBPK model predictions
(+ with error bars: single data points or shaded regions: 2.5, 25, 50, 75, and
97.5% population-based predictions) A-175
Figure A-37. Comparison of best-fitting (out of 50,000 posterior samples) PBPK model
prediction and Kim et al. (2009) TCA blood concentration data for mice gavaged with
2,140 mg/kgTCE A-176
Figure A-38. Comparison of best-fitting (out of 50,000 posterior samples) PBPK model
prediction and Kim et al. (2009) DCVG blood concentration data for mice gavaged with
2,140 mg/kgTCE A-177
Figure A-39. PBPK model predictions for the fraction of intake undergoing GSH conjugation in
mice continuously exposed orally to TCE A-178
Figure A-40. PBPK model predictions for the fraction of intake undergoing GSH conjugation in
mice continuously exposed via inhalation to TCE A-179
Figure A-41. Comparison of Liu et al. (2009) rat data (boxes) and PBPK model predictions (+
with error bars: single data points or shaded regions: 2.5, 25, 50, 75, and 97.5%
population-based predictions) A-180
Figure A-42. Assumed drinking water patterns as a function of time since beginning of
exposure A-184
Figure A-43. PBPK model predictions for TCA in blood and liver of male B6C3Fi mice from
Mahleetal. (1999) A-186
xix
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Figure A-44. PBPK model predictions for TCA in blood and liver of male B6C3Fi mice from
Green (2003a, 2003b) A-187
Figure A-45. Distribution of fractional absorption fit to each TCA drinking water kinetic study
group in mice, using LFIL drinking water intake patterns A-188
Figure C-l. Meta-analysis of NHL and overall TCE exposure. Rectangle sizes reflect relative
weights of the individual studies. The bottom diamond represents the summary RR
estimate C-ll
Figure C-2. Funnel plot of SE by log RR estimate for TCE and NHL studies C-l6
Figure C-3. Cumulative meta-analysis of TCE and NHL studies, progressively including studies
with increasing SEs C-l7
Figure C-4. Meta-analysis of NHL and TCE exposure—highest exposure groups. Rectangle
sizes reflect relative weights of the individual studies. The bottom diamond represents
the RRm estimate C-24
Figure C-5. Meta-analysis of kidney cancer and overall TCE exposure. Random-effects model;
fixed-effect model same. Rectangle sizes reflect relative weights of the individual
studies. The summary estimate is in the bottom row, represented by the diamond. ... C-36
Figure C-6. Funnel plot of SE by log RR estimate for TCE and kidney cancer studies C-37
Figure E-l. Comparison of average fold-changes in relative liver weight to control and exposure
concentrations of <2 g/L in drinking water for TCA and DCA in male B6C3Fi mice for
14-30 days E-209
Figure E-2. Comparisons of fold-changes in average relative liver weight and gavage dose of
(top panel) male B6C3Fi mice for 10-28 days of exposure and (bottom panel) in male
B6C3Fi and Swiss mice E-211
Figure E-3. Comparison of fold-changes in relative liver weight for data sets in male B6C3Fi,
Swiss, and NRMI mice between TCE studies [duration 28-42 days]) and studies of direct
oral TCA administration to B6C3Fi mice [duration 14-28 days]) E-214
Figure E-4. Fold-changes in relative liver weight for data sets in male B6C3Fi, Swiss, and
NRMI mice reported by TCE studies of duration 28-42 days using internal dose-metrics
predicted by the PBPK model described in Section 3.5: (A) dose-metric is the median
estimate of the daily AUC of TCE in blood, (B) dose-metric is the median estimate of the
total daily rate of TCE oxidation E-216
Figure E-5. Comparison of Ito et al. and David et al. data for DEHP tumor induction from
(Guyton et al., 2009) E-335
Figure F-l. Regression of TCE in air (ppm) and TCA in urine (mg/g creatinine) based on data
from Ikedaetal. (1972) F-3
Figure F-2. BMD modeling of Johnson et al. (2003) using nested log-logistic model, with
applied dose, without LSC, with 1C, and without the high-dose group, using a BMR of
0.05 extra risk (top panel) or 0.01 extra risk (bottom panel) F-l 1
xx
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Figure F-3. BMD modeling of Johnson et al. (2003) using nested log-logistic model, with
TotOxMetabBW34 dose-metric, without LSC, with 1C, and without the high-dose group,
using a BMR of 0.01 extra risk F-13
Figure F-4. BMD modeling of Johnson et al. (2003) using nested log-logistic model, with
AUCCBld dose-metric, without LSC, with 1C, and without the high-dose group, using a
BMR of 0.01 extra risk F-14
Figure F-5. BMD modeling of fetal eye defects from Narotsky et al. (1995) using nested
log-logistic model, with applied dose, with both LSC and 1C, using a BMR of 0.05 extra
risk F-17
Figure F-6. BMD modeling of fetal eye defects from Narotsky et al. (1995) using nested
log-logistic model, with applied dose, without either LSC or 1C, using a BMR of 0.05
extra risk F-17
Figure F-7. BMD modeling of fetal eye defects from Narotsky et al. (1995) using nested
Rai-VanRyzin model, with applied dose, without either LSC or 1C, using a BMR of 0.05
extra risk F-18
Figure F-8. BMD modeling of prenatal loss reported in Narotsky et al. (1995) using nested
log-logistic model, with applied dose, without LSC, with 1C, using a BMR of 0.05 extra
risk (top panel) or 0.01 extra risk (bottom panel) F-20
Figure F-9. BMD modeling of prenatal loss reported in Narotsky et al. (1995) using nested
Rai-VanRyzin model, with applied dose, without LSC, with 1C, using a BMR of 0.05
extra risk (top panel) or 0.01 extra risk (bottom panel) F-21
Figure F-10. BMD modeling of NTP (1988) toxic nephropathy in female Marshall rats F-29
Figure F-l 1. Derivation of FIECgg and FIEDgg corresponding to the rodent idPOD from NTP
(1988) toxic nephropathy in rats F-30
Figure F-12. BMD modeling of Woolhiser et al. (2006) for increased kidney weight in female
Sprague-Dawley rats F-31
Figure F-13. Derivation of FIECgg and FIEDgg corresponding to the rodent idPOD from
Woolhiser et al. (2006) for increased kidney weight in rats F-32
Figure F-14. Derivation of FIECgg and FIEDgg corresponding to the rodent idPOD from
Keil et al. (2009) for decreased thymus weight in mice F-33
Figure F-l5. Derivation of FIECgg and FIEDgg corresponding to the rodent idPOD from Johnson
et al. (2003) for increased fetal cardiac malformations in female Sprague-Dawley rats
using the total oxidative metabolism dose-metric F-35
Figure G-l. Female B6C3Fi mice—applied doses: combined and individual tumor extra-risk
functions G-l 8
Figure G-2. Female B6C3Fi mice—applied doses: posterior distribution of BMDc for combined
risk G-18
xxi
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Figure G-3. B6C3Fi female mice inhalation exposure—applied doses: combined and individual
tumor extra-risk functions G-21
Figure G-4. B6C3Fi female mice inhalation exposure—applied doses: posterior distribution of
BMDc for combined risk G-21
Figure G-5. Maltoni Sprague-Dawley male rats—applied doses: combined and individual tumor
extra-risk functions G-24
Figure G-6. Maltoni Sprague-Dawley male rats—applied doses: posterior distribution of BMDc
for combined risk G-24
Figure G-7. Female B6C3Fi mice—internal dose-metric (total oxidative metabolism): combined
and individual tumor extra-risk functions G-27
Figure G-8. Female B6C3Fi mice—internal dose-metric (total oxidative metabolism): posterior
distribution of BMDc for combined risk G-27
Figure G-9. B6C3Fi female mice inhalation exposure—internal dose-metric: combined and
individual tumor extra-risk functions G-30
Figure G-10. B6C3Fi female mice inhalation exposure—internal dose-metric: posterior
distribution of BMDc for combined risk G-30
Figure G-ll. Maltoni Sprague-Dawley male rats—internal dose-metric: combined and
individual tumor extra-risk functions G-33
Figure G-12. Maltoni Sprague-Dawley male rats—internal dose-metric: posterior distribution of
BMDc for combined risk G-33
xxn
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A. PBPK MODELING OF TCE AND METABOLITES—DETAILED METHODS AND
RESULTS
A.l. THE HIERARCHICAL BAYESIAN APPROACH TO CHARACTERIZING PBPK
MODEL UNCERTAINTY AND VARIABILITY
The Bayesian approach for characterizing uncertainty and variability in PBPK model
parameters, used previously for TCE in Bois (2000a, b) and Hack et al. (2006), is briefly
described here as background. Once a PBPK model structure is specified, characterizing the
model reduces to calibrating and making inferences about model parameters. The use of least-
squares point estimators is limited by the large number of parameters and small amounts of data.
The use of least-squares estimation is reported after imposing constraints for several parameters
(Clewell et al., 2000; Fisher, 2000). This is reasonable for a first estimate, but it is important to
follow-up with a more refined treatment. This is implemented by a Bayesian approach to
estimate posterior distributions on the unknown parameters, a natural choice, and almost a
compulsory consequence given the large number of parameters and relatively small amount of
data, and given the difficulties of frequentist estimation in this setting.
As described by Gelman et al. (1996), the Bayesian approach to population PBPK
modeling involves setting up the overall model in several stages. A nonlinear PBPK model, with
predictions denoted/ describes the absorption, distribution, metabolism, and excretion of a
compound and its metabolites in the body. This model depends on several, usually known,
parameters such as measurement times t, exposure E, and measured covariates (p. Additionally,
each subject /' in a population has a set of unmeasured parameters 0,. A random effects model
describes their population variability P(Qj u, E2), and a prior distribution ,P(u, E2) on the
population mean [j, and covariance E2 (often assumed to be diagonal) incorporates existing
scientific knowledge about them. Finally, a "measurement error" model P(y |_/[0, (p, E, f\, a2)
describes deviations (with variance a2) between the data_y and model predict!ons/(which of
course depends on the unmeasured parameters 0, and the measured parameters t, E, and (p). This
"measurement error" level of the hierarchical model typically also encompasses intrasubject
variability as well as model misspecification, but for notational convenience we refer to it here as
"measurement error." Because these other sources of variance are lumped into a single
"measurement error," a prior distribution of its variance a2 must be specified even if the actual
analytic measurement error is known. All of these components are illustrated graphically in
Figure A-l.
A-l
-------
Square nodes denote fixed or observed quantities; circle notes represent uncertain
or unobserved quantities, and the nonlinear model outputs are denoted by the
inverted triangle. Solid arrows denote a stochastic relationship represented by a
conditional distribution [A^-B means B ~ P(B\A)\ while dashed arrows represent
a function relationship [B =J(A)]. The population consists of subjects /', each of
which undergoes one or more experiments^' with exposure parameters Ey with
datayyki collected at times %/a, where k denotes different types of outputs and /
denotes the different time points. The PBPK model produces outputs fyki for
comparison with the datay^i. The difference between them ("measurement
error") has variance o\, with a fixed prior distribution Pr, which in this case is the
same for the entire population. The PBPK model also depends on measured
covariates (j), (e.g., body weight) and unobserved model parameters 9, (e.g.,
VMAX). The parameters 9, are drawn from a population with mean (j, and variance
Z2, each of which is uncertain and has a prior distribution assigned to it.
Source: Gelman et al. (1996).
Figure A-l. Hierarchical population statistical model for PBPK model
parameter uncertainty and variability.
A-2
-------
The posterior distribution for the unknown parameters is obtained in the usual manner by
multiplying: (1) the prior distribution for the population mean and variance and the
"measurement" error P([i, E2) /"(a2); (2) the population distribution for the subject parameters
P(Q u, E2); and (3) the likelihood P(y \ 0, a2), where for notational convenience, the dependence
on/ cp, E, and t (which are taken as fixed for a given data set) is dropped:
a, E2, a2 | y) oc P(n, E2) P(o2) P(Q \ u, E2) P(y 0, a2) (Eq. A-l)
Here, each subject's parameters 0, have the same sampling distribution (i.e., they are
independently and identically distributed), so their joint prior distribution is:
,Z2) (Eq.A-2)
Different experiments^ = !...«,- may have different exposure and different data collected
and different time points. In addition, different types of measurements k=l...rik (e.g., TCE
blood, TCE breath, TCA blood, etc.) may have different errors, but errors are otherwise assumed
to be iid. Since the subjects are treated as independent given Q\...n, the total likelihood function is
simply
P(y | 0, a2) = H/= I...H n/ = !...»(/ Ht = \...m Il/ = \...m,^p^y^i \ 6b ^k2, %«) (Eq. A-3)
where n is the number of subjects, n,y is the number of experiments in that subject, m is the
number of different types of measurements, Nyk is the number (possibly 0) of measurements
(e.g., time points) for subject / of type k in experiment j, and %/a are the times at which
measurements for subject /' of type k were made in experiment/
Given the large number of parameters, complex likelihood functions, and nonlinear
PBPK model, Markov chain Monte Carlo (MCMC) simulation was used to generate samples
from the posterior distribution. An important practical advantage of MCMC sampling is the
ability to implement inference in nearly any probability model and the possibility to report
inference on any event of interest. MCMC simulation was introduced by Gelfand and Smith
(1990) as a generic tool for posterior inference. See Gilks et al. (1995) for a review. In addition,
because many parameters are allowed to vary simultaneously, the local parameter sensitivity
analyses often performed with PBPK models (in which the changes in model predictions are
assessed with each parameter varied by a small amount) are unnecessary.1 In the context of
PBPK models, the MCMC simulation can be carried out as described by Hack et al. (2006). The
:In particular, local sensitivity analyses are typically used to assess the impact of alternative parameter estimates on
model predictions, inform experimental design, or assist prioritizing risk assessment research. Only the first purpose
is relevant here; however, the full uncertainty and variability analysis allows for a more comprehensive assessment
than can be done with sensitivity analyses. Separately, such analyses could be done to design experiments and
prioritize research that would be most likely to help reduce the remaining uncertainties in TCE toxicokinetics, but
that is beyond the scope of this assessment.
A-3
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simulation program MCSim (version 5.0.0) was used to implement MCMC posterior simulation,
with analysis of the results performed using the R statistical package. Simulation-based
parameter estimation with MCMC posterior simulation gives rise to an additional source of
uncertainty. For instance, averages computed from the MCMC simulation output represent the
desired posterior means only asymptotically, in the limit as the number of iterations goes to
infinity. Any implementation needs to include a convergence diagnostic to judge practical
convergence. The potential scale-reduction-factor convergence diagnostic R of Gelman et al.
(1996) was used here, as it was in Hack et al. (2006).
A.2. EVALUATION OF THE HACK ET AL. (2006) PBPK MODEL
U.S. EPA obtained the original model code for the version of the TCE PBPK model
published in Hack et al. (2006) and conducted a detailed evaluation of the model, focusing on the
following areas: convergence, posterior estimates for model parameters, and comparison of
model predictions with in vivo data.
A.2.1. Convergence
As noted in Hack et al. (2006), the diagnostics for the MCMC simulations (three chains
of length 20,000-25,000 for each species) indicated that additional samples might further
improve convergence. A recent analysis of tetrachloroethylene pharmacokinetics indicated the
need to be especially careful in ensuring convergence (Chiu and Bois, 2007). Therefore, the
number of MCMC samples per chain was increased to 75,000 for rats (first 25,000 discarded)
and 175,000 for mice and humans (first 75,000 discarded). Using these chain lengths, the vast
majority of the parameters had potential scale reduction factors R < 1.01, and all population
parameters had R < 1.05, indicating that longer chains would be expected to reduce the SD (or
other measure of scale, such as a CI) of the posterior distribution by less than this factor (Gelman
et al.. 2003).
In addition, analysis of autocorrelation within chains using the R-CODA package
(Plummer et al., 2006) indicated that there was significant serial correlation, so additional
"thinning" of the chains was performed in order to reduce serial correlations. In particular, for
rats, for each of three chains, every 100th sample from the last 50,000 samples was used; and for
mice and humans, for each of three chains, every 200th sample from the last 100,000 samples
was used. This thinning resulted in a total of 1,500 samples for each species available for use for
posterior inference.
Finally, an evaluation was made of the "convergence" of dose-metric predictions—that
is, the extent to which the SD or CIs for these predictions would be reduced with additional
samples. This is analogous to a "sensitivity analysis" performed so that most effort is spent on
parameters that are most influential in the result. In this case, the purpose is to evaluate whether
one can sample chains only long enough to ensure convergence of predictions of interest, even if
A-4
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certain more poorly identified parameters take longer chains to converge. The motivation for
this analysis is that for a more complex model, running chains until all parameters have R < 1.01
or 1.05 may be infeasible given the available time and resource. In addition, as some of the
model parameters had prior distributions derived from "visual fitting" to the same data, replacing
those distributions with less informative distributions (in order to reduce bias from "using the
same data twice") may require even longer chains for convergence.
Indeed, it was found that ^-values for dose-metric predictions approached one more
quickly than PBPK model input parameters. The most informative simulations were for mice,
which converged the slowest and, thus, had the most potential for convergence-related error.
Results for rats could not be assessed because the model converged so rapidly, and results for
humans were similar to those in mice, though the deviations were all less because of the more
rapid convergence. In the mouse model, after 25,000 iterations, many PBPK model parameters
had ^-values >2, with >25% >1.2. However, all dose-metric predictions had R < 1.4, with the
>96% of them <1.2 and the majority of them <1.01. In addition, when compared to the results of
the last 100,000 iterations (after the total of 175,000 iterations), >90% of the medians estimates
shifted by <20%, with the largest shifts <40% (for GSH metabolism dose-metrics, which had no
relevant calibration data). Tail quantiles had somewhat larger shifts, which was expected given
the limited number of samples in the tail, but still >90% of the 2.5 and 97.5 percentile quantiles
had shifts of <40%. Again, the largest shifts, on the order of twofold, were for GSH-related
dose-metrics that had high uncertainty, so the relative impact of limited sample size is small.
Therefore, the additional simulations performed in this evaluation, with three- to
sevenfold longer chains, did not result in much change in risk assessment predictions from the
original Hack et al. (2006) results. Thus, assessing prediction convergence appears sufficient for
assessing convergence of the TCE PBPK model for the purposes of risk assessment prediction.
A.2.2. Evaluation of Posterior Distributions for Population Parameters
Posterior distributions for the population parameters were first checked for whether they
appeared reasonable given the prior distributions. Inconsistency between the prior and posterior
distributions may indicate an insufficiently broad prior distribution (i.e., overconfidence in their
specification), a mis-specification of the model structure, or an error in the data. Parameters that
were flagged for further investigation were those for which the interquartile ranges (intervals
bounded by the 25th and 75th percentiles) of the prior and posterior distributions did not overlap.
In addition, lumped metabolism and clearance parameters for TCA, TCOH, and TCOG were
checked to make sure that they remained physiological—e.g., metabolic clearance was not more
than hepatic blood flow and urinary clearance not more than kidney blood flow (constraints that
were not present in the Hack et al. (2006) priors).
In mice, population mean parameters that had lack of overlap between priors and
posteriors included the affinity of oxidative metabolism (lnKM), the TCA plasma-blood
A-5
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concentration ratio (TCAPlas), the TCE stomach to duodenum transfer coefficient (InKTSD),
and the urinary excretion rates of TCA and TCOG (InkUrnTCAC and InkUrnTCOGC). For KM,
this is not unexpected, as previous investigators have noted inconsistency in the KM values
between in vitro values (upon which the prior distribution was based) and in vivo values derived
from oral and inhalation exposures in mice (Greenberg et al., 1999; Abbas and Fisher, 1997).
For the other mean parameters, the central estimates were based on visual fits, without any other
a priori data, so it is reasonable to assume that the inconsistency is due to insufficiently broad
prior distributions. In addition, the population variance for the TCE absorption coefficient from
the duodenum (kAD) was rather large compared to the prior distribution, likely due to the fact
that oral studies included TCE in both oil and aqueous solutions, which are known to have very
different absorption properties. Thus, the larger population variance was required to
accommodate both of them. Finally, the estimated clearance rate for glucuronidation of TCOH
was substantially greater than hepatic blood flow. This is an artifact of the one-compartment
model used for TCOH and TCOG, and suggests that first-pass effects are important for TCOH
glucuronidation. Therefore, the model would benefit from the addition of a separate liver
compartment so that first-pass effects can be accounted for, particularly when comparing across
dose-routes.
In rats, the only population mean or variance parameter for which the posterior
distribution was somewhat inconsistent with the prior distribution was the population mean for
the InKM. While the interquartile regions did not overlap, the 95th percentile regions did, so the
discordance was relatively minor. However, as with mice, the estimated clearance rate for
glucuronidation of TCOH was substantially greater than hepatic blood flow.
In humans, some of the chemical-specific parameters for which priors were established
using visual fits had posterior distributions that were somewhat inconsistent, including the
oxidative split between TCA and TCOH, biliary excretion of TCOG (InkBileC), and the TCOH
distribution volume (VBodC). More concerning was the fact that the posterior distributions for
several physiological volumes and flows were rather strongly discordant with the priors and/or
near their truncation limits, including gut, liver, and slowly perfused blood flow, the volumes of
the liver and rapidly perfused compartments. In addition, a number of tissue partition
coefficients were somewhat inconsistent with their priors, including those for TCE in the gut,
rapidly perfused, and slowly perfused tissues, and TCA in the body and liver. Finally, a number
of population variances (for TCOH clearance [InClTCOHC], urinary excretion of TCOG
[InkUrnTCOGC], ventilation-perfusion ratio [InVPRC], cardiac output [InQCC], fat blood flow
and volume [QFatC and VFatC], and TCE blood-air partition coefficient [PBC]) were somewhat
high compared to their prior distributions, indicating much greater population variability than
expected.
A-6
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A.2.3. Comparison of Model Predictions With Data
A schematic of the comparisons between model predictions and data are shown in
Figure A-2. In the hierarchical population model, subject-specific parameters were estimated for
each data set used in calibrating the model (posterior subject-specific 9, in Figure A-2). Because
these parameters are in a sense "optimized" to the experimental data themselves, the subject-
specific predictions (posterior subject-specific >^ in Figure A-2) using these parameters should
be accurate by design. Poor fits to the data using these subject-parameters may indicate a
misspecification of the model structure, prior parameter distributions, or an error in the data. In
addition, it is useful to generate "population-based" parameters (posterior population 9) using
only the posterior distributions for the population means (u) and variances (E2), instead of the
estimated subject-specific parameters. These population predictions provide a sense as to
whether the model and the predicted degree of population uncertainty and variability adequately
account for the range of heterogeneity in the experimental data. Furthermore, assuming the
subject-specific predictions are accurate, the population-based predictions are useful to identify
whether one or more if the data sets are "outliers" with respect to the predicted population. In
addition, a substantial number of in vivo data sets was available in all three species that were not
previously used for calibration. Thus, it is informative to compare the population-based model
predictions, discussed above, to these additional "validation" data in order to assess the
predictive power of the PBPK model.
A-7
-------
MCMC outputs
Posterior
Posterior I2
Posterior subject-
specific
e.
Posterior population
9
Posterior population
prediction;
Yjki
9i
Group/
Individuj
EIJ
Vi
Experiment j
all
Posterior group:specific
prediction
Yijki
Two sets of posterior predictions were generated: population predictions
(diagonal hashing) and subject-specific predictions (vertical hashing).
Figure A-2. Schematic of how posterior predictions were generated for
comparison with experimental data.
A.2.3.1. Mouse Model
A.2.3.1.1. Subject-specific and population-based predictions
Initially, the sampled subject-specific parameters were used to generate predictions for
comparison to the calibration data. Because these parameters were "optimized" for each subject,
these "subject-specific" predictions should be accurate by design. However, unlike for the rat
(see below), this was not the case for some experiments (this is partially responsible for the
slower convergence). In particular, the predictions for TCE and TCOH concentrations for the
Abbas and Fisher (1997) data were poor. In addition, TCE blood concentrations for the
Greenberg et al. (1999) data were consistently overpredicted. These data are discussed further in
Table A-1.
A-8
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Table A-1. Evaluation of Hack et al.
PBPK model predictions for in vivo data in mice
Reference
Simulation #
Calibration
data
Discussion
Abbas et al. (19971
41-42
These data are only published as an abstract. They consist of TCA and TCOH blood and urine data from
TCA and TCOH i.v. dosing. Blood levels of TCA and TCOH are fairly accurately predicted. From
TCOH dosing, urinary TCOG excretion is substantially overpredicted, and from TCA dosing, urinary
TCA excretion is substantially overpredicted.
Abbas and Fisher
(1997)
3-6
Results for these data were mixed. TCA levels were the best fit. The calibration data included TCA blood
and liver data, which were well predicted except at the earliest time-point. In addition, TCA
concentrations in the kidney were fairly consistent with the surrogate TCA body concentrations predicted
by the model. Urinary TCA was well predicted at the lower two and highest doses, but somewhat
underpredicted (though still in the 95% confidence region) at 1,200 mg/kg.
TCE levels were in general not well fit. Calibration data included blood, fat, and liver concentrations,
which were predicted poorly particularly at early and late times. One reason for this is probably the
representation of oral uptake. Although both the current model and the original Abbas and Fisher (1997)
model had two-compartments representing oral absorption, in the current model uptake can only occur
from the second compartment. By contrast, the Abbas and Fisher (1997) model had uptake from both
compartments, with the majority occurring from the first compartment. Thus, the explanation for the poor
fit, particularly of blood and liver concentrations, at early times is probably simply due to differences in
modeling oral uptake. This is also supported by the fact that the oral uptake parameters tended to be
among those that took the longest to converge.
Subject-specific blood TCOH predictions were poor, with underprediction at early times and
overprediction at late times. Population-based blood TCOH predictions tended to be underpredicted,
though generally within the 95% confidence region. Subject-specific urinary TCOG predictions were
fairly accurate except at the highest dose. These predictions are also probably affected by the apparent
misrepresentation of oral uptake. In addition, a problem as found in the calibration data in that data on
free TCOH was calibrated against predictions of total TCOH (TCOH+TCOG).
A number of TCOH and TCOG measurements were not included in the calibration—among them
tissue concentrations of TCOH and tissue and blood concentrations of TCOG. Blood concentrations (the
only available surrogate) were poor predictors of tissue concentrations of TCOH and TCOG (model
generally underpredicted). For TCOG, this may be due in part to the model assumption that the
distribution volume of TCOG is equal to that of TCOH.
A-9
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Table A-l. Evaluation of Hack et al.
PBPK model predictions for in vivo data in mice (continued)
Reference
Simulation #
Calibration
data
Discussion
Fisher et al. (1991)
1-2
(open-
chamber)
Venous blood TCE concentrations were somewhat underpredicted (a common issue with inhalation
exposures in mice below) (Greenberg et al.. 1999). but within the 95% confidence region of both subject-
specific and population-based predictions. Plasma TCA levels were well predicted, with most of the data
near the interquartile region of both subject-specific and population-based predictions (but with substantial
scatter in the male mice). However, it should be noted that only a single exposure concentration for each
sex was used in calibration, with six additional exposures (three for each sex) not included (see
simulations 21-26, below).
7-16 (closed-
chamber)
Good posterior fits were obtained for these data—closed-chamber data with initial concentrations from
300 to 10,000 ppm. Some variability in VMAX, however, was noted in the posterior distributions for that
parameter. Using subject-specific VMAX values resulted in better fits to these data. However, there
appears to be a systematic trend of lower estimated apparent VMAX at higher exposures. Similarly,
posterior estimates of cardiac output and the ventilation-perfusion ratio declined (slightly) with higher
exposures. These could be related to documented physiological changes (e.g., reduced ventilation rate and
body temperature) in mice when exposed to some volatile organics.
21-26 (open-
chamber,
additional
exposures)
Data from three additional exposures for each sex were available for comparison to model predictions.
Plasma TCA levels were generally well predicted, though the predictions for female mice data showed
some systematic overprediction, particularly at late times (i.e., data showed shorter apparent half-life).
Blood TCE concentrations were consistently overpredicted, sometimes by almost an order of magnitude,
except in the case of female mice at 236 ppm, for which predictions were fairly accurate.
Fisher and Allen
(1993)
31-36
Predictions for these gavage data were generally fairly accurate. There was a slight tendency to
overpredict TCA plasma concentrations, with predictions tending to be worse in the female mice. Blood
levels of TCE were adequately predicted, though there was some systematic underprediction at 2-6 hrs
after dosing.
Green and Prout
(1985)
40
This datum consists of a single measurement of urinary excretion of TCA at 24 hrs as a fraction of dose,
from TCA i.v. dosing. The model substantially overpredicts the amount excreted. Whereas Green and
Prout (1985) measured 35% excreted at 24 hrs, the model predicts virtually complete excretion at 24 hrs.
Greenberg et al.
(1999)
17-18
The calibration data included blood TCE, TCOH, and TCA data. Fits to blood TCA and TCOH were
adequate, but as with the Fisher et al. (1991) inhalation data, TCE levels were overpredicted (outside the
95% confidence region during and shortly after exposure).
As with Abbas and Fisher (1997). there were additional data in the study that was not used in calibration,
including blood levels of TCOG and tissue levels of TCE, TCA, TCOH, and TCOG. Tissue levels of
TCE were somewhat overpredicted, but generally within the 95% confidence region. TCA levels were
adequately predicted, and mostly in or near the interquartile region. TCOH levels were somewhat
underpredicted, though within the 95% confidence region. TCOG levels, for which blood served as a
surrogate for all tissues, were well predicted in blood and the lung, generally within the interquartile
region. However, blood TCOG predictions underpredicted liver and kidney concentrations.
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Table A-l. Evaluation of Hack et al. (2006) PBPK model predictions for in vivo data in mice (continued)
Reference
Larson and Bull
(1992a)
Prout et al. (1985)
Templin et al. (1993)
Simulation #
37-39
19
27-30 (urinary
excretion at
different doses)
20
Calibration
data
A/
A/
Discussion
Blood TCA predictions were fairly accurate for these data. However, TCE and TCOH blood
concentrations were underpredicted by up to an order of magnitude (outside the 95% confidence region).
Part of this may be due to uncertain oral dosing parameters. Urinary TCA and TCOG were also generally
underpredicted, in some cases outside of the 95% confidence region.
Fits to these data were generally adequate — within or near the interquartile region.
These data consisted of mass balance studies of the amount excreted in urine and exhaled unchanged at
doses from 10 to 2,000 mg/kg. TCA excretion was consistently overpredicted, except at the highest dose.
TCOG excretion was generally well predicted — within the interquartile range. The amount exhaled was
somewhat overpredicted, with a fourfold difference (but still within 95% confidence) at the highest dose.
Blood TCA levels from these data were well predicted by the model. Blood TCE and TCOH levels were
well predicted using subject-specific parameters, but did not appear representative using population-
derived parameters. However, this is probably a result of the subject-specific oral absorption parameter,
which was substantially different than the population mean.
A-ll
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Next, only samples of the population parameters (means and variances) were used, and
"new subjects" were sampled from appropriate distributions using these population means and
variances. These "new subjects" then represent the predicted population distribution,
incorporating both variability in the population as well as uncertainty in the population means
and variances. These "population-based" predictions were then compared to both the data used
in calibration, as well as the additional data identified that was not used in calibration. The
PBPK model was modified to accommodate some of the different outputs (e.g., tissue
concentrations) and exposure routes (TCE, TCA, and TCOH i.v.) used in the "noncalibration"
data, but otherwise it is unchanged.
A.2.3.1.1.1. Subject-specific predictions and calibration data
(See "Supplementary data for TCE assessment: Hack mouse subject calibration," 2011)
A.2.3.1.1.2. Population-based predictions and calibration and additional evaluation data
(See "Supplementary data for TCE assessment: Hack mouse population calibration
evaluation." 2011)
A.2.3.1.2. Conclusions regarding mouse model
A.2.3.1.2.1. TCE concentrations in blood and tissues not well-predicted
The PBPK model for the parent compound does not appear to be robust. Even subject-
specific fits to data sets used for calibration were not always accurate. For oral dosing data, there
is clearly high variability in oral uptake parameters, and the addition of uptake through the first
(stomach) compartment should improve the fit. Unfortunately, inaccurate TCE uptake
parameters may lead to inaccurately estimated kinetic parameters for metabolites, TCA and
TCOH, even if current fits are adequate.
The TCE data from inhalation experiments also are not well estimated, particularly blood
levels of TCE. While fractional uptake has been hypothesized, direct evidence for this is
lacking. In addition, physiologic responses to TCE vapors (reduced ventilation rates, lowered
body temperature) are a possibility. These are weakly supported by the closed-chamber data, but
the amount of the changes is not sufficient to account for the low blood levels of TCE observed
in the open-chamber experiments. It is also not clear what role presystemic elimination due to
local metabolism in the lung may play. It is known that the mouse lung has a high capacity to
metabolize TCE (Green et al., 1997b). However, in the Hack et al. (2006) model, lung
metabolism is limited by flow to the tracheobronchial region. An alternative formulation for
lung metabolism in which TCE is available for metabolism directly from inhaled air (similar to
that used for styrene) (Sarangapani et al., 2003), may allow for greater presystemic elimination
of TCE, as well as for evaluating the possibility of wash-in/wash-out effects. Furthermore, the
potential impact of other extrahepatic metabolism has not been evaluated. Curiously, predictions
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for the tissue concentrations of TCE observed by Greenberg et al. (1999) were not as discrepant
as those for blood. A number of these hypotheses could be tested; however, the existing data
may not be sufficient to distinguish them. The Merdink et al. (1998) study, in which TCE was
given by i.v. (thereby avoiding both first-pass in the liver and any fractional uptake issue in the
lung), may be somewhat helpful, but unfortunately only oxidative metabolite concentrations
were reported, not TCE concentrations.
A.2.3.1.2.2. TCA blood concentrations well predicted following TCE exposures, but TCA
flux and disposition may not be accurate
TCA blood and plasma concentrations following TCE exposure are consistently well
predicted. However, the total flux of TCA may not be correct, as evidenced by the varying
degrees of consistency with urinary excretion data. Of particular importance are TCA dosing
studies, none of which were included in the calibration. In these studies, total recovery of
urinary TCA was found to be substantially less than the administered dose. However, the current
model assumes that urinary excretion is the only source of clearance of TCA, leading to
overestimation of urinary excretion. This fact, combined with the observation that under TCE
dosing, the model appears to give accurate predictions of TCA urinary excretion for several data
sets, strongly suggests a discrepancy in the amount of TCA formed from TCE. That is, since the
model appears to overpredict the fraction of TCA that appears in urine, it may be reducing TCA
production to compensate. Inclusion of the TCA dosing studies (including some oral dosing
studies), along with inclusion of a nonrenal clearance pathway, would probably be helpful in
reducing these discrepancies. Finally, improvements in the TCOH/TCOG submodel, below,
should also help to ensure accurate estimates of TCA kinetics.
A.2.3.1.2.3. TCOH/TCOG submodel requires revision and recalibration
Blood levels of TCOH and TCOG were inconsistently predicted. Part of this is due to the
problems with oral uptake, as discussed above. In addition, the problems identified with the use
of the Abbas and Fisher (1997) data (i.e., free TCOH vs. total TCOH), mean that this submodel
is not likely to be robust.
An additional concern is the overprediction of urinary TCOG from the Abbas et al.
(1997) TCOH i.v. data. Like the case of TCA, this indicates that some other source of TCOH
clearance (not to TCA or urine—e.g., to DCA or some other untracked metabolite) is possible.
This pathway can be considered for inclusion, and limits can be placed on it using the available
data.
Also, like for TCA, the fact that blood and urine are relatively well predicted from TCE
dosing strongly suggests a discrepancy in the amount of TCOH formed from TCE. That is, since
the model appears to overpredict the fraction of TCOH that appears in urine, it may be reducing
A-13
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TCOH production to compensate. Including the TCOH dosing data would likely be helpful in
reducing these discrepancies.
Finally, as with the rat, the model needs to ensure that any first-pass effect is accounted
for appropriately. Importantly, the estimated clearance rate for glucuronidation of TCOH is
substantially greater than hepatic blood flow. As was shown in Okino et al. (2005), in such a
situation, the use of a single compartment model across dose routes will be misleading because it
implies a substantial first-pass effect in the liver that cannot be modeled in a single compartment
model. That is, since TCOH is formed in the liver from TCE, and TCOH is also glucuronidated
in the liver to TCOG, a substantial portion of the TCOH may be glucuroni dated before reaching
systemic circulation. This suggests that a liver compartment for TCOH is necessary.
Furthermore, because substantial TCOG can be excreted in bile from the liver prior to systemic
circulation, a liver compartment for TCOG may also be necessary to address that first-pass
effect.
The addition of the liver compartment will necessitate several changes to model
parameters. The distribution volume for TCOH will be replaced by two parameters: the
liverblood and body:blood partition coefficients. Similarly for TCOG, liverblood and
body:blood partition coefficients will need to be added. Clearance of TCOH to TCA and TCOG
can be redefined as occurring in the liver, and urinary clearance can be redefined as coming from
the rest of the body. Fortunately, there are substantial data on circulating TCOG that has not
been included in the calibration. These data should be extremely informative in better estimating
the TCOH/TCOG submodel parameters.
A.2.3.1.2.4. Uncertainty in estimates of total metabolism
Closed-chamber data are generally thought to provide a good indicator of total
metabolism. Both subject-specific and population-based predictions of the only available closed-
chamber data (Fisher et al., 1991) were fairly accurate. Unfortunately, no additional closed-
chamber data were available. In addition, the discrepancies in observed and predicted TCE
blood concentrations following inhalation exposures remain unresolved. Hypothesized
explanations such as fractional uptake or presystemic elimination could have a substantial impact
on estimates of total metabolism.
In addition, no data are directly informative as to the fraction of total metabolism in the
lung, the amount of "untracked" hepatic oxidative metabolism (parameterized as "FracDCA"), or
any other extrahepatic metabolism. The lung metabolism as currently modeled could just as well
be located in other extrahepatic tissues, with little change in calibration. In addition, it is
difficult to distinguish between untracked hepatic oxidative metabolism and GSH conjugation,
particularly at low doses.
A-14
-------
A.2.3.2. Rat Model
A.2.3.2.1. Subject-specific and population-based predictions
As with the mouse mode, initially, the sampled subject-specific parameters were used to
generate predictions for comparison to the calibration data. Because these parameters were
"optimized" for each subject, these "subject-specific" predictions should be accurate by design,
and indeed they were, as discussed in more detail in Table A-2.
Next, as with the mouse, only samples of the population parameters (means and
variances) were used, and "new subjects" were sampled from appropriate distribution using these
population means and variances. These "new subjects" then represent the predicted population
distribution, incorporating both variability in the population as well as uncertainty in the
population means and variances. These "population-based" predictions were then compared to
both the data used in calibration, as well as the additional data identified that were not used in
calibration. The Hack et al. (2006) PBPK model used for prediction was modified to
accommodate some of the different outputs (e.g., tissue concentrations) and exposure routes (i.v.,
i.a., and p.v.) used in the "noncalibration" data, but otherwise unchanged.
A-15
-------
Table A-2. Evaluation of Hack et al.
PBPK model predictions for in vivo data in rats
Reference
Simulation #
Calibration
data
Discussion
Andersen et al.
(1987b)
7-11
Good posterior fits were obtained for these data—closed-chamber data with initial concentrations of 100-
4,640 ppm.
Barton et al.
(1995)
17-20
It was assumed that the closed-chamber volume was the same as for Andersen et al. (1987b). However, the
initial chamber concentrations are not clear in the paper. The values that were used in the simulations do not
appear to be correct, since in many cases the time-course is inaccurately predicted even at the earliest time-
points. Conclusions as to these data need to await definitive values for the initial chamber concentrations,
which were not available.
Bernauer et al.
(1996)
1-3
Urinary time-course data (see Figure 6-7) for TCA, TCOG, and NAcDCVC was given in concentration units
(mg/mg creat-hr), whereas total excretion at 48 hrs (see Table 2) was given in molar units (mmol excreted). In
the original calibration files, the conversion from concentration to cumulative excretion was not consistent
(i.e., the amount excreted at 48 hrs was different). The data were revised using a conversion that forced
consistency. One concern, however, is that this conversion amounts to 6.2 mg creatinine over 48 hrs, or
1.14 micromol/hr. This seems very low for rats; Trevisan et al. (2001). in samples from 195 male control rats,
found a median value of 4.95 micromol/hr, a mean of 5.39 micromol/hr, and a 1-99* percentile range of 2.56-
10.46 micromol/hr.
In addition, the NAcDCVC data were revised in include both 1,2- and 2,2-isomers, since the goal of the GSH
pathway is primarily to constrain the total flux. Furthermore, because of the extensive interorgan processing of
GSH conjugates, and the fact that excretion was still ongoing at the end of the study (48 hrs), the amount of
NAcDCVC recovered can only be a lower bound on the amount ultimately excreted in urine. However, the
model does not attempt to represent the excretion time-course of GSH conjugates—it merely models the total
flux. This is evinced by the fact that the model predicts complete excretion by the first time point of 12 hrs,
whereas in the data, there is still substantial excretion occurring at 48 hrs.
Posterior fits to these data were poor in all cases except urinary TCA at the highest dose. In all other cases,
TCOH/TCOG and TCA excretion was substantially overpredicted, though this is due to the revision of the data
(i.e., the different assumptions about creatinine excretion). Unfortunately, of the original calibration data, this
is the only one with TCA and TCOH/TCOG urinary excretion. Therefore, that part of the model is poorly
calibrated. On the other hand, NAcDCVC was underpredicted for a number of reasons, as noted above.
Because of the incomplete capture of NAcDCVC in urine, unless the model can accurately portray the time-
course of NAcDCVC in urine, it should probably not be used for calibration of the GSH pathway, except
perhaps as a lower bound.
A-16
-------
Table A-2. Evaluation of Hack et al.
PBPK model predictions for in vivo data in rats (continued)
Reference
Simulation #
Calibration
data
Discussion
Birner et al.
(19931
21-22
These data only showed urine concentrations, so a conversion was made to cumulative excretion based on an
assumed urine flow rate of 22.5 mL/d. Based on this, urinary NAcDCVC was underestimated by 100- to
1,000-fold. Urinary TCA was underestimated by about twofold in females (barely within the 95% CI), and
was accurately estimated in males. Note that data on urinary flow rate from Trevisan et al. (2001) in samples
from 195 male control rats showed high variability, with a GSD of 1.75, so this may explain the discrepancy in
urinary TCA. However, the underestimation of urinary NAcDCVC cannot be explained this way.
Dallas et al.
(1991)
23-24
At the lower (50 ppm) exposure, arterial blood concentrations were consistently overpredicted by about
2.5-fold, while at the higher (500 ppm) exposure, arterial blood was overpredicted by 1.5-2-fold, but within
the range of variability. Exhaled breath concentrations were in the middle of the predicted range of variability
at both exposure levels. The ratio of exhaled breath and arterial blood should depend largely on the blood-air
partition coefficient, with minor dependence on the assumed dead space. This suggests the possibility of some
unaccounted-for variability in the partition coefficient (e.g., posterior mean estimated to be 15.7; in vitro
measured values from the literature are as follows: 25.82 (Sato etal.. 1977). 21.9 (Gargas etal.. 1989).
25.8 (Koizumi. 1989). 13.2 (Fisher etal.. 1989). posterior). Alternatively, there may be a systematic error in
these data, since, as discussed below, the fit of the model to the arterial blood data of Keys et al. (2003) was
highly accurate.
Fisher et al.
(1989)
25-28
Good posterior fits were obtained for these data (in females)—closed-chamber data with initial concentrations
from 300 to 5,100 ppm. There was some slight overprediction of chamber concentrations (i.e., data showed
more uptake/metabolism) at the lower doses, but still within the 95% CI.
Fisher et al.
(1991)
4-6
Good posterior fits were obtained from these data—plasma levels of TCA and venous blood levels of TCE.
Green and Prout
(1985)
29-30
In naive rats at 500 mg/kg, urinary excretion of TCOH/TCOG and TCA at 24 hrs was underpredicted
(twofold), although within the 95% CI. With bile-cannulated rats at the same dose, the amount of TCOG in
bile was well within the 95% CI. Urinary TCOH/TCOG was still underpredicted by about twofold, but again
still within the 95% CI.
Jakobson et al.
(1986)
31
The only data from the experiment (500 ppm in female rats) were venous blood concentrations during
exposure. There were somewhat overpredicted at early times (outside of 95% CI for first 30 min) but was well
predicted at the termination of exposure. This suggests some discrepancies in uptake to tissues that reach
equilibrium quickly—the model approaches the peak concentration at a faster rate than the data suggest.
A-17
-------
Table A-2. Evaluation of Hack et al.
PBPK model predictions for in vivo data in rats (continued)
Reference
Simulation #
Calibration
data
Discussion
Kaneko et al.
(19941
32-35
In these inhalation experiments (50-1,000 ppm), urinary excretion of TCOH/TCOG and TCA are consistently
overpredicted, particularly at lower doses. The discrepancy decreases systematically as dose increases, with
TCA excretion accurately predicted at 1,000 ppm (TCOH/TCOG excretion slightly below near the lower
95% CI at this dose). This suggests a discrepancy in the dose-dependence of TCOH, TCOG, and TCA
formation and excretion.
On the other hand, venous blood TCE concentrations postexposure are well predicted. TCE blood
concentrations right at the end of the exposure are overpredicted; however, concentrations are rapidly declining
at this point, so even a few minutes delay in obtaining the blood sample could explain the discrepancy.
Keys et al. (2003)
36-39
These experiments collected extensive data on TCE in blood and tissues following i.a., oral, and inhalation
exposures. For the i.a. exposure, blood and tissue concentrations were very well predicted by the model, even
with the use of the rapidly perfused tissue concentration as a surrogate for brain, heart, kidney, liver, lung, and
spleen concentrations. Similarly accurate predictions were found with the higher (500 ppm) inhalation
exposure. At the lower inhalation exposure (50 ppm), there was some minor overprediction of concentrations
(twofold), particularly in fat, but values were still within the 95% CIs.
For oral exposure, the GI absorption parameters needed to be revised substantially to obtain a good fit. When
the values reported by Keys et al. (2003) were used, the model generally had accurate predictions.
Two exceptions were the values in the gut and fat in the first 30 min after exposure. In addition, the liver
concentration was overpredicted in the first 30 min, and underpredicted at 2-4 hrs, but still within the 95% CI
during the entire period.
Kimmerle and
Eben(1973b)
40^4
In these inhalation experiments (49-3,160 ppm), urinary excretion of TCOH/TCOG was systematically
overpredicted (>twofold; outside 95% CI), while excretion of TCA was accurately predicted. In addition,
elimination by exhaled breath was substantially overpredicted at the lowest exposure. Blood TCOH levels
were accurately predicted, but blood TCE levels were overpredicted at the 55 ppm. Part of the discrepancies
may be due to limited analytic sensitivities at the lower exposures.
Larson and Bull
(1992a)
12-14
The digitization in the calibration file did not appear to be accurate, as there was a 10-fold discrepancy with the
original paper in the TCOH data. The data were replaced this those used by Clewell et al. (2000) and Bois
(2000b). Except for the TCOH data, differences between the digitizations were <20%.
Adequate posterior predictions were obtained for these data (oral dosing from 200 to 3,000 mg/kg). All
predictions were within the 95% CI of posterior predictions. Better fits were obtained using subject-specific
posterior parameters, for which gut absorption and TCA urinary excretion parameters were more highly
identified.
A-18
-------
Table A-2. Evaluation of Hack et al.
PBPK model predictions for in vivo data in rats (continued)
Reference
Simulation #
Calibration
data
Discussion
Lash et al.
45^6
In these corn-oil gavage experiments, almost all of the measurements appeared to be systematically low,
sometimes by many orders of magnitude. For example, at the lowest dose (263 mg/kg), urinary excretion of
TCOH/TCOG and TCA, and blood concentrations of TCOH were overpredicted by the model by around
>105-fold. TCE concentrations in blood and tissues at 2, 4, and 8 hrs were underpredicted by 103- to 104-fold.
Many studies, including those using the corn oil gavage (Hissink et al.. 2002: Green and Prout 1985). with
similar ranges of oral doses show good agreement with the model, it seems likely that these data are aberrant.
Lee et al. (1996)
47-61
This extensive set of experiments involved multiroute administration of TCE (oral, i.v., i.a., or portal vein),
with serial measurements of arterial blood concentrations. For the oral route (8-64 mg/kg), the GI absorption
parameters had to be modified. The values from Keys et al. (2003) were used, and the resulting predictions
were quite accurate, albeit a more prominent peak was predicted. Predictions >30 min after dosing were
highly accurate.
For the i.v. route (0.71-64 mg/kg), predictions were also highly accurate in almost all cases. At the lower
doses (0.71 and 2 mg/kg), there was slight overprediction in the first 30 min after dosing. At highest dose
(64 mg/kg), there was slight underprediction between 1 and 2 hrs after dosing. In all cases, the values were
within the 95% CI.
For the i.a. route (0.71-16 mg/kg), all predictions were very accurate.
For the p.v. route (0.7-64 mg/kg), predictions still remained in the 95% CI, although there was more variation.
At the lowest dose, there was overprediction in the first 30 min after dosing. At the highest two doses (16 and
64 mg/kg), there was slight underprediction between 1 and 5 hrs after dosing. This may in part be because a
pharmacodynamic change in metabolism (e.g.. via direct solvent injury proposed by Lee et al.. 2000a).
62-69
In the p.v. and i.v. exposures, blood and liver concentrations were accurately predicted. For oral exposures,
the GI absorption parameters needed to be changed. While the values from Keys et al. (2003) led to accurate
predictions for lower doses (2-16 mg/kg), at the higher doses (48^32 mg/kg), much slower absorption was
evident. Comparisons at these higher dose are not meaningful without calibration of absorption parameters.
Prout et al. (1985)
15
Adequate posterior fits were obtained for these data—rat dosing at 1,000 mg/kg in corn oil. All predictions
were within the 95% CI of posterior predictions. Better fits were obtained using subject-specific posterior
parameters, for which gut absorption and TCA urinary excretion parameters were more highly identified.
Stenner et al.
(1997)
70
As with other oral exposures, different GI absorption parameters were necessary. Again, the values from Keys
et al. (2003) were used, with some success. Blood TCA levels were accurately predicted, while TCOH blood
levels were systematically underpredicted (up to 10-fold).
Additional data with TCOH and TCA dosing, including naive and bile-cannulated rats, can be added when
those exposure routes are added to the model. These could be useful in better calibrating the enterohepatic
recirculation parameters.
A-19
-------
Table A-2. Evaluation of Hack et al.
PBPK model predictions for in vivo data in rats (continued)
Reference
Templin et al.
(1995b)
Simulation #
16
Calibration
data
A/
Discussion
Adequate posterior fits were obtained for blood TCA from these data — oral dosing at 100 mg/kg in Tween.
Blood levels of TCOH were underpredicted, while the time-course of TCE in blood exhibited an earlier peak.
Better fits were obtained using subject-specific posterior parameters, for which gut absorption and TCA
urinary excretion parameters (and to a lesser extent glucuronidation of TCOH and biliary excretion of TCOG)
were more highly identified.
NAc-l,2-DCVC = N-acetyl-S-(l,2-dichlrovinyl)-L-cysteine; NAc-2,2-DCVC = N-acetyl-S-(2,2-dichlrovinyl)-L-cysteine; NAcDCVC = NAc-l,2-DCVC and
NAc-2,2-DCVC.
A-20
-------
A.2.3.2.1.1. Subject-specific predictions and calibration data
(See "Supplementary data for TCE assessment: Hack mouse subject calibration," 2011)
A.2.3.2.1.2. Population-based predictions and calibration and additional evaluation data
(See "Supplementary data for TCE assessment: Hack mouse subject calibration," 2011)
A.2.3.2.2. Conclusions regarding rat model
A.2.3.2.2.1. TCE concentrations in blood and tissues generally well-predicted
The PBPK model for the parent compound appears to be robust. Multiple data sets not
used for calibration with TCE measurements in blood and tissues were simulated, and overall the
model gave very accurate predictions. A few data sets seemed somewhat anomalous—Dallas
et al. (1991), Kimmerle and Eben (1973b), and Lash et al. (2006). However, data from Kaneko
et al. (1994). Keys et al. (2003). and Lee et al. (2000a: 1996) were all well simulated, and
corroborated the data used for calibration (Templin et al., 1995b: Larson and Bull, 1992a: Fisher
et al., 1991; Prout et al., 1985). Particularly important is the fact that tissue concentrations from
Keys et al. (2003) were well simulated.
A.2.3.2.2.2. Total metabolism probably well simulated, but ultimate disposition is less
certain
Closed-chamber data are generally thought to provide a good indicator of total
metabolism. Two closed-chamber studies not used for calibration were available—Barton et al.
(1995) and Fisher et al. (1989). Additional experimental information is required to analyze the
Barton et al. (1995) data, but the predictions for the Fisher et al. (1989) data were quite accurate.
However, the ultimate disposition of metabolized TCE is much less certain. Clearly, the
flux through the GSH pathway is not well constrained, with apparent discrepancies between the
N-acetyl-S-(l,2-dichlorovinyl)-L-cysteine (NAc-l,2-DCVC) data of Bernauer et al. (1996) and
Birner et al. (1993). Moreover, each of these data has limitations—in particular, the Bernauer
et al. (1996) data show that excretion is still substantial at the end of the reporting period, so that
the total flux of mercapturates has not been collected. Moreover, there is some question as to the
consistency of the Bernauer et al. (1996) data (see Table 2 vs. Figures 6 and 7), since a direct
comparison seems to imply a very low creatinine excretion rate. The Birner et al. (1993) data
only report concentrations—not total excretion—so a urinary flow rate needs to be assumed.
In addition, no data are directly informative as to the fraction of total metabolism in the
lung or the amount of "untracked" hepatic oxidative metabolism (parameterized as "FracDCA").
The lung metabolism could just as well be located in other extrahepatic tissues, with little change
in calibration. In addition, there is a degeneracy between untracked hepatic oxidative
metabolism and GSH conjugation, particularly at low doses.
The ultimate disposition of TCE as excreted TCOH/TCOG or TCA is also poorly
estimated in some cases, as discussed in more detail below.
A-21
-------
A.2.3.2.2.3. TCOH/TCOG submodel requires revision and recalibration
TCOH blood levels of TCOH were inconsistently predicted in noncalibration data sets
(well predicted for Larson and Bull (1992a): Kimmerle and Eben (1973b): but not Stenner et al.
(1997)1 or Lash et al. (2006). and the amount of TCE ultimately excreted as TCOG/TCOH also
appeared to be poorly predicted. The model generally underpredicted TCOG/TCOH urinary
excretion (underpredicted Green and Prout (1985), overpredicted Kaneko et al. (1994),
Kimmerle and Eben (1973b), and Lash et al. (2006)). This may in part be due to discrepancies in
the Bernauer et al. (1996) data as to the conversion of excretion relative to creatinine.
Moreover, there are relatively sparse data on TCOH in combination with a relatively
complex model, so the identifiability of various pathways—conversion to TCA, enterohepatic
recirculation, and excretion in urine—is questionable.
This could be improved by the ability to incorporate TCOH dosing data from Merdink
et al. (1999) and Stenner et al. (1997), the latter of which included bile duct cannulation to better
estimate enterohepatic recirculation parameters. However, the TCOH dosing in these studies is
by the i.v. route, whereas with TCE dosing, TCOH first appears in the liver. Thus, the model
needs to ensure that any first-pass effect is accounted for appropriately. Importantly, the
estimated clearance rate for glucuronidation of TCOH is substantially greater than hepatic blood
flow. That is, since TCOH is formed in the liver from TCE, and TCOH is also glucuronidated in
the liver to TCOG, a substantial portion of the TCOH may be glucuroni dated before reaching
systemic circulation. Thus, suggests that a liver compartment for TCOH is necessary.
Furthermore, because substantial TCOG can be excreted in bile from the liver prior to systemic
circulation, a liver compartment for TCOG may also be necessary to address that first-pass
effect.
The addition of the liver compartment will necessitate several changes to model
parameters. The distribution volume for TCOH will be replaced by two parameters: the
liver:blood and body:blood partition coefficients. Similarly for TCOG, liver:blood and
body:blood partition coefficients will need to be added. Clearance of TCOH to TCA and TCOG
can be redefined as occurring in the liver, and urinary clearance can be redefined as coming from
the rest of the body.
Finally, additional clearance of TCOH (not to TCA or urine—e.g., to DCA or some other
untracked metabolite) is possible. This may in part explain the discrepancy between the accurate
predictions to blood data along with poor predictions to urinary excretion (i.e., there is a missing
pathway). This pathway can be considered for inclusion, and limits can be placed on it using the
available data.
A-22
-------
A.2.3.2.2.4. TCA submodel would benefit from revised submodel and incorporating TCA
dosing studies
While blood levels of TCA were well predicted in the one noncalibration data set
(Stenner et al., 1997), the urinary excretion of TCA was inconsistently predicted (underpredicted
in Green and Prout (1985): overpredicted in Kaneko et al. (1994) and Lash et al. (2006):
accurately predicted in Kimmerle and Eben (1973b)]). Because TCA is, in part, derived from
TCOH, a more accurate TCOH/TCOG submodel would probably improve the TCA submodel.
In addition, there are a number of TCA dosing studies that could be used to isolate the
TCA kinetics from the complexities of TCE and TCOH. These could be readily incorporated
into the TCA submodel.
Finally, as with TCOH, additional clearance of TCA (not to urine—e.g., to DCA or some
other untracked metabolite) is possible. This may in part explain the discrepancy between the
accurate predictions to blood data along with poor predictions to urinary excretion (i.e., there is a
missing pathway). As with TCOH, this pathway can be considered for inclusion, and limits can
be placed on it using the available data.
A.2.3.3. Human Model
A.2.3.3.1. Subject-specific and population-based predictions
As with the mouse and rat models, initially, the sampled subject-specific parameters were used to
generate predictions for comparison to the calibration data. Because these parameters were
"optimized" for each subject, these "subject-specific" predictions should be accurate by design.
However, unlike for the rat, this was not the case for some experiments (this is partially
responsible for the slower convergence), although the inaccuracies were generally less than those
in the mouse. For example, alveolar air concentrations were systematically overpredicted for
several data sets. There was also variability in the ability to predict the precise time-course of
TCA and TCOH blood levels, with a few data sets more difficult for the model to accommodate.
These data are discussed further in Table A-3. Next, only samples of the population parameters
(means and variances) were used, and "new subjects" were sampled from appropriate
distribution using these population means and variances. These "new subjects" then represent
the predicted population distribution, incorporating both variability as well as uncertainty in the
population means and variances. These "population-based" predictions were then compared to
both the data used in calibration, as well as the additional data identified that was not used in
calibration. The Hack et al. (2006) PBPK model was modified to accommodate some of the
different outputs (e.g., arterial blood, intermittently collected urine, retained dose) and exposure
routes (TCA i.v., oral TCA, and TCOH) used in the "noncalibration" data, but otherwise
unchanged.
A-23
-------
Table A-3. Evaluation of Hack et al.
PBPK model predictions for in vivo data in humans
Reference
Simulation
number
Calibration
data
Discussion
Bartonicek (1962'
38-45
The measured minute-volume was multiplied by a factor of 0.7 to obtain an estimate for alveolar ventilation
rate, which was fixed for each subject. These data are difficult to interpret because they consist of many single
data points. It is easiest to go through the measurements one at a time:
Alveolar retention (1—exhaled dose/inhaled dose during exposure) and. Retained dose (inhaled dose—exhaled
dose during exposure): Curiously, retention was generally underpredicted, which in many cases retained dose
was accurately predicted. However, alveolar retention was an adjustment of the observed total retention:
TotRet = (CInh - CExh)/CInh = QAlv x (CInh - CAlv)/(MV x CInh), so that
AlvRet = TotRet x (QAlv/MV), with QAlv/MV assumed to be 0.7.
Because retained dose is the more relevant quantity, and is less sensitive to assumptions about QAlv/MV, then
this is the better quantity to use for calibration.
Urinary TCOG: This was generally underpredicted, although generally within the 95% CI. Thus, these
data will be informative as to intersubject variability.
Urinary TCA: Total collection (at 528 hrs) was accurately predicted, although the amount collected at
72 hrs was generally underpredicted, sometimes substantially so.
Plasma TCA: Generally well predicted.
Bernauer et al.
(1996)
1-3
Subject-specific predictions were good for the time-courses of urinary TCOG and TCA, but poor for total
urinary TCOG+TCA and for urinary NAc-l,2-DCVC. One reason for the discrepancy in urinary excretion of
TCA and TCOG is that the urinary time-course data (see Figures 4-5 in the manuscript) for TCA, TCOG, and
NAc-l,2-DCVC was given in concentration units (mg/mg creat-hr), whereas total excretion at 48 hrs (see
Table 2 in the manuscript) was given in molar units (mmol excreted). In the original calibration files, the
conversion from concentration to cumulative excretion was not consistent (i.e., the amount excreted at 48 hrs
was different). For population-based predictions, the data were revised using a conversion that forced
consistency. One concern, however, is that this conversion amounts to 400-500 mg creatinine over 48 hrs, or
200-250 mg/d, which seems rather low. For instance, Araki (1978) reported creatinine excretion of
11.5 ± 1.8 mmol/24 hrs (mean± SD) in nine subjects, corresponding to 1,300 ± 200 mg/d.
In addition, for population-based predictions, the data were revised include both the NAc-l,2-DCVC and the
N acetyl-S-(2,2-dichlorovinyl)-L-cysteine isomer (the combination denoted NAcDCVC), since the goal of the
GSH pathway is primarily to constrain the total flux. Furthermore, because of the extensive interorgan
processing of GSH conjugates, and the fact that excretion was still ongoing at the end of the study (48 hrs), the
amount of NAcDCVC recovered can only be a lower bound on the amount ultimately excreted in urine.
However, the model does not attempt to represent the excretion time-course of GSH conjugates—it merely
models the total flux. This is evinced by the fact that the model predicts complete excretion by the first time
point of 12 hrs, whereas in the data, there is still substantial excretion occurring at 48 hrs.
A-24
-------
Table A-3. Evaluation of Hack et al.
PBPK model predictions for in vivo data in humans (continued)
Reference
Bernauer et al.
(1996)
(continued)
Bloemen et al.
(2001)
Chiu et al. (2007)
Fernandez et al.
(1977)
Simulation
number
1-3
(continued)
72-75
66-71
Calibration
data
Discussion
Population-based posterior fits to these data were quite good for urinary TCA and TCOH, but not for
NAcDCVC in urine. Because of the incomplete capture of NAcDCVC in urine, unless the model can
accurately portray the time-course of NAcDCVC in urine, it should probably not be used for calibration of the
GSH pathway, except perhaps as a lower bound.
Like Bartonicek (1962). these data are more difficult to interpret due to their being single data points for each
subject and exposure. However, in general, posterior population-based estimates of retained dose, urinary
TCOG, and urinary TCA were fairly accurate, staying within the 95% CI, and mostly inside the interquartile
range. The data on GSH mercapturates are limited — first they are all nondetects. In addition, because of the
48-56 hrs collection period, excretion of GSH mercapturates is probably incomplete, as noted above in the
discussion of Bernauer et al. (1996).
The measured minute-volume was multiplied by a factor of 0.7 to obtain an estimate for alveolar ventilation
rate, which was fixed for each subject. Alveolar air concentrations of TCE were generally well predicted,
especially during the exposure period. Postexposure, the initial drop in TCE concentration was generally
further than predicted, but the slope of the terminal phase was similar. Blood concentrations of TCE were
consistently overpredicted for all subjects and occasions.
Blood concentrations of TCA were consistently overpredicted, though mostly staying in the lower
95% confidence region. Blood TCOH (free) levels were generally overpredicted, in many cases falling below
the 95% confidence region, though in some cases the predictions were accurate. On the other hand, total
TCOH (free+glucuronidated) was well predicted (or even underpredicted) in most cases — in the cases where
free TCOH was accurately predicted, total TCOH was underpredicted. The free and total TCOH data reflect
the higher fraction of TCOH as TCOG than previously reported (e.g., Fisher et al. (1998) reported no
detectable TCOG in blood).
Data on urinary TCA and TCOG were complicated by some measurements being saturated, as well as the
intermittent nature of urine collection after d 3. Thus, only the nonsaturated measurements for which the time
since the last voiding was known were included for direct comparison to the model predictions. Saturated
measurements were kept track of separately for comparison, but were considered only rough lower bounds.
TCA excretion was generally overpredicted, whether looking at unsaturated or saturated measurements (the
latter, would of course, be expected). Urinary excretion of TCOG generally stayed within the 95% confidence
range.
Alveolar air concentrations are somewhat overestimated. Other measurements are fairly well predicted.
A-25
-------
Table A-3. Evaluation of Hack et al.
PBPK model predictions for in vivo data in humans (continued)
Reference
Simulation
number
Calibration
data
Discussion
Fisher et al.
(1998)
13-33
The majority of the data used in the calibration (both in terms of experiments and data points) came from this
study. In general, the subject-specific fits to these data were good, with the exception of alveolar air
concentrations, which were consistently overpredicted. In addition, for some subjects, the shape of the TCOH
time-course deviated from the predictions (#14, 24, 29, and 30)—the predicted peak was too "sharp," with
underprediction at early times. Simulation #23 showed the most deviation from predictions, with substantial
inaccuracies in blood TCA, TCOH, and urinary TCA.
Interestingly, in the population-based predictions, in some cases the predictions were not very
accurate—indicating that the full range of population variability is not accounted for in the posterior
simulations. This is particularly the case with venous blood TCE concentrations, which are generally
underpredicted in population estimates (although in some cases the predictions are accurate).
One issue with the way in which these data were utilized in the calibration is that in some cases, the same
subject was exposed to two different concentrations, but in the calibration, they were treated as separate
"subjects." Thus, parameters were allowed to vary between exposures, mixing intersubject and interoccasion
variability. It is recommended that in subsequent calibrations, the different occasions with the same subject be
modeled together. This will also allow identification of any dose-related changes in parameters (e.g.,
saturation).
Kimmerle and
Eben (1973a)
46-57
Blood TCE levels are generally overpredicted for both single and multiexposure experiments. However, levels
at the end of exposure are rapidly changing, so some of those values may be better predicted if the "exact"
time after cessation of exposure were known.
Blood TCOH levels are fairly accurately predicted, although in some subjects in single exposure experiments,
there is a tendency to overpredict at early times and underpredict at late times. In multiexposure experiments,
the decline after the last exposure was somewhat steeper than predicted. Urinary excretion of TCA and TCOH
was well predicted.
Only grouped data on alveolar air concentrations were available, so they were not used.
Lapare et al.
(1995)
34
Predictions for these data were not accurate. However, there was an error in some of the exposure
concentrations used in the original calibration. In addition, the last exposure "occasion" in these experiments
involved exercise/workload, and so should be excluded. Finally, subject data are available for these
experiments.
62-65
(individual
data)
Taking into account these changes, population-based predictions were somewhat more accurate. However,
alveolar air concentrations and venous blood TCE concentrations were still overpredicted.
A-26
-------
Table A-3. Evaluation of Hack et al.
PBPK model predictions for in vivo data in humans (continued)
Reference
Simulation
number
Calibration
data
Discussion
Monster et al.
(1976)
5-6 (summary
data)
Subject-specific predictions were quite good, except that for blood TCA concentrations exhibited a higher
peak that predicted. However, TCOH values were entered as free TCOH, whereas the TCOH data were
actually total (free + glucuronidated) TCOH. Therefore, for population-based predictions, this change was
made. In addition, as with the Monster et al. (1979a) data, minute-volume and exhaled air concentrations were
measured and incorporated for population-based predictions. Finally, subject-specific data are available, so, in
this case, those data should replace the grouped data in any revised calibration. These individual data also
included estimates of retained dose based on complete inhaled and exhaled air samples during exposure.
For population-based predictions, as with the Monster et al. (1979a) data, grouped urinary and blood
TCOH/TCOG was somewhat underpredicted in the population-based predictions, and grouped alveolar and
blood TCE concentrations were somewhat overpredicted.
58-61
(individual
data)
The results for the individual data were similar, but exhibited substantially greater variability that predicted.
For instance, in subject A, blood TCOH levels were generally greater than the 95% CI at both 70 and 140 ppm,
whereas predictions for blood TCOH in subject D were quite good. In another example, for blood TCE levels,
predictions for subject B were quite good, but those for subject D were poor (substantially overpredicted).
Thus, it is anticipated that adding these individual data will be substantially informative as to intersubject
variability, especially since all four individuals were exposed at two different doses.
Monster et al.
(1979a)
Subject-specific predictions for these data were quite good. However, TCA values were entered as plasma,
whereas the TCA data were actually in whole blood. Therefore, for population-based predictions, this change
was made. In addition, two additional time-courses were available that were not used in calibration: exhaled
air concentrations and total TCOH blood concentrations. These were added for population-based predictions.
In addition, the original article had data on ventilation rate, which as incorporated into the model. The minute
volume needed to be converted to alveolar ventilation rate for the model, but this required adjusted for an extra
dead space volume of 0.15 L due to use of a mask, as suggested in the article. The measured mean minute
volume was 11 L/min, and with a breathing rate of 14 breaths/min (assumed in the article), this corresponding
to a total volume of 0.79 L. Subtracting the 0.15 L of mask dead space and 0.15 L of physiological dead space
(suggested in the article) gives 0.49 L of total physiological dead space. Thus, the minute volume of 11 L/min
was adjusted by the factor 0.49/0.79 to give an alveolar ventilation rate of 6.8 L/min, which is a reasonably
typical value at rest.
Due to extra nonphysiological dead space issue, some adjustment to the exhaled air predictions also needed to
be made. The alveolar air concentration CAlv was, therefore, estimated based on the formula
A-27
-------
Table A-3. Evaluation of Hack et al.
PBPK model predictions for in vivo data in humans (continued)
Reference
Simulation
number
Calibration
data
Discussion
Monster et al.
(1979a)
(continued)
4 (continued)
CAlv = (CExh x VTot - CInh x VDs)/VAlv
where CExh is the measured exhaled air concentration, VTot is the total volume (alveolar space VAlv of
0.49 L, physiological dead space of 0.15 L, and mask dead space of 0.15 L), VDs is the total dead space of
0.3 L, and CInh is the inhaled concentration.
Population-based predictions for these data lead to slight underestimation urinary TCOG and blood TCOH
levels, as well as some overprediction of alveolar air and venous blood concentrations by factors of 3~10-fold.
Muller et al.
(1975: 1974.
1972)
7-10
Subject-specific predictions for these data were good, except for alveolar air concentrations. However, several
problems were found with these data as utilized in the original calibration:
• Digitization problems, particular with the time axis in the multiday exposure study (Simulation 9) that led
to measurements taken prior to an exposure modeled as occurring during the exposure. The original
digitization from Bois (2000b) and Clewell et al. (2000) was used for population-based estimates.
• Original article showed TCA as measured in plasma, not blood as was assumed in the calibration.
• Blood was taken from the earlobe, which is thought to be indicative of arterial blood concentrations, rather
than venous blood concentrations.
• TCOH in blood was free, not total, as Ertle et al. (1972) (cited in Methods) had no use of p-glucuronidase
in analyzing blood samples. Separate free and total measurements were done in plasma (not whole blood),
but these data were not included.
• Simulation 9, contiguous data on urinary excretion were only available out to 6 d, so only that data should
be included.
• Simulation 10, is actually the same as the first day of simulation 9, from Muller et al. (1975: 1972) (the
data were reported in both papers), and, thus, should be deleted.
These were corrected in the population-based estimates. Alveolar air concentration measurements remained
overpredicted, while the change to arterial blood led to overprediction of those measurements during exposure
(but postexposure predictions were accurate).
Muller et al.
(1974)
81-82 (TCA
and TCOH
dosing)
The experiment with TCA showed somewhat more rapid decline in plasma levels than predicted, but still well
within the 95% confidence range. Urinary excretion was well predicted, but only accounted for 60% of the
administered dose—this is not consistent with the rapid decline in TCA plasma levels (10-fold lower than peak
at the end of exposure), which would seem to suggest the majority of TCA has been eliminated. With TCOH
dosing, blood levels of TCOH were overpredicted in the first 5 hrs, perhaps due to slower oral absorption (the
augmented model used instantaneous and complete absorption). TCA plasma and urinary excretion levels
were fairly well predicted. However, urinary excretion of TCOG was near the bottom of the 95% CI; while, in
the same individuals with TCE dosing (Simulation 7), urinary excretion of TCOG was substantially greater
(near slightly above the interquartile region). Furthermore, total TCA and TCOG urinary excretion accounted
for <40% of the administered dose.
A-28
-------
Table A-3. Evaluation of Hack et al.
PBPK model predictions for in vivo data in humans (continued)
Reference
Paykoc and
Powell (19451
Sato et al. (1977)
Stewart et al.
(1970)
Triebig et al.
(1976)
Simulation
number
35-37
76
11
12
Calibration
data
A/
A/
Discussion
Population-based fits were good, within the inner quartile region.
Both alveolar air and blood concentrations are overpredicted in this model. Urinary TCA and TCOG, on the
other hand, are well predicted.
Subject-specific predictions for these data were good, except for some alveolar air concentrations. However, a
couple of problems were found with these data as utilized in the original calibration:
• The original article noted that individuals took a lunch break during which there was no exposure. This
was not accounted for in the calibration runs, which a assumed a continuous 7-hr exposure. The exposures
were, therefore, revised with a 3-hr morning exposure (9-12), a 1 hr lunch break (12-1), and 4-hr
afternoon exposure (1-5), to mimic a typical workday. The times of the measurements had to be revised as
well, since the article gave "relative" rather than "absolute" times (e.g., x hr postexposure).
• Contiguous data on urinary excretion were only available out to 1 1 d, so only that data should be included
(see Table 2).
With these changes, population-based predictions of urinary TCA and TCOG were still accurate, but alveolar
air concentrations were overpredicted.
Only two data points are available for alveolar air, and blood TCA and TCOH. Only one data point is
available on blood TCE. Alveolar air was underpredicted at 24 hrs. Blood TCA and TCOH were within the
95% confidence ranges. Blood TCE was overpredicted substantially (outside 95% confidence range).
A-29
-------
A.2.3.3.1.1. Subject-specific predictions and calibration data
(See "Supplementary data for TCE assessment: Hack mouse subject calibration," 2011)
A.2.3.3.1.2. Population-based predictions and calibration and additional evaluation data
(See "Supplementary data for TCE assessment: Hack mouse subject calibration," 2011)
A.2.3.3.2. Conclusions regarding human model
A.2.3.3.2.1. TCE concentrations in blood and air are often not well-predicted
Except for the Chiu et al. (2007) during exposure, TCE alveolar air levels were
consistently overpredicted. Even in Chiu et al. (2007), TCE levels postexposure were
overpredicted, as the drop-off after the end of exposure was further than predicted. Because
predictions for retained dose appear to be fairly accurate, this implies that less clearance is
occurring via exhalation than predicted by the model. This could be the result of additional
metabolism or storage not accounted for by the model.
Except for the Fisher et al. (1998) data, TCE blood levels were consistently
overpredicted. Because the majority of the data used for calibration was from Fisher et al.
(1998), this implies that the Fisher et al. (1998) data had blood concentrations that were
consistently higher than the other studies. This could be due to differences in metabolism and/or
distribution among studies.
Interestingly, the mouse inhalation data also exhibited inaccurate prediction of blood
TCE levels. Hypotheses such as fractional uptake or presystemic elimination due to local
metabolism in the lung have not been tested experimentally, nor is it clear that they can explain
the discrepancies.
Due to the difficulty in accurately predicted blood and air concentrations, there may be
substantial uncertainty in tissue concentrations of TCE. However, such potential model errors
can be characterized estimated and estimated as part of a revised calibration.
A.2.3.3.2.2. TCA blood concentrations well predicted following TCE exposures, but some
uncertainty in TCA flux and disposition
TCA blood and plasma concentrations and urinary excretion, following TCE exposure,
are generally well predicted. Even though the model's central estimates overpredicted the Chiu
et al. (2007) TCA data, the CIs were still wide enough to encompass those data.
However, the total flux of TCA may not be correct, as evidenced by TCA dosing studies,
none of which were included in the calibration. In these studies, total recovery of urinary TCA
was found to be substantially less than the administered dose. However, the current model
assumes that urinary excretion is the only source of clearance of TCA. This leads to
overestimation of urinary excretion. This fact, combined with the observation that under TCE
A-30
-------
dosing, the model appears to give accurate predictions of TCA urinary excretion for several data
sets, strongly suggests a discrepancy in the amount of TCA formed from TCE. That is, since the
model appears to overpredict the fraction of TCA that appears in urine, it may be reducing TCA
production to compensate. Inclusion of the TCA dosing studies, along with inclusion of a
nonrenal clearance pathway, would probably be helpful in reducing these discrepancies. Finally,
improvements in the TCOH/TCOG submodel, below, should also help to insure accurate
estimates of TCA kinetics.
A.2.3.3.2.3. TCOH/TCOG submodel requires revision and recalibration
Blood levels of TCOH and urinary excretion of TCOG were generally well predicted.
Additional individual data show substantial intersubject variability than can be incorporated into
the calibration. Several errors as to the measurement of free or total TCOH in blood need to be
corrected.
A few inconsistencies with noncalibration data sets stand out. The presence of
substantial TCOG in blood in the Chiu et al. (2007) data are not predicted by the model.
Interestingly, only two studies that included measurements of TCOG in blood (rather than just
total TCOH or just free TCOH)—Muller et al. (1975). which found about 17% of total TCOH to
be TCOG, and Fisher et al. (1998). who could not detect TCOG. Both of these studies had
exposures at 100 ppm. Interestingly, Muller et al. (1975) reported increased TCOG (as fraction
of total TCOH) with ethanol consumption, hypothesizing the inhibition of a glucuronyl
transferase that slowed glucuronidation. This also would result in a greater half-life for TCOH in
blood with ethanol consumptions, which was observed.
An additional concern is the overprediction of urinary TCOG following TCOH
administration from the Muller et al. (1974) data. Like the case of TCA, this indicates that some
other source of TCOH clearance (not to TCA or urine—e.g., to DCA or some other untracked
metabolite) is possible. This pathway can be considered for inclusion, and limits can be placed
on it using the available data.
Also, as for TCA, the fact that blood and urine are relatively well predicted from TCE
dosing strongly suggests a discrepancy in the amount of TCOH formed from TCE. That is, since
the model appears to overpredict the fraction of TCOH that appears in urine, it may be reducing
TCOH production to compensate.
Finally, as with the rat and mice, the model needs to ensure that any first-pass effect is
accounted for appropriately. Particularly for the Chiu et al. (2007) data, in which substantial
TCOG appears in blood, since TCOH is formed in the liver from TCE, and TCOH is also
glucuronidated in the liver to TCOG, a substantial portion of the TCOH may be glucuronidated
before reaching systemic circulation. Thus, suggests that a liver compartment for TCOH is
necessary. Furthermore, because substantial TCOG can be excreted in bile from the liver prior
to systemic circulation, a liver compartment for TCOG may also be necessary to address that
A-31
-------
first-pass effect. In addition, in light of the Chiu et al. (2007) data, it may be useful to expand the
prior range for the KM of TCOH glucuronidation.
The addition of the liver compartment will necessitate several changes to model
parameters. The distribution volume for TCOH will be replaced by two parameters: the
liverblood and body:blood partition coefficients. Similarly for TCOG, liverblood and
body:blood partition coefficients will need to be added. Clearance of TCOH to TCA and TCOG
can be redefined as occurring in the liver, and urinary clearance can be redefined as coming from
the rest of the body. Fortunately, there are in vitro partition coefficients for TCOH. It may be
important to incorporate the fact that Fisher et al. (1998) found no TCOG in blood. This can be
included by having the TCOH data be used for both free and total TCOH (particularly since that
is how the estimation of TCOG was made—by taking the difference between total and free).
A.2.3.3.2.4. Uncertainty in estimates of total metabolism
Estimates of total recovery after TCE exposure (TCE in exhaled air, TCA and TCOG in
urine) have been found to be only 60-70% (Chiu et al.. 2007: Monster et al.. 1979a. 1976). Even
estimates of total recovery after TCA and TCOH dosing have found 25-50% unaccounted for in
urinary excretion (Muller et al., 1974: Paykoc and Powell, 1945). Bartonicek (1962) found some
TCOH and TCA in feces, but this was about 10-fold less than that found in urine, so this cannot
account for the discrepancy. Therefore, it is likely that additional metabolism of TCE, TCOH,
and/or TCA are occurring. Additional metabolism of TCE could account for the consistent
overestimation of TCE in blood and exhaled breath found in many studies. However, no data are
directly informative as to the fraction of total metabolism in the lung, the amount of "untracked"
hepatic oxidative metabolism (parameterized as "FracDCA"), or any other extrahepatic
metabolism. The lung metabolism as currently modeled could just as well be located in other
extrahepatic tissues, with little change in calibration. In addition, it is difficult to distinguish
between untracked hepatic oxidative metabolism and GSH conjugation, particularly at low
doses.
A.3. PRELIMINARY ANALYSIS OF MOUSE GAS UPTAKE DATA: MOTIVATION
FOR MODIFICATION OF RESPIRATORY METABOLISM
Potential different model structures can be investigated using the core PBPK model
containing averaged input parameters, since this approach saves computational time and is more
efficient when testing different structural hypotheses. This approach is particularly helpful for
quick comparisons of data with model predictions. During the calibration process, this approach
was used for different routes of exposure and across all three species. For both mice and rats, the
closed-chamber inhalation data resulted in fits that were considered not optimal when visually
examined. Although closed-chamber inhalation usually combines multiple animals per
experiment, and may not be as useful in differentiating between individual and experimental
A-32
-------
uncertainty (Hack et al., 2006), closed-chamber data do describe in vivo metabolism and have
been historically used to quantify averaged in vivo Michaelis-Menten kinetics in rodents.
There are several assumptions used when combining PBPK modeling and closed-
chamber data to estimate metabolism via regression. The key experimental principles require a
tight, sealed, or air-closed system where all chamber variables are controlled to known set points
or monitored, that is all except for metabolism. For example, the inhalation chamber is
calibrated without an animal, to determine normal absorption to the empty system. This empty
chamber calibration is then followed with a dead animal experiment, identical in every way to
the in vivo exposure, and is meant to account for every factor other than metabolism, which is
zero in the dead animal. When the live animal(s) are placed in the chamber, oxygen is provided
for, and carbon dioxide accumulated during breathing is removed by absorption with a chemical
scrubber. A bolus injection of the parent chemical, TCE, is given and this injection time starts
the inhalation exposure. The chemical inside the chamber will decrease with time, as it is
absorbed by the system and the metabolic process inside the rodent. Since all known processes
contributing to the decline are quantified, except for metabolism, the metabolic parameters can
be extracted from the total chamber concentration decline using regression techniques.
The basic structure for the PBPK model that is linked to closed-chamber inhalation data
has the same basic structure as described before. The one major difference is the inclusion of
one additional equation that accounts for mass balance changes inside the inhalation chamber or
system, and connects the chamber with the inhaled and exhaled concentrations breathed in and
out by the animal:
(Eq. A-4)
ch
where
RATS = number of animals in the chamber
QP = alveolar ventilation rate
Cx = exhaled concentration
ACH = net amount of chemical inside chamber
Vch = volume of chamber
KLOSS = loss rate constant to glassware.
An updated model was developed that included updated physiological and chemical-
specific parameters as well as GSH metabolism in the liver and kidney, as discussed in
Chapter 3. The PBPK model code was translated from MCSim to use in Matlab®
(version 7.2.0.232, R2006a, Natick, MA) using their m language. This PBPK model made use of
fixed or constant, averaged values for physiological, chemical and other input parameters; there
were no statistical distributions attached to each average value. As an additional step in quality
A-33
-------
control, mass balance was checked for the MCSim code, and comparisons across both sets of
code were made to ensure that both sets of predictions were the same.
The resulting simulations were compared to mice gas uptake data (Fisher et al., 1991)
after some adjustments of the fat compartment volumes and flows based on visual fits, and
limited least-squares optimization of just VMAX (different for males and females) and KM (same
for males and females). The results are shown in the top panels of Figures A-3 and A-4, which
showed poor fits particularly at lower chamber concentrations. In particular, metabolism is
observed to be faster than predicted by simulation. This is directly related to metabolism of TCE
being limited by hepatic blood flow at these exposures. Indeed, Fisher et al. (1991) was able to
obtain adequate fits to these data by using cardiac output and ventilation rates that were about
twofold higher than is typical for mice. Although their later publication reporting inhalation
experiments (Greenberg et al., 1999) used the lower values from Brown et al. (1997) for these
parameters, they did not revisit the Fisher et al. (1991) data with the updated model. In addition,
the Hack et al. (2006) model estimated the cardiac output and ventilation rate and for these
experiments to be about twofold higher than typical. However, it seems unlikely that cardiac
output and ventilation rate were really as high as used in these models, since TCE and other
solvents typically have CNS-depressing effects. In the mouse, after the liver, the lung has the
highest rate of oxidative metabolism, as assessed by in vitro methods (see footnote in
Section 3.5.4.2 for a discussion of why kidney oxidative metabolism is likely to be minor
quantitatively). In addition, TCE administered via inhalation is available to the lung directly, as
well as through blood flow. Therefore, it was hypothesized that a more refined treatment of
respiratory metabolism may be necessary to account for the additional metabolism.
A-34
-------
10
E 10
Q_
Q.
C
O
ts
S 10
c
o
O
o 10
10
10
E 10
Q-
Q.
g
to
g 10
c
o
O
_Q
E
CD ,
o 10
10
123456
time (h)
234
time (h)
Figure A-3. Limited optimization results for male closed-chamber data from
Fisher et al. (1991) without (top) and with (bottom) respiratory metabolism.
A-3 5
-------
10
£ 10
Q.
Q.
S 10
c
o
O
CU
_Q
E
5 1
6 10
10
2 3
time (h)
Figure A-4. Limited optimization results for female closed-chamber data
from Fisher et al. (1991) without (top) and with (bottom) respiratory
metabolism.
A-36
-------
The structure of the updated respiratory metabolism model is shown in Figure A-5, with
the mathematical formulation shown in the model code in Section A.6, where the "D" is the
diffusion rate, "concentrations" and "amounts" are related by the compartment volume, and the
other symbols have their standard meanings in the context of PBPK modeling. In brief, this is a
more highly "lumped" version of the Sarangapani et al. (2003) respiratory metabolism model for
styrene combined with a "continuous breathing" model to account for a possible wash-in/wash-
out effect. In brief, upon inhalation (at a rate equal to the full minute volume, not just the
alveolar ventilation), TCE can either: (1) diffuse between the respiratory tract lumen and the
respiratory tract tissue; (2) remain in the dead space; or (3) enter the gas exchange region. In the
respiratory tract tissue, TCE can either be "stored" temporarily until exhalation, during which it
diffuses to the "exhalation" respiratory tract lumen, or be metabolized. In the dead space, TCE is
transferred directly to the "exhalation" respiratory tract lumen at a rate equal to the minute-
volume minus the alveolar ventilation rate, where it mixes with the other sources. In the gas
exchange region, it undergoes transfer to and from blood, as is standard for PBPK models of
volatile organics. Therefore, if respiratory metabolism is absent (VMAxClara = 0), then the
model reduces to a wash-in/wash-out effect where TCE is temporarily adsorbed to the
respiratory tract tissue, the amount of which depends on the diffusion rate, the volume of the
tissue, and the partition coefficients.
QP*C1
QM*CInh
1
Respiratory
Tract During
Inhalation
(AInhResp)
nhResp
i
D*CResp
^
D*CInhResp
VMaxClara*C
Respiratory
Tract Tissue
(AResp)
D*CResp
^
D*CExhResp
^
:Resp/(KMClara + CResp)
QM*CExhResp
1
Respiratory
Tract During
Exhalation
(AExhResp)
A
(QM - QP)*CInhResp
r
L
QP*CArt/PB
Alveolar (Gas Exchange) Region
QC*CVen
|QC*
CArt
Figure A-5. Respiratory metabolism model for updated PBPK model.
A-37
-------
The results of the same limited optimization, now with additional parameters VMAxClara,
KMClara, and D being estimated simultaneously with the hepatic VMAX and KM, are shown in the
bottom panels of Figures A-2 and A-3. The improvement in the model fits is obvious, and these
results served as a motivation to include this respiratory metabolism model for analysis by the
more formal Bayesian methods.
A.4. DETAILS OF THE UPDATED PBPK MODEL FOR TCE AND ITS
METABOLITES
The structure of the updated PBPK model and the statistical population model are shown
graphically in Chapter 3, with the model code shown below in Section A.7. Details as to the
model structure, equations, and parameter values and prior distributions are given below.
A.4.1. PBPK Model Structure and Equations
The equations below, along with the parameters defined in Table A-4, specify the PBPK
model. The ordinary differential equations are shown in bold, with the remaining equations
being algebraic definitions. The same equations are in the PBPK model code, with some
additional provisions for unit conversions (e.g., ppm to mg/L) or numerical stability (e.g.,
truncating small values at 10"15, so states are never negative). For reference, the stoichiometric
adjustments for molecular weights are given by the following:
# Molecular Weights
TCE: MWTCE=131.39
DCVC: MWDCVC = 216.1
TCA: MWTCA=163.5
TCOH: MWTCOH= 149.5
TCOG: MWTCOHGluc = 325.53
NAcDCVC: MWNADCVC = 258.8
# Stoichiometry
StochTCATCE = MWTCA/MWTCE;
StochTCATCOH = MWTCA/MWTCOH;
StochTCOHTCE = MWTCOH/MWTCE;
StochGlucTCOH = MWTCOHGluc/MWTCOH;
StochTCOHGluc = MWTCOH/MWTCOHGluc;
StochTCEGluc = MWTCE/MWTCOHGluc;
StochDCVCTCE = MWDCVC/MWTCE;
StochN = MWNADCVC/MWDCVC;
A-3 8
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships
Parameter
Body weight
Description
(units)
Body weight
(kg)
Formula
-
Baseline value or
parameter
Body weight0
Description
Standard body
weight
Mouse
0.03
Rat
0.3
Human F/M
60/70
Scaling
parameter
-
Sources(s)
a
Flows
QC
QP
DResp
Cardiac output
(L/hr)
Alveolar
ventilation
(L/hr)
Diffusion
clearance rate
(L/hr)
QC = QCC0 x exp(lnQCC)
x body weight0 75
QP = QC x VPR0
x exp(lnVPR)
DResp = QP
x exp(lnDRespC)
QCC0
VPR0
Cardiac output
allometrically
scaled
Ventilation-
perfusion ratio
11.6
2.5
13.3
1.9
16/16
0.96/0.96
InQCC
InVPRC
InDRespC
b
c
d
Physiological blood flows to tissues
QFat
QGut
QLiv
QSlw
QKid
QRap
FracPlas
Blood flow to
fat (L/hr)
Blood flow to
gut (L/hr)
Hepatic artery
blood flow
(L/hr)
Blood flow to
slowly perfused
tissues (L/hr)
Blood flow to
kidney (L/hr)
Blood flow to
rapidly
perfused tissues
(L/hr)
Fraction of
blood that is
plasma
QFat = QC x QFatCo
x QFatC
QGut = QC x QGutC0
x QGutC
QLiv = QC x QLivCo
x QLivC
QSlw = QC x QSlwC0
xQSlwC
QKid = QC x QKidCo
x QKidC
QRap = QC-(QFat
+ QGut + QLiv + QSlw
+ QKid)
FracPlas = FracPlas0
x FracPlasC
QFatCo
QGutC0
QLivCo
QSlwC0
QKidCo
FracPlaso
Fraction of blood
flow to fat
Fraction of blood
flow to gut
Fraction of blood
flow to hepatic
artery
Fraction of blood
flow to slowly
perfused tissues
Fraction of blood
flow to kidney
Fraction of blood
that is plasma
0.07
0.141
0.02
0.217
0.091
0.52
0.07
0.153
0.021
0.336
0.141
0.53
0.085/0.05
0.21/0.19
0.065/0.065
0.17/0.22
0.085/0.05
0.21/0.19
0.065/0.065
QFatC
QGutC
QLivC
QSlwC
QKidC
FracPlasC
e
e
e
e
e
e
f
A-39
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
Parameter
Description
(units)
Formula
Baseline value or
parameter
Description
Mouse
Rat
Human F/M
Scaling
parameter
Sources(s)
Physiological volumes
Wat
VGut
VLiv
VRap
VRespLum
VResp
VRespEff
VKid
VBld
VSlw
VPlas
Volume of fat
(L)
Volume of gut
(L)
Volume of liver
(L)
Volume of
rapidly
perfused tissues
(L)
Volume of
respiratory tract
lumen (L)
Volume of
respiratory tract
tissue (L)
Effective air
volume of
respiratory tract
tissue
Volume of
kidney (L)
Volume of
blood (L)
Volume of
slowly perfused
tissue (L)
Volume of
plasma (L)
Wat = body weight x
WatC0
x WatC
VGut = body weight x
VGutC0
x VGutC
VLiv = body weight x
WivC0
x WivC
VRap = body weight x
VRapC0
x VRapC
VRespLum = body weight
x VRespLumCo
x VRespLumC
VResp = body weight x
VRespCo x
VRespC
VRespEff = VResp
x PResp x PB
VKid = body weight
x VKidCo x VKidC
VBld = body weight x
VBldCo
xVBldC
VSlw = body weight x
VperfCo
- (Wat + VGut + VLiv
+ VRap + VResp + VKid
+ VBld)
VPlas = FracPlas x VBld
WatC0
VGutC0
VLivCo
VRapC0
VRespLumCo
VRespCo
VKidCo
VBldCo
VperfCo
-
Fraction of
body weight
that is fat
Fraction of
body weight
that is gut
Fraction of
body weight
that is liver
Fraction of
body weight
that is rapidly
perfused
Respiratory
lumen volume
as fraction body
weight
Fraction of
body weight
that is
respiratory tract
Fraction of
body weight
that is kidney
Fraction of
body weight
that is blood
Fraction of
body weight
that is blood
perfused
-
0.07
0.049
0.055
0.1
0.004667
0.0007
0.017
0.049
0.8897
-
0.07
0.032
0.034
0.088
0.004667
0.0005
0.007
0.074
0.8995
-
0.317/0.199
0.022/0.02
0.023/0.025
0.093/0.088
0.002386/0.002386
0.00018/0.00018
0.0046/0.0043
0.068/0.077
0.85778/0.8560
-
WatC
VGutC
WivC
VRapC
VRespLum
C
VRespC
VKidC
VBldC
-
g
g
g
g
g
g
g
g
g
g
h
A-40
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
Parameter
VBod
VBodTCOH
Description
(units)
Volume body
for TCA
submodel (L)
Volume body
for TCOH and
TCOG
submodels (L)
Formula
VBod = Wat + VGut
+ VRap + VResp + VKid
+ VSlw
VBodTCOH = VBod
+ VBld
Baseline value or
parameter
Description
Mouse
Rat
Human F/M
Scaling
parameter
Sources(s)
i
j
TCE distribution/partitioning
PB
PFat
PGut
PLiv
PRap
PResp
PKid
PSlw
TCE blood-air
partition
coefficient
TCE fat-blood
partition
coefficient
TCE gut-blood
partition
coefficient
TCE liver-
blood partition
coefficient
TCE rapidly
perfused-blood
partition
coefficient
TCE
respiratory tract
tissue-blood
partition
coefficient
TCE kidney-
blood partition
coefficient
TCE slowly
perfused-blood
partition
coefficient
PB=PB0xPBC
PFat=PFatC0x
exp(PFatC)
PGut=(PGutC0)x
exp(lnPGutC)
PLiv = (PLivCo)
x exp(lnPLivC)
PRap = (PRapCo)
x exp(lnPRapC)
Presp = (PRespCo)
x exp(lnPRespC)
PKid = (PKidC0)
x exp(lnPKidC)
PSlw = (PSlwCo) x
exp(lnPSlwC)
PB0
PFatC0
PGutCo
PLivCo
PRapCo
PRespCo
PKidCo
PSlwCo
TCE blood-air
partition
coefficient
TCE fat-blood
partition
coefficient
TCE gut-blood
partition
coefficient
TCE liver-blood
partition
coefficient
TCE rapidly
perfused-blood
partition
coefficient
TCE respiratory
tract tissue-
blood partition
coefficient
TCE kidney-
blood partition
coefficient
TCE slowly
perfused-blood
partition
coefficient
15
36
1.9
1.7
1.9
2.6
2.1
2.4
22
27
1.4
1.5
1.3
1.0
1.3
0.58
9.5
67
2.6
4.1
2.6
1.3
1.6
2.1
PBC
PFatC
InPGutC
InPLivC
InPRapC
InPRespC
InPKidC
InPSlwC
k
1
m
n
0
P
q
r
A-41
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
Parameter
Description
(units)
Formula
Baseline value or
parameter
Description
Mouse
Rat
Human F/M
Scaling
parameter
Sources(s)
TCA distribution/partitioning
TCAPlas
PBodTCA
PLivTCA
TCA blood-
plasma
concentration
ratio
Free TCA
body -plasma
partition
coefficient
Free TCA
liver-plasma
partition
coefficient
TCAPlas = FracPlas
+ (l-FracPlas)
x PRBCPlasTCAo
x exp(lnPRBCPlasTCAC)
PBodTCA = TCAPlas
x PBodTCACo
x exp(lnPBodTCAC)
PLivTCA = TCAPlas
x PLivTCACo
x exp(lnPLivTCAC)
PRBCPlasTCAo
PBodTCAC0
PLivTCACo
TCA red blood
cell-plasma
partition
coefficient
Free TCA body-
blood partition
coefficient
Free TCA liver-
blood partition
coefficient
0.5
0.88
1.18
0.5
0.88
1.18
0.5/0.5
0.52
0.66
InPRBCPlas
TCAC
InPBodTCA
C
InPLivTCA
C
S
t
t
TCA plasma binding
kDissoc
BMax
Protein TCA
dissociation
constant
(microM)
Protein
concentration
(microM)
kDissoc = kDissoco x
exp(lnkDissocC)
BMax = BMaxkDo
x kDissoc
x exp(lnBMaxkDC)
kDissoco
BMaxkDo
Protein TCA
dissociation
constant
(microM)
BMax/kDissoc
ratio
107
0.88
275
1.22
182
4.62
InkDissocC
InBMaxkD
C
u
u
TCOH and TCOG distribution/partitioning
PBodTCOH
PLivTCOH
PBodTCOG
PLivTCOG
TCOH body-
blood partition
coefficient
TCOH liver-
blood partition
coefficient
TCOG body-
blood partition
coefficient
TCOG liver-
blood partition
coefficient
PBodTCOH
= PBodTCOHo
x exp(lnPBodTCOHC)
PBodTCOH
= PLivTCOHo
x exp(lnPLivTCOHC)
PBodTCOG = PBodTCOGo
x exp(lnPBodTCOGC)
PBodTCOG = PLivTCOGo x
exp(lnPLivTCOGC)
PBodTCOHo
PLivTCOHo
PBodTCOGo
PLivTCOGo
TCOH body-
blood partition
coefficient
TCOH liver-
blood partition
coefficient
TCOG body-
blood partition
coefficient
TCOG liver-
blood partition
coefficient
1.11
1.3
1.11
1.3
1.11
1.3
1.11
1.3
0.91
0.59
0.91
0.59
InPBodTCO
HC
InPLivTCO
HC
InPBodTCO
GC
InPLivTCO
GC
V
V
w
w
DCVG distribution/partitioning
VDCVG
DCVG
distribution
volume (L)
VDCVG = VBld
+ (VBod+VLiv)
x exp(lnPeffDCVG)
InPeffDCV
G
X
A-42
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
Parameter
Description
(units)
Formula
Baseline value or
parameter
Description
Mouse
Rat
Human F/M
Scaling
parameter
Sources(s)
TCE metabolism
VMAX
KM
FracOther
FracTCA
VMAXDCVG
VMAXforTCE
hepatic
oxidation
(mg/hr)
KM for TCE
hepatic
oxidation
(mg/L blood)
Fraction of
TCE oxidation
not to TCA or
TCOH
Fraction of
TCE oxidation
to TCA
VMAXforTCE
hepatic GSH
conjugation
(mg/hr)
VMAX= VMAXOx VLiv
x exp(lnVMAXC)
KM = KMo x exp(lnKMC)
[Mouse and Rat]
KM=VMAX/(C1C0
x VLiv x exp(lnClC))
[Human]
FracOther
= exp(lnFracOtherC)/
(l+exp(lnFracOtherC))
FracTCA = (1 -FracOther) x
logitFracTCAo
x exp(lnFracTCAC)/
(1 + logitFracTCAo
x exp(lnFracTCAC))
VMAXDCVG
= VMAXDCVG0 x VLiv
x exp(lnVMAXDCVGC)
[Mouse and Rat]
VMAXDCVG = VLiv
x C1DCVG0
x exp(lnClDCVGC)
x KMDCVGo
x exp(lnKMDCVGC)
[Human]
VMAXO
KMo
C1C0
logitFracTCAo
VMAXDCVG0
CIDCVGo
KMDCVGo
VMAX per kg
liver for TCE
hepatic
oxidation
(mg/hr/kg liver)
KM for TCE
hepatic
oxidation
(mg/L)
VMAX/KMper
kg liver for TCE
hepatic
oxidation (L
blood/hr/kg
liver)
Log of ratio of
fraction to TCA
to fraction not
to TCA
VMAX per kg
liver for TCE
GSH
conjugation
(mg/hr/kg liver)
VMAX/KMper
kg liver for TCE
GSH
conjugation (L
blood/hr/kg
liver)
KM for TCE
GSH
conjugation
(mg/L blood)
2,700
36
0.32
300
600
21
0.32
66
255
66
0.32
19
2.9
lnVMAXC
InKMC
InCIC
InFracOther
C
InFracTCA
C
lnVMAXDC
VGC
InClDCVG
C
InKMDCV
GC
y
y
y
z
aa
bb
bb
bb
A-43
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
Parameter
KMDCVG
VMAXKidDCVG
KMKidDCVG
Description
(units)
KM for TCE
hepatic GSH
conjugation
(mg/L blood)
VMAXforTCE
kidney GSH
conjugation
(mg/hr)
KM for TCE
kidney GSH
conjugation
(mg/L blood)
Formula
KMDCVG = VMAXDCVG/
(CIDCVGo
x exp(lnClDCVGC)
[Mouse and Rat]
KMDCVG = KMDCVG0
x exp(lnKMDCVGC)
[Human]
VMAXKidDCVG
= VMAXKidDCVG0
x VKid
x exp(lnVMAXKidDCVGC)
[Mouse and Rat]
VMAXKidDCVG = VKid
x ClKidDCVGo
x exp(lnClKidDCVGC)
x KMKidDCVGo
x exp(lnKMKidDCVGC)
[Human]
KMKidDCVG
= VMAXKidDCVG/
(ClKidDCVGo
x exp(lnClKidDCVGC)
[Mouse and Rat]
KMKidDCVG
= KMKidDCVGo
x exp(lnKMKidDCVGC)
[Human]
Baseline value or
parameter
CIDCVGo
KMDCVGo
VMAXKidDCVG0
ClKidDCVGo
KMKidDCVGo
ClKidDCVGo
KMKidDCVGo
Description
VMAX/KMper
kg liver for TCE
hepatic GSH
conjugation (L
blood/hr/kg
liver)
KM for TCE
GSH
conjugation
(mg/L blood)
VMAX per kg
kidney for TCE
GSH
conjugation
(mg/hr/kg
kidney)
VMAX/KM per
kg kidney for
TCE GSH
conjugation (L
blood/hr/kg
liver)
KM for TCE
GSH
conjugation
(mg/L blood)
VMAX/KMper
kg kidney for
TCE kidney
GSH
conjugation (L
blood/hr/kg
liver)
KM for TCE
GSH
conjugation
(mg/L blood)
Mouse
1.53
60
0.34
Rat
0.25
6.0
0.026
Human F/M
2.9
230
2.7
2.7
Scaling
parameter
InClDCVG
C
InKMDCV
GC
lnVMAXKid
DCVGC
InClKidDC
VGC
InKMKidD
CVGC
InClDCVG
C
InKMKidD
CVGC
Sources(s)
bb
bb
bb
bb
bb
bb
bb
A-44
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
Parameter
Description
(units)
Formula
Baseline value or
parameter
Description
Mouse
Rat
Human F/M
Scaling
parameter
Sources(s)
TCE metabolism (respiratory tract)
KMClara
VMAXClara
FracLungSys
KM for TCE
lung oxidation
(mg/L air)
VMAXforTCE
lung oxidation
(mg/hr)
Fraction of
respiratory
oxidation
entering
systemic
circulation
KMClara
= exp(lnKMClara)
VMAXClara = VMAX
x VMAXLungLiv0
x exp(lnVMAXLungLivC)
FracLungSys
= exp(lnFracLungSysC)/
(l+exp(lnFracLungSysC))
VMAXLungLiv0
Ratio of lung to
liver total VMAX
(mg/hr per
mg/hr)
0.07
0.0144
0.0138/
0.0128
lnVMAXLun
gLivC
InFracLung
SysC
cc
cc
dd
TCOH metabolism
VMAXTCOH
KMTCOH
VMAXGluc
KMGluc
kMetTCOH
VMAX for
TCOH
oxidation to
TCA (mg/hr)
KM for TCOH
oxidation to
TCA (mg/L air)
VMAX for
TCOH
glucuroni-
dation (mg/hr)
KM for TCOH
glucuroni-
dation (mg/L
air)
Rate constant
for TCOH
other clearance
VMAXTCOH= body weight*
x exp(lnVMAXTCOHC)
[Mouse and Rat]
VMAXTCOH = body weight4
x exp(lnC!TCOHC
+ InKMTCOHC)
[Human]
KMTCOH
= exp(lnKMTCOHC)
VMAXGluc = body weight *
x exp(lnVMAXGlucC)
[Mouse and Rat]
VMAXGluc = body weight*
x exp(lnC!GlucC
+ InKMGlucC)
[Human]
KMGluc
= exp(lnKMGlucC)
kMetTCOH = body weight'74
x exp(lnkMetTCOHC)
lnVMAXTC
OHC
InClTCOH
C
InKMTCO
HC
InKMTCO
HC
lnVMAXGluc
C
InClGlucC
InKMGlucC
InKMGlucC
InkMetTCO
HC
A-45
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
Parameter
Description
(units)
(/hr)
Formula
Baseline value or
parameter
Description
Mouse
Rat
Human F/M
Scaling
parameter
Sources(s)
TCA metabolism/clearance
kUmTCA
kMetTCA
Rate constant
for TCA
excretion to
urine (/hr)
Rate constant
for other TCA
clearance (/hr)
kUrnTCA = GFR_body
weight
exp(lnkUmTCAC)
x body weight /VPlas
kMetTCA = body weight" '/4
x exp(lnkMetTCAC)
GFR_body weight
Glomerular
filtration rate
per kg body
weight (L/h/kg)
0.6
0.522
0.108
InkUmTCA
C
InkMetTCA
C
ee
TCOG metabolism/clearance
kBile
kEHR
kUmTCOG
Rate constant
for other
TCOG
excretion to
bile (/hr)
Rate constant
for other bile
TCOG
reaborption as
TCOH (/hr)
Rate constant
for TCOH
excretion to
urine (/hr)
kBile = body weight" '/4
x exp(lnkBileC)
kEHR = body weight"'7'
x exp(lnkEHRC)
kUmTCOG = GFR_body
weight
exp(lnkUrnTCOGC)
x body weight/(VBodTCOH
x PBodTCOG)
GFR_body weight
Glomerular
filtration rate
per kg body
weight
(L/hr/kg)
0.6
0.522
0.108
InkBileC
InkEHRC
InkUmTCO
GC
ee
DCVG metabolism
kDCVG
kNAT
kBioact
Rate constant
for DCVC
formation from
DCVG (/hr)
Rate constant
for urinary
excretion of
NAcDCVC
(/hr)
Rate constant
for other bio-
activation of
DCVC (/hr)
kDCVG = body weight" v'
x exp(lnkDCVGC)
kNAT = body weight" *
x exp(lnkNATC)
kKidBioact = body weight" *
x exp(lnkKidBioactC)
InkDCVGC
InkNATC
InkKidBioa
ctC
ff
gg
gg
A-46
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
Parameter
Description
(units)
Formula
Baseline value or
parameter
Description
Mouse
Rat
Human F/M
Scaling
parameter
Sources(s)
Oral uptake/transfer coefficients
kTSD
kAS
kAD
kASTCA
kASTCOH
TCE gavage
stomach-
duodenum
transfer
coefficient (/hr)
TCE gavage
stomach-
absorption
coefficient (/hr)
TCE gavage
duodenum-
absorption
coefficient (/hr)
TCA stomach
absorption
coefficient (/hr)
TCOH stomach
absorption
coefficient (/hr)
kTSD = exp(lnkTSD)
kAS = exp(lnkAS)
kAD = exp(lnkAD)
kASTCA
= exp(lnkASTCA)
kASTCOH
= exp(lnkASTCOH)
1.4
1.4
0.75
0.75
0.75
InkTSD
InkAS
InkAD
InkASTCA
InkASTCO
H
hh
hh
hh
hh
hh
Explanatory note. Unless otherwise noted, the model parameter is obtained by multiplying: (1) the "baseline value" (equals one if not specified); (2) the
scaling parameter (or for those beginning with "In," which are natural-log transformed, exp[lnXX]); and (3) any additional scaling as noted in the second to last
column. Unless otherwise noted, all log-transformed scaling parameters have baseline value of 0 (i.e., exp[lnXX] has baseline value of 1) and all other scaling
parameters have baseline parameters of 1.
aUse measured value if available.
blf QP is measured, then scale by QP using VPR. Baseline values are from Brown et al. (1997) (mouse and rat) and International Commission on Radiological
Protection (ICRP) Publication 89 (2003) (human).
°Use measured QP, if available; otherwise scale by QC using alveolar VPR. Baseline values are from Brown et al. (1997) (mouse and rat) and ICRP
Publication 89 (2003) (human).
dScaling parameter is relative to alveolar ventilation rate.
Tat represents adipose tissue only. Gut is the GI tract, pancreas, and spleen (all drain to the portal vein). Slowly perfused tissue is the muscle and skin. Rapidly
perfused tissue is the rest of the organs, plus the bone marrow and lymph nodes, the blood flow for which is calculated as the difference between the cardiac
output (QC) and the sum of the other blood flows. Baseline values are from Brown et al. (1997) (mouse and rat) and ICRP Publication 89 (2003) (human).
fThis is equal to 1 minus the hematocrit (measured value used if available). Baseline values from control animals in (Hejtmancik et al.. 2002) (mouse and rat)
and ICRP Publication 89 (2003) (human).
A-47
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
8Fat represents adipose tissue only, and the measured value is used, if available. Gut is the GI tract, pancreas, and spleen (all drain to the portal vein). Rapidly
perfused tissue is the rest of the organs, plus the bone marrow and lymph nodes, minus the tracheobronchial region. The respiratory tissue volume is
tracheobronchial region, with an effective air volume given by multiplying by its tissue:air partition coefficient (= tissue:blood times blood:air). The slowly
perfused tissue is the muscle and skin. This leaves a small (10-15% of body weight) unperfused volume that consists mostly of bone (minus marrow) and the GI
tract contents. Baseline values are from Brown et al. (1997) (mouse and rat) and ICRP Publication 89 (2003) (human), except for volumes of the respiratory
lumen, which are from Sarangapani et al. (2003).
hDerived from blood volume using FracPlas.
'Sum of all compartments except the blood and liver.
JSum of all compartments except the liver.
kMouse value is from pooling Abbas and Fisher (1997) and Fisher et al. (1991). Rat value is from pooling Sato et al. (1977). Gargas et al. (1989). Barton et al.
(1995). Simmons et al. (2002). Koizumi (1989). and Fisher et al. (1989). Human value is from pooling Sato and Nakajima (1979). Sato et al. (1977). Gargas
et al. (1989). Fiserova-Bergerova et al. (1984). Fisher et al. (1998). and Koizumi (1989).
'Mouse value is from Abbas and Fisher (1997). Rat value is from pooling Barton et al. (1995). Sato et al. (1977). and Fisher et al. (1989). Human value is from
pooling Fiserova-Bergerova et al. (1984). Fisher et al. (1998). and Sato et al. (1977).
mValue is the geometric mean of liver and kidney (relatively high uncertainty) values.
"Mouse value is from Fisher et al. (1991). Rat value is from pooling Barton et al. (1995). Sato et al. (1977). and Fisher et al. (1989). Human value is from
pooling Fiserova-Bergerova et al. (1984) and Fisher et al. (1998).
"Mouse value is geometric mean of liver and kidney values. Rat value is the brain value from Sato et al. (1977). Human value is the brain value from Fiserova-
Bergerova et al. (1984).
pMouse value is the lung value from Abbas and Fisher (1997). Rat value is the lung value from Sato et al. (1977). Human value is from pooling lung values
from Fiserova-Bergerova et al. (1984) and Fisher et al. (1998).
qMouse value is from Abbas and Fisher (1997). Rat value is from pooling Barton et al. (1995) and Sato et al. (1977). Human value is from pooling Fiserova-
Bergerova et al. (1984) and Fisher et al. (1998).
'Mouse value is the muscle value from Abbas and Fisher (1997). Rat value is the muscle value from pooling Barton et al. (1995). Sato et al. (1977). and Fisher et
al. (1989). Human value is the muscle value from pooling Fiserova-Bergerova et al. (1984) and Fisher et al. (1998).
"Scaling parameter is the effective partition coefficient between red blood cells and plasma. Thus, the TCA blood-plasma concentration ratio depends on the
plasma fraction. Baseline value is based on the blood-plasma concentration ratio of 0.76 in rats (Schultz et al.. 1999).
'in vitro partition coefficients were determined at high concentration, when plasma binding is saturated, so should reflect the free blood:tissue partition
coefficient. To get the plasma partition coefficient, the partition coefficient is multiplied by the blood:plasma concentration ratio (TCAPlas). In vitro values
were from Abbas and Fisher (1997) in the mouse (used for both mouse and rat) and from Fisher et al. (1998). Body values based on measurements in muscle.
"Values are based on the geometric mean of estimates based on data from Lumpkin et al. (2003). Schultz et al. (1999). Templin et al. (1995b: 1993). and Yu et al.
(2000). Scaling parameter for BMAX is actually the ratio of BMAX/kD, which determines the binding at low concentrations.
vData are from Abbas and Fisher (1997) in the mouse (used for the mouse and rat) and Fisher et al. (1998) (human).
"Used in vitro measurements in TCOH as a proxy, but higher uncertainty is noted.
"The scaling parameter (only used in the human model) is the effective partition coefficient for the "body" (nonblood) compartment, so that the distribution
volume VDCVG is given by VBld + exp(lnPeffDCVG) x (VBod + VLiv).
A-48
-------
Table A-4. PBPK model parameters, baseline values, and scaling relationships (continued)
yBaseline values have the following units: for VMAX, mg/hr/kg liver; for KM, mg/L blood; and for clearance (Cl), L/hr/kg liver (in humans, KM is calculated from
KM = VMAx/(exp(lnClC) x Vliv). Values are based on in vitro (microsomal and hepatocellular preparations) from Elfarra et al. (1998), Lipscomb et al. (1998b;
1998c. 1997). Scaling from in vitro data based on 32 mg microsomal protein/g liver and 99 x 106 hepatocytes/g liver (Barter etal. 2007). Scaling of KM from
microsomes were based on two methods: (1) assuming microsomal concentrations equal to liver tissue concentrations and (2) using the measured microsome:air
partition coefficient and a central estimate of the blood:air partition coefficient. For KM from human hepatocyte preparations, the measured hepatocyte:air
partition coefficient and a central estimate of the blood:air partition coefficient was used.
zScaling parameter is ratio of "DCA" to "non-DCA" oxidative pathway (where DCA is a proxy for oxidative metabolism not producing TCA or TCOH).
Fraction of "other" oxidation is exp(lnFracOtherC)/(l + exp[lnFracOtherC]).
aaScaling parameter is ratio of TCA to TCOH pathways. Baseline value based on geometric mean of Lipscomb et al. (1998b) using fresh hepatocytes and
Bronley-DeLancey et al. (2006) using cryogenically-preserved hepatocytes. Fraction of oxidation to TCA is (1 -
FracOther) x exp(lnFracTCAC)/(l + exp[lnFracTCAC]).
bbBaseline values are based on in vitro data. In the mouse and rat, the only in vitro data are at 1 or 2 mM (Lash etal.. 1998b; Lash etal.. 1995). In most cases,
rates at 2 mM were increased over the same sex/species at 1 mM, indicating VMAX has not yet been reached. These data therefore put lower bounds on both
VMAX (in units of mg/hr/kg tissue) and clearance (in units of L/hr/kg tissue), so those are the scaling parameters used, with those bounds used as baseline values.
For humans, data from Lash et al. (1999a) in the liver (hepatocytes) and the kidney (cytosol) and Green et al. (1997b) (liver cytosol) was used to estimate the
clearance in units of L/hr/kg tissue and KM in units of mg/L in blood.
ccScaling parameter is the ratio of the lung to liver VMAX (each in units of mg/hr), with baseline values based on microsomal preparations (mg/hr/mg protein)
assayed at ~1 mM (Green etal.. 1997b). further adjusted by the ratio of lung to liver tissue masses (Publication 89. ICRP. 2003; Brown etal.. 1997).
ddScaling parameter is the ratio of respiratory oxidation entering systemic circulation (translocated to the liver) to that locally cleared in the lung. Fraction of
respiratory oxidation entering systemic circulation is exp(lnFracLungSysC)/(l + exp[lnFracLungSysC]).
eeBaseline parameters for urinary clearance (L/hr) were based on glomular filtration rate per unit body weight (L/hr/kg body weight) from Lin (1995). multiplied
by the body weights cited in the study. For TCA, these were scaled by plasma volume to obtain the rate constant (/hr), since the model clears TCA from plasma.
For TCOG, these were scaled by the effective distribution volume of the body (VBodTCOH x PBodTCOG) to obtain the rate constant (/hr), since the model
clears TCOG from the body compartment.
ffHuman model only.
88Rat and human models only.
^Baseline value for oral absorption scaling parameter are as follows: kTSD and kAS, 1.4/hr, based on human stomach half time of 0.5 hr; kAD, kASTCA, and
kASTCOH, 0.75/hr, based on human small intestine transit time of 4 hrs (Publication 89. ICRP. 2003). These are noted to have very high uncertainty.
A-49
-------
A.4.1.1. TCE Submodel
The TCE submodel is a whole-body, flow-limited PBPK model, with gas respiratory
exchange, oral absorption, and metabolizing and nonmetabolizing tissues (see Figures A-6 and
A-7).
I Exhaled air
l (CMixEXh)
" QIVTCInh
T DRfiSf
Respiratory Cln
Trart 1 iimffn ^
Inhal
ation
(AlnhResp)
^
i
wr.RP.p-
hResp) Respiratory
Tract Tissue
fc fAPncr^
QM*CMixExh
DPesp*(rPesp- "
CExhResp) Respiratory
^ Tract Lumen
* Exha
ation
X (AExhReso)
* Oxidation
1 (VMaxClara,
l _ _KjyiCla_ra| _
\ '
l
l
Dead space
QP*ClnhResp (QM-QP)*ClnhResp QP*CArt_tmp/PB
r
Gas Exchange
k
QC*CVen
From venous blood
QC*CArt_tmp ,
QC*CArt ,
To rest of body
L
r ^ *
r ^m
Intra-
arterial
_dpse_(klA)_
Figure A-6. Submodel for TCE gas exchange, respiratory metabolism, and
arterial blood concentration.
A-50
-------
To gas exchange
QC*
t
QRap*CVRap
QSIw*CVSIw
4
Venous
Blood
(ABIcO
QFat*CVFat
4
From gas exchange
QC*CArt
\at\S WJ
RaPid|V |QRap*CArt
Perfused *
(ARap)
Slowly |QSIw*CArt
Derfused <
Perfused
(ASIw)
QGut*CVGut
Gut (AGut)
|QFat*CArt
Fat (AFat) * -^r
WVJUl v-'VVJUl ir^
^QGUt+CVLiv Liver (ALiv)
QKid*CVKid
4
QGut*CArt
QLiv*CArt "
-*
Kidne
AStom
_A_Duodj Duodenum
"" (ADuod)
^LV-/l I If
*kJSD*AStom
^ ^.
Portal Vein
dose (kPV)
^. >
~ "
-. Oxidation
v JVjyiax^KM^ (
iManey K _^^_jr
(AKid) |oKid*CArt ^W^ v ^ •- -"i-- - -L. /
/ upnnnc; \ ! Conjugation I | Conjugation ,
( v^IUUb j (VMaxKidDCVG, I (VMaxDCVG, i
V /M?A / i KMKidDCVG) ' , KMDCVG) I
^ilSlXi-^ \_ / \. s
\.-l Submodel for TCE oral absorption, tissue distribution, and
ism
Figure A-7 ,
metabolism.
A.4.1.1.1. Gas exchange, respiratory metabolism, arterial blood concentration, and
closed-chamber concentrations
For an open-chamber concentration and a closed-chamber concentration of ACh/VCh,
the rates of change for the amount in the respiratory lumen during inhalation (AInhResp, in mg),
the amount in the respiratory tract tissue (AResp, in mg), and the respiratory lumen during
exhalation (AExhResp, in mg) are given by the following:
d(AInhResp)/dt = (QM x CInh + DResp x (CResp - CInhResp)
- QM x CInhResp)
d(AResp)/dt = (DResp x (CInhResp + CExhResp - 2
x CResp) - RAMetLng)
d(AExhResp)/dt = (QM x (CInhResp - CExhResp) + QP
x (CArt_tmp/PB-CInhResp) + DResp
x (CResp-CExhResp))
(Eq. A-5)
(Eq. A-6)
(Eq. A-7)
A-51
-------
where
CInh = inhaled concentration (mg/L) = ACh/VCh + Cone
QM = minute volume (L/hour) = QP/0.7
CInhResp = concentration in respiratory lumen during inhalation (mg/L)
= AInhResp/VRespLum
CResp = concentration in respiratory tract tissue (mg/L)
= AResp/VRespEff
CExhResp = concentration in respiratory lumen during exhalation (mg/L)
= AExhResp/VRespLum
RAMetLng = rate of metabolism in respiratory tract tissue
= (VMAxClara x CResp)/(KMClara + CResp)
CArt_tmp = arterial blood concentration after gas exchange
= (QC x CVen + QP x CInhResp)/(QC + (QP/PB))
Because alveolar breath concentrations can include desorption from the respiratory tract
tissue, the concentration at the alveolae (CArt_tmp/PB) may not equal the measured
concentration in end-exhaled breath. It is therefore assumed that the ratio of the measured end-
exhaled breath concentration to the concentration in the absence of desorption is the same as the
ratio of the rate of TCE leaving the lumen to the rate of TCE entering the lumen:
CAlv/(CArt_tmp/PB) = (QM x CMixExh)/{(QP x CArt_tmp/PB (Eq. A-8)
+ (QM-QP) x CInhResp)}
That is, it is assumed that desorption occurs proportionally throughout the "breath." The
concentration of arterial blood entering circulation needs to add the contribution from the i.a.
dose (lADose in mg/kg, infused over a time period TChng):
CArt = CArtJmp + klA/QC (Eq. A-9)
where
klA = (lADose x body weight)/TChng
For closed-chamber experiments, the additional differential equation for the amount in
the chamber (ACh, in mg) is:
d(ACh)/dt = Rodents x (QM x CMixExh - QM x ACh/VCh) - kLoss x Ach (Eq. A-10)
where rodents is the number of animals in the chamber, and kLoss is the chamber loss rate
(per hour).
A .4.1.1.2. Oral absorption to gut compartment
For oil-based gavage, the dose PDose is defined in terms of units of mg/kg, entering the
stomach during a time TChng, with rates of change in the stomach (AStom, in mg) and
duodenum (ADuod, in mg):
A-52
-------
d(AStom)/dt = kStom - AStom x (kAS + kTSD) (Eq. A-l 1)
d(ADuod)/dt = (kTSD x AStom) - kAD x ADuod (Eq. A-12)
where
kStom = rate of TCE entering stomach (mg/hour) = (PDose x body
weight)/TChng
Note that there is absorption to the gut from both the stomach and duodenal
compartments. Analogous equations are defined for aqueous gavage, with the expectation that
absorption and transfer coefficients would differ with the different vehicle. In particular, the
aqueous gavage dose PDoseAq is defined in terms of units of mg/kg, entering the stomach
during a time TChng, with rates of change in the stomach (AStomAq, in mg) and duodenum
(ADuodAq, in mg):
d(AStomAq)/dt = kStomAq - AStomAq x (kASAq + kTSDAq) (Eq. A-13)
d(ADuodAq)/dt = (kTSDAq x AStomAq) - kADAq x ADuodAq (Eq. A-14)
where
kStomAq = rate of TCE entering stomach (mg/hour) = (PDoseAq x body
weight)/TChng
For drinking water, the rate Drink is defined in terms of mg/kg-day, and it is assumed that
absorption is direct to the gut:
kDrink = (Drink x body weight)/24.0 (Eq. A-15)
Therefore, the total rate of absorption to the gut via oral exposure (RAO, in mg/hour) is:
RAO = kDrink + (kAS x AStom) + (kAD x ADuod) + (kASAq (Eq. A-16)
x AStomAq) + (kADAq x ADuodAq)
The differential equation for the gut compartment (AGut, in mg) is, therefore, given by:
d(AGut)/dt = QGut x (CArt - CVGut) + RAO (Eq. A-17)
where
CVGut = concentration in the gut (mg/L) = AGut/VGut/PGut
A.4.1.1.3. Nonmetabolizing tissues
The differential equations for nonmetabolizing tissues (rapidly perfused, ARap, in mg;
slowly perfused, ASlw, in mg; and fat, AFat, in mg) follow the standard flow-limited form:
d(ARap)/dt = QRap x (CArt - CVRap) (Eq. A-18)
A-53
-------
d(ASlw)/dt = QSlw x (CArt - CVSlw) (Eq. A-19)
d(AFat)/dt = QFat x (CArt - CVFat) (Eq. A-20)
where
CVRap = venous blood concentration leaving rapidly perfused issues
= ARap/VRap/PRap
CVSlw = venous blood concentration leaving slowly perfused issues
= ASlw/VSlw/PSlw
CVFat = venous blood concentration leaving fat
= AFat/VFat/PFat
A .4.1.1.4. Liver compartment
The liver has two metabolizing pathways:
RAMetLivl = Rate of TCE oxidation by P450 in liver (mg/hour) (Eq. A-21)
= (VMAX x CVLiv)/(KM + CVLiv)
RAMetLiv2 = Rate of TCE metabolized to S-dichlorovinyl glutathione
(DCVG_ in liver (mg/hour)
= (VMAXDCVG x CVLiv) (KMDCVG + CVLiv) (Eq. A-22)
Some experiments also had portal vein dosing (PVDose in mg/kg, infused over a time
period TChng), with a rate entering the liver of:
kPV = (PVDose x body weight)/TChng (Eq. A-23)
The differential equation for TCE in liver (ALiv, in mg) is thus:
d(ALiv)/dt = (QLiv x (CArt - CVLiv)) + (QGut x (CVGut (Eq. A-24)
- CVLiv)) - RAMetLivl - RAMetLivl + kPV
where
CVLiv = venous blood concentration leaving liver
= ALiv/VLiv/PLiv
A.4.1.1.5. Kidney compartment
The kidney has one metabolizing pathway, GSH conjugation:
RAMetKid = Rate of TCE metabolized to DCVG in kidney (mg/hour) (Eq. A-25)
= (VMAXKidDCVG x CVKid)/(KMKidDCVG + CVKid)
The differential equation for TCE in kidney (AKid, in mg) is thus:
d(AKid)/dt = (QKid x (CArt - CVKid)) - RAMetKid (Eq. A-26)
A-54
-------
where
CVKid = venous blood concentration leaving kidney = AKid/VKid/PKid
A .4.1.1.6. Venous blood compartment
The venous blood compartment (ABld, in mg) has inputs both from the venous blood
exiting tissues as well as from an IV dose (IVDose in mg/kg infused during a time TChng), and
output to the gas exchange region:
d(ABld)/dt = (QFat x CVFat + QGutLiv x CVLiv + QSlw (Eq. A-27)
x CVSlw + QRap x CVRap + QKid x CVKid)
+ kIV-CVenxQC
klV = IV infusion rate
= (IVDose x body weight)/TChng
CVen = concentration in mixed venous blood
= ABld/VBld
where
A.4.1.2. TCOH Submodel
The TCOH submodel is a simplified whole-body, flow-limited PBPK model, with only a
body (ABodTCOH, in mg) and liver (ALivTCOH, in mg) compartment (see Figure A-8).
(QGut+QLiv)*
CVLivTCOH
Liver TCE
Oxidation*
(1 - FracTCA -
LungTCE
Oxidation*
FracLungSys
(1 - FracTCA -
_ FracOther^ _ /
Enterohepatic
Recirculation
(kEHR*
. .AB'teTCOG} _ /
Figure A-8. Submodel for TCOH.
(QGut+QLiv)*
CTCOH
Oxidation to TCA
(VMaxTCOH,
KMTCOH)
Glucuronindation'
to TCOG
(VMaxGluc,
Clearance to
Other
, (kMetTCOH)
A-55
-------
A .4.1.2.1. Blood concentration
The venous blood concentration, including an IV dose (IVDoseTCOH in mg/kg infused
during a time TChng), is given by
CTCOH = (QBod x CVBodTCOH + QGutLiv (Eq. A-28)
x CVLivTCOH + kIVTCOH)/QC
where
CVBodTCOH = ABodTCOH/VBodTCOH/PBodTCOH
CVLivTCOH = ALivTCOH/VLiv/PLivTCOH
klVTCOH = IV infusion rate
= (IVDoseTCOH x body weight)/TChng
and the partition coefficients for the body:blood and liverblood are PBodTCOH and
PLivTCOH, respectively, QGutLiv is the sum of the portal vein and hepatic artery blood flows,
QBod is the remaining blood flow, VLiv is the liver volume, and VBodTCOH is the remaining
perfused volume.
A.4.1.2.2. Body compartment
The rate of change of the amount of TCOH in the body compartment is
d(ABodTCOH)/dt = QBod x (CTCOH - CVBodTCOH) (Eq. A-29)
A.4.1.2.3. Liver compartment
The liver has three metabolizing pathways:
RAMetTCOHTCA = Rate of oxidation of TCOH to TCA (mg/hour) (Eq. A-30)
= (VMAXTCOH x CVLivTCOH)/(KMTCOH
+ CVLivTCOH)
RAMetTCOHGluc = Amount of glucuronidation to TCOG (mg/hour) (Eq. A-31)
= (VMAXGluc x CVLivTCOH)/(KMGluc
+ CVLivTCOH)
RAMetTCOH = Amount of TCOH metabolized to other (e.g., DCA) (Eq. A-32)
= kMetTCOH x ALivTCOH
Some experiments also had oral dosing (PODoseTCOH in mg/kg, entering the stomach
over a time TChng):
d(AStomTCOH)/dt = kStomTCOH - AStomTCOH x kASTCOH (Eq. A-33)
kStomTCOH = (PODoseTCOH x body weight)/TChng; (Eq. A-34)
A-56
-------
# TCOH PO dose rate into stomach
kPOTCOH = AStomTCOH x kASTCOH; # TCOH oral absorption rate
(mg/hour) (Eq. A-35)
In addition, there are three additional sources of TCOH:
Production in the liver from TCE (a fraction of hepatic oxidation) (Eq. A-36)
= (1.0 - FracOther - FracTCA) x StochTCOHTCE x RAMetLivl
Production in the lung from TCE (a fraction of lung oxidation) (Eq. A-37)
= (1.0 - FracOther - FracTCA) x StochTCOHTCE
x FracLungSys x RAMetLng
Enterohepatic recirculation (rate kEHR) from TCOG in the bile (Eq. A-38)
(amount ABileTCOG) = StochTCOHGluc x RARecircTCOG
= StochTCOHGluc x kEHR x ABileTCOG
Note that StochTCOHTCE is the ratio of molecular weights of TCOH and TCE,
StochTCOHGluc is the ratio of molecular weights of TCOH and TCOG, FracOther is the
fraction of TCE oxidation not producing TCA or TCOH, FracTCA is the fraction of TCE
oxidation producing TCA, and FracLungSys is the fraction of lung TCE oxidation that is
translocated to the liver and not locally cleared.
The differential equation for TCOH in liver (ALivTCOH, in mg) is thus:
d(ALivTCOH)/dt = kPOTCOH + QGutLiv x (CTCOH -
CVLivTCOH) (Eq. A-39)
- RAMetTCOH - RAMetTCOHTCA - RAMetTCOHGluc
+ ((1.0 - FracOther - FracTCA) x StochTCOHTCE
x (RAMetLivl + FracLungSys x RAMetLng))
+ (StochTCOHGluc x RARecircTCOG)
A.4.1.3. TCOG Submodel
The TCOG submodel is a simplified whole-body, flow-limited PBPK model, with body
(ABodTCOG, in mg), liver (ALivTCOG, in mg), and bile (ABileTCOG) compartments (see
Figure A-9).
A-57
-------
Blood
L__(.QTC_OG)_
Body
(ABodTCOG)
Liver
(ALivTCOG) |(QGut+QLiv)*
I
I Glucuronindation
I ofTCOH
I
I
I TCOG in l
I urine I
I I
kBile
, ALivTCOG
CTCOG
Bile
(ABileTCOG)
Enterohepatic N
Recirculation .
(kEHR * |
.ABileTCOG} _ /
Figure A-9. Submodel for TCOG.
A.4.1.3.1. Blood concentration
The venous blood concentration is given by:
CTCOG = (QBod x CVBodTCOG + QGutLiv x CVLivTCOG)/QC (Eq. A-40)
where
CVBodTCOG = ABodTCOG/VBodTCOH/PBodTCOG
CVLivTCOG = ALivTCOG/VLiv/PLivTCOG
and the partition coefficients for the body:blood and liverblood are PBodTCOG and
PLivTCOG, respectively, QGutLiv is the sum of the portal vein and hepatic artery blood flows,
QBod is the remaining blood flow, VLiv is the liver volume, and VBodTCOH is the remaining
perfused volume.
A.4.1.3.2. Body compartment
The body compartment is flow limited, with urinary excretion rate (mg/hour):
RUrnTCOG = kUrnTCOG x ABodTCOG (Eq. A-41)
So the rate of change of the amount of TCOG in the body compartment is:
d(ABodTCOG)/dt = QBod x (CTCOG - CVBodTCOG) -
RUrnTCOG (Eq. A-42)
A-58
-------
Thus, the amount excreted in urine (AUrnTCOG, mg) is given by:
d(AUrnTCOG)/dt = RUrnTCOG (Eq. A-43)
A.4.1.3.3. Liver compartment
The liver is flow limited, with one input, glucuronidation of TCOH (defined above in the
TCOH submodel):
StochGlucTCOH x RAMetTCOHGluc (Eq. A-44)
and one additional output, excretion in bile:
RBileTCOG = rate of excretion in bile (mg/hour) = kBile x
ALivTCOG (Eq. A-45)
The rate of change of the amount of TCOG in the liver is, therefore:
d(ALivTCOG)/dt = QGutLiv x (CTCOG - CVLivTCOG) (Eq. A-46)
+ (StochGlucTCOH x RAMetTCOHGluc) - RBileTCOG
A.4.1.3.4. Bile compartment
The bile compartment has one input, excretion of TCOG in bile from the liver (defined
above) and one output, enterohepatic recirculation to TCOH in the liver (defined above in the
TCOH submodel), with rate of change:
d(ABileTCOG)/dt = RBileTCOG - RARecircTCOG (Eq. A-47)
A.4.1.4. TCA Submodel
The TCA submodel is the same as that in Hack et al. (2006), with an error in the plasma
flow to the liver corrected (see Figure A-10). In brief, TCA in plasma is assumed to undergo
saturable plasma protein binding. TCA in tissues is assumed to be flow limited, but with the
tissue partition coefficient reflecting equilibrium with the free concentration of TCA in plasma.
A-59
-------
dVTCAjX ^^
L
c
(QBo
QBodPlas*
VBodTCAFree
QGutLivPlas*
•M/I ;.,-rr* A ir^««
Plasma
(APIasTCA)
kUrnTCA* , .
APIasTCAFree 1 1
dPIas+QGutLivPlas)*
CPIasTCABnd
Body
(ABodTCA)
Liver
(ALivTCOG)
QBodPlas*
CPIasTCAFree
kASTCA*
AStomTCA
^ i
1 urine '
1 I
X* Oral dose
x^_(PODoseTCA)_^
t
Stomach
QGutLivPlas*
r^Din^-rr* A ir^««
1
1
1
f
i
I
1
1
V
Liver TCE
Oxidation*
FracTCA
Lung TCE
Oxidation*
FracLungSys*
FracTCA
V
I
I
I
/
/
V
\
\
\
\
/
/
Other Clearance
(kMet*ALivTCA)
'
! Oxidation of TCOHI
I
I
Figure A-10 Submodel for TCA.
A .4.1.4.1. Plasma binding and concentrations
For an i.v. dose of TCA given by IVDoseTCA (mg/kg during an infusion period of
TChng), the rate of the change of the amount of total TCA in plasma (APIasTCA, in mg) is:
d(APlasTCA)/dt = klVTCA + (QBodPlas x CVBodTCA) (Eq. A-48)
+ (QGutLivPlas x CVLivTCA) - (QCPlas x CPlasTCA) - RUrnTCAplas
where
klVTCA = rate of IV infusion of TCA = (IVDoseTCA x body
weight)/TChng
QBodPlas = plasma flow from body = QBod x FracPlas
QGutLivPlas = plasma flow from liver = (QGut + QLiv) x FracPlas
CVBodTCA = venous concentration leaving body = CPIasTCABnd +
CVBodTCAFree
A-60
-------
CVBodTCAFree = free venous concentration leaving body
= (ABodTCA/VBod/PBodTCA)
CVLivTCA = venous concentration leaving liver
= CPlasTCABnd + CVLivTCAFree
CVLivTCAFree = free venous concentration leaving liver
= (ALivTCA/VLiv/PLivTCA)
QCPlas = total plasma flow
= QC x FracPlas
RUrnTCAplas = rate of urinary excretion of TCA from plasma
= kUrnTCA x APlasTCAFree
The free (CPlasTCAFree) and bound (CPlasTCABnd) concentrations are calculated from
the total concentration (CPlasTCA = APlasTCA/VPlas) by solving the equations:
CPlasTCABndMole = BMax x CPlasTCAFreeMole/(kDissoc (Eq. A-49)
+ CPlasTCAFreeMole)
CPlasTCABndMole = CPlasTCAMole - CPlasTCAFreeMole (Eq. A-50)
Here the suffix "Mole" means that all concentrations are in micromole/L, because BMax
and kDissoc in Table A-4 are given in those units. These lead to explicit solutions of:
CPlasTCAFreeMole = (sqrt(a x a + b) - a)/2 (Eq. A-51)
where
a = kDissoc + BMax - CPlasTCAMole
b = 4.0 x kDissoc x CPlasTCAMole
CPlasTCABlasTCAMoleCPlasTCAFreeMole
These concentrations are converted to mg/L (CPlasTCABnd, CPlasTCAFree) by
multiplying by the molecular weight in mg/|imoles. The amount of free TCA in plasma is, thus:
APlasTCAFree = CPlasTCAFree x VPlas. (Eq. A-52)
Here, VPlas is derived from the blood volume and hematocrit (see Table A-4).
A.4.1.4.2. Urinary excretion
Urinary excretion is modeled as coming from the plasma compartment, so the rate of
change of TCA in urine (AUrnTCA, in mg) is:
d(AUrnTCA)/dt = RUrnTCA (Eq. A-53)
where
RUrnTCA = RUrnTCAplas
A-61
-------
For some human data (Chiu et al., 2007), urinary excretion was only collected during
certain time periods, with data missing in other time periods. Thus, a switch UrnMissing was
defined, which equals 0 during times of urine collection and 1 when urinary data are missing.
The total amount of urinary TCA "collected" (AUrnTCA_collect, in mg) is, thus, given by:
d(AUrnTCA collect)/dt = (1-UrnMissing) x RUrnTCA (Eq. A-54)
A.4.1.4.3. Body compartment
The body compartment is flow limited, with the rate of change for the amount of TCA in
the body (ABodTCA, in mg) given by:
d(ABodTCA)/dt = QBodPlas x (CPlasTCAFree - CVBodTCAFree) (Eq. A-55)
A .4.1.4.4. Liver compartment
The rate of change for the amount of TCA in the liver (ALivTCA, in mg) is given by:
d(ALivTCA)/dt = QGutLivPlas x (CPlasTCAFree - CVLivTCAFree) (Eq. A-56)
+ (FracTCA x StochTCATCE x (RAMetLivl + FracLungSys x RAMetLng))
+ (StochTCATCOH x RAMetTCOHTCA) - RAMetTCA + kPOTCA
The first term reflects the free TCA in plasma flowing into and out of the liver
compartment, the second term reflects production of TCA from liver (adjusted for molecular
weights and fractional yield of TCA) and lung (adjusted for molecular weights, fraction of lung
metabolism translocated to the liver, and fractional yield of TCA) metabolism of TCE, the third
term reflects production of TCA from TCOH, the fourth term reflects other clearance of TCA
from the liver, and the fifth term reflects absorption from the stomach of TCA. The contribution
from liver metabolism of TCE is adjusted for molecular weights and production of oxidative
metabolites other than TCA. The rate of clearance of TCA is given by:
RAMetTCA = kMetTCA x ALivTCA (Eq. A-57)
The oral intake rate of TCA (mg/hour) includes a one-compartment stomach. So for an
oral dose of PODoseTCA (in mg/kg), occurring over a time TChng, the rate of change of TCA in
the stomach (AStomTCA, in mg) is given by:
d(AStomTCA)/dt = kStomTCA - AStomTCA x kASTCA (Eq. A-58)
where
kStomTCA = rate of input into stomach
= (PODoseTCA x body weight)/TChng
A-62
-------
The rate of absorption into the liver is, thus,
kPOTCA = AStomTCA x kASTCA (Eq. A-59)
A.4.1.5. GSH Conjugation Submodel
The GSH conjugation submodel only tracks DCVG, DCVC, and urinary excretion of
NAc-DCVC (see Figure A-l 1).
' Liver
1
'
Conjugation
Kidney
Conjugation
DCVG
(ADCVGmol)
kDCVG*
ADCVGmol
1
z
DCVC
(ADCVC)
kNAT* kKidBioact*
ADCVC ^ \ ADCVC
Urine "^ J' Bio- "
actiation
Figure A-ll. Submodel for TCE GSH conjugation metabolites.
The rate of change for DCVG (ADCVGmol, in mmoles) depends on production from
TCE in the liver and metabolism to DCVC:
d(ADCVGmol)/dt = RAMetLiv2/MWTCE - RAMetDCVGmol (Eq. A-60)
where
RAMetDCVGmol = rate of metabolism of DCVG to DCVC
= kDCVG x ADCVGmol
The rate of change of DCVC (ADCVC, in mg) depends on the production from TCE in
the kidney (adjusted for molecular weights), production from DCVG, urinary excretion as NAc-
DCVC (rate constant kNAT), and other bioactivation (rate constant kKidBioact):
d(ADCVC)/dt = RAMetDCVGmol x MWDCVC (Eq. A-61)
+ RAMetKid x StochDCVCTCE - ((kNAT + kKidBioact) x ADCVC)
where
RAUrnDCVC = Rate of NAcDCVC excretion into urine
= kNAT x ADCVC
A-63
-------
The rate of change of the amount of NAc-DCVC excreted (AUrnNDCVC, in mg) is
given (adjusted for molecular weights) by:
d(AUrnNDCVC)/dt = StochN x RAUrnDCVC (Eq. A-62)
For the rat model, the DCVG compartment is "turned off' by setting kDCVG to an
arbitrarily high value.
A.4.2. Model Parameters and Baseline Values
The multipage Table A-4 describes all the parameters of the updated PBPK model, their
baseline values (which are used as central estimates in the prior distributions for the Bayesian
analysis), and any scaling relationship used in their calculation. More detailed notes are included
in the comments of the model code (see Section A.7).
A.4.3. Statistical Distributions for Parameter Uncertainty and Variability
A.4.3.1. Initial Prior Uncertainty in Population Mean Parameters
The following multipage Table A-5 describes the initial prior distributions for the
population mean of the PBPK model parameters. For selected parameters, rat prior distributions
were subsequently updated using the mouse posterior distributions, and human prior distributions
were then updated using mouse and rat posterior distributions (see Section A.4.3.2).
A.4.3.2. Interspecies Scaling to Update Selected Prior Distributions in the Rat and
Human
As shown in Table A-5, for several parameters, there is little or no in vitro or other prior
information available to develop informative prior distributions, so many parameters had
lognormal or log-uniform priors that spanned a wide range. Initially, the PBPK model for each
species was run with the initial prior distributions in Table A-5, but, in the time available for
analysis (up to about 100,000 iterations), only for the mouse did all of these parameters achieve
adequate convergence. Additional preliminary runs indicated replacing the log-uniform priors
with lognormal priors and/or requiring more consistency between species could lead to adequate
convergence. However, an objective method of "centering" the lognormal distributions that did
not rely on the in vivo data (e.g., via visual fitting or limited optimization) being calibrated
against was necessary in order to minimize potential bias.
Therefore, the approach taken was to consider three species sequentially, from mouse to
rat to human, and to use a model for interspecies scaling to update the prior distributions across
species (the original prior distributions define the prior bounds). This sequence was chosen
because the models are essentially "nested" in this order—the rat model adds to the mouse model
the "downstream" GSH conjugation pathways, and the human model adds to the rat model the
intermediary DCVG compartment. Therefore, for those parameters with little or no independent
A-64
-------
data only, the mouse posteriors were used to update the rat priors, and both the mouse and rat
posteriors were used to update the human priors. A list of the parameters for which this scaling
was used to update prior distributions is contained in Table A-6, with the updated prior
distributions. The correspondence between the "scaling parameters" and the physical parameters
generally follows standard practice, and were explicitly described in Table A-4. For instance,
VMAX and clearance rates are scaled by body weight to the % power, whereas KM values are
assumed to have no scaling, and rate constants (inverse time units) are scaled by body weight to
the -Vi power.
A-65
-------
Table A-5. Uncertainty distributions for the population mean of the PBPK model parameters
Scaling (sampled)
parameter
Mouse
Distribution"
SD or Mm
Truncation
(± nxSD) or
Max
Rat
Distribution
SDorMin
Truncation
(± nxSD) or
Max
Human
Distribution
SDorMin
Truncation
(± nxSD) or
Max
Notes/
Source
Flows
InQCC
InVPRC
InDRespC
TrancNormal
TrancNormal
Uniform
0.2
0.2
-11.513
4
4
2.303
TrancNormal
TrancNormal
Uniform
0.14
0.3
-11.513
4
4
2.303
TrancNormal
TrancNormal
Uniform
0.2
0.2
-11.513
4
4
2.303
a
a
b
Physiological blood flows to tissues
QFatC
QGutC
QLivC
QSlwC
QKidC
FracPlasC
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
0.46
0.17
0.17
0.29
0.32
0.2
2
2
2
2
2
3
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
0.46
0.17
0.17
0.3
0.13
0.2
2
2
2
2
2
3
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
0.46
0.18
0.45
0.32
0.12
0.05
2
2
2
2
2
3
a
a
a
a
a
c
Physiological volumes
VFatC
VGutC
VLivC
VRapC
VRespLumC
VRespEffC
VKidC
VBldC
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
0.45
0.13
0.24
0.1
0.11
0.11
0.1
0.12
2
2
2
2
2
2
2
2
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
0.45
0.13
0.18
0.12
0.18
0.18
0.15
0.12
2
2
2
2
2
2
2
2
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
0.45
0.08
0.23
0.08
0.2
0.2
0.17
0.12
2
2
2
2
2
2
2
2
a
a
a
a
a
a
a
a
A-66
-------
Table A-5. Uncertainty distributions for the population mean of the PBPK model parameters (continued)
Scaling (sampled)
parameter
Mouse
Distribution"
SDorMin
Truncation
(± nxSD) or
Max
Rat
Distribution
SDorMin
Truncation
(±nxSD)or
Max
Human
Distribution
SDorMin
Truncation
(±nxSD)or
Max
Notes/
Source
TCE distribution/partitioning
InPBC
InPFatC
InPGutC
InPLivC
InPRapC
InPRespC
InPKidC
InPSlwC
TruncNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
0.25
0.3
0.4
0.4
0.4
0.4
0.4
0.4
3
3
3
3
3
3
3
3
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
0.25
0.3
0.4
0.15
0.4
0.4
0.3
0.3
3
3
3
3
3
3
3
3
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
TrancNormal
0.2
0.2
0.4
0.4
0.4
0.4
0.2
0.3
3
3
3
3
3
3
3
3
a
TCA distribution/partitioning
InPRBCPlasTCAC
InPBodTCAC
InPLivTCAC
Uniform
TrancNormal
TrancNormal
-4.605
0.336
0.336
4.605
3
3
TrancNormal
TrancNormal
TrancNormal
0.336
0.693
0.693
3
3
3
Uniform
TrancNormal
TrancNormal
-4.605
0.336
0.336
4.605
3
3
e
f
TCA plasma binding
InkDissocC
InBMaxkDC
TrancNormal
TrancNormal
1.191
0.495
3
3
TrancNormal
TrancNormal
0.61
0.47
3
3
TrancNormal
TrancNormal
0.06
0.182
3
3
g
TCOH and TCOG distribution/partitioning
InPBodTCOHC
InPLivTCOHC
InPBodTCOGC
InPLivTCOGC
TrancNormal
TrancNormal
Uniform
Uniform
0.336
0.336
-4.605
-4.605
3
3
4.605
4.605
TrancNormal
TrancNormal
Uniform
Uniform
0.693
0.693
-4.605
-4.605
3
3
4.605
4.605
TrancNormal
TrancNormal
Uniform
Uniform
0.336
0.336
-4.605
-4.605
3
3
4.605
4.605
DCVG distribution/partitioning
InPeffDCVG
Uniform
-6.908
6.908
Uniform
-6.908
6.908
Uniform
-6.908
6.908
h
TCE Metabolism
InVMAxC
lnKMC
InCIC
InFracOtherC
TrancNormal
TrancNormal
Uniform
0.693
1.386
-6.908
3
3
6.908
TrancNormal
TrancNormal
Uniform
0.693
1.386
-6.908
3
3
6.908
TrancNormal
TrancNormal
Uniform
0.693
1.386
-6.908
3
3
6.908
i
i
i
h
A-67
-------
Table A-5. Uncertainty distributions for the population mean of the PBPK model parameters (continued)
Scaling (sampled)
parameter
InFracTCAC
InVMAxDCVGC
InClDCVGC
lnKMDCVGC
InVMAxKidDCVGC
InClKidDCVGC
lnKMKidDCVGC
InVMAxLungLivC
lnKMClara
InFracLungSysC
Mouse
Distribution"
TruncNormal
Uniform
Uniform
Uniform
Uniform
TruncNormal
Uniform
Uniform
SDorMin
1.163
-4.605
-4.605
-4.605
-4.605
1.099
-6.908
-6.908
Truncation
(± nxSD) or
Max
3
9.21
9.21
9.21
9.21
3
6.908
6.908
Rat
Distribution
TruncNormal
Uniform
Uniform
Uniform
Uniform
TruncNormal
Uniform
Uniform
SDorMin
1.163
-4.605
-4.605
-4.605
-4.605
1.099
-6.908
-6.908
Truncation
(±nxSD)or
Max
3
9.21
9.21
9.21
9.21
3
6.908
6.908
Human
Distribution
TruncNormal
TruncNormal
TruncNormal
TruncNormal
TruncNormal
TruncNormal
Uniform
Uniform
SDorMin
1.163
4.605
1.386
4.605
1.386
1.099
-6.908
-6.908
Truncation
(±nxSD)or
Max
3
3
3
3
3
3
6.908
6.908
Notes/
Source
j
k
k
k
k
k
k
i
h
h
TCOH metabolism
InVMAxTCOHC
InClTCOHC
lnKMTCOH
InVMAxGlucC
InClGlucC
lnKMGluc
InkMetTCOHC
Uniform
Uniform
Uniform
Uniform
Uniform
-9.21
-9.21
-9.21
-6.908
-11.513
9.21
9.21
9.21
6.908
6.908
Uniform
Uniform
Uniform
Uniform
Uniform
-9.21
-9.21
-9.21
-6.908
-11.513
9.21
9.21
9.21
6.908
6.908
Uniform
Uniform
Uniform
Uniform
Uniform
-11.513
-9.21
-9.21
-6.908
-11.513
6.908
9.21
4.605
6.908
6.908
h
h
TCA metabolism/clearance
InkUrnTCAC
InkMetTCAC
Uniform
Uniform
-4.605
-9.21
4.605
4.605
Uniform
Uniform
-4.605
-9.21
4.605
4.605
Uniform
Uniform
-4.605
-9.21
4.605
4.605
h
TCOG metabolism/clearance
InkBileC
InkEHRC
InkUrnTCOGC
Uniform
Uniform
Uniform
-9.21
-9.21
-6.908
4.605
4.605
6.908
Uniform
Uniform
Uniform
-9.21
-9.21
-6.908
4.605
4.605
6.908
Uniform
Uniform
Uniform
-9.21
-9.21
-6.908
4.605
4.605
6.908
h
DCVG metabolism
InFracKidDCVCC
InkDCVGC
Uniform
Uniform
-6.908
-9.21
6.908
4.605
Uniform
Uniform
-6.908
-9.21
6.908
4.605
Uniform
Uniform
-6.908
-9.21
6.908
4.605
h
A-68
-------
Table A-5. Uncertainty distributions for the population mean of the PBPK model parameters (continued)
Scaling (sampled)
parameter
Mouse
Distribution"
SDorMin
Truncation
(± nxSD) or
Max
Rat
Distribution
SDorMin
Truncation
(±nxSD)or
Max
Human
Distribution
SDorMin
Truncation
(±nxSD)or
Max
Notes/
Source
DCVC metabolism/clearance
InkNATC
InkKidBioactC
Uniform
Uniform
-9.21
-9.21
4.605
4.605
Uniform
Uniform
-9.21
-9.21
4.605
4.605
Uniform
Uniform
-9.21
-9.21
4.605
4.605
h
Oral uptake/transfer coefficients
InkTSD
InkAS
InkTD
InkAD
InkASTCA
InkASTCOH
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
-4.269
-6.571
-4.605
-7.195
-7.195
-7.195
4.942
7.244
0
6.62
6.62
6.62
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
-4.269
-6.571
-4.605
-7.195
-7.195
-7.195
4.942
7.244
0
6.62
6.62
6.62
Uniform
Uniform
Uniform
Uniform
Uniform
Uniform
-4.269
-6.571
-4.605
-7.195
-7.195
-7.195
4.942
7.244
0
6.62
6.62
6.62
h
h
Explanatory note. All population mean parameters have either truncated normal (TruncNormal) or uniform distributions. For those with TruncNormal
distributions, the mean for the population mean is 0 for natural-log transformed parameters (parameter name starting with "In") and one for untransformed
parameters, with the truncation at the specified number (n) of SDs. All uniformly distributed parameters are natural-log transformed, so their untransformed
minimum and maximum are exp(Min) and exp(Max), respectively.
"Uncertainty based on coefficient of variation (CV) or range of values in Brown et al. (1997) (mouse and rat) and a comparison of values from ICRP
Publication 89 (2003). Brown et al. (1997). and Price et al. (2003) (human).
bNoninformative prior distribution intended to span a wide range of possibilities because no independent data are available on these parameters. These priors for
the rat and human were subsequently updated (see Section A.4.3.2).
'Because of potential strain differences, uncertainty in mice and rat assumed to be 20%. In humans, Price et al. (2003) reported variability of about 5%, and this
is also used for the uncertainty in the mean.
dFor partition coefficients, it is not clear whether interstudy variability is due to intersubject or assay variability, so uncertainty in the mean is based on interstudy
variability among in vitro measurements. For single measurements, uncertainty SD of 0.3 was used for fat (mouse) and 0.4 for other tissues was used. In
addition, where measurements were from a surrogate tissue (e.g., gut was based on liver and kidney), an uncertainty SD 0.4 was used.
eSingle in vitro data point available in rats, so a GSD of 1.4 was used. In mice and humans, where no in vitro data was available, a noninformative prior was
used.
fSingle in vitro data points available in mice and humans, so a GSD of 1.4 was used. In rats, where the mouse data was used as a surrogate, a GSD of 2.0 was
used, based on the difference between mice and rats in vitro.
gGSD for uncertainty based on different estimates from different in vitro studies.
hNoninformative prior distribution.
A-69
-------
Table A-5. Uncertainty distributions for the population mean of the PBPK model parameters (continued)
'Assume twofold uncertainty GSD in VMAX, based on observed variability and uncertainties of in vitro-to-in vivo scaling. For KM and C1C, the uncertainty is
assumed to be fourfold, due to the different methods for scaling of concentrations from TCE in the in vitro medium to TCE in blood.
JUncertainty GSD of 3.2-fold reflects difference between in vitro measurements from Lipscomb et al. (1998b) and Bronley-DeLancey et al. (2006).
kln mice and rats, the baseline values are notional lower-limits on VMAX and clearance, however, the lower bound of the prior distribution is set to 100-fold less
because of uncertainty in in vitro-in vivo extrapolation, and because Green et al. (1997b) reported values 100-fold smaller than Lash et al. (1998b: 1995). In
humans, the uncertainty GSD in clearance is assumed to be 100-fold, due to the difference between Lash et al. (1998b) and Green et al. (1997bX For KM, the
uncertainty GSD of fourfold is based on differences between concentrations in cells and cytosol.
'Uncertainty GSD of threefold was assumed due to possible differences in microsomal protein content, the fact that measurements were at a single concentration,
and the fact that the human baseline values was based on the limit of detection.
A-70
-------
Table A-6. Updated prior distributions for selected parameters in the rat
and human
Scaling parameter
InDRespC
InPBodTCOGC
InPLivTCOGC
InFracOtherC
InVMAxDCVGC
InClDCVGC
InVMAxKidDCVGC
InClKidDCVGC
InVMAxLungLivC
lnKMClara
InFracLungSysC
InVMAxTCOHC
InClTCOHC
lnKMTCOH
InVMAxGlucC
InClGlucC
lnKMGluc
InkMetTCOHC
InkUrnTCAC
InkMetTCAC
InkBileC
InkEHRC
InkUrnTCOGC
InkNATC
InkKidBioactC
Initial prior bounds
exp(min)
1.0 x ID'5
1.0 x ID'2
1.0 x ID'2
1.0 x 1Q-3
1.0 x 1Q-2
1.0 x 10'2
1.0 x 10'2
1.0 x 10'2
3.7 x 10'2
1.0 x 10'3
1.0 x 10'3
1.0 x 10'4
1.0 x 10'5
1.0 x 10'4
1.0 x 10'4
1.0 x lO'4
1.0 x lO'3
1.0 x lO'5
1.0 x 1Q-2
1.0 x 1Q-4
1.0 x 1Q-4
1.0 x 1Q-4
1.0 x 1Q-3
1.0 x 1Q-4
1.0 x 1Q-4
exp(max)
1.0 x 101
1.0 x 102
1.0 x 102
1.0 x 103
1.0 x 104
1.0 x 104
1.0 x 104
1.0 x 104
2.7 x 101
1.0 x 103
1.0 x 103
1.0 x 104
1.0 x 103
1.0 x 104
1.0 x 104
1.0 x 102
1.0 x 103
1.0 x 103
1.0 x 102
1.0 x 102
1.0 x 102
1.0 x 102
1.0 x 103
1.0 x 102
1.0 x 102
Updated rat prior
exp(u)
1.22
0.42
1.01
0.02
2.61
0.36
2.56
1.22
2.77
0.01
4.39
1.65
0.93
69.41
30.57
3.35
0.11
0.61
1.01
0.01
8.58
exp(o)
5.21
5.47
5.31
6.82
42.52
15.03
22.65
15.03
6.17
6.69
11.13
5.42
5.64
5.58
6.11
5.87
5.42
5.37
5.70
6.62
6.05
Updated human prior
exp(u)
1.84
0.81
2.92
0.14
2.80
0.02
3.10
0.37
4.81
3.39
11.13
2.39
0.09
0.45
3.39
0.22
16.12
0.00
0.01
exp(o)
4.18
5.10
4.31
4.76
4.71
4.85
8.08
4.44
4.53
4.35
4.57
4.62
4.22
4.26
4.44
4.71
4.81
6.11
6.49
Notes: updated rat prior is based on the mouse posterior; and the updated human priors are based on combining the
mouse and rat posteriors, except in the case of InkNATC and InkKidBioactC, which are unidentified in the mouse
model. Columns labeled exp(min) and exp(max) are the exponentiated prior bounds; columns labeled exp(u) and
exp(o) are the exponentiated mean and SD of the updated prior distributions, which are normal distributions
truncated at the prior bounds.
The scaling model is given explicitly as follows. If 9, are the "scaling" parameters
(usually also natural-log-transformed) that are actually estimated, and A is the "universal"
(species-independent) parameter, then 9, = A + s/, where et is the species-specific "departure"
from the scaling relationship, assumed to be normally distributed with variance oe2. This
"scatter" in the interspecies scaling relationship is assumed to have a SD of 1.15 = ln(3.16), so
that the unlogarithmically transformed 95% CI spans about 100-fold (i.e., exp(2o) = 10). This
implies that 95% of the time, the species-specific scaling parameter is assumed be within 10-fold
higher or lower than the "species-independent" value. However, the prior bounds, which
A-71
-------
generally span a wider range, are maintained so that if the data strongly imply an extreme
species-specific value, they can be accommodated. In addition, the model transfers the marginal
distributions for each parameter across species, so correlations between parameters are not
retained. This is a restriction on the software used for conducting MCMC analyses, however,
assuming independence will lead to a "broader" joint distribution, given the same marginal
distributions. Thus, this assumption tends to reduce the weight of the interspecies scaling as
compared to the species-specific calibration data.
Therefore, the mouse model gives an initial estimate of "A," which is used to update the
prior distribution for 9r = A + sr in the rat (alternatively, since there is only one species at this
stage, one could think of this as estimating the rat parameter using the mouse parameter, but with
a cross-species variance is twice the allometric scatter variance). The rat and mouse together
then give a "better" estimate of A, which is used to update the prior distribution for 0/, = A + s/, in
the human, with the assumed distribution for s/,. This approach is implemented by
approximating the posterior distributions by normal distributions, deriving heuristic "data" for
the specific-specific parameters, and then using these pseudo-data to derive updated prior
distributions for the other species parameters. Specifically, the procedure is as follows:
1. Run the mouse model.
2. Use the mouse posterior to derive the mouse "pseudo-data" Dm (equal to the posterior
mean) and its uncertainty om2 (equal to the posterior variance).
3. Use the Dm as the prior mean for the rat. The prior variance for the rat is 2oe2 + om2,
which accounts for two components of species-specific departure from "species-
independence" (one each for mouse and rat), and the mouse posterior uncertainty.
4. Match the rat posterior mean and variance to the values derived from the normal
approximation (posterior mean = (Dm/(2oe2 + om2) + Dr/or2}/{l/(2oe2 + om2) + l/or2};
posterior variance = {l/(2oe2 + om2) + I/O,2}"1), and solve for the rat "data" Dr and its
uncertainty or2.
5. Use, om2, and or2 to derive the updated prior mean and variance for the human model.
For the mean (={Dm/(oe2 + om2) + Dr/(oe2 + or2)}/{ l/(oe2 + om2) + l/(oE2 + or2)}), it is the
weighted average of the mouse and rat, with each weight including both posterior
uncertainty and departure from "species-independence." For the variance (={ l/(oe2
+ om2) + l/(oe2 + Or2)}"1 + ae2), it is the variance in the weighted average of the mouse and
rat plus an additional component of species-specific departure from "species-
independence."
Formally, then, the probability of 0, given A can be written as:
P(Qf | A) = 9(0, -A, GE2) (Eq. A-63)
A-72
-------
where (p(x, a2) is the normal density centered on 0 with variance a2. Let D, be a heuristic
"datum" for species /', so the likelihood given 0, is adequately approximated by:
P(Dt 0,) = q>(D,- - 0,-, G,2)
(Eq. A-64)
Therefore, considering A to have a uniform prior distribution, then running the mouse
model gives a posterior of the form:
P(A,
m ) oc P(A) P(Qm A) P(Dm 0m) oc cp(0m - A, GE2) cp(Dm - 0m, Gm2)(Eq. A-65)
From the MCMC posterior, the values of Dm and om2 are simply the mean and variance of
the scaled parameter 0m.
Now, adding the rat data gives:
P(A, 0m, Qr | Dm, Dr) ex: P(A) P(0m | A) P(Dm \ 0m) P(Qr \ A) P(Dr 0r) (Eq. A-66)
oc y(Qm-A, Ge2) (p(Dm - 0m, om2)
-------
This distribution is also normal with:
= (Dm/(2oE2 + Gm2) + Dr/or2}/{ 1/(2GE2 + om2) + l/or2}
VAR(0r) = (l/(2oE2 + Gm2) + I/a,2}'1
= harmonic mean of variances
(Eq. A-70)
= weighted mean of Dr
(Eq. A-71)
Thus, using the mean and variance of the posterior distribution from the MCMC analysis,
Dr and or2 can be derived.
Now, Dm, cm2, Dr, and or2 are known, so the analogous "mouse + rat" based prior used in
the human model can be derived. As with the rat prior, the human prior is based on a normal
approximation of the posterior for^4, and then incorporates a random term for cross-species
variation (allometric scatter):
P(A, 0m, 0r,
| Dm, Dr, D/0 (Eq. A-72)
ex: P(A) P(Qm | A) P(Dm | 0m) P(Qr A) P(Dr \ 0r) P(Qh A) P(Dh 0/0
QC
m-A, oe2) (p(Dm - 0m, cm2) y(Qr-A, Ge)
-^, GE2)9(D/7-0/7,G/72)
- 0r, cr)
Consider marginalizing first over 0m, then over 0r, and then over A:
I P(A, 0m, 0r, Qh | Dm, Dr, D/,) d0m d0r dA
oc [\P(A) {\P(
-------
which is given by:
Dm+r = E(A\ Dm Dr) = (Dm/(oE2
om2) + Dr/(oE2 + or2)}/{ l/(oE2 + om2) + l/(oE2
or2)}
= weighted mean of Dm and Dr
= VARG4J Dm Dr) = { l/(oE2 + o
= harmonic mean of variances
l/(oE
At this point, these values are used in the normal approximation of the combined rodent
posterior, which will be incorporated into the cross-species extrapolation as described in Step 5
above.
The results of these calculations for the updated prior distributions, are shown in
Table A-6. With this methodology for updating the prior distributions, adequate convergence
was achieved for the rat and human after 1 10,000-140,000 iterations.
A.4.3.3. Population Variance: Prior Central Estimates and Uncertainty
The following multipage Table A-7 describes the uncertainty distributions used for the
population variability in the PBPK model parameters.
Table A-7. Uncertainty distributions for the population variance of the
PBPK model parameters
Scaling (sampled)
parameter
Mouse
CV
cu
Rat
CV
CU
Human
CV
CU
Notes/source
Flows
InQCC
InVPRC
InDRespC
0.2
0.2
0.2
2
2
0.5
0.14
0.3
0.2
2
2
0.5
0.2
0.2
0.2
2
2
0.5
a
Physiological blood flows to tissues
QFatC
QGutC
QLivC
QSlwC
QKidC
FracPlasC
0.46
0.17
0.17
0.29
0.32
0.2
0.5
0.5
0.5
0.5
0.5
0.5
0.46
0.17
0.17
0.3
0.13
0.2
0.5
0.5
0.5
0.5
0.5
0.5
0.46
0.18
0.45
0.32
0.12
0.05
0.5
0.5
0.5
0.5
0.5
0.5
a
Physiological volumes
VFatC
VGutC
VLivC
VRapC
VRespLumC
VRespEffC
VKidC
VBldC
0.45
0.13
0.24
0.1
0.11
0.11
0.1
0.12
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.45
0.13
0.18
0.12
0.18
0.18
0.15
0.12
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.45
0.08
0.23
0.08
0.2
0.2
0.17
0.12
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
a
A-75
-------
Table A-7. Uncertainty distributions for the population variance of the
PBPK model parameters (continued)
Scaling (sampled)
parameter
Mouse
CV
cu
Rat
CV
CU
Human
CV
CU
Notes/source
TCE distribution/partitioning
InPBC
InPFatC
InPGutC
InPLivC
InPRapC
InPRespC
InPKidC
InPSlwC
0.25
0.3
0.4
0.4
0.4
0.4
0.4
0.4
2
2
2
2
2
2
2
2
TCA distribution/partitioning
InPRBCPlasTCAC
InPBodTCAC
InPLivTCAC
0.336
0.336
0.336
0.25
0.3
0.4
0.15
0.4
0.4
0.3
0.3
2
2
2
0.333
0.333
2
0.333
2
2
0.577
0.333
0.336
0.693
0.693
0.185
0.2
0.4
0.4
0.4
0.4
0.2
0.3
2
2
2
0.333
1
2
1.414
2
2
1.414
1.414
b
0.336
0.336
0.336
2
2
2
c
b
TCA plasma binding
InkDissocC
InBMaxkDC
1.191
0.495
2
2
0.61
0.47
2
2
0.06
0.182
2
2
b
TCOH and TCOG distribution/partitioning
InPBodTCOHC
InPLivTCOHC
InPBodTCOGC
InPLivTCOGC
0.336
0.336
0.4
0.4
2
2
2
2
0.693
0.693
0.4
0.4
2
2
2
2
0.336
0.336
0.4
0.4
2
2
2
2
b
b
d
d
DCVG distribution/partitioning
InPeffDCVG
0.4
2
0.4
2
0.4
2
b
TCE metabolism
InVMAxC
lnKMC
InCIC
InFracOtherC
InFracTCAC
InVMAxDCVGC
InClDCVGC
lnKMDCVGC
InVMAxKidDCVGC
InClKidDCVGC
lnKMKidDCVGC
InVMAxLungLivC
lnKMClara
InFracLungSysC
0.824
0.270
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
1
1
2
2
2
2
2
2
2
2
2
0.806
1.200
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
1
1
2
2
2
2
2
2
2
2
2
0.708
0.944
0.5
1.8
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.26
1.41
2
2
2
2
2
2
2
2
2
e
f
g
f
A-76
-------
Table A-7. Uncertainty distributions for the population variance of the
PBPK model parameters (continued)
Scaling (sampled)
parameter
Mouse
cv cu
Rat
CV
CU
Human
CV
CU
Notes/source
TCOH metabolism
InVMAxTCOHC
InClTCOHC
lnKMTCOH
InVMAxGlucC
InClGlucC
lnKMGluc
InkMetTCOHC
0.5
0.5
0.5
0.5
0.5
2
2
2
2
2
0.5
0.5
0.5
0.5
0.5
2
2
2
2
2
0.5
0.5
0.5
0.5
0.5
2
2
2
2
2
f
TCA metabolism/clearance
InkUrnTCAC
InkMetTCAC
0.5
0.5
2
2
0.5
0.5
2
2
0.5
0.5
2
2
f
TCOG metabolism/clearance
InkBileC
InkEHRC
InkUrnTCOGC
0.5
0.5
0.5
2
2
2
0.5
0.5
0.5
2
2
2
0.5
0.5
0.5
2
2
2
f
f
DCVG metabolism/clearance
InFracKidDCVCC
InkDCVGC
0.5
0.5
2
2
0.5
0.5
2
2
0.5
0.5
2
2
f
DCVC metabolism/clearance
InkNATC
InkKidBioactC
0.5
0.5
2
2
0.5
0.5
2
2
0.5
0.5
2
2
f
Oral uptake/transfer coefficients
InkTSD
InkAS
InkTD
InkAD
InkASTCA
InkASTCOH
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
h
Explanatory note. All population variance parameters (V_pname, for parameter "pname") have Inverse-Gamma
distributions, with the expected value given by CV and coefficient of uncertainty given by CU (i.e., SD of V_pname
divided by expected value of V_pname) (notation the same as Hack et al. (2006)). Under these conditions, the
Inverse-Gamma distribution has a shape parameter is given by a = 2 + 1/CU2 and scale parameter (3 = (ol - 1) CV2.
In addition, it should be noted that, under a normal distribution and a uniform prior distribution on the population
variance, the posterior distribution for the variance given n data points with a sample variance s2 is given by and
Inverse-Gamma distribution with a = (n - l)/2 and (3 = a s2. Therefore, the "effective" number of data points is
given by n = 5 + 2/CU2 and the "effective" sample variance is s2 = CV2 acor|aT/(a - 1).
"For physiological parameters, CV values generally taken to be equal to the uncertainty SD in the population mean,
most of which were based on variability between studies (i.e., not clear whether variability represents uncertainty or
variability). Given this uncertainty, CU of 2 assigned to cardiac output and ventilation-perfusion, while CU of
0.5 assigned to the remaining physiological parameters.
A-77
-------
Table A-7. Uncertainty distributions for the population variance of the
PBPK model parameters (continued)
bAs discussed above, it is not clear whether interstudy variability is due to intersubject or assay variability, so the
same central were assigned to the uncertainty in the population mean as to the central estimate of the population
variance. In the cases where direct measurements were available, the CU for the uncertainty in the population
variance is based on the actual sample n, with the derivation discussed in the notes preceding this table. Otherwise,
a CU of 2 was assigned, reflecting high uncertainty.
TJsed value from uncertainty in population in mean in rats for all species with high uncertainty.
dNo data, so assumed CV of 0.4 with high uncertainty.
Tor mice and rats, based on variability in results from Lipscomb et al. (1998c) and Elfarra et al. (1998) in
microsomes. Since only pooled or mean values are available, CU of one was assigned (moderate uncertainty). For
humans, based on variability in individual samples from Lipscomb et al. (1997) (microsomes), Elfarra et al. (1998)
(microsomes), and Lipscomb et al. (1998c) (freshly isolated hepatocytes). High uncertainty in clearance (InCIC)
reflects two different methods for scaling concentrations in microsomal preparations to blood concentrations:
(1) assuming microsomal concentration equals liver concentration and then using the measured liverblood partition
coefficient to convert to blood and (2) using the measured microsome:air partition coefficient and then using the
measured blood:air partition coefficient to convert to blood.
fNo data on variability, so a CV of 0.5 was assigned, with a CU of 2.
Tor mice and rats, no data on variability, so a CV of 0.5 was assigned, with a CU of 2. For humans, sixfold
variability based on in vitro data from Bronley-DeLancy et al. (2006). but with high uncertainty.
hNo data on variability, so a CV of 2 was assigned (larger than assumed for metabolism due to possible vehicle
effects), with a CU of 2.
A.4.3.4. Likelihood Function and Prior distributions for Residual Error Estimates
From Equation A-3 for the total likelihood function, different measurement types may
have different partial likelihoods. In all cases except one, the likelihood was assumed to be
lognormal, with probability density for a particular measurements/a at time %/a given by:
P(yijki | 0i, Gljk2, tljki) = (27io2y1/2 exp[{- In yijkl - In ^(0;, tljki)}2/(2oljk2)] (Eq. A-76)
As before, the subject is labeled /', the study is labeledy, the type of measurement is
labeled &, and the different time points are labeled /. The parameters 0, are the "scaling
parameters" at the subject-level, shown in Table A-4, whereas the parameters o^2 represent the
"residual error" variance a2. This error term may include variability due to measurement error,
intrasubject and intrastudy heterogeneity, as well as model misspecification. The available in
vivo measurements to which the model was calibrated are listed in Table A-8. The variances for
each of the corresponding residual errors were given log-uniform distributions. For all
measurements, the bounds on the log-uniform distribution were 0.01 and 3.3, corresponding to
GSDs bounded by 1.11 and 6.15. The lower bound was set to prevent "over-fitting," as was
done in Bois (2000a) and Hack et al. (2006).
A-78
-------
Table A-8. Measurements used for calibration
Measurement
abbreviation
RetDose
CAlvPPM
CInhPPM
Cart
CVen
CBldMix
CFat
CGut
CKid
CLiv
CMus
AExhpost
CTCOH
CLivTCOH
CPlasTCA
CBldTCA
CLivTCA
AUrnTCA
AUrnTCA_collect
ABileTCOG
CTCOG
CTCOGTCOH
CLivTCOGTCOH
AUrnTCOGTCOH
AUrnTCOGTCOH_c
ollect
CDCVGmol
CDCVG_ND
AUrnNDCVC
AUrnTCTotMole
TotCTCOH
Mouse
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
Rat
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
Human
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
A/
Measurement description
Retained TCE dose (mg)
TCE concentration in alveolar air (ppm)
TCE concentration in closed-chamber (ppm)
TCE concentration in arterial blood (mg/L)
TCE concentration in venous blood (mg/L)
TCE concentration in mixed arterial and venous blood (mg/L)
TCE concentration in fat (mg/L)
TCE concentration in gut (mg/L)
TCE concentration in kidney (mg/L)
TCE concentration in liver (mg/L)
TCE concentration in muscle (mg/L)
Amount of TCE exhaled postexposure (mg)
Free TCOH concentration in blood (mg/L)
Free TCOH concentration in liver (mg/L)
TCA concentration in plasma (mg/L)
TCA concentration in blood (mg/L)
TCA concentration in liver (mg/L)
Cumulative amount of TCA excreted in urine (mg)
Cumulative amount of TCA collected in urine (noncontinuous
sampling) (mg)
Cumulative amount of bound TCOH excreted in bile (mg)
Bound TCOH concentration in blood (mg/L)
Bound TCOH concentration in blood in free TCOH equivalents
(mg/L)
Bound TCOH concentration in liver in free TCOH equivalents
(mg/L)
Cumulative amount of total TCOH excreted in urine (mg)
Cumulative amount of total TCOH collected in urine
(noncontinuous sampling) (mg)
DCVG concentration in blood (mmol/L)
DCVG nondetects from Lash et al. (1999b)
Cumulative amount of NAcDCVC excreted in urine (mg)
Cumulative amount of TCA+total TCOH excreted in urine
(mmol)
Total TCOH concentration in blood (mg/L)
where:
-------
was assumed, as is standard in analytical chemistry, that the detection limit represents a response
from a blank sample at 3 SDs. Because detector responses near the detection limit are generally
normally distributed, the likelihood for observing a nondetect given a model-predicted value of
fijki(Qi, tijki) is equal to:
P(= ND| Qh tljkl) = 0(3 x {1 -fl]kl(Qt, tljkl)/L D), (Eq. A-77)
The rat and human models differed from the mouse model in terms of the hierarchical
structure of the residual errors. In the mouse model, all of the studies were assumed to have the
same residual error, as shown in Figure A-l, so that the residual error is only indexed by k, the
type of measurement: o/t2. This appeared reasonable because there were fewer studies, and there
appeared to be less variation between studies. In the rat and human models, each of which used
a much larger database of in vivo studies, residual errors were assumed to be the same within a
study, but may differ between studies, and so are labeled by study y and the type of measurement
k: Cjk2. The updated hierarchical structures are shown in Figure A-12. Initial attempts to use a
single set of residual errors led to large residual errors for some measurements, even though fits
to many studies appeared reasonable. Residual errors were generally reduced when study-
specific errors were used, except for some data sets that appeared to be outliers (discussed
below).
A-80
-------
Symbols have the same meaning as Figure A-l, with modifications for the rat and
human. In particular, in the rat, each "subject" consists of animals (usually
comprising multiple dose groups) of the same sex, species, and strain within a
study (possibly reported in more than one publication, but reasonably presumed to
be of animals in the same "lot"). Animals within each subject are presumed to be
"identical," with the same PBPK model parameters, and each such subject is
assigned its own set of "residual" error variances a2*. In humans, each "subject"
is a single person, possibly exposed in multiple experiments, and each subject is
assigned a set of PBPK model parameters drawn from the population. However,
in humans, "residual" error variances are assigned at an intermediate level of the
hierarchy—the "study" level, o\m—rather than the subject or the population
level.
Figure A-12. Updated hierarchical structure for rat and human models.
A.4.4. Summary of Bayesian Posterior Distribution Function
As described in Section A. 1, the posterior distribution for the unknown parameters is
obtained in the usual Bayesian manner by multiplying:
(1) The prior distributions for the population mean of the scaling parameter(u) (see
Sections A.4.3.1-A.4.3.2), the population variance of the scaling parameters(E2) (see
Section A.4.3.3), and the "residual" error (a2) (see Section A.4.3.4);
A-81
-------
(2) The population distribution, assumed to be a truncated normal distribution, for the subject
parameters (0 | u, E2); and
(3) The likelihood functions (y | 0, a2), (see Section A.4.3.4)
as follows:
i, a2) (Eq. A-78)
Each subject's parameters 0, have the same sampling distribution (i.e., they are
independently and identically distributed), so their joint prior distribution is:
(0 u, E2) = n=i...« (0*1 u, 22) (Eq. A-79)
Different experiments^ = !...«,- may have different exposure and different data collected
and different time points. In addition, different types of measurements k=\...n^ (e.g., TCE
blood, TCE breath, TCA blood, etc.) may have different errors, but errors are otherwise assumed
to be independently and identically distributed. Because the subjects are treated as independent
given 0i...w, the likelihood function is simply:
where n is the number of subjects, % is the number of experiments in that subject, m is the
number of different types of measurements, Nyk is the number (possibly 0) of measurements
(e.g., time points) for subject /' of type k in experiment j, and tijki are the times at which
measurements for subject /' of type k were made in experiment/
The MCSim software (version 5.0.0) was used to sample from this distribution.
A.5. RESULTS OF UPDATED PBPK MODEL
The evaluation of the updated PBPK model was discussed in Chapter 3. Detailed results
in the form of tables and figures are provided in this section.
A.5.1. Convergence and Posterior Distributions of Sampled Parameters
For each sampled parameter (population mean and variance and the variance for residual
errors), summary statistics (median, [2.5, 97.5%] CI) for the posterior distribution are tabulated
in Tables A-9, A-10, A-12, A-13, A-15, and A-16 below. In addition, the potential scale
reduction factor R, calculated from comparing four independent chains, is given. For each
species, graphs of the prior and posterior distributions for the population mean and variance
parameters are shown in Figures A-13 to A-18 for mice, A-19 to A-24 for rats, and A-25 to A-30
A-82
-------
for humans. Finally, posterior correlations between population mean parameters are given in
Tables A-l 1, A-14, and A-17, which show parameter pairs with correlation coefficients >0.25.
In addition, posterior distributions for the subject-specific parameters are summarized in
supplementary figures accessible here:
• Mouse: (Supplementary data for TCE assessment: Mouse posterior by subject, 2011)
• Rat: (Supplementary data for TCE assessment: Rat posterior by subject, 2011)
• Human: (Supplementary data for TCE assessment: Human posterior by subject, 2011)
A-83
-------
Table A-9. Posterior distributions for mouse PBPK model population
parameters
Sampled parameter"
InQCC
InVPRC
QFatC
QGutC
QLivC
QSlwC
InDRespC
QKidC
FracPlasC
VFatC
VGutC
VLivC
VRapC
VRespLumC
VRespEffC
VKidC
VBldC
InPBC
InPFatC
InPGutC
InPLivC
InPRapC
InPRespC
InPKidC
InPSlwC
InPRBCPlasTCAC
InPBodTCAC
InPLivTCAC
InkDissocC
Posterior distributions reflecting uncertainty in population distribution
Population (geometric) mean
Median (2.5, 97.5%)
1.237 (0.8972, 1.602)
0.8076 (0.6434, 1.022)
1.034(0.5235, 1.55)
1.183 (1.002, 1.322)
1.035 (0.8002, 1.256)
0.9828 (0.6043, 1.378)
1.214(0.7167,2.149)
0.995 (0.5642, 1.425)
0.8707(0.5979, 1.152)
1.329 (0.8537, 1.784)
0.9871 (0.817, 1.162)
0.8035 (0.5609, 1.093)
0.997(0.8627, 1.131)
0.9995(0.8536, 1.145)
1(0.8537,1.148)
1.001 (0.8676, 1.134)
0.9916(0.8341, 1.153)
0.9259 (0.647, 1.369)
0.9828(0.7039, 1.431)
0.805 (0.4735, 1.418)
1.297 (0.7687, 2.039)
0.9529 (0.5336, 1.721)
0.9918 (0.5566, 1.773)
1.277 (0.7274, 2.089)
0.92 (0.5585, 1.586)
2.495(1.144,5.138)
0.8816 (0.6219, 1.29)
0.8003 (0.5696, 1.15)
1.214 (0.2527, 4.896)
R
1
1
1
1
1
1
1.002
1
1.001
1.002
1
1.013
1
1
1.001
1
1.001
1
1.001
1
1
1
1.001
1
1.001
1.001
1.003
1.003
1.003
Population GSD
Median (2.5, 97.5%)
1.402(1.183,2.283)
1.224(1.108, 1.63)
0.436 (0.3057, 0.6935)
0.1548(0.1101,0.2421)
0.1593(0.1107,0.2581)
0.275(0.1915,0.4425)
1.215(1.143, 1.375)
0.3001 (0.21, 0.48)
0.1903(0.1327,0.3039)
0.4123 (0.2928, 0.6414)
0.1219(0.085,0.1965)
0.2216(0.1552,0.3488)
0.09384(0.06519,0.1512)
0.1027(0.07172,0.1639)
0.1032(0.07176,0.1652)
0.09365(0.06523,0.1494)
0.1126(0.07835,0.1817)
1.644 (1.278, 3.682)
1.321(1.16,2.002)
1.375(1.198,2.062)
1.415(1.21,2.342)
1.378(1.203,2.141)
1.378(1.2,2.066)
1.554 (1.265, 2.872)
1.411 (1.209,2.3)
1.398(1.178,2.623)
1.27(1.158, 1.609)
1.278(1.157, 1.641)
2.71 (1.765, 8.973)
R
1
1.001
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1.001
1
1
1
1
1
1.001
1.001
1
1.001
1
A-84
-------
Table A-9. Posterior distributions for mouse PBPK model population
parameters (continued)
Sampled parameter"
InBMaxkDC
InPBodTCOHC
InPLivTCOHC
InPBodTCOGC
InPLivTCOGC
InPeffDCVG
InkTSD
InkAS
InkTD
InkAD
InkASTCA
InkASTCOH
InVMAxC
lnKMC
InFracOtherC
InFracTCAC
InVMAxDCVGC
InClDCVGC
InVMAxKidDCVGC
InClKidDCVGC
InVMAxLungLivC
lnKMClara
InFracLungSysC
InVMAxTCOHC
lnKMTCOH
InVMAxGlucC
lnKMGluc
InkMetTCOHC
InkUrnTCAC
InkMetTCAC
InkBileC
InkEHRC
InkUrnTCOGC
InFracKidDCVCC
InkDCVGC
InkNATC
InkKidBioactC
Posterior distributions reflecting uncertainty in population distribution
Population (geometric) mean
Median (2.5, 97.5%)
1.25(0.6793,2.162)
0.8025(0.5607, 1.174)
1.526 (0.9099, 2.245)
0.4241 (0.1555, 1.053)
1.013 (0.492, 2.025)
0.9807 (0.008098, 149.6)
5.187(0.3909,69.34)
1.711(0.3729, 11.23)
0.1002(0.01304,0.7688)
0.2665 (0.05143, 1.483)
3.986(0.1048, 141.9)
0.7308 (0.006338, 89.75)
0.6693(0.4093, 1.106)
0.07148(0.0323,0.1882)
0.02384(0.003244,0.1611)
0.4875 (0.2764, 0.8444)
1.517(0.02376, 1,421)
0.1794(0.02333,79.69)
1.424(0.04313,704.9)
0.827 (0.04059, 167.2)
2.903 (0.487, 12.1)
0.01123(0.001983,0.09537)
3.304(0.2619, 182.1)
1.645 (0.6986, 3.915)
0.9594 (0.2867, 2.778)
65.59 (27.58, 232.5)
31.16(6.122, 137.3)
3.629 (0.7248, 9.535)
0.1126(0.04083,0.2423)
0.6175 (0.2702, 1.305)
0.9954(0.316,3.952)
0.01553(0.001001,0.0432)
7.874 (2.408, 50.28)
1.931(0.01084, 113.7)
0.2266(0.001104, 16.46)
0.1175(0.0008506, 14.34)
0.07506 (0.0009418, 12.35)
R
1.002
1
1
1.004
1.002
1.041
1.001
1.001
1
1.003
1
1.001
1.005
1
1.006
1.002
1.001
1.013
1.014
1.019
1.001
1.012
1.011
1.005
1.007
1.018
1.015
1.009
1.012
1.027
1.003
1.008
1
1.018
1.011
1.024
1.035
Population GSD
Median (2.5, 97.5%)
1.474(1.253,2.383)
1.314(1.17, 1.85)
1.399(1.194,2.352)
1.398(1.207,2.156)
1.554 (1.279, 2.526)
1.406 (1.206, 2.379)
5.858 (2.614, 80)
4.203 (2.379, 18.15)
5.16(2.478,60.24)
4.282 (2.378, 20.21)
5.187(2.516,58.72)
5.047 (2.496, 54.8)
1.793(1.49,2.675)
2.203(1.535,4.536)
1.532(1.265,2.971)
1.474(1.258,2.111)
1.53(1.263,2.795)
1.528(1.261,2.922)
1.533(1.262,2.854)
1.527 (1.263, 2.874)
4.157(1.778,29.01)
1.629 (1.278, 5.955)
1.543(1.266,3.102)
1.603(1.28,2.918)
1.521 (1.264, 2.626)
1.487(1.254,2.335)
1.781 (1.299, 5.667)
1.527 (1.265, 2.626)
1.757(1.318,3.281)
1.508 (1.262, 2.352)
1.502(1.26,2.453)
1.534 (1.264, 2.767)
3.156(1.783, 12.18)
1.53 (1.264, 2.77)
1.525(1.263,2.855)
1.528(1.264,2.851)
1.527(1.263,2.84)
R
1
1.001
1
1
1
1
1
1
1
1
1
1
1
1.001
1
1
1
1
1
1
1.018
1.003
1.001
1
1
1
1.002
1
1.003
1.002
1
1
1.001
1
1
1
1.001
aThese "sampled parameters" are scaled one or more times (see Table A-4) to obtain a biologically-meaningful
parameter, posterior distributions of which are summarized in Tables 3-36 through 3-40). For natural log
transformed parameters (name starting with "In"), values are for the population geometric means and SDs.
A-85
-------
Table A-10. Posterior distributions for mouse residual errors
Measurement
CInhPPM
CVen
CBldMix
CFat
CKid
CLiv
AExhpost
CTCOH
CLivTCOH
CPlasTCA
CBldTCA
CLivTCA
AUrnTCA
CTCOGTCOH
CLivTCOGTCOH
AUrnTCOGTCOH
TotCTCOH
Residual error GSD
Median (2.5, 97.5%)
1.177(1.16, 1.198)
2.678(2.354,3.146)
1.606(1.415, 1.96)
2.486(2.08,3.195)
2.23 (1.908, 2.796)
1.712(1.543,1.993)
1.234(1.159,1.359)
1.543 (1.424, 1.725)
1.591(1.454,1.818)
1.396(1.338,1.467)
1.488(1.423,1.572)
1.337(1.271, 1.43)
1.338(1.259,1.467)
1.493 (1.38, 1.674)
1.63 (1.457, 1.924)
1.263 (1.203, 1.355)
1.846(1.506,2.509)
R
1.001
1.001
1.001
1
1
1
1
1
1
1.001
1.001
1
1
1.001
1
1
1.002
Note: the hierarchical statistical model for residual errors did not separate by subject.
A-86
-------
Table A-ll. Posterior correlations for mouse population mean parameters
Mouse
Parameter 1
InKMGluc
InClDCVGC
InkMetTCAC
InKMTCOH
InClKidDCVGC
InkUrnTCAC
InkDissocC
InkEHRC
InVMAxC
InKMClara
InBMaxkDC
InKMGluc
InkBileC
InkEHRC
InPBodTCOGC
InFracTCAC
InkMetTCAC
InkDissocC
InPSlwC
Parameter 2
InVMAxGlucC
InVMAxDCVGC
InkUrnTCAC
InVMAxTCOHC
InVMAxKidDCVGC
InPRBCPlasTCAC
InPBodTCAC
InkMetTCOHC
VLivC
InVMAxLungLivC
InPLivTCAC
InKMTCOH
InkEHRC
InKMTCOH
InVMAxGlucC
InVMAxTCOHC
InPBodTCAC
InPLivTCAC
QFatC
Correlation coefficient.
0.765
-0.553
-0.488
0.464
-0.394
0.358
0.328
0.314
-0.305
0.302
0.299
0.293
-0.280
-0.273
0.269
-0.267
0.264
0.253
-0.252
Note: only parameter pairs with correlation coefficient >0.25 are listed.
A-87
-------
Mouse
M_QFatC
M_QGutC'
o
o ™
o
o
•gf
1
Prior Posterior
M QLivC
^ -
o
CD
Q
i
Prior Posterior
M FraePlasC
Prior
M VR
i
Posterior
apC
to
O
if)
O
CN
O
0
o
Prior Posterior
M VBIdC
o
o
o
o -
[
1
Prior Posterior
M InPLivC
E
**! _
o
o
m
o ~
i
E
E
IO
IT)
O
1 1
Prior Posterior
M QSiwC
CN ~
CN
I "™
CD
I
O
f
i i
Pr or Posterior
M VFatC
E
E
CN __
O
CO
Q
Pr or Posterior
M VRespLumC
Pr or Posterior
M inPBC
i i
Pr or Posterior
MJnPRapC
O)
Q
CO
O
IN
O
Q -
I
to
I
o ~
o
o
SO
o -
Prior Posterior
M InDRespC *
CO
-
1*--
o
O
!
Pro" Posterior
M VGutC _
Pror Posterior
M VRespEffC
CN
O
O
CD
O
to
SO
-
m
o
m
CO —
Pror Posterior
M InPFatC
E
3
if)
O
O
to
o ~
I
Pror Posterior
MJnPRespC
m
C3 ~
O
O
in
o —
i
E
E
Prior Posterior
M QKidC
Pr or Posterior
M VLivC
Prior Posterior
M VKidC
Pr or Posterior
M InPGutC
Pr or Posterior
M InPKidC
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-13. Prior and posterior mouse population mean parameters
(Part 1).
A-88
-------
Mouse
M InPRBCPIasTCAC
M InPBodTCAC
M InPLivTCAC
in
o
o
o
o -
i
Prior Posterior
M InkDissocC
::
CM
E
B
O -
CM _
in __
CD*
O
o
CD
1 I
Prior Posterior
M InPBodTCOGC
r*-
CM
•Kf
-s-
i
Prior Posterior
M InkAS
CO -
CM
CO
*
»-
CM -
O -
I ~
CM „
CO „
Prior Posterior
M InkASTCOH
CD -
CM -
CN
CO
O
o
o
o
T
1
Prior Posterior
M inFracTCAC
CM -
CM
^
CO -
CM -
O -
"3"
i ""
•*•
CO __
CD*
O
CM
o -
I
CD
o -
1 1
Prior Posterior
M InBMaxkDC
E
E
CD
CD
O
CM
I
CO
Q -
1 1
Pr or Posterior
M InPLivTCOGC
-
CM „
CO
f
Pr or Posterior
M InkTD
CD -
CM _
CO
I ™"
Pr or Posterior
M InVMaxC
*
M^
,_
n
i ~"
i i
Pr or Posterior
M nVMaxDCVGC
CO -
CM ~
o -
T-
{
--
Prior Posterior
M nPBodTCOHC
__
i 1
Hr~^
i
Pr or Posterior
M InPeffDCVG
-p
-L
Pr or Posterior
M InkAD
^^
Pr or Posterior
M InKMC*
r^
Pr or Posterior
M InCIDCVGC
^p
CO _
CD*
5N „
CM
I
so
o —
1
If)
Q
O
in
o —
I
I
I ~
CO —
n
CS|
I ~
CO
1
1 *
CS|
I ~
CO
1
CO —
I ~
E
3
Prior Posterior
M InPLivTCOHC*
E
3
Pr or Posterior
M nkTSD
E
3
Prior Posterior
M InkASTCA
E
3
Pr or Posterior
M InFracOtherC
^
Pr or Posterior
M InVMaxKldDCVGC
Prior Posteriw
Pricr Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-14. Prior and posterior mouse population mean parameters
(Part 2).
A-89
-------
Mouse
M InCIKidDCVGC
MJnVMaxLungLlvC
M InKMCIara '
MJnFraclungSysC
CM ~
T ~"
CN
i
i
Prior Posterior
M nVMaxTCQHC
—
»-
i
Prior Posterior
M InkMetTCOHC
-*-
„-
CM „
i
Prior Posterior
M InkEHRC
-t-
SO -
CN -
SO
1 ~
i
Prior Posterior
M InkNATC
CM -
O -
CO _
so -
CM ~
CM
1
SO
f
i 1
Pr or Pos erior
M InKMTCOH
—
U"> -
1
Pr or Posterior
M InkUrnTCAC
-*-
CM -
O -
I ™"
I ~
i i
Pr or Posterior
M InkUrnTCOGC
^
CD -
CN -
CN
I ~
CD _
Pr or Posterior
M InkKidBioactC
^
so —
CN
CD
I ~
I
Pror Posterior
M InVMaxGlucC
"*"
--
CM _
I ~
Pror Posterior
M InkMetTCAC
I
O —
I ™"
f-
!
Pr or Posterior
M nFracKidDCVCC
Prior
CN —
O —
T-
K3
I ~
Posterior
--
^_
-L
Prior Posterior
M InKMGluc
T
^^
Pr or Posterior
M InkBileC
1 I
Pr or Posterior
M InkDCVGC
-p
Pr or Posterior
Prior Posterio*
Prio* Posterio*
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-15. Prior and posterior mouse population mean parameters
(Part 3).
A-90
-------
Mouse
VJnQCC'
V_QFatC
g-
E
o
CM _
d
o
Q
o
—
1 d
Prior Posterior
V QLivC o
d
in
d
o
d
© -
d
Prior Posterity
V FracPlasC
Prior
V VRi
i
Posterior
ipC
Prior Posterior
V VBIdC
!
Prior Posterior
V InPLivC
P
pE
E
d
ro
d
d "™
d ~"
^E
E
i i
Pr or Pos erior
V QSIwC
Pr or Posterior
V VFatC
d
CO
d
o
o
d
CO
o -
CM
o -
©
o ~~
d
CM
o -
d
©
p ~
I I
Pr or Pos erior
V VRespLurnC
in
g-
©
in
o ~
©
in
o
o ~
0
if)
O
if)
©
O
d ~"1
if)
©
CO
d
d ~
Pr or Posterior
V InPBC*
p
I i
Prior Posterior
VJnPRapC
E
EE
E
if)
CM
O ~
d
if)
d
in
©
o -
©
o ""
© ™"
o
o
d ~"
CO
d
d ~"
P _
d
if
© ~
o
CM
©
1
Pror Posterior
VJnDRespC
Pror Posterior
V VGutC
!
Pr or Posterior
V YRespEffC
Prior Posterior
V inPFatC
^
P3E
P
Prior Posterior
VJnPRespC
E
EE
P
if)
d
if)
o —
d
o
d
CO
o _
d
CM
o
in
© ~
d
if)
8-
d
if)
d
CO
d
©
CM
O
© ~~
O
Prior Posterior
V QKidC
Pr or Posterior
V VLivC
Pr or Posterior
V VKdC
Pr or Posterior
V InPGutC
E
EE
E
Prior Posterior
V inPKidC
t=
^^
P
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-16. Prior and posterior mouse population variance parameters
(Part 1).
A-91
-------
Mouse
V InPRBCPIasTCAC
p
pE
P
i
Prior Poster lor
V InkDissocC
E
EE
E
Prior Posterior
V InPBodTCOGC
E
EE
E
i
Prior Posterior
V InkAS
E
E^
p
Prior Posterior
V InkASTCOH
E
EE
P
!
Prior Posterior
V InFracTCAC
E
EE
p
Prior Posterior
V InBMaxkDC
Prior Posterior
V InPLivTCOGC
Prior Posterior
V InkTD
Prior Posterior
V InVMaxC
Priw Posterior
V InVMaxDCVGC
V InPBodTCAC
Prior Posterior
V InPBodTCOHC
Prior Posterior
V InPeffDCVG
Prior Posterior
V InkAD
Prior Posterior
V InKMC*
Prior Posterior
V InCIDCVGC
V InPLivTCAC
E
E^
p
Prior Posterior
V InPLivTCOHC
^
Pr
E
Pr
E
Pr
V
E
^E
E
or Poster! or
V InkTSD
pE
E
or Posterior
V InkASTCA
PE
E
or Posterior
InFracOtherC
pE
P
Prior Posterior
V nVMaxKidDCVGC
E
pE
E
Prior Postericr
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-17. Prior and posterior mouse population variance parameters
(Part 2).
A-92
-------
Mouse
V InCIKidDCVGC
E
pE
E
i
Prior Poster ior
V InVMaxTCQHC
P
^E
3
Prior Posterity
V inkMetTCOHC
E
pE
3
i
Prior Posterior
V InkEHRC
E
^E
5
i
Prior Posterior
V InkNATC
E
pE
3
VJnVMaxLungLlvC *
VJnKMCIara
Prior Posterior
V InKMTCOH
Prior Posterior
V InVMaxGlucC
Prior Posterior
V InkUrnTCAC
Prior Posterior
V InkMetTCAC
Prior Posterior
V InkUrnTCOGC *
Prior Posterior
V InFracKidDCVCC
Prior Posterior
V InkKidBioactC
Prior Posterior
VJn FraclunpSysC
^
^E
E
Prior Poster! or
V InKMGiuc
iE
3
Pr or Posterior
V InkBileC
E
Pr
E
PE
P
or Posterior
V InkDCVGC
PE
P
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-18. Prior and posterior mouse population variance parameters
(Part 3).
A-93
-------
Table A-12. Posterior distributions for rat PBPK model population
parameters
Sampled parameter
InQCC
InVPRC
QFatC
QGutC
QLivC
QSlwC
InDRespC
QKidC
FracPlasC
VFatC
VGutC
VLivC
VRapC
VRespLumC
VRespEffC
VKidC
VBldC
InPBC
InPFatC
InPGutC
InPLivC
InPRapC
InPRespC
InPKidC
InPSlwC
InPRBCPlasTCAC
InPBodTCAC
InPLivTCAC
InkDissocC
InBMaxkDC
InPBodTCOHC
InPLivTCOHC
InPBodTCOGC
InPLivTCOGC
InkTSD
InkAS
InkAD
InkASTCA
InkASTCOH
InVMAxC
Posterior distributions reflecting uncertainty in population distribution
Population (geometric) mean
Median (2.5, 97.5%)
1.195(0.9285, 1.448)
0.6304 (0.4788, 0.8607)
1.167(0.8321, 1.561)
1.154(0.988, 1.306)
1.029 (0.8322, 1.223)
0.9086(0.5738, 1.251)
2.765 (1.391, 5.262)
1.002(0.8519,1.152)
1.037 (0.8071, 1.259)
0.9728 (0.593, 1.378)
0.9826(0.8321, 1.137)
0.9608(0.7493, 1.19)
0.9929(0.8563, 1.133)
1.001 (0.7924, 1.21)
0.999 (0.7921, 1.208)
0.999(0.8263,1.169)
1.002(0.8617,1.141)
0.8551 (0.6854, 1.065)
1.17(0.8705,1.595)
0.8197 (0.5649, 1.227)
1.046 (0.8886, 1.234)
1.021 (0.6239, 1.675)
0.993 (0.5964, 1.645)
0.9209 (0.6728, 1.281)
1.258(0.9228, 1.711)
0.9763 (0.6761, 1.353)
1.136(0.6737, 1.953)
1.283(0.6425,2.491)
1.01 (0.5052, 2.017)
0.9654 (0.5716, 1.733)
0.9454 (0.4533, 1.884)
0.926(0.3916,2.196)
1.968 (0.09185, 14.44)
7.484 (2.389, 26.92)
3.747(0.2263,62.58)
2.474 (0.2542, 28.35)
0.1731(0.04001,0.7841)
1.513(0.1401,17.19)
0.6896(0.01534,25.81)
0.8948 (0.6377, 1.293)
R
1.034
1.012
1
1
1.002
1.001
1.018
1.001
1.002
1
1
1.015
1.001
1
1.001
1
1
1.001
1.003
1
1.001
1.002
1.001
1
1.001
1
1.008
1.008
1.002
1.02
1.045
1.013
1.031
1.017
1.01
1.004
1.018
1.002
1.001
1.028
Population GSD
Median (2.5, 97.5%)
1.298(1.123,2.041)
1.446(1.247,2.011)
0.4119(0.2934,0.6438)
0.1613 (0.1132,0.2542)
0.1551 (0.1092,0.2483)
0.2817(0.1968,0.4493)
1.21 (1.142, 1.358)
0.1185(0.08284,0.1871)
0.1785(0.1272,0.2723)
0.4139 (0.2924, 0.6552)
0.1187(0.08296,0.1873)
0.1682(0.1168,0.2718)
0.1093(0.07693,0.175)
0.1636(0.116,0.2601)
0.1635(0.1161,0.2598)
0.1361(0.09617,0.2167)
0.1096(0.07755,0.176)
1.317(1.232,1.462)
1.333 (1.247, 1.481)
1.362(1.198,1.895)
1.152(1.115, 1.214)
1.373 (1.201, 1.988)
1.356(1.197, 1.948)
1.304(1.201, 1.536)
1.364(1.263, 1.544)
1.276(1.159, 1.634)
1.631(1.364,2.351)
1.651(1.356,2.658)
1.596(1.315,2.774)
1.412(1.234,2.01)
1.734(1.39,3.151)
1.785(1.382,4.142)
1.414(1.208,2.571)
1.41(1.208,2.108)
6.777 (2.844, 87.29)
10.16(4.085,143.7)
4.069(2.373,14.19)
4.376 (2.43, 22.83)
4.734 (2.444, 35.2)
1.646(1.424,2.146)
R
1.031
1.005
1
1
1
1
1.001
1
1
1.002
1
1.001
1
1
1
1
1
1.001
1.001
1
1
1
1
1
1
1
1.003
1
1
1
1.002
1.003
1
1
1
1
1.009
1
1.001
1.021
A-94
-------
Table A-12. Posterior distributions for rat PBPK model population
parameters (continued)
Sampled parameter
lnKMC
InFracOtherC
InFracTCAC
InVMAxDCVGC
InClDCVGC
InVMAxKidDCVGC
InClKidDCVGC
InVMAxLungLivC
lnKMClara
InFracLungSysC
InVMAxTCOHC
lnKMTCOH
InVMAxGlucC
lnKMGluc
InkMetTCOHC
InkUrnTCAC
InkMetTCAC
InkBileC
InkEHRC
InkUrnTCOGC
InkNATC
InkKidBioactC
Posterior distributions reflecting uncertainty in population distribution
Population (geometric) mean
Median (2.5, 97.5%)
0.0239(0.01602,0.04993)
0.344 (0.0206, 1.228)
0.2348(0.122,0.4616)
7.749 (0.2332, 458.8)
0.3556(0.06631,2.242)
0.2089(0.04229, 1.14)
184(26.29, 1312)
2.673(0.4019, 14.16)
0.02563(0.005231,0.197)
2.729 (0.04124, 63.27)
1.832(0.6673,6.885)
22.09(3.075, 131.9)
28.72 (10.02, 86.33)
6.579(1.378,23.57)
2.354(0.3445, 15.83)
0.07112(0.03934,0.1329)
0.3554(0.1195,0.8715)
8.7(1.939,26.71)
1.396(0.2711,6.624)
20.65 (2.437, 138)
0.002035 (0.0004799,
0.01019)
0.006618 (0.0009409,
0.0367)
R
1.001
1.442
1.028
1.088
1.018
1.011
1.02
1.002
1.01
1.027
1.041
1.186
1.225
1.119
1.287
1.076
1.036
1.05
1.091
1.041
1.01
1.039
Population GSD
Median (2.5, 97.5%)
2.402(1.812,4.056)
3 (1.332, 10.04)
1.517(1.264,2.393)
1.534(1.262,2.804)
1.509(1.261,2.553)
1.542(1.263,2.923)
1.527(1.265,2.873)
4.833(1.599,48.32)
1.66 (1.279, 18.74)
1.536(1.267,2.868)
1.667(1.292,3.148)
1.629 (1.276, 3.773)
2.331(1.364,5.891)
2.046(1.309, 10.3)
1.876(1.283, 11.82)
1.513(1.27,2.327)
1.528(1.263,2.444)
1.65 (1.282, 5.494)
1.647 (1.277, 5.582)
1.595 (1.269, 5.257)
1.523(1.261,2.593)
1.52(1.261,2.674)
R
1.001
1.353
1.001
1.001
1
1.001
1.001
1.002
1.002
1.001
1.002
1.017
1.126
1.125
1.182
1.003
1.001
1.017
1.005
1.026
1.001
1
A-95
-------
Table A-13. Posterior distributions for rat residual errors
Measurement
CInhPPM
CMixExh
Cart
CVen
CBldMix
CFat
CGut
CKid
CLiv
CMus
AExhpost
CTCOH
CPlasTCA
Subject"
Subject 3
Subject 16
Subject 2
Subject 2
Subject 6
Subject 4
Subject 7
Subject 8
Subject 9
Subject 10
Subject 1 1
Subject 16
Subject 18
Subject 12
Subject 9
Subject 16
Subject 9
Subject 9
Subject 9
Subject 12
Subject 16
Subject 9
Subject 6
Subject 10
Subject 14
Subject 15
Subject 6
Subject 10
Subject 1 1
Subject 13
Subject 17
Subject 18
Subject 4
Subject 5
Subject 1 1
Subject 19
Residual error GSD
Median (2.5, 97.5%)
1.124(1.108, 1.147)
1.106(1.105, 1.111)
1.501 (1.398, 1.65)
1.174(1.142, 1.222)
1.523(1.321, 1.918)
1.22(1.111,1.877)
1.668 (1.489, 1.986)
1.45(1.234,2.065)
1.571(1.426, 1.811)
4.459 (2.754, 6.009)
1.587 (1.347, 2.296)
1.874 (1.466, 2.964)
1.676(1.188,3.486)
1.498(1.268,2.189)
1.846(1.635,2.184)
2.658(1.861,4.728)
1.855 (1.622, 2.243)
1.469(1.354, 1.648)
1.783(1.554,2.157)
1.744 (1.401, 2.892)
1.665(1.376,2.411)
1.653 (1.494, 1.919)
1.142(1.108, 1.239)
1.117(1.106, 1.184)
1.166(1.107, 1.475)
1.125(1.106, 1.237)
1.635(1.455, 1.983)
1.259(1.122, 1.868)
1.497(1.299, 1.923)
1.611(1.216,3.556)
1.45(1.213,2.208)
1.142(1.107, 1.268)
1.134(1.106, 1.254)
1.141(1.107, 1.291)
1.213(1.136, 1.381)
1.201(1.145, 1.305)
R
1
1
1
1
1.002
1
1.001
1.014
1
1
1.002
1.011
1.003
1
1
1.001
1
1
1
1
1.001
1
1.003
1.004
1
1
1.002
1.009
1.01
1.001
1.004
1
1
1
1
1
A-96
-------
Table A-13. Posterior distributions for rat residual errors (continued)
Measurement
CBldTCA
CLivTCA
AUrnTCA
ABileTCOG
CTCOG
AUrnTCOGTCOH
AUrnNDCVC
AUrnTCTotMole
TotCTCOH
Subject"
Subject 4
Subject 5
Subject 6
Subject 1 1
Subject 17
Subject 18
Subject 19
Subject 19
Subject 1
Subject 6
Subject 8
Subject 10
Subject 17
Subject 19
Subject 6
Subject 17
Subject 1
Subject 6
Subject 8
Subject 10
Subject 17
Subject 1
Subject 6
Subject 7
Subject 14
Subject 15
Subject 17
Residual error GSD
Median (2.5, 97.5%)
1.134(1.106, 1.258)
1.14(1.107,1.289)
1.59(1.431,1.878)
1.429(1.292, 1.701)
1.432 (1.282, 1.675)
1.193(1.12,1.358)
1.214(1.153, 1.327)
1.666(1.443,2.104)
1.498(1.125,2.18)
1.95(1.124,5.264)
1.221 (1.146, 1.375)
1.18(1.108,1.444)
1.753(1.163,4.337)
1.333 (1.201, 1.707)
2.129(1.128,5.363)
2.758 (1.664, 5.734)
1.129(1.106, 1.232)
1.483(1.113,4.791)
1.115(1.106, 1.162)
1.145(1.107, 1.305)
2.27(1.53,4.956)
1.168(1.11, 1.33)
1.538(1.182,3.868)
1.117(1.106, 1.153)
1.121 (1.106, 1.207)
1.162(1.108, 1.358)
1.488(1.172,2.366)
R
1
1
1.001
1.001
1.03
1.004
1
1
1.135
1.003
1.003
1.007
1.001
1
1.003
1.028
1.004
1.002
1
1
1.009
1.002
1.002
1.001
1
1
1.015
aThe nineteen subjects are: (1) Bernauer et al. (1996): (2) Dallas et al. (1991): (3) Fisher et al. (1989) females;
(4) Fisher et al. (1991) females; (5) Fisher et al. (1991) males; (6) Green and Prout (1985). Prout et al. (1985). male
OA rats; (7) Hissink et al. (2002); (8) Kaneko et al. (1994) (9) Keys et al. (2003); (10) Kimmerle and Eben (1973b);
(11) Larson and Bull (1992b. a); (12) Lee et al. QOOQa); (13) Merdink et al. (19991: (14) Prout et al. (19851 AP rats;
(15) Prout et al. (1985) OM rats; (16) Simmons et al. (2002); (17) Stenner et al. (1997); (18) Templin et al. (1995b);
and (191 Yuetal. (20001.
A-97
-------
Table A-14. Posterior correlations for rat population mean parameters
Rat
Parameter 1
InkNATC
InkBileC
InKMTCOH
InKMGluc
InClKidDCVGC
InkUrnTCAC
InVMAxC
InBMaxkDC
InkUrnTCOGC
InPFatC
InClKidDCVGC
InKMGluc
InPLivTCOGC
InBMaxkDC
InClDCVGC
FracPlasC
InClDCVGC
InkEHRC
InkBileC
InFracLungSysC
InFracOtherC
InFracLungSysC
InkMetTCAC
InkMetTCAC
InKMTCOH
InFracTCAC
InDRespC
InFracOtherC
Parameter 2
InVMAxKidDCVGC
InPLivTCOGC
InVMAxTCOHC
InVMAxGlucC
InkNATC
InPBodTCAC
VLivC
InkUrnTCAC
InPBodTCOGC
VFatC
InVMAxKidDCVGC
InKMTCOH
InVMAxGlucC
InPBodTCAC
InClKidDCVGC
InPRBCPlasTCAC
InkNATC
InVMAxGlucC
InkUrnTCOGC
InFracOtherC
InkMetTCOHC
InKMTCOH
InPBodTCAC
VLivC
InPBodTCOGC
InKMTCOH
InVPRC
InKMTCOH
Correlation coefficient
-0.599
-0.587
0.567
0.506
-0.497
0.421
-0.417
0.397
-0.389
-0.385
0.384
0.383
0.358
0.352
0.343
-0.337
-0.331
0.322
0.307
0.304
-0.296
-0.271
0.264
-0.261
-0.260
0.258
0.254
-0.252
Note: only parameter pairs with correlation coefficient >0.25 are listed.
A-98
-------
Rat
CO
o
o
T_
CO
CD
O
N-
O
CN
O
*~
CO
o
CD
O
CD
O
CO
O
o
CO
CD
CO _j
O
O
,_
CO
o -
o
CN
O
CM
O "~
!
CD
O "™
!
1
Prior Posterior
M QLivC
"Bj"
(D
O
Prior Pos erior
M FracPiasC
E
Prior
M VR
3
Posterior
apC
Prior Posterior
M VBldC
in
0
G ~"
to __
CD
O
r—
o
o
o
o
o -
i
i
Prior Posterior
M nPLivC
3
o
o
o
o ~
!
^
in
o
in
o
I 1
Pr or Posterior
M QSiwC
E
3
CN -
-
Pr or Posterior
M VFatC
E
3
CN
O
Q
o
Pr or Posterior
M VRespLumC _
Pr or Posterior
M nPBC
E
3
1 !
Pr or Posterior
MJnPRapC
-
^_
CD
O
o
CN
O
CN
i
tD
O ~
O
o
in
^
I !
Prior Posterior
MJnDRespC
-s-
1 !
Prior Posterior
M VGutC
i i
Prior Posterior
M VRespEffC
-
O ""
o
CN
o
CO
o
-
*-
o> __
o
5-
o
"*"
cQ
o
1 !
Prior Posterior
M InPFatC
3
I i
Prior Posterior
MJnPRespC
in
o
o
o
o ~
o
o
CN
o -
CD
o —
3
) |
Prior Posterior
M QKidC
Prior Posterior
M VLvC
Prior Posterior
M VKidC
Prior Posterior
M InPGutC
E
3
i i
Prior Posterior
M inPKidC
E
3
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-19. Prior and posterior rat population mean parameters (Part 1).
A-99
-------
Rat.seqpriors.test4
M InPRBCPIasTCAC
M InPBodTCAC
M InPLivTCAC
E
E
1
Prior Posterior
M InkDissocC
E
E
(S3
O
O
CM
O ~
!
SD
Q -
i
to
o
o
o
sn
o ~
I
Prior Pos erior
M InPBodTCOGC
CO ~
l -
I ""
Prior Posterior
M nkTD
CM -
CN
1
! ""
Prior Posterior
M inVMaxC
*
--
o -
CNI
s
i
Prior Posterior
M nVMaxDCVGC
o -
CN
!
I ""
E
E
0
o
o
o
I 1
Pr or Posterior
M InBMaxkDC
E
E
p _
o
o
o
T
Pr or Posterior
M InPLivTCOGC *
1
CN -
O -
CN
Pr or Posterior
IV! nkAD
*
to -
CN -
CN
T-
Pr or Posterior
M InKMC *
^
^
o -
CN
'
1 ~
1 !
Pr or Posterior
M InCIDCVGC
^
(O -
,-f. -
fS] _
O -
E
^
I !
Prior Posterior
M InPBodTCOHC
t
E
1 !
Prior Posterior
M nkTSD
E
E
i i
Prior Posterior
M InkASTCA
F=
"
—
^
1 !
Prior Posterior
M InFracQtherC*
E
^
I i
Prior Posterior
M InVMaxKidDCVGC *
, 1
N
^
O
O
o
o
p _
o
o
p
T
CD —
Tf -
CN
CO _
CD -
CN _
CD _
CM _
CN „
CD -
O -
-if
| 1
) |
Prior Posterior
M inPLvTCOHC
_L
Prior Posterior
M nkAS
r^^
-L
Prior Posterior
M InkASTCOH
r——.
^^
Prior Posterior
M InFracTCAC *
^^
i i
Prior Posterior
M nCIKidDCVGC*
r ^
T-l =^.
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-20. Prior and posterior rat population mean parameters (Part 2).
A-100
-------
Rat.seqpriors,test4
MJnVMaxLungLivC
M InKIVOara
MJnFrg^LungSysC
M InVMaxTCOHC
Prior Posterior
M InKMTCOH *
Prior Posterior
M InVMaxGiucC
Prior Posterior
M InKMGluc
Prior Poster! or
M InkMetTCOHC
Prior Posterior
M InkUrnTCAC
Prior Posterior
M InkMetTCAC
Prior Posterior
M InkBileC*
Prior Posterior
M inkEHRC *
Prior Posterior
M InkUrnTCOGC
Prior Posterior
M InkNATC
Prior Posterior
M InkKidBioaetC
Prior Posterior
(N —
o —
T-
(N —
O —
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-21. Prior and posterior rat population mean parameters (Part 3).
A-101
-------
Rat
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Rat.seqpriors.test4
E
1
Prior Posterior
V QLivC
Prior
V Fr
Prior
V \
d
CO
d
CN
Q
d ~
o
d
m
d
o
d
m
o -
Q
i
Posterior
acPlasC
^RE
Posterior
»pC
d
CO __
o
CN
o
CD ~
CD
O -
d
O -
d
o
d
i
Prior Posterior
V VBIdC
1
Prior Posterior
V InPLivC
CN
CD
OQ
O ,,,
CD*
•f
CD _
CD*
CD ~
CO
d
d "
E
M ^
t I
Pr or Posterior
V QSIwC
I i
Pr or Posterior
V VFstC
Pr or Pos erior
V VRespLumC
Pr or Posterior
V nPBC
1 I
Pr or Posterior
VJnPRapC
E
Ep
P
-sfr
d
cO
CD
CN
O
d
CD
O _
d
o ~
d
o
i !
Prior Posterior
V inDRespC
03
O -
CD
O _
d
o -
CD
(N
CD _
CD
in
(N
O ""
o
o
d
(XJ
O ~
O
d
! !
Prior Posterior
V VGutC
i 1
Prior Posterior
V VRespEffC
•si-
CD ™
d
o
CD
! !
Prior Posterior
V InPFatC
d
0
to
o
o
to
o ~
d
s
o
CN
d
in
o _
d
CO
o ~
o
O —
d
CO
o
o
! I
Prior Posterior
VJnPRespC
o
d
E
EE
p
m
o
o ~
o
i 1
Prior Posterior
V QKdC
i i
Prior Posterior
V VLvC
Prior Posterior
V VKidC
i i
Prior Posterior
V InPGutC
E
EE
3
i i
Prior Posterior
V InPKidC
i
t
5
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Figure A-22. Prior and posterior rat population variance parameters (Part 1).
A-102
-------
Rat.seqpriors.test4
V InPRBCPIasTCAC
1
Prior Posterior
V InkDissocC
E
3^
p
Prior Pos erior
V InPBodTCOGC
E
^E
p
Prior Posterior
V nkTD
E
3E
3
Prior Posterior
V InVMaxC
<=d
t
i
Prior Posterior
V nVMaxDCVGC
E
^E
3
Prior Posterior
V InBMaxkDC
Prior Posterior
V InPLivTCOGC
Prior Posterior
V InkAD
Prior Posterior
V InKMC
Prior Posterior
V InClDCVGC
V InPBodTCAC
Prior Posterior
V InPBodTCOHC
Prior Posterior
V InkTSD
Prior Posterior
V InkASTCA
Prior Posterior
V InFracOtherC *
Prior Posterior
V InVMaxKidDCVGC
V InPLivTCAC
E
3^
^
i i
Prior Posterior
V nPLivTCOHC
E
3E
3
Prior Posterior
V InkAS
^
^E
3
Prior Posterior
V InkASTCOH
E
3E
p
Prior Posterior
V inFracTCAC
E
EE
3
i i
Prior Posterior
V nCIKidDCVGC
E
EE
3
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-23. Prior and posterior rat population variance parameters
(Part 2).
A-103
-------
Rat.seqpriors.test4
VJnVMaxlungLivC'
E
3
i
Prior Posterior
V InKMTCOH
&
Prior Posterior
V InkllrnTCAG
E
3E
3
Prior Posterior
V inkUrnTCOGC
^
^E
3
V InKMCIara
Prior Posterior
V InVMaxGlucC *
Prior Posterior
V InkMetTCAC
Prior Posterior
V InkNATC
VJnFracLungSysC
Prior Posterior
V InKMGluc
Prior Posterior
V InkBileC
Prior Posterior
V InkKidBioaotC
V InVMaxTCOHC
—
^
i i
Prior Posterior
V InkMetTCOHC
^
Prior Posterior
V InkEHRC
==,
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-24. Prior and posterior rat population variance parameters
(Part 3).
A-104
-------
Table A-15. Posterior distributions for human PBPK model population
parameters
Sampled parameter
InQCC
InVPRC
QFatC
QGutC
QLivC
QSlwC
InDRespC
QKidC
FracPlasC
VFatC
VGutC
VLivC
VRapC
VRespLumC
VRespEffC
VKidC
VBldC
InPBC
InPFatC
InPGutC
InPLivC
InPRapC
InPRespC
InPKidC
InPSlwC
InPRBCPlasTCAC
InPBodTCAC
InPLivTCAC
InkDissocC
InBMaxkDC
InPBodTCOHC
InPLivTCOHC
InPBodTCOGC
InPLivTCOGC
InPeffDCVG
InkASTCA
InkASTCOH
InVMAxC
InCIC
Posterior distributions reflecting uncertainty in population distribution
Population (geometric) mean
Median (2.5, 97.5%)
0.837 (0.6761, 1.022)
1.519(1.261, 1.884)
0.7781 (0.405, 1.143)
0.7917(0.6631,0.925)
0.5099(0.1737,0.8386)
0.7261 (0.4864, 0.9234)
0.626 (0.3063, 1.013)
1.007(0.9137, 1.103)
1.001 (0.9544, 1.047)
0.788 (0.48, 1.056)
1 (0.937, 1.067)
1.043 (0.8683, 1.23)
0.9959(0.9311, 1.06)
1.003 (0.8461, 1.164)
1(0.8383, 1.159)
0.9965 (0.8551, 1.14)
1.013 (0.9177, 1.108)
0.9704(0.8529, 1.101)
0.8498 (0.7334, 0.9976)
1.095 (0.7377, 1.585)
0.9907 (0.6679, 1.441)
0.93 (0.6589, 1.28)
1.018(0.6773, 1.5)
0.9993 (0.8236, 1.219)
1.157(0.8468,1.59)
0.3223 (0.04876, 0.8378)
1.194(0.929, 1.481)
1.202 (0.8429, 1.634)
0.9932 (0.9387, 1.053)
0.8806 (0.7492, 1.047)
1.703(1.439,2.172)
1.069 (0.7643, 1.485)
0.7264(0.1237,2.54)
6.671 (1.545, 24.87)
0.01007 (0.003264, 0.03264)
4.511(0.04731,465.7)
8.262 (0.0677, 347.9)
0.3759 (0.2218, 0.5882)
12.64 (5.207, 39.96)
R
1.038
1.007
1.014
1.017
1.031
1.011
1.197
1.009
1.01
1.005
1.007
1.047
1.006
1.001
1.001
1.007
1.003
1.001
1.002
1.029
1.01
1.003
1.015
1.003
1.018
1.007
1.043
1.046
1.012
1.038
1.019
1.028
1.003
1.225
1.004
1
1
1.026
1.028
Population GSD
Median (2.5, 97.5%)
1.457(1.271,1.996)
1.497(1.317,1.851)
0.6272(0.4431,0.9773)
0.1693(0.1199,0.2559)
0.4167 (0.2943, 0.6324)
0.3166(0.2254,0.4802)
1.291(1.158,2.006)
0.1004(0.07307,0.1545)
0.04275(0.03155,0.06305)
0.3666 (0.2696, 0.5542)
0.06745(0.04923,0.1038)
0.1959(0.1424,0.3017)
0.06692(0.04843,0.1027)
0.1671 (0.1209,0.255)
0.1672(0.1215,0.259)
0.1425(0.1037,0.2183)
0.1005(0.07265,0.1564)
1.216(1.161,1.307)
1.188(1.113,1.366)
1.413(1.214,2.05)
1.338(1.203,1.683)
1.528(1.248,2.472)
1.32(1.192, 1.656)
1.155(1.097,1.287)
1.69(1.383,3.157)
5.507 (3.047, 19.88)
1.327(1.185, 1.67)
1.285(1.162,1.648)
1.043 (1.026, 1.076)
1.157(1.085, 1.37)
1.409 (1.267, 1.678)
1.288(1.165, 1.629)
11.98(5.037, 185.3)
5.954(2.653,23.68)
1.385(1.201,2.03)
5.467(2.523,71.06)
5.481(2.513,67.86)
2.21 (1.862, 2.848)
4.325 (2.672, 9.003)
R
1.036
1.008
1
1.019
1.009
1.005
1.083
1
1
1
1
1.003
1
1
1
1
1
1.002
1.002
1.002
1
1.001
1
1
1.008
1.003
1.018
1.007
1.003
1.012
1.011
1.002
1.017
1.052
1.001
1
1
1.003
1.016
A-105
-------
Table A-15. Posterior distributions for human PBPK model population parameters
(continued)
Sampled parameter
InFracOtherC
InFracTCAC
InClDCVGC
lnKMDCVGC
InClKidDCVGC
lnKMKidDCVGC
InVMAxLungLivC
lnKMClara
InFracLungSysC
InClTCOHC
lnKMTCOH
InClGlucC
lnKMGluc
InkMetTCOHC
InkUrnTCAC
InkMetTCAC
InkBileC
InkEHRC
InkUrnTCOGC
InkDCVGC
InkNATC
InkKidBioactC
Posterior distributions reflecting uncertainty in population distribution
Population (geometric) mean
Median (2.5, 97.5%)
0.1186(0.02298,0.2989)
0.1315(0.07115,0.197)
2.786 (1.326, 5.769)
1.213 (0.3908, 4.707)
0.04538(0.001311,0.1945)
0.2802(0.1096, 1.778)
3.772(0.8319,9.157)
0.2726(0.02144, 1.411)
24.08(6.276,81.14)
0.1767(0.1374,0.2257)
2.221 (1.296, 4.575)
0.2796 (0.2132, 0.3807)
133.4(51.56,277.2)
0.7546(0.1427,2.13)
0.04565 (0.0324, 0.06029)
0.2812(0.1293,0.5359)
6.855 (3.016, 20.69)
0.1561(0.09511,0.2608)
15.78(6.135,72.5)
7.123(5.429,9.702)
0.0003157 (0.0001087, 0.002305)
0.06516(0.01763,0.1743)
R
1.061
1.026
1.08
1.029
1.204
1.097
1.035
1.041
1.016
1.011
1.02
1.056
1.02
1.007
1.005
1.004
1.464
1.1
1.007
1.026
1.008
1.001
Population GSD
Median (2.5, 97.5%)
3.449(1.392,9.146)
2.467 (1.916, 3.778)
2.789(1.867,4.877)
4.43 (2.396, 18.56)
3.338(1.295,30.46)
1.496(1.263,2.317)
2.228(1.335,21.89)
11.63(1.877,682.7)
1.496(1.263,2.439)
1.888(1.624,2.307)
2.578(1.782,4.584)
1.955(1.583,2.418)
1.573(1.266,4.968)
5.011(2.668, 15.71)
1.878(1.589,2.48)
2.529(1.78,4.211)
1.589(1.27,3.358)
1.699(1.348,2.498)
9.351(4.93,29.96)
1.507(1.311, 1.897)
1.54(1.261,3.306)
1.523(1.262,2.987)
R
1.102
1.01
1.02
1.035
1.095
1.001
1.014
1.041
1.001
1.01
1.015
1.079
1.011
1.002
1.006
1.002
1.015
1.015
1.003
1.008
1
1
A-106
-------
Table A-16. Posterior distributions for human residual errors
Measurement
RetDose
CAlvPPM
CVen
CTCOH
CPlasTCA
CBldTCA
zAUrnTCA
zAUrnTCA_collect
AUrnTCOGTCOH
AUrnTCOGTCOH_collect
CDCVGmol
zAUrnNDCVC
TotCTCOH
Subject3
Subject 4
Subject 1
Subject 4
Subject 5
Subject 1
Subject 3
Subject 4
Subject 5
Subject 1
Subject 3
Subject 5
Subject 7
Subject 2
Subject 7
Subject 1
Subject 2
Subject 4
Subject 5
Subject 1
Subject 2
Subject 3
Subject 4
Subject 6
Subject 7
Subject 3
Subject 5
Subject 1
Subject 3
Subject 4
Subject 6
Subject 7
Subject 3
Subject 5
Subject 1
Subject 6
Subject 1
Subject 4
Subject 5
Residual error GSD
Median (2.5, 97.5%)
1.131(1.106, 1.25)
1.832(1.509,2.376)
1.515(1.378,1.738)
1.44(1.413, 1.471)
1.875(1.683,2.129)
1.618(1.462, 1.862)
1.716(1.513,2.057)
2.948 (2.423, 3.8)
1.205(1.185, 1.227)
1.213 (1.187, 1.247)
2.101(1.826,2.571)
1.144(1.106,2.887)
1.117(1.106, 1.17)
1.168(1.123, 1.242)
1.138(1.126, 1.152)
1.119(1.106, 1.178)
1.488(1.351, 1.646)
1.438(1.367,1.537)
1.448(1.414,1.485)
1.113(1.105,1.149)
1.242(1.197,1.301)
1.538(1.441, 1.67)
1.158(1.118,1.228)
1.119(1.106,1.181)
1.999(1.178,3.903)
2.787(2.134,4.23)
1.106(1.105,1.112)
1.11(1.105, 1.125)
1.124(1.107,1.151)
1.117(1.106, 1.157)
1.134(1.106, 1.348)
1.3(1.111,2.333)
1.626 (1.524, 1.767)
1.53 (1.436, 1.656)
1.167(1.124, 1.244)
1.204(1.185, 1.226)
1.247(1.177, 1.366)
1.689 (1.552, 1.9)
R
1.001
1.015
1
1
1.018
1
1.001
1.007
1.012
1
1.001
1.123
1.001
1
1.003
1
1.018
1.002
1.001
1.001
1.001
1
1
1
1.003
1.001
1.001
1
1.001
1.001
1.003
1.004
1
1.009
1
1.011
1.009
1.001
aThe seven subjects are: (1) Fisher et al. (1998): (2) Paycok and Powell (1945): (3) Kimmerle and Eben (1973a)
(4) Monster et al. (1976): (5) Chiu et al. (2007): (6) Bernauer et al. (1996): and (7) Muller et al. (1974).
A-107
-------
Table A-17. Posterior correlations for human population mean parameters
Human
Parameter 1
InkBileC
InClKidDCVGC
InClGlucC
InkMetTCAC
InClKidDCVGC
InClKidDCVGC
InKMTCOH
InkMetTCAC
InClKidDCVGC
InkEHRC
InClDCVGC
InBMaxkDC
InFracOtherC
InFracOtherC
InFracOtherC
InFracOtherC
InClDCVGC
InClDCVGC
Parameter 2
InPLivTCOGC
InKMKidDCVGC
InkEHRC
InPLivTCAC
InDRespC
InkEHRC
InPBodTCAC
InPBodTCAC
InkBileC
InPBodTCOHC
InkDCVGC
InPBodTCAC
InQCC
InkDCVGC
VLivC
InPLivTCOGC
InFracOtherC
VLivC
Correlation coefficient
-0.649
-0.567
0.438
-0.392
-0.324
-0.301
0.289
0.283
-0.277
-0.277
0.269
0.267
0.260
-0.258
0.257
-0.256
-0.256
-0.252
Note: only parameter pairs with correlation coefficient >0.25 are listed.
A-108
-------
Human, seqpriors.vt
E
3
Prior Posterior
M QLivC *
E
3
Prior Posterior
M FracPiasC
E
3
Pr or Posterior
M VRapC
E
3
Pr or Posterior
M VBIdC
E
3
Prior Posterior
M nPLivC
E
3
o
o
^
*_
o
CD
O
*
o
to
o
Pr or Posterior
M QSIwC*
*
o -
1 ~"
7-
1 i
Pr or Posterior
M VFatC
o
o
*
o
o
_
o
d
1 !
Pr or Posterior
M VRespLurnC
CO
o"
o" ™"
E
^
o ""
o
1 !
Pr or Posterior
^ M nPBC
o
Q ~
O
O
"*.
i
O
o
o
O ™"
1
*
o
CN
O
o
o
Pr or Poslerior
MJnPRapC
E
^
o
o
o ~
o —
E
3
-r-
T-
0,
O
1
Prior Posterior
MJnDRespC
*
!
Prior Posterior
M VGutC
E
3
i
Prior Posterior
M VRespEffC
E
3
O
O
CO
o
o
CO
o
o
CO
-
o
i 0
Prior Posterior
M InPFatC
*
i
Prior Posterior
MJnPRespC
E
3
m
o
o
o
i
o
o ~"
p
o ~
E
^
Pror Posterior
M QKidC
1=
3
Prior Posterior
M VLivC
E
3
Pror Posterior
M VKidC
i —
i
^
Pror Posterior
M InPGutC
E
3
Pror Posterior
M inPKidC
i —
i
3
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-25. Prior and posterior human population mean parameters
(Part 1).
A-109
-------
Human, seqpriors.vt
MJnPRBCPIasTCAC
MJnPBodTCAC
H_lnPLivTCAC
o
o
CM
I
CO
o
o
o
o
o
o ™"
1
(•"J -
CM -
i ""
i ~
CO -
** -
(M _
CM
1
EM -
o -
i ~
CM _
i
(M _
O ™
i ~
E
E
Prior Posterior
M irtkDisseeC
E
E
Prior Posterior
M nPBodTCOGC
E
3
Pr or Posterior
M inkASTCOH
E
E
Pr or Posterior
M InFracTCAC*
Prior Posterior
M nKMKidDCVGC
E
3
cs-
CM „
^
CO
o
o
O ~"
Pr or Posterior
M inBMaxkDC
o
o
o ~
1
i
E
3
o
o
1
to
O ""
1 i
Pr or Posterior
M nPLvTCOGC
co-
--
-
E
3
-------
Human.seqpriors.vl
M InCITCOHC
--
if
—
CO -
CM -
0 -
T ~
1 i
Prior Posterior
M InkMetTCOHC
CO -
o -
CM
E
3
O -
CM
(O _
I ""
i
Prior Posterior
M inkEHRC
o -
**• _
*
CM -
o -
i
Prior Posterior
M InkKdBioactC*
CM
1
cp_
1
^
o -
1 1
Pr or Posterior
M InkUmTCAC
^
o -
T ~"
i i
Pr or Posterior
M InkUrnTCOGC
Pror
*
Posterior
•<* -
""*• „
1
K>
f
31
*-
1 I
Pror Posterior
M InkMetTCAC
*
^ _
O —
T ~
i i
Pror Posterior
M InkDCVGC*
Pror
i
Posterior
i ~
r-~
" ^^
Pr or Posterior
M InkBiieC
t j |
1 | =•
Pr or Posterior
M [nkNATC*
E^
Pr or Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-27. Prior and posterior human population mean parameters
(Part 3).
A-lll
-------
Human, seqpriors.vt
iE
3
Prior Posterior
V QLivC
p
i
3
Prior Posterior
V FracPlasC
F
3
o
CM
d
o
o
d
o
o _.
o
**.
o "™
d
o"
o
i
d
(D
d
d ~
E
Pr or Posterior
V QSlwC
Pror
V VF
Pr or Posterior
V VRapC
F
3
O ™
o
CM
O —
d
Pr or Posterior
V VBIdC
F
3
Prior Posterior
V InPUvC
E
E^
^
CO
O —
o
S.
o"
8-
d
d
d
d
CM
O
i
Posterior
atC
E|
3
1 !
Pr or Posterior
V VRespLymC
Pror
V
^
3
d "™
o
o
o
o ~
d
o
o —
d
o
o —
d
o
d
o -
d
r-t
O ™
d
^
o _
d
s.
d
o —
o
1
Prior Posterior
VJnDRespG
Prior
V VG
3
!
Posterior
utC
E|
3
i
Prior Posterior
V VRespEffC
i
Poslerior
nPBC
o
d
d
fM
O —
d
^
3
i
Prior Posterior
V InPFatC
E
Pr or Poslerior
VJnPRapC
E
^E
3
to
d
d
d ~
EE
3
in
CD
if)
p ~
d
o
O ~"
o
o
CD
CD
q -
d
o —
d
o —
d
q -
d
O ™
o
LO
d
d ~
d
Pr or Posterior
V QKdC
Prior
V
VL
3
Posterior
vC
E
3
Pror Posterior
V VKrJC
E
E
Pror Posterior
V InPGutC
E
i
Prior Posterior
VJnPRespC
E
3s±a
d
o
d
to
O —
d
o —
d
EE
E
Pr or Posterior
V InPKMC
E
=±
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-28. Prior and posterior human population variance parameters
(Part 1).
A-112
-------
8-
Human.seqpriors.v1
V_lnPRBCPIasTCAC *
Prior Posterior
V InkDissocC
Prior Posterior
V InPBodTCOGC '
Prior Posterior
V inkASTCOH
Prior Posterior
V InFracTCAC*
Prior Posterior
V InKMWdDCVGC
Prior Posterior
V InBMaxkDC
Prior Posterior
V InPLivTCOGC *
Prior Posterior
V InVMaxC
Prior Posterior
V InCIDCVGC *
Prior Posterior
VJnVyaxLungllvC *
VJnPBodTCAC
Prior Posterior
V InPBodTCOHC
Prior Posterior
V InPeffDCVG
Prior Posterior
V InCIC *
Prior Posterior
V InKMDCVGC *
Prior Posterior
V InKMCIara *
VJnPLivTCAC
E
3^
p
Prior Posterior
V InPLivTCOHC
E
3&
^
Prior Posterior
V InkASTCA
^
^E
P
Prior Posterior
V nFracOtherC*
Prior Posterior
V nCIKidDCVGC
Prior Posterior
V InFracLungSysC
E
3E
P
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-29. Prior and posterior human population variance parameters
(Part 2).
A-113
-------
Human, seqpriors.vl
V InCITCOHC *
V InKMTCOH *
V InCIGlueC "
Prior Posterior
V InkMetTCOHC*
Prior Posterior
V InkUrnTCAC *
Prior Posterior
V InkMetTCAC*
Prior Posterior
V InkBileC
Prior Posterior
V InkEHRC
Prior Posterior
V InkUrnTCOGC *
Prior Posterior
V InkDCVGC
Prior Posterior
V InkNATC
Prior Posterior
V InkKidHoactC
Prior Posterior
Prior Posterior
Prior Posterior
Prior Posterior
Thick lines are medians, boxes are interquartile regions, and error bars are (2.5,
97.5%) CIs. Parameters labeled with "*" have nonoverlapping interquartile
regions.
Figure A-30. Prior and posterior human population variance parameters
(Part 3).
A-114
-------
A.5.2. Comparison of Model Predictions with Data
Time-course graphs of calibration and evaluation data compared to posterior predictions
are shown in Figures A-31 to A-35. For each panel, the boxes are the experimental data, the
solid red line is the prediction using the posterior mean of the subject-specific parameters (only
shown for calibration data), and the shaded regions (or + with error bars, for single data points)
are bounded by the 2.5, 25, 50, 75, and 97.5% population-based predictions.
A-115
-------
A.5.2.1.
Mouse Data and Model Predictions
Abo « el a I S7a Male Uou»
1200 pig/kg TCE Ora I gavage I all
ADM* si al 57a Mile Mouse
1200 mgftg TCE Oral gavage (odi
6 o
< *-'
o
20 40 60 80 100 120 140
t|hr)
Abbas etal 97aM»le Mouse
I200m;|fkgTCE Orjl gsvage loll
0.5 1.0 20 5.0 20.0
t (tin
Abbas e! •!. 97. Mate Mouse
1200 mgftg TCE Oral giveg* Iwl)
0.5 2,0 5.0
l(hrl
Abbes el al 97a Male Mouse
1200 mg/kg TCE Oral gavage fdD
0.5 2,0 5.0
t(hr)
AlJtas >l al 97. Mate MOUM
' I'OO m»'Kg TCE Oral gavage (o-l)
?:
If.
~ ?
5 -
Abba-, el al. 97a Mai. Moun
1200 mg/kg TCE Oral gavag* loil}
05 10 2.0 50 10
Uhr)
Abbas el al 97a Mate Mouse
1200 nig/kg TCE Oral gavage (ad)
-
05 1.0 30 50 100
HhcJ
Abbatelal 97aMal>Mou»
2000 r-.iv'kn TCE Oral gavage lo.ll
05 10 50 200
Uhr)
ABbat el al 97a Male Mouse
2000 ms/kg TCE Oral gavege (o*l
EH
i"
mglkg TCE Oral gavage roil)
- 0.1 0.5 20 50 200
Abbn-,et«l 97iMals Mou»
1200 ma*g TCE Orsl gnuage icil:.
0.5 1.0 2.0 5.0 20.0
l(hr)
flbbas «t al 97a Mala Mouse
1200 mo*9 TCE Onl savage (oil)
0.5 1 0 1.0 5.0 10.0
I (fir)
Abbas el al. SJa Male Mouse-
1200 mgfrg TCE Oral gavage (oil)
20 40 60 SO 100 120 140
Khr|
Abbas et al S7a Male Mouse
2000 moAg TCE D.ol gauage [oil)
50
100
toin
1SO
0.5 10 20 5,0
20.0 500
OS 10 2.0 5.0 100
Figure A-31. Comparison of mouse calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions).
A-116
-------
Abbas el fll. 97ft Male
20QQ ingjitg ICE Orel r, wiqe i&ij
Abbes «t al S7a Male Mouse
2000 mgrttg ICE Oral garage (
2000 mg/kg TCE Ornl gavege (oil)
0.5 10 2.0
Abbas el at. 97a Mole Mouse
2000 mgrtg TCE Oral g
0.5 1.0 20 50 10.0
i (hi)
Abbes el al 97g Male Mouse
7000 moAg TCE Oral gavage (Oil)
r.
ECM
il-i
0.5 1.0 JO
SO 100
Abbii « al S7a Mite Mome
2DDD mgAg TCE Oral gavH^fl [al'i
05 10 20 5.0 10.C
Ml")
Abw, el 31 e?i Wale Mots*
2000 mgfltg TCE Orjl gjvsge I oil)
rJ
sl
vage (oil)
0.5 1.0 2.0 5.0 10.1
t(tir>
Abbas ft al 97a Male Mouse
300 mgAg TCE Oral g«vage (oil)
0,5 10 2.0 5.0
200 500
Abbts at al- 97a Male Mouse
2000 mgAg TCE Oral gavage (oil)
! *
I*
20 40 SO
fiO 100 120 un
t(hr)
Abbes PI al 97a Male
300 mgftg TCE Oral gavage •;o-'j
0.5 1.0 2.0 50 2C
tfliO
Afalwseial 97a Male Mouse
iOO mgAg TCE Oral gsvnge (oil)
Q.5 1.0 2.0 5.0 10.0
Iftri
0.5 1 0 2.0 5.0 10,0
I(hr)
Abba-. »il 97i Male Mouvi
2000 mg/kg TCE Oralgavage iml.
50 100 150
t(hr)
Abtjn^Kal 57a Male Mouse
300 mgritg TCE Oral gavage (oil)
1.0 2.0 5.0 100
lint i
AJiba^ et al. 97a Male Mouse
300 mg/Vg TCE Oral gavage (OH)
0,5 1.0 2.0 50
tOti
200 50.0
Figure A-31. Comparison of mouse calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-117
-------
AbbHietal 97a MaleMoUse
300 "Ki.'Xg TOE Oral gavage mil-
Abbas et D| 97a Male Mouse
300 mgjHg TCE Oral g*vag« foil)
Abbas BE al 37a Male Mouse
300 mg/Kg TCE Orel gavagg (oij
It-
fe
05 10 20
Abbas et it 57n Male Mou»
300 mgAg TOE Oral gavage (all)
£5-
tf
I*.
0.5 1.0 20
I (hi)
Abbas el il 97a Male Mouse
600 mgAg TCE Oral gavage (oil)
Bi-
ll
05 10 2.0 5.0
AbbDS et al 97a Mule Mouse
GOO rrigAg TCE Oral givage (oil)
fl
111
0.5 1 0 2.0 50 10.0
Abbas et el 97a Male Mouse
600 mgAg TCE Oral gavage (oil)
£ °
II
0.5 10 20
05 10 2.0 5.0 10J
t(hi)
Abbas et nl 97j Male Mouse
300 mgAg TCE Oral gavage [oil)
Is-l
20 40 60
80 100 120 140
t ihr)
Abb«».»l 9?s Male Mouse
BOO nigAg TCE Oral gavage i.oit
05 1.0 20 50 19,0
tfhr)
Abbas et al 97a Male Mod so
600 mgJkg TCE Oral g*vag* (oil)
Is"
0,5 10 2.0 5.0 20.0
tfhrj
Abba^elol 97«M«le Mouse
600 m^flcg TCE Onl gavage (oil)
£ o
II
5 0 10 0 '-
1.0 2.0
Mb!)
Is.
1.0 20
t(t»>
5.0 100
Abbas X al 97* Male Mono
eiccreted in urine frng TCOH equi
12 5 10
ii ii
_»••••
i • •
20 40 60
f:
11"
5 :
o ,
80 100
t (hr)
Abbas il al 97a Male Mouse
600 mo/kg TCE Oral gavaga lo:i>
0,5 10
3,0
t(hr)
Abbas « al 97a Male Mouse
GOO mg/kg TCE Onl givigii
-------
Abbas el •)! 3"-
u *"
5*>-
p 0
S°-
o n
E" -
i°-
s
|o_
s"
. • • •
•
•
20 40 60 60 100 140
t (hn
Abba'- M al. 97b Mate Mouse
100 mrjKg TCDH iv
O 1 I I I
o 20 40 60 80 100 140
I {ho
Fisher etal 91 Female Mouse
42 ppm TCE 4 hr inhalation
L.
0.05 0.10 0,20 O.SO 1.00 2.00
1 (hr]
Ftsher et a I. 91 Female Mouse
235 pptn TCE 4 hr inhalation
3 4 5 6 7 S
16*3
Fisher et at 9! Female Mouse
236 ppm TCE 4 tir inhalation
18-
Is-
-i
fisher et >l 91 F>m>le Mru5c
369 ppm TCE 4 h! inhalation
2.0 3.S 3.0 3.5 4.0 4.5 50
I (hi)
Fisher et at 91 Fumale Mouse
389 ppm TCE 4 hr inhalation
2.0 2.5 3.0 35 4.0 45 5.0
I (tin
o~
5 10
t(hil
0-5
Abbas et al. 97b Male Mau
1DO mg/kgTGOH iv
fs-i
005 0.10 0.20 050 1.00 Z.OO
Frsher et al 61 Female Mouse
42 ppm TCE 4 h- Inhalation
*«.
E O
2.0
I
i -
*S-
2.5
3.0
l(hr)
3.5
Fisher et al. fli Female Mouse
368 ppm TCE Jhr inhalation
5 10
tint]
Fnher et al 91 Female
BBSppmTCEIhrmr
20 25 3.0 3.5 4.0 4.6 50
I (hi]
Figure A-31. Comparison of mouse calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-119
-------
1100 ppin TC E Cloud Ch I mtw
F,-f.er et al 91 Female Mause
300 gpm TCE Cloced Chimber
Fishflr et B| 91 FefnalB Mcuse
370D mm TCE Cnnw Ctumbir
123
Mir)
FisHer elftl 91 i-?n-n t. Mouse
700 ppm TCE Closed Chambei
E o
r:
.5 HI-
0.5 1.0 1.5 2.0
t(hr)
M'-hErelel 91 Male Mouse
110 ppm TC E 4 hr Inhalation
J!.n-
So-
2.5 3.0 3.5 4.0
t Ihrl
Fishei IN 01 31 Mate MO-J-.P
366 ppm TCE 4 hr inhalation
Fjsh&r el al. 91 Male Mou
748 ppm TC E A h r inhral a
2.D
25 3.0 35 4.0 4.5
MM
D5 1.0 15
1(1")
F'5he' et a). 91 Female Mou-ie
7000 ppm TCE Closed Chamber
Fisher el Hi 91 Male MDLJSB
297 ppm TCE 4 hr inhalation
10 15 20
tlhtl
Fisher et al 91 Male Mouse
388 ppm TCE 4 hr inhalBbon
2.0 25 30 3.5 4.0 4.5
tthr)
Fhher et al 91 Male Mouse
1BDO ppm TCE Closed Chamber
05 10 1.5 20 2.S
r SI-
1 2 3 4 5 B
Khr)
Fisher et .1 91 Male Mouse
110ppni TCE 4 hr inhalation
Fisher Et al. 91 Male MOUSE
237 ppm TCE 4 hr inhalation
20 2.5 3.0 3.5 4.0
t [hrj
Fisher el al 91 Male Mou»
74B ppm TCE 4 hr inhalation
5 10 J
Khr!
Fixherfllal 91 Male Mouse
1DDQ ppm TCE Glased Chamber
0.0 05 10 1.5
tlhr)
Figure A-31. Comparison of mouse calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-120
-------
el al 91 Mala M
ffi TCE Close
Fisher el al. 91 Male Mouse
3800 pprn TCE CIo»d Chamber
Fisher et ol SI Mule MOUSB
SfiOO ppm TCE Closed Clumber
0,0 0.5 1.0 1.5 2.0 2.5 3.0
Hhr)
FisherelRl 93 Ferrate Mouse
2GQQ mg/kg TCE Oral gavage (oil)
1234
1(1")
Fitter et al 93 Fernalv Wau^c
2000 mg/Vg TCE Of 91 gavage l
005 0.20 100 5.00 2000
Fisher Bl al. 93 female Mouse
457 mg/kg TCE Orel govsge (c*
F.Ehe: Bl at 93 Female Mouse
973 mg*g TCE Oral gav.g. (oil)
1.1 02 0.5 1.0 2.0 0.1 O.S 2.0 5.0 200
' ir,!if i et al. 93 Ma*e Mouse
30QQ mgftg TCE Oral gavagE inl)
Fisher et «l 93 Male MOUM
200D mg/kg TCE Oral gavage (oil)
O.OS 0.20 1 00 5.00 20 00
i OBJ
Fishef et Bl. 33 Male Mdyit
487 rng.'Kci TCE Oral gavngo (trf)
0.05 020 O.SO 2.00 500
Ithr)
Fisher et al. 93 Mate Mouse
973 mgftg TCE Oral gavage (Dill
18-
•a
:
05 10 1.5 2.0
005 0.20 1.00 500 2000
1234
t(hr)
487 mg/kg TCE Oral savage «nl)
0.5 2.0 5.0
Khri
Fisher e1 al. 93 Female Mouse
973 mg/kg TCE Oral g
E •"
8 :
0.1 02 05 1.0 2.0
t [hr!
Fisher el al 93 Male Mouse
487 mg/kg TCE Oral gavage (rj'J)
0.5 20 50 JO.O
Ktiri
Fisher at al 93 Male Mouse
973 mg/kg TCE Oral gavaga (Dd?
E *n
1 1 1 1 i 1 1—
O.D5 020 050 2.00 5.00
tlht)
Figure A-31. Comparison of mouse calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-121
-------
i
(.•.-<) 01-0
HOOi 6uil auun a
1 "
w *
» °-
go
•LJ
>
B-
5
Ss-
1
B0_
r
£
*
u
£ o-
tf
I
0 -
r-
is-
£
!r
§
o
1
g*-
e«-
ir
B g< i an
T
•
1
f
1
15 20 25 30
l(hr]
Green a H 96 Mile Menu
£00 rr,9.> u TCE Oral gawftge fad]
1
I
I
15 20 25 30
I (hr)
Prr.u1ot.3l B^MalcMw«*
1 000 mi^Jtg TCE Onl gBUagt («li
B==:^^
•^^
0.5 1.0 20 50
t(hn
«
o_
o
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o
o_
i o
?«-
§rJ-
|g.
|
5 T*^
8.0-
•Q
S ui —
A
LU O-
f
jS-
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• 0
i"
I"
I5"
I
LU 0-
s
t
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S.C&-
i *o-
II
I
UJ O —
s -
TCA in blood lir
-02 5e+00
jj
Grpen *t 01 8$ M«l* Mou»
10 ms/kg TCE Onlg»>g> ;c..n
I
30 40 50 60 70
ttho
Green Bt al 8S Male Mau»
tDCO m^kg TCE Oral g»v»q« I0rf>
• •
30 40 50 60 70
Mhr>
Gr*en «t al a$ Male Mouse
2000 mjlkg TCE Oral givnjt loin
• •
30 40 50 60 70
Iff!')
Green at al. 96 Male Moute
600 mgAg TCE Oral gavage (oil)
30 40 50 60 70
tlhr)
Prout *t al, 85 Male Mouva
1000 mgrVg TCE Oral garage tout
^^^^^^^V
— ^
12 5 10 20 S
I (hi)
fS'
•
. : O
5 "rti~
c O
TJ
is
•
<
- O_
Q
e»
£
«-
r*-
*--
o-
d>-
«-
p-i-
OJ-
<--
!-;
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E"
s -
I
S,s"
1
55-
II-
rcOH m blood rmgfl
-02 1e+00
3 5*°
i vwi rial. SSMflio MQUS«
10 rngflfg TCE Oral pvigi (oil]
1
15 20 25 30
Uhr)
Gtaei at al -.':';• Mala Mduse
1 000 mg/kg TCE Oral gsvage roili
T
15 20 25 30
tlhr)
Grvwi A al 85 Mate Mou»
2000 mg/Hg TCE Orvl guvetgB (ail)
I
15 20 25 30
t(hr)
OMI « al .86 Male Mouw
&00 my^Kg TCE Oral gauage (ad)
1
15 20 25 30
tlhn
Prouletal 8SMale Mouw?
1000 mq/Kg TCE Orel gavegp (oil)
0.5 1.0 Z.O 50
I On]
Figure A-31. Comparison of mouse calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-122
-------
Prcirl
-
•5 -
e .
!§.
5 :
e-
et ol 99 Male Mouse
100 ppm TCE4hr mhalstjon
20 2.5 3.0 35 40 45
:
-------
Greenbergelal. 99 Male Mouse
600 ppm TCE 4 hi inhalation
g el at 99 Male Mo
600 ppfr, TCE 4 h
Greenberg et ol 99 Mai* Mouse
600 ppm TCE 4 hf inhalation
-J
So
o 5
E *~
i •
10
tfhr)
erg et al 99 Male Mouse
500 ppm TCE 4 hr inhalation
> ^
n-\
IT.
t(hn
Larson Wat. 92a Male Mouse
187 mgftg TCE Oral givigg (aq)
o-
E
I
I:
0,3 0.4
0.5
tfhrj
0.6 0.7
{.arum et al 92a Male Mcuse
2-000 mgftg TCE Qral gavage
-------
5 ;
£ SI
ss:
s!
Larson et n 92b Male Mouse
20 mgrttg TCA Onil gavaga (aq)
0.6 1.0 2.0 50 1
tOir)
Merdrt H Bl 93 Mile Mouse
IQOmgAg TCEiv
05 1.0
t din
Templin « II 93 Male Mouse
£00 nigAg TCE Oral gavage (aqj
LnrsDoetnl 32h Male Mouje
TCA Onl g»vige [iql
Merdinkelal. 98 MQlB
100 mgAg TCEi
OS 1.0 20 50 100
tlhr)
Templm et al S3 Male Mouse
500 mg*i9 TCE Oral gavaoe (aq^
0.5 1020 !
t(hr)
Templln el al 95 Male Mouse
500 mgflig TCE Oral gavagp (nq)
!"I
S°-
c O
•I '"_
ID
tlhn
0.5
1.0
MM
2.0
Figure A-31. Comparison of mouse calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-125
-------
A.5.2.2.
Rat Data and Model Predictions
Bernauer et al 96 Male Rat
40 ppm TCE 6 hf inhalation
I,.
So
8
I
8 ^
k- T-
I
15 20 25 30 35 40 45
Bernauer et al 96 Male Rat
80 ppm TCE 6 hf inhalation
I
15 20 25 30 35 40 45
Mhr)
Bernauer et al 96 Male Rat
160 ppm TCE 8 hr inhalation
SH
- 2-
8
g
15 20 25 30 35 40 45
l(hr!
Dallas etal 91 Male Rat
50 ppm TCE 2 hr inhalation
1
BenaLetetal 96 Male Rat
40 pprn TCE 6 hr inhalation
15 20 25 30 35 40 45
' (hr)
Benaijer et al. 96 Male Pal
90 ppm TCE 6 hr inhalation
'= °
1°
fc
15 20 25 30 35 40 45
t(hr)
Bemeuer et si 9B Male Rat
1
-------
Fisher etal B9 Female Rat
300 ppm TCE Closod Chamber
Fitfi er et at. S9 Fema le Rat
5100 ppm TCE CJosed Chamb
Fisher et al 91 Female Rat
600 ppm TCE 4 hr inhalation
I-
I I II I I I
0.2 0.4 0.6 0-8 1.0 1.2 1 4
Fisher rl at. 91 Female Rat
fiDD ppm TCE 4 hr inhalation
c
5 10
t*r)
20
Green etal. 85 Male Rat
=00 rngdig TCE Oral gavage (oil)
2 5 10 20 50
t»t)
Fisher st al 91 Male Rat
505 ppm TCE 4 hi inhalation
sS-
UJ 'c~
O
Fisher et al. SI Female Fiat
600 ppm TCE 4 h r in h alatjon
Green Bt
lOmgfkgTCAiv
fg
•E P-
£8-
g1
5!
3:
I
15
25
t(hr}
30
Green etal.85Male Rat
500 mgfcg TCE Oral gavage rnilj
3
i
SI-
5 10
t(hr)
20
Fisher et al 91 Male Fiat
505 ppm TCE 4 hr inhalation
10
20
Green et al. 85 Male Rat
75 nig/kg TCA Of a! gavage (aq)
If 0
m
S
£ "
•n
a T
~ 0 —
^ p-
1
* 0 1 1 1 1
g 15 20 25 30
20
25
30
20 25
30
urine inn mo 1]
0.20
IS °
COHB
?g_
Green et si. 85 Male Rat
£00 riig '-9 TCE Oral gavage (D|) bile cannulated
1
-
15
20
30
Green et al. 85 Male Ret
SOO mgrVg TCE Oral gavage (oil) bile cannulated
s-
s-
o-
If
i-a
18
I*'
•S M
UJ O
'
15 20 25 30
ttf")
Prout etal. 35 Male Rat
1000 m^hg TCE Oral gavage [oil)
05 1.0 2.0
5.0 100
Prautetal 85 Male Rat
1000 mgAg TCEOralgovageioil)
10
tlhrl
20
Figure A-32. Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-127
-------
Proutetal 85 Male Rat
1000 mgAg TCE Oral gavage (oil)
E
I -
is-
t(ht|
HIssinkBtal 02 Male Rat
10mg/kgTCEiv
0.5
1.0 2,0
tdic)
5.0
Hissmk et al 02 Mile Rat
100 ms*g TCE Oral gavage (oil)
20 50
HhrJ
100
Hissiik et al. 02 Male Rat
1000 mg/kg TCE Oral gavagefol
0.5 1.0
20 5.0 10.0
Hhi)
Kaneka et al 94 Male Rat
50 ppm TCE 6 tu inhalation
20
30
Klii)
Prnut et al. 35 Male Ral
IQQOrnrj'kg TCE Oral gavagg [oil}
"3 ^
•s ^_
cla! KMale Ra-
75 msiha TCE iv
20
too
HrsBinfc et at. •:: i.'i.j- Rat
100 ma*g TCE Oral flavaga (oil)
05
10 2.0
t(hr)
5.0
i"in;."j et al. 94 Msle Rat
50 pprn TCE e hr intialatiorr
20
30
40
— Kanekoetal. 94 Male Rat
100 ppm TCE 6 nr inhalation
g
1*
.s
r-
1
« o-
/
[^
£=^8 i •
10
20
M
Pi-
1"
I 8"
3 IT>-
£ °
•a
3
si.
« °-
a
m
go-
unketal 02 Male Rat
10 mgAg TCE iv
• I I Ik
) 20 50 I
t(hr(
Hissinketal 02 Male Rat
75 mg/kg TCE iv
05
1 0 2.0
I (lir)
50
Hissink et al D2 Male Rat
1000 mg/kg TCE Oral gavage (oil)
10
20 50
tttir)
100
Ksneko et al. 94 Male Rat
50 ppm TCE 6 hr inhalation
10
Ksnehoetal. 94 Male Rat
100 ppm TCE 6 tir inhalation
S
t(hr)
Figure A-32. Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-128
-------
Kaneka et a\ 34 Male Rat
100 ppm TCE 6 hr inhalation
Kaneka etal 94 Male Rat
1000 ppm TCE 6 hi inhalation
Kaneko et al. 94 Male Rat
1000 ppm TCE 6 hr inhalation
10
20
30
3
i- a-
II:
3
!>'
« 01
.-. o
I?.
.£. QJ
0.1
Kaneka et al 94 Male Rat
1000 ppm TCE 6 hr inhalation
30
40
Kaneko et si S4 Male Rat
500 ppm TCE 6 hr inhalation
20
40
Keys etal 03 Mole Rat
8 mg/kg TCE ia
0.2
0.5
Hhrj
10
ysKal. OSMale Rat
8 mg/kg TCE la
0.5
tlhr)
I.O 2.0
^
D>
e
.2
t»
.6 '
Is-
IP-
8
20 30 40
1(1")
Kinekoetal. 94 Male Rat
500 ppm TCE S hr inhalation
20
30
t (hr)
Keys et . 03 Male Rat
8 m^lty TCEia
!!-
i i i i r i i
0.1 0.5 2.0 5.0 20.0
t(hr>
Keys et al. 03 Msle Rat
a mgykg TCE ia
I'-
ll-
0.1 02
0.5
1 0 2.0
Keys et al 03 Male Rat
50 ppm TCE 2 hi inhalation
8 si
o.i o.:
0.5 1.0
5.0
7 6 9 10 11
t(hr|
Kaneko et al. 94 Male Rat
500 ppm TCE 6 hr inhalation
Key; et al 03 Male Rat
8 mg/kg TCE ia
I*
£ -
.E 5
gt:
8-
I
0.1 0.2 0.5 1.0 2.0
tlhr)
Keys etal. 03 Male Rat
8-
0.1 0.2
0.5
MW
10 30
Keys et al. 03 Male Rat
50 ppm TCE 2 hr inhalation
a-
0.5 1.0 2.0
tlhr)
Figure A-32. (Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25
50 75, and 97.5% population-based predictions) (continued).
A-129
-------
Keyi Etal. 03 Male Rat
50 ppm TCE 2 hr inhalation
Keys el al. 03 Male Rat
50 ppm TCE 2 hr inhalation-
KeysetBl. 03 Male Rat
50 ppm TCE 2 hr inhalation
0.5 1.0
tflitl
Keys e»al. 03 Male Rat
60 pern TCE 2 hi inhalation
n-
0.1 0.2 0.5 1.0 2.0
tdlil
Keys a B| 03 Male Ral
500 ppm TCE 2 hr inhalation
50
I1
S-8.
0.1 Q.2
0.5 1.0
t(hr)
2.0
5.0
Keys etal. 03 Male Rat
500 ppm TCE 2 hr inhalation
0.1 0.2
05 10 2.0
HIM)
Keys Etal. 03 Male Rat
mgfltg TCE Oral gavage (aq)
5.0
i?:
0.1 0.2
05
t(hi)
1 0 2.0
•§
E .
s -
—i—
0.2
—I 1—
0.5 1.0
'(1*0
—\—
20
Keyset al 03 Male Rat
500 pprn TCE 2 hr inhalation
I
01 0.2
0.5 1.0 20
t (hr)
5.0
Keys ..! al 03 Male Rat
500 ppm TCE 2hrinhala«m
IB"
J+ •
$
0.1 0.2 0.5 1.0 2.0
t [hr)
Hoys et al. 03 Male Rat
Q mg/kg TCE Oml gavage (aq)
5.0
in »
^ -\
03
?,
0.1 0.5 2.0 50 20.0
tlhrl
Keys et al 03 Male Rat
3 mgrttg TCE Oral gavage (aq)
0.1 0.2
0.5 1.0 2.0
0.5 1.0
tlhr)
2.0
Keys et al. 03 Male Rat
500 ppm TCE 2 hr inhalation
ul rj_
2.0 2.5 3,0
3,5 4,0 45
tlhrt
Keys etal 03 Male Rat
500 ppm TCE 2 hr tnhalalion
5 0
s.
= '"-
0.1 0.2
0.5 1.0
ttht)
2.0
5.0
Keys etal. 03 Male Rat
8 r:VVi TCE Oral gavage /
01 02
0.5
tltir)
2.0
Keys et al. 03 Male Rat
8 mg/kg TCE Oral gauage (aq)
?"
.6
05
t(h,(
Figure A-32. Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-130
-------
Keys et si 03 Male Rat
8 mgAg TCE Oral gavage (aq)
Kifnmefle Et al. 73 Male ft a
49 ppm TCE 4. hr ir>tialatii>
Kin-iir&rle et al 73 Mate Rat
49 ppm TCE 4 hr inhalation
0.2 0.5 1.0 2.0
t(hr)
KimrneTle et al. 73 Male Rat
4fl pcm TCE 4 hr inhalation
I§H
S1
IsJ
35 40 45
tOlr)
Kimmerle et al. 73 Male Rat
175 ppm TCE 4 hf inhalation
567
9 10 11 12
Kinnmeile et al. 73 Male Rat
330 ppm TCE 4 hr inhalation
10 11 12
Kimmetlp et al 73 Male Rat
3000 ppm TCE 4 lit inhalation
4.2
4.4 46
I (hi)
50
3--
axcreted in urine (mg 1
012345
i i i i i i
1 •
35 40 45
t(hr)
KimmeflBstal
MppmTCE4 h
KimniBrte at al 73 Male Rat
17S ppni TCE 4 lir i
I10"
'i
E ro-
S rsi-
r , i
30 40 50 60 70
t(nr)
Hi -nn n-1 e e I a I .'3 M.j|p R Bl
330 ppm TCE 4 hr inhalation
f
E UD-
a
T3 •V-
H rj-
S
1- 0-
r
"
30 40 50 60 70
t(hr)
Larson et B\_ 92A Male Rat
20 irig/kg TCA Oral geiuage (aq>
0.5 1,0 30 50 10.0
i s~
(haled post-«x
02 0.4
u °
i-
^=J
1 1 ) 1 1 1 <
j.I) 5.5 6.0 6.5 7 0 75 BO
Kimmerie et al 73 Male Rat
'| 175 ppm TCE 4 hr inhalation
S"
e (mg TCOH
15 20
cretad in urin
5 10
£ 0-
• • •
0 30 40 50 60 70
ftflti
A Kimmerie et al. 73 Male Rat
'| 330 ppm TCE 4 hr inhalation
a
X
o
E _
|^_
^ ir>-
lo-
• • •
rj} 1 1 1 1 1
0 30 40 50 60 70
S t thr)
Kirnmerl* et al. 73 Male Rat
3000 ppm TC E 4 hr i nhal ation
4.2
44 4.6
Hnf)
5.0
Larson et al 92A Male Rat
100 tng/kg TCA Oral gavage (a
_g-
^:
0.5 1 0 2.0 50
Khrl
Figure A-32. Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-131
-------
Larson etal. 92BMale Ret
197 mgftg TCE Dial gavage (aq)
La'san et B\. 92B Mais Rat
197 mgftg TCE Oral gavagg
Larson el el 92BMale Rat
592 mg/kg TC E Oral gavaga ia
20
5
UNO
10
Lee et al H>A Male Ral
IBmgdigTCEiv
0.02
0.04 0.06
Ml")
•.-•:•,:..-• --I .-.• 1,1.•!- i::.'
100 msftg TCOH ivf_aq
00«
5 S
tdir)
I -
o ^
A o
1 *'
si
Larson et al- 92B MaJe Rat
3000 mg/kg TCE Oral gavage faq)
2 5 10 20
tlhr)
Larson etal 92BMalQ Rat
592 mg/kg TCE Oral gavage «aql
10
tthr)
Lee et»l. DOA Male Rat
16 mg/kg TCE pv
002
0.04 O.OS
Ktirt
0.08
Proutetil as Male Rat
10mgAg TCE Oral gavage (oil)
nd TCOH accreted in urine i
000 0.004 0.008 001
i I I I 1 1 I
g ° 30 40 50 BO 70
K Mho
Figure A-32 Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-132
-------
Prout etal. 85 Mate Rat
iQmg/kg Tr.E Oral govage foil)
S
35
trtir)
Prautetal. 85 Male Rat
5DO mpAg TCE Oral (javageM)
30
40
50
t(hr|
SO
70
Proutetal aSMaleRal
10 mg/kg TCE Oral gavage (oil)
40
50
(Art
SO
Pi out etal. 85 Mate Rat
2QOO fng/kg TCE Oral gavage (ol)
30
40
50
70
Pt out el a I 85 Male Rat
mg/Hg TC.E Oral gsvagg (oil)
—I—
40
—I—
50
Khr)
—i—
SO
Proul et a\ 35 '•.
IQQOrng'kg TOE Oal gavago [oil>
u
T3 O
3C 40 50 50
Prtrutetel SSMaleRal
500 mg*g TCE Orel gflvnge (o«lj
P
« o
" rN
25
30
v
Is-
O
i§-
i
8-
is.
I"-
35 40
t (hr)
45
1000 mg/kg TCE Oral gavage ^il>
30
50
tlhr)
70
Prouletal.
Omg/)tg TCE Oral savage (oilI
40
50
70
c Et al. D2 Mala Ral
2000 ppm TtE 1 hJ mhala(iot>
0.5
1.5
2.0
Praul Btal.SSMale Rat
1000 mg/KgTCE Qralgavage (oil)
25 30 35
Prout etal. 85 Male Rat
10 mg/kg TCE Oral gavage (oil)
30
8-
40 50
(Ihr)
SO
Proutetal 9S Male Rat
1000 tng/kg TCE Oial gavage foi
30
40
50
tlhr)
60
Prautetal.86M«leRat
GOO nig/hg TCE Oral gavngB
30 40 50 BO
tlhrt
Simmons et al 02 Male Rat
2000 ppm TCE 1 hr Inhalation
0.5
1 0
i (tin
70
70
Figure A-32 Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-133
-------
Simmons et al. D2 Male Rat
2000 ppm TCE 1 hr inhalation
Simmons et at. D2 Male Ra!
4QQO ppm TCE 1 *w mhalatiort
Simmons et al 02 Male Rat
4000 ppm TCE 1 hrinhalalion
LU *rt
p«n
s-
0.5 1.0 1.5
t(hr)
Simmons et al. 02 Male Rat
4000 ppm TCE 1 hr inhalation
0.5
1 0
1.5
Simmons et al 02 Male Rat
200 ppm TCE 1 hr inhalation
0.5
10
1.5
Simmons et al. OZ Male Rat
3000 ppm TCE Closed Chamber
I 2 3 4
t(hc)
Stenneretal.97MaleRat
IQOmgJkgTCE id
2,0
2.0
0.5 1.0 20 5.0
t(hr)
0.5 10 1.5
KM)
Simmons et al. 02 Male Ra!
300 ppm TCE 1 hr inhalation
05
1.0
1.5
Simmons et al 02 Male Rat
100 ppm TCE Closed Chamber
2
t(hr>
Simmons et al. OZ Male Rat
500 ppm TCE Closed Chamber
1234
t(hr)
Stenner Et al. 97 Mele Ra
1QOmg/kg TCE id
0.5 1.0
2.0
5.0
a-
fsl
05 10 1 5
t(hr|
Simmons et al 02 Male Ral
200 ppm TCE t hr inhalation
0.5
10
Mhr)
1 5
Simmons et al. 02 Male Rat
1000 ppm TCE Closed Chamber
012345
tlhr)
Slenneretal.87MaleRat
lOQmgftgTCEid
20
0.5 10
20 5.0 10.0
t(hf)
Stenner et al. 97 Male Rat
mg TCOH equiv
20 30
.E £-
•3 *r> —
« o—
u
o
D
K
IQOmg/kgTCOHfv
•
30 40 50 60
tlhrl
Figure A-32 Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-134
-------
Etenner EL Hi. 97 Male Rat
100 mg/yg TCOH iv
et al. 97 Male Re
100 rng*g TCOH iv
ennerelal 97 Male Rat
tOO rag/kg TCOfl iv
fa:
r-
§:
05
3?.
fl
£
» CN-
2.0 5.0 10.0 200
t(hr)
Stenner et al 97 Male Rat
Sterrner et al. 57 MaJe Rol
50
t(hrt
Stenner et al 97 Male Rat
6 mgAg TCOH iv bile cannulaled 5 mgfcg TCOH iv bile ceuinulated 20 mofcg TCOH iv bile cannulated
R
i
i
H-
!«-
S 0
3
5 °-
i °
So"
1- O-
1
S —
I—
|n-
*
'i N—
i
"S ^~
a
£o-
1
1 1 1 i 0 1 1 1 1 (B 1 1 1 1
30 40 50 60 30 40 50 60 p 30 40 50 60
toin MUD " Mhn
Blenne-r et al 97 Male Rat
20 niLj-ky TCOH (v bile cannulated
30
40
50
60
|
IS-
IS-
Hhc)
Tempkn et al 95 Male Rat
100 mgftg TCE Or»! gawagg (aq)
oo-
|
o ^
COG accrued in urine fmg TCOH tqu
0 5 10 15 20
I
30 40 50 60
Stenner etal. 97 Mate Rat
100 mg-'ftg TCOH iv t. IP cannulated
5ter>ner *t al. a? Male Rat
100 mgfcg TCOH rv bile cannulaied
30 40 50 SO
Templin et al. 95 Male Rat
100 mg/kg TCE Oral gavag^ (aq)
0.5 10 20 50 10.0
MM
0-5 1.0
M*r)
J.O
Stenner et al. 97 Male Rat
lOOmg&gTCGH iv btlecannulaled
t -
05 1.0
20 5.0 10.0 20.0
ttbr)
Templin et al 95 Male Rot
100 rng.'Kg TOE Oral gavage M
LI
1
c
S
0.5 1 0 2 0 50 20.0 50 0
IfH
Yuetal 00 Male Rat
I mg^Kg TCA iv
B Q_
*S-
0.1
0.5 20 5.0 20.0
KM
Figure A-32. Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-135
-------
Vu -:t si 'JL« Male Rat
1 mg/kg TCA tv
Yu at at. 00 Male Rat
1 mgftg TCA w
Yuetal 00 Male Rat
1 mg/kg TCA iv
o-
§§-
E ,--
ll:
0.1
0.1
~° T^'
!«.
01 O
58
as _
0.5
20 SO
200
Vu el al. 00 Male Rat
10 mg/kg TCA iv
0.5
2.0 50
200
Vu at al. 00 Male Rat
10 mg/Kg TC A iv
10
15
t(hr)
Yu at al 00 Male Rat
50 mg/kg TCA Iv
20
2.0
MM
200
sl
33:
S LO
32-
0.1
0.5
2.0 5.0
t(hr)
Vu 01 >l. 00 Male Rai
50 mgiha TCA iv
01 0.5 20 50 200
t.ffll)
Yu -- , i 00 Male Rat
IDm^kg TCAiv
10
15
1:.
0.1 0.5 2.0 5.0 200
tflh)
Yu otal. 00 Mile Rat
50 m^hg TCAiv
3°:
20.0
0.1
15
MhO
Yustal OOMaleRuJ
10 mg/kg TCAiv
0.1 0.5 2.0 5.0 20.0
Uh<)
Yuetal 00Male Rat
SO m^gTCAiv
0.5
2.0 5.0
20.0
Figure A-32. Comparison of rat calibration data (boxes) and PBPK model
predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-136
-------
LeesHI 96 Male Kit
D-71 mg/kg TCE la
Lee el al. 96 Mil? Rat
S nigftig TCE .n
Lee a! at. 96 Male Rat
16mgfligTCE m
&•=<
§ j^
0.02
Lee eta I 96 Malt R si
071 mg/kg TCEiv
0 10 0.5U
Leaetnl 96 Mule Rat
3 mgAg TCE [v
1000 *- 002
010 050
r(hr)
2.00 10.00
BmgflcgTCEi.
0.05 0 20 0.50 ;
tM
Lee Bt al. 96 Mate Rat
16 mgrtnj TCE w
Leeetal 96 Mole Rat
54 tng'kg TCE iv
'.10 0.50 2C
Ktir)
Lee e! al. 96 Male Rat
0.71 rngfrg TCEp«
l?
a,;
2
E
"- 0,02
050
tffirt
200
1000
0.01 0 05
0.50
Ml")
LseeUI 96 Mali Rat
Bmgthg TCE pv
Lee etui 96 Male Rat
1 1 1 1 1 1
0.05 0 20 0 50 2,00 5,00
I (tir)
Lee etal. 93 Malt Rat
64 mtfkg TCE pv
- 0.02 0,10 0.50 2.00
t (tin
Lee si 3 96 Mali Rat
8 iTtg/kg TCE Orel gavage (aqt
10.00 - 0.02
0.10 0,50 2.00
I(H')
Lee el at as Male Rat
16 mg/kg TCE Oral gauage f»q)
10.00 - 0.05 0.20 0.50 2.00 5.00
Leaetal
S4 ma/kg TCE Oral gavage (aq)
£.„
10.00 0.02
0.10
0,50
KM)
2.00
10.00
0.01 0.05
a. so
s.oo
Figure A-33. Comparison of rat evaluation data (boxes) and PBPK model
predictions (+ with error bars: single data points or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions).
A-137
-------
Fitfier et al 91 Male Ral
S?9 ppm TCE 4 hr inhalation
l 86 Male Rat
500 ppm TCE 6 hr inhalation
Lee etal.ODB Mule Rat
2 mg/kg TCE Oral gavage \ a
I
£
^ o
O CM"
468
t(hr)
le*»tal 008 Male Rol
3 nig/kg TCE Oral gavage (aqj
10
0.5 1.0
Mhr)
LeeetdJ OWMMl Ret
16 mg/kg TCE Oral gauase fapj
S
tg
-s f
005
0,20
1.00
500
0.05 020
1.00
5.00
lee eta 1.008 Male Rat
144 mgAg TCE Oral gavage (eq)
Leeelal. DOB Male Rat
43? mg/lcg TCE Oral gavage <
5 •
!*•
s
0.10
0.50
t(hrj
2.00
1000
0.05 0.20
1.00
i(hr)
5.00
Bructner et al. XX Male Rat
50 ppm TCE 2 hr inhalation
Bnickner el al XX Male Rat
50 ppm TCE 2 hr inhalation
05 0.20 0.50 2.00
llhr)
Lee el «l OOP. Mele Rat
4S rn^rltg TCE Oral gavage (aql
S
E
B *
-002
B O-
.£
Ul O'
01 0.2
0.5
t(hr|
1.0 2.0
C> I 0.2
0.5
Mhr)
10 20
Bruckneretal.XXMaleRBl
50 ppm TCE 2 hr inhalation
Bruckner et Bl. M Male Rat
50 ppm TCE 2 hr inhalation
!d
til CM
g-l
I
0.1 0,2
0.5
tltirt
1 0 2.0
0.1 0.2
050
HUD
2.00
1000
Bnicknetetal. XX Male Rat
50 ppm TCE 2 hr inhalation
0.1 0.2
0.5 10
Mhr)
2.0
ErucknEt et al. XX Male Rat
50 ppm TCE 2 hr inhalation
0.1 0.2
0.5
t(hr)
1.0 3.0
Brucknei et ni. fc\ MBle Ral
SOD ppm TCE 2 hr inhalation
0.2
0-5 1 0 2.0
Mdrt
Figure A-33. Comparison of rat evaluation data (boxes) and PBPK model
predictions (+ with error bars: single data points or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-138
-------
Bruckner etal.XX Male Rat
500 ppm TCE 2 hr inhalation
BFUckne.- etui X^^.'.aieRa-
500ppm TCE 2 hr inhalation
Bruckner el a! XX Male Rat
600 ppm TCE 2 nr inhalation
20 2.5 3.0 3.5 4.0 45 5.0
8
o
«
s -
5-
EnjcknBT d al XX Male Rat
SOD ppm TCE 2 hr inhalation
0.1 0.2
0.5 1.0 2.0
5.0
DSouzB SI Bl B5 M«le R.I
10rr>8fkgTCEw
|T-
.£ -
0.02 0.10 050 2.00
tlhrt
Andasm Bt al. B7MaJtRat
100 ppm TCE Closed ChambEr
D.O
1 0 1.5
Hhr)
2.0
Andersen et al 87 Male Rat
2000 ppm TCE Closed Chamber
tH_
§ O^
I
!t
LU
^.
Kill)
01 0.2 05 1.0 2.0
Bruckner el al XX Mala Rat
500 ppm TCE 2 hrinhalabon
?-
-
E O
! rs-
01 0,2
0.5 1.0 20
t (hr)
DSouja el al 85 Male Rat
ZSmjJ/kfjTCElif
50
0.1 0.2
05 1.0 2.0
t [hr)
5.0
Andersen e[ al. Bf7 Male Rat
150 ppm TCE Closed Chamber
0.0 05
1.0 1.5
Kill)
2.0 25
Andersen et al. B7 Male Rat
5000 ppm TCE Closed Chamber
IS*
3 4
Khr)
Is"
O.I 0.2
0.5 1.0 2.0 5.0
t(hr)
DStuza et al 85 Male Rat
5 ma/kg TCErw
002
^002
0.10 0.50
I (ft)
2.00
Diojaaela. 86 Mele Ral
10 mg/kg TCE Oral gavage (aq)
010
0.50
t!hr)
2.00
10.00
A/idersen et al. 87 Mele Rflt
1000 ppm TCE Closed Chamber
t(hr>
Figure A-33. Comparison of rat evaluation data (boxes) and PBPK model
predictions (+ with error bars: single data points or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-139
-------
A.5.2.3.
Human Data and Model Predictions
Fisher Hal 1988 Human »l
105 S ppm TCE 4 hr inhalation
5 10 20 50
KM
Fisher etal 1999 Human #1 -iE-ex-Ma
t D5.5 ppm TC E 4 hr inhalation
S-
s
Khrt
105 t ppm TCE 4 hr inhalation
3r.
10 20 50
i (tin
iet el al t938 Human 02 |SBK=M
49.3 ppm TCE 4 ht inhalation
E
•n °
o o
I T-
O o
U
ISI 51 nil 998 Human *1
1(h5 5 ppm TCE 4 hr mhalatoon
Fisher el al 1693 Human *t
10S SppmTCE 4 hr
si
S;!
s
0.5 10 2.0 5.9 20.0 500
t(h<)
Flihar at fll. 1998 Human ft1 tsex=Mal&>
IDS 5 ppm TCE 4 hr aihalalron
1 2 5
11 hi]
Fisher SI fll 1993 Human #1 t«x=Malal
105.5 ppm TCE 4 hr inhaltiiw
Si:
r-
0.5 10 20 5.0 ro.0 200
MhO
hll 91.11 I SHS Human «3 i-p:.-Vals
;L> ^ ppm TCE 4 hr inhalation
20
Fisher et il 1999 Mumsn *2
4S.3 ppm TCE 4 hr inhalation
P:
la:
Fisher at al. 1998 Human #2 (5ex=MalBf
49.3 npm TCE 4 hr inhalation
0.5 1.0 2.0 5.0
KM
Fisher Bt al 1338 Human #? |&ex=Male!
49.3 ppm TCE 4 hr mhala&cn
0.5 1.0 2.0 5.0 10.0 30.0
t(rir)
Fisher « ol 1S99 Muma
49.3 ppm TCE 4 hf inhalation
1 2, 3 *
t(ho
Freh«r ol iiI 1998 H,imon »3 «P« = Ma!ei
5S.2 ppm TCE 4 nr inhalation
2.0 5.0 10.0 20.0
r(ht)
Fiiher el .-il 1399 u.im.in ii ,
5S.2 ppm TCE 4 hr mhala
1"
•
TJ O
I -
P o
j> m.
£ o
05 1.0 2.0 50
II nr)
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions).
A-140
-------
Fisher et BI.19Q8 Human S3 (sex=Mele)
55.2 ppm TCE 4 tit inhalation
Fisher et all 99B Human X3 i.-,f, = Mal
55 2 ppm TCE 4 hr inhalarton
Fisher et a!1998 Human 83 (
55.2 ppm TCE 4 hr inhalation
10
e' el al t?33 Human S3 i.sE-, = M
55.2 ppm TCE 4 hr inhalation
20
0.5 1.0 2.0 5.0 200 50,0
t -
= §-
1 2 5
KM
Fisher gl ,:l i-'l Human «3 .•„:• -;.ijk
l015ppmTCE4litmhalaliDri
10
20
tlhr)
50
Fisher Bl al.19SB Human *3 i>.} : = Mdl.ji
101.5 ppm TCE 4 tu inhalaliDn
05 1.0 2.0 50 100 200
tlbi)
Fisher et g|.1998 Human #3 (sa<=Male)
101 5 ppm TCE 4 hi inhalation
a>
S
S o
.E Q'
-D
to.
3-_
10
20
t(hr)
50
Fisher et al.193B Human 84 .-,». = Mais-.
53.1 ppm TCE 4 h Tin halation
S -
10
20
10 20
Fisher el al 1999 Human 83 (ie»=Male)
101 S ppm TCE 4 hr inhalation
S-
100
0.5 1.0 20
50
l(hr)
200 50 0
Fisher el el,1998 Human Si (-a>='.1alai
101 5 ppm TCE 4 hr inhalation
B _
I
0.5 1.0 2.0 5.0
1*0
fisher et al 1993 Human #4 (sex=Mate)
53 1 ppm TCE 4 hr inhalation
S8H
100
10
20
t (tir)
50
Fisher et Bl 199B Human *1 (sex^Mele)
53.1 ppmTCE4hnnhalabon
IS"
TJ 1O-
°d
3 ~
Is
0.5
1.0
2.0
Mhr)
5.0
10.0
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-141
-------
Fisher et al.1998 Human #4 ey=Malei
97.6 ppm TCE 4 hi inhalation
I' H
In!
-S-|
d
5
t*r!
10
Fishet et a.l.1998 Human #4 (sex=Male)
97 S ppm TCE 4 hr inhalation
;°-
10 20 50
t(h')
Fisher e! al.1938 Human tfJ ,-,B .-Malei
97 8 ppm TCE 4 hr inhalanon
20
t(hr)
F i-.-ior et al 1996 Human M (
97 8 ppm TCE 4 hr inhalaeon
0.5 1.0 2,0 5.0 20.0 500
t (hr)
et Bl 1939 Human #4 (s&*=MaleJ
97.8ppni TCE 4 hr inhalation
2
tlhr)
Fisher et al 1996 Human S4 (sex-Male]
97.8 ppm TCE 4 hr inhalation
is-
.5
0.5
1.0
2.0
tlhr)
5.0
10.0
5
ttbr)
10
20
Fisher et si. 1998 Humnn fS (se>=Male)
105 S ppm TCE 4 hi inhalation
Fisher et ai 1998 Human #5 (sex=Male)
105.5ppmTCE4 hrinhalation
i o-
O o
O "T>
go-
S o-
g*.
20
t[h«
50 100
00 8
!s:
20
Hhi)
50
100
Fisher et al 1 tpg Human #6 (sex=Ma!e)
105 5 ppm TCE 4 hr inhalabon
iei et al.193E Human *E ,.-,». = Malti
105.5 ppm TC.E 4 hf inhalation
05 1.0 20 5.0 20.0 50.0
tlhr)
Fisher et al 1998 Human IB isE«=Malei
105.5 ppm TCE 4 hr inhalation
I?-
o_
S
-------
Fisher et el.lflSS Human flS (sex=Male]
105 5 ppm TCE 4 hr inhalation
Fisher et al.193G Human ffi ..-ie.< = Mal&.
105.5 ppm TCE t ta inhalation
et al.1998 Hjman fl6 (3ex=Male)
102 6 ppm TCE 4 hr inhalation
2 5 10 2(
t(hr)
Fisher et al.1998 Human W (sex=Male>
102 6 ppm TCE4hr inhalation
0.5 1.0 2.0 5.0 200 500
tdicl
Fisher et Hi 1998HumBn 98 (SBX=Male)
102.6 ppm TCE 4 hf inhalation
i°~
.E -
0.5
1.0
5.0
tihr)
Fisher et al.lSgBHuraan #7 (sex=Male)
102 ppm TCE 4 hr inhalation
a
E
So
S r-'
10 20 50
t(Nr)
1'i.i.c- el jl l'?3 Hu manile (sex-Male)
102 6 ppm TCE J hi inhalolion
i?:
*z
2
Mill)
Frcher et a I 1 93B Human fle (
102.6ppmTCE4li(mtialaliDD
I a.
id
f ° 0.5 1.0
2.0 5:0
t [hr)
10.0 20.0
Fisher et al.1 998 Human #7 (5e*=Male)
102 ppm TCE 4 hr inhalation
20
Htlr)
50
100
S 7 8
tihi)
9 10
Fisher et al.1998 Human B7 (ux^Uale)
102 ppm TCE 4 hr inhalation
Fisher el al 199S Human S7 (spx=Male]
102 pprn TCE 4 hr inhalation
o_
^ ^
E -
tfl.0
5
I (hi)
10
10 20
tthr)
Fisher el al 1998 Human #6 (sex*Male)
1026ppmTCE4hrinhBlslion
p-
iJ
0.5 1 0
2.0 50 10.0 20.0
t(hr)
Fishet et BM998 Human tS [«x=Ml
102.6 ppm TCE 4 hnnhalelion
I -
If:
•i
df
x "
10 20
tthr)
50
fisher ft ai 1898 Human *7 x«Malej
102 ppm TCE 4 hr inhalation
0.5
20
t(hr)
5.0
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-143
-------
Fcsher et Bl. 1998 Human ffl (sex=Male>
102 ppm TCE 4 hr inhalation
Fisher et al.1998 Human ff~ ..^ &.< - M a IE-.
102 ppm TCE 4 hr inhalation
Fisher et ai 1998 Human SB (sex=Me]e)
101-1 ppm TCE 4 hr inhalation
f
E p
isd
2 5 10 2<
t(hr)
Fisher et al.1998 Human «8 (sex-Male)
101 1 ppm TCE 4 hr inhalation
i 10 20 50
t(hr)
Fi J-o e! al IT-9 Human (18 I-.B . .M.-ilei
101.1 ppmTCE4himhalaljon
10
20
t(hr)
50
Fisher et al 1998 Human »B i-.e« - M.i
101.1 ppm TCE 4 hr inhalation
I "
3 -
ir
a
Ir*
10 12 14 16 18
t(hr)
Fisher et al 199S Human SB i^e/=MaleJ
101.1 ppm TCE4hr inhalation
0 50
100 150
t (hr)
200 250
Fisher el al 1998 Human tte (s»x=Male)
101 1 ppm TCE 4 111 inhalation
2
Khrl
Fisher et al.1998 Human *8 (sex-Malei
101 1 ppmTCE4hnnhaliition
8-
a
i">-
G
E S_
0.5 1.0 2.0
50 10.0 200
05
1.0
2.0
5.0 -§ ° 0.5 1.0
~
20 5.0
t (hr)
10.0 20.0
Fisher et a].1S9B Human «S (sex=Male)
101 1 ppm TCE 4 hr inhalation
Fisher et sl.1998 Human #9 (se>=Male)
103 4 ppm TCE 4 hi inhalation
fisher et al 1998 Human #9 (sex^Mate)
103.4ppmTCE4 hr inhalation
frr
10
2C
Hhrj
50
100
Fisher et al.1998 Human *9 (ux^Male)
103.4 ppm TCE 4 hr inhalation
20
flhr)
Fpsher el all 998 Human B9 (
103 4 ppm TCE 4 hr inhalation
50
100 150
t(hr)
200 2SO
Fisher et aM 998 Human »9 (se^Malej
103.4 ppm TCE 4 hr inhalation
I
fg,
E o-l
0.5 1.0 2.0
5.0 10.0 200
O.S 1.0
t(nr)
2.0 5.0
t(hr)
10.0 20.0
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-144
-------
Fisher et al. 1998 Human fl9 . ::&y=Male|
1034 ppm TCE 4 hr inhalation
§
s -
0.5 1 0 2.0
5.0 10.0 200
Fisher ul ji TJ3S Human Slu
55.1 pprn TCE 4 hr inhalation
2
Mhrl
Fiihei et al.1998 Human 910 (sen=Female)
101.4 ppm TCE 4 hr inhalation
!*.
20
t (hr>
50
100
10
20
tthr)
50
100
Fisher et al.1998 Human #10
-------
Fisher eta! 1998 Human #11 (sex= Female)
53 ppm TCE a h» inhalation
Fisher et aM990 Human #11 !se';-F
53 ppm TCE 4 hr inhalation
Frcher et al 1998 Human #11 {5ex=Female>
53 ppm TCE 4 hr inhalation
£
•g-
s H
E
— O
"S ">-
10
20
t flirt
Fisher el al-1998 Human #11 (sen-Female)
53 pp«n TCE 4 hi inhalation
.5 m
LU O —
u o
18.
10 20
t(nr)
r el FI| 1998 Human #11 <:•»-Female1)
53 ppm TCE 4 hr inhalation
0.5
1 0
2.0
50
5
T (hr)
10
20
FiLher etal 1998 Human »11 (sex=Fematej
97.7 ppm TCE 4 hr tnhalatron
Fisher et al 1998 Human #11 (3ey= Female)
97.7 ppm TCE 4 hr inhalation
P:
01 _
§°_
I":
.= <=_
10
20
tlhr)
100
Fisfier etai.t998 Human #11 (sex=Fennlet
97.7 ppm TCE 4 hr inhalation
12 14
Fisher et al.1998 Human #11 (sex=Fomale(
37 7 ppm TCE 4 hr i
0.5
10 2.0
tlhr)
5.0
Fisher et el. 1998 Human »11 (sew Female)
? 97.7 ppm TCE 4 hr inhalation
i:
T5 O-
is.
O o
"a
Is-
I*
10 15
tlhr)
20
5
t(ht)
Fishei et al 1998 Human #11 lse»"Femnle)
S3 ppm TCE 4 hr inhalation
18
20
tlhrl
SO
100
Fi'.her et al 1998 Human 911 ..sex-Fc-
97.7 ppm TCE 4 hr inhalation
0.5 1.0 2.0 5.0
20.0 500
Fisha el al. 1898 Human #11 (5ex=Fem»lel
97 7 ppm TCE 4 hr mhalabnn
y
05
1.0 2.0 5.0 10.0
tlhr)
Fisher et al.1998 Human ffl 1 rsex=Female) ^
97 7 ppm TCE 4 hr inhalation .|
Frsher et al 1998 Human *12 (sex= Female)
102.5 ppm TCE 4 hr inhalation
10 15 20
20
!
-------
Fi sh er et al. 1993 Human VI2 (3ex= Female)
102 5 ppm TCE 4 nr inhalation
et al.19QS Human 912 fsex=FEmale>
102.5 ppm TCE * t\t inhalation
Frsher et al. 1998 Human #12 j3ex=Fe
102.5 ppm TCE 4 hr inhalation
M
.£ «
I I 1 I I I I I
05 1.0 2.0 5.0 200 500
Fisher etal 1998 Human *12 (sen-Female)
102 5 ppm TCE 4 hr inhalation
0.5
1.0
2.0
'(1*0
1 0 20 5.0 100
tlhr,
500
F.shsr et all 935 Human # 1? <5e»=Female)
10? 5 ppm TCE 4 hi mnalalion
FI-.I-M el jll'Wi Human *12 I sex-Female)
102 5 ppm TCE 4 hr inhalation
I
I H
5 -
15-
0.5
1 0 2.0
tlhii
50
0.5 1.0 2.0 5.0 10,0
t (hr)
50.0
20
t(hr)
50
100
!«
5 u
Fit-her etal.,998 Human *13 |.36U= Female)
102 ppm TCE 4 hr inhalation
Fisher si al.1998 Human #12- - -:e>. = Fc-nalL-'.
102 ppm TCE 4 hr inhalation
Fraher et al 1998 Human 913 (sen=Fe
102ppm TCE4 hr inhalation
-
i "-
5 10 20 50 100
tflir)
0.5 1.0 20 5.0
20.0 50.0
0.5
1.0
20
tthr)
5.0
an*13(sex= Femalrt
102 ppm TCE 4 hr inhalation
Fisher ft al.l 388 Human #13 « el al.1898 Human #13(s«=Female)
102 ppm TCE 4 hr inhalation
Is-
fSi
h-i
KH
2 S
tftr)
10 20
10 20
Fisher etal 1SU6 Human *13 (M*"= Female)
102 ppm TC6 4 hr inhalation
r et al.1998 Human 914 [sex=FemBle;i
102 ppm TCE 4 hr inhalation
Fisher et al.1998 Human 914 ^sex=Fe
102 ppm TCE 4 hr inhalation
20
tlhr)
SO
100 o
20
50
8 10
Uhrl
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-147
-------
Fisher EtaM998 Human #14 ise'*= Female.
102 ppm TC E 4 hr inhalation
Fisher et al.1998 Human ff 14 {se*=FemaleJ
102 ppm TCE 4 hr inhalation
Fnher el al.lSSS Hu
102 ppm TCE 4 hr inhalation
0.5 1.0 2.0 5.0 20.0 500
tr.hr)
Fisher at aU898 Human #14 {se»=Female)
102 ppm TCE 4 hr inhalation
2.0
t(hr)
et al.1 99B Human #14
101 ppm TCE 4 hr inhalation
Fisher et al. 1888 Human #15 (sex= Female I
101 ppm TCE 4 hr inhalation
i:H
0.5
1.0
2.0
tftr)
50
Fisher ital.1998 Human ,115 -.s: = Fe
lot ppm TCE- 4 hr inhalation
10.0
male}
1°
is
Ftsher et al.1998 Human S16 fsex=Female;i
103.^ ppm TCE* Ju mhalalion
20
tffu)
100 §
O-
1
E
5
t(hr)
Fisher el al 1598 Human *14 ;™. --Fe
102 ppm TCE 4 hr inhalation
20
nale)
5-
20
t(hr)
50
100
Frcher et 31.1998 Human 915 (sen=Fe
101 ppoi TCE4 nt inhalation
0.5
1.0
2.0
ttnr)
5.0
Fishet et al.1898 Human #15(5*1= Female)
101 ppm TCE 4 hr inhalation
5 ^
= 0.5
1.0
20
t (hrt
5.0 100
Fisher et al.1998 Human »16 |sex=Fe
103.3 ppm TCE 4 hr inhalation
S
s
« *--
20
Mhr)
SO
100
50
100 150 200 250
Mhr)
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-148
-------
Fisher 0-lal 1933 Human #16 {sex=FEmale)
103 3 ppm TCE4tif tnhalabon
Fisher et al.l9S8 Human #16^ev=FB
103.3 ppm TCE i h< inhalation
-i et at. 1998 Human #16 isex=Female}
103.3 ppm TCE 4 hr inhalalior>
!?:
E. a>_
102 ppm TC E 4 hr inhalation
0.5 10 2.0 5.0
t[h«
Payknc et al.1945 Human S18 (sex=unknDwn)
*y,-\
10
20 50 100
t(hr)
1
E
8S.
O
" en
>- o-
0,5 1 0 2.0 5.0 10.0 20.0
t(hr)
r et al.1933Human #1fl(aex= Femate'j
103.3 ppm TCE 4 hi mhalalion
1.5 1 0 20
t(hr)
Fishei el a! 1998 Human *17 Is
102 ppm TCE 4 hr inhalation
If,
§*_,
ill
20 50 100
IlhK
Fisher BI al I998 Human *17 shw et al. 1388 Human #17 (sex= Female)
I'M
o ^
102 ppm TCE 4 hr inhalatjon
5
t(hi)
Paykocetal1945Humanfl1S^e)i=unhn e
329moAgTr;A 1 hr IW
20 50 100
Khrl
10 20 50 100
Uhfl
Fisher et al 1998 Human »17 (sejt=Female)
102 ppm TCE 4 ht inhalation
5
tthr)
10
20
FishH et sl.1998 Human #17
-------
Paykac et al.1945 Human #19 {sex=u
63.06 mg/kg TCA 1 hr IV
Paykoc et al 1945 Human ff19 <5K*=ij
63.06 mg/kg TCA 1 hr IV
Paykac Bt al.1S4£ Human »1S (se!f=unknawn)
53.06 mg/kg TCA1 hr IV
5 o
r:
ISH
e
I s-l
80 100 120
tfhr)
Poykac Dt al 1 345 Human #20 (sex=unknown)
24 9mjfkgTCA1 hr IV
40 60
80 100 120
Mnr)
Paykoc et a I 1945 Human #20(iex=unfcnowTiJ
24 8 mgfkg TCA 1 hr IV
- Kimmerle and Eben 1373 Human #21 (sex=Fetnale)
40 ppm TCE 4 hr inhalation
I":
iL -
's
40 50 BO 70 80 90 100
-^ Kimmeile nnd Eben 1973 Human fl21 (sex=Female)
'5 40 ppm TCE 4 hf mrialation
40 50 SO 70 SO 90 100
tlhi)
KimmerlB and Eben 1973 Humsr>ff21 (sey=Fernale]
40 ppm TCE 4 hr inhalation
10 20 50 100
linn
Kimmerle and Eben 1973 Human Sf21 (sex=Femalej
40 ppm TCE 4 hf inhalation
§-
-
InJ
150
200
tlbi)
250
10 20
t(hr)
50
4.0 4.5
5.0
tthr)
55
6.0
Kimmefle and Eben 1973 Human #21 (sex=Female) Kimmefle and Eben 1973 Human' #21 (se*=Fernflle)
40 ppm TCE 4 hr inhalation 40 ppm TCE 4 hr inhalation
^ Kimmerle and Eben 1973 Human #22 (sex=Fem9le)
| 44 ppm TCE 4 hi inhalation
If:
5 10 20 50 100
t(h«
150 200
tlhr)
250
20
tthr)
50 100
^ Kimmerle and Eben 1973 Human #22 fsex=Femn!et K-immerle and Eben 1973 Human S22 (sex=FBmaleJ
•g_ 44 ppm TCE 4 hr inhalalion 44 ppm TCE 4 hr inhalation
Kimmerle and Eben 1973 Human *22 (sex=FemeleJ
44 ppm TCE 4 hr inhalation
5 -\
&
00-
5s;
Re-
8-
B
13-
150 200 250
Khr)
10 -JO
Khr)
50
4.0 4.5 5.0 5.5 8,0 6,5 70
tthr)
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-150
-------
KLimmerle and Eben 1973 Human S22 (sex=FEmBle>
44 pp-ni TOE 4 hf ' • 11 ir/, i,.. 11.> i
KJnvneTle and Ebsn 1973 Human 322 (sex=FemaleJ
44 pp*n TCE 4 hr inhalation
_. Kimmerle and Eben 1373 Human #23 r 3e
~ 44 ppm TCE 4 hr inhalation
5 10 20 50 100
t(hr)
^ Kimmerie and Eben 1973 Human #23 (ser=Female>
44 ppm TCE 4 hi inhalation
20
t(hr)
50 100
Kim-merle and Eben 1973 Human #23 (sex = Female)
44 ppm TCE 4 hr inhalation
Kimmerle and Eben 1973 Human #23 (sex-Female]
44 ppm TCE 4 hr inhalation
IE
!§:
•- o_
= o
LD
^ q_
150
200
tOir)
250
50
Kimmeile and Eben 1973 Human #23 fsex=FemEle)
44 ppm TCE 4 hf mnalation
KimmerlB and Eben 1573 Human K23 (sex=Fernale)
44 ppm TCE 4 hr inhalation
4.0 4.5 5.0 55 SO S.5 70
Kimmerle and Eben .973 Human S24 (sex=Ffimale
~ 44 ppm TCE 4 Mr inhalation
H;
Is
y "*""
-------
and Eben 1973 Human ¥25 (sex=MaleJ
*0 ppm TOE 4 h» inhalation
KmrmeHE and Eben 1973 Human #25 (S
40 ppm TCE 4 hr inhalation
KiiTL-nerle and Eben 1973 Human #25 {5EX=Male)
40 ppm TCE 4 hr inhalation
E °-
4.0
4.5
5.0
Ifflr)
—r~
10
—i—
20
t(nr)
—i—
so
—I—
100
150
200
t(hr)
250
— KimmartB «nd Eben 1973 Human *26
tlhr)
e arid Eben 1973 Human #26 |>Ex=Mflle]
40 ppm TCE 4 hr inhalation
go
Is-
I :
Is-
II
^H
4.0
4.5
5.0
trtlrt
55
6.0
10
20
t (hr)
50 100
150
ZOO
tlhr)
250
Kimmerle and Eben 1973 Human #27
-------
S28(sex=Male) — Krmmerle and Eben 1973 Human #28 (s
44 ppm TCE 4 hr inhalation
Kimmerle and Eben 1973 Human #2B (^
44 ppm TCE 4 hr inhalation
£
I
Q
Kirrri trk- gnd Eben 1973 Human *28 (six-Male)
44 ppm TCE 4 hi inhalation
200
'(ht)
KrmmeHe and Eben 1973 Human #29 (sexBMale)
44 ppm TCE 4 hr inhalation
10 20
t(hr|
50 100
Kimmerle and Eben 1973 Human #28 Isex-Male)
44 ppm TCE 4 hr inhalation
^
,= o_
I"
.E ID-
4.0 45 50 5,5 6,0 6.5 7,0
Uhr)
10
20
50
100
150
t Chrl
200
tlhrl
250
— Kimmerle and Eben 1973 Human 3J29 (sex=unlcncwn) -^ KimmerJp and Eben 1973 Human #29 (sex=unknown) Kimmerte and Eben 1973 Human 923 [3ex=unknowrt]
~
n TCE 4 lit .nhalalion for 5 days
48 ppm TCE 4 ht pnhalalion for 5 days
48 ppm TCE 4 hf tnhalalion fa S days
§
en
§ .
8 20 40 GO 80 100
140
So-l |
0 250 300 350 400 450 500 550
^ t (nO
i*
50 100 150 200 250
tlhr)
end Eben 1973 Human #29 (sex=unhrtown) Kimfrtefle and Eben 1973 Human #29 tse<=uiihnown) Kimmerte and Eben 1973 Human #29 (5ex=unknown)
43 ppm TCE4hrinhaliiban far 5 days
9 ppm TCE 4 hr .nn.il.nor Ira !, da>' =
48 ppm TCE 4 hr inhalation for 5 days
a _
.18-
'~
fS-
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3 0_
£ ^
^ 10-
<
L>
H 0-
-
i
10 20
HtiH
50 100
20 40 60 SO 100
MM
140
t(hr)
— KimrnerlB and Eben 1973 Human S30 (sex=unknown) ^ Krmmefle and Eben 1973 Humen S30 tsex=unknown) Kimmerte and Eben 1973 Human *3D (sex=unknown)
48 ppm TCE 4 nr inhalation for 5 days
48 ppm TCE 4 hr inMalion for E days
49 ppm TCE 4 hr inhalalion for 5 days
I -
^^^
^^
-------
= ne and Eben 1973 Human £3C (sex=unknown)
46 ppm TCE 4 hr inhalation for 5 days
er=unknown)
)ppm TCE 4 hr inhalation for 5 days 43 ppm TCE 4 hr inhalation for 5 da;.".
50 100 150 200 250
" t Oirj
to
O
in-
10 20 50 100
tlhr)
KsmnrerlBBnd Eben 1973 Human S32 (sex=unknawn) KrmmertE and Eben 1973 Human fl32 ls&x=unknown) _
48 ppm TCE 4 hr mhalaSon for 5 days 48 ppm TCE 4 hr inhalation for b days
5 10 20
Mfir*
Monster etal.1976 Human »33 ^e>=W
I
t
N-
I
•c O~
S
Tl O
% IN
SS m^
• n,"
_^-— -
C^--
L-- — " "
50 100 150 200 250
tlht)
250 300 350 400 450 500 550
tin.)
20 30 40 50 60 70 80 90
Uh,,
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-154
-------
Monster et a! 1976 Human S33 (sex=Male)
65 ppm TOE 4 h» inhalation
etal 1976 Human #33 (sex=MalB)
65 pp
140 ppnn TCE 4 hr inhalation
3-
10 20 30 40 50 60 70
ttttn
Monster el a! 1976 Human 833 isex'
140 ppm TCE 4 hr inhalation
18-
|"-
.18-
i _
5 ID 20
t(hr)
Monger et all 976 Human #33 (sex
65 ppm TCE 4 hr inhalation
10
15
20
Monger el al 1976 Human S3 5 (ra-
140 ppm TCE 4 hr inhalation
10
20 30
t(Ni)
40
Monster etil. 1976 Human #33 (sex=Molei
MO ppm TCE 4 hr inhalation
Retained dose (mg]
0 200 400 (500 80
!
2.5
i.O 3.5 4.0 15 5.0 5.5
tlhr)
I
5 -I
• o
£
-------
Monster el al 1976 Human ff34 fsax=Male)
68 ppm TCE i ht inhalation
etal 1976 Human #34 (sex-=MalE)
68 ppm TCE 4 hr inhalation
eretal 1376 Human #34 (sex=
68 ppm TCE 4 hf inhalation
8-
150 200
Monster el el 1976 Human «34 (s«»-Male)
68 ppm TCE 4 hr inhalation
10 20
t(hr)
Monster el at 1976 Human #34 (-,?:.: =Mab!
68 ppm TCE 4 hr inhalation
ISH
!s:
L:
10
20 30
tlhr)
10
Monster el aM976 Human 1F34 «sex=Male)
139 ppm TCE 4 hr inhalation
138 ppm TCE 4 hr inhalation
1
ll:
.E
<
0
*~ fj
?:
20
t[hr]
50 100
100
200
t (hr)
300
400
Monster el aM87S Human #34 ias«=Malei
138 ppm TCE 4 hr inhalatjon
Monster etal.t876 Human «34 (3ei=M
138 ppm TCE 4 Kr inhalation
S -
*
25 3-0 3.5
40
t(hr)
4.5 5.0 5.5
Monster el nl.1976 Human #34 lse»»
138 ppm TCE 4 hr inhalation
fg.
20 30 40 50 60 70 80
tlhr)
Monster e! al 1976 Human 834 Iss>x=Mala j
138 ppm TCE4 hr inhalation
5
18
10 20
50 100
ttrir)
Monster etal.4976 Human #34
-------
Manstsr et aJ.1976 Human S35 (sex=Ma|e)
70 ppm TOE 4 h» inhalation
Mansler elal 1976 Hurnsn S3S (sex =
70 ppm TCE 4 hr inhalation
Monster et al 1S76 Human Sjii {sex=Male
70 ppm TCE 4 hr inhalation
-
10
20 30
tflit)
Monster el al 1976 Human *35 (in-Mili)
70 ppm TCE 4 hi inhalation
i.
25 3.0 3.5 4.0 45 5.0 5.5
Monster M al 1976 Human *35 (xegt'Male)
142 ppm TCE 4 hr inhalation
20 30 40 50 60 70 80 90
Monster el al 1976 Human 035 'Sex=Malel
142 ppm TCE 4 hr inhalation
40
100
200
t*r!
300
400
Monster el al.1876 Human *3S (asi=Male)
142 ppm TCE 4 hr inhalation
O m
Si:
10
50
HIM)
Monster el al.1976 Human 836 ise»=Male)
76 ppm TCE 4 hi inhalation
20
60
t (hr)
80
too
Monster el al 1976 Human tB6 (lex-IJ
142 ppm TCE 4 hr inhalation
10 20
30 40
Hhr)
50 60 70
'.'01 a a el il 1976 Huniati *35 (sex=Melej
142 ppm TCE 4 hr inhalation
1s-
20
40
60
t(ht)
100
Monster etal 1976 Human 836 (s«=Male)
76 ppm TCE 4 hr inhalation
200 300
tint)
400 500
I «
O
O
5 10 15 2
I OH)
Monster et al.1976 Human #55 (sex^
142 ppm TCE 4 hr Inhalation
20 50
t(tir)
Monster u! il 1976 Human *35 isei-
142 ppm ICE4 hr inhalation
'
2.5 3.0 3.5 4.0 4.5 5.0 55
tlhr)
Monster etaUSTS Human *36 (»x=M>k>)
76 ppm TCE 4 hr inhalation
!§:
Is-
I 10-
i r>l-
§0
B S
I*
IS.
.£ O
20
40
60
tltir)
80
Monster et al.1976 Human tt36 (sex^Male)
76 ppm TCE 4 hr inhalation
20 30
t(hr)
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-157
-------
Monster el ai 1976 Human 836 (sex=Male)
76 ppm TOE 4 hf inhalation
l 1976 Hurnsn S36 (sex^Male)
76 ppm TCE 4 hr inhalation
Monster etal 1S76 Human #36 |se>=Maie;
76 ppm TCE 4 hr inhalation
2-5 3.0 3.5 40 45 5.0 5.5
l flirt
Monster el al 1976 Human *36 (sei'Male)
140 ppm TCE 4 hr inhalation
s-
20
40
60
tlhr)
SO
100
Monster el al 1976 Human 036 .;se-<=MalB!
140 ppm TCE 4 hr inhalation
10 20
t flirt
50
Monsta et al.)B76 Human#35 (sei=Malf!
140 ppm TCE 4 hr inhalation
8-
20 40 60 80 100
tint)
Chiu et al 2007 Human *37 (se»=Male)
1 ppm TCE 6 hr inhalation
£•=-
1 2
10 20 50 100
Iff")
O _
ri *•
10 20 30
t(nr)
Monder etal 1376 Human *irj (SB:.:
140 ppm TCE 4 hr inhalation
B?:
"£
10
20
t(hi)
50
er et al 1976 Human ffiS (5ex=Malai
140 ppm TCE 4 hr inhalation
25 3.0 3.5 4.0 4.5 5.0 5.5
t [hr>
Chiu et al. Z007 Human #37 (sex=Mile)
1 ppm TCE B hr inhahjtion
g8J
s.
^~ - - 1
20 40 60 80 100 120
tlhrl
Chiu eta] 2007 Human £37
-------
Chin et B! 2007 Human V37 (5ex=MalE>
^ ppm TCE 6 hr inhalation
Chiu EtaJ. 2007 Human S37 fsejr=Male;.
1 ppm TOE 6 hr inhalation
Chiu Et a! 2007 Human S37 (sex=MalE)
1 ppm TCE 6 hr inhalation
10 20 50 100
L.liiu el a i 2007 Human *37 (se»-Male)
1 ppm TCE 6 hr inhalation
I
s .
0.5
2,0 50
20.0 50,0
Chiu a al 2007 Human #37 [se«=Malsl
1 ppm TCE 6 ht inhalation
*
10 20
tlhrt
50 100
Chiu el al. 2007 Human #38
1 ppm TCE S hr inhalation
Chiu et al 2007 Human #38 -
5 10 20 50 100
t(h,l
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-159
-------
i
i S
Chin etal 2007 Human #3E !se» = r>
I ppm TCE 6 hr inhalation
Chiu eta] 2QQ7 Human £38
Chiu atal 2007 Human MB (s««=Mala;
1 ppm TCE 6 hr inhalation
10
20
Mhrl
50 100
Chiu et al. 2007 Human t31 ..se> =
1 ppm TCE 6 h( inhalation
" 0.5 2.0 5.0
20.0 1000
Chiu et al. Z007 Human. #33 lseK=Msle)
1 ppm TCE 6 hr inhalation
5 10 20 50 100
1*0
Chiu et al 2007 Human *39 («v=
1 ppm TCE 6 hr inhalation
I?:
1°
* *_
o ^
!i
5 10 20 50
tlhrl
Chiu Btal 2007 Human S3B(sen=Male;.
1 ppm TCE 6 hr inhalation
5 10 20 50 100
tlnr)
Chiu et al. 2007 Human *39 (5ex=M ale)
1 ppm TCE 6 hr inhalation
fo-
r.
!si
O 20 40 60 80 tOO 120 140
O J ,. L
0.5
2,0 50 20.0 50.0
Mh,l
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-160
-------
Chin et H! 2007 Human #39 lsex=MaIe)
i ppm TCE 6 hr inhalation
Chiu etal 2007 Human S39fseir=Male;.
1 ppm TOE 6 hr inhalation
Chiu etal. 2007 Human £39
1 ppm TCE 6 hr inhalation
t :
is
It:
.E
jn
13 s-
• »
PH
< W)~
y S-
5 10 20
t(hi)
50
20 50
t (hr)
100
Chiu a ml 2007 Human (MO l5e«=Male,
•t ppm TCE 6 hf inhalation
Chiu c-ldl 2007 Human MO |s«x=Male;,
1 ppm TCE 6 hr inhalation
"s-
*$-
o
"§-
0.5
2.0 5.0
t flirt
200
1000
5 10 20
t(hr}
50
Chiu el a). 2007 Human #40
-------
Chlu et H! 2007 Human #41 (sex=MaiE}
^ ppm TCE 6 hr inhalation
Chiu Eta], 2001 Human S41 (sej<=Male;.
1 ppm TCE 6 hr inhatetion
Chiu etai. 2007 Human S41 S:=MH,B;
1 ppm TCE 6 hr inhalation
50
Chiu et at 2007 Human #41 (sex=Maie)
1 ppm TCE 6 hr inhalation
Is-
I":
| 8-
!;
30 35
i (hi)
40 45
50
IOC
Chiu el ml 2007 Human S41 5e«=Male,
•t ppm TCE 6 hr inhalation
Chiu atal 2007 Human #41 |5ex=MaleJ
1 ppm TCE 6 hr inhalation
B 7_
I *
?-
5 10 20 50 100
tlhr)
Chiu el al. 2007 Human #41
1 ppm TCE 6 hr inhalation
15
t (hr)
Chiu etal. Z007 Human *41 (seK=Male)
1 pfKti TCE 3 hr inhalation
«.
is-
£ „.
I
I
5 10 20 50 f
t (Mr) K
Chiu stal 2007 Human #41 (sei-Male)
1 ppm TCE 6 hr inhalation
10 20
t|hr)
50 100
Chiu et a 2007 Human «41 .• w =W alev
1 ppm TCE 6 hi inhalation
I?:
I*
0.5 2,0 50 200
t(hrl
Chiu et al. 2007 Human Ml (sex=l
1 ppm TCE 6 hf inhalation
(00.0
ig'
£ -
UJ
4 5
tthr)
Chiu et al 2007 Human *4Z («x=
t ppm TCE 6 hr inhalation
5 10 20 50
tow
Chiu et al 2007 Human 842 !se» =
t ppm TCE 6 hr inhalation
40
80
tlhr)
100
120
Chiu Bt il 2007 Human S42 (sen=Male;.
1 ppm TCE 6 hr inhalation
30 40 50 60 70 80
t(hr)
Chiu et al. 2007 Human
-------
Chiu Bin! 2007 Human #42 (se*«=MalE)
i ppm TCE 6 hr inhalation
Chiu Etaj. 2007 Human ff
1 pprm TCE 6 hr inhalation
Chiu etai 2007 Human S42 (sex =Male;
1 ppm TCE 6 hr inhalation
?0
^^^=r" "^
• •
•
•
T »
O *~
O
I?"
"%£
e
E -
l»"
0 ?-
^ssf**^^
^^^ ^^^^^,
^^^x/^J
J
!«•
^ o
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13 OJ
^ CD
< "
K °^
1
1 1 11 1 1 _ * 1 ] III 1 " 1 t I i 1 1
) 1 3 4 5 6 {§ *~ 1 2 5 10 20 50 Q 10 12 14 16 18
t(hr) K i(hr) t (hr)
L
o °-
Bemauer et al.1996 Human JW3 (sex-Male)
40 ppm TCE 6 hi inhalation
30
50
Bernauer at al 19&6 Human S43 l.sex=Male)
80 ppm TCE 6 hf inhalation
LJ O.
•>«•
&0
la:
10 20
30
I (hr]
40 50
i ti.
81-
I?:
§"
Bemauer et al.1955 Human #43 isex=Malet
160 ppm TCE 6 hr inhalation
10
20 30 40 50
tftr)
Muller Etal 1974 Human W4 (s
2.&4S mg/kg TCA oral
r
0.5
2.0 5.0 200 1000
t
Bemauer el al. 1996 Human
IfiO ppm TCE 6 Kr inhiaiation
- o_
i?-
10 20 30 40 50
t(hr)
Muiler ft 31.1974 Human M4 15
2.646 rngftrg TCA oral
I
a
1s-
20 40 80 80 100 140
Bemauer et al 1996 Human *»3 (sex^Mate)
40 ppm TCE 6 hr inhalation
0.0-
£ o-
S=>"
10 20 30 40
Barnauer &tal.1996 Human 042 =Male)
130 ppm TCE 6 hr inhalation
E o
— pj_
loH
10 20
30
t(tir)
40 50
I
M
Mullsr it Bl 1974 Human *44 ise» =MalB)
10 mg/kg TCOH oral
40 60 80 100 120
tlhrl
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-163
-------
Muller et al IS74 Human #44 (sex=M»le>
lOmg/kgTCOHoral
Muller el al 1974 Human #44 ise
lOmg/kg TCOH oral
Muller PI al 1974 Human «fl (ssx^Mals)
10 mg/Vg TCOH oral
Is
= + -
0.5 2.0 5.0 20.0 100.0
Mhr)
II;
0,5 2.0 5.0 20,0 50.0
20 40 60 80 100 140
t (hi)
Figure A-34. Comparison of human calibration data (boxes) and PBPK
model predictions (red line: using the posterior mean of the subject-specific
parameters; + with error bars: single data points; or shaded regions: 2.5, 25,
50, 75, and 97.5% population-based predictions) (continued).
A-164
-------
Bartomceh 1962 Human ffl l3E
20Q ppm TC£ 5 hr inhalation
BartDnlcBk 1982 Human #1 He.t=unhnownJ
200 ppm Tr.E 5 \w inhalation
niceh 1962 Human S1 I se.» =uit(nawn J
200 ppm TCE 5 hr inhalation
X
o
po
1
Pll
In-
Bn-
.
I
f
0 ^
O
Is-
•
|s-
1 0-
! 1 i f rn til
300 400 500 600 700 o SO CO 70
*"""
UK-
* ^->
Li
•
•
1 > 1 1 1 1 1 F I
80 90 1 00 50 BO 70 80 90 1 00
K t(hr) " tlhr} t(hrt
Bartomcek 1962 Human fPl [ssx^unhnauni BartamcBk 1932 Hum.
an #1 fsBx^unluiDwn) Sartomcelt 1982 Human #1 [ SB»=unknDwn )
200 ppm TCE 5 hr inhalation 200 opm TCE Shr inhalation 200 DDm TCE 5 hr inhalation
_
1-
1-
-
"1 -
.
g_
II-
« "
» *
ac
o-
t
1 1 1 1 1 II
F
Is-
^ 0-
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•
i i i i
0.6 O.B 1,0 1 2 1 4 3 4 5 6 7 300 400 500 600 700
t (nrj t (hr) t hr>
Bartonicek 1962 Human 91 [*ex= unknown) Bartonlcek 1962 Human #? {seK-unkn own) - QartonJcek 19fi2 Human #2
»
|
600 700 § 50 GO 70 fiO 90 1 00
tlhll " tjht) H Hhfl
BartDniceh 1962 Human £2 (5e>=untcnewn) BartanicEt 19ff2 Human fl2^E»r-untmDwn) BartoniceK 1962 Human *P2 tEB^=unt'nmvn)
200 ppm TCE Shr inhalation ioO pprn TCE S hr inhalation 200 pool TCEShr Inhalfltton
g-
I,-
|s-
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^
o-
1 1
k
a
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So
sg-
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50 EO 70 50 90 100 O.G 0.6 1.0 1 2 1 4 3 4 5 6 7
LftiO tfhrl If if»
Bartonicek 1362 Human JF2 <:..EX= unknown! Earlcm^Ek 1932 Hum
an tt?ISEX=unknDwn) -. fiartanicek i9«2 Human S3 |BB«=UnWvn)
300 ppm TCE 5 hr Inhalation 300 ppm TCE S hr inhalation 200 ppm TCE 5 hr Inhalation
Is"
1 0
c ^
I- 0-
1 I
|o_
§ o
: A collected in
20 4(J 6
"- o-
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300 40C 500 SCO TOO 50 60 70
gg-
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jl-
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t
flO 90 100 o 300 400 500 GOO 700
t(«r) t("') " Hnr)
Figure A-35. Comparison of human evaluation data (boxes) and PBPK
model predictions (+ with error bars: single data points or shaded regions:
2.5, 25, 50, 75, and 97.5% population-based predictions).
A-165
-------
- Bartonlce^ 1DSI1 Hum
an #5 (».?*= Unknown) BartQnlcBk 19S2 Hum
0n fl3 | s ELV =unh n own ) Bartonieek 1952 Hum
an S3 1 sea=unknDwn i
•| 200 ppm TCE 5 hr inhalation 200 ppm TCE 5 hr inhalation 200 ppm TCE S hi ^halation
O"
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20 60
So-
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0 511 BO 70
i
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TCA in plevn
10 20
•
BO 90 100 50 GO 70
o
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o—
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1
90 90 ion os o.a 1
0 12 1 *
H Hhfi 1 (hr) tltlft
Bartonltek 1962 Human 1O (se«-untmcwnt Qartonicek 1962 Human #3 15e**unknown) Banonicek 1962 Human K3 ise)(=untfnQwn)
200 ppm TC 6 6 hr Inhalation 200 pom TCE S hr inhalation 200 ppm TCE S hr Inhalation
£§:
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0-
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jj:
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5 6 300 400 500
i
in unnv (mgi
so an
H '*""
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S
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:
600 700 50 60 70
*0 90 100
tOin iihn tihr)
Bartonieek 1962 Human W (sex=unknown} Bartonicek 1962 Human W{sex=gnknBrwr>) Sartanicek 1962 Hum
an it--'- i -E ,-'ji|i:nc...Mii
| 200ppmTCE5ininnalaaon 200 ppm TCE S hr inhala lion 200 ppm TCE 5 nr inhalation
E
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0 300 400 500
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80 90 100 0.6 0.8 1
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S tihii tjhi) t(hr)
Bartonicek 1962 Human *4 (sex=unfcnown\ Bartrjmcek 1962 Human *fcMsa<=unlmown) .- Bartoniceh 1S62Human #5(sex=unKnowi1
200 ppm TC E 5 hr inhalation 200 ppm TCE
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So
E 0-
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CArtcrttedin
100 30
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5 6 300 400 MO
S hr inhalaljon | 200 ppm TCE fi nr Inhalation
1
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•£ —
£ o— i
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800 700 0 300 400 5(10
600 70t>
t(hr) MhO >- Uhri
Bartonicelt 1962 Human »5 (sex=unfcnown| Bartonicsk 1982 Hum
Qnfl5|5E.i.--un1tnown) Bartoniceh 1982 Mum
an B5 [Ee?=LnHnawnj
•|_ 2^0 ppm TCE S hr Inhalation 200 ppm TCE 6 hr ir.haiBl.wi 200 ppni TCE 6 r» inhalntlor
I o_
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Bartonlceli 1062 Human #& (SE>= Unknown)
200 pprn TCE & nr inhalation
EartDnlcek 19
200 pprn TCE &hr inhalation
iceh '96? Human 35 lae/=unfcnnwn 1
200 ppm TCE 6 hi Inhalation
fi-
Retained tit
400
t
= o
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- Bartonltek 1962 Human H6 (sei-untrnown) - Bartanleek 1902 Human #6^er*tinknDwn!i fiartoniceti 1962 Human #6 lse»=untfnDwn)
200 ppm TCE S hr Inhalation | 200 pprn TCE S hr inhalation 200 ppm TCE 5 nt inhalation
r I,
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8 *
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Bertonicek 1962 Human we (sex= unknown) Earromcek i G&l HUUUM Hf£. i-.e.---.)nhi.:." i. Bartanicek 1962 Human #6 isexauiittriown J
200 ppm TCE 6 hf inhalation 200 ppm TCE 5 \» inhalation 200 ppm TCE 5 fir inhilution
1-
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t hi\ t(hr) t'hr)
Barlomoek 19C2 Human #G tse*=imfcnowrtV .-. BartDrtittk IBS^ Humflfi #7teex=f=unKnDwni
200 ppm TCE 5 hr inhalation § 200 ppm TCE 5 hr inhalaboo % 200 ppm TCE S hr inhalation
!-:
!§-
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9
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2D CO
S 0 —
1
0 100 WO 600 700 0 50 60 70 80 90 100
tihr) " MhN K Khn
EartoniceK 1962 Human S7 (sex=unknown| BaftDirlcek 1382 Human fl7 [3e-»=unhnown) Bartoniceh 1962 Htjrrran »7 |ES!i=ijnlinnwn.i
200 pprn TCE & hr Inhnlation 200 ppm TCE 5 hr inhalation 200 ppm TCE 6 nr Inhalntlon
fa
^ o
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8-
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I ft.) KM) llhri
Figure A-35 Comparison of human evaluation data (boxes) and PBPK
model predictions (+ with error bars: single data points or shaded regions:
2.5, 25, 50, 75, and 97.5% population-based predictions) (continued).
A-167
-------
Bartonlcek 1962 Human £7 (SEX= Unknown)
200 pprn TCE S hr inhalation
Bartanlcsk 1983 Human #7 (SEir^unkT
200 ppm TCE 5 Kr inhalation
Bartomcek '• CIS. Hdman »•? (sey=unk
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s-i
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70 80 90 100 o 300
[1
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Figure A-35. Comparison of human evaluation data (boxes) and PBPK
model predictions (+ with error bars: single data points or shaded regions:
2.5, 25, 50, 75, and 97.5% population-based predictions) (continued).
A-168
-------
Lapar
el al 1B95 Human 711 ( s ex = unknown)
muflipte ppivi TGE inhalation
Lapare et al 1395 Human #11 [se>=un
multiple ppm TCE inhalation
Lapare et al 1995 Human #11 is
muhipte ppm TCE in hat.
*"
100
t(hr>
Uipan? el al 1995 Human B11 (sen=uol(nown)
multiple ppm TCE inhalation
0 50 100 150 ZOO
I(hr)
Lapara et al 1995 Human #12 [se-=(jnknown:]
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1000
Lapare Bl al.1995 Human *12 (sex=unknown)
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860
900
950
1000
50
100 150
t Chrf
200
50
100
nun
150 200
Laparp tn al 1935 Human S12 ^ex=unknown)
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Btal i'j
multiple ppm TCE Inhalation
Bloemen et el 2001 Human »t3 (sex=Male?
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950
900 950
t ftrt
1000
850
900
950
1000
Eloemen et aF.2001 Human #13 (se«=Mal*l
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Bloernei ft al .2001 Human
Bloemen et al ZDOf Human #13 (seK=Malei
| 50 ppm TCE 15 nun inhalation 9 limps
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MO ppoi TC£ 1£ mm Inhalation 8 times
BlaemBii =1 al I'O'IM Human ff 12 (EeN=Male/
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Figure A-35. Comparison of human evaluation data (boxes) and PBPK
model predictions (+ with error bars: single data points or shaded regions:
2.5, 25, 50, 75, and 97.5% population-based predictions) (continued).
A-170
-------
50 ppm TCE 15 m n inhalation 8 times 50 ppm TC£ 15 mlrt inhalation Climes •* 1 00 ppm TCE I5mtn Inhalation 8 times
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Blaemen el al 2001 Human #15 <;n-Male) Eloemmi elal.2001 hunur «•:,.;-,.., l.iai.- . Blnemen pi al 2001 Human »18(se.r=Male>
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50 ppm TOE i5 mm inhalation fi li^as 50 ppm TCE 15 min irrhalatinn SlirneE- -| IQQppm TCE 15m
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Figure A-35. Comparison of human evaluation data (boxes) and PBPK
model predictions (+ with error bars: single data points or shaded regions:
2.5, 25, 50, 75, and 97.5% population-based predictions) (continued).
A-171
-------
Fernandez et B! 1277 Hunan S1B (sex=MaleJ
< 60 ppm TCE 8 hr inhalation
Manner etal t979 Human #13 (sea=MalEt)
70 ppm TC-E 4 hr inhalation for 5 days
Monster et al 1379 Human #19 (se*=Male;
70 ppm TCE A hr inhalation for 5 days
10
3D 40
tr.hr/
Monster el al1979 Human *rS (sen-Male)
7Dppni TCE 4 hr inhalation for 5 days
T:
s:
C SO 100 150 200 250 300
tOltl
Monster et aM97S Human 819 r,sex=Male)
70 ppm TCE 4 lit inhalation for 5 days
S-
1 • •
20 50 100
t(hr)
M.HIO, ..> Bi • 3/1 Human «CO ,v-"'.1fli-i
10Q ppm TCE 6 hr inhalation
5 10 20
HhH
50
Muller Etal 1974 Human 020 •,.•>< M.= .7
100 ppm TCE 6 hr inhalation
10 20
50
1?
Manser Br ai t979 Human *19 (sn'Male)
70 ppm TCE 4 hr inhalation for 5 days
5,5-
20
50
100
Monger el al 1979 Human #19 (sex = Male)
70 ppm TCE 4 ht inhalation for 5 days
< 9
10
20
t(hr)
50 100
Munereta(.fB74 Human *!0
100 ppm TCE 3 hr inhalation
5 10
t(hr)
50
iret ai ig?j Human *2D(»i«Male)
100 ppm TCt 6 hr inhalation
Ifrl
40
30
Kh)
50 100 150 200
Uhr)
Monster et al 1979 Human #19 {seir=Male
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§-
I
0.6
1.0
tlhrl
1.2
14
MuHer et al 1974 Human *2Q lseH=
I
3
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L.
a
3
30
40 50
tthr)
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Mu»ei et al.1974 Human KO (sex=Mals)
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5 -
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5 10 20 50
tlhr)
Muller 51 BI 1975 Human 531 |;e»-Mals)
100 ppm TCE 6 hr inhalation
f-.
10 15
Khrl
20
Figure A-35. Comparison of human evaluation data (boxes) and PBPK
model predictions (+ with error bars: single data points or shaded regions:
2.5, 25, 50, 75, and 97.5% population-based predictions) (continued).
A-172
-------
Mulleretal 1B75 Human »21 (sex=Male)
100 ppm TCE 6 hi inhalation
Muller Ft 3l.1975Humantt21 {sex=Male)
100 ppm TCE 6 hr inhalation
Muller et al.1975 Human #21 {se»=-MalE)
100 ppm TCE 6 hr inhalation
2 5 10 20
t(hr)
Muller dal 1975 Human «C1 (sex-Mole)
100 ppm TC6 6 hr inhalation
5
t(hr)
20
Muller •:-• al 1975 Human K'- ise»--Malc-.i
50 ppm TCE 6 lif inhBlalion for 5 days
»1
100
200
trtu]
300
400
Stewsrt etaMSf-OHumen #^3 (se>=un|mawn}
200 ppm TCE 7 hr .nhalalino for S days
50
1 00 150
HtlH
200 250
7re:bio et II .1976 Human «24 (sex-Male)
136 ppm TCE 6 hr inhalation
15
Khr)
20
5 1
t(tir)
tt al.1975 Human #21 (
100 ppm TCE 6 hr inhalation
J
it:
^
*&
10 15
t chr)
Muller at si 1975 Human *I2 {te«=Male)
50 ppm TCE6 hi inhalation for 5 days
50
10D 150
tthrj
200
Stewart et al. 1970 Human #23 (sex=unknown)
200 ppm TCE 7 hr inhalatinQ far 5 days
;-
10 20
tlhr)
50 100
Trelbrg el a 1.1976 Human S24 (SBX=Male)
136 ppm TOE 6 hr inhalation
I*
15
tfhr)
Muller al al-1975 Human *:: (sex=Male)
50 ppm TCE 6 hr inhalation for 5 days
Mullet at all '375 Human *22 <5ex=>Mal«)
50 ppm TCE 6 hrtnhalalion for 5 days
111
20 40 60 80 100 120 140
tltir)
Stewart etaM970 Human #23 (sex=unKnown)
200 ppm TCE 7 hr inhabitancy (of 5 days
•a
50 100 ISO 200 250
tlhr)
Treibig eta* 1976 Human »24 (5ex=Malej
136 ppm TCE 6 rir inhalation
c 10-
5-
£ IN-
O-
15
Mho
Figure A-35. Comparison of human evaluation data (boxes) and PBPK
model predictions (+ with error bars: single data points or shaded regions:
2.5, 25, 50, 75, and 97.5% population-based predictions) (continued).
A-173
-------
Trsitug et al 1B76 Human KM
50
50
Sato etal.1977 Human V25 (sej<=Malel
1 DO ppm TCE 4 hi inhalation
20 50
l(hr)
5eto el al.1877 Human ff25 (3eK
100 ppm TCE 4 hi inhalatio
g -
10
20 50
t(hr)
100 200
Fernandez et si 1977 Human #26 (se«=MalB)
54 ppm TCE 8 nr inhalation
P
53-
H i'
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.E T-
5 D
10
100
200
I (hi)
Fernindez st al 1977 Human 927 (s»«=Male)
97 ppm TCE 8 hr inhala&on
fo-
BTS-I
20
50
I (hi)
100
200
Sato BI al 1977 Human #25
-------
A.6.1. TCE Metabolite Toxicokinetics in Mice: Kim et al. (2009)
Kim et al. (2009) measured TCA, DC A, DCVG, and DCVC in blood of male B6C3Fi
mice following a single gavage dose of 2,140 mg/kg. Of these data, only TCA and DCVG blood
concentrations are predicted by the updated PBPK model, so only those data are compared with
PBPK model predictions (prior values for the distribution volume and elimination rate constant
of DCVG were used, as there were no calibration data informing those parameters). The TCA
data were within the interquartile region of the PBPK model population predictions, as shown in
Figure A-36. The DCVG data were at the lower end of the PBPK model population predictions,
but within the 95% range.
Kim et al. (2009) Male Mouse
2140 mg/kg/d TCE corn oil gavage (single dose)
\
0.5
I
1.0
I
2.0
I
5.0
\
\
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Kim etal. (2009) Male Mouse
2140 mg/kg/d TCE corn oil gavage (single dose)
•5
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0
10
15
20
t(hr)
Figure A-37. Comparison of best-fitting (out of 50,000 posterior samples)
PBPK model prediction and Kim et al. (2009) TCA blood concentration data
for mice gavaged with 2,140 mg/kg TCE.
A-176
-------
O
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8
10
12
t(hr)
Figure A-38. Comparison of best-fitting (out of 50,000 posterior samples)
PBPK model prediction and Kim et al. (2009) DCVG blood concentration
data for mice gavaged with 2,140 mg/kg TCE.
A-177
-------
03
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10'
1CT
10"
oral exposure (mg/kg/d continuous)
Lines and error bars represent the median and 95th percentile CI for the posterior
predictions, respectively (also reported in Section 3.5.7.3.1). Filled circles
represent the predictions from the sample (out of 50,000 total posterior samples)
which provides the best fit to the Kim et al. (2009) TCA and DCVG blood
concentration data for mice gavaged with 2,140 mg/kg TCE.
Figure A-39. PBPK model predictions for the fraction of intake undergoing
GSH conjugation in mice continuously exposed orally to TCE.
A-178
-------
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Inhalation exposure (ppm continuous)
Lines and error bars represent the median and 95th percentile CI for the posterior
predictions, respectively (also reported in Section 3.5.7.3.1). Filled circles
represent the predictions from the sample (out of 50,000 total posterior samples)
which provides the best fit to the Kim et al. (2009) TCA and DCVG blood
concentration data for mice gavaged with 2,140 mg/kg TCE.
Figure A-40. PBPK model predictions for the fraction of intake undergoing
GSH conjugation in mice continuously exposed via inhalation to TCE.
An additional note of interest from the Kim et al. (2009) data is the interstudy variability
in TCA kinetics. In particular, the TCA blood concentrations reported by Kim et al. (2009) are
twofold lower than those reported by Abbas and Fisher (1997) in the same sex and strain of
mouse, with a very similar corn oil gavage dose of 2,000 mg/kg [as compared to 2,140 mg/kg
used in Kim et al. (2009)1.
A.6.2. TCE Toxicokinetics in Rats: Liu et al. (2009)
Liu et al. (2009) measured TCE in blood of male rats after treatment with TCE by i.v.
injection (0.1, 1.0, or 2.5 mg/kg) or aqueous gavage (0.0001, 0.001, 0.01, 0.1, 1, 2.5, 5, or
10 mg/kg). Almost all of the data from gavage exposures were within the interquartile region of
the PBPK model population predictions, with all of it within the 95% CI, as shown in Figure A-
41. For i.v. exposures, the data at 1 and 2.5 mg/kg were well simulated, but the time-course data
at 0.1 mg/kg were substantially different in shape from that predicted by the PBPK model, with a
A-179
-------
lower initial concentration and longer half-life. The slower elimination rat at 0.1 mg/kg was
noted by the study authors through use of noncompartamental analysis. There is no clear
explanation for this discrepancy, particularly since the gavage data at this and even lower doses
were well predicted by the PBPK model.
Liu eta HUGOS) Male Ret
0.1 mg/fcgTCE iv 0 1 mg/kg
Line! el, iSCKr) Male Rat
fl mgAcg TCE \
Liu el Bl, (200B1 Male Ral
2.5 mg/kg TCE iu 2.5 mg/kg
0.20 050
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0.001 mq.'V.q TCC aqueous gavage 0.001 mgiVq 0.01 mgAg TCE aqueous gavage 0.01 mg/kg
8
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Liu etal.(ZOQB} Male Ral
0.1 mgfltg TC E aqu eou s gavage 0.1m g/kg
0.05
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Liu eta I .(2009) Male Ral
1 rug/kg TCEaqu9ousg3v,ige 1 mg/kg
t —
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t(hr)
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-------
A.6.3.1. Analysis Using Evans et al. (2009) and Chiu et al. (2009) PBPK Model
TCA blood and liver concentrations were reported by Mahle et al. (1999) for male
B6C3Fi mice and male F344 rats exposed to 0.1 g/L to 2 g/L TCA in drinking water for 3 or
14 days (12-270 mg/kg-day in mice and 7-150 mg/kg-day in rats). For mice, these data were all
within the 95% CI of PBPK model population predictions, with about half of these data within
the interquartile region. For rats, all of these data, except those for the 3-day exposure at 0.1 g/L,
were within the 95% CI of the PBPK model predictions. In addition, the median rat predictions
were consistently higher than the data, although this could be explained by interstudy (strain, lot,
etc.) variability.
TCA blood concentrations were reported by Green (2003a) for male and female B6C3Fi
mice exposed to 0.5-2.5 g/L TCA in drinking water for 5 days (130-600 mg/kg-day in males and
160-750 mg/kg-day in females). Notably, these animals consumed around twice as much water
per day as compared to the mice reported by Mahle et al. (1999), and therefore, received
comparatively higher doses of TCA for the same TCE concentration in drinking water.
In male mice, the data at the lower two doses (130 and 250 mg/kg-day) were within the
interquartile region of the PBPK model predictions. The data for male mice at the highest dose
(600 mg/kg-day) were below the interquartile region, but within the 95% CI of the PBPK model
predictions. In females, the data at the lower two doses (160 and 360 mg/kg-day) were mostly
below the interquartile region, but within the 95% CI of the PBPK model predictions, while
about half of the data at the highest dose were just below the 95% CI.
TCA blood, plasma, and liver concentrations were reported by Green (2003b) for male
PPARa-null mice, male 129/sv mice (the background strain of the PPARa-null mice), and male
and female B6C3Fi mice, exposed to 1.0 or 2.5 g/L TCA in drinking water for 5 days (male
B6C3Fi only) to 14 days.2 In male PPARa-null mice, plasma and blood concentrations were
within the interquartile region of the PBPK model predictions, while liver concentrations were
below the interquartile region but within the 95% CI. In male 129/sv mice, the plasma
concentrations were within the interquartile region of the PBPK model predictions, while blood
and liver concentrations were below the interquartile region but within the 95% CI. In male
B6C3Fi mice, all data were within the 95% CIs of the PBPK model predictions, with about half
within the interquartile region, and the rest above (plasma concentrations at the lower dose) or
below (liver concentrations at all but the lowest dose at 5 days). In female B6C3Fi mice, plasma
concentrations were below the interquartile region but within the 95% confidence region, while
liver and blood concentrations were at or below the lower 95% confidence bound.
2Sweeney et al. (2009) reported that blood concentrations in Green (2003b) were incorrect due to an arithmetic error
owing to a change in chemical analytic methodology, and should have been multiplied by 2. This correction was
included in the present analysis.
A-181
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Overall, the predictions of the TCA submodel of the updated TCE PBPK model appear
consistent with these data on the toxicokinetics of TCA after drinking water exposure in male
rats and male mice. In female mice, the reported concentrations tends to be at the low end of or
lower than those predicted by the PBPK model. Importantly, the data used for calibrating the
mouse PBPK model parameters were predominantly in males, with only Fisher et al. (1991) and
Fisher and Allen (1993) reporting TCA plasma levels in female mice after TCE exposure. In
addition, median PBPK model predictions at higher doses (>300 mg/kg-day), even in males,
tended to be higher than the concentrations reported. While TCA kinetics after TCE exposure
includes predicted internal production at these higher levels, previously published data on TCA
kinetics alone only included doses up to 100 mg/kg, and only in males. Therefore, these results
suggest that the median predictions of the TCA submodel of the updated TCE PBPK model are
somewhat less accurate for female mice and for higher doses of TCA (>300 mg/kg-day) in mice,
though the 95% CIs still cover the majority of the reported data. Finally, the ratio of blood to
liver concentrations of-1.4 reported in the mouse experiments in Mahle et al. (1999) were
significantly different from the ratios of-2.3 reported by Green (2003b), a difference for which
there is no clear explanation given the similar experimental designs and common use the B6C3Fi
mouse strain. Because median PBPK model predictions for the blood to liver concentration ratio
for these studies are -1.3, they are more consistent with the Mahle et al. (1999) data than with
the Green (2003b) data.
A.6.3.2. Summary of Results From Chiu of Bayesian Updating of Evans et al. (2009)
and Chiu et al. (2009) Model Using TCA Drinking Water Data
Sweeney et al. (2009) also suggested that the available data, in conjunction with
deterministic modeling using the TCA portion of the Hack et al. (2006) TCE PBPK model,
supported a hypothesis that the bioavailability of TCA in drinking water in mice is substantially
<100%. Classically, oral bioavailability is assessed by comparing blood concentration profiles
from oral and i.v. dosing experiments, because blood concentration data from oral dosing alone
cannot distinguish fractional uptake from metabolism. Schultz et al. (1999) made this
comparison in rats at a single dose of 82 mg/kg, and reported an empirical bioavailability of
116%, consistent with complete absorption. A priori, there would not seem to be a strong reason
to suspect that oral absorption in mice would be significantly different from that in rats. As
discussed above in the evaluation of Hack et al. (2006) model, available data strongly support
clearance of TCA in addition to urinary excretion, based on the finding of < 100% recovery in
urine after i.v. dosing. In addition, as the current TCE PBPK model assumes 100% absorption
for orally-administered TCA, and the PBPK model predictions are consistent with these data, it
is likely that the limited bioavailability determined by Sweeney et al. (2009) was confounded by
this additional clearance pathway unaccounted for by Hack et al. (2006). Therefore, Chiu
A-182
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conducted a Bayesian reanalysis of the TCE mouse PBPK model, the results of which are
summarized here.
In brief, the TCA submodel from Evans et al. (2009) and Chiu et al. (2009) is augmented
by the addition of a fractional absorption parameter for drinking water exposures and parameters
reestimated by adding the newly available TCA drinking water kinetic studies in mice. Being
nocturnal animals, rodents do not have a steady pattern of drinking water consumption
throughout the day. It has been suggested that a 90/10%-split between dark-cycle (night
time)/light-cycle (day time) drinking water consumption is a reasonable approximation (Yuan,
1995), and that pattern is assumed here. Most analyses assume something similar (e.g., Sweeney
et al., 2009, assumed 100% consumption during the dark cycle).
However, TCA kinetics from drinking water exposures also depends on the relationship
between the times of the light/dark cycle and the times of specimen collection (i.e., at what time
during the cycle did exposure begin [when is 'V = 0"])? These data are not specified in any of
the available technical reports cited by Sweeney et al. (2009). Therefore, in the present analysis,
three different assumptions that represent a range of possibilities were made, and the results of
each were carried through the analysis. These patterns are shown in Figure A-42 and designated
low-12/high-12 (LH), low-6/high-12/low-6 (LHL), and high-12/low-12 (HL). In the first, it is
assumed that the start of exposure coincided exactly with the start of the light cycle; in the
second, it is assumed that the start of exposure was exactly in the middle of the light cycle; and
in the last case, it is assumed that the start of exposure was exactly at the end of the light cycle.
A priori, one of the first two patterns (LH and LHL) would appear to be most likely, but the last
pattern (HL) was included for completeness. Sweeney et al. (2009) assumed drinking water
intake was most similar to the LH pattern.
A-183
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£•
"O
2
.> —
S 1
£ a
"cb
t
1.8
1.4
1 1
1
0.8
0.6
0.4
0.2
Low-12/High
-12(LH(
12 18 24 30 36
time since beginning of exposure
|_
I
Low-»Hign-12/low-6 (LHL)
0 6 12 18 24 30 36 42
time since beginning of exposure
?
1.4
0.8
0.8
0.4
Hiih-12/Low-12(HLj
6 12 18 24 30 36
time since beginning of exposure
The upper left panel (LH) assumes that t = 0 is at the beginning of the "light" part
of the "light/dark" cycle (light is dashed grey line at the bottom, dark is thick
black line at the bottom). The upper right panel (LHL) assumes that t = 0 is in the
middle of the "light" part of the cycle. The lower left panel (HL) assumes that
t = 0 is at the end of the "light" part of the cycle.
Figure A-42. Assumed drinking water patterns as a function of time since
beginning of exposure.
A-184
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As was done by Evans et al. (2009) and Chiu et al. (2009). the PBPK parameter
estimation is performed in a hierarchical Bayesian population statistical framework, with
calculations performed using MCMC, using posteriors from the earlier analysis as priors for the
reanalysis. A total of six different model runs were made using the "harmonized" PBPK model,
as shown in Table A-18, using different assumptions for fractional absorption and for drinking
water intake patterns. Comparisons between different modeling assumptions (i.e., fixing or
estimating fractional absorption; assumed drinking water patterns) were made using the deviance
information criterion (DIG) (Spiegelhalter et al., 2002). The DIG is a Bayesian analogue to the
AIC and is used in a similar manner, with smaller values indicating better model fits. As with
the AIC, "small" differences in DIG (e.g., <5, as suggested by the WinBUGS "DIC page"
[http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/dicpage.shtmll) are not likely to be important, but
much lower values suggest substantially better fitting models. Results of these comparison are
also shown in Table A-18. Adding the fractional absorption parameter decreases the DIC by
about 100 units, which strongly supports inclusion of the parameter. In addition, in both cases of
fixed and fitted fractional absorption, the lowest DIC was for the LHL drinking water intake
pattern, with the second lowest DIC for the LH pattern, with a difference of 33 units in DIC.
Given that these model runs are highly favored relative to the others, the rest of this summary
reports the results for the "LHL.fitted" run (see Chiu, 2011, for additional details).
Table A-18. Summary characteristics of model runs
Run
designation
LH.fixed
LHL.fixed
HL.fixed
LH.fitted
LHL.fitted
HL.fitted
Drinking water pattern
Low-12/high-12
Low-6/high-12/low-6
High-12/low-12
Low-12/high-12
Low-6/high-12/low-6
High-12/low-12
Fractional absorption
Fixed
A/
A/
A/
Fitted
A/
A/
A/
Convergence
R< .04
R< .09
R< .05
R< .05
R< .11
R< .12
DIC
895
877
897
764
731
781
Posterior model fits for the LHL.fitted runs are shown in Figures A-43 and A-44, using a
representative sample from the converged MCMC chain. A dose-dependent fractional
absorption can account for the less-than-proportional increase in TCA blood concentrations
between the middle and high dose groups observed in Mahle et al. (1999) (see Figure A-43) and
among all of the dose groups observed in Green (2003a, 2003b) (see Figure A-44).
A-185
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Mahte et ai. (2001} m 86C3F1 {3 d) ILHL.fitteetl
Mahleetat, (2001) m B6C3F1 (14 d) [LKLfitted]
<
O
0
80 90
Mahleetal. (2001) m B6C3F1 (14d) [LHL.fittedl
300 310 320 330 340 350 360
Mahte et al. |2001J m 86C3F1 (3 d) [LHL.fitted]
_» o -
-£
*j
<
o
F
300 310 320 330 340 350 360
t (Hours)
40 50 60 TO 80 90
t (hours)
Three- and 14-day exposures to 0.08 (data: open circles, predictions: solid line),
0.8 (data: open triangle, predictions: dashed line), and 2 g/L TCA in drinking
water (data: crosses, predictions: dotted line). Predictions use a representative
parameter sample from the converged MCMC chain for the LHL drinking water
intake pattern.
Figure A-43. PBPK model predictions for TCA in blood and liver of male
B6C3Fi mice from Mahle et al. (1999).
A-186
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Green (2003a) m B6C3F1 (5 d) [LHL.fitted]
Green (2003b) m B6C3F1 (5 d) [LHL.fitted]
20
40
60
80
100
120
t (hours)
Green (2003b) m B6C3F1 (14 d) [LHL.fitted]
85 90 95 100 105 110 115 120
t (hours)
Green (2003b) m B6C3F1 (14 d) [LHL.fitted]
o
i-
t -1
<
o
300 305 310 315 320 325 330 335
t (hours)
Green (2003b) m B6C3F1 (5 d) [LHL.fitted]
300 305 310 315 320 325 330 335
t (hours)
.- o
< ^
o
85 90
95
100 105
t (hours)
110 115 120
Green (2003a): 5-day drinking water exposures to 0.5 (data: open circle;
predictions: solid line), 1 (data: open triangle; predictions: dashed line), and
2.5 g/L TCA (data: crosses; predictions: dotted lines). Green (2003b): 5- and
14-day drinking water exposures to 1 (data: open circle; predictions: solid line)
and 2.5 g/L TCA (data: open triangle; predictions: dashed line). Predictions use a
representative parameter sample from the converged MCMC chain for the LHL
drinking water intake pattern.
Figure A-44. PBPK model predictions for TCA in blood and liver of male
B6C3Fi mice from Green (2003a, 2003b).
A-187
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As was done by Sweeney et al. (2009), fractional absorption is separately estimated for
each drinking water dose group, and the results are fit to a parametric model, shown in
Figure A-45. Several features of the data and analysis are worth noting. First, there is a general
trend for decrease in fractional absorption with increasing concentration, evident even within
studies. Second, there appears to be substantial interstudy and intrastudy variability in the
apparent fractional absorption. This is particularly evident across strains in Green (2003b)—the
PPARa-null and 129/sv mice appear to have substantially higher fractional absorption than the
B6C3Fi mice, even though in all strains, there appeared to be a decreasing trend with increasing
TCA concentration. Third, the fractional absorption estimates increase as the "start of exposure"
is assumed to be later and later in the "light" cycle. Fourth, the estimated fractional absorption at
low concentrations is fairly high, at >80%. Finally, the estimates for fractional absorption from
the current analysis are 3-4 times greater than those reported by Sweeney et al. (2009). Because
hepatic clearance was not included in the previous Hack et al. (2006) version of the TCE model
used by Sweeney et al. (2009), and this could partially explain why they found a very low
fractional absorption to be necessary to provide a fit to the observed data from drinking water
exposures.
Low-6/High-12/Low-6 Drinking Water Intake
1 2
TCA Concentration (g/L)
O Mahleetal. (2001)B6C3F1
D Green (2003a,2003b)
B6C3F1
A Green (2003a,2003b)
sv129(PPARalpha-null and
wild)
^^•MichaelisMenten Fit
Sweeneyetal. (2009)
estimatesfor Mahle et al.
(2001)B6C3F1
Sweeney etal. (2009)
estimatesfor Green
(2003a,2003b)B6C3F1
Michaelis-Menten Fit to
Sweeney etal. (2009)
Fits are to a Michaelis-Menten function for "effective" concentration Ceg- = Cmax ;
C/(Ci/2 + C), so that the fractional absorption Fabs = Ceff/C = Cmax/(Ci/2 + C).
Sweeney et al. (2009) estimates of Fabs, along with a Michaelis-Menten fit, are
included for comparison. The ratio Cmax/Ci/2 gives the fractional uptake at low
concentrations.
Figure A-45. Distribution of fractional absorption fit to each TCA drinking
water kinetic study group in mice, using LHL drinking water intake
patterns.
A-188
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In sum, comparing model results with complete- and less-than-complete-fractional
absorption, it is evident (e.g., through the much lower DIG) that including a concentration-
dependent fractional absorption substantially improves model fits. Thus, these
data are consistent with reduced bioavailability from drinking water, particularly at higher TCA
drinking water concentrations. However, the estimates of fractional absorption are three- to
fourfold higher than those estimated by Sweeney et al. (2009). In addition, there appeared to be
substantial inter- and intrastudy variability, with the fractional absorption for some mouse strains
estimated to be nearly complete even at the higher TCA drinking water concentrations. Thus, on
the whole, adding a fractional absorption parameter substantially improves the PBPK model
predictions, though the degree of absorption is greater than that reported by Sweeney et al.
(2009) and appears to be variable between studies and mouse strains. Data are lacking as to a
mechanistic basis for reduced absorption of TCA at higher doses. Biliary excretion is a
possibility, though data from rats suggest that the degree of biliary excretion of TCA is rather
modest (Stenner et al., 1997). It is also possible that the nonlinearity in TCA kinetics reflects a
difference in clearance processes, such as saturation of renal reabsorption, which would lead to
increased urinary clearance and reduced internal dose. This could be tested experimentally by
simultaneously measuring blood and urinary kinetics of TCA at different doses. However, this
would not explain differences between drinking water and gavage dosing.
The degree of interexperimental variability raises the question of whether the apparent
fractional absorption may be due, in part, to experimental factors, such as analytical errors due to
incomplete/inadequate procedures to prevent TCA degradation or experimental losses in
estimating drinking water consumption rates. With respect to TCA degradation, Mahle et al.
(1999) appeared to be specifically aware of the issue and froze biological samples prior to
analysis in order to address it. However, lacking any external validation, the extent to which this
was completely successful is unclear. On the other hand, Green (2003a, 2003b) did not appear to
have any particular procedure designed to address TCA degradation. Thus, the extent and
impact of TCA degradation is not clear, though it may be a plausible explanation for the degree
of variability observed across data sets. With respect to drinking water consumption,
experimental variance is notable with respect to reported drinking water consumption rates, with
Green (2003a) > Green (2003b) > Mahle et al. (1999) > other TCA drinking water studies. One
may hypothesize that the actual drinking water consumption rates are roughly equal, with
differences in reported values reflecting experimental losses. However, in this case, reported
drinking water consumption would inversely correlate with fractional absorption, and no such
correlation is evident. In addition, this does not explain the consistent dose-related trends within
a study or data set, even if the slope of the trend varies between experiments.
A-189
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Overall, then, it may be more accurate to characterize the fractional absorption as an
empirical parameter reflecting unaccounted-for biological processes as well as experimental
variation.
A.7. UPDATED PBPK MODEL CODE
The following pages contain the updated PBPK model code for the MCSim software
(version 5.0.0). Additional details on baseline parameter derivations are included as inline
documentation. Example simulation files containing prior distributions and experimental
calibration data are available electronically:
• Mouse ("Supplementary data for TCE assessment: Mouse population example," 2011)
• Rat ("Supplementary data for TCE assessment: Rat population example," 2011)
• Human ("Supplementary data for TCE assessment: Human population example," 2011)
A-190
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#### HISTORY OF HACK ET AL. (2006) MODEL
# Model code to correspond to the block diagram version of the model
# Edited by Deborah Keys to incorporate Lapare et al. 1995 data
# Last edited: August 6, 2004
# Translated into MCSim from acslXtreme CSL file by Eric Hack, started 31Aug2004
# Removed nonessential differential eguations (i.e., AUCCBld) for MCMC runs.
# Changed QRap and QSlw calculations and added QTot to scale fractional flows
# back to 1 after sampling.
# Finished translating and verifying results on 15Sep2004.
# Changed QSlw calculation and removed QTot 21Sep2004.
# Removed diffusion-limited fat uptake 24Sep2004.
#### HISTORY OF U.S. EPA (2009) MODEL (CHIU ET AL., 2009)
# Extensively revised by U.S. EPA June 2007-June 2008
# - Fixed hepatic plasma flow for TCA-submodel to include
# portal vein (i.e., QGutLivPlas -- originally was just
# QLivPlas, which was only hepatic artery).
# - Clearer coding and in-line documentation
# - Single model for 3 species
# - Revised physiological parameters, with discussion of
# uncertainty and variability,
# - In vitro data used for default metabolism parameters,
# with discussion of uncertainty and variability
# - added TCE blood compartment
# - added TCE kidney compartment, with GSH metabolism
# - added DCVG compartment
# - added additional outputs available from in vivo data
# - removed DCA compartment
# - added IA and PV dosing (for rats)
# - Version 1.1 -- fixed urinary parameter scaling
# — fixed VBod in kUrnTCOG (should be VBodTCOH)
# - Version 1.1.1 -- changed some truncation limits (in commments only)
# - Version 1.2 --
# -- removed TB compartment as currently coded
# -- added respiratory oxidative metabolism:
# 3 states: AInhResp, AResp, AExhResp
# -- removed clearance from respiratory metabolism
# - Version 1.2.1 -- changed oral dosing to be similar to IV
# - Version 1.2.2 -- fixed default lung metabolism (additional
# scaling by lung/liver weight ratio)
# - Version 1.2.3 — fixed FracKidDCVC scaling
for
NOTE — lines
AExc, #(vrisk) excreted in feces from gavage (currently 0)
AO, #(vrisk) total absorbed
InhDose, # Amount inhaled
— TCE in the body
ARap, # Amount in rapidly perfused tissues
ASlw, # Amount in slowly perfused tissues
AFat, # Amount in fat
AGut, # Amount in gut
ALiv, # Amount in liver
AKid, # Amount in Kidney -- previously in Rap tissue
ABld, # Amount in Blood -- previously in Rap tissue
AInhResp, # Amount in respiratory lumen during inhalation
AResp, # Amount in respiratory tissue
AExhResp, # Amount in respiratory lumen during exhalation
— TCA in the body
AOTCA, #(vrisk)
AStomTCA, # Amount of TCA in stomach
APlasTCA, # Amount of TCA in plasma #comment out for
ABodTCA, # Amount of TCA in lumped body compartment
ALivTCA, # Amount of TCA in liver
— TCA metabolized
AUrnTCA, # Cumulative Amount of TCA excreted in urine
AUrnTCA_sat, # Amount of TCA excreted that during times that had
# saturated measurements (for lower bounds)
AUrnTCA collect,# Cumulative Amount of TCA excreted in urine during
# collection times (for intermittent collection)
— TCOH in body
AOTCOH,
AStomTCOH,
ABodTCOH,
ALivTCOH,
— TCOG in body
ABodTCOG,
ALivTCOG,
ABileTCOG,
ARecircTCOG,
— TCOG excreted
AUrnTCOG, # Amount of TCOG excreted in urine
AUrnTCOG_sat, # Amount of TCOG excreted that during times that had
# saturated measurements (for lower bounds)
AUrnTCOG_collect,# Cumulative Amount of TCA excreted in urine during
# collection times (for intermittent collection)
— DCVG in body
ADCVGIn, #(vrisk)
ADCVGmol, # Amount of DCVG in body in mmoles
AMetDCVG, #(vrisk)
#(vrisk)
# Amount of TCOH in stomach
# Amount of TCOH in lumped body compartment
# Amount of TCOH in liver
# Amount of TCOG in lumped body compartment
# Amount of TCOG in liver
# Amount of TCOG in bile (incl. gut)
#(vrisk)
States = {
##— TCE uptake
AStom,
ADuod,
NAcDCVC excreted
AUrnNDCVC,
Other states for TCE
ACh,
AExh,
# Amount of NAcDCVC excreted
# Amount in closed chamber -- mice and rats only
# Amount exhaled
AExhExp, # Amount exhaled during expos [to calc. retention]
Metabolism
A-191
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AMetLivl, #(vrisk) Amount metabolized by P450 in liver
AMetLiv2, #(vrisk) Amount metabolized by GSH conjugation in liver
AMetLng, #(vrisk) Amount metabolized in the lung
AMetKid, #(vrisk)
AMetTCOHTCA, #(vrisk) Amount of TCOH metabolized to TCA
AMetTCOHGluc, #(vrisk) Amount of TCOH glucuronidated
AMetTCOHOther, #(vrisk)
AMetTCA, #(vrisk) Amount of TCA metabolized
#-- Other Dose metrics
AUCCKld, t (vrisk)
AUCCRap, # (vrisk)
AUCCTCOH, t (vrisk)
AUCCBodTCOH,
AUCTotCTCOH,
AUCPlasTCAFree,
AUCPlasTCA,
AUCLlvTCA,
AUCCDCVG t (vrisk)
# (vrisk)
t (vrisk)
t (vrisk)
# (vrisk)
t (vrisk)
# Inhalation exposure cone.
# IV dose (mg/kg)
# Oral gavage dose (mg/kg)
# Drinking water dose (mg/kg-day)
# Inter-arterial
# Portal Vein
Inputs = {
##— TCE dosing
Cone,
IVDose,
PDose,
Drink,
lADose,
PVDose,
##— TCA dosing
IVDoseTCA,
PODoseTCA,
##— TCOH dosing
IVDoseTCOH, # IV dose (mg/kg) of TCOH
PODoseTCOH, # Oral dose (mg/kg) of TCOH
##-- Potentially time-varying parameters
QPmeas, # Measured value of Alveolar ventilation QP
TCAUrnSat, # Flag for saturated TCA urine
TCOGUrnSat, # Flag for saturated TCOG urine
UrnMissing # Flag for missing urine collection times
#*** Outputs for mass balance check
MassBalTCE,
TotDose,
TotTissue,
MassBalTCOH,
TotTCOHIn,
TotTCOHDose,
TotTissueTCOH,
TotMetabTCOH,
MassBalTCA,
TotTCAIn,
TotTissueTCA,
MassBalTCOG,
TotTCOGIn,
TotTissueTCOG,
MassBalDCVG,
MassBalDCVC,
AUrnNDCVCeguiv,
#*** Outputs that are potential dose metrics
TotMetab, #(vrisk) Total metabolism
TotMetabBW34, #(vrisk) Total metabolism/BW"3/4
ATotMetLiv, #(vrisk) Total metabolism in liver
AMetLivlLiv, #(vrisk) Total oxidation in liver/liver volume
AMetLivOther, #(vrisk) Total "other" oxidation in liver
AMetLivOtherLiv, #(vrisk) Total "other" oxidation in liver/liver vol
AMetLngResp, #(vrisk) oxiation in lung/respiratory tissue volume
AMetGSH, #(vrisk) total GSH conjugation
AMetGSHBW34, # (vrisk) total GSH conjugation/BW'v3/4
ABioactDCVCKid, #(vrisk) Amount of DCVC bioactivated/kidney volume
# NEW
TotDoseBW34, # (vrisk) mg intake / BW"3/4
AMetLivlBW34, #(vrisk) mg hepatic oxidative metabolism / BW-3/4
TotOxMetabBW34, #(vrisk) mg oxidative metabolism / BW-3/4
TotTCAInBW, #(vrisk) TCA production / BW
AMetLngBW34, #(vrisk) oxiation in lung/BW"3/4
ABioactDCVCBW34, #(vrisk) Amount of DCVC bioactivated/BW"3/4
AMetLivOtherBW34, # (vrisk) Total "other" oxidation in liver/BW'v3/4
#*** Outputs for comparison to in vivo data
# TCE
RetDose, # human - = (InhDose - AExhExp)
CAlv, # needed for CAlvPPM
CAlvPPM, # human
CInhPPM, # mouse, rat
CInh, # needed for CMixExh
CMixExh, # rat - Mixed exhaled breath (mg/1)
CArt, # rat, human - Arterial blood concentration
CVen, # mous e, rat, human
CBldMix, # rat - Concentration in mixed arterial+venous blood
# (used for cardiac puncture)
CFat, # mouse, rat - Concentration in fat
CGut, # rat
CRap, # needed for unlumped tissues
CSlw, # needed for unlumped tissues
CHrt, # rat - Concentration in heart tissue [use CRap]
CKid, # mouse, rat - Concentration in kidney
CLiv, # mouse, rat - Concentration in liver
CLung, # mouse, rat - Concentration in lung [use CRap]
CMus, # rat - Concentration in muscle [use CSlw]
CSpl, # rat - Concentration in spleen [use CRap]
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CBrn, # rat - Concentration in brain [use CRap]
zAExh, # mouse
zAExhpost, # rat - Amount exhaled post-exposure (mg)
# TCOH
CTCOH, # mouse, rat, human - TCOH concentration in blood
CKidTCGH, # mouse - TCOH concentration in kidney
CLivTCOH, # mouse - TCOH concentration in liver
CLungTCOH, # mouse - TCOH concentration in lung
# TCA
CPlasTCA, # mouse, rat, human - TCA concentration in plasma
CBldTCA, # mouse, rat, human - TCA concentration in blood
CBodTCA, # needed for CKidTCA and CLungTCA
CKidTCA, # mouse - TCA concentration in kidney
CLivTCA, # mouse, rat - TCA concentration in liver
CLungTCA, # mouse - TCA concentration in lung
zAUrnTCA, # mouse, rat, human - Cumulative Urinary TCA
zAUrnTCA_collect, # human - TCA measurements for intermittent collection
zAUrnTCA sat, # human - Saturated TCA measurements
zAUrnNDCVC,
AUrnTCTotMole,
# mouse - TCOG concentration in blood (in TCOH-eguiv)
# mouse - TCOG concentration in kidney (in TCOH-eguiv)
# mouse - TCOG concentration in liver (in TCOH-eguiv)
# mouse - TCOG concentration in lung (in TCOH-eguiv)
# mouse, rat, human - Cumulative Urinary TCOG (in TCOH-eguiv)
# human - TCOG (in TCOH-eguiv) measurements for
# intermittent collection
at, # human - Saturated TCOG (in TCOH-eguiv) measurements
# Other
CDCVGmol, # concentration of DCVG (mmol/1)
CDCVGmolO, # Dummy variable without likelihood (for plotting)#(vl.2.3.1)
CDCVG_ND, # Non-detect of DCVG (<0.05 pmol/ml= 5e-5 mmol/1 )#(vl.2.3.1)
# Output -ln(likelihood)#(vl.2.3.1)
# rat, human - Cumulative urinary NAcDCVC
# rat, human - Cumulative urinary TCOH+TCA in mmoles
TotCTCOH, # mouse, human - TCOH+TCOG Concentration (in TCOH-eguiv)
TotCTCOHcomp, # ONLY FOR COMPARISON WITH HACK
# ONLY FOR COMPARISON WITH HACK
# human - sampled value of alveolar ventilation rate
# PARAMETERS #(vrisk)
QCnow, # (vrisk) #Cardiac output (L/hr)
QP, # (vrisk) #Alveolar ventilation (L/hr)
QFatCtmp, # (vrisk) #Scaled fat blood flow
QGutCtmp, # (vrisk) #Scaled gut blood flow
QLivCtmp, # (vrisk) #Scaled liver blood flow
QSlwCtmp, # (vrisk) #Scaled slowly perfused blood flow
QRapCtmp, # (vrisk) #Scaled rapidly perfused blood flow
QKidCtmp, # (vrisk) #Scaled kidney blood flow
DResp, # (vrisk) #Respiratory lumen:tissue diffusive clearance rate
VFatCtmp, # (vrisk) #Fat fractional compartment volume
VGutCtmp, # (vrisk) #Gut fractional compartment volume
VLivCtmp, # (vrisk) #Liver fractional compartment volume
VRapCtmp, # (vrisk) #Rapidly perfused fractional compartment volume
VRespLumCtmp, # (vrisk) # Fractional volume of respiratory lumen
VRespEffCtmp, # (vrisk) #Effective fractional volume of respiratory tissue
VKidCtmp, # (vrisk) #Kidney fractional compartment volume
VBldCtmp, # (vrisk) #Blood fractional compartment volume
VSlwCtmp, # (vrisk) #Slowly perfused fractional compartment volume
VPlasCtmp, # (vrisk) #Plasma fractional compartment volume
VBodCtmp, # (vrisk) #TCA Body fractional compartment volume [not incl.
blood+liver]
VBodTCOHCtmp, # (vrisk) #TCOH/G Body fractional compartment volume [not incl.
liver]
PB, # (vrisk) #TCE Blood/air partition coefficient
PFat, # (vrisk) #TCE Fat/Blood partition coefficient
PGut, # (vrisk) #TCE Gut/Blood partition coefficient
PLiv, # (vrisk) #TCE Liver/Blood partition coefficient
PRap, # (vrisk) #TCE Rapidly perfused/Blood partition coefficient
PResp, # (vrisk) #TCE Respiratory tissue:air partition coefficient
PKid, # (vrisk) #TCE Kidney/Blood partition coefficient
PSlw, # (vrisk) #TCE Slowly perfused/Blood partition coefficient
TCAPlas, # (vrisk) #TCA blood/plasma concentration ratio
PBodTCA, # (vrisk) #Free TCA Body/blood plasma partition coefficient
PLivTCA, # (vrisk) #Free TCA Liver/blood plasma partition coefficient
kDissoc, # (vrisk) #Protein/TCA dissociation constant (umole/L)
BMax, # (vrisk) #Maximum binding concentration (umole/L)
PBodTCOH, # (vrisk) #TCOH body/blood partition coefficient
PLivTCOH, # (vrisk) #TCOH liver/body partition coefficient
PBodTCOG, # (vrisk) #TCOG body/blood partition coefficient
PLivTCOG, # (vrisk) #TCOG liver/body partition coefficient
VDCVG, # (vrisk) #DCVG effective volume of distribution
kAS, # (vrisk) #TCE Stomach absorption coefficient (/hr)
kTSD, # (vrisk) #TCE Stomach-duodenum transfer coefficient (/hr)
kAD, # (vrisk) #TCE Duodenum absorption coefficient (/hr)
kTD, # (vrisk) #TCE Duodenum-feces transfer coefficient (/hr)
kASTCA, # (vrisk) #TCA Stomach absorption coefficient (/hr)
kASTCOH, # (vrisk) #TCOH Stomach absorption coefficient (/hr)
VMAX, # (vrisk) #VMAX for hepatic TCE oxidation (mg/hr)
KM, # (vrisk) #KM for hepatic TCE oxidation (mg/L)
FracOther, # (vrisk) #Fraction of hepatic TCE oxidation not to TCA+TCOH
FracTCA, # (vrisk) #Fraction of hepatic TCE oxidation to TCA
VMAXDCVG, # (vrisk) #VMAX for hepatic TCE GSH conjugation (mg/hr)
KMDCVG, # (vrisk) #KM for hepatic TCE GSH conjugation (mg/L)
VMAXKidDCVG, # (vrisk) #VMAX for renal TCE GSH conjugation (mg/hr)
KMKidDCVG, # (vrisk) #KM for renal TCE GSH conjugation (mg/L)
FracKidDCVC, # (vrisk) #Fraction of renal TCE GSH conj. "directly" to DCVC
# (vrisk) #(i.e., via first pass)
VMAXClara, # (vrisk) #VMAX for Tracheo-bronchial TCE oxidation (mg/hr)
KMClara, # (vrisk) #KM for Tracheo-bronchial TCE oxidation (mg/L)
FracLungSys, # (vrisk) #Fraction of respiratory metabolism to systemic circ.
VMAXTCOH, # (vrisk) #VMAX for hepatic TCOH->TCA (mg/hr)
KMTCOH, # (vrisk) #KM for hepatic TCOH->TCA (mg/L)
VMAXGluc, # (vrisk) #VMAX for hepatic TCOH->TCOG (mg/hr)
KMGluc, # (vrisk) #KM for hepatic TCOH->TCOG (mg/L)
kMetTCOH, # (vrisk) #Rate constant for hepatic TCOH->other (/hr)
kUrnTCA, # (vrisk) #Rate constant for TCA plasma->urine (/hr)
kMetTCA, # (vrisk) #Rate constant for hepatic TCA->other (/hr)
A-193
-------
kBile, # (vrisk) #Rate constant for TCOG liver->bile (/hr)
kEHR, t (vrisk) fLumped rate constant for TCOG bile->TCOH liver (/hr)
kUrnTCOG, # (vrisk) #Rate constant for TCOG->urine (/hr)
kDCVG, # (vrisk) fRate constant for hepatic DCVG->DCVC (/hr)
kNAT, # (vrisk) #Lumped rate constant for DCVC->Urinary NAcDCVC (/hr)
kKidBioact, # (vrisk) #Rate constant for DCVC bioactivation (/hr)
## Misc
RUrnTCA, #(vrisk)
RUrnTCOGTCOH, #(vrisk)
RUrnNDCVC, #(vrisk)
RAO,
CVenMole,
CPlasTCAMole,
CPlasTCAFreeMole
# Molecular Weights
MWTCE = 131.39;
MWDCA = 129.0;
MWDCVC = 216.1;
MWTCA = 163.5;
MWChlor = 147.5;
MWTCOH = 149.5;
MWTCOHGluc = 325.53;
MWNADCVC = 258.8;
# TCE
t DCA
t DCVC
t TCA
t Chloral
t TCOH
t TCOH-Gluc
t N Acetyl DCVC
# Stoichiometry
StochChlorTCE =
StochTCATCE =
StochTCATCOH =
StochTCOHTCE =
StochGlucTCOH =
StochTCOHGluc =
StochTCEGluc =
StochDCVCTCE =
StochN =
StochDCATCE =
MWChlor / MWTCE;
MWTCA / MWTCE;
MWTCA / MWTCOH;
MWTCOH / MWTCE;
MWTCOHGluc / MWTCOH;
MWTCOH / MWTCOHGluc;
MWTCE / MWTCOHGluc;
MWDCVC / MWTCE;
MWNADCVC / MWDCVC;
MWDCA / MWTCE;
# These are the actual model parameters used in "dynamics.
# Values that are assigned in the "initialize" section,
# are all set to 1 to avoid confusion.
# Cardiac output (L/hr)
# Alveolar ventilation (L/hr)
# Alveolar ventilation-perfusion ratio
# Scaled fat blood flow
# Scaled gut blood flow
# Fat compartment volume (L)
# Gut compartment volume (L)
# Liver compartment volume (L)
# Rapidly perfused compartment volume (L)
# Volume of respiratory lumen (L air)
= 1; #(vrisk) volume for respiratory tissue (L)
# Effective volume for respiratory tissue (L air) = V(tissue)
coefficient
# Kidney compartment volume (L)
# Blood compartment volume (L)
# Slowly perfused compartment volume (L)
# Plasma compartment volume [fraction of blood] (L)
# TCA Body compartment volume [not incl. blood+liver] (L)
# TCOH/G Body compartment volume [not incl. liver] (L)
VFat
VGut
VLiv
VRap
VRespLum
VRespEfftmp
VRespEff = 1;
Resp:Air partition
# Distribution/part
PB = 1;
PFat = 1;
PGut = 1;
PLiv = 1;
PRap = 1;
PResp = 1;
PKid = 1;
PSlw = 1;
TCAPlas = 1;
PBodTCA = 1;
PLivTCA = 1;
kDissoc = 1;
BMax = 1;
PBodTCOH = 1;
PLivTCOH = 1;
PBodTCOG = 1;
PLivTCOG = 1;
VDCVG = 1;
itioning
# TCE Blood/air partition coefficient
# TCE Fat/Blood partition coefficient
# TCE Gut/Blood partition coefficient
# TCE Liver/Blood partition coefficient
# TCE Rapidly perfused/Blood partition coefficient
# TCE Respiratory tissue:air partition coefficient
# TCE Kidney/Blood partition coefficient
# TCE Slowly perfused/Blood partition coefficient
# TCA blood/plasma concentration ratio
# Free TCA Body/blood plasma partition coefficient
# Free TCA Liver/blood plasma partition coefficient
# Protein/TCA dissociation constant (umole/L)
# Protein concentration (UNITS?)
# TCOH body/blood partition coefficient
# TCOH liver/body partition coefficient
# TCOG body/blood partition coefficient
# TCOG liver/body partition coefficient
# DCVG effective volume of distribution
# TCE Stomach-duodenum transfer coefficient (/hr
# TCE Stomach absorption coefficient (/hr)
# TCE Duodenum-feces transfer coefficient (/hr)
# TCE Duodenum absorption coefficient (/hr)
# TCA Stomach absorption coefficient (/hr)
# TCOH Stomach absorption coefficient (/hr)
# TCE Metabolism
VMAX = 1;
KM = 1;
FracOther = 1;
FracTCA = 1;
VMAXDCVG = 1;
# VMAX for hepatic TCE oxidation (mg/hr)
# KM for hepatic TCE oxidation (mg/L)
# Fraction of hepatic TCE oxidation not to TCA+TCOH
# Fraction of hepatic TCE oxidation to TCA
# VMAX for hepatic TCE GSH conjugation (mg/hr)
A-194
-------
KMDCVG =1; # KM for hepatic TCE GSH conjugation (mg/L)
VMAXKidDCVG = 1; # VMAX for renal TCE GSH conjugation (mg/hr)
KMKidDCVG =1; # KM for renal TCE GSH conjugation (mg/L)
VMAXClara = 1; # VMAX for Tracheo-bronchial TCE oxidation (mg/hr)
KMClara =1; # KM for Tracheo-bronchial TCE oxidation (mg/L)
# but in units of air concentration
FracLungSys = 1; # Fraction of respiratory oxidative metabolism that
Global Sampling Parameters
# VMAX for hepatic TCOH->TCA (mg/hr)
# KM for hepatic TCOH->TCA (mg/L)
# VMAX for hepatic TCOH->TCOG (mg/hr)
# KM for hepatic TCOH->TCOG (mg/L)
# Rate constant for hepatic TCOH->other
# TCA metabolism/clearance
kUrnTCA = 1; # Rate constant for TCA plasma->urine (/hr)
kMetTCA = 1; # Rate constant for hepatic TCA->other (/hr)
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# TCOG metabolism/clearance
kBile = 1; # Rate constant for TCOG liver->bile (/hr)
kEHR = 1; # Lumped rate constant for TCOG bile->TCOH liver (/hr)
kllrriTCOG = 1; # Rate constant for TCOG->urine (/hr)
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# DCVG metabolism
kDCVG = 1; # Rate constant for hepatic DCVG->DCVC (/hr)
FracKidDCVC =1; # Fraction of renal TCE GSH conj. "directly" to DCV(
(i.e., via first pass)
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# DCVC metabolism/clearance
kNAT = 1; # Lumped rate constant for DCVC-MJrinary NAcDCVC (/hr)
kKidBioact = 1; # Rate constant for DCVC bioactivation (/hr)
# Number of rodents in closed chamber data
# Chamber volume for closed chamber data
# Rate constant for closed chamber air loss
# Initial chamber concentration (ppm)
# IV infusion duration (hour)
## Flag for species, sex -- these are global parameters
BW = 0.0; # Species-specific defaults during initialization
BW75 = 0.0; #(vrisk) Variable for BVT3/4
Male =1.0; # 1 = male, 0 = female
Species = 1.0; # 1 = human, 2 = rat, 3 = mouse
BWmeas =0.0; # Body weight
VFatCmeas = 0.0; # Fractional volume fat
PBmeas =0.0; # Measured blood-air partition coefficient
Hematocritmeas = 0.0; # Measured hematocrit -- used for FracPlas = 1 - HCt
CDCVGmolLD = 5e-5; # Detection limit of CDCVGmol#(vl.2.3.1)
# These parameters are potentially sampled/calibrated in the MCMC or MC
# analyses. The default values here are used if no sampled value is given.
# M indicates population mean parameters used only in MC sampling
# V_ indicates a population variance parameter used in MC and MCMC sampling
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to alveolar ventilation rate in dynamics
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
Partition Coefficients for TCE
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
Partition Coefficients for TCA
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Plasma Binding for TCA
InkDissocC = 0.0; # Scaled to species-specific central estimates
InBMaxkDC = 0.0; # Scaled to species-specific central estimates
# Partition Coefficients for TCOH and TCOG
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
A-195
-------
# TCE Metabolism
InVMAXC = 0.0;
InKMC = 0.0;
InCIC = 0.0;
InFracOtherC
InFracTCAC
InVMAXDCVGC
estimates
InClDCVGC = 0.0;
InKMDCVGC = 0.0;
InVMAXKidDCVGC
estimates
InClKidDCVGC
InKMKidDCVGC
InVMAXLungLivC
# Scaled by liver weight and species-specific central estimates
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
=0.0; t Ratio of DCA to non-DCA
=0.0; t Ratio of TCA to TCOH
=0.0; # Scaled by liver weight and species-specific central
# Scaled to species-specific central estimates
# Scaled to species-specific central estimates
=0.0; # Scaled by kidney weight and species-specific central
= 0.0; # Scaled to species-specific central estimates
= 0.0; # Scaled to species-specific central estimates
=0.0; t Ratio of lung VMAX to liver VMAX,
# Scaled to species-specific central estimates
# now in units of air concentration
# TCOH Metabolism
InVMAXTCOHC = 0.0; # Scaled by BW0.75
InClTCOHC = 0.0; # Scaled by BST0.75
InKMTCOH =0.0; #
InVMAXGlucC = 0.0; # Scaled by BST0.75
InClGlucC = 0.0; # Scaled by BW"0.75
InKMGluc =0.0; #
InkMetTCOHC =0.0; # Scaled by BW-0.25
# TCA Metabolism/clearance
InkUrnTCAC = 0.0; # Scaled by (plasma volume)~-l and species-specific
central estimates
InkMetTCAC =0.0; # Scaled by BW"-0.25
# TCOG excretion and reabsorption
InkBileC =0.0; # Scaled by BW"-0.25
InkEHRC =0.0; # Scaled by BST-0.25
central estimates
# DCVG metabolism
InFracKidDCVCC =0.0; # Ratio of "directly" to DCVC to systemic DCVG
InkDCVGC =0.0; # Scaled by BW"-0.25
# DCVC metabolism
InkNATC =0.0; # Scaled by BW"-0.25
# Closed chamber parameters
NRodents =1; #
VChC =1; t
InkLossC =0; #
# These are given truncated normal or uniform distributions, depending on
# what prior information is available. Note that these distributions
# reflect uncertainty in the population mean, not inter-individual
# variability. Normal distributions are truncated at 2, 3, or 4 SD.
# For fractional volumes and flows, 2xSD
# For plasma fraction, 3xSD
# For cardiac output and ventilation-perfusion ratio, 4xSD
# For all others, 3xSD
# For uniform distributions, range of Ie2 to Ie8 fold, centered on
# central estimate.
#
M_lnQCC
M_lnVPRC
M_QFatC
M_QGutC
M_QLivC
M_QSlwC
M_QKidC
M_FracPlasC
M_lnDRespC = 1.0;
M_VFatC = 1.0;
M_VGutC = 1.0;
M_VLivC = 1.0;
M_VRapC = 1.0;
M_VRespLumC = 1.0;
M_VRespEffC = 1.0;
M_VKidC = 1.0;
M_VBldC = 1.0;
M_lnPBC = 1.0;
M_lnPFatC = 1.0;
M_lnPGutC = 1.0;
M_lnPLivC = 1.0;
M_lnPRapC = 1.0;
M_lnPRespC
M_lnPKidC = 1.0;
M_lnPSlwC = 1.0;
M_lnPRBCPlasTCAC
M_lnPBodTCAC
M_lnPLivTCAC
M_lnkDissocC
M_lnBMaxkDC
M_lnPBodTCOHC
M_lnPLivTCOHC
M_lnPBodTCOGC
M_lnPLivTCOGC
M_lnPeffDCVG
M InkTSD = 1.0;
A-196
-------
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
InkAS = 1.0;
InkTD = 1.0;
InkAD = 1.0;
InkASTCA
InkASTCOH
InVMAXC = 1.0;
InKMC = 1.0;
InCIC = 1.0;
InFracOtherC
InFracTCAC
InVMAXDCVGC
InClDCVGC
InKMDCVGC
InVMAXKldDCVGC
InClKldDCVGC
InKMKldDCVGC
InVMAXLungLlvC
InKMClara
InFracLungSysC
InVMAXTCOHC
InClTCOHC
InKMTCOH
InVMAXGlucC
InClGlucC
InKMGluc
InkMetTCOHC
InkUrnTCAC
InkMetTCAC
InkBlleC
InkEHRC = 1.0;
InkUrnTCOGC
InFracKldDCVCC
InkDCVGC
InkNATC = 1.0;
InkKidBioactC
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
= 1.0;
These are given InvGamma(alpha,beta) distributions. The parameterization
for alpha and beta is given by:
alpha = (n-1)12
beta = s"2* (n-1)/2
where n = number of data points, and s"2 is the sample variance
Sum(x_i"2)/n - "2.
Generally, for parameters for which there is no direct data, assume a
value of n = 5 (alpha = 2). For a sample variance s"2, this gives
an expected value for the standard deviation = 0.9*s,
a median [2.58,97.58] of 1.1*5 [0.6*s , 2.9*s] .
_lnQCC = 1.0;
_lnVPRC = 1.0;
_QFatC = 1.0;
_QGutC = 1.0;
_QLivC = 1.0;
_QSlwC = 1.0;
V_QKidC = 1.0;
V_FracPlasC
V_lnDRespC = 1.0;
V_VFatC = 1.0;
V_VGutC = 1.0;
V_VLivC = 1.0;
V_VRapC = 1.0;
V_VRespLumC = 1.0;
V_VRespEffC = 1.0;
V_VKidC = 1.0;
V_VBldC = 1.0;
V_lnPBC = 1.0;
V_lnPFatC = 1.0;
V_lnPGutC = 1.0;
V_lnPLivC = 1.0;
V_lnPRapC = 1.0;
V_lnPRespC
V_lnPKidC = 1.0;
V_lnPSlwC = 1.0;
V_lnPRBCPlasTCAC
V_lnPBodTCAC
V_lnPLivTCAC
V InkDissocC
V_lnBMaxkDC
V_lnPBodTCOHC
V_lnPLivTCOHC
V_lnPBodTCOGC
V_lnPLivTCOGC
V_lnPeffDCVG
V_lnkTSD = 1.0;
V_lnkAS = 1.0;
V_lnkTD = 1.0;
V_lnkAD = 1.0;
V_lnkASTCA
V_lnkASTCOH
V_lnVMAXC = 1.0;
V_lnKMC = 1.0;
V_lnClC = 1.0;
V_lnFracOtherC
V_lnFracTCAC
V_lnVMAXDCVGC
V_lnClDCVGC
V_lnKMDCVGC
V_lnVMAXKidDCVGC
V_lnClKidDCVGC
V_lnKMKidDCVGC
V InVMAXLungLivC
V_lnKMClara
V InFracLungSysC
V_lnVMAXTCOHC
V_lnClTCOHC
V_lnKMTCOH
V_lnVMAXGlucC
V_lnClGlucC
V_lnKMGluc
V_lnkMetTCOHC
V InkUrnTCAC
A-197
-------
V_lnkMetTCAC
V_lnkBileC
V_lnkEHRC = 1.0;
V_lnkUrnTCGGC
V_lnFracKidDCVCC
V_lnkDCVGC
V_lnkNATC = 1.0;
V InkKidBioactC
Ve_CKidTCOGTCOH = 1;
Ve_CLivTCOGTCOH = 1;
Ve_CLungTCOGTCOH = 1;
Ve_AUrnTCOGTCOH = 1;
Ve AUmTCOGTCOH collect
Ve_RetDose
Ve_CAlv = 1;
Ve_CAlvPPM
Ve_CInhPPM
Ve_CInh = 1;
Ve_CMixExh
Ve_CArt = 1;
Ve CVen = 1;
Ve CBldMix
Ve_CDCVGmol
Ve_zAUrnNDCVC
Ve_AUrnTCTotMole
Ve_TotCTCOH
Ve_QPsamp = 1 ;
Defaults for input parameters
#— TCE dosing
Cone = 0.0;
IVDose = 0.0;
PDose = 0.0;
Drink = 0.0;
lADose = 0.0;
PVDose = 0.0;
t— TCA dosing
IVDoseTCA =0.0
# Inhalation exposure cone. (ppm)
# IV dose (ing/kg)
# Oral gavage dose (ing/kg)
# Drinking water dose (mg/kg-day)
# Intraarterial dose (ing/kg)
# Portal vein dose (ing/kg)
;# IV dose (mg/kg) of TCA
PODoseTCA = 0.0;# Oral dose (mg/kg) of TCA
tt— TCOH dosing
IVDoseTCOH = 0.0;# IV dose (mg/kg) of TCOH
PODoseTCOH = 0.0;# Oral dose (mg/kg) of TCOH
##-- Potentially time-varying parameters
QPmeas =0.0; # Measured value of Alveolar ventilation QP
TCAUrnSat = 0.0;# Flag for saturated TCA urine
TCOGUrnSat = 0.0;# Flag for saturated TCOG urine
UrnMissing = 0.0;# Flag for missing urine collection times
Initialize
Ve_CPlasTCA
Ve_CBldTCA
Ve_CBodTCA
Ve_CKidTCA
Ve_CLivTCA
Ve CLungTCA
Ve zAUrnTCA
Parameter Initialization and Scaling
Model Parameters
QC
VPR
QPsamp
QFatCtmp
QGutCtmp
QLivCtmp
QSlwCtmp
DResptmp
QKidCtmp
FracPlas
VFat
VGut
VLiv
VRap
[used in dynamics):
Cardiac output (L/hr)
Venti1ation-perfusion ratio
Alveolar ventilation (L/hr)
Scaled fat blood flow
Scaled gut blood flow
Scaled liver blood flow
Scaled slowly perfused blood flow
Respiratory lumen:tissue diffusive clearance rate
Scaled kidney blood flow
Fraction of blood that is plasma (1-hematocrit)
Fat compartment volume (L)
Gut compartment volume (L)
Liver compartment volume (L)
Rapidly perfused compartment volume (L)
A-198
-------
VKid
VBld
VSlw
VPlas
VBod
Kidney compartment volume (L)
Blood compartment volume (L)
Slowly perfused compartment volume (L)
Plasma compartment volume [fraction of blood] (L)
TCA Body compartment volume [not incl. blood+liver]
PB TCE Blood/air partition coefficient
PFat TCE Fat/Blood partition coefficient
PGut TCE Gut/Blood partition coefficient
PLiv TCE Liver/Blood partition coefficient
PRap TCE Rapidly perfused/Blood partition coefficient
PResp TCE Respiratory tissue:air partition coefficient
PKid TCE Kidney/Blood partition coefficient
PSlw TCE Slowly perfused/Blood partition coefficient
TCAPlas TCA blood/plasma concentration ratio
PBodTCA Free TCA Body/blood plasma partition coefficient
PLivTCA Free TCA Liver/blood plasma partition coefficient
kDissoc Protein/TCA dissociation constant (umole/L)
BMax Maximum binding concentration (umole/L)
PBodTCOH TCOH body/blood partition coefficient
PLivTCOH TCOH liver/body partition coefficient
PBodTCOG TCOG body/blood partition coefficient
PLivTCOG TCOG liver/body partition coefficient
kAS TCE Stomach absorption coefficient (/hr)
kTSD TCE Stomach-duodenum transfer coefficient (/hr)
kAD TCE Duodenum absorption coefficient (/hr)
kTD TCE Duodenum-feces transfer coefficient (/hr)
kASTCA TCA Stomach absorption coefficient (/hr)
kASTCOH TCOH Stomach absorption coefficient (/hr)
VMAX VMAX for hepatic TCE oxidation (mg/hr)
KM KM for hepatic TCE oxidation (mg/L)
FracOther Fraction of hepatic TCE oxidation not to TCA+TCOH
FracTCA Fraction of hepatic TCE oxidation to TCA
VMAXDCVG VMAX for hepatic TCE GSH conjugation (mg/hr)
KMDCVG KM for hepatic TCE GSH conjugation (mg/L)
VMAXKidDCVG VMAX for renal TCE GSH conjugation (mg/hr)
KMKidDCVG KM for renal TCE GSH conjugation (mg/L)
VMAXClara VMAX for Tracheo-bronchial TCE oxidation (mg/hr)
KMClara KM for Tracheo-bronchial TCE oxidation (mg/L)
FracLungSys Fraction of respiratory metabolism to systemic circ.
VMAXTCOH VMAX for hepatic TCOH->TCA (mg/hr)
KMTCOH KM for hepatic TCOH->TCA (mg/L)
VMAXGluc VMAX for hepatic TCOH->TCOG (mg/hr)
KMGluc KM for hepatic TCOH->TCOG (mg/L)
kMetTCOH Rate constant for hepatic TCOH->other (/hr)
kUrnTCA Rate constant for TCA plasma->urine (/hr)
kMetTCA Rate constant for hepatic TCA->other (/hr)
kBile Rate constant for TCOG liver->bile (/hr)
kEHR Lumped rate constant for TCOG bile->TCOH liver (/hr)
kUrnTCOG Rate constant for TCOG->urine (/hr)
kDCVG Rate constant for hepatic DCVG->DCVC (/hr)
Lumped rate constant for DCVC->Urinary NAcDCVC (/hr)
Rate constant for DCVC bioactivation (/hr)
Number of rodents in closed chamber data
Chamber volume for closed chamber data
Rate constant for closed chamber air loss
Parameters used (not assigned here)
BW Body weight in kg
Species 1 = human (default), 2 = rat, 3 = mouse
Male 0 = female, 1 (default) = male
CC Closed chamber initial concentration
Sampling/sealing parameters (assigned or sampled)
InQCC
InVPRC
InDRespC
QFatC
QGutC
QLivC
QSlwC
QKidC
FracPlasC
VFatC
VGutC
VLivC
VRapC
VRespLumC
VRespEffC
VKidC
VBldC
InPBC
InPFatC
InPGutC
InPLivC
InPRapC
InPSlwC
InPRespC
InPKidC
InPRBCPlasTCAC
InPBodTCAC
InPLivTCAC
InkDissocC
InBMaxkDC
InPBodTCOHC
InPLivTCOHC
InPBodTCOGC
InPLivTCOGC
InPeffDCVG
InkTSD
In kAS
InkTD
In kAD
InkASTCA
InkASTCOH
InVMAXC
InKMC
InCIC
InFracOtherC
InFracTCAC
A-199
-------
# InVMAXDCVGC
# InClDCVGC
# InKMDCVGC
# InVMAXKidDCVGC
# InClKidDCVGC
# InKMKidDCVGC
# InVMAXLungLivC
# InKMClara
# InFracLungSysC
# InVMAXTCOHC
# InClTCOHC
# InKMTCOH
# InVMAXGlucC
# InClGlucC
# InKMGluc
# InkMetTCOHC
# InkUrnTCAC
# InkMetTCAC
# InkBileC
# InkEHRC
# InkUrnTCGGC
# InFracKidDCVCC
# InkDCVGC
# InkNATC
# InkKidBioactC
# NRodents
# VChC
# InkLossC
# Input parameters
# none
# Mouse: QP/BW=116.5 ml/min/100 g (Brown et al. 1997, Tab. 31), VPR=2.5
# Assume uncertainty CV of 0.2 similar to QC, truncated at 4xCV
# Consistent with range of QP in Tab. 31
# Rat: QP/BW=52.9 ml/min/100 g (Brown et al. 1997, Tab. 31), VPR=1.9
# Assume uncertainty CV of 0.3 similar to QC, truncated at 4xCV
# Used larger CV because Tab. 31 shows a very large range of QP
# Human: Average of Male VE=9 1/min, resp. rate=12 /min,
# dead space=0.15 1 (QP=7.2 1/min), and Female
# VE=6.5 1/min, resp. rate=14 /min, dead space=0.12 1
# (QP=4.8 1/min), VPR =0.96
# Assume uncertainty CV of 0.2 similar to QC, truncated at 4xCV
# Consistent with range of QP in Tab. 31
QPsamp = QC*VPR;
# Respiratory diffusion flow rate
# Will be scaled by QP in dynamics
# Use log-uniform distribution from le-5 to 10
DResptmp = exp(InDRespC);
# Fractional Flows scaled to the appropriate species
# Fat = Adipose only
# Gut = GI tract + pancreas + spleen (all drain to portal vein)
# Liv = Liver, hepatic artery
# Slw = Muscle + Skin
# Kid = Kidney
# Rap = Rapidly perfused (rest of organs, plus bone marrow, lymph, etc.),
# derived by difference in dynamics
# use measured value of > 0, otherwise use 0.03 for mouse,
# 0.3 for rat, 60 for female human, 70 for male human
BW = (BWmeas > 0.0 ? BWmeas : (Species ==3 ? 0.03 : (Species == 2
0 ? 60.0 : 70.0) )));
BW75 = pow(BW, 0.75) ;
BW25 = pow(BW, 0.25);
Cardiac Output and alveolar ventilation (L/hr)
QC = exp(lnQCC) * BW75 * # Mouse, Rat, Human (default)
(Species == 3 ? 11.6 : (Species == 2 ? 13.3 : 16.0 ) ) ;
# Mouse: C0=13.98 +/- 2.85 ml/min, BW=30 g (Brown et al. 1997, Tab. 22)
# Uncertainty CV is 0.20
# Rat: CQ=110.4 ml/min +/- 15.6, BW=396 g (Brown et al. 1997, Tab. 22,
# p 441). Uncertainty CV is 0.14.
# Human: Average of Male CQ=6.5 1/min, BW=73 kg
# and female C0= 5.9 1/min, BW=60 kg (ICRP #89, sitting at rest)
# From Price et al. 2003, estimates of human perfusion rate were
# 4.7-6.5 for females and 5.5-7.1 1/min for males (note
# portal blood was double-counted, and subtracted off here)
# Thus for uncertainty use CV of 0.2, truncated at 4xCV
# Variability from Price et al. (2003) had CV of 0.14-0.20,
# so use 0.2 as central estimate
VPR = exp(lnVPRC)*
(Species == 3 ? 2.5 : (Species == 2 ? 1.9 : 0.96 ));
QLivCtmp = QLivC*
(Species == 3 ? 0.02 : (Species == 2 ? 0.021 : 0.065 ));
QSlwCtmp = QSlwC*
(Species == 3 ? 0.217 : (Species == 2 ? 0.336 : (Male == 0 ? 0.17 : 0.22)
# Plasma Flows to Tissues (L/hr)
## Mice and rats from Hejtmancik et al. 2002,
## control F344 rats and B6C3F! mice at 19 weeks of age
## However, there appear to be significant strain differences in rodents, so
## assume uncertainty CV=0.2 and variability CV=0.2.
## Human central estimate from ICRP. Well measured in humans, from Price et al.,
## human SD in hematocrit was 0.029 in females, 0.027 in males,
## corresponding to FracPlas CV of 0.047 in females and
## 0.048 in males. Use rounded CV = 0.05 for both uncertainty and
variability
## Use measured 1-hematocrit if available
A-200
-------
## Truncate distributions at 3xCV to encompass clinical "normal range"
FracPlas = (Hematocritmeas > 0.0 ? (1-Hematocritmeas) : (FracPlasC *
(Species == 3 ? 0.52 : (Species == 2 ? 0.53 : (Male == 0 ? 0.615 :
0.567)))));
# Tissue Volumes (L)
# Fat = Adipose only
# Gut = GI tract (not contents) + pancreas + spleen (all drain to portal vein)
# Liv = Liver
# Rap = Brain + Heart + (Lungs-TB) + Bone marrow + "Rest of the body"
# VResp = Tracheobroneial region (trachea+broncial basal+
# broneial secretory+bronchiolar)
# Kid = Kidney
# Bid = Blood
# Slw = Muscle + Skin, derived by difference
# residual (assumed unperfused) = (Bone-Marrow)+GI contents+other
VFat = BW * (VFatCmeas > 0.0 ? VFatCmeas : (VFatC * (Species ==3 ? 0.07 :
(Species == 2 ? 0.07 : (Male == 0 ? 0.317 : 0.199) ))));
VGut = VGutC * BW *
(Species == 3 ? 0.049 : (Species == 2 ? 0.032 : (Male == 0 ? 0.022 :
0.020) ));
VLiv = VLivC * BW *
(Species == 3 ? 0.055 : (Species == 2 ? 0.034 : (Male == 0 ? 0.023 :
0.025) ));
VRap = VRapC * BW *
(Species == 3 ? 0.100 : (Species == 2 ? 0.088 : (Male == 0 ? 0.093 :
0.088) )) ;
VRespLum = VRespLumC * BW *
(Species == 3 ? (0.00014/0.03) : (Species == 2 ? (0.0014/0.3) : (0.167/70)
)); # Lumenal volumes from Styrene model (Sarangapani et al. 2002)
VRespEfftmp = VRespEffC * BW *
(Species == 3 ? 0.0007 : (Species == 2 ? 0.0005 : 0.00018 ) ) ;
# Respiratory tract volume is TB region
# will be multiplied by partition coef. below
VKid = VKidC * BW *
(Species == 3 ? 0.017 : (Species == 2 ? 0.007 : (Male == 0 ? 0.0046 :
0.0043) ));
VBld = VBldC * BW *
(Species == 3 ? 0.049 : (Species == 2 ? 0.074 : (Male == 0 ? 0.068 :
0.077) ));
VSlw = (Species == 3 ? 0.8897 : (Species == 2 ? 0.8995 : (Male == 0 ?
0.85778 : 0.856))) * BW
- VFat - VGut - VLiv - VRap - VRespEfftmp - VKid - VBld;
# Slowly perfused:
# Baseline mouse: 0.8897-0.049-0.017-0.0007-0.1-0.055-0.049-0.07= 0.549
# Baseline rat: 0.8995 -0.074-0.007-0.0005-0.088-0.034-0.032-0.07= 0.594
# Baseline human F: 0.85778-0.068-0.0046-0.00018-0.093-0.023-0.022-0.317= 0.33
# Baseline human M: 0.856-0.077-0.0043-0.00018-0.088-0.025-0.02-0.199= 0.4425
# Partition coefficients
PB = (PBmeas > 0.0 ? PBmeas : (exp(lnPBC) * (Species == 3 ? 15. : (Species
2 ? 22. : 9.5 )))); # Blood-air
# Mice: pooling Abbas and Fisher 1997, Fisher et al. 1991
# each a single measurement, with overall CV = 0.07.
# Given small number of measurements, and variability
# in rat, use CV of 0.25 for uncertainty and variability.
# Rats: pooling Sato et al. 1977, Gargas et al. 1989,
# Barton et al. 1995, Simmons et al. 2002, Koizumi 1989,
# Fisher et al. 1989. Fisher et al. measurement substantially
# smaller than others (15 vs. 21-26). Recent article
# by Rodriguez et al. 2007 shows significant change with
# age (13.1 at PND10, 17.5 at adult, 21.8 at aged), also seems
# to favor lower values than previously reported. Therefore
# use CV = 0.25 for uncertainty and variability.
# Humans: pooling Sato and Nakaj ima 1979, Sato et al. 1977,
# Gargas et al. 1989, Fiserova-Bergerova et al. 1984,
# Fisher et al. 1998, Koizumi 1989
# Overall variability CV = 0.185. Consistent with
# within study inter-individual variability CV = 0.07-0.22.
# Study-to-study, sex-specific means range 8.1-11, so
# uncertainty CV = 0.2.
PFat = exp(lnPFatC) * # Fat/blood
(Species == 3 ? 36. : (Species == 2 ? 27. : 67. ));
# Mice: Abbas and Fisher 1997. Single measurement. Use
# rat uncertainty of CV = 0.3.
# Rats: Pooling Barton et al. 1995, Sato et al. 1977,
# Fisher et al. 1989. Recent article by Rodriguez et al.
# (2007) shows higher value of 36., so assume uncertainty
# CV of 0.3.
# Humans: Pooling Fiserova-Bergerova et al. 1984, Fisher et al. 1998,
# Sato et al. 1977. Variability in Fat:Air has CV = 0.07.
# For uncertainty, dominated by PB uncertainty CV = 0.2
# For variability, add CVs in guadrature for
# sgrt(0.07^2+0.185"2)=0.20
PGut = exp(lnPGutC) * # Gut/blood
(Species == 3 ? 1.9 : (Species == 2 ? 1.4 : 2.6 ));
# Mice: Geometric mean of liver, kidney
# Rats: Geometric mean of liver, kidney
# Humans: Geometric mean of liver, kidney
# Uncertainty of CV = 0.4 due to tissue extrapolation
PLiv = exp(lnPLivC) * # Liver/blood
(Species ==3 ? 1.7 : (Species ==2 ? 1.5 : 4.1 ));
# Mice: Fisher et al. 1991, single datum, so assumed uncert CV = 0.4
# Rats: Pooling Barton et al. 1995, Sato et al. 1977,
# Fisher et al. 1989, with little variation (range 1.3-1.7).
# Recent article by Rodriguez et al.reports 1.34. Use
# uncertainty CV = 0.15.
# Humans: Pooling Fiserova-Bergerova et al. 1984, Fisher et al. 1998
# almost 2-fold difference in Liver:Air values, so uncertainty
# CV = 0.4
PRap = exp(lnPRapC) * # Rapidly perfused/blood
(Species ==3 ? 1.9 : (Species ==2 ? 1.3 : 2.6 ));
# Mice: Similar to liver, kidney. Uncertainty CV = 0.4 due to
# tissue extrapolation
# Rats: Use brain values Sato et al. 1977. Recent article by
# Rodriguez et al. (2007) reports 0.99 for brain. Uncertainty
A-201
-------
# CV of 0.4 due to tissue extrapolation.
# Humans: Use brain from Fiserova-Bergerova et al. 1984
# Uncertainty of CV = 0.4 due to tissue extrapolation
PResp = exp(InPRespC) * # Resp/blood =
(Species ==3 ? 2.6 : (Species ==2 ? 1.0 : 1.3 ));
# Mice: Abbas and Fisher 1997, single datum, so assumed uncert CV = 0.4
# Rats: Sato et al. 1977, single datum, so assumed uncert CV = 0.4
# Humans: Pooling Fiserova-Bergerova et al. 1984, Fisher et al. 1998
# > 2-fold difference in lung:air values, so uncertainty
# CV = 0.4
VRespEff = VRespEfftmp * PResp * PB; # Effective air volume
PKid = exp(lnPKidC) * # Slowly perfused/blood
(Species ==3 ? 2.1 : (Species ==2 ? 1.3 : 1.6 ));
# Mice: Abbas and Fisher 1997, single datum, so assumed uncert CV = 0.4
# Rats: Pooling Barton et al. 1995, Sato et al. 1977. Recent article
# by Rodriguez et al. (2007) reports 1.01, so use uncertainty
# CV of 0.3. Pooled variability CV = 0.39.
# Humans: Pooling Fiserova-Bergerova et al. 1984, Fisher et al. 1998
# For uncertainty, dominated by PB uncertainty CV = 0.2
# Variability in kidney:air CV = 0.23, so add to PB variability
# in guadrature sgrt(0.23"2+0.185"2)=0.30
PSlw = exp(InPSlwC) * # Slowly perfused/blood
(Species == 3 ? 2.4 : (Species == 2 ? 0.58 : 2.1 ));
# Mice: Muscle - Abbas and Fisher 1997, single datum, so assumed
# uncert CV = 0.4
# Rats: Pooling Barton et al. 1995, Sato et al. 1977,
# Fisher et al. 1989. Recent article by Rodriguez et al. (2007)
# reported 0.72, so use uncertainty CV of 0.25. Variability
# in Muscle:air and muscle:blood ~ CV = 0.3
# Humans: Pooling Fiserova-Bergerova et al. 1984, Fisher et al. 1998
# Range of values 1.4-2.4, so uncertainty CV = 0.3
# Variability in muscle:air CV =0.3, so add to PB variability
# in guadrature sgrt(0.3"2+0.185"2)=0.35
# TCA partitioning
TCAPlas = FracPlas + (1 - FracPlas) * 0.5 * exp(InPRBCPlasTCAC);
# Blood/Plasma concentration ratio. Note dependence
# on fraction of blood that is plasma. Here
# exp(InPRBCPlasTCA) = partition coefficient
# C(blood minus plasma)/C(plasma)
# Default of 0.5, corresponding to Blood/Plasma
# concentration ratio of 0.76 in
# rats (Schultz et al 1999)
# For rats, Normal uncertainty with GSD = 1.4
# For mice and humans, diffuse prior uncertainty of
# 100-fold up/down
PBodTCA = TCAPlas * exp(InPBodTCAC) *
(Species == 3 ? 0.88 : (Species == 2 ? 0.88 : 0.52 ));
# Note -- these were done at 10-20 microg/ml (Abbas and Fisher 1997),
# which is 1.635-3.27 mmol/ml (1.635-3.27 x 10^6 microM).
# At this high concentration, plasma binding should be
# saturated -- e.g., plasma albumin concentration was
# measured to be P=190-239 microM in mouse, rat, and human
# plasma by Lumpkin et al. 2003, or > 6800 molecules of
# TCA per molecule of albumin. So the measured partition
# coefficients should reflect free blood-tissue partitioning.
# Used muscle values, multiplied by blood:plasma ratio to get
# Body:Plasma partition coefficient
# Rats = mice from Abbas and Fisher 1997
# Humans from Fisher et al. 1998
# Uncertainty in mice, humans GSD = 1.4
# For rats, GSD = 2.0, based on difference between mice
# and humans.
PLivTCA = TCAPlas * exp(InPLivTCAC) *
(Species == 3 ? 1.18 : (Species == 2 ? 1.18 : 0.66 ));
# Multiplied by blood:plasma ratio to get Liver:Plasma
# Rats = mice from Abbas and Fisher 1997
# Humans from Fisher et al. 1998
# Uncertainty in mice, humans GSD = 1.4
# For rats, GSD = 2.0, based on difference between mice
# and humans.
# Binding Parameters for TCA
# GM of Lumpkin et al. 2003; Schultz et al. 1999;
# Templin et al. 1993, 1995; Yu et al. 2000
# Protein/TCA dissociation constant (umole/L)
# note - GSD = 3.29, 1.84, and 1.062 for mouse, rat, human
kDissoc = exp(InkDissocC) *
(Species == 3 ? 107. : (Species == 2 ? 275. : 182. ));
# BMax = NSites * Protein concentration. Sampled parameter is
# BMax/kD (determines binding at low concentrations)
# note - GSD = 1.64, 1.60, 1.20 for mouse, rat, human
BMax = kDissoc * exp(InBMaxkDC) *
(Species == 3 ? 0.88 : (Species == 2 ? 1.22 : 4.62 ));
# TCOH partitioning
# Data from Abbas and Fisher 1997 (mouse) and Fisher et al.
# 1998 (human). For rat, used mouse values.
# Uncertainty in mice, humans GSD = 1.4
# For rats, GSD = 2.0, based on difference between mice
# and humans.
PBodTCOH = exp(lnPBodTCOHC) *
(Species == 3 ? 1.11 : (Species == 2 ? 1.11 : 0.91 ));
PLivTCOH = exp(lnPLivTCOHC) *
(Species == 3 ? 1.3 : (Species == 2 ? 1.3 : 0.59 ));
# TCOG partitioning
# Use TCOH as a proxy, but uncertainty much greater
# (e.g., use uniform prior, 100-fold up/down)
PBodTCOG = exp(lnPBodTCOGC) *
(Species == 3 ? 1.11 : (Species == 2 ? 1.11 : 0.91 ));
PLivTCOG = exp(lnPLivTCOGC) *
(Species == 3 ? 1.3 : (Species == 2 ? 1.3 : 0.59 ));
# DCVG distribution volume
# exp(InPeffDCVG) is the effective partition coefficient for
# the "body" (non-blood) compartment
# Diffuse prior distribution: loguniform le-3 to Ie3
VDCVG = VBld + # blood plus body (with "effective" PC)
exp(InPeffDCVG) * (VBod + VLiv);
A-202
-------
# transfer from stomach centered on 1.4/hr, range up or down 100-fold,
# based on human stomach half-time of 0.5 hr.
kTSD = exp(lnkTSD);
# stomach absorption centered on 1.4/hr, range up or down 1000-fold
kAS = exp(InkAS);
# assume no fecal excretion -- 100% absorption
kTD = 0.0 * exp(lnkTD);
# intestinal absorption centered on 0.75/hr, range up or down
# 1000-fold, based on human transit time of small intestine
# of 4 hr (95% throughput in 4 hr)
kAD = exp(lnkAD);
kASTCA = exp(InkASTCA);
kASTCOH = exp(lnkASTCOH);
TCE Oxidative Metabolism Constants
For rodents, in vitro microsomal data define priors (pooled).
For human, combined in vitro microsomoal+hepatocellular individual data
define priors.
All data from Elfarra et al. 1998; Lipscomb et al. 1997, 1998a,b
For VMAX, scaling from in vitro data were (Barter et al. 2007):
32 mg microsomal protein/g liver
99 x Ie6 hepatocytes/g liver
Here, human data assumed representative of mouse and rats.
For KM, two different scaling methods were used for microsomes:
Assume microsomal concentration = liver concentration, and
use central estimate of liver:blood PC (see above)
Use measured microsome:air partition coefficient (1.78) and
central estimate of blood:air PC (see above)
For human KM from hepatocytes, used measured human hepatocyte:air
partition coefficient (21.62, Lipscomb et al. 1998), and
central estimate of blood:air PC.
Note that to that the hepatocyte:air PC is similar to that
found in liver homogenates (human: 29.4+/-5.1 from Fiserova-
Bergerova et al. 1984, and 54 for Fisher et al. 1998; rat:
27.2+7-3.4 from Gargas et al. 1989, 62.7 from Koisumi 1989,
43.6 from Sato et al. 1977; mouse: 23.2 from Fisher et al. 1991).
For humans, sampled parameters are VMAX and C1C (VMAX/KM), due to
improved convergence. VMAX is kept as a parameter because it
appears less uncertain (i.e., more consistent across microsomal
and hepatocyte data).
# Central estimate of VMAX is 342, 76.2, and 32.3 (micromol/min/
# kg liver) for mouse, rat, human. Converting to /hr by
# * (60 min/hr * 0.1314 mg/micromol) gives
# 2700, 600, and 255 mg/hr/kg liver
# Observed variability of about 2-fold GSD. Assume 2-fold GSD for
# both uncertainty and variability
VMAX = VLiv*exp(lnVMAXC)*
(Species == 3 ? 2700. : (Species == 2 ? 600. : 255.));
# For mouse and rat central estimates for KM are 0.068-1.088 and
# 0.039-0.679 mmol/1 in blood, depending on the scaling
# method used. Taking the geometric mean, and converting
# to mg/1 by 131.4 mg/mmol gives 36. and 21. mg/1 in blood.
# For human, central estimate
# for Cl are 0.306-3.95 1/min/kg liver. Taking the geometric
# mean and converting to /hr gives a central estimate of
# 66 . 1/hr/kg.
# KM is then derived from KM = VMAX/ (Cl*Vliv) (central estimate
# of
# Note uncertainty due to scaling is about 4 -fold.
# Variability is about 3-fold in mice, 1 . 3-fold in rats , and
# 2- to 4- fold in humans (depending on scaling) .
KM = (Species == 3 ? 36 . *exp (InKMC) : (Species == 2 ? 21 . *exp (InKMC) :
VMAX/ (VLiv*66. *exp (InCIC) ) ) ) ;
# Oxidative metabolism splits
# Fractional split of TCE to DCA
# exp (InFracOtherC) = ratio of DCA to non-DCA
# Diffuse prior distribution: logunif orm le-4 to Ie2
FracOther = exp (InFracOtherC) /( 1+exp (InFracOtherC) );
# Fractional split of TCE to TCA
# exp(lnFracTCAC) = ratio of TCA to TCOH
# TCA/TCOH = 0.1 from Lipscomb et al . 1998 using fresh hepatocytes,
# but TCA/TCOH - 1 from Bronley-DeLancey et al 2006
# GM = 0.32, GSD =3.2
FracTCA = 0 . 32* exp (InFracTCAC) * ( 1 -FracOther ) / (1+0 . 32* exp (InFracTCAC) ) ;
# TCE GSH Metabolism Constants
# Human in vitro data from Lash et al . 1999 , define human priors .
# VMAX (nmol/min/ KM (mM)
# gtissue)
CLeff (ml/min/
g tissue)
[high affinity pathway only] [total]
-423 0.0055-0.023
-211
* estimated visually from Fig 1, Lash et al. 1999
** Fig 1A, data from 50-500 ppm headspace at 60 min
and Fig IB, data at 100-5000 ppm in headspace for 120 min
*** Fig IB, 30-100 ppm headspace, converted to blood concentration
using blood:air PC of 9.5
**** Fig 1A, data at 50 ppm headspace at 120 min and Fig IB, data at
25 and 50 ppm headspace at 120 min.
Overall, human liver hepatocytes are probably most like the
intact liver (e.g., accounting for the competition between
GSH conjugation and oxidation). So central estimates based
on those: CLeff - 0.32 ml/min/g tissue, KM - 0.022 mM in blood.
CLeff converted to 19 1/hr/kg; KM converted to 2.9 mg/1 in blood
However, uncertainty in CLeff is large (values in cytosol
-100-fold larger). Moreover, Green et al. 1997 reported
DCVG formation in cytosol that was -30,000-fold smaller
than Lash et al. (1998) in cytosol, which would be a VMAX
-300-fold smaller than Lash et al. (1998) in hepatocytes.
Uncertainty in KM appears smaller (-4-fold)
CLC: GM = 19., GSD = 100; KM: GM = 2.9., GSD = 4.
In addition, at a single concentration, the variability
in human liver cytosol samples had a GSD=1.3.
For the human kidney, the kidney cytosol values are used, with the same
uncertainty as for the liver. Note that the DCVG formation rates
in rat kidney cortical cells and rat cytosol are guite similar
A-203
-------
(see below).
CLC: GM = 230., GSD = 100; KM: GM = 2.7., GSD = 4.
Rat and mouse in vitro data from Lash et al. 1995,1998 define rat and mouse
priors. However, rats and mice are only assayed at 1 and 2 mM
providing only a bound on VMAX and very little data on KM.
Rate at 2 mM Equivalent CLeff
blood cone. at 2 mM
(nmol/min/ (mM) (ml/min/
g tissue) g tissue)
hepatocytes :
liver cytosol:
kidney cells :
kidney cytosol:
kidney cytosol:
4.4-16
8.0-12
0.79-1.1 2.2
0.53-0.75 1.1-2.0
6.2-9.3
2.0
1.7-2.0
0.000
0.000
0.91-2.0 0.003
1
1
36-0 . i
27-0.
1-0.0
0.0022-0.0079
0.0040-0.0072
00049
00068
0.018-0.036
# In most cases, rates were increased over the same sex/species at 1 mM,
# indicating VMAX has not yet been reached. The values between cells
# and cytosol are more much consistent that in the human data.
# These data therefore put a lower bound on VMAX and a lower bound
# on CLC. To account for in vitro-in vivo uncertainty, the lower
# bound of the prior distribution is set 100-fold below the central
# estimate of the measurements here. In addition, Green et al.
# (1997) found values 100-fold smaller than Lash et al. 1995, 1998.
# Therefore diffuse prior distributions set to le-2-le4.
# Rat liver: Bound on VMAX of 4.4-16, with GM of 8.4. Converting to
# mg/hr/kg tissue (* 131.4 ng/nmol * 60 min/hr * Ie3 g/kg / Ie6 mg/ng)
# gives a central estimate of 66. mg/hr/kg tissue. Bound on CL of
# 0.0022-0.0079, with GM of 0.0042. Converting to 1/hr/kg tissue
# (* 60 min/hr) gives 0.25 1/hr/kg tissue.
# Rat kidney: Bound on VMAX of 0.53-1.1, with GM of 0.76. Converting
# to mg/hr/kg tissue gives a central estimate of 6.0 mg/hr/kg.
# Bound on CL of 0.00027-0.00068, with GM of 0.00043. Converting
# tol/hr/kg tissue gives 0.026 1/hr/kg tissue.
# Mouse liver: Bound on VMAX of 36-40, with GM of 38. Converting
# to mg/hr/kg tissue gives a central estimate of 300. mg/hr/kg.
# Bound on CL of 0.018-0.036, with GM of 0.025. Converting
# tol/hr/kg tissue gives 1.53 1/hr/kg tissue.
# Mouse kidney: Bound on VMAX of 6.2-9.3, with GM of 7.6. Converting
# to mg/hr/kg tissue gives a central estimate of 60. mg/hr/kg.
# Bound on CL of 0.0031-0.0102, with GM of 0.0056. Converting
# tol/hr/kg tissue gives 0.34 1/hr/kg tissue.
VMAXDCVG = VLiv*(Species == 3 ? (300.*exp(InVMAXDCVGC)) :
(66.*exp(lnVMAXDCVGC)) : (2.9*19.*exp(InClDCVGC+lnKMDCVGC))));
KMDCVG = (Species == 3 ? (VMAXDCVG/(VLiv*1.53*exp(InClDCVGC);
2 ? (VMAXDCVG/(VLiv*0.25*exp(InClDCVGC))) : 2.9*exp(InKMDCVGC)));
VMAXKidDCVG = VKid*(Species == 3 ? (60.*exp(InVMAXKidDCVGC)) : (Species
2 ? (6.0*exp(InVMAXKidDCVGC)) : (2.7*230.*exp(InClKidDCVGC+lnKMKidDCVGC))));
KMKidDCVG = (Species == 3 ? (VMAXKidDCVG/(VKid*0.34*exp(InClKidDCVGC))) :
(Species == 2 ? (VMAXKidDCVG/(VKid*0.026*exp(InClKidDCVGC))) :
2.7* exp(InKMKidDCVGC)));
# For humans, used detection limit of 0.03
# Additional scaling by lung/liver weight ratio
# from Brown et al. Table 21 (mouse and rat) or
# ICRP Pub 89 Table 2.8 (Human female and male)
# Uncertainty - 3-fold truncated at 3 GSD
VMAXClara = exp(InVMAXLungLivC) * VMAX *
(Species == 3 ? (1.03/1.87*0.7/5.5): (Species == 2 ?
(0.08/0.82*0.5/3.4) : (0.03/0.33*(Male == 0 ? (0.42/1.4) : (0.5/1.8)))));
KMClara = exp(InKMClara);
# Fraction of Respiratory Metabolism that goes to system circulation
# (translocated to the liver)
FracLungSys = exp(InFracLungSysC)/(1 + exp(InFracLungSysC));
# TCOH Metabolism Constants (mg/hr)
# No in vitro data. So use diffuse priors of
# le-4 to Ie4 mg/hr/kg"0.75 for VMAX
# (4e-5 to 4000 mg/hr for rat),
# le-4 to Ie4 mg/1 for KM,
# and le-5 to Ie3 l/hr/kg"0.75 for Cl
# (2e-4 to 2.4e4 1/hr for human)
VMAXTCOH = BW75*
(Species == 3 ? (exp(InVMAXTCOHC)) : (Species == 2 ?
(exp(lnVMAXTCOHC)) : (exp(InClTCOHC+lnKMTCOH))));
KMTCOH = exp(lnKMTCOH);
VMAXGluc = BW75*
(Species == 3 ? (exp(InVMAXGlucC)) : (Species == 2 ?
(exp(InVMAXGlucC)) : (exp(InClGlucC+lnKMGluc))));
KMGluc = exp(InKMGluc);
# No in vitro data. So use diffuse priors of
# le-5 to Ie3 kg"0.25/hr (3.5e-6/hr to 3.5e2/hr for human)
kMetTCOH = exp(InkMetTCOHC) / BW25;
# TCA kinetic parameters
# Central estimate based on GFR clearance per unit body weight
# 10.0,8.7,1.8 ml/min/kg for mouse, rat, human
# (= 0.6, 0.522, 0.108 1/hr/kg) from Lin 1995.
# = CL_GFR / BW (BW=0.02 for mouse, 0.265 for rat, 70 for human)
# kUrn = CL_GFR / VPlas
# Diffuse prior with uncertainty of up,down 100-fold
kUrnTCA = exp(InkUrnTCAC) * BW / VPlas *
(Species == 3 ? 0.6 : (Species == 2 ? 0.522 : 0.108));
# No in vitro data. So use diffuse priors of
# le-4 to Ie2 /hr/kg"0.25 (0.3/hr to 35/hr for human)
kMetTCA = exp(InkMetTCAC) / BW25;
# TCOG kinetic parameters
# No in vitro data. So use diffuse priors of
# le-4 to Ie2 /hr/kg^O.25 (0.3/hr to 35/hr for human)
kBile = exp(lnkBileC) / BW25;
kEHR = exp(lnkEHRC) / BW25;
# Central estimate based on GFR clearance per unit body weight
# 10.0,8.7,1.8 ml/min/kg for mouse, rat, human
# (= 0.6, 0.522, 0.108 1/hr/kg) from Lin 1995.
# = CL_GFR / BW (BW=0.02 for mouse, 0.265 for rat, 70 for human)
# kUrn = CL_GFR / VBld
# Diffuse prior with Uncertainty of up,down 1000-fold
kUrnTCOG = exp(InkUrnTCOGC) * BW / (VBodTCOH * PBodTCOG) *
A-204
-------
# DCVG Kinetics (/hr)
# Fraction of renal TCE GSH conj. "directly" to DCVC via "first pass
# exp(InFracOtherCC) = ratio of direct/non-direct
# Diffuse prior distribution: loguniform le-3 to Ie3
# FIXED in vl.2.3
# In ".in" files, set to 1, so that all kidney GSH conjugation
# is assumed to directly produce DCVC (model lacks identiflability
# otherwise).
FracKidDCVC = exp(InFracKidDCVCC)/(1 + exp(InFracKidDCVCC));
# No in vitro data. So use diffuse priors of
# le-4 to Ie2 /hr/kg"0.25 (0.3/hr to 35/hr for human)
kDCVG = exp(lnkDCVGC) / BW25;
# DCVC Kinetics in Kidney (/hr)
# No in vitro data. So use diffuse priors of
# le-4 to Ie2 /hr/kg"0.25 (0.3/hr to 35/hr for human)
kNAT = exp(lnkNATC) / BW25;
kKidBioact = exp(InkKidBioactC) / BW25;
# CC data initialization
Rodents = (CC > 0 ? NRodents : 0.0); # Closed chamber simulation
VCh = (CC > 0 ? VChC - (Rodents * BW) : 1.0);
# Calculate net chamber volume
kLoss = (CC > 0 ? exp(lnkLossC) : 0.0);
FracPlas
Temporary variables used:
none
Temporary variables assigned:
QP
DResp
QCnow
QFat
QGut
QLiv
QSlw
QKid
QGutLiv
QRap
QCPlas
QBodPlas
QGutLivPlas
Initial amount in chamber
These done here in dynamics in case QCnow changes
Blood Flows to Tissues (L/hr)
QFat = (QFatCtmp) * QCnow; #
QGut = (QGutCtmp) * QCnow; #
QLiv = (QLivCtmp) * QCnow; #
QSlw = (QSlwCtmp) * QCnow; #
QKid = (QKidCtmp) * QCnow; #
QGutLiv = QGut + QLiv; #
QRap = QCnow - QFat - QGut - QLiv - QSlw - QKid;
QRapCtmp = QRap/QCnow; #(vrisk)
QBod = QCnow - QGutLiv;
# State Variables with dynamics:
# none
# Input Variables:
# QPmeas
# Other State Variables and Global Parameters:
# QC
# VPR
# DResptmp
# QPsamp
# QFatCtmp
# QGutCtmp
# QLivCtmp
# QSlwCtmp
# QKidCtmp
Plasma Flows to Tissues (L/hr)
QCPlas = FracPlas * QCnow; #
QBodPlas = FracPlas * QBod; #
QGutLivPlas = FracPlas * QGutLiv; #
# State Variables with dynamics:
# AStom
# ADuod
# AStomTCA
# AStomTCQH
# Input Variables:
# IVDose
# PDose
# Drink
A-205
-------
Cone
IVDoseTCA
PODoseTCA
IVDoseTCOH
PODoseTCOH
Other State Variables and Global Parameters:
ACh
CC
VCh
MWTCE
BW
TChng
kAS
kTSD
kAD
kTD
kASTCA
kASTCOH
Temporary variables used:
none
Temporary variables assigned:
klV - rate into CVen
klA - rate into CArt
kPV - rate into portal vein
kStom - rate into stomach
kDrink - incorporated into RAO
RAO - rate into gut (oral absorption - both gavage and drinking water)
CInh - inhalation exposure concentration
klVTCA - rate into blood
kStomTCA - rate into stomach
kPOTCA - rate into liver (oral absorption)
klVTCOH - rate into blood
kStomTCOH - rate into stomach
kPOTCOH - rate into liver (oral absorption)
Notes:
For oral dosing, using "Spikes" for instantaneous inputs
Inhalation Concentration (mg/L)
CInh uses Cone when open chamber (CC=0) and
ACh/VCh when closed chamber COO.
#### TCE DOSING
## IV route
klV = (IVDose * BW) / TChng;# IV infusion rate (mg/hr)
# (IVDose constant for duration TChng)
BW) / TChng; # IA infusion rate (mg/hr)
BW) / TChng; # PV infusion rate (mg/hr)
* BW) / TChng;# PO dose rate (into stomach) (mg/hr)
# Total rate of absorption including gavage and drinking water
RAO = kDrink + (kAS * AStom) + (kAD * ADuod);
## Inhalation route
CInh = (CC > 0 ? ACh/VCh : Conc*MWTCE/24450.0); # in mg/1
#### TCA Dosing
klVTCA = (IVDoseTCA * BW) / TChng; # TCA IV infusion rate (mg/hr)
kStomTCA = (PODoseTCA * BW) / TChng; # TCA PO dose rate into stomach
dt(AStomTCA) = kStomTCA - AStomTCA * kASTCA;
kPOTCA = AStomTCA * kASTCA; # TCA oral absorption rate (mg/hr)
#### TCOH Dosing
klVTCOH = (IVDoseTCOH * BW) / TChng;#TCOH IV infusion rate (mg/hr)
kStomTCOH = (PODoseTCOH * BW) / TChng; # TCOH PO dose rate into stomach
dt(AStomTCOH) = kStomTCOH - AStomTCOH * kASTCOH;
kPOTCOH = AStomTCOH * kASTCOH;# TCOH oral absorption rate (mg/hr)
# Amount in rapidly perfused tissues
# Amount in slowly perfused tissues
# Amount in fat
# Amount in gut
# Amount in liver
# Amount in Kidney -- currently in Rap tissue
# Amount in Blood -- currently in Rap tissue
# Amount of TCE in closed chamber
ARap,
ASlw,
AFat,
AGut,
ALiv,
AInhResp,
AResp,
AExhResp,
AKid,
ABld,
ACh,
Input Variables:
none
Other State Variables and Global Parameters:
VRap
PRap
VSlw
PSlw
VFat
PFat
VGut
PGut
VLiv
PLiv
VRespLum
VRespEff
FracLungSys
VKid
PKid
VBld
VMAXClara
KMClara
# Amount of TCE in duodenum -- for oral dosing only (mg)
dt(ADuod) = (kTSD * AStom) - (kAD + kTD) * ADuod;
# Rate of absorption from drinking water
kDrink = (Drink * BW) / 24.0; #Ingestion rate via drinking water (mg/hr)
A-206
-------
VMAX
KM
VMAXDCVG
KMDCVG
VMAXKldDCVG
KMKldDCVG
Temporary variables used:
QM
QFat
QGutLiv
QSlw
QRap
QKid
klV
QCnow
CInh
QP
RAO
Temporary variables assigned:
QM
CRap
CSlw
CFat
CGut
CLiv
CInhResp
CResp
CExhResp
ExhFactor
CMixExh
CKid
CVRap
CVSlw
CVFat
CVGut
CVLiv
CVTB
CVKid
CVen
RAMetLng
CArt_tmp
CArt
CAlv
RAMetLivl
RAMetLiv2
RAMetKid
Notes:
# Tissue Concentrations (mg/L)
CRap = ARap/VRap;
CSlw = ASlw/VSlw;
CFat = AFat/VFat;
CGut = AGut/VGut;
CLiv = ALiv/VLiv;
CKid = AKid/VKid;
# Venous Concentrations (mg/L)
CVRap = CRap / PRap
CVSlw = CSlw / PSlw
CVFat = CFat / PFat
CVGut = CGut / PGut
CVLiv = CLiv / PLiv
CVKid = CKid / PKid
# Concentration of TCE in mixed venous blood (mg/L)
CVen = ABld/VBld;
# Dynamics for blood
dt(ABld) = (QFat^CVFat + QGutLiv^CVLiv + QSlw^CVSlw +
QRap*CVRap + QKid*CVKid + klV) - CVen * QCn<
QM = QP/0.7; # Minute-volum
CInhResp = AInhResp/VRespLum;
CResp = AResp/VRespEf f ;
CExhResp = AExhResp/VRespLum;
RAMetLng = VMAXClara * CResp/ (KMClara + CResp) ;
dt(AResp) = (DResp* (CInhResp + CExhResp - 2*CResp) - RAMetLng);
CArt_tmp = (QCnow*CVen + QP*CInhResp) / (QCnow + (QP/PB));
dt(AExhResp) = (QM* (CInhResp-CExhResp) + QP* (CArt_tmp/PB-CInhResp)
DResp* (CResp-CExhResp) ) ;
CMixExh = (CExhResp > 0 ? CExhResp : le-15); # mixed exhaled breath
Concentration in alveolar air (mg/L)
# Correction factor for exhaled air to account for
# absorption/desorption/metabolism in respiratory tissue
t = 1 if DResp = 0
ExhFactor_den = (QP * CArt_tmp / PB + (QM-QP) *CInhResp) ;
ExhFactor = (ExhFactor_den > 0) ? (
QM * CMixExh / ExhFactor_den) : 1;
# End-exhaled breath (corrected for absorption/
# desorption/metabolism in respiratory tissue)
CAlv = CArt_tmp / PB * ExhFactor;
Concentration in arterial blood entering circulation (mg/L)
CArt = CArt tmp + klA/QCnow; # add inter-arterial dose
#**** Non—metabolizing tissues ******************
# Amount of TCE in rapidly perfused tissues (mg)
dt(ARap) = QRap * (CArt - CVRap);
# Amount of TCE in slowly perfused tissues
dt(ASlw) = QSlw * (CArt - CVSlw);
# Amount of TCE in fat tissue (mg)
dt(AFat) = QFat*(CArt - CVFat);
# Amount of TCE in gut compartment (mg)
dt(AGut) = (QGut * (CArt - CVGut)) + RAO;
#*
A-207
-------
RAMetLivl = (VMAX * CVLlv) / (KM + CVLlv);
Rate of TCE metabolized to DCVG in liver (mg)
RAMetLiv2 = (VMAXDCVG * CVLiv) / (KMDCVG + CVLiv);
Dynamics for amount of TCE in liver (mg)
dt(ALiv) = (QLiv * (CArt - CVLiv)) + (QGut * (CVGut - CVLiv))
- RAMetLivl - RAMetLiv2 + kPV; # added PV dose
^*** Kidney **^*^*************^*^*********
Rate of TCE metabolized to DCVG in kidney
RAMetKid = (VMAXKidDCVG * CVKid)
Amount of TCE in kidney compartment (mg)
dt(AKid) = (QKid * (CArt - CVKid)) - RAMetKid;
State Variables with dynamics:
ABodTCOH
ALivTCOH
Input Variables:
none
Other State Variables and Global Parameters:
ABileTCOG
kEHR
VBodTCOH
PBodTCOH
VLiv
PLivTCOH
VMAXTCOH
KMTCOH
VMAXGluc
KMGluc
kMetTCOH - hepatic metabolism of TCOH (e.g.,
FracOther
FracTCA
StochTCOHTCE
StochTCOHGluc
FracLungSys
Temporary variables used:
QBod
QGutLiv
QCnow
kPOTCOH
RAMetLivl
RAMetLng
Temporary variables assigned:
CVBodTCOH
CVLivTCOH
CTCOH
RAMetTCOHTCA
RAMetTCOHGluc
RAMetTCOH
RARecircTCOG
Notes:
# Rate of oxidation of TCOH to TCA (mg/hr)
RAMetTCOHTCA = (VMAXTCOH * CVLivTCOH) /
# Amount of glucuronidation to TCOG (mg/hr)
RAMetTCOHGluc = (VMAXGluc * CVLivTCOH)
# Amount of TCOH metabolized to other (e.g., DCA)
RAMetTCOH = kMetTCOH * ALivTCOH;
# Amount of TCOH-Gluc recirculated (mg)
RARecircTCOG = kEHR * ABileTCOG;
# Amount of TCOH in liver (mg)
dt(ALivTCOH) = kPOTCOH + QGutLiv * (CTCOH - CVLivTCOH)
- RAMetTCOH - RAMetTCOHTCA - RAMetTCOHGluc
+ ((1.0 - FracOther - FracTCA) * StochTCOHTCE
(RAMetLivl + FracLungSys^RAMetLng))
+ (StochTCOHGluc * RARecircTCOG);
State Variables with dynamics:
APlasTCA
ABodTCA
ALivTCA
AUrnTCA
AUrnTCA_sat
AUrnTCA_collect
Input Variables:
TCAUrnSat
UrnMissing
Other State Variables and Global Parameters:
VPlas
MWTCA
kDissoc
BMax
kMetTCA — hepatic metabolism of TCA (e.g., to DC
VBod
PBodTCA
PLivTCA
kllrnTCA
FracTCA
StochTCATCE
StochTCATCOH
FracLungSys
Temporary variables used:
klVTCA
kPOTCA
QBodPlas
QGutLivPlas
A-208
-------
QCPlas
RAMetLivl
RAMetTCOHTCA
RAMetLng
Temporary variables assigned:
CPlasTCA
CPLasTCAMole
a, b, c
CPlasTCAFreeMole
CPlasTCAFree
APlasTCAFree
CPlasTCABnd
CBodTCAFree
CLivTCAFree
CBodTCA
CLivTCA
CVBodTCA
CVLivTCA
RUrnTCA
RAMetTCA
Notes:
Concentration of TCA in plasma (umoles/L)
CPlasTCA = (APlasTCA<1.0e-15 ? l.Oe-15 : APlasTCA/VPlas);
Concentration of free TCA in plasma in (umoles/L)
CPlasTCAMole = (CPlasTCA / MWTCA) * 1000.0;
a = kDissoc+BMax-CPlasTCAMole;
b = 4.0*kDissoc*CPlasTCAMole;
c = (b < 0.01*a*a ? b/2.0/a : sqrt(a*a+b)-a);
CPlasTCAFreeMole = 0.5*c;
Concentration of free TCA in plasma (mg/L)
CPlasTCAFree = (CPlasTCAFreeMole * MWTCA) / 1000.0;
APlasTCAFree = CPlasTCAFree * VPlas;
Concentration of bound TCA in plasma (mg/L)
CPlasTCABnd = (CPlasTCA
-------
Amount of TCOG in liver (rag)
RBileTCOG = kBile * ALivTCOG;
dt(ALivTCOG) = QGutLiv * (CTCOG - CVLivTCOG)
+ (StochGlucTCOH * RAMetTCOHGluc) - RBileTCOG;
Amount of TCOH-Gluc excreted in urine (mg)
dt(AUrnTCOG) = RUrnTCOG;
dt(AUrnTCOG_sat) = TCOGUrnSat*(1-UrnMissing)*RUrnTCOG;
# Saturated, but not missing collection times
dt(AUrnTCOG_collect) = (1-TCOGUrnSat)*(1-UrnMissing)*RUrnTCOG;
# Not saturated and not missing collection times
State Variables with dynamics:
ADCVGmol
Input Variables:
none
Other State Variables and Global Parameters:
kDCVG
FracKidDCVC # Fraction of kidney DCVG going to DCVC in first pass
VDCVG
Temporary variables used:
RAMetLiv2
RAMetKid
Temporary variables assigned:
RAMetDCVGmol
CDCVGmol
Assume negligible GGT activity in liver as compared to kidney,
supported by in vitro data on GGT (even accounting for 5x
greater liver mass relative to kidney mass), as well as lack
of DCVC detected in blood.
"FracKidDCVC" Needed to account for "first pass" in
kidney (TCE->DCVG->DCVC without systemic circulation of DCVG).
Rate of metabolism of DCVG to DCVC
RAMetDCVGmol = kDCVG * ADCVGmol;
Dynamics for DCVG in blood
dt(ADCVGmol) = (RAMetLiv2 + RAMetKid*(1-FracKidDCVC)) / MWTCE
- RAMetDCVGmol;
Concentration of DCVG in blood (in mmoles/1)
CDCVGmol = ADCVGmol / VDCVG;
ADCVC
AUrnNDCVC
Variables:
none
State Variables and Global Parameters:
# MWDCVC
# FracKidDCVC
# StochDCVCTCE
t kNAT
t kKidBioact
# StochN
# Temporary variables used:
# RAMetDCVGmol
t RAMetKid
rary variables assigned:
RAUrnDCVC
Amount of DCVC in kidney (mg)
dt(ADCVC) = RAMetDCVGmol * MWDCVC
+ RAMetKid * FracKidDCVC * StochDCVCTCE
- ((kNAT + kKidBioact) * ADCVC);
Rate of NAcDCVC excretion into urine (mg)
RAUrnDCVC = kNAT * ADCVC;
Dynamics for amount of N Acetyl DCVC excreted (mg)
dt(AUrnNDCVC) = StochN * RAUrnDCVC;
RUrnNDCVC = StochN * RAUrnDCVC; #(vrisk)
# Total intake from inhalation (mg)
RInhDose = QM * CInh;
dt(InhDose) = RInhDose;
# Amount of TCE absorbed by non-inhalation routes (mg)
dt(AO) = RAO + klV + klA + kPV; #(vrisk)
t Total dose
TotDose = InhDose + AO; #(vrisk)
# Total in tissues
TotTissue = #(vrisk)
ARap + ASlw + AFat + AGut + ALiv + AKid + ABld + #(vrisk)
AInhResp + AResp + AExhResp; #(vrisk)
# Total metabolized
dt(AMetLng) = RAMetLng; #(vrisk)
dt(AMetLivl) = RAMetLivl; #(vrisk)
dt(AMetLiv2) = RAMetLiv2; #(vrisk)
dt(AMetKid) = RAMetKid; #(vrisk)
ATotMetLiv = AMetLivl + AMetLiv2; #(vrisk)
TotMetab = AMetLng + ATotMetLiv + AMetKid; t(vrisk)
AMetLivOther = AMetLivl * FracOther; #(vrisk)
AMetGSH = AMetLiv2 + AMetKid; #(vrisk)
# Amount of TCE excreted in feces (mg)
RAExc = kTD * ADuod; #(vrisk)
dt(AExc) = RAExc; #(vrisk)
# Amount exhaled (mg)
A-210
-------
RAExh = QM * CMixExh;
dt(AExh) = RAExh;
# Mass balance
TCEDlff = TotDose - TotTlssue - TotMetab; #(vrisk)
MassBalTCE = TCEDlff - AExc - AExh; #(vrisk)
#**** Mass Balance for TCOH ***************************************************
# Total production/intake of TCOH
dt(ARecircTCOG) = RARecircTCOG; #(vrisk)
dt(AOTCOH) = kPOTCOH + klVTCOH; f(vrisk)
TotTCOHIn = AOTCOH + ((1.0 - FracOther - FracTCA) * #(vrisk)
StochTCOHTCE * (AMetLivl + FracLungSys*AMetLng)) + #(vrisk)
(StochTCOHGluc * ARecircTCOG); #(vrisk)
TotTCOHDose = AOTCOH + ((1.0 - FracOther - FracTCA) * #(vrisk)
StochTCOHTCE * (AMetLivl + FracLungSys*AMetLng)); #(vrisk)
# Total in tissues
TotTissueTCOH = ABodTCOH + ALivTCOH; #(vrisk)
# Total metabolism of TCOH
dt(AMetTCOHTCA) = RAMetTCOHTCA; f(vrisk)
dt(AMetTCOHGluc) = RAMetTCOHGluc; #(vrisk)
dt(AMetTCOHOther) = RAMetTCOH; #(vrisk)
TotMetabTCOH = AMetTCOHTCA + AMetTCOHGluc + AMetTCOHOther; #(vrisk)
# Mass balance
MassBalTCOH = TotTCOHIn - TotTissueTCOH - TotMetabTCOH; f(vrisk)
# Total production/intake of TCA
dt(AOTCA) = kPOTCA + klVTCA; #(vrisk)
TotTCAIn = AOTCA + (FracTCA*StochTCATCE*(AMetLivl + f(vrisk)
FracLungSys*AMetLng)) + (StochTCATCOH*AMetTCOHTCA); #(vrisk)
# Total in tissues
TotTissueTCA = APlasTCA + ABodTCA + ALivTCA; #(vrisk)
# Total metabolism of TCA
dt(AMetTCA) = RAMetTCA; #(vrisk)
# Mass balance
TCADiff = TotTCAIn - TotTissueTCA - AMetTCA; #(vrisk)
MassBalTCA = TCADiff - AUrnTCA; f(vrisk)
# Total production of TCOG
TotTCOGIn = StochGlucTCOH * AMetTCOHGluc; f(vrisk)
# Total in tissues
TotTissueTCOG = ABodTCOG + ALivTCOG + ABileTCOG; #(vrisk)
# Mass balance
MassBalTCOG = TotTCOGIn - TotTissueTCOG - f(vrisk)
ARecircTCOG - AUrnTCOG; f(vrisk)
# Total production of DCVG
dt(ADCVGIn) = (RAMetLiv2 + RAMetKid*(1-FracKidDCVC)) / MWTCE; #(vrisk)
t Metabolism of DCVG
dt(AMetDCVG) = RAMetDCVGmol; #(vrisk)
# Mass balance
MassBalDCVG = ADCVGIn - ADCVGmol - AMetDCVG; #(vrisk)
dt(ADCVCIn) = RAMetDCVGmol * MWDCVC f(vrisk)
+ RAMetKid * FracKidDCVC * StochDCVCTCE;#(vrisk)
# Bioactivation of DCVC
dt(ABioactDCVC) = (kKidBioact * ADCVC);f(vrisk)
# Mass balance
AUrnNDCVCeguiv = AUrnNDCVC/StochN;
MassBalDCVC = ADCVCIn - ADCVC - ABioactDCVC - AUrnNDCVCeguiv;#(vrisk)
#**** AUCs in mg-hr/L unless otherwise noted ********************
#AUC of TCE in arterial blood
dt(AUCCBld) = CArt; #(vrisk)
#AUC of TCE in liver
dt(AUCCLiv) = CLiv; #(vrisk)
#AUC of TCE in kidney
dt(AUCCKid) = CKid; #(vrisk)
#AUC of TCE in rapidly perfused
dt(AUCCRap) = CRap; #(vrisk)
#AUC of TCOH in blood
dt(AUCCTCOH) = CTCOH; #(vrisk)
#AUC of TCOH in body
dt(AUCCBodTCOH) = ABodTCOH / VBodTCOH; #(vrisk)
#AUC of free TCA in the plasma (mg/L * hr)
dt(AUCPlasTCAFree) = CPlasTCAFree; #(vrisk)
#AUC of total TCA in plasma (mg/L * hr)
dt(AUCPlasTCA) = CPlasTCA; #(vrisk)
#AUC of TCA in liver (mg/L * hr)
dt(AUCLivTCA) = CLivTCA; f(vrisk)
#AUC of total TCOH (free+gluc) in TCOH-eguiv in blood (mg/L * hr)
dt(AUCTotCTCOH) = CTCOH + CTCOGTCOH; #(vrisk)
#AUC of DCVG in blood (mmol/L * hr) — NOTE moles, not mg
dt(AUCCDCVG) = CDCVGmol; f(vrisk)
#**** Static outputs for comparison to data ******************************
t TCE
RetDose = ((InhDose-AExhExp) > 0 ? (InhDose - AExhExp) : le-15);
CAlvPPM = (CAlv < l.Oe-15 ? l.Oe-15 : CAlv * (24450.0 / MWTCE));
CInhPPM = (ACh< l.Oe-15 ? l.Oe-15 : ACh/VCh*24450.0/MWTCE);
# CInhPPM Only used for CC inhalation
CArt = (CArt < l.Oe-15 ? l.Oe-15 : CArt);
CVen = (CVen < l.Oe-15 ? l.Oe-15 : CVen);
CBldMix = (CArt+CVen)/2;
CFat = (CFat < l.Oe-15 ? l.Oe-15 : CFat);
CGut = (CGut < l.Oe-15 ? l.Oe-15 : CGut);
A-211
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CRap = (CRap < l.Oe-15 ? l.Oe-15
CSlw = (CSlw < l.Oe-15 ? l.Oe-15
CHrt = CRap;
CKid = (CKld < l.Oe-15 ? l.Oe-15
CLlv = (CLlv < l.Oe-15 ? l.Oe-15
CLung = CRap;
CMus = (CSlw < l.Oe-15 ? l.Oe-15
CSpl = CRap;
CBrn = CRap;
zAExh = (AExh
zAExhpost = ((
# Misc
CVenMole = CVen / MWTCE;
CPlasTCAMole = (CPlasTCAMole < l.Oe-15 ? l.Oe-15 : CPlasTCAMole);
CPlasTCAFreeMole = (CPlasTCAFreeMole < l.Oe-15 ? l.Oe-15 :
CPlasTCAFreeMole);
< l.Oe-15 ? l.Oe-15 : AExh);
AExh - AExhExp) < l.Oe-15 ? l.Oe-15 : AExh - AExhExp);
CTCOH = (CTCOH < l.Oe-15 ? l.Oe-15 : CTCOH);
CBodTCOH = (ABodTCOH < l.Oe-15 ? l.Oe-15 : ABodTCOH/VBodTCOH);
CKidTCOH = CBodTCOH;
CLivTCOH = (ALivTCOH < l.Oe-15 ? l.Oe-15
CLungTCOH = CBodTCOH;
t TCA
CPlasTCA = (CPlasTCA < l.Oe-15
CBldTCA = CPlasTCA*TCAPlas;
CBodTCA = (CBodTCA < l.Oe-15 ? l.Oe-15
CLivTCA = (CLivTCA < l.Oe-15 ? l.Oe-15
CKidTCA = CBodTCA;
CLungTCA = CBodTCA;
zAUrnTCA = (AUrnTCA < l.Oe-15 ? l.Oe-15 : AUrnTCA);
zAUrnTCA_sat = (AUrnTCA_sat < l.Oe-15 ? l.Oe-15 : AUrnTCA_sat);
zAUrnTCA_collect = (AUrnTCA_collect < l.Oe-15 ? l.Oe-15 :
AUrnTCA_collect);
t TCOG
zABileTCOG = (ABileTCOG < l.Oe-15 ? l.Oe-15 : ABileTCOG);
# Concentrations are in TCOH-eguivalents
CTCOG = (CTCOG < l.Oe-15 ? l.Oe-15 : CTCOG);
CTCOGTCOH = (CTCOG < l.Oe-15 ? l.Oe-15 : StochTCOHGluc*CTCOG);
CBodTCOGTCOH = (ABodTCOG < l.Oe-15 ? l.Oe-15 :
StochTCOHGluc*ABodTCOG/VBodTCOH);
CKidTCOGTCOH = CBodTCOGTCOH;
CLivTCOGTCOH = (ALivTCOG < l.Oe-15 ? l.Oe-15 :
StochTCOHGluc*ALivTCOG/VLiv);
CLungTCOGTCOH = CBodTCOGTCOH;
AUrnTCOGTCOH = (AUrnTCOG < l.Oe-15 ? l.Oe-15 : StochTCOHGluc*AUrnTCOG);
AUrnTCOGTCOH_sat = (AUrnTCOG_sat < l.Oe-15 ? l.Oe-15 :
StochTCOHGluc*AUrnTCOG_sat);
AUrnTCOGTCOH_collect = (AUrnTCOG_collect < l.Oe-15 ? l.Oe-15 :
StochTCOHGluc*AUrnTCOG_collect);
f Other
CDCVGmol = (CDCVGmol < l.Oe-15 ? l.Oe-15 : CDCVGmol);
CDCVGmolO = CDCVGmol; #(vl.2.3.2)
CDCVG_NDtmp = CDFNormal(3*(1-CDCVGmol/CDCVGmolLD));
# Assuming LD = 3*sigma blank, Normally distributed
CDCVG_ND = ( CDCVG_NDtmp < 1.0 ? ( CDCVG_NDtmp >= le-100 ? -
log(CDCVG_NDtmp) : -log(le-100)) : le-100 );
#(vl.2.3.2)
zAUrnNDCVC =(AUrnNDCVC < l.Oe-15 ? l.Oe-15 : AUrnNDCVC);
AUrnTCTotMole = zAUrnTCA / MWTCA + AUrnTCOGTCOH / MWTCOH;
TotCTCOH = CTCOH + CTCOGTCOH;
TotCTCOHcomp = CTCOH + CTCOG; # ONLY FOR COMPARISON WITH HACK
ATCOG = ABodTCOG + ALivTCOG; # ONLY FOR COMPARISON WITH HACK
TotTCAInBW = TotTCAIn/BW;#(vrisk)
# Scaled by BW"3/4
TotMetabBW34 = TotMetab/BW75;f(vrisk)
AMetGSHBW34 = AMetGSH/BW75;#(vrisk)
TotDoseBW34 = TotDose/BW75;#(vrisk)
AMetLivlBW34 = AMetLivl/BW75;#(vrisk)
TotOxMetabBW34 = (AMetLng+AMetLivl)/BW75;#(vrisk)
AMetLngBW34 = AMetLng/BW75; #(vrisk)
ABioactDCVCBW34 = ABioactDCVC/BW75;#(vrisk)
AMetLivOtherBW34 = AMetLivOther/BW75; #(vrisk)
# Scaled by tissue volume
AMetLivlLiv = AMetLivl/VLiv; #(vrisk)
AMetLivOtherLiv = AMetLivOther/VLiv; f(vrisk)
AMetLngResp = AMetLng/VRespEfftmp; #(vrisk)
ABioactDCVCKid = ABioactDCVC/VKid;#(vrisk)
VFatCtmp = VFat/BW; #(vrisk)
VGutCtmp = VGut/BW; #(vrisk)
VLivCtmp = VLiv/BW; #(vrisk)
VRapCtmp = VRap/BW; #(vrisk)
VRespLumCtmp = VRespLum/BW; #(vrisk)
VRespEffCtmp = VRespEfftmp/BW; #(vrisk)
VKidCtmp = VKid/BW; #(vrisk)
VBldCtmp = VBld/BW; #(vrisk)
VSlwCtmp = VSlw/BW; #(vrisk)
VPlasCtmp = VPlas/BW; #(vrisk)
VBodCtmp = VBod/BW; #(vrisk)
VBodTCOHCtmp = VBodTCOH/BW; #(vrisk)
A-212
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B. SYSTEMATIC REVIEW OF EPIDEMIOLOGIC STUDIES ON CANCER AND TCE
EXPOSURE
B.I. INTRODUCTION
The epidemiologic evidence on TCE is large with over 50 studies and includes
occupational cohort studies, case-control studies, both nested within a cohort (nested case-
control study) or population-based, and geographic-based studies. The analysis of epidemiologic
studies on cancer and TCE serves to document essential design features, exposure assessment
approaches, statistical analyses, and potential sources of confounding and bias. These studies are
described below and reviewed according to criteria to assess: (1) their ability to inform weight
of evidence evaluation for TCE exposure and a cancer hazard and (2) their utility for
examination using meta-analysis approaches. A secondary goal of the qualitative review is to
provide transparency on study strengths and weaknesses, providing background for inclusion or
exclusion of individual studies for quantitative treatment using meta-analysis approaches.
Individual study qualities are discussed according to specific criteria in Sections B.2.1 to B.2.8.,
and rationale for studies examined using meta-analysis approaches, the systematic review,
contained in Section B.2.9. Appendix C contains a full discussion of the meta-analysis, its
analytical methodology, including sensitivity analyses, and findings. This analysis supports
discussion of site-specific cancer observations in Chapter 4 where a presentation may be found
of study findings with assessment and discussion of observations according to a study's weight
of evidence and potential for alternative explanations, including bias and confounding.
B.2. METHODOLOGIC REVIEW OF EPIDEMIOLOGIC STUDIES ON CANCER
AND TCE
Epidemiologic studies considered in this analysis assess the relationship between TCE
exposure and cancer, and are identified using several sources and their utility for characterizing
hazard and quantitative treatment is based on recommendations in NRC (2006). A thorough
search of the literature was carried out through December 2010 without restriction on year of
publication or language using the following approaches: a search of the bibliographic databases
PubMed (http://www.ncbi.nlm.nih.gov/ pubmed/), TOXNET (http://toxnet.nlm.nih.gov/), and
EMBASE (http://www.embase.com/) using the terms "trichloroethylene cancer epidemiology"
and ancillary terms, "degreasers," "aircraft, aerospace or aircraft maintenance workers," "metal
workers," and "electronic workers," "trichloroethylene and cohort," or "trichloroethylene and
case-control;" bibliographies of reviews of the TCE epidemiologic literature such as those of the
Institute of Medicine (TOM. 2003). NRC (2009. 2006). and Scott and Chiu (2006) and review of
bibliographies of individual studies for relevant studies not identified in the previous two
B-l
-------
approaches. The search strategy identified studies that were either published or available on-line
(in press). NRC (2006) noted "a full review of the literature should identify all published studies
in which there was a possibility that TCE was investigated, even though results per se may not
have been reported."
Additional steps of U.S. EPA staff to identify studies not published in the literature
included contacting primary investigators for case-control studies of liver, kidney and lymphoma
and occupation, asking for information on analyses examining TCE uniquely and a review of
ATSDR or state health department community health surveys or statistics reviews for
information on TCE exposure and cancer incidence or mortality.
The breadth of the available epidemiologic database on TCE and cancer is wide
compared to that available for other chemicals assessed by U.S. EPA. However, few studies
were designed with the sole, or primary, objective of this report—to characterize the magnitude
of underlying association, if such exists, between TCE and cancer. Yet, many studies in the
body of evidence can provide information for identifying cancer hazard and dose-response
inferences. The weight a study contributes to the overall evidence on TCE and cancer depends
on a number of characteristics regarding the design, exposure assessment, and analysis
approaches. Epidemiologic studies were most informative for analysis if they approached ideals
described below, as evaluated using objective criteria for identifying a cancer hazard.
Seventy-five studies potentially relevant to health assessment of TCE exposure and
cancer and identified from the above comprehensive search are presented in Tables B-l, B-2, and
B-3. The studies vary widely in their approaches to study design, exposure assessment, and
statistical analysis; for these reasons, studies vary in their usefulness for identifying cancer
hazard. Studies are reviewed according to a set of a priori guidelines of their utility for assessing
TCE exposure and cancer according to the below criteria. Studies approaching criteria ideals
contribute greater weight in the weight of evidence analysis than studies with significant
deficiencies. These criteria are not meant to be used to "accept" or "reject" a particular study for
identifying cancer hazard. Rather, they are to be used as measurement tools for evaluating a
study's ability to identify TCE exposure and cancer outcomes. Studies suitable for meta-analysis
treatment are selected according to specific criteria identified in Section B.2.9.4. Individual
study descriptions and abstract sheets according to these criteria are found in Section B.3.
Appendix C describes meta-analysis methods and findings.
B-2
-------
Table B-l. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure
Reference
Description
Study group (N)
Comparison group (N)
Exposure assessment and other information
Aircraft and aerospace workers
Radican et al.
2008), Blair
et al. (1998)
Civilian aircraft-maintenance
workers with at least 1 yr in 1952-
1956 at Hill Air Force Base, Utah.
Vital status (VS) to 1990 (Blair et
al.. 1998) or 2000 (Radican et al..
2008): cancer incidence 1973-1990
(Blair etal. 1998).
14,457 (7,204 ever exposed to TCE).
Incidence (Blairetal.. 1998) and
mortality rates (Radican et al.. 2008:
Blair etal.. 1998) of nonchemical
exposed subjects.
Most subjects (n = 10,718) with potential exposure to 1-25 solvents.
Cumulative TCE assigned to individual subjects using JEM.
Exposure-response patterns assessed using cumulative exposure,
continuous or intermittent exposures, and peak exposure. TCE
replaced in 1968 with 1,1,1-trichloroethane and was discontinued in
1978 in vapor degreasing activities. Median TCE exposures were
about 10 ppm for rag and bucket; 100-200 ppm for vapor
degreasing. Poisson regression analyses controlled for age, calendar
time, sex (Blair etal.. 1998). or Cox proportional hazard model for
age and race.
Krishnadasan
et al. (2007)
Nested case-control study within a
cohort of 7,618 workers employed
for between 1950 and 1992, or who
had started employment before 1980
at Boeing/Rockwell/
Rocketdyne (SSFL [the UCLA
cohort of (Morgenstern et al..
1997)1). Cancer incidence 1988-
1999.
326 prostate cancer cases,
1,805 controls.
Response rate:
Cases, 69%; Controls, 60%.
JEM for TCE, hydrazine, PAHs, benzene, and mineral oil
constructed from company records, walk-through, or interviews.
Lifestyle factors obtained from living subjects through mail and
telephone surveys. Conditional logistic regression controlled for
cohort, age at diagnosis, physical activity, SES and other
occupational exposure (benzene, PAHs, mineral oil, hydrazine).
Aerospace workers with >2 yrs of
employment at Rockwell/
Rocketdyne (now Boeing) and who
worked at SSFL, Ventura,
California, from 1950 to 1993 (the
UCLA cohort of (Morgenstern et al.
1997)). Cancer mortality as of
December 31, 2001. Cancer
incidence 1988-2000 for subjects
alive as of 1988.
6,044 (2,689 with high cumulative
exposure to TCE). Mortality rates of
subjects in lowest TCE exposure
category.
5,049 (2,227 with high cumulative
exposure to TCE). Incidence rates of
subjects in lowest TCE exposure
category.
JEM for TCE, hydrazine, PAHs, mineral oil, and benzene. IH
ranked each job title ranked for presumptive TCE exposure as high
(3), medium (2), low (1), or no (0) exposure for 3 time periods
(1951-1969, 1970-1979, 1980-1989). Cumulative TCE score: low
(<3), medium (>3-12), high (>12) assigned to individual subjects
using JEM. Cox proportional hazard, controlled for time, since 1st
employment, SES, age at diagnosis, and hydrazine.
B-3
-------
Table B-l. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure
(continued)
Reference
Description
Study group (N)
Comparison group (N)
Exposure assessment and other information
Boice et al.
(2QQ6b)
Aerospace workers with >6 months
employment at Rockwell/
Rocketdyne (SSFL and nearby
facilities) from 1948 to 1999 (IEI
cohort, IEI [2005]). VS to 1999.
41,351, 1,642 male hourly test stand
mechanics (1,111 with potential TCE
exposure).
Mortality rates of U.S. population and for exposure
California population. Internal
referent groups including male hourly
nonadministrative Rocketdyne
workers; male hourly,
nonadministrative SSFL workers; and
test stand mechanics with no potential
exposure to TCE.
Potential TCE exposure assigned to test stands workers only whose
tasks included the cleaning or flushing of rocket engines (engine
flush) (n = 639) or for general utility cleaning (n = 472); potential
to large quantities of TCE was much greater during
engine flush than when TCE used as a utility solvent. JEM for TCE
and hydrazine without semiquantitative intensity estimates.
Exposure to other solvents not evaluated due to low potential for
confounding (few exposed, low exposure intensity, or not
carcinogenic). Exposure metrics included employment duration,
employment decade, years worked with potential TCE exposure, and
years worked with potential TCE exposure via engine cleaning,
weighted by number of tests. Lifetable (SMR); Cox proportional
hazard controlling for birth year, hire year, and hydrazine exposure.
Boice et al.
(1999)
Aircraft-manufacturing workers
with at least 1 yr >1960 at Lockheed
Martin (Burbank, California). VS to
1996.
77,965 (2,267 with potential routine
TCE exposures and 3,016 with
routine or intermittent TCE
exposure).
Mortality rates of U.S. population
(routine TCE exposed subjects) and
non-exposed internal referents
(routine and intermittent TCE
exposed subjects).
12% with potential routine mixed solvent exposure and 30% with
route or intermittent solvent exposure. JEM for potential TCE
exposure on: (1) routine basis; or (2) intermittent or routine basis
without semiquantitative intensity estimate. Exposure-response
patterns assessed by any exposure or duration of exposure and
internal control group. Vapor degreasing with TCE before 1966 and
perchloroethylene, afterwards. Lifetable analyses (SMR); Poisson
regression analysis adjusting for birth date, starting employment
date, finishing employment date, sex, and race.
Morgan et al.
(1998)
Aerospace workers with >6 months
1950-1985 at Hughes (Tucson,
Arizona). VSto 1993.
20,508 (4,733 with TCE exposures).
Mortality rates of U.S. population for
overall TCE exposure; mortality rates
of all-other cohort subjects (internal
referents) for exposure-response
analyses.
TCE exposure intensity assigned using JEM. Exposure-response
patterns assessed using cumulative exposure (low vs. high) and job
with highest TCE exposure rating (peak, medium/high exposure vs.
no/low exposure). "High exposure" job classification defined as
>50 ppm. Vapor degreasing with TCE 1952-1977, but limited IH
data <1975. Limited IH data before 1975 and medium/ low rankings
likely misclassified given temporal changes in exposure intensity not
fully considered (NRC. 2006).
Costa et al.
f!989)
Aircraft manufacturing workers
employed 1954-1981 at plant in
Italy. VSto 1981.
8,626 subjects
Mortality rates of the Italian
population.
No exposure assessment to TCE and job titles grouped into one of
four categories: blue- and white-collar workers, technical staff, and
administrative clerks. Lifetable (SMR).
B-4
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Table B-l. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure
(continued)
Reference
Garabrant et
al. (1988)
Description
Aircraft manufacturing workers
>4 yrs employment and who had
worked at least 1 d at San Diego,
California, plant 1958-1982. VS to
1982.
Study group (N)
Comparison group (N)
14,067
Mortality rates of U.S. population.
Exposure assessment and other information
TCE exposure assessment for 70 of 14,067 subjects; 14 cases of
esophageal cancer and 56 matched controls. For these 70 subjects,
company work records identified 37% with job title with potential
TCE exposure without quantitative estimates. Lifetable (SMR).
Cohorts identified from biological monitoring (U-TCA)
Hansen et al.
(2001)
Anttila et al.
(1995)
Axelson et al.
(1994)
Workers biological monitored using
U-TCA and air-TCE, 1947-1989.
Cancer incidence from 1964 to
1996.
Workers biological monitored using
U-TCA, 1965-1982. VS 1965-
1991 and cancer incidence 1967-
1992.
Workers biological monitored using
U-TCA, 1955-1975. VS to 1986
and cancer incidence 1958-1987.
803 total
Cancer incidence rates of the Danish
population.
3,974 total (3,089 with U-TCA
measurements).
Mortality and cancer incidence rates
of the Finnish population.
1,4,21 males
Mortality and cancer incidence rates
of Swedish male population.
712 with U-TCA, 89 with air-TCE measurement records, 2 with
records of both types. U-TCA from 1947 to 1989; air TCE
measurements from 1974. Historic median exposures estimated
from the U-TCA concentrations were: 9 ppm for 1947-1964, 5 ppm
for 1965-1973, 4 ppm for 1974-1979, and 0.7 ppm for 1980-1989.
Air TCE measurements from 1974 onward were 19 ppm (mean) and
5 ppm (median). Overall, median TCE exposure to cohort as
extrapolated from air TCE and U-TCA measurements was 4 ppm
(arithmetic mean, 12 ppm). Exposure metrics: year 1st employed,
employment duration, mean exposure, cumulative exposure.
Exposure metrics: employment duration, average TCE intensity,
cumulative TCE, period 1st employment. Lifetable analysis (SIR).
Median U-TCA, 63 umol/L for females and 48 umol/L for males;
mean U-TCA was 100 umol/L. Average 2.5 U-TCA measurements
per individual. Using the Ikeda et al. (1972) relationship for TCE
exposure to U-TCA, TCE exposures were roughly 4 ppm (median)
and 6 ppm (mean). Exposure metrics: years since 1st measurement.
Lifetable analysis (SMR, SIR).
Biological monitoring for U-TCA from 1955 and 1975. Roughly %
of cohort had U-TCA concentrations equivalent to <20 ppm TCE.
Exposure metrics: duration exposure, mean U-TCA. Lifetable
analysis (SMR, SIR).
B-5
-------
Table B-l. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure
(continued)
Reference
Description
Study group (N)
Comparison group (N)
Exposure assessment and other information
Other cohorts
Clapp and
Hoffman
(2008)
Deaths between 1969 and 2001
among employees >5 yrs
employment duration at an IBM
facility (Endicott, New York).
360 deaths
Proportion of deaths among New
York residents during 1979 to 1998.
No exposure assessment to TCE. PMR analysis.
Female workers 1st employed
1973-1997 at an electronics (RCA)
manufacturing factory (Taoyuan,
Taiwan). Cancer incidence 1979-
2001 (Sung etal. 2007). Childhood
leukemia 1979-2001 among first
born of female subjects in (Sung et
al. 2008)
63,982 females and 40,647 females
with 1st live born offspring.
Cancer incidence rates of Taiwan
population (Sung et al.. 2007).
Childhood leukemia incidence rates
of first born live births of Taiwan
population (Sung et al.. 2008).
No exposure assessment. Chlorinated solvents including TCE and
perchloroethylene found in soil and groundwater at factory site.
Company records indicated TCE not used 1975-1991 and
perchloroethylene 1975-1991 and perchloroethylene after 1981. No
information for other time periods. Exposure-response using
employment duration. Lifetable analysis (SMR, SIR) (Sung etal..
2007: Chang etal.. 2005: Chang et al.. 2003) or Poisson regression
adjusting for maternal age, education, sex, and birth year (Sung et
al.. 2008).
Chang et al.
(2005: 2003)
Male and female workers employed
1978-1997 at electronics factory as
studied by Sung et al. (2007). VS
from 1985 to 1997 and cancer
incidence 1979-1997.
86,868 total
Incidence (Chang et al.. 2005) or
mortality (Chang et al.. 2003) rates
Taiwan population.
ATSDR
(2004a)
Workers 1952-1980 at the View-
Master factory (Beaverton, Oregon).
616 deaths 1989-2001
Proportion of deaths between 1989
and 2001 in Oregon population.
No exposure information on individual subjects. TCE and other
VOCs detected in well water at the time of the plant closure in 1998
were TCE, 1,220-1,670 ug/L; 1,1-DCE, up to 33 ug/L; and,
perchloroethylene up to 56 ug/L. PMR analysis.
Raaschou-
Nielsen et al.
(2003)
Blue-collar workers employed
>1968 at 347 Danish TCE-using
companies. Cancer incidence
through 1997.
40,049 total (14,360 with presumably
higher level exposure to TCE).
Cancer incidence rates of the Danish
population.
Employers had documented TCE usage but no information on
individual subjects. Blue-collar vs. white-collar workers and
companies with <200 workers were variables identified as increasing
the likelihood for TCE exposure. Subjects from iron and metal,
electronics, painting, printing, chemical, and dry cleaning industries.
Median exposures to TCE were 40-60 ppm for the years before
1970, 10-20 ppm for 1970-1979, and approximately 4 ppm for
1980-1989. Exposure metrics: employment duration, year 1st
employed, and # employees in company. Lifetable (SIR).
B-6
-------
Table B-l. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure
(continued)
Reference
Description
Study group (N)
Comparison group (N)
Exposure assessment and other information
Male uranium-processing plant
workers >3 months employment
1951-1972 at DOE facility
(Fernald, Ohio). VS 1951-1989,
cancer.
3,814 white males monitored for
radiation (2,971 with potential TCE
exposure).
Mortality rates of the U.S.
population; non-TCE exposed
internal controls for TCE exposure-
response analyses.
JEM for TCE, cutting fluids, kerosene, and radiation generated by
employees and industrial hygienists. Subjects assigned potential
TCE according to intensity: light (2,792 subjects), moderate
(179 subjects), heavy (no subjects). Lifetable (SMR) and conditional
logistic regression adjusted for pay status, date first hire, radiation.
Henschler et
al. (1995)
Male workers >1 yr 1956-1975 at
cardboard factory (Arnsberg region,
Germany). VS to 1992.
169 exposed; 190 unexposed.
Mortality rates from German
Democratic Republic (broad
categories) or RCC incidence rates
from Danish population, German
Democratic, or non-TCE exposed
subjects.
Walk-through surveys and employee interviews used to identify
work areas with TCE exposure. TCE exposure assigned to renal
cancer cases using workman's compensation files. Lifetable (SMR,
SIR) or Mantel-Haenszel.
Greenland et
al. (1994)
Cancer deaths, 1969-1984, among
pensioned workers employed <1984
at GE transformer manufacturing
plant (Pittsfield, Massachusetts),
and who had job history record;
controls were noncancer deaths
among pensioned workers.
512 cases, 1,202 controls.
Response rate:
Cases, 69%;
Controls, 60%.
Industrial hygienist assessment from interviews and position
descriptions. TCE (no/any exposure) assigned to individual subjects
using JEM. Logistic regression.
Sinks et al.
(1992)
Workers employed 1957-1980 at a
paperboard container manufacturing
and printing plant (Newnan,
Georgia). VS to 1988. Kidney and
bladder cancer incidence through
1990.
2,050 total
Mortality rates of the U.S. population.
bladder and kidney cancer incidence
rates from the Atlanta-SEER registry
for the years 1973-1977.
No exposure assessment to TCE; analyses of all plant employees
including white- and blue-collar employees. Assignment of work
department in case-control study based upon work history; Material
Safety Data Sheets identified chemical usage by department.
Lifetable (SMR, SIR) or conditional logistic regression adjusted for
hire date and age at hire, and using 5- and 10-yr lagged employment
duration.
Blair et al.
(1989)
Workers employed 1942-1970 in
U.S. Coast. VS to 1980.
3,781 males of whom 1,767 were
marine inspectors (48%).
Mortality rates of the U.S. population.
Mortality rates of marine inspectors
also compared to that of
noninspectors.
No exposure assessment to TCE. Marine inspectors worked in
confined spaces and had exposure potential to multiple chemicals.
TCE was identified as one of 10 potential chemical exposures.
Lifetable (SMR) and directly adjusted RRs.
B-7
-------
Table B-l. Description of epidemiologic cohort and PMR studies assessing cancer and TCE exposure
(continued)
Reference
Description
Study group (N)
Comparison group (N)
Exposure assessment and other information
Shannon et al.
(1988)
Workers employed >6 months at GE
lamp manufacturing plant, 1960-
1975. Cancer incidence from 1964
to 1982.
1,870 males and females, 249 (13%)
in coiling and wire-drawing area.
Cancer incidence rates from Ontario
Cancer Registry.
No exposure assessment to TCE. Workers in coiling and wire
drawing (CWD) had potential exposure to many chemicals including
metals and solvents. A 1955-dated engineering instruction sheet
identified TCE used as degreasing solvent in CWD. Lifetable
(SMR).
Shindell and
Ulrich (1985)
Workers employed >3 months at a
TCE manufacturing plant 1957-
1983. VSto 1983.
2,646 males and females
Mortality rates of the United States
population.
No exposure assessment to TCE; job titles categorized as either
white- or blue-collar. Lifetable analysis (SMR).
Wilcosky et
al. (1984)
Respiratory, stomach, prostate,
lymphosarcoma, and lymphatic
leukemia cancer deaths 1964-1972
among 6,678 active and retired
production workers at a rubber plant
(Akron, Ohio); controls were a 20%
age-stratified random sample of the
cohort.
183 cases (101 respiratory,
33 prostate, 30 stomach,
9 lymphosarcoma and 10 lymphatic
leukemia cancer deaths).
JEM without quantitative intensity estimates for 20 exposures
including TCE. Exposure metric: ever held job with potential TCE
exposure.
DOE = U.S. Department of Energy; IEI = International Epidemiology Institute; Los Angeles; VS = vital status.
B-8
-------
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure
Reference
Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Bladder
Pesch et al.
(2000a)
Histologically confirmed urothelial
cancer (bladder, ureter, renal
pelvis) cases from German
hospitals (five regions) in 1991-
1995; controls randomly selected
from residency registries matched
on region, sex, and age.
1,035 cases
4,298 controls
Cases, 84%; controls, 71%
Occupational history using job title or self-reported exposure. JEM and
ITEM to assign exposure potential to metals and solvents (chlorinated
solvents, TCE, perchloroethylene). Lifetime exposure to TCE exposure
examined as 30th, 60th, and 90th percentiles (medium, high, and substantial) of
exposed control exposure index. Duration used to examine occupational title
and job task duties and defined as 30th, 60th, and 90th percentiles (medium,
long, and very long) of exposed control durations.
Logistic regression with covariates for age, study center, and smoking.
Siemiatycki
(1994). (1991)
Male bladder cancer cases, age 35-
75 yrs, diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
484 cases
533 population controls; 740
other cancer controls
Cases, 78%; controls, 72%
JEM to assign 294 exposures including TCE on semiquantitative scales
categorized as any or substantial exposure. Other exposure metrics included
exposure duration in occupation or job title.
Logistic regression adjusted for age, ethnic origin, SES, smoking, coffee
consumption, and respondent status [occupation or job title] or Mantel-
Haenszel stratified on age, income, index for cigarette smoking, coffee
consumption, and respondent status (TCE).
Brain
De Roos et al.
(2001):
Olshan et al.
(1999)
Neuroblastoma cases in children of
<19 yrs selected from Children's
Cancer Group and Pediatric
Oncology Group with diagnosis in
1992-1994; population controls
(random digit dialing) matched to
control on birth date.
504 cases
504 controls
Cases, 73%; controls, 74%
Telephone interview with parent using questionnaire to assess parental
occupation and self-reported exposure history and judgment-based attribution
of exposure to chemical classes (halogenated solvents) and specific solvents
(TCE). Exposure metric was any potential exposure.
Logistic regression with covariate for child's age and material race, age, and
education.
Heineman et al.
(1994)
White, male cases, age >30 yrs,
identified from death certificates in
1978-1981; controls identified
from death certificates and
matched for age, year of death, and
study area.
300 cases
386 controls
Cases, 74%; controls, 63%
In-person interview with next-of-kin; questionnaire assessing lifetime
occupational history using job title and JEM of Gomez et al. (1994).
Cumulative exposure metric (low, medium, or high) based on weighted
probability and duration.
Logistic regression with covariates for age and study area.
B-9
-------
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Colon and rectum
Goldberg et al.
(20011:
Siemiatycki
(1991)
Male colon cancer cases, 35-
75 yrs, from 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
497 cases
533 population controls and
740 cancer controls
Cases, 82%; controls, 72%
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); potential
TCE exposure defined as any or substantial exposure.
Logistic regression adjusted for age, ethnic origin, birthplace, education,
income, parent's occupation, smoking, alcohol consumption, tea
consumption, respondent status, heating source SES, smoking, coffee
consumption, and respondent status [occupation, some chemical agents] or
Mantel-Haenszel stratified on age, income, index for cigarette smoking,
coffee consumption, and respondent status [TCE].
Dumas et al.
(2000);
Simeiatycki
(1991)
Male rectal cancer cases, age 35-
75 yrs, diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
292 cases
533 population controls and
740 other cancer controls
Cases, 78%; controls, 72%
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); potential
TCE exposure defined as any or substantial exposure.
Logistic regression adjusted for age, education, respondent status, cigarette
smoking, beer consumption, and BMI [TCE] or Mantel-Haenszel stratified on
age, income, index for cigarette smoking, coffee consumption, ethnic origin,
and beer consumption [TCE].
Fredriksson et
al. (1989)
Colon cancer cases aged 30-75 yrs
identified through the Swedish
Cancer Registry among patients
diagnosed in 1980-1983;
population-based controls were
frequency-matched on age and sex
and were randomly selected from a
population register.
329 cases
658 controls
Not available
Mailed questionnaire assessing occupational history with telephone interview
follow-up. Self-reported exposure to TCE defined as any exposure.
Mantel-Haenszel stratified on age, sex, and physical activity.
B-10
-------
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Esophagus
Parent et al.
(2000a).
Siemiatycki
(1991)
Male esophageal cancer cases, 35-
75 yrs, diagnosed in 19 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
292 cases
533 population controls;
740 subjects with other
cancers
Cases, 78%; controls, 72%
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); potential
TCE exposure defined as any or substantial exposure.
Logistic regression adjusted for age, education, respondent status, cigarette
smoking, beer consumption, and BMI [solvents] or Mantel-Haenszel
stratified on age, income, index for cigarette smoking, coffee consumption,
ethnic origin, and beer consumption [TCE].
Lymphoma
Purdue et al.
(2011);
Cases aged 20-74 with
histologically-confirmed NHL
(B-cell diffuse and follicular,
T-cell, lymphoreticular) without
HIV in 1998-2000 and identified
from four SEER areas (Los
Angeles County and Detroit
metropolitan area, random sample;
Seattle_Puget Sound and Iowa, all
consecutive cases); population
controls aged 20-74 yrs with no
previous diagnosis of HIV
infection or NHL, identified
through: (1) if >65 yrs of age,
random digit dialing; or (2) if
>65 yrs, identified from Medicare
eligibility files and stratified on
geographic area, age, and race.
1,321 cases
1,057 controls
Cases, 76%; controls, 78%
In-person interview using questionnaire or computer-assisted personal
interview questionnaire specific for jobs held for >1 yr since the age of
16 yrs, hobbies, and medical and family history. For occupational history,
32 job- or industry-specific interview modules asked for detailed information
on individual jobs and focused on solvents exposure, including TCE,
assessment by expert industrial hygienist blinded to case and control status by
levels of probability, frequency, and intensity. Exposure metric of overall
exposure, average weekly exposure, years exposed, average exposure
intensity, and cumulative exposure.
Logistic regression adjusted for sex, age, race, education, and SEER site.
B-ll
-------
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Gold et
al.QOll)
Cases aged 35-74 with
histologically-confirmed multiple
myeloma in 2000-2002 and
identified from SEER areas
(Detroit, Seattle-Puget Sound);
population controls.
181 cases
481 controls
Cases, 71%; controls, 52%
In-person interview using computer-assisted personal interview questionnaire
for jobs held >1 yr since 1941 (cases) or 1946 (controls) and since age 18 yrs.
For occupational history, 20 occupations, job- or industry-specific interview
modules asked for detailed information on individual jobs held at least 2 yrs
and focused on solvents exposure, including TCE, assessment by expert
industrial hygienist blinded to case and control status by levels of probability,
duration, and cumulative exposure.
Logistic regression adjusted for sex, age, race, education, and SEER site.
Cocco et al.
(2010)
Cases aged >17 yrs with lymphoma
(B-cell, T-cell, CLL, multiple
myeloma, Hodgkin) in 1998-2004
and residents of referral areas from
seven European countries (Czech
Republic, Finland, France,
Germany, Ireland, Italy, and
Spain); hospital (four participating
countries) or population controls
(all others); controls from:
(1) Germany and Italy selected by
random digit dialing from general
population and matched
(individually in German and group-
based in Italy) to cases by sex, age
and residence area, and; (2) for all
other countries, matched hospital
controls with diagnoses other than
cancer, infectious diseases and
immundeficient diseases.
2,348 cases
2,462 controls
Cases, 88%; controls, 81%
hospital and 52% population
In-person interviews using same structured questionnaire translated to the
local language for information on sociodemographic factors, lifestyle, health
history, and all full-time job held >1 yr. Assessment by industrial hygienists
in each participating center to 43 agents, including TCE, by confidence,
exposure intensity, and exposure frequency. Exposure metric of overall TCE
exposure and cumulative TCE exposure for subjects assessed with high
degree of confidence (defined as low, medium, and high).
Logistic regression adjusted for age, gender, education and study center.
German
centers:
Seidler et al.
(2007); Mester
et al. (2006);
Becker et al.
(2004)
NHL and Hodgkin lymphoma
cases aged 18-80 yrs identified
through all hospitals and
ambulatory physicians in six
regions of Germany between 1998
and 2003; population controls were
identified from population registers
and matched on age, sex, and
region.
710 cases
710 controls
Cases, 87%; controls, 44%
In-person interview using questionnaire assessing personal characteristics,
lifestyle, medical history, UV light exposure, and occupational history of all
jobs held for >1 yr. Exposure of a prior interest were assessed using job task-
specific supplementary questionnaires. JEM used to assign cumulative
quantitative TCE exposure metric, categorized according to the distribution
among the control persons (50th and 90th percentile of the exposed controls).
Conditional logistic regression adjusted for age, sex, region, smoking, and
alcohol consumption.
B-12
-------
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Wang et al.
(2009)
Cases among females aged 21 and
84 yrs with NHL in 1996-2000 and
identified from Connecticut Cancer
Registry; population-based female
controls: (1) if <65 yrs of age,
having Connecticut address
stratified by 5-yr age groups
identified from random digit
dialing; or (2) >65 yrs of age, by
random selection from Centers for
Medicare and Medicaid Service
files.
601 cases
717 controls
Cases, 72%; controls, 69%
(<65 yrs), 47% (>65 yrs)
In-person interview with using questionnaire assessment specific jobs held
for >1 yr. Intensity and probability of exposure to broad category of organic
solvents and to individual solvents, including TCE, estimated using JEM
(DosemecietaL 1999; Gomez etal. 1994) and assigned blinded. Exposure
metric of any exposure, exposure intensity (low, medium/high), and exposure
probability (low, medium/high).
Logistic regression adjusted for age, family history of hematopoietic cancer,
alcohol consumption and race.
Costantini et al.
(2008); Miligi
et al. (2006)
Cases aged 20-74 with NHL,
including CLL, all forms of
leukemia, or multiple myeloma
(MM) in 1991-1993 and identified
through surveys of hospital and
pathology departments in study
areas and in specialized
hematology centers in eight areas
in Italy; population-based controls
stratified by 5-yr age groups and by
sex selected through random
sampling of demographic or of
National Health Service files.
1,428 NHL + CLL, 586
Leukemia,
263, MM
1,278 controls (leukemia
analysis)
1,100 controls (MM
analysis)
Cases, 83%; controls, 73%
In-person interview primarily at interviewee's home (not blinded) using
questionnaire assessing specific jobs, extra occupational exposure to solvents
and pesticides, residential history, and medical history. Occupational
exposure assessed by job-specific or industry-specific questionnaires. JEM
used to assign TCE exposure and assessed using intensity (two categories)
and exposure duration (two categories). All NHL diagnoses and 20% sample
of all cases confirmed by panel of three pathologists.
Logistic regression with covariates for sex, age, region, and education.
Logistic regression for specific NHL included an additional covariate for
smoking.
Persson and
Fredriksson
(1999):
Combined
analysis of
NHL cases in
Persson et al.
1993); Persson
etal. (1989)
Histologically confirmed cases of
B-cell NHL, age 20-79 yrs,
identified in two hospitals in
Sweden: Oreboro in 1964-1986
(Persson et al.. 1989) and in
Linkoping between 1975 and 1984
(Persson et al.. 1993); controls
were identified from previous
studies and were randomly selected
from population registers.
199 NHL cases,
479 controls
Cases, 96% (Oreboro),
90% (Linkoping);
controls, not reported
Mailed questionnaire to assess serf reported occupational exposures to TCE
and other solvents.
Mantel-Haenszel %2.
B-13
-------
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Nordstrom et
al. (19981
Histologically-confirmed cases in
males of hairy-cell leukemia
reported to Swedish Cancer
Registry in 1987-1992 (includes
one case latter identified with an
incorrect diagnosis date);
population-based controls
identified from the National
Population Registry and matched
(1:4 ratio) to cases for age and
county.
Ill cases
400 controls
Cases, 91%; controls, 83%
Mailed questionnaire to assess self reported working history, specific
exposure, and leisure time activities.
Univariate analysis for chemical-specific exposures (any TCE exposure).
Fritschi and
Siemiatycki
(1996a);
Siemiatycki
(1991)
Male NHL cases, age 35-75 yrs,
diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
215 cases
533 population controls
(Group 1) and
1,900 subjects with other
cancers (Group 2)
Cases, 83%; controls, 71%
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales).
Exposure metric defined as any or substantial exposure.
Logistic regression adjusted for age, proxy status, income, and ethnicity
(solvents) or Mantel-Haenszel stratified by age, BMI, and cigarette smoking
(TCE).
Serf-administered questionnaire assessing serf-reported solvent exposure;
phone follow-up with subject, if necessary.
Mantel-Haenszel %2.
Hardell et al.
(1994: 1981)
Histologically-confirmed cases of
NHL in males, age 25-85 yrs,
admitted to Swedish (Umea)
hospital between 1974 and 1978;
living controls (1:2 ratio) from the
National Population Register,
matched to living cases on sex,
age, and place of residence;
deceased controls from the
National Registry for Causes of
Death, matched (1:2 ratio) to dead
cases on sex, age, place of
residence, and year of death.
105 cases
335 controls
Response rate not available
B-14
-------
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Persson et al.
(1993): Persson
et al. (1989)
Histologically confirmed cases of
Hodgkin lymphoma, age 20-80
yrs, identified in two hospitals in
Sweden: Oreboro in 1964-1986
(Persson et al.. 1989) and in
Linkoping between 1975 and 1984
(Persson et al.. 1993): controls
54 cases (1989 study);
3 leases (1993 study)
275 controls (1989 study);
204 controls (1993 study)
Response rate not available
Mailed questionnaire to assess self reported occupational exposures to TCE
and other solvents.
Logistic regression with adjustment for age and other exposure; unadjusted
Mantel-Haenszel %2.
randomly selected from population
registers.
Childhood leukemia
Shu et al.
(2004; 1999)
Childhood leukemia cases, <15 yrs,
diagnosed between 1989 and 1993
by a Children's Cancer Group
member or affiliated institute;
population controls (random digit
dialing), matched for age, race, and
telephone area code and exchange.
1,842 cases
1,986 controls
Cases, 92%; controls, 77%
Telephone interview with mother, and whenever available, fathers using
questionnaire to assess occupation using job-industry title and serf-reported
exposure history. Questionnaire included questions specific for solvent,
degreaser, or cleaning agent exposures.
Logistic regression with adjustment for maternal or paternal education, race,
and family income. Analyses of paternal exposure also included age and sex
of the index child.
Childhood leukemia (<19 yrs of
age) diagnosed in 1969-1989 and
who were resident of Woburn,
Massachusetts; controls randomly
selected from Woburn public
School records, matched for age.
19 cases
37 controls
Cases, 91%; controls, not
available
Questionnaire administered to parents separately assessing demographic and
lifestyle characteristics, medical history information, environmental and
occupational exposure, and use of public drinking water in the home.
Hydraulic mixing model used to infer delivery of TCE and other solvents
water to residence.
Logistic regression with composite covariate, a weighted variable of
individual covariates.
McKinney et al.
(1991)
Incident childhood leukemia and
NHL cases, 1974-1988, ages not
identified, from three geographical
areas in England; controls
randomly selected from children of
residents in the three areas and
matched for sex and birth health
district.
109 cases
206 controls
Cases, 72%; controls, 77%
In-person interview with questionnaire with mother to assess maternal
occupational exposure history, and with father and mother, as surrogate, to
assess paternal occupational exposure history. No information provided in
paper whether interviewer was blinded as to case and control status.
Matched pair design using logistic regression for univariate and multivariate
analysis.
B-15
-------
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Lowengart et
al. (1987)
Population
Childhood leukemia cases aged
<10 yrs and identified from the Los
Angeles (California) Cancer
Surveillance Program in 1980-
1984; controls selected from
random digit dialing or from
friends of cases and matched on
age, sex, and race.
Study group (N)
Comparison group (N)
Response rates
123 cases
123 controls
Cases, 79%; controls,
not available
Exposure assessment and other information
Telephone interview with questionnaire to assess parental occupational and
serf-reported exposure history.
Matched (discordant) pair analysis.
Melanoma
Fritschi and
Siemiatycki
(1996b);
Siemiatycki
(1991)
Male melanoma cases, age 35-
75 yrs, diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
103 cases
533 population controls and
533 other cancer controls
Cases, 78%; controls, 72%
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); potential
TCE exposure defined as any or substantial exposure.
Logistic regression adjusted for age, education, and ethnic origin (TCE) or
Mantel-Haenszel stratified on age, income, index for cigarette smoking, and
ethnic origin (TCE).
Prostate
Aronson et al.
(1996):
Siemiatycki
(1991)
Male prostate cancer cases, age
35-75 yrs, diagnosed in 16 large
Montreal-area hospitals in 1979-
1985 and histologically confirmed;
controls identified concurrently at
18 other cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
449 cases
533 population controls
(Group 1) and
other cancer cases from
same study (Group 2)
Cases, 81%; controls, 72%
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales).
Logistic regression adjusted for age, ethnic origin, SES, Quetlet, and
respondent status (occupation) or Mantel-Haenszel stratified on age, income,
index for cigarette smoking, ethnic origin, and respondent status (TCE).
B-16
-------
Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Renal cell
Moore et al.
(20101
Cases aged 20-74 yrs from four
European countries (Czech
Republic, Poland, Russia,
Romania) with histologically-
confirmed kidney cancer in 1999-
2003; hospital controls with
diagnoses unrelated to smoking or
genitourinary disorders in 1998-
2003 and frequency matched by
sex, age, and study center.
1,097 cases (825 renal cell
carcinomas)
1,184 controls
Cases, 90-99%; controls,
90.3-96%
In-person interview using questionnaire for information on lifestyle habits,
smoking, antopometric measures, personal and family medical history, and
occupational history. Specialized job-specific questionnaire for specific jobs
or industries of interest focused on solvents exposure, including.TCE, with
exposure assignment by expert blinded to case and control status by
frequency, intensity and confidence of TCE exposure. Exposure metric of
overall exposure, duration (total hours, years) and cumulative exposure.
Logistic regression adjusted for sex, age, and study center. BMI,
hypertension, smoking, and residence location also included in initial models
but did not alter ORs by >10%.
Charbotel et al.
(2009; 2006)
Cases from Arve Valley region in
France identified from local
urologists files and from area
teaching hospitals; age- and sex-
matched controls chosen from file
of same urologist as who treated
case or recruited among the
patients of the case's general
practitioner.
87 cases
316 controls
Cases, 74%; controls, 78%
Telephone interview with case or control, or, if deceased, with next-of-kin
(22% cases, 2% controls). Questionnaire assessing occupational history,
particularly, employment in the screw cutting jobs, and medical history.
Semiquantitative TCE exposure assigned to subjects using a task/TCE-
Exposure Matrix designed using information obtained from questionnaires
and routine atmospheric monitoring of workshops or biological monitoring
(U-TCA) of workers carried out since the 1960s. Cumulative exposure,
cumulative exposure with peaks, and TWA.
Conditional logistic regression with covariates for tobacco smoking and BMI.
Briining et al.
(2003)
Histologically-confirmed cases
1992-2000 from German hospitals
(Arnsberg); hospital controls
(urology department) serving area,
and local geriatric department, for
older controls, matched by sex and
age.
134 cases
401 controls
Cases, 83%; controls, not
available
In-person interviews with case or next-of-kin; questionnaire assessing
occupational history using job title. Exposure metrics included longest job
held, JEM of Pannett et al. (1985) to assign cumulative exposure to TCE and
perchloroethylene, and exposure duration.
Logistic regression with covariates for age, sex, and smoking.
Pesch et al.
(2000b)
Histologically-confirmed cases
from German hospitals (five
regions) in 1991-1995; controls
randomly selected from residency
registries matched on region, sex,
and age.
935 cases
4,298 controls
Cases, 88%; controls, 71%
In-person interview with case or next-of-kin; questionnaire assessing
occupational history using job title (JEM approach), serf-reported exposure,
or job task (JTEM approach) to assign TCE and other exposures.
Logistic regression with covariates for age, study center, and smoking.
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Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Parent et al.
(2000a);
Siemiatycki
(1991)
Male RCC cases, age 35-75 yrs,
diagnosed in 16 large Montreal-
area hospitals in 1979-1985 and
histologically confirmed; controls
identified concurrently at 18 other
cancer sites; age-matched,
population-based controls
identified from electoral lists and
random digit dialing.
142 cases
533 population controls
(Group 1) and
other cancer controls
(excluding lung and bladder
cancers) (Group 2)
Cases, 82%; controls, 71%
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (about 300 exposures on semiquantitative scales);
TCE defined as any or substantial exposure.
Mantel-Haenszel stratified by age, BMI, and cigarette smoking (TCE) or
logistic regression adjusted for respondent status, age, smoking, and BMI
(occupation, job title).
Dosemeci et al.
1999)
Histologically-confirmed cases,
1988-1990, white males and
females, 20-85 yrs, from
Minnesota Cancer Registry;
controls stratified for age and sex
using random digit dialing, 21-
64 yrs, or from HCFA records, 64-
85 yrs.
438 cases
687 controls
Cases, 87%; controls, 86%
In-person interviews with case or next-of-kin; questionnaire assessing
occupational history of TCE using job title and JEM of Gomez et al. (1994).
Exposure metric was any TCE exposure.
Logistic regression with covariates for age, smoking, hypertension, and BMI.
Vamvakas et al.
(1998)
Cases who underwent nephrectomy
in 1987-1992 in a hospital in
Arnsberg region of Germany;
controls selected accident wards
from nearby hospital in 1992.
58 cases
84 controls
Cases, 83%; controls, 75%
In-person interview with case or next-of-kin; questionnaire assessing
occupational history using job title or serf-reported exposure to assign TCE
and perchloroethylene exposure.
Logistic regression with covariates for age, smoking, BMI, hypertension, and
diuretic intake.
Multiple or other sites
Lee et al.
(2003)
Liver, lung, stomach, colorectal
cancer deaths in males and females
between 1966 and 1997 from two
villages in Taiwan; controls were
cardiovascular and cerebral-
vascular disease deaths from same
underlying area as cases.
53 liver,
39 stomach,
26 colorectal, and
41 lung cancer cases;
286 controls
Response rate not reported
Residence as recorded on death certificate.
Mantel-Haenszel stratified by age, sex, and time period.
Kernan et al.
(1999)
Pancreatic deaths, 1984-1993, in
24 states; noncancer death and non-
pancreatic disease death controls,
frequency matched to cases by age,
gender, race, and state.
63,097 pancreatic cancer
cases
252,386 noncancer
population controls
Response rate not reported
Usual occupation and industry on death certificate coded to standardized
occupation codes and industry codes for 1980 U.S. census. Potential
exposure to 11 chlorinated hydrocarbons, including TCE, assessed using JEM
of Gomez etal. (1994).
Logistic regression adjusted for age, marital status, gender, race, and
metropolitan and residential status.
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Table B-2. Case-control epidemiologic studies examining cancer and TCE exposure (continued)
Reference
Population
Study group (N)
Comparison group (N)
Response rates
Exposure assessment and other information
Siemiatycki
(1991)
Male cancer cases, 1979-1985, 35-
75 yrs, diagnosed in 16 Montreal-
area hospitals, histologically
confirmed; cancer controls
identified concurrently; age-
matched, population-based controls
identified from electoral lists and
random digit dialing.
857 lung and
117 pancreatic cancer cases
533 population controls
(Group 1) and other cancer
cases from same study
(Group 2)
Cases, 79% (lung), 71%
(pancreas); controls, 72%
In-person interviews (direct or proxy) with segments on work histories (job
titles and serf-reported exposures); analyzed and coded by a team of chemists
and industrial hygienists (294 exposures on semiquantitative scales); TCE
defined as any or substantial exposure.
Mantel-Haenszel stratified on age, income, index for cigarette smoking,
ethnic origin, and respondent status (lung cancer) and age, income, index for
cigarette smoking, and respondent status (pancreatic cancer).
HCFA = Health Care Financing Administration; NCI =; UV = ultra-violet
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Table B-3. Geographic-based studies assessing cancer and TCE exposure
Reference
Description
Analysis approach
Exposure assessment
Broome County, New York studies
ATSDR
(2006a.
2008b)
Total, 22 site-specific, and
childhood cancer incidence from
1980 to 2001 among residents in
two areas in Endicott, New York.
SIR among all subjects (ATSDR. 2006a) or
among white subjects only (ATSDR. 2008b) with
expected numbers of cancers derived using age-
specific cancer incidence rates for New York
State, excluding New York City. Limited
assessment of smoking and occupation using
medical and other records in lung and kidney
cancer subjects (ATSDR. 2008b).
Two study areas, Eastern and Western study areas,
identified based on potential for soil vapor intrusion
exposures as defined by the extent of likely soil vapor
contamination. Contour lines of modeled VOC soil
vapor contamination levels based on exposure model
using GIS mapping and soil vapor sampling results
taken in 2003. The study areas were defined by 2000
Census block boundaries to conform to model predicted
areas of soil vapor contamination. TCE was the most
commonly found contaminant in indoor air in Eastern
study area at levels ranging from 0. 18 to 140 ug/m3,
with tetrachloroethylene, cis-l,2-dichloroethene, 1,1,1-
trichloroethane, 1,1-DCE, 1,1-dichloroethane, and
Freon 1 13 detected at lower levels. Perchloroethylene
was most common contaminant in indoor air in Western
study area with other VOCs detected at lower levels.
Maricopa County, Arizona studies
Aickin et al.
(1992): Aickin
(2004)
Cancer deaths, including leukemia,
1966-1986, and childhood (<19 yrs
old) leukemia incident cases (1965-
1986), Maricopa County, Arizona.
Standardized mortality rate ratio from Poisson
regression modeling. Childhood leukemia
incidence data evaluated using Bayes methods and
Poisson regression modeling.
Location of residency in Maricopa County, Arizona, at
the time of death as surrogate for exposure. Some
analyses examined residency in West Central Phoenix
and cancer. Exposure information is limited to TCE
concentration in two drinking water wells in 1982.
Pima County, Arizona studies
ADHS (1995.
1990)
Cancer incidence in children
(<19 yrs old) and testicular cancer in
1970-1986 and 1987-1991, Pima
County, Arizona.
Standardized incidence RR from Poisson
regression modeling using method of Aickin et al.
(1992). Analysis compares incidence in Tucson
Airport Area to rate for rest of Pima County.
Location of residency in Pima, County, Arizona, at the
time of diagnosis or death as surrogate for exposure.
Exposure information is limited to monitoring since
1981 and include VOCs in soil gas samples (TCE,
perchloroethylene, 1,1-DCE, 1,1,1-trichloroacetic acid);
PCBs in soil samples, and TCE in municipal water
supply wells.
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Table B-3. Geographic-based studies assessing cancer and TCE exposure (continued)
Reference
Description
Analysis approach
Exposure assessment
Other
Coyle et al.
(2005)
Incident breast cancer cases among
men and women, 1995-2000,
reported to Texas Cancer Registry.
Correlation study using rank order statistics of
mean average annual breast cancer rate among
women and men and atmospheric release of
12 hazardous air pollutants.
Reporting to EPA Toxic Release Inventory the number
of pounds released for 12 hazardous air pollutants,
(carbon tetrachloride, formaldehyde, methylene
chloride, styrene, tetrachloroethylene, TCE, arsenic,
cadmium, chromium, cobalt, copper, and nickel).
Morgan and
Cassady
(2002)
Incident cancer cases, 1988-1989,
among residents of 13 census tracts
in Redlands area, San Bernardino
County, California.
SIR for all cancer sites and 16 site-specific
cancers; expected numbers using incidence rates
of site-specific cancer of a four-county region
between 1988 and 1992.
TCE and perchlorate detected in some county wells; no
information on location of wells to residents,
distribution of contaminated water, or TCE exposure
potential to individual residents in studied census tracts.
Vartiainen
et al. (1993)
Total cancer and site-specific cancer
cases (lymphoma sites and liver)
from 1953 to 1991 in two Finnish
municipalities.
SIR with expected number of cancers and site-
specific cancers derived from incidence of the
Finnish population.
Monitoring data from 1992 indicated presence of TCE,
tetrachloroethylene and 1,1,1-trichloroethane in
drinking water supplies in largest towns in
municipalities. Residence in town used to infer
exposure to TCE.
Cohn et al.
(1994b);
Fagliano et al.
(1990)
Incident leukemia and NHL cases,
1979-1987, from 75 municipalities
and identified from the New Jersey
State Cancer Registry. Histological
type classified using WHO scheme
and the classification of NIH
Working Formulation Group for
grading NHL.
Logistic regression modeling adjusted for age.
Monitoring data from 1984 to 1985 on TCE,
trihalomethanes, and VOCs concentrations in public
water supplies, and historical monitoring data
conducted in 1978-1984.
Incident bladder cancer cases and
deaths, 1978-1985, among residents
of nine northwestern Illinois
counties.
SIR and SMR by county of residence and zip
code; expected numbers of bladder cancers using
age-race-sex specific incidence rates from SEER
or bladder cancer mortality rates of the U.S.
population from 1978 to 1985.
Exposure data are lacking for the study population with
the exception of noting one of two zip code areas with
observed elevated bladder cancer rates also had
groundwater supplies contaminated with TCE,
perchloroethylene, and other solvents.
Isacson et al.
f!985)
Incident bladder, breast, prostate,
colon, lung, and rectal cancer cases
reported to Iowa cancer registry
between 1969 and 1981.
Age-adjusted site-specific cancer incidence in
Iowa towns with populations of 1,000-10,000 and
who were serviced by a public drinking water
supply.
Monitoring data of drinking water at treatment plant in
each Iowa municipality with populations of 1,000-
10,000 used to infer TCE and other VOC
concentrations in finished drinking water supplies.
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Category A: Study Design
• Clear articulation of study objectives or hypothesis. The ideal is a clearly stated
hypothesis or study objectives and the study is designed to achieve the identified
objectives.
• Selection and characterization in cohort studies of exposure and control groups and of
cases and controls (case-control studies) is adequate. The ideal is for selection of cohort
and referents from the same underlying population and differences between these groups
are due to TCE exposure or level of TCE exposure and not to physiological, health status,
or lifestyle factors. Controls or referents are assumed to lack or to have background
exposure to TCE. These factors may lead to a downward bias including one of which is
known as "healthy worker bias," often introduced in analyses when mortality or
incidence rates from a large population such as the U.S. population are used to derive
expected numbers of events. The ideal in case-control studies is cases and controls are
derived from the same population and are representative of all cases and controls in that
population. Any differences between controls and cases are due to exposure to TCE
itself and not to confounding factors related to both TCE exposure and disease.
Additionally, the ideal is for controls to be free of any disease related to TCE exposure.
In this latter case, potential bias is toward the null hypothesis.
Category B: Endpoint Measured
• Levels of health outcome assessed. Three levels of health outcomes are considered in
assessing the human health risks associated with exposure to TCE: biomarkers of effects
and susceptibility, morbidity, and mortality. Both morbidity as enumerated by incidence
and mortality as identified from death certificates are useful indicators in risk assessment
for hazard identification. The ideal is for accurate and predictive indicator of disease.
Incidence rates are generally considered to provide an accurate indication of disease in a
population and cancer incidence is generally enumerated with a high degree of accuracy
in cancer registries. Death certifications are readily available and have complete national
coverage but diagnostic accuracy is reduced and can vary by specific diagnosis.
Furthermore, diagnostic inaccuracies can contribute to death certificates as a poor
surrogate for disease incidence. Incidence, when obtained from population-based cancer
registries, is preferred for identifying cancer hazards.
• Changes in diagnostic coding systems for lymphoma, particularly NHL. Classification of
lymphomas today is based on morphologic, immunophenotypic, genotypic, and clinical
features and is based upon the WHO classification, introduced in 2001, and incorporation
of WHO terminology into International Classification of Disease (ICD)-0-3. ICD
Versions 7 and earlier had rubrics for general types of lymphatic and hematopoietic
cancer, but no categories for distinguishing specific types of cancers, such as acute
leukemia. Epidemiologic studies based on causes of deaths as coded using these older
ICD classifications typically grouped together lymphatic neoplasms instead of examining
individual types of cancer or specific cell types. Before the use of immunophenotyping,
these grouping of ambiguous diseases such as NHL and Hodgkin lymphoma may be have
misclassified. Lymphatic tumors coding, starting in 1994 with the introduction of the
Revised European-American Lymphoma classification, the basis of the current WHO
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classification, was more similar to that presently used. Misclassification of specific types
of cancer, if unrelated to exposure, would have attenuated estimate of RR and reduced
statistical power to detect associations. When the outcome was mortality, rather than
incidence, misclassification would be greater because of the errors in the coding of
underlying causes of death on death certificates (IOM, 2003). Older studies that
combined all lymphatic and hematopoietic neoplasms must be interpreted with care.
Category C: TCE-Exposure Criteria
• Adequate characterization of exposure. The ideal is for TCE exposure potential known
for each subject and quantitative assessment (job-exposure-matrix approach) of TCE
exposure assessment for each subject as a function of job title, year exposed, duration,
and intensity. Consideration of job task as additional information supplementing job title
strengthens assessment increases specificity of TCE assignment. The assessment
approach is accurate for assigning TCE intensity (TCE concentration or a TWA) to
individual study subjects and estimates of TCE intensity are validated using monitoring
data from the time period. The objective for cohort and case-controls studies is to
differentiate TCE exposed subjects from subjects with little or no TCE exposure. A
variety of dose-metrics may be used to quantify or classify exposures for an
epidemiologic study. They include precise summaries of quantitative exposure,
concentrations of biomarkers, cumulative exposure, and simple qualitative assessments of
whether exposure occurred (yes or no). Each method has implicit assumptions and
potential problems that may lead to misclassification. Exposure assessment approaches
in which it was unclear that the study population was actually exposed to TCE are
considered inferior since there may be a lower likelihood or degree of exposure to study
subjects compared to approaches that assign known TCE exposure potential to each
subject.
Category D: Follow-up (Cohort)
• Loss to follow-up. The ideal is complete follow-up of all subjects; however, this is not
achievable in practice, but it seems reasonable to expect loss to follow-up not to exceed
10%. The bias from loss to follow-up is indeterminate. Random loss may have less
effect than if subjects who are not followed have some significant characteristics in
common.
• Follow-up period allows full latency period for over 50% of the cohort. The ideal to
follow all study subjects until death. Short of the ideal, a sufficient follow-up period to
allow for cancer induction period or latency over 15 or 20 years is desired for a large
percentage of cohort subjects.
Category E: Interview Type (Case-control)
• Interview approach. The ideal interviewing technique is face-to-face by trained
interviewers with >90% of interviews with cases and control subjects conduced face-to-
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face. The effect on the quality of information from other types of data collection is
unclear, but telephone interviews and mail-in questionnaires probably increase the rate of
misclassification of subject information. The bias is toward the null hypothesis if the
proportion of interview by type is the same for case and control, and of indeterminate
direction otherwise.
• Blinded interviewer. The ideal is for the interviewer to be unaware whether the subject is
among the cases or controls and the subject to be unaware of the purpose and intended
use of the information collected. Although desirable for case-control studies, blinding is
usually not possible to fully accomplish because subject responses during the interview
provide clues as to subject status. In face-to-face and telephone interviews, potential bias
may arise from the interviewer expects regarding the relationship between exposure and
cancer incidence. The potential for bias from face-to-face interviews is probably less
than with mail-in interviews. Some studies have assigned exposure status in a blinded
manner using a JEM and information collected in the unblinded interview. The potential
for bias in this situation is probably less with this approach than for nonblinded
assignment of exposure status.
Category F: Proxy Respondents
• Proxy respondents. The ideal is for data to be supplied by the subject because the subject
generally would be expected to be the most reliable source; <10% of either total cases or
total controls for case-control studies. A subject may be either deceased or too ill to
participate, however, making the use of proxy responses unavoidable if those subjects are
to be included in the study. The direction and magnitude of bias from use of proxies is
unclear, and may be inconsistent across studies.
Category G: Sample Size
• The ideal is for the sample size is large enough to provide sufficient statistical power to
ensure that any elevation of effect in the exposure group, if present, would be found, and
to ensure that the confidence bounds placed on RR estimates can be well-characterized.
Category H: Analysis Issues
• Control for potentially confounding factors of importance in analysis. The ideal in cohort
studies is to derive expected numbers of cases based on age-sex- and time-specific cancer
rates in the referent population and in case-control studies by matching on age and sex in
the design and then adjusting for age in the analysis of data. Age and sex are likely
correlated with exposure and are also risk factors for cancer development. Similarly,
other factors such as cigarette smoking and alcohol consumption are risk factors for
several site-specific cancers reported as associative with TCE exposure. To be a
confounder of TCE, exposure to the other factor must be correlated, and the association
of the factor with the site-specific cancer must be causal. The expected effect from
controlling for confounders is to move the estimated RR estimate closer to the true value.
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• Statistical methods are appropriate. The ideal is that conclusions are drawn from the
application of statistical methods that are appropriate to the problem and accurately
interpreted.
• Evaluation of exposure-response. The ideal is an examination of a linear exposure-
response as assessed with a quantitative exposure metric such as cumulative exposure.
Some studies, absent quantitative exposure metrics, examine exposure response
relationships using a semi quantitative exposure metric or by duration of exposure. A
positive dose-response relationship is usually more convincing of an association as causal
than a simple excess of disease using TCE dose-metric. However, a number of reasons
have been identified for a lack of linear exposure-response finding and the failure to find
such a relationship means little from an etiological viewpoint and does not minimize an
observed association with overall TCE exposure.
• Documentation of results. The ideal is for analysis observations to be completely and
clearly documented and discussed in the published paper, or provided in supplementary
materials accompanying publication.
B.2.1. Study Designs and Characteristics
The epidemiologic designs investigating TCE exposure and cancer include cohort studies
of occupationally exposure populations, population case-control studies, and geographic studies
of residents in communities with TCE in water supplies or ambient air. Analytical
epidemiologic studies, which include case-control and cohort designs, are generally relied on for
identifying a causal association between human exposure and adverse health effects (U.S. EPA,
2005b) due to their clear ability to show exposure precedes disease occurrence. In contrast,
ecologic studies such as health surveys of cancer incidence or mortality in a community during a
specified time period (i.e., geographic-based studies identified in Table B-3, provide correlations
between rates of cancer and exposure measured at the geographic level).
An epidemiologic study's ability to inform a question on TCE and cancer depends on
clear articulation of study objective or hypothesis and adequate selection of exposed and control
group in cohort studies and cases and controls in case-control studies are important. As the body
of evidence on TCE has grown over the past 20 years, so has the number of studies with clearly
articulated hypothesis. All Nordic cohort studies (Raaschou-Nielsen et al., 2003; Hansen et al.,
2001; Anttila et al., 1995; Axel son etal., 1994) are designed to examine cancer and TCE, albeit
some with limited statistical power, as are recent cohort studies of U.S. occupationally exposed
populations (Radican et al.. 2008: Boice et al.. 2006b: Zhao et al.. 2005: Boiceetal.. 1999: Ritz.
1999a). Exposure assessment approaches in these studies distinguished subjects with varying
potentials for TCE exposure, and in some cases, assigned a semi quantitative TCE exposure
surrogate to individual study subjects. Three case-control studies nested in cohorts, furthermore,
examined TCE exposure and site-specific cancer, albeit a subject's potential and overall
prevalence of TCE exposure greatly varied between these studies (Krishnadasan et al., 2007:
Greenland et al., 1994: Wilcosky et al., 1984). Typically, studies of all workers at a plant or
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manufacturing facility (Clapp and Hoffman. 2008: 2008: 2007: Chang et al.. 2005: 2004a: Chang
et al.. 2003: Sinks etal.. 1992: Blair etal.. 1989: Costa etal.. 1989: Garabrant et al.. 1988:
Shannon etal., 1988: Shindell andUlrich, 1985) are not designed to evaluate cancer and TCE
specifically, given their inability to identify varying TCE exposure potential for individual study
subjects; rather, such studies evaluate the health status of the entire population working at that
facility. Bias associated with exposure misclassification is greater in these studies, and for this
and other reasons more fully discussed below, they are of limited utility for informing
evaluations on TCE exposure and cancer.
Recent case-control studies with hypotheses specific for TCE exposure include the
kidney cancer case-control studies of Vamvakas et al. (1998), B riming et al. (2003), and
Charbotel et al. (2009: 2006). More common, population-based, case-control studies assess
occupational exposure to organic solvents, using a JEM approach for exposure assessment to
examine organic solvent categories (i.e., aliphatic hydrocarbons, or specific solvents such as
TCE). The case-control studies of Costas et al. (2002) and Lee et al. (2003) were also designed
to examine possible association with contaminated drinking water containing TCE and other
solvents detected at lower concentrations. The hypothesis of Siemiatycki (1991) and ancillary
publications (Goldberg et al., 2001: Dumas et al., 2000: Parent et al., 2000a: Fritschi and
Siemiatycki, 1996a: Siemiatycki etal., 1994) explored possible association between 20 site-
specific cancers and occupational title or chemical exposures, including TCE exposure, using a
contemporary exposure assessment approach for more focused research investigation.
Cases and control selection in most population-based case-control studies of TCE
exposure are considered a random sample and representative of the source population [Gold et
al., 2011: Cocco etal., 2010: Moore etal., 2010: Charbotel et al., 2009: Seidler et al., 2007:
Charbotel et al., 2006: Miligi et al., 2006: Shu et al., 2004: Briining et al., 2003: Lee et al., 2003:
Costas et al., 2002: DeRoos etal., 2001: Pesch et al., 2000a, 2000b: Dosemeci et al., 1999:
Kernan et al., 1999: Persson and Fredrikson, 1999: Nordstrom et al., 1998: Hardell et al., 1994:
Heineman et al., 1994: McKinney et al., 1991: Lowengart et al., 1987Siemiatycki et al., 1991
(and related publications: Siemiatycki et al., 1994: Aronson et al., 1996: Fritschi and Siemiatycki
1996b: Dumas et al., 2000: Parent et al., 2000b; Goldberg et al., 2001, and Fritschi and
Siemiatvcki, 1996a)1.
Case and control selection in Vamvakas et al. (1998), a study conducted in the Arnsberg
area of Germany, is subject to criticism regarding possible selection bias resulting from
differences in selection criteria, cases worked in small industries and controls from a wider
universe of industries; differences in age, controls being younger than cases with possible lower
exposure potentials; and temporal difference in case and control selection, controls selected only
during the last year of the study period with possible lower exposure potential if exposure has
decreased over period of the study (NRC, 2006). The potential for selection bias in Briining et
al. (2003), another study in the same area as Vamvakas et al. (1998) but of later period of
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observation, was likely reduced compared to Vamvakas et al. (1998) due to the broader region of
southern Germany from which cases were identified and interviewing cases and controls during
the same time. One case-control study nested in a cohort (Greenland et al., 1994) included
subjects whose deaths were reported to and known by the employer, e.g., occurred among vested
or pensioned employees or among currently employees. A 10-15-year employment period was
required for subjects in this study to receive a pension; deaths among employees who left
employment before this time were not known to the employer and not included the study.
Survivor bias, a selection bias, may be introduced by excluding nonpensioned workers or those
who leave employment before becoming vested in a company's retirement plan is more likely
than in a study of all employees with complete follow-up. The use of pensioned deaths as
controls, as was done in this study, would reduce potential bias if both cases and control had the
same likelihood of becoming pensioned. That is, the probability for becoming a pensioned
worker is similar for all deaths and unrelated to the likelihood of exposure or magnitude of
exposure and disease. No information was available in (Greenland et al., 1994) to evaluate this
assumption.
Geographic-based and ecological studies of TCE contaminated water supplies typically
focus on estimating cancer or other disease rates in geographically circumscribed populations
who are geospatially located with a source containing TCE, e.g., a hazardous waste site, well
water, or air. These studies are often less informative for studying cancer due to their inability to
estimate incidence rate ratios, essential for causal inferences, inferior exposure assessment
approach, and to possible selection biases. Ecological studies also are subject to bias known as
"ecological fallacy" since variables of exposure and outcome measured on an aggregate level do
not represent association at the individual level. Consideration of this bias is important for
diseases with more than one risk factor, such as the site-specific cancers evaluated in this
assessment.
B.2.2. Outcomes Assessed in TCE Epidemiologic Studies
The epidemiologic studies consider at least three levels of health outcomes in their
examinations of human health risks associated with exposure to TCE: biomarkers of effects and
susceptibility, morbidity, and mortality (NRC, 2006). Few susceptibility biomarkers have been
examined and these are not specific to TCE (NRC, 2006). By far, the bulk of the literature on
cancer and TCE exposure is of cancer morbidity (Goldetal., 2011; Purdue et al., 2011; Cocco et
al.. 2010: Moore et al.. 2010: Charbotel et al.. 2009: Wang et al.. 2009: Sung et al.. 2008: Seidler
et al.. 2007: ATSDR, 2006a; Charbotel et al.. 2006: Miligi et al.. 2006: Coyle et al.. 2005:
Aickin. 2004: Shu et al.. 2004: Briming et al.. 2003: Raaschou-Nielsen et al.. 2003: Costas et al..
2002: Morgan and Cassadv. 2002: DeRoos et al.. 2001: Hansen et al.. 2001: Dumas et al.. 2000:
Pesch et al., 2000a, 2000b: Dosemeci etal., 1999: Persson and Fredrikson, 1999: Nordstrom et
al.. 1998: Vamvakas et al.. 1998: APRS. 1995: Anttila et al.. 1995: Axel son et al.. 1994:. Cohn
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et al., 1994b:: Hardell et al.. 1994: Persson et al.. 1993: Vartiainen et al.. 1993: McKinnev et al..
1991: Siemiatvcki. 1991: APRS. 1990: Fredriksson et al.. 1989: Shannon et al.. 1988:
Lowengart et al., 1987: Isacson et al., 1985), mortality (Clapp and Hoffman, 2008: Radican et
al.. 2008: Boice et al.. 2006b: ATSDR. 2004a: Lee etal.. 2003: Boiceetal.. 1999: Kernan et al..
1999: Ritz. 1999a: Morgan et al.. 1998: Greenland et al.. 1994: Heineman et al.. 1994: Aickin et
al.. 1992: Blair etal.. 1989: Costa etal.. 1989: Garabrant et al.. 1988: Shindell and Ulrich. 1985:
Wilcosky et al.. 19841 or both (Sung et al.. 2007: Chang et al.. 2005: Zhao et al.. 2005: Chang et
al.. 2003: Blair etal.. 1998: Henschler et al.. 1995: Sinks etal.. 1992).
Mortality is readily identified from death certificates; however, diagnostic accuracy from
death certificates varies by the specific diagnosis (Brenner and Gefeller, 1993). Incident cancer
cases are enumerated more accurately by tumor registries and by hospital pathology records and
cases identified from these sources are considered to have less bias resulting from disease
misclassification than cause or underlying cause of death as noted on death certificates. Studies
of incidence are preferred, particularly for examining association with site-specific cancers
having high 5-year survival rates or which may be misclassified on death certificate.
Misclassification of the cause of death as noted on death certificates attenuates statistical power
through errors of outcome identification. This nondifferential misclassification of outcome in
cohort studies will lead to attenuation of rate ratios, although the magnitude of is difficult to
predict (NRC, 2006). Cancer registries are used for cases diagnosed in more recent time periods
and cohorts whose entrance dates are 30 or 40 years may miss many incident cancers and
reduced statistical power as a consequence. Two studies examine both cancer incidence and
mortality (Zhao et al.. 2005: Blair etal.. 1998). The lapse of >20 years in Blair et al. (1998) and
38 years in Zhao et al. (2005) between date of cohort identification and cancer incidence
ascertainment suggests these studies are missing cases and limits incidence examinations.
B.2.3. Disease Classifications Adopted in TCE Epidemiologic Studies
Disease coding and changes over time are important in epidemiologic evaluations,
particularly in evaluation of heterogeneity or consistency of observations from a body of
evidence. The ICD, published by WHO, is used to code underlying and contributing cause of
death on death certificates and is updated periodically, adding to diagnostic inconsistency for
cross-study comparisons (NRC, 2006). Tumor registries use the International Classification of
Diseases-Oncology (ICD-O) for coding the site and the histology of neoplasms, principally
obtained from a pathology report.
The epidemiologic studies of TCE exposure have used a number of different
classification systems (Scott and Chiu, 2006). A number of studies classified neoplasms
according to ICD-O (Gold et al.. 2011: Purdue etal.. 2011: Moore etal.. 2010: Chang et al..
2005: Costas et al.. 2002: Siemiatvcki. 1991) or to ICD-9 (Zhao et al.. 2005: Kernan etal.. 1999:
Ritz, 1999a: Nordstrom et al., 1998). Other ICD revisions used in recent studies include ICDA-8
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(Blair etal.. 1998: Greenland et al.. 1994: Blair etal.. 1989). ICD-7 (Raaschou-Nielsen et al..
2003: Hansenetal., 2001: Anttila et al., 1995: Axel son et al., 1994), or several ICD revisions,
whichever was in effect at the date of death (Radican et al., 2008: Morgan et al., 2000: Boice et
al., 1999: Morgan et al., 1998: Garabrant et al., 1988). In this latter case, changes in disease
classification over revisions are not harmonized or receded to a common classification; and
diagnostic inconsistencies and disease misclassification errors leads to a greater likelihood for
bias in these studies. Greatest weight is placed on studies where all cases or deaths are classified
using current classification systems. However, association in studies adopting older revisions,
ICD 7 (Raaschou-Nielsen et al.. 2003: Hansen etal.. 2001: Anttila et al.. 1995: Axel son et al..
1994), for example, is noteworthy given the narrow consideration of lymphoid neoplasms
compared to contemporary classification systems. Consistency examinations of the overall body
of evidence using meta-analysis methods and examination of heterogeneity will need to consider
study differences in coding in interpreting findings.
A major shift in thinking occurred around 1995 with the Revised European-American
Lymphoma (REAL) classification of grouping diseases of the blood and lymphatic tissues along
their cell lines compared to previous approaches to group lymphomas by a cell's physical
characteristics. It was increasing recognized that some NHLs and corresponding lymphoid
leukemias were different phases (solid and circulating) of the same disease entity (Morton et al.,
2007). Many concepts of contemporary knowledge of lymphomas are incorporated in the WHO
Classification of Neoplastic Diseases of the Hematopoietic and Lymphoid Tissues, an
international consensus scheme for classifying leukemia and lymphoma now in use and the
predecessor to REAL (IARC, 2001). Both the ICD-O, 3rd edition, and ICD-10 have adopted the
WHO classification framework.
The only study coding NHLs using the WHO classification is (Cocco et al., 2010). Other
NHL studies have adopted older lymphoma classification systems, either the NCI's Working
Formulation (Costantini et al., 2008: Miligi et al., 2006) or other systems coding lymphomas
according to NCI's Working Formulation (i.e., International Classification of Disease-
Oncology, 2nd Edition (Gold et al., 2011: Purdue et al., 2011: Wang et al., 2009)1) that divided
lymphomas into low-grade, intermediate-grade and high grade, with subgroups based on cell
type and presentation, or Rappaport (Hardell etal., 1994: 1981), with groupings based on
microscopic morphology (Lymphoma Information Network, 2008). Both Purdue et al. (2011)
and Gold et al. (2011) provide equivalent ICD-O-3 morphology codes
(http://www.seer.cancer.gov/tools/conversion/ICDO2-3manual.pdf, accessed April 6, 2011,).
Lowengart et al. (1987), Persson et al. (1993: 1989), McKinney et al. (1991), and Persson and
Fredriksson (1999) do not provide information in their published articles on lymphomas
classification systems used in these studies.
Implications of classification changes are most significant for NHL. As noted by the
IOM (2003), in Revision 7 and earlier editions of the ICD, all lymphatic and hematopoietic
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neoplasms were grouped together instead of treated as individual types of cancer (such as
Hodgkin lymphoma) or specific cell types (such as acute lymphocytic leukemia). One limitation
of this treatment was the amalgamation of these relatively rare cancers would increase the
apparent sample size but could also result in diluted estimates of effect if etiologic heterogeneity
of different lymphoma subtypes existed (i.e., different sites of cancer were not associated in
similar ways with the exposures of interest). Additionally, immunophenotyping was not
available, leading to decreased ability to distinguish ambiguous diseases, and diagnoses of these
cancers may have been misclassified; for example, NHL may have been grouped with other
lymphatic and hematopoietic cancers to increase statistical power or misclassified as Hodgkin
lymphoma, for example. Examination of distinct lymphoma subtypes is expected to reduce
disease misclassification bias. Five case-control studies on NHL include analysis of lymphoma
subtype and TCE exposure (Gold et al., 2011; Purdue et al., 2011; Cocco et al., 2010; Costantini
et al.. 2008; Miligi et al.. 2006).
A change in liver cancer coding occurred between ICDA-8 and ICD-9 and is important to
consider in examinations of liver cancer observations across the TCE studies. With ICD-9, liver
cancer "not specified as primary or secondary" was moved from the grouping of secondary
malignant neoplasms and added to the larger class of malignant liver neoplasms. Thus, a similar
grouping of liver cancer causes is necessary to cross-study comparisons. For example, an
examination of liver cancer, based on ICDA-8, would need to include codes for liver and
intrahepatic bile duct (code 155) and liver, not specified as primary or secondary (code 197.8),
but, for ICD-9, would include liver and intrahepatic bile duct (code 155) only. The effect of
adding "liver cancer, not specified as primary or secondary" to the larger liver and intrahepatic
bile duct category in ICD-9 was a twofold increase in the overall liver cancer mortality (Percy et
al.. 1990).
B.2.4. Exposure Classification
Adequacy of exposure assessment approaches and their supporting data are a critical
determinant of a study's contribution in a weight-of-evidence evaluation (Checkoway et al.,
1989). Exposure assessment approaches in studies of TCE and cancer vary greatly. At one
extreme, studies assume subjects are exposed by residence in a defined geographic area
(ATSDR, 2008b, 2006a; Coyle et al.. 2005; Aickin, 2004; Lee et al.. 2003; Morgan and Cassadv,
2002; ADHS, 1995; Cohn et al., 1994b; Vartiainen et al.. 1993; Aickin etal., 1992; ADHS,
1990; Isacson et al., 1985) or by employment in a plant or job title (Clapp and Hoffman, 2008;
Sung et al.. 2008; Sung et al.. 2007; Chang et al.. 2005; ATSDR, 2004a; Chang et al.. 2003; Blair
etal.. 1989; Costa et al.. 1989; Garabrant et al.. 1988; Shannon et al.. 1988; Shindell and Ulrich.
1985). This is a poor exposure surrogate given potential for TCE exposure can vary in these
broad categories depending on job function, year, use of personal protection, and, for residential
exposure, pollutant fate and transport, water system distribution characteristics, percent of time
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per day in residence, presence of mitigation devices, drinking water consumption rates, and
showering times. Another example comprises measurement from a subset of workers with jobs
where TCE is routinely used to infer TCE exposure and TCE intensity to all subjects. In both
examples, exposure misclassification potential may be extensive and with a downward bias in
risk estimates.
At the other extreme and preferred given a reduced likelihood for misclassification bias,
quantitative exposure assessment based upon a subject's job history, job title, and monitoring
data are used to develop estimates of TCE intensity and cumulative exposure (quantitative
exposure metrics or measures) and is known as JEM approaches. Peak exposure is also well
characterized. Addition to JEM approaches of information on job tasks (JTEM) associated with
exposure such as that done by Pesch et al. (2000a, 2000b) is expected to reduce potential
exposure misclassification. In between these two extremes, semi quantitative estimates of low,
medium, and high TCE exposure are assigned to subjects. Twenty-one studies assigned a
quantitative or semi quantitative TCE surrogate metrics to individual subjects using a JEM,
JTEM, or expert knowledge: (Siemiatycki, 1991) (and related publications (Goldberg et al.,
2001; Dumas et al., 2000; Parent et al., 2000a: Aronson et al., 1996; Fritschi and Siemiatycki,
1996a, b; Siemiatvcki et al.. 1994): Blair et al. (1998) and follow-up by Radican et al. (2008):
Morgan et al. (1998). Vamvakas et al. (1998). Kernan et al. (1999). Ritz (1999a). Pesch et al.
(2000a. 2000b). Briining et al. (2003). Zhao et al. (2005). Miligi et al. (2006). Charbotel et al.
(2009: 2006). Krishnadansen et al. (2007). Seidler et al. (2007). Costantini et al. (2008). Wang et
al. (2009). Cocco et al. (2010). Gold et al. , Moore et al. (2010). and Purdue et al. (2011).
Thirteen other studies assigned a qualitative TCE surrogate metric (ever exposed or never
exposed), less preferred to a semi-quantitative exposure surrogate given greater likelihood for
error associated exposure misclassification, using general job classification of job title by
reference to industrial hygiene records indicating a high probability of TCE use, individual
biomarkers, JEMs, water distribution models, for cohort studies, or obtained from subjects using
questionnaire for case-control studies. The 13 studies were: Wilcosky et al. (1984), Lowengart
et al. (1987). McKinney et al. (1991). Greenland et al. (1994). Hardell et al. (1994). Nordstrom et
al. (1998). Shu et al. (1999). Boice et al. (2006b: 1999). Dosemeci et al. (1999). Persson and
Fredriksson (1999). Costas et al. (2002). and Raaschou-Nielsen et al. (2003). Without
quantitative measures, however, it is not possible to quantify exposure difference between
groupings nor is it possible to compare similarly named categories across studies. Exposure
misclassification for dichotomous exposure defined in these studies, if nondifferential, would
downward bias resulting risk estimates.
Zhao et al. (2005), Krishnadansen et al. (2007), and Boice et al. (2006b) are studies with
overlap in some subjects, but with different exposure assessment approaches, more fully
discussed in Section B.3.1.1, with implication on study ability to identify cancer hazard. While
these studies used job title to assign TCE exposure potential, Zhao et al. (2005) and
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Krishnadansen et al. (2007) developed a semiquantitative estimate of TCE exposure potential,
whereas Boice et al. (2006b) classified subjects as either "exposed" or "unexposed" using a
qualitative surrogate. These studies, furthermore, identify TCE exposure potentially differently
for possibly similar job titles. For example, jobs as instrument mechanics, inspectors, test stand
engineers, and research engineers are identified with medium potential exposure in Zhao et al.
(2005) and Krishnadansen et al. (2007): however, these job titles were considered in Boice et al.
(2006b) as having background exposure and were combined with unexposed subjects, the
referent population in Cox Proportional Hazard analyses.
Three Nordic cohorts have TCE exposure as indicated from biological markers, assigning
TCE exposure to subjects using either concentration of TCA in urine or TCE in blood (Hansen et
al., 2001; Anttila et al., 1995: Axel son et al., 1994). The utility of a biomarker depends on it
selectivity and the exposure situation. Urinary TCA (U-TCA) is a nonselective marker since
other chlorinated solvents besides TCE are metabolized to TCA and resultant urinary
elimination. If TCE is the only exposure, urinary TCE may be a useful marker; however, in
setting with mixed exposure, urinary TCA may serve as an integrated exposure marker of several
chlorinated solvents. The Nordic studies used the linear relationship found for average inhaled
TCE vs. U-TCA: TCE (mg/m3) = 1.96; U-TCA (mg/L) = 0.7 for exposures <375 mg/m3
(69.8 ppm) (Ikedaet al., 1972). This relationship shows considerable variability among
individuals, which reflects variation in urinary output and activity of metabolic enzymes.
Therefore, the estimated inhalation exposures are only approximate for individuals but can
provide reasonable estimates of group exposures. There is evidence of nonlinear formation of
U-TCA above about 400 mg/m3 or 75 ppm of TCE. The half-life of U-TCA is about 100 hours.
Therefore, the U-TCA value represents roughly the weekly average of exposure from all sources,
including skin absorption. The Ikeda et al. (1972) relationship can be used to convert urinary
values into approximate airborne concentration, which can lead to misclassification if
tetrachloroethylene and 1,1,1-trichloroethane are also being used because they also produce
U-TCA. In most cases, the Ikeda et al. (1972) relationship provides a rough upper boundary of
exposure to TCE.
B.2.5. Follow-up in TCE Cohort Studies
Cohort studies are most informative if vital status is ascertained for all cohort subjects
and if the period of time for disease ascertainment is sufficient to allow for long latencies,
particularly for cancer detection and death, in the case of mortality studies. Inability to ascertain
vital status for all subjects, or, conversely, subjects who are loss-to-follow-up, can affect the
validity of observations and lead to biased results. Both power and rate ratios estimated in
cohort studies can be underestimated due to bias introduced if the follow-up period was not long
enough to account for latency (NRC, 2006). The probability of loss to follow-up may be related
to exposure, disease, or both. The multiple-stage process of cancer development occurs over
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decades after first exposure and studies with full latent periods are considered to provide greater
weight to the evaluation compared to cohort studies with shortened follow-up period and lower
percentage of subjects whose vital status was known on the date follow-up ended. Vital status
ascertainment for over 90% of all cohort studies and long mean follow-up periods, about
15 years of longer, characterized many occupational cohort studies on TCE and cancer (Blair et
al.. 1998: Anttilaetal.. 1995: Costa etal.. 1989: Garabrant et al.. 1988) and the follow-up study
of Radican et al. (2008: Boice et al.. 2006b: Zhao et al.. 2005: Raaschou-Nielsen et al.. 2003:
Boice et al., 1999: Ritz, 1999a: Morgan et al., 1998). Information is lacking in two biomarker
studies (Hansen et al., 2001: Axel son et al., 1994), additionally, to estimate the mean follow-up
period for TCE-exposed subjects; although Hansen et al. (2001) state "some workers were
followed for as long as 50 years after their exposure, which allowed the detection of cancers with
long latency periods." Other studies of TCE and cancer did not identify a latent period,
information for calculating a latent period, or contained other deficiencies in follow-up criteria
(Sung et al.. 2007: Chang et al.. 2005: Henschler et al.. 1995: Sinks etal.. 1992: Blair et al..
1989: Costa etal.. 1989: Shannon et al.. 1988: Wilcosky et al.. 1984). PMR studies, based only
on deaths and which lack information on person-year structure as cohort studies, by definition,
do not contain information on cancer latent periods or follow-up (Clapp and Hoffman, 2008:
ATSDR, 2004a).
B.2.6. Interview Approaches in Case-Control Studies of Cancer and TCE Exposure
Interview approaches and the percentage of subjects with information obtained from
proxy or next-of-kin respondents need consideration in interpreting population and hospital-
based, case-control studies in light of possible biases. Biases resulting from proxy respondent or
from low participation related to mailed questionnaires are not relevant to cohort or geographic
studies since information is obtained from local, national, or corporate records. Both face-to-
face and telephone interviews are common and valid approaches used in population or hospital-
based case-control studies. Important to each is the use of a structured questionnaires combined
with intensive training as ways to minimize a high potential for biases often associated with
mailed questionnaires (Blatter et al., 1997: Schlesselman, 1982). Studies with information
limited to job title, type of business and dates of employment and aided with computer or job-
exposure-matrix approaches are preferred to studies of job title only; the added approaches can
reduce exposure misclassification bias and improve disease risk estimates (Stewart et al., 1996).
Moreover, interview with respondents other than the individual case or control, through proxy or
next-of-kin respondents, may also introduce bias in case-control studies. Proxy respondents are
used when cases or control are either too sick to respond or if deceased. This bias would dampen
observed associations if proxy respondents did not fully provide accurate information. Boyle et
al. (1992), for example, in their study of several site-specific cancers and occupational exposures
found low sensitivity, or correct reporting, for occupational exposure to solvents among proxy
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respondents. The weight-of-evidence analysis on TCE and cancer, for this reason, places
greatest weight on observations from studies which obtain information on personal, medical, and
occupational histories from each case and control with lesser weight is placed on studies where
>10% of interviews are with proxy respondents.
Many of the more recent case-control studies include face-to-face (Gold et al., 2011;
Purdue etal.. 2011: Cocco et al.. 2010: Moore etal.. 2010: Wang et al.. 2009: Seidler et al..
2007: Miligi et al.. 2006: Bruning etal.. 2003: Costas et al.. 2002: Pesch et al., 2000a, 2000b:
Dosemeci etal., 1999: Vamvakas et al., 1998: McKinney etal., 1991: Siemiatycki, 1991) or
telephone (Charbotel et al.. 2009: Charbotel et al.. 2006: Shu et al.. 2004: Shu etal.. 1999:
Lowengart et al., 1987) interviews. Few of these studies included interviewers who were blinded
or did not know the identity of who is a case and who is a control. Although desirable for case-
control studies, blinding is usually not possible to fully accomplish because subject responses
during the interview provide clues as to subject status. For this reason, the lack of blinded
interviewers is not considered a serious limitation. More importantly, most studies assigned
exposure to cases and controls in a blinded manner
Information obtained from mailed questionnaire predominantly characterized older
Nordic studies (Persson and Fredrikson, 1999: Nordstrom et al., 1998: Hardell etal., 1994:
Persson et al.. 1993: Fredriksson et al.. 1989: Persson et al.. 1989: Hardell et al.. 1981). One
case-control study did not ascertain information from a questionnaire or through interviews,
instead using occupation coded on death certificates to infer TCE exposure potential (Kernan et
al., 1999). In all studies except Costas et al. (2002) and Kernan et al. (1999), assignment of
potential TCE exposure to cases and controls, to different degrees depending on each study, is
based on self-reported information on job title, and in some cases, to specific chemicals.
More common to the case-control studies on TCE and cancer was possible bias related to
a higher percentage of proxy interviews. Seven studies (Gold et al., 2011: Purdue et al., 2011:
Moore etal., 2010: Wang et al., 2009: Pesch et al., 2000a, 2000b: Dosemeci et al., 1999)
excluded subjects with proxy interviews and the percentage of proxy interview among subjects
in one other study is <10% (Nordstrom et al., 1998). Charbotel et al. (2009: 2006) furthermore
presents analyses for data they considered as better quality, including higher confidence
exposure information and excluding proxy respondents, in addition to analyses using both living
and proxy respondents. A consideration of proxy interviews in studies of childhood cancers,
which include an examination of paternal occupational exposure, is needed given a greater
likelihood for bias if fathers are not directly interviewed and the father's occupational
information is provided only by the child's mother. A good practice is for statistical analyses
examining paternal occupational exposure to include only cases and controls with direct
information provided by the fathers, such as De Roos et al. (2001), the only childhood cancer
study (neuroblastoma) to exclude the use of proxy information.
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B.2.7. Sample Size and Approximate Statistical Power
Cancer is generally considered a rare disease compared to more common health outcomes
such as cardiovascular disease. Of all site-specific cancers, endocrine cancers of the breast
prostate and lung cancer are most common, with age-adjusted incidence rates of 126 per
100,000 women (breast), 163 per 100,000 men (prostate), and 63.9 per 100,000 men and women
(lung) (Ries et al., 2008). Several site-specific cancers including kidney cancer, liver cancer, and
NHL that are of interest to TCE are rarer and consideration of study size and the influence on
statistical power are factors for judging a study's validity and assessment of a study's
contribution to the overall weight of evidence for identifying a hazard. For example, the age-
adjusted incidence rates of NHL, liver and intrahepatic bile duct cancer, and kidney, renal, and
pelvis cancer in the United States population are 19.5 per 100,000, 6.4 per 100,000, and 13.2 per
100,000; rates vary by sex and race. Age-adjusted mortality rates for these cancers are lower:
7.3 per 100,000 (NHL), 5.0 per 100,000 (liver and intrahepatic bile duct), 4.2 per 100,000
(kidney and renal pelvis). Rates of the childhood cancer, acute lymphocytic leukemia, are even
lower: 1.6 (incidence) and 0.5 (mortality) per 100,000 (Ries et al.. 2008).
Only very large cohort or case-control studies would have a sufficient number of cases
and statistical power to estimate excess risks and exposure-response relationships (NRC, 2006).
Observations from studies with large numbers of TCE-exposed subjects, given consideration of
exposure conditions and other criteria discussed in this section, can provide useful information
on hazard and may provide quantitative information on possible upper bound TCE cancer risks.
Alternatively, studies of small numbers of subjects or cases and controls, typically, studies with
statistical power <80% to detect risk of a magnitude of <2, are not likely to provide useful
evidence for or against the hypothesis that TCE is a human carcinogen.
Studies with either a large number of TCE-exposed subjects or with large numbers of
total deaths, cancer deaths, or cancer cases among TCE-exposed subjects are the cohort studies
of Blair et al. (1998), Raaschou-Nielsen et al. (2003), and Zhao et al. (2005), and the case-control
studies of Pesch et al. (2000a, 2000b), Shu et al. (2004; 1999) [paternal exposure assessment,
only]), Wang et al. (2009) and Cocco et al. (2010), with >50 TCE-exposed cases. The cohorts of
Boice et al. (2006b; 1999) and Morgan et al. (1998), like that of Blair et al. (1998), comprised
over 10,000 subjects both with and without potential TCE exposure; however, the number of
subjects and the percentage of the larger cohort identified with TCE exposure in these studies
was less than that in Blair et al. (1998): 23% of all subjects in Morgan et al. (1998), 3% in Boice
et al. (1999), 2% in Boice et al. (2006b) compared to 50% in Blair et al. (1998). Moreover,
although the cohorts of Garabrant et al. (1988), Chang et al. (2005) and Sung et al. (2007) are
also of population sizes > 10,000, these studies of employees at one manufacturing facility lack
assignment of potential TCE exposure to individual subjects and include subjects with varying
exposure potential, some of whom are likely with very low to no exposure potential to TCE.
Rate ratios estimated from cohorts that include unexposed subjects would be underestimated,
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although the magnitude of this bias cannot be calculated given the absence in individual studies
of information on the percentage of subjects lacking potential TCE exposure.
Examination of the statistical power or ability to detect a rate ratio magnitude for site-
specific cancer in an epidemiologic study informs weight-of-evidence evaluations and provides
perspective on a study's validity and robustness of observations. Although statistical power
calculations are traditionally carried out during the design phase for sample size estimation,
examination of a study's statistical power post hoc is one of several tools to evaluate a study's
validity; however, such calculations must be interpreted in context of exposure conditions in the
study. Given the lower average exposure concentrations in the cohort studies and in population
case-control studies, an assumption of low RRs is plausible. Approximate statistical power to
detect a RR of 2.0 with a = 0.05 was calculated for site-specific cancers in cohort and
geographic-based studies according to the methods of Beaumont and Breslow (1981), as
suggested by NRC (2006), and are found in Table B-4. Approximate statistical power was
calculated for kidney, NHL, and liver cancers as examples. Radican et al. (2008), the previous
follow-up of this cohort by Blair et al. (1998), and Raaschou-Nielsen et al. (2003) have over 80%
statistical power to detect RR of 2.0 for kidney and liver cancers and NHL and overall TCE
exposure. However, while these studies may appear sufficient for examining overall TCE
exposure and RRs of 2.0, they have a greatly reduced ability to detect underlying risks of this
magnitude in analyses using rank-ordered exposure- or duration-response analyses. Other
studies with fewer TCE-exposed subjects and of similar or lower exposure conditions as Blair et
al. (1998) will decreased statistical power to detect most site-specific cancer risks of <2.0.
Statistical power in Morgan et al. (1998) and Boice et al. (1999) approaches that in Blair et al.
(1998) and Raaschou-Nielsen et al. (2003). As further identified in Table B-4, Garabrant et al.
(1988) and Morgan and Cassady (2002) each had over 80% statistical power to detect RRs of 2.0
for liver and kidney cancer and reflects the number of subjects in each of these studies.
However, underlying risk in both studies and other studies such as these which lack
characterization of TCE exposure to individual subjects is likely lower than 2.0 because of
inclusion of subjects with varying exposure potential, including low exposure potential. Case-
control studies such as Charbotel et al. (2006) and B riming et al. (2003) examine higher level
exposure to TCE than average exposure in the population case-control studies, and although
these two studies contain fewer subjects than population case-control studies such as Cocco et al.
(2010), a higher statistical power is expected related to the different and higher exposure
conditions and to the higher prevalence of exposure.
Overall, except for a few studies noted above, the body of evidence has limited statistical
power for evaluating low level cancer risk and TCE. For this reason, studies reporting
statistically significant association between TCE and site-specific cancer are noteworthy if
positive biases such as confounding are minimal.
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Table B-4. Approximate statistical power (%) in cohort and geographic-based studies to detect an RR = 2
Exposure group
NHL
Kidney
Liver
Reference
Cohort studies — incidence
Aerospace workers (Rocketdyne)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
Not reported
Referent
97.0
58.2
Not reported
Referent
43.8
18.7
Not reported
Referent
Not reported
Not reported
All employees at electronics factory (Taiwan)
Males
Females
Not reported
Not reported
Not reported
92.1a
16.9
15.4
Danish blue-collar worker with TCE exposure
Any exposure, all subjects
Employment duration, males
<1 yr
1-4.9 yrs
>5yrs
Employment duration, females
<1 yr
1-4.9 yrs
>5yrs
100.0
98.4
99.4
97.7
40.3
48.4
39.6
100.0
96.6
98.4
97.0
30.1
37.1
31.9
100.0
85.2
92.7
93.1
27.3
34.1
30.5
Zhao et al. (2005)
Chang et al. (2005)
Raaschou-Nielsen et al. (2003)
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Table B-4. Approximate statistical power (%) in cohort and geographic-based studies to detect an RR = 2
(continued)
Exposure group
NHL
Kidney
Liver
Biologically -monitored Danish workers
Any TCE exposure
Cumulative exposure (Ikeda)
<17 ppm-yr
>17 ppm-yr
Mean concentration (Ikeda)
<4ppm
4+ppm
Employment duration
<6.25 yr
>6.25
37.9
17.9
20.3
21.0
23.6
18.3
20.1
47.9
Not reported
Not reported
Not reported
35.7
Not reported
Not reported
Not reported
Aircraft maintenance workers from Hill Air Force Base
TCE subcohort
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
Not reported
Referent
79.5
63.1
70.8
Referent
28.2
0 cases
34.1
Not reported
Referent
67.8
49.4
58.4
Referent
0 cases
0 cases
Not reported
Referent
58.2
44.7
47.4
Referent
0 cases
0 cases
0 cases
Biologically -monitored Finnish workers
All subjects
Mean air-TCE (Ikeda extrapolation)
<6ppm
6+ppm
53.8
36.8
25.6
70.4
Not reported
Not reported
56.5
23.2
17.4
Reference
Hansenetal. (2001)
Blair et al. (1998)
Anttila et al. (1995)
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Table B-4. Approximate statistical power (%) in cohort and geographic-based studies to detect an RR = 2
(continued)
Exposure group
NHL
Kidney
Liver
Cardboard manufacturing workers in Arnsberg, Germany
Exposed workers
Not reported
16.3
Not reported
Biologically -monitored Swedish workers
Any TCE exposure, males
Any TCE exposure, females
43.5
Not reported
59.6
Not reported
40.1
Not reported
Cardboard manufacturing workers, Atlanta area, Georgia
All subjects
Not reported
27.9
Not reported
Reference
Henschler et al. (1995)
Axelson et al. (1994)
Sinks et al. (1992)
Cohort studies — mortality
Aerospace workers (Rocketdyne)
Any TCE (utility /engine flush)
Any exposure to TCE
Low cumulative TCE score
Medium cumulative TCE score
High TCE score
56.0
Not reported
Referent
97.0
55.4
43.5
Not reported
Referent
57.6
26.4
42.6
Not reported
Referent
Not reported
Not reported
View-Master employees
Males
Females
40.9
74.1
17.3
24.1
23.4
0 deaths
All employees at electronics factory (Taiwan)
Males
Females
49.8
79.0
0 deaths
37.5
16.9
15.4
United States uranium-processing workers (Fernald)
Any TCE exposure
Light TCE exposure, >2 yrs duration
Modified TCE exposure, >2 yrs duration
91.6b
20. 9b
59.7C
0 deaths'
10.1
0.08
Aerospace workers (Lockheed)
Routine exposure
Duration of exposure, routine -intermittent
Oyrs
<1 yr
88.4
Referent
81.7
71.3
Referent
66.3
72.9
Referent
73.6
Boice et al. (2006b)
Zhao et al. (2005)
ATSDR (2004a)
Chang et al. (2003)
Ritz ( 1999a)
Boice et al. (1999)
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Table B-4. Approximate statistical power (%) in cohort and geographic-based studies to detect an RR = 2
(continued)
Exposure group
1-4 yrs
>5yrs
p for trend
NHL
73.5
78.5
Kidney
60.3
63.8
Liver
63.5
67.3
Aerospace workers (Hughes)
TCE subcohort
Low intensity (<50 ppm)
High intensity (>50 ppm)
42.6, 79.6d
22.1
31.8
65.5
33.3
50.1
65.6
34.7
49.2
Aircraft maintenance workers (Hill Air Force Base, Utah)
TCE subcohort
92.7
81.5
87.9
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
62.1
43.1
54.8
50.7
37.1
44.9
61.4
44.7
52.8
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
TCE subcohort
18.2
0 deaths
22.0
99.9
0 deaths
8.4
11.5
94.4
0 deaths
0 deaths
19.1
99.7
Males, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
>25 ppm-yr
83.0
64.9
75.7
43.8
53.0
33.4
59.4
70.6
50.9
Females, cumulative exposure
0
<5 ppm-yr
5-25 ppm-yr
38.9
0 deaths
0 deaths
12.4
25.9
0 deaths
Reference
Morgan etal. (1998)
Blair et al. (1998)
Radican et al. (2008)
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Table B-4. Approximate statistical power (%) in cohort and geographic-based studies to detect an RR = 2
(continued)
Exposure group
>25 ppm-yr
NHL
49.2
Kidney
21.1
Liver
32.2
Cardboard manufacturing workers in Arnsberg, Germany
TCE exposed workers
Cardboard manufacturing workers, Atlanta area, Georgia
19.6b
45.3b
16.0
17.3
Not reported
Not reported
Coast Guard employees (US)
Marine inspectors
31.8
31.8
38.6
Aircraft manufacturing plant employees (Italy)
All subjects
94. lb
Not reported
63.1
Aircraft manufacturing plant employees (San Diego, California)
All subjects
95.1e,74.2f
90.9
77.9
Reference
Henschler et al. (1995)
Sinks et al. (1992)
Blair et al. (1989)
Costa et al. (1989)
Garabrant et al. (1988)
Geographic-based studies
Residents in two study areas in Endicott, New York
Residents of 13 census tracts in Redlands, California
90.8
100
41.7
100.0
31.8
98.7
Finnish residents
Residents of Hausjarvi
Residents of Huttula
98.8
98.7
Not reported
Not reported
84.2
83.2
ATSDR (2006a)
Morgan and Cassady (2002)
Vartiainen et al. (1993)
"Kidney cancer and other urinary organs, excluding bladder, as reported in Sung et al. (2008).
bAll cancers of hematopoietic and lymphatic tissues.
°Bladder and kidney cancer, as reported in NRC (2006).
dBased on number of observed cases of NHL reported in Mandel et al. (2006).
eLymphosarcoma and reticulosarcoma.
fOther lymphatic and hematopoietic tissue neoplasms.
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B.2.8. Statistical Analysis and Result Documentation
Appropriate analysis approaches characterize most cohort and case-control studies on
TCE cancer. Many studies clearly documented statistical analyses, evaluated possible
confounding factors, and included an examination of exposure-response. In occupational cohort
studies, potential confounding factors other than age, sex, race, and calendar year are, generally,
not evaluated. Expected numbers of outcomes (deaths or incident cancers) were calculated using
life table analysis and an external comparison group, national or regional population mortality or
incidence rates (Sung et al.. 2007: 2006b: Chang et al.. 2005: ATSDR. 2004a: Chang et al..
2003: Raaschou-Nielsen et al.. 2003: Boiceetal.. 1999: Blair etal.. 1998: Morgan et al.. 1998:
Anttila et al.. 1995: Henschler et al.. 1995: Axel son et al.. 1994: Sinks etal.. 1992: Blair et al..
1989: Costa etal.. 1989: Garabrant et al.. 1988: Shannon et al.. 1988: Shindell andUlrich. 1985).
Risk ratios are also presented in some cohort studies using proportional hazard and logistic
regression statistical methods using mortality or incidence rates of non-TCE exposed cohort
subjects as referent or internal controls (Radican et al., 2008: Boice et al., 1999: Ritz, 1999a:
Blair et al., 1998). Use of a non-TCE exposed referent group employed at the same facility as
exposed generally reduces downward bias or bias having potential associations masked by a
healthy worker work or other factors such as smoking that may be more similar within an
occupational cohort than between the cohort and the general population. However, the
advantage is minimized if subjects with lower TCE exposure potential are included in the
referent group as in Boice et al. (2006b). One referent group (the Santa Susanna Field
Laboratory [SSFL] group) of Boice et al. (2006b) included individuals with low TCE potential, a
treatment different from the overlapping study of Zhao et al. (2005) whose exposure assessment
adopted a semi-quantitative approach, grouping subjects identified with low TCE exposure
potential separately from subjects with no TCE exposure potential. A second referent group of
all Rocketdyne workers in Boice et al. (2006b) for whom TCE exposure potential was not
examined may, also, have potential for greater than background exposure since TCE use was
widespread and rocket engine cleaning occurred at other locations besides at test sites
(Morgenstern, 1998). The inclusion of nonexposed subjects in the low-exposure group can
obscure resultant associations due to misclassification bias (Stewart and Correa-Villaseor, 1991).
Cohort studies additionally evaluate a limited number of other factors associated with
employment which could be easily obtained from company and other records such as hire date,
time since first employment, SES or pay status, and termination date (2006b; Zhao et al., 2005:
Boiceetal.. 1999: Greenland et al.. 1994). and three studies (Boice et al.. 2006b: Zhao et al..
2005: Ritz, 1999a) included a limited evaluation of smoking using information collected by
survey on smoking patterns from a subgroup of subjects. Neither analysis of Morgan et al.
(1998) nor Zhao et al. (2005) control for race, although Morgan et al. (1998) stated that "data
concerning race were too sparse to use." The direction of any bias introduced depends on
proportion of nonwhites in the referent (internal) group compared to TCE-exposed and on
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differences between racial groups in site-specific cancer incidence and mortality rates. Blair et
al. (1998), furthermore, presumed all subjects of unknown race were white, an assumption with
little associated error as shown later by Radican et al. (2008) whose RR estimates were adjusted
for race in follow-up analysis of this cohort.
The case-control studies on TCE are better able than cohort studies to evaluate other
possible confounders besides age and sex using logistic regression approaches since such
information can be obtained directly through interview and questionnaires. The case-control
studies of Hardell et al. (1994), Nordstrom et al. (1998), and Persson and Fredriksson (1999) lack
evaluation of possible confounding factors other than age, sex, and other demographic
information used to match control subjects to case subjects. RCC case-control studies included
evaluation of suggested risk factors for RCC such as smoking (Charbotel et al., 2006; Bruning et
al., 2003: Pesch et al., 2000b: Vamvakas et al., 1998: Siemiatvcki, 1991), weight, or obesity
(Charbotel et al., 2006: Dosemeci et al., 1999), and diuretics (Dosemeci etal., 1999: Vamvakas
et al., 1998). Moore et al. (2010) examined the effect on RCC by smoking in univariate analyses
and reported a change in their OR of <10% compared to that for TCE and RCC. They concluded
that smoking was not a confounder of the observed association with TCE. NHL and childhood
leukemia case-control studies included evaluation and control for possible confounding due to
smoking (Seidler et al., 2007: Costas et al., 2002: Siemiatvcki, 1991), alcohol consumption
(Seidler et al., 2007: Costas et al., 2002), and education (Costantini et al., 2008: Miligi et al.,
2006), although etiological factors for these cancers are not well identified other than a
suggestion of a role of immune function and some infectious agents in NHL (Alexander et al.,
2007b). Smoking was not controlled in other NHL case-control studies; however, neither
smoking nor alcohol is a strong risk factor for NHL (Besson et al., 2006: Morton et al., 2005).
Cohort studies additionally evaluate a limited number of other factors associated with
employment which could be easily obtained from company and other records such as hire date,
time since first employment, SES or pay status, and termination date (2006b: Zhao et al., 2005:
Boiceetal., 1999: Greenland et al., 1994), and three studies (Boice et al., 2006b: Zhao et al.,
2005: Ritz, 1999a) included a limited evaluation of smoking using information collected by
survey on smoking patterns from a subgroup of subjects. Neither analysis of Morgan et al.
(1998) nor Zhao et al. (2005) control for race, although Morgan et al. (1998) stated that "data
concerning race were too sparse to use." The direction of any bias introduced depends on
proportion of nonwhites in the referent (internal) group compared to TCE-exposed and on
differences between racial groups in site-specific cancer incidence and mortality rates. Blair et
al. (1998), furthermore, presumed all subjects of unknown race were white, an assumption with
little associated error as shown later by Radican et al. (2008) whose RR estimates were adjusted
for race in follow-up analysis of this cohort.
The case-control studies on TCE are better able than cohort studies to evaluate other
possible confounders besides age and sex using logistic regression approaches since such
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information can be obtained directly through interview and questionnaires. The case-control
studies of Hardell et al. (1994), Nordstrom et al. (1998) and Persson and Fredriksson (1999) lack
evaluation of possible confounding factors other than age, sex and other demographic
information used to match control subjects to case subjects. RCC case-control studies included
evaluation of suggested risk factors for RCC such as smoking (Charbotel et al., 2006; Bruning et
al.. 2003: Pesch et al.. 2000b: Vamvakas et al.. 1998: Siemiatvcki, 1991). weight, or obesity
(Charbotel et al., 2006: Dosemeci et al., 1999), and diuretics (Dosemeci etal., 1999: Vamvakas
et al., 1998). Moore et al. (2010) examined the effect on RCC by smoking in univariate analyses
and reported a change in their OR of <10% compared to that for TCE and RCC. They concluded
that smoking was not a confounder of the observed association with TCE. NHL and childhood
leukemia case-control studies included evaluation and control for possible confounding due to
smoking (Seidler et al., 2007: Costas et al., 2002: Siemiatycki, 1991), alcohol consumption
(Seidler et al., 2007: Costas et al., 2002), education (Costantini et al., 2008: Miligi et al., 2006),
although etiological factors for these cancers are not well identified other than a suggestion of a
role of immune function and some infectious agents in NHL (Alexander et al., 2007b). Smoking
was not controlled in other NHL case-control studies; however, neither smoking nor alcohol is a
strong risk factor for NHL (Besson et al., 2006: Morton et al., 2005).
Mineral oils such as cutting fluids or hydrazine common to some job titles with potential
TCE exposure as machinists, metal workers, and test stand mechanics are included as covariates
in statistical analyses of Zhao et al. (2005), Boice et al. (2006b), and Charbotel et al. (2009:
2006) or evaluated as a single exposure for cases and controls in Moore et al., 2010 (Moore et
al., 2010) and Karami et al. (Karami etal., 2011: 2010). Two other kidney case-control studies
of TCE exposure examined the effect of cutting oil as a single occupational exposure on kidney
cancer risk (Karami et al., 2011: Bruning et al., 2003). In Bruning et al. (2003), cutting oil
exposure did not appear highly correlated with TCE exposure as only five cases reported
exposure to cutting oils compared to 25 cases reporting TCE exposure. Karami et al. (2011),
who examined mineral oil or cutting fluid exposure among cases and controls in Moore et al.
(2010), reported an OR of 0.8 (95% CI: 0.6, 1,1) and 1.1 (95% CI: 0.8, 1.4), for cutting oil mists
or other mineral oil mists respectively, and provides little evidence for confounding in Moore et
al. (2010) by cutting or mineral oil exposures. Moreover, cutting oils and mineral oils have not
been associated with kidney cancer in other cohort or case-control studies (Mirer, 2010: NIOSH,
1998). In all other studies, exposure to cutting oils or to hydrazine did not greatly affect
magnitude of risk estimates for TCE exposure.
Geographical studies do not examine possible confounding factors other than sex, age
and calendar year. These studies are generally health surveys using publically-available records
such as death certificates and lack information on other risk factors such as smoking and
exposure to viruses, important to Lee et al. (2003), introduces uncertainties for informing
evaluations of TCE and cancer.
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B.2.9. Systematic Review for Identifying Cancer Hazards and TCE Exposure
The epidemiological studies on cancer and TCE are reviewed systematically and
transparently using criteria to identify studies for meta-analysis. Section B.3 contains a
description of and comment on 79 studies of varying qualities for identifying cancer hazard, a
question complementary but separate from that examined using meta-analysis. This section
identifies of the studies reviewed, studies in which there is a high likelihood of TCE exposure in
individual study subjects (e.g., based on JEMs, biomarker monitoring, or industrial hygiene data
indicating a high probability of TCE use) and were judged to have met the inclusion criteria
identified below. Lack of inclusion of an individual study in the meta-analysis does not
necessarily imply an inability to identify cancer hazard. Not all questions associated with
identifying a cancer hazard are addressed using meta-analyses and the 79 studies with varying
abilities approached, to sufficient degrees, the standards of epidemiologic design and analysis,
identified in the beginning of Section B.2.
The NRC (2006) suggested U.S. EPA conduct a new meta-analysis of the epidemiologic
data on TCE to synthesize the epidemiologic data on TCE exposure. Meta-analysis approaches
are feasible for examining cancers of the liver, kidney, and NHL given most studies presented
risks for these sites in their published papers and these cancer sites are of interest given
observations in the animal studies. Examination of site-specific cancers other than kidney
cancer, liver cancer, and NHL, such as for childhood leukemia, bladder cancer, esophageal
cancer, or cervical cancer is more difficult and not recommended due to fewer available high-
quality studies. NRC (2006) specifically suggested EPA to:
1. Document essential design features, exposure, and results from the epidemiologic
studies—Information on study design, exposure assessment approach, statistical
analysis, and other aspects important to interpreting observations in a weight of
evidence evaluation for individual studies is found in Section B.3 and site-specific
estimated RRs or measures of association are presented in Chapter 4;
2. Analyze the epidemiologic studies to discriminate the amount of exposure experience
by the study population; exclude studies in meta-analysis based on objective criteria
(e.g., studies in which it was unclear that the study population was exposed)—Section
B.3. describes exposure assessment approach for individual studies and inclusion
criteria for identifying studies for meta-analysis are identified below;
3. Classify studies in terms of objective characteristics, such as on the basis of the
study's design characteristics or documentation of exposure—Section B.3. groups
studies by study design, analytical designs and geographic-based designs, with
discussion of factors important to study design, endpoint measured, exposure
assessment approach, study size, and statistical analysis methods including
adjustment for potential confounding exposures;
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4. Assess statistical power of each study—Table B-4 presents power calculations for
cohort studies;
5. Combine case-control and cohort studies in the analysis, unless it introduces
substantial heterogeneity—Appendix C discusses the meta-analysis statistical
methods and findings;
6. Testing of heterogeneity (e.g., fixed or random effect models)—Appendix C
discusses the meta-analysis statistical methods and findings;
7. Perform a sensitivity analysis in which each study is excluded from the analysis to
determine whether any study significantly influences the finding—Appendix C
discusses the meta-analysis statistical methods and findings.
Studies selected for inclusion in the meta-analysis met the following criteria: (1) cohort
or case-control designs; (2) evaluation of incidence or mortality; (3) adequate selection in cohort
studies of exposure and control groups and of cases and controls in case-control studies; (4) TCE
exposure potential inferred to each subject and quantitative assessment of TCE exposure for each
subject by reference to industrial hygiene records indicating a high probability of TCE use,
individual biomarkers, JEMs, water distribution models, or obtained from subjects using
questionnaire (case-control studies); and (5) RR estimates for kidney cancer, liver cancer, or
NHL adjusted, at minimum, for possible confounding of age, sex, and race. Table B-5 in
Section B.2.9.4 identifies studies included in the meta-analysis and studies that did not meet the
inclusion criteria and the primary reasons for their deficiencies.
B.2.9.1. Cohort Studies
The cohort studies (Radican et al.. 2008: Sung et al.. 2008: Krishnadasan et al.. 2007:
Sung et al.. 2007: Boice et al.. 2006b: Chang et al.. 2005: Zhao et al.. 2005: Chang et al.. 2003:
Raaschou-Nielsen et al.. 2003: Hansen et al.. 2001: Boice etal.. 1999: Ritz. 1999a: Blair et al..
1998: Morgan et al.. 1998: Anttila et al.. 1995: Henschler et al.. 1995: Axel son et al.. 1994:
Greenland et al.. 1994: Sinks et al.. 1992: Blair etal.. 1989: Costa et al.. 1989: Garabrant et al..
1988: Shannon et al.. 1988: Shindell and Ulrich. 1985: Wilcosky et al.. 1984). with data on the
incidence or morality of site-specific cancer in relation to TCE exposure range in size (803
(Hansen etal.. 2001) to 86,868 (Chang et al.. 2005: Chang et al.. 2003)). and were conducted in
Denmark, Sweden, Finland, Germany, Taiwan, and the United States (see Table B-l). Three
case-control studies nested within cohorts (Krishnadasan et al., 2007; Greenland et al., 1994;
Wilcosky et al., 1984) are considered as cohort studies because the summary risk estimate from a
nested case-control study, the OR, was estimated from incidence density sampling and is
considered an unbiased estimate of the hazard ratio, similar to an RR estimate from a cohort
study. Two studies of deaths within a cohort were included in the group, but these studies lacked
information on the person-year structure (i.e., both are PMR studies, and did not satisfy the meta-
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analysis inclusion criteria for analytical study design [(Clapp and Hoffman, 2008; AT SDR,
2004a)1).
Cohort and nested case-control study designs are analytical epidemiologic studies and are
generally relied on for identifying a causal association between human exposure and adverse
health effects (Zhou et al., 2003). Some subjects in the Hansen et al. study are also included in a
study reported by Raaschou-Nielsen et al. (2003): however, any contribution from the former to
the latter are minimal given the large differences in cohort sizes of these studies (Raaschou-
Nielsen et al., 2003; Hansen etal., 2001). Similarly, some females in Chang et al. (2005: 2003),
a large cohort of 70,735 female and 16,133 male subjects, are included in Sung et al. (2007), a
cohort of 63,982 female electronic workers from the same factory who were followed an
additional 4-year period than subjects in Chang et al. (2005: 2003). Cancer observations for
female subjects in these studies are considered as equivalent since they are derived from
essentially the same population. Krishnadasan et al. (2007) is a nested case-control study of
prostate cancer with cases and controls drawn from subjects in a large cohort of aerospace
workers as subjects in Zhao et al. (2005), who did not report on prostate cancer, and met all of
the inclusion criteria except that for reporting an RR estimate for cancer of the kidney, liver or
NHL.
Eleven of the cohort studies met all five inclusion criteria: the cohorts of Blair et al.
(1998) and its further follow-up by Radican et al. (2008), Morgan et al. (1998), Boice et al.
(2006b: 1999) and Zhao et al. (2005) of aerospace workers or aircraft mechanics; Axelson et al.
(1994), Anttila et al. (1995), Hansen et al. (2001), and Raaschou-Nielsen et al. (2003) of Nordic
workers in multiple industries with TCE exposure; and Greenland et al. (1994) of electrical
manufacturing workers. All 11 cohort studies adopted statistical methods, e.g., life table
analysis, Poisson regression analysis, or Cox Proportional Hazard analysis, that met
epidemiologic standards, and were able to control for age, race, sex, and calendar time trends in
cancer rates. Statistical analyses in Boice et al. (1999) adjusted for demographic variable such as
age, race, and sex, and also included date of first employment and terminating date of
employments, which may have decreased the statistical power of their analyses due to
collinearity between age, first and last employment dates. Statistical analyses in Zhao et al.
(2005) and Boice et al. (2006b) adjusted for potential effects by other occupational exposures on
cancer and both Raaschou-Nielsen et al. (2003) and Zhao et al. (2005) examined possible
confounding by smoking on TCE exposure and cancer risks using indirect approaches.
Of the 11 studies, 2 studies reported risk estimates for both site-specific cancer incidence
and mortality (Zhao et al., 2005; Blair et al., 1998), 4 studies reported risk estimates for cancer
incidence only (Krishnadasan et al., 2007; Raaschou-Nielsen et al., 2003; Hansen etal., 2001;
Anttila et al., 1995; Axelson et al., 1994), and four studies reported risk estimates for mortality
only (Radican et al., 2008: 2006b: Boice etal., 1999: Morgan et al., 1998). Incidence
ascertainment in two cohorts began 21 (Blair etal., 1998) and 38 years (Zhao et al., 2005) after
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the inception of the cohort. Specifically, Zhao et al. (2005) note "results may not accurately
reflect the effects of carcinogenic exposure that resulted in nonfatal cancers before 1988."
Because of the issues concerning case ascertainment raised by this incomplete coverage,
incidence observations must be interpreted in light of possible bias reflecting incomplete
ascertainment of incident cases. Furthermore, use of an internal referent population, nonexposed
subjects drawn from the same or nearby facilities as exposed workers, in Blair et al. (1998) and
Radican et al. (2008) for overall TCE exposure, and in Blair et al. (1998). Morgan et al. (1998).
Boice et al. (1999). Zhao et al. (2005). Boice et al. (2006b). and Radican et al. (2008) for rank-
ordered TCE exposure is expected to reduce bias associated with the healthy worker effect.
Morgan et al. (1998) presents risk estimates for overall TCE exposure comparing mortality in
their TCE subcohort to that expected using mortality rate of the U.S. population in an
Environmental Health Strategies Final Report and sent to U.S. EPA by Paul Cammer, Ph.D., on
behalf of the Trichloroethylene Issues Group (EHS, 1997). The final report also contained risk
estimates from internal analyses of rank-order TCE exposure and published as Morgan et al.
(1998). Both internal cohort analyses of the rank-ordered exposure, presented in both the final
report of Environmental Health Strategies (1997) and Morgan et al. (1998), and overall TCE
exposure, available in the final report or upon request, are based on the same group of internal
referents, nonexposed TCE subjects employed at the same facility.
Subjects in these studies had a high likelihood or potential for TCE exposure, although
estimated average exposure intensity for overall TCE exposure in some cohorts was considered
as <10 or 20 ppm (TWA). The exposure assessment techniques used in these cohort studies
included a detailed JEM (Blair etal., 1998; Greenland et al., 1994): its follow-up by Radican et
al. (2008) (2008: Boice et al.. 2006b: Zhao et al.. 2005: Boice etal.. 1999: Morgan etal.. 1998):
Radican et al. (2008), biomonitoring data (Hansen etal., 2001: Anttila et al., 1995: Axel son et
al., 1994), or use of industrial hygiene data on TCE exposure patterns and factors that affect such
exposure (Raaschou-Nielsen et al., 2003), with high probability of TCE exposure potential to
individual subjects. The JEM in six studies provided rank-ordered surrogate metrics for TCE
exposure (Hansen etal., 2001: Blair etal., 1998: Anttila et al., 1995: Axel son et al., 1994) and its
follow-up by Radican et al. (2008: Zhao et al., 2005), a strength compared to use of duration of
employment as an exposure surrogate, e.g., Boice et al. (2006b: 1999) or Raaschou-Nielsen et al.
(2003), which is a poorer exposure metric given subjects may have differing exposure intensity
with similar exposure duration (NRC, 2006). Rank-ordered TCE dose surrogates for low and
medium exposure from the JEM of Morgan et al. (1998) are uncertain because of a lack of
information on frequency of exposure-related tasks and on temporal changes (NRC, 2006): only
the high category for TCE exposure is unambiguous. The nested case-control study of
Greenland et al. (1994) examined TCE as one of seven exposures and potential assigned to
individual cases and controls using a job-exposure-matrix approach. However, the low exposure
prevalence, missing job history information for 34% of eligible subjects, and study of pensioned
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workers only were other factors judged to lower this study's sensitivity for cancer hazard
identification.
The remaining cohort studies (Chang et al.. 2005: Chang etal.. 2003: Ritz, 1999a:
Henschler et al.. 1995: Sinks etal.. 1992: Blair etal.. 1989: Costa etal.. 1989: Garabrant et al..
1988: Shannon et al.. 1988: Shindell and Ulrich. 1985: Wilcosky et al.. 1984): Sung et al., (Sung
et al., 2008: 2007) less satisfactorily meet inclusion criteria. These studies, while not meeting
the meta-analysis inclusion criteria, can inform the hazard analysis although their findings are
weighted less than for observations in the other studies, and observations may have alternative
causes. Reasons for study insufficiencies varied. Nine studies do not assign TCE exposure
potential to individual subjects (Clapp and Hoffman, 2008: Sung et al., 2008: Sung et al., 2007:
Chang et al.. 2005: ATSDR, 2004a: Chang et al.. 2003: Sinks etal.. 1992: Costa et al.. 1989:
Garabrant et al., 1988: Shindell and Ulrich, 1985) all subjects are presumed as "exposed"
because of employment in the plant or facility although individual subjects would be expected to
have differing exposure potentials.
TCE exposure potential is ambiguous in both Wilcosky et al. (1984) and Ritz (1999a),
two studies of low potential, low intensity TCE exposure compared to studies using exposure
assessment approaches supported by information on job titles, tasks, and industrial hygiene
monitoring data. Furthermore, high correlation in Ritz (1999a) between TCE and other
exposures, particularly cutting fluids and radiation, may not have been sufficiently controlled in
statistical analyses. Ritz et al. (1999a), furthermore, did not report estimated RRs for kidney or
NHL separately; rather, presenting RR estimates for kidney and bladder cancer combined and for
all hemato- and lymphopoietic cancers.
Two studies do not sufficiently define the underlying cohort or there is uncertainty in
cancer case or death ascertainment (Henschler et al., 1995: Shindell and Ulrich, 1985).
Furthermore, magnitude of observed risk in Henschler et al. (1995), ATSDR (2004a), and Clapp
and Hoffman (2008) must be interpreted in a weight-of-evidence evaluation in light of possible
bias introduced through use of analysis of proportion of deaths (PMR) in ATSDR (2004a) and
Clapp and Hoffman (2008), or to inclusion of index kidney cancer cases in Henschler et al.
(1995).
B.2.9.2. Case-Control Studies
Case-control studies on TCE exposure are of several site-specific cancers and include
bladder cancer (Pesch et al., 2000a: Siemiatycki etal., 1994: Siemiatycki, 1991): brain cancer
(De Roos et al., 2001: Heineman et al., 1994): childhood lymphoma or leukemia (Shu et al.,
2004: Costas et al., 2002: Shu etal., 1999: McKinnev et al., 1991: Lowengart et al., 1987): colon
cancer (Goldberg et al., 2001: Siemiatycki, 1991), esophageal cancer (Parent et al., 2000b:
Siemiatycki, 1991): liver cancer (Lee et al., 2003): lung cancer (Siemiatycki, 1991): lymphoma
(Hardell et al., 1994) [NHL, Hodgkin lymphoma], (Nordstrom et al., 1998: Fritschi and
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Siemiatycki, 1996a: Siemiatycki, 1991), [hairy cell leukemia], (Persson and Fredrikson, 1999)
[NHL], (Miligi et al.. 2006) [NHL and CLL], (Seidler et al.. 2007) [NHL, Hodgkin lymphoma],
(Costantini et al., 2008) [leukemia types, CLL included in Miligi et al. (2006), Wang et al.
(2009) [NHL], (Coccoetal., 2010) [NHL, CLL, MM]; (Gold et al., 2011) [MM]; Purdue et al.
(2011) [NHL]; melanoma (Fritschi and Siemiatycki, 1996a; Siemiatycki, 1991); rectal cancer
(Dumas et al., 2000: Siemiatvcki, 1991): RCC, a form of kidney cancer (Moore et al., 2010:
Charbotel et al., 2009: Charbotel et al., 2006: Briming et al., 2003: Parent et al., 2000a: Pesch et
al., 2000b; Dosemeci etal., 1999; Vamvakas et al., 1998; Siemiatvcki, 1991); pancreatic cancer
(Siemiatycki, 1991); and prostate cancer (Aronson et al., 1996; Siemiatycki, 1991). No case-
control studies of reproductive cancers (breast or cervix) and TCE exposure were found in the
peer-reviewed literature.
Several of the above publications are studies of cases and controls drawn from the same
underlying population with a common control series. Miligi et al. (2006) and Costantini et al.
(2008) presented observations from the Italian multicenter lymphoma population case-control
study; Miligi et al. (2006) on occupation or specific solvent exposures and NHL, and who also
included CLL and Hodgkin lymphoma in the overall NHL category, and Costantini et al. (2008)
who examined leukemia subtypes, and included CLL as a separate disease outcome. Seidler et
al. (2007) analyzed independently the German subjects of the six European country, multicenter
lymphoma population case-control study (EPILYMPH study) of Cocco et al. (2010). Each
study adopted a different approach to calculate cumulative exposure and apparent inconsistency
in their conclusions may reflect the slightly different ranking of cases and controls in each study
(personal communication from Pierluigi Cocco to Cheryl Siegel Scott). Gold et al. (2011) and
Purdue et al. (2011) presented observations from the NCI-SEER population case-control studies
and share a common control series; Purdue et al. (2011) of NHL in four SEER reporting areas
and Gold et al. (2011) of multiple myeloma in two of the four SEER sites. Pesch et al. (2000a,
2000b), a multiple center population case- control study of urothelial cancers in Germany,
presented observations on TCE and bladder cancer, including cancer of the ureter and renal
pelvis, in Pesch et al. (2000a) and RCC in Pesch et al. (2000b). Siemiatycki (1991), a case-
control of occupational exposures and several site-specific cancers (bladder, colon, esophagus,
lung, rectum, pancreas, and prostate) and designed to generate hypotheses about possible
occupational carcinogens, presents risk estimates associated with TCE exposure using Mantel-
Haentszel methods. Subsequent publications examine either TCE exposure (analyses of
melanoma and colon cancers) or job title/occupation (all other cancer sites) using logistic
regression methods (Goldberg et al., 2001; Dumas et al., 2000; Parent et al., 2000a; Aronson et
al., 1996: Fritschi and Siemiatvcki, 1996b, a; Siemiatvcki et al., 1994).
The population case-control studies with data on cancer incidence or mortality
(Siemiatycki, 1991 [and related publications, Goldberg et al., 2001; Dumas et al., 2000; Parent et
al., 2000a; Aronson et al., 1996; Fritschi and Siemiatvcki, 1996b; Siemiatvcki et al., 1994], Gold
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etal.. 2011: Purdue et al.. 2011: Coccoetal.. 2010: Moore etal.. 2010: Wang et al.. 2009:
Costantini et al.. 2008: Seidler et al.. 2007: Charbotel et al.. 2006: Miligi et al.. 2006: Shu et al..
2004: Briming etal.. 2003: Lee et al.. 2003: Costas et al.. 2002: DeRoos etal.. 2001: Pesch
et al., 2000a. 2000b: Dosemeci et al.. 1999: Kernan et al.. 1999: Persson and Fredrikson. 1999:
Nordstrom et al.. 1998: Vamvakas et al.. 1998: Hardell et al.. 1994: Heineman et al.. 1994:
McKinney et al., 1991: Lowengart et al.. 1987) in relation to TCE exposure range in size, from
small studies with <100 cases and control (Costas et al., 2002) to multiple-center studies large-
scale studies of over 2,000 cases and controls (Costantini et al.. 2008: Miligi et al., 2006: Shu et
al.. 2004: Pesch et al., 2000a. 2000b: Shu et al.. 1999). and were conducted in Sweden, Germany,
Italy, Taiwan, Canada, and the United States (see Table B-2).
Fifteen of the case-control studies met the meta-analysis inclusion criteria identified in
Section B.2.9 (Purdue et al.. 2011: Cocco et al.. 2010: Moore etal.. 2010: Charbotel et al.. 2009:
Wang et al.. 2009: Seidler et al.. 2007: Charbotel et al.. 2006: Miligi et al.. 2006: Briining et al..
2003: Pesch et al.. 2000b: Dosemeci etal.. 1999: Persson and Fredrikson. 1999: Nordstrom et al..
1998: Hardell et al., 1994: Siemiatycki, 1991). They were of analytical study design, cases and
controls were considered to represent underlying populations and selected with minimal potential
for bias; exposure assessment approaches included assignment of TCE exposure potential to
individual subjects using information obtained from face-to-face, mailed, or telephone
interviews; analyses methods were appropriate, well-documented, included adjustment for
potential confounding exposures, with RR estimates and associated CIs reported for kidney
cancer, liver cancer, or NHL. All thirteen studies evaluated TCE exposure potential to individual
cases and controls and a structured questionnaire sought information on self-reported
occupational history and specific exposures such as TCE. Three studies assigned TCE exposure
potential to cases and controls using self-reported information (Nordstrom et al., 1998; Hardell et
al., 1994) and two of these studies used judgment to assign potential exposure intensity (Persson
and Fredrikson, 1999; Nordstrom et al., 1998). Persson and Fredriksson (1999) also assigned
TCE exposure potential from both occupational and leisure use, the only study to do so. The 10
other studies assigned TCE exposure potential using self-reported job title and occupational
history, a superior approach compared to use of a JEM supported by expert judgment and
information on only self-reported information given its expect greater specificity (Purdue et al.,
2011: Coccoetal.. 2010: Moore et al.. 2010: Charbotel et al.. 2009: Wang et al.. 2009: Seidler et
al.. 2007: Charbotel et al.. 2006: Miligi et al.. 2006: Briining etal.. 2003: Pesch et al.. 2000b:
Dosemeci etal., 1999; Siemiatycki, 1991). Pesch et al. (2000b) assigned TCE exposure potential
using both JEM and JTEM. The inclusion of task information is considered superior to exposure
assignment using only job title since it likely reduces potential misclassification and, for this
reason, RR estimates in Pesch et al. (2000b) for TCE from a JTEM are preferred. All studies
except Hardell et al. (1994) and Dosemeci et al. (1999) developed a semiquantitative or
quantitative TCE exposure surrogate.
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These studies to varying degrees were considered as stronger studies for weight-of
evidence characterization of hazard. Both Briining et al. (2003) and Charbotel et al. (2006),
(2009) had a priori hypotheses for examining RCC and TCE exposure. Strengths of both studies
are in their examination of populations with potential for high exposure intensity and in areas
with high frequency of TCE usage and their assessment of TCE potential. An important feature
of the exposure assessment approach of Charbotel et al. (2006) is their use of a large number of
studies on biological monitoring of workers in the screw-cutting industry a predominant industry
with documented TCE exposures as support. The other studies were either large multiple-center
studies (Purdue etal.. 2011: Cocco et al.. 2010: Moore etal.. 2010: Wang et al.. 2009: Miligi et
al., 2006: Pesch et al., 2000b): or reporting from one location of a larger international study
(Seidler et al.. 2007: Dosemeci etal.. 1999). In contrast to Briining et al. (2003) and Charbotel et
al. (2009: 2006), two studies conducted in geographical areas with widespread TCE usage and
potential for exposure to higher intensity, a lower exposure prevalence to TCE is found (any
TCE exposure: 15% of cases [(Dosemeci et al., 1999): 6% of cases (Miligi et al., 2006): 13% of
cases (Seidler et al., 2007): 13% of cases (Wang et al., 2009)1) and most subjects identified as
exposed to TCE probably had minimal contact (3% of cases with moderate/high TCE exposure
[(Miligi et al., 2006): 1% of cases with high cumulative TCE (Seidler et al., 2007): 2% of cases
with high intensity, but of low probability TCE exposure (Wang et al., 2009)]). This pattern of
lower exposure prevalence and intensity is common to community-based, population case-
control studies (Teschke et al., 2002).
Fifteen case-control studies did not meet specific inclusion criterion (Gold et al., 2011;
Costantini et al., 2008: Shu et al., 2004: Lee etal., 2003: Costas et al., 2002: Goldberg et al.,
2001: Dumas et al., 2000: Parent et al., 2000a: Pesch et al., 2000a: Kernan et al., 1999: Shu et al.,
1999: Vamvakas et al., 1998: Fritschi and Siemiatycki, 1996b: Siemiatycki, 1991). Costantini et
al. (2008) and Gold et al. (2011) examined multiple myeloma or leukemias, not included in older
NHL classification schemes, although these neoplasms are now considered as lymphomas under
the WHO Lymphoma Classification. Vamvakas et al. (1998) has been subject of considerable
controversy (Cherrie et al., 2001: Mandel, 2001: Green and Lash, 1999: McLaughlin and Blot,
1997: Bloemen and Tomenson, 1995: Swaen, 1995) with questions raised on potential for
selection bias related to the study's controls. This study was deficient in the criterion for
adequacy of case and control selection. Briining et al. (2003), a study from the same region as
Vamvakas et al. (1998), is considered a stronger study for identifying cancer hazard since it
addresses many of the deficiencies of Vamvakas et al. (1998). Lee et al. (2003), in their study of
hepatocellular cancer, assigns one level of exposure to all subjects in a geographic area, and
inherent measurement error and misclassification bias because not all subjects are exposed
uniformly. Additionally, statistical analyses in this study did not control for hepatitis viral
infection, a known risk factor for hepatocellular cancer and of high prevalence in the study area.
Ten of 12 studies reported RR estimates for site-specific cancers other than kidney, liver, and
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NHL (Shu et al.. 2004: Costas et al.. 2002: Goldberg etal.. 2001: Dumas et al.. 2000: Parent et
al.. 2000b: Pesch et al., 2000a: Kernanetal.. 1999: Shu etal.. 1999: Aronson et al.. 1996:
Fritschi and Siemiatvcki. 1996b: Siemiatvcki etal.. 1994: Garabrant et al.. 1988).
B.2.9.3. Geographic-Based Studies
The geographic-based studies (ATSDR. 2008b. 2006a: Aickin. 2004: Morgan and
Cassadv. 2002: APRS. 1995: Cohn et al., 1994b: Vartiainen et al.. 1993: AickinetaL 1992:
ADHS, 1990: Mallin, 1990: Isacson etal., 1985) with data on cancer incidence (all studies) are
correlation studies to examine cancer outcomes of residents living in communities with TCE and
other chemicals detected in groundwater wells or in municipal drinking water supplies. These
eight studies did not meet inclusion criteria and were deficient in a number of criteria.
All geographic-based studies are surveys of cancer rates for a defined time period among
residents in geographic areas with TCE contamination in groundwater or drinking water
supplies, or soil and are not of analytical designs such as cohort and case-control designs. A
major shortcoming in all studies is, also, their low level of detail to individual subjects for TCE
potential. The exposure surrogate is assigned to a community, town, or a geographically-defined
area such as a contiguous grouping of census tracts as an aggregate level, typically based on
limited number of water monitoring data from a recent time period and is a poor exposure
surrogate because potential for TCE exposure can vary in these broad categories depending on
job function, year, use of personal protection, and, for residential exposure, pollutant fate and
transport, water system distribution characteristics, percent of time per day in residence, presence
of mitigation devices, drinking water consumption rates, and showering times. Additionally,
ATSDR (2008b), the only geographic-based study to examine other possible risk factors on
individual subjects, reported that smoking patterns and occupational exposures may partly
contribute to the observed elevated rates of kidney and renal pelvis cancer and lung cancer in
subjects living in a community with contaminated groundwater and with TCE exposure potential
from vapor intrusion into residences.
B.2.9.4. Recommendation of Studies for Treatment Using Meta-Analysis Approaches
All studies are initially considered for inclusion in the meta-analysis; however, as
discussed throughout this section, some studies are better than others for inclusion in a
quantitative examination of cancer and TCE. Twenty-six of the studies included in the meta-
analysis (statistical methods and findings discussed in Appendix C) met the following five
inclusion criteria: (1) cohort or case-control designs; (2) evaluation of incidence or mortality;
(3) adequate selection in cohort studies of exposure and control groups and of cases and controls
in case-control studies; (4) TCE exposure potential inferred to each subject and quantitative
assessment of TCE exposure assessment for each subject by reference to industrial hygiene
records indicating a high probability of TCE use, individual biomarkers, JEMs, water
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distribution models, or obtained from subjects using questionnaire (case-control studies); and
(5) RR estimates for kidney cancer, liver cancer, or NHL adjusted, at minimum, for possible
confounding of age, sex, and race. The twenty-six studies that met these inclusion are:
Siemiatycki (1991). Axelson et al. (1994), Greenland et al. (1994), Hardell et al. (19941 Anttila
et al. (1995), Blair et al. (1998), Morgan et al. (19981 Nordstrom et al. (1998), Dosemeci et al.
(1999), Boice et al. (2006b: 1999), Persson and Fredriksson (1999), Pesch et al. (2000b), Hansen
et al. (20011 Briining et al. (2003). Raaschou-Nielsen et al. (2003). Zhao et al. (2005). Miligi et
al. (2006). Charbotel et al. (2006). Seidler et al. (2007). Radican et al. (2008). Wang et al.
(2009). Cocco et al. (2010). Moore et al. (2010). and Purdue et al. (2011). Table B-5 identifies
studies included in the meta-analysis and studies that did not meet the inclusion criteria and the
primary reasons for their deficiencies.
Table B-5. Summary of rationale for study selection for meta-analysis
Decision
outcome
Studies
Primary reason(s)
Studies recommended for meta-analysis:
Siemiatycki (1991): Axelson et al.
(1994): Hardell (1994): Greenland et al.
1994): Anttila et al. (1995): Morgan et
al. (1998): Nordstrom et al. (1998): Boice
et al. (2006b: 1999): Dosemeci et al.,
1999): Persson and Fredriksson, (1999):
Pesch et al. (2000b): Hansen et al.
2001): Briining et al. (2003): Raaschou-
Nielsen et al. (2003): Zhao et al. (2005):
Miligi et al. (2006): Chaibotel et al.
(2006): Radican et al. (2008) [Blair et al.
1998). incidence]; Wang et al. (2009):
Cocco et al. (2010): Moore et al. (2010):
Purdue et al. (2011)
Analytical study designs of cohort or case-control
approaches; evaluation of cancer incidence or cancer
mortality. Specifically identified TCE exposure potential to
individual study subjects by reference to industrial hygiene
records, individual biomarkers, JEMs, water distribution
models, industrial hygiene data indicating a high probability
of TCE use (cohort studies), or obtained information on TCE
exposure from subjects using questionnaire (case-control
studies). Reported results for kidney cancer, liver cancer, or
NHL with RR estimates and corresponding CIs (or
information to allow calculation).
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Table B-5. Summary of rationale for study selection for meta-analysis
(continued)
Decision
outcome
Studies
Primary reason(s)
Studies not recommended for meta-analysis:
ATSDR (2004a); Clapp and Hoffman,
(20081: Cohn et al. (1994b)
Weakness with respect to analytical study design (i.e.,
geographic-based, ecological, or PMR design)
Wilcosky et al. (1984): Isacson et al.
1985): Shindell and Ulrich (1985):
Garabrant et al. (1988): Shannon et
al.(1988): Blair et al. (1989): Costa et al.
(1989): ADHS (1995. 1990): Mallin
1990): Aickin et al. (1992): Sinks et al.
(1992): Vartiainen et al. (1993): Morgan
and Cassady (2002): Lee et al. (2003):
Aickin (2004): Chang et al. (2005: 2003):
Coyle et al. (2005): ATSDR (2008b,
2006a): Sung et al. (2008: 2007)
TCE exposure potential not assigned to individual subjects
using JEM, individual biomarkers, water distribution models,
or industrial hygiene data indicating a high probability of
TCE use (cohort studies).
Lowengart et al. (1987): Fredriksson et
al. (1989): McKinney et al. (1991):
Heineman et al. (1994): Siemiatycki et al.
(1994): Aronson et al. (1996): Fritchi and
Siemiatycki (1996b): Dumas et al.
(2000): Kernan et al.(1999): Shu et al.
(2004: 1999): Parent et al. (Parent et al..
2000b): Pesch et al., (2000a): De Roos et
al. (2001): Goldberg et al. (2001): Costas
et al. (2002): Krishnadasan et al. (2007):
Costantini et al. (2008): Gold et al.
2011)
Cancer incidence or mortality reported for cancers other than
kidney, liver, or NHL.
Subjects monitored for radiation exposure with likelihood for
potential confounding. Cancer mortality and TCE exposure
not reported for kidney cancer and all hemato- and
lymphopoietic cancer reported as broad category.
Henschler et al. (1995)
Incomplete identification of cohort and index kidney cancer
cases included in case series.
Vamvakas et al. (1998)
Control selection may not represent case series with potential
for selection bias.
There is some overlap between the cohorts of Zhao et al. (2005) and Boice et al. (2006b);
each cohort is identified from a population of workers, but these studies differ on cohort
definition, cohort identification dates, disease outcome examined, and exposure assessment
approach. Zhao et al. (2005), who adopted a semi quantitative approach for TCE exposure
assessment, is preferred to Boice et al. (2006b), whose TCE subcohort included subjects with a
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lower likelihood for TCE exposure and duration of exposure, a poor exposure metric given that
subjects may have differing exposure intensity with similar exposure duration (NRC, 2006).
Additionally, a larger number of site-specific cancer deaths identified with potential TCE
exposure is observed by Zhao et al. (2005) compared to Boice et al. (2006b): e.g., 95 lung cancer
cases with medium or high TCE exposure (Zhao et al., 2005) and 51 lung cancer cases with any
TCE exposure (Boice et al., 2006b) (see further discussion in Section B.3.1.1.1.3). Radican et al.
(2008) studied the same subjects as Blair et al. (1998), adding an additional 10 years of follow-
up and updating mortality. Observed site-specific cancer mortality risk estimates in Radican et
al. (2008) did not change appreciably and were consistent with those reported in Blair et al.
(1998) and is preferred. Blair et al. (1998) who also presented incidence RR estimates is
recommended for inclusion in sensitivity analyses. Charbotel et al. (2006) is preferred to
Charbotel et al. (2009), who examined kidney cancer risk and TCE exposure at the existing
French occupational exposure limit for cases and controls from their earlier publication
(Charbotel et al., 2009): the earlier publication contained more extensive analyses and included
exposure-response analyses using several exposure metrics and multiple exposure categories.
Cocco et al. (2010) is preferred to Seidler et al. (2007), whose subjects are included in the larger
multicenter population case-control study. In conclusion, twenty-four studies in which there is a
high likelihood for TCE exposure and judged to have met, to a sufficient degree, the standards of
epidemiologic design and analysis, are identified in a systematic review of the epidemiologic
literature and for examination using meta-analysis.
B.3. INDIVIDUAL STUDY REVIEWS AND ABSTRACTS
B.3.1. Cohort Studies
B.3.1.1. Studies of Aerospace Workers
Seven papers reported on cohort studies of aerospace or aircraft maintenance and
manufacturing workers in large facilities.
B.3.1.1.1. Studies of SSFL workers.
TCE exposure to workers at SSFL, an aerospace facility located nearby Los Angeles,
California, operated by Rocketdyne/Atomics International, formerly a division of Boeing and
currently owned by Pratt-Whitney, is subject of two research efforts: (1) the University of
California at Los Angeles (UCLA) study, overseen by the California Department of Health
Services and funded by the U.S. Department of Energy (DOE) (Morgenstern et al., 1999: Ritz et
al., 1999: Morgenstern et al., 1997), with two publications on TCE exposure and cancer
incidence (Krishnadasan et al., 2007: Zhao et al., 2005) and mortality (Zhao et al., 2005) and (2)
the International Epidemiology Institute study (IEI), funded by Boeing after publication of the
initial UCLA reports, of all Rocketdyne employees which included a mortality analysis of TCE
exposure in a subcohort of SSFL test stand mechanics (Boice et al., 2006b). In addition to
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chemical exposure, both groups examine radiation exposure and cancer among Rocketdyne
workers monitored for radiation (Boice et al., 2006a: Ritz et al., 2000).
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B.3.1.1.1.1. International Epidemiology Institute study of Rocketdyne workers.
B.3.1.1.1.1.1. Boice et al. (2006b).
B.3.1.1.1.1.1.1. Author's abstract.
Objective: The objective of this study was to evaluate potential health risks
associated with testing rocket engines. Methods: A retrospective cohort mortality
study was conducted of 8372 Rocketdyne workers employed 1948 to 1999 at the
Santa Susanna Field Laboratory (SSFL). Standardized mortality ratios (SMRs)
and 95% confidence intervals (CIs) were calculated for all workers, including
those employed at specific test areas where particular fuels, solvents, and
chemicals were used. Dose-response trends were evaluated using Cox
proportional hazards models. Results: SMRs for all cancers were close to
population expects among SSFL workers overall (SMR = 0.89; CI = 0.82-0.96)
and test stand mechanics in particular (n = 1651; SMR = 1.00; CI = 0.86-1.1.6),
including those likely exposure to hydrazines (n = 315; SMR = 1.09; CI = 0.75-
1.52)ortrichloroethylene(TCE)(n=llll; SMR= 1.00; CI = 0.83-1.19).
Nonsignificant associations were seen between kidney cancer and TCE, lung
cancer and hydrazines, and stomach cancer and years worked as a test stand
mechanic. No trends over exposure categories were statistically significant.
Conclusion: Work at the SSFL rocket engine test facility or as a test stand
mechanic was not associated with a significant increase in cancer mortality overall
or for any specific cancer.
B.3.1.1.1.1.1.2. Study description and comment.
Boice et al. (2006b) examined all cause, all cancer and site-specific mortality in a
subcohort of 1,651 male and female test stand mechanics who had been employed on or after
1949 to 1999, the end of follow-up, for at least 6 months at SSFL. Subjects were identified from
41,345 male and female Rocketdyne workers at SSFL (n = 8.372) and two nearby facilities
(32,979). Of the 1,642 male test stand mechanics, 9 females were excluded due to few numbers,
personnel listing in company phone directories were used to identify test stand assignments (and
infer potential specific chemical exposures) for 1,440 subjects, and of this group, 1,111 male test
stand mechanics were identified with potential TCE exposure either from the cleaning of rocket
engines between tests or from more generalized use as a utility degreasing solvent. Cause-
specific mortality is compared to several referents: (1) morality rates of the U.S. population; (2)
mortality rates of California residents; (3) hourly nonadministrative workers at SSFL and two
nearby facilities; and (4) 1,598 SSFL hourly workers; however, the published paper does not
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clearly present details of all analyses. For example, the referent population is not identified for
the SMR analysis of the 1,111 male subjects with TCE potential exposure and analyses
examining exposure duration present point estimates and p-values from tests of linear trend, but
not always CIs (e.g., Boice et al. (2006b) Table 7, table footnotes).
Exposure assessment to TCE is qualitative without attempt to characterize exposure level
as was done in the exposure assessment approach of Zhao et al. (2005) and Krishnadsen et al.
(2007). Test stand mechanics were nonadministrative hourly positions and had the greatest
potential for chemical exposures to TCE and hydrazine. Potential exposure to chemicals also
existed for other subjects associated with test stand work such as instrument mechanics,
inspectors, test stand engineers, and research engineers potential for chemical exposure, although
Boice et al. (2006b) considered their exposure potential lower compared to that received by test
stand mechanics and, thus, were not included in the cohort. Like that encountered by UCLA
researchers, work history information in the personnel file was not specific to identify work
location and test stand and Boice et al. (2006b) adopted ancillary information, company phone
directories, as an aid to identify subjects with greater potential for TCE exposure. From these
aids, investigators identified rocket stand assignment for 1,440 or 87% of the SSFL test stand
mechanics. Bias is introduced through missing information on the other 211 subjects or if phone
directories were not available for the full period of the study. Test stand mechanics, if exposed,
had the likelihood for exposure to high TCE concentrations associated with flushing or cleaning
of rocket engines; 593 of the 1,111 subjects (53%) were identified as having potential TCE
exposure through rocket engine cleaning. The removal or flushing of hydrocarbon deposits in
fuel jackets and in liquid oxygen dome of large engines entailed the use of 5 to 100 gallons of
TCE, with TCE use starting around 1956 and ceased by the late 1960's at all test stands except
one which continued until 1994. No information was provided on test stand and working
conditions or the frequency of exposure-related tasks, and no atmospheric monitoring data were
available on TCE. A small number of these subjects (121) also had potential exposure to
hydrazines. The remaining 518 subjects in the TCE subcohort were presumed exposed to TCE
as a utility solvent. Information on use of TCE as a utility solvent is lacking except that TCE as
a utility solvent was discontinued in 1974 except at one test stand where it was used until 1984.
These subjects have a lower likelihood of exposure compared to subjects with TCE exposure
from cleaning rocket engines.
Several study design and analysis aspects limit this study for assessing risks associated
with TCE exposure. Overall, exposures were likely substantially misclassified and their
frequency likely low, particularly for subjects identified with TCE use as a utility solvent who
comprise roughly 50% of the TCE subcohort. Analyses examining number of years employed at
SSFL or worked as test stand mechanic as a surrogate for cumulative exposure has a large
potential for misclassification bias due to the lack of air monitoring data and inability to account
to temporal changes in TCE usage. Moreover, the exposure metric used in some dose-response
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analyses is weighted by the number of workers without rationale provided and would introduce
bias if the workforce changed over the period covered by this study. Some information suggests
that this was likely: (1) the number of cohort subjects entering the cohort decreased over the
time period of this study, as much as a 20% decrease between 1960s and 1970s, and (2) ancillary
information (http://www.thewednesdayreport.com/twr/twr48v7.htm, accessed March 11, 2008;
DOE Closure Project, http://www.etec.energy.gov/Reading-Room/DeSoto.html, accessed March
11, 2008). Study investigators did not carry out exposure assessment for referents and no
information is provided on potential TCE exposure. If referents had more than background
exposure, likely for other hourly subjects with direct association with test stand work but with a
job title other than test stand mechanic, the bias introduced leads to an underestimation of risk.
TCE use at SSFL was widespread and rocket engine cleaning occurred at other locations besides
at test sites (Morgenstern et al., 1999), locations from which the referent population arose.
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Boice JD, Marano DE, Cohen SS, Mumma MT, Blott WJ, Brill AB, Fryzek JP, Henderson BE, McLaughlin JK. (2006b)
Mortality among Rocketdyne workers who tested rocket engines, 1948-1999. J Occup Environ Med 48:1070-1092.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
From abstract "objective of this study was to evaluate potential health risks associated with testing
rocket engines."
54,384 Rocketdyne workers of which 41,351 were employed on or after 1-1-1948 and for at least
6 months at SSFL or nearby facilities. Of the 41,351 subjects, 1,651 were identified as having a job
title of test stand mechanic and exposure assignments could be made for 1,440 of these subjects.
Site-specific mortality rates of U.S. population and of all-other Rocketdyne employees. Potential TCE
exposures of all other subjects (referents) not documented but investigators assumed referents are
unexposed to TCE.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality from 1948 to 12-31-1999.
Coding to ICD in use at time of death.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Qualitative exposure assessment, any TCE exposure. No quantitative information on TCE intensity
by job title or to individual subjects or referents.
Missing exposure potential to 12% of test stand mechanics; potential exposure hydrazine and/or TCE
assigned to 1,440 of 1,651 test stand mechanics. Of 1,440 test stand mechanics, 1,1 IT identified with
potential TCE exposure, 518 of the 1,111 identified as having presumed high intensity exposure from
the cleaning of rocket engines. The remaining 593 subjects with potential exposure to TCE through
use as "utility solvent," a job task with low likelihood or potential for TCE exposure.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
0.4% for test stand mechanic cohort (1,65 1 subjects).
35 yrs average follow-up; 88% of 1,651 test stand mechanics >20-yr follow-up.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
TCE exposed subcohort — 391 total deaths, 121 cancer deaths.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
SMR analysis restricted to male hourly test stand mechanics using U.S. population rates as referent —
no adjustment of potential confounders other than age and calendar-year.
Cox proportional hazard models examining TCE exposure adjusted for birth year, year of hire and
potential hydrazine exposure. Race was not included in Cox proportional hazard analysis.
SMR analysis and Cox proportional hazard.
Duration of exposure (employment): 2-sided tests for linear trend.
All analyses are not presented in published paper. Follow-up correspondence of C Scott, U.S. EPA, to
J. Boice, of 12-31-06 and 02-28-07 remain unanswered as of November 15, 2007.
"Zhao et al. (2005), whose study period and base population overlaps that of Boice et al. (2006b), identified a larger number of subjects with potential TCE
exposures; 2,689 subjects with TCE score >3, a group having medium to high cumulative TCE exposure.
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B.3.1.1.1.2. UCLA studies of Rocketdyne workers.
B .3.1.1.1.2.1. Krishnadasan et al. (2007).
B.3.1.1.1.2.1.1. Author's abstract.
Background To date, little is known about the potential contributions of
occupational exposure to chemicals to the etiology of prostate cancer. Previous
studies examining associations suffered from limitations including the reliance on
mortality data and inadequate exposure assessment. Methods We conducted a
nested case-control study of 362 cases and 1,805 matched controls to examine the
association between occupational chemical exposures and prostate cancer
incidence. Workers were employed between 1950 and 1992 at a nuclear energy
and rocket engine-testing facility in Southern California. We obtained cancer
incidence data from the California Cancer Registry and seven other state cancer
registries. Data from company records were used to construct a job exposure
matrix (JEM) for occupational exposures to hydrazine, trichloroethylene (TCE),
poly cyclic aromatic hydrocarbons (PAHs), benzene, and mineral oil.
Associations between chemical exposures and prostate cancer incidence were
assessed in conditional logistic regression models. Results With adjustment for
occupational confounders, including socioeconomic status, occupational physical
activity, and exposure to the other chemicals evaluated, the odds ratio for
low/moderate TCE exposure was 1.3; 95%CI=0.8 to 2.1, and for high TCE
exposure was 2.1; 95%CI=1.2 to 3.9. Furthermore, we noted a positive trend
between increasing levels of TCE exposure and prostate cancer (p-value for
trend=0.02). Conclusion Our results suggest that high levels of TCE exposure
are associated with prostate cancer among workers in our study population.
B.3.1.1.1.2.2. Zhaoetal. (2005).
B.3.1.1.1.2.2.1. Author's abstract.
Background A retrospective cohort study of workers employed at a California
aerospace company between 1950 and 1993 was conducted; it examined cancer
mortality from exposures to the rocket fuel hydrazine. Methods In this study, we
employed a job exposure matrix (JEM) to assess exposures to other known or
suspected carcinogens—including trichloroethylene (TCE), polycyclic aromatic
hydrocarbons (PAHs), mineral oils, and benzene—on cancer mortality (1960-
2001) and incidence (1988-2000) in 6,107 male workers. We derived rate-
(hazard-) ratios estimates from Cox proportional hazard models with time-
dependent exposures. Results High levels of TCE exposure were positively
associated with cancer incidence of the bladder (rate ratio (RR): 1.98, 95%
confidence interval (CI) 0.93-4.22) and kidney (4.90; 1.23-19.6). High levels of
exposure to mineral oils increased mortality and incidence of lung cancer (1.56;
1.02-2.39 and 1.99; 1.03-3.85), and incidence of melanoma (3.32; 1.20-9.24).
Mineral oil exposures also contributed to incidence and mortality of esophageal
and stomach cancers and of non-Hodgkin lymphoma and leukemia when
adjusting for other chemical exposures. Lagging exposure measures by 20 years
changed effect estimates only minimally. No associations were observed for
benzene or PAH exposures in this cohort. Conclusions Our findings suggest that
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these aerospace workers who were highly exposed to mineral oils experienced an
increased risk of developing and/or dying from cancers of the lung, melanoma,
and possibly from cancers of the esophagus and stomach and non-Hodgkin
lymphoma and leukemia. These results and the increases we observed for TCE
and kidney cancers are consistent with findings of previous studies.
B.3.1.1.1.2.3. Study description and comment
The source population for Krishnadasen et al. (2007) and Zhao et al. (2005) is the UCLA
chemical cohort of 6,044 male workers with >2 years of employment Rocketdyne between 1950
and 1993, who engaged in rocket testing at SSFL before 1980 and who have never been
monitored for radiation. Zhao et al. (2005) examined cancer mortality between 1960 and 2001,
an additional 7 years from earlier analyses of the chemical subcohort (Morgenstern et al., 1999;
Ritz et al., 1999), and cancer incidence (5,049 subjects) between 1988 and 2000, matching
cohort subjects to names in California's Cancer Registry and eight other state cancer registries.
Deaths before 1998 are coded using ICD, 9th revision, and ICD-10 after this date; ICD-0 was
used to code cancer incidence with leukemia, lymphoma, and other lymphopoietic tumors
grouped on the basis of morphology codes. A total of 600 cancer deaths and 691 incident
cancers were identified during the study period.
Krishnadasen et al. (2007) adopted a nested case-control design to examine occupational
exposure to several chemicals and prostate cancer incidence in a cohort, which included the
SSFL chemically-exposed subjects and an additional 4,607 workers in the larger cohort who
were enrolled in the company's radiation monitoring program. A total of 362 incident prostate
cancers were identified between 1988 and 12-31-1999. Controls were randomly selected from
the original cohorts using risk-set sampling and a 5:1 matching ratio on age at start of
employment, age at diagnosis, and cohort.
Both studies are based on the same exposure assessment approach. Walk-through visits,
interviews with managers and workers, job descriptions manual, and historical facility reports
supported the development of a JEM with jobs ranked on a scale of 0 (no exposure) to 3 (highly
exposure) on presumptive exposure reflecting relative intensity of that exposure over three
temporal periods: 1950-1960, 1970s, 1980-1990. Of the 6,044 subjects, 2,689 had TCE
exposure scores of >3 and 2,643 with an exposure score >3 for hydrazine. Workers with job
titles indicating technical or mechanical work on rocket engines were presumed to have high
hydrazine rocket fuel exposure and high TCE exposure, which was used in cleaning rocket
engines and parts. Although fewer subjects had exposure to benzene (819 subjects) or mineral
oil (1,499 subjects), a high percentage of these subjects were also exposed to TCE. TCE use was
widespread at the facility and other mechanics, maintenance and utility workers, and machinists
were presumed as having exposure. No details were provided for job titles other than rocket test
stand mechanics for assigning TCE exposure intensity and historical trends in TCE usage. Air
monitoring data were absent for any chemicals prior to 1985 and investigators could not link
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study subjects to specific work locations and rocket-engine test stands. As a result, exposures
were probably substantially misclassified, particularly those with low to moderate TCE
exposure. Cumulative intensity score was the sum of the job-and time-specific intensity score
and years in job. Exposure classification was assigned blinded to survival status and cause of
death.
Proportional hazards modeling in calendar time with both fixed and time-dependant
predictors was used by Zhao et al. (2005) to estimate exposure effects on site-specific cancer
incidence and mortality for a combined exposure group of medium and high exposure intensity
with workers with no to low exposure intensity as referents. Variables in the proportional hazard
model included time since first employment, SES, age at diagnosis or death, and exposure to
other chemical agents including benzene, polycyclic aromatic hydrocarbons (PAHs) mineral oil,
and hydrazine. Krishnadasen et al. (2007) fit conditional logistic regression models to their data
adjusting of cohort, age at diagnosis, occupation physical activity, SES and all other chemical
exposure levels. Both publications include exposure-response analysis and presents-values for
linear trend. Race was not controlled in either study given the lack of recording on personnel
records. Smoking histories was available for only a small percentage of the cohort; for those
subjects reporting smoking information, mean cumulative TCE score did not differ between
smokers and nonsmokers.
This study develops semi quantitative exposure levels and is strength of the exposure
assessment. However, potential for exposure misclassification exists and would be of a
nondifferential direction. Rocket engine test stand mechanics had likely exposure to TCE,
kerosene, and hydrazine fuels; no information is available as to exposure concentrations.
Statistical analyses in both Zhao et al. (2005) and Krishnadansan et al. (2007) present risk
estimates for TCE that were adjusted for these other chemical exposures. Other strengths of this
study include a long follow-up period for mortality, greater than an average time of 29 years of
which 16 at SSFL, use of internal referents and the examination of cancer incidence, although
under ascertainment of cases is likely given only eight state cancer registries were used to
identify cases and incidence ascertained after 1981, 40 years after the cohort's initial definition
date.
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Krishnadasan A, Kennedy N, Zhao Y, Morgenstern H, Ritz B. (2007). Nested case-control study of occupational chemical
exposures and prostate cancer in aerospace and radiation workers. Am J Ind Med 50:383-390.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Nested case-control study of the UCLA chemical and radiation cohorts (Morsenstern et al., 1999,
1997) to assess occupational exposures including TCE and prostate cancer.
4,607 radiation cohort + 6,107 Santa Susana chemical cohort (Zhao et al., 2005; Ritz et al..
1999), excluded 1,410 deaths before 1988 (date of cancer incidence follow-up).
Incident prostate cancer cases identified from eight State cancer registries (California, Nevada,
Arizona, Texas, Washington Florida, Arkansas, and Oregon). Controls were randomly selected from
the original cohorts using risk-set sampling.
362 cases and 1,805 controls (100% participation rate).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Prostate cancer incidence.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
TCE exposure assigned to cases and controls based on longest job held at company as identified from
personnel records. Cumulative exposure — ranked exposure intensity score for TCE by three time
periods — using method of Zhao et al. (2005).
Blinded ranking of exposure status.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Employment records were used to assign exposure. 734 subjects (249 cases and 485 controls, or 33%
of all cases and controls) were interviewed via telephone or sent a mailed questionnaire to obtain
medical history, education and personal information on physical activity level and smoking history.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No proxy interviews.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
Any TCE exposure: 135 cases (37%) and 668 controls (37%).
High cumulative TCE exposure: 45 cases (12%) and 124 controls (7%).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Cohort, age at diagnosis, occupational physical activity, SES, other chemical exposures (benzene,
PAHs, mineral oil, hydrazine). No adjustment for race due to lacking information; affect of race on OR
examined using information from survey of workers still alive in 1999. Few African American
workers (n = 7), TCE levels did not vary greatly with race.
Crude and adjusted conditional logistic regression.
/>-value for trend with exposure lag (0 yrs, 20 yr).
Adequate.
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Zhao Y, Krishnadasan A, Kennedy N, Morgenstern H, Ritz B. (2005). Estimated effects of solvents and mineral oils on cancer
incidence and Mortality in a cohort of aerospace workers. Am J Ind Med 48:249-258.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls in
case-control studies is adequate
From introduction "one aim of this new investigation was to determine whether these aerospace
workers also developed cancers from exposures to other chemicals including trichloroethylene
(TCE), polycyclic aromatic hydrocarbons (PAHs), mineral oils, and benzene."
6,107 male workers employed for >2 years and before 1980 at SSFL. Internal referents (no or low
TCE exposure).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence between 1988 and 2000.
Mortality between 1950 and 2001.
ICD-0 for cancer incidence. Leukemia, lymphomas, and other lymphopoietic malignancies grouped
on the basis of morphology codes.
Mortality: ICD-9, before 1998, and ICD-10 thereafter. Incidence: ICD-Oncology
Lymphoma and leukemia grouping includes lymphosarcoma and reticulosarcoma, Hodgkin
lymphoma, other malignant neoplasm of the lymphoid and histiocytic tissue, multiple myeloma and
immunoproliferative neoplasms, and all leukemias except chronic lymphoid leukemia. The
following incident tumors were also included: Hodgkin lymphoma, leukemia, polycythemia vera,
chronic myeloproliferative disease, myelosclerosis, eosinophilic conditions, platelet diseases, and red
blood cell diseases.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of JEM
and quantitative exposure estimates
Cumulative exposure — ranked exposure intensity score for TCE by three time periods
Blinded ranking of exposure status.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
99% follow-up for mortality (6,044 of 6,107 subjects).
Average latency = 29 yrs (Ritz et al., 1999).
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers of
total cancer incidence studies; numbers of exposed cases
and prevalence of exposure in case-control studies
600 cancer deaths, 621 cancer cases.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published paper
Documentation of results
Time since first employment, SES, age (at incidence or mortality), exposure to other carcinogens,
including hydrazine. No adjustment for race. Indirectly assessment of smoking through examination
of smoking distribution by chemical exposure. Mean TCE cumulative exposure scores of smokers
and nonsmokers is not statistically significant different.
Cox proportional hazards modeling in calendar time with both fixed and time -dependent predictors.
Exposure lagged 10 and 20 yrs.
Test for monotonic trend of cumulative exposure, two-sided />-value for trend.
Liver cancer results are not reported in published paper.
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B .3.1.1.1.3. Comment on the SSFL studies
Rocketdyne workers at SSFL are subject of two separate and independent studies. Both
research groups draw subjects from the same underlying source population, Rocketdyne workers
including those at SSFL; however, the methods adopted to identify study subjects and to define
TCE exposure differ with each study. A subset of SSFL workers is common to both studies;
however, no information exist in final published reports (IEI, 2005; Morgenstern et al., 1999,
1997) to indicate the percentage overlap between cohorts or between observed number of site-
specific events.
Notable differences in both study design and analysis including cohort identification,
endpoint, exposure assessment approaches, and statistical methods exist between Zhao et al.
(2005) and Krishnadasan et al. (2007), whose source population is the UCLA cohort, and Boice
et al. (2006b) whose source population is the LEI cohort. A perspective of each study's
characteristics may be obtained from Table B-6.
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Table B-6. Characteristics of epidemiologic investigations of Rocketdyne workers
Study
Source population
TCE subcohort
Pay-type (hourly)
Job title with
potential TCE
exposure
Exposure metric
Endpoint
Statistical analysis
Observed number
of deaths:
Total cancer
Lung
Kidney
Bladder
NHL/Leukemia
Boice et al. (2006b)
41,351 administrative/scientific and nonadministrative male
and female employees between 1949 and 1999 at
Rocketdyne SSFL and two nearby facilities
1,111 male test stand mechanics with potential TCE
exposure
100% of TCE subcohort
Test stand mechanics identified with greatest potential for
TCE exposure
Other job titles with direct association with test stand work —
instrument mechanics, inspectors, test stand engineers, and
research engineers — identified with lower exposure potential
to TCE and included in referent population
Qualitative, yes/no, and employment duration
Mortality as of 1999
SMR
Proportional hazards modeling with covariates for birth year,
hire year, and potential exposure to hydrazine.
121
51
7
5
6
Zhao et al. (2005)
-55,000 subjects of SSFL and two nearby facilities employed between 1950
and 1993
6,107 males working at SSFL before 1980 and identified as test stand
personnel, of whom 2,689 males had exposure scores greater than no- to low-
TCE exposure potential
11.3%
High potential exposure group included job titles as propulsion/test
mechanics or technicians; Medium potential exposure group included
propulsion/test inspector, test or research engineer, and instrumentation
mechanic; Low-exposure potential included employees who, according to job
title may have been present during engine test firings but without direct
contact
Cumulative exposure score = £ (exposure score (0-3) x number of years in
job)
Mortality as of 200 1 and Incidence as of 2000
Proportional hazards modeling with covariates for time since first
employment, SES, age at event, and exposure to all other carcinogens,
including hydrazine
600
No/low, 99
Medium, 62
High, 33
No/low, 7
Medium, 7
High, 3
No/low, 8
Medium, 6
High, 3
No/low, 27
Medium, 27
High, 6
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A number of strengths and limitations underlie these studies. First, the Zhao et al. (2005)
and Krishnadasan et al. (2007) analyses is of a larger population and of more cancer cases or
deaths; 600 cancer deaths and 691 cancer cases in Zhao et al. (2005) compared to 121 cancer
deaths in the TCE subcohort of Boice et al. (2006b), and for prostatic cancer among all
Rocketdyne workers, 362 incident prostatic cancer cases in Krishnandasan et al. (2007)
compared to 193 deaths in Boice et al. (2006b). Second, exposed populations appear
appropriately selected in the three studies although questions exist regarding the referent
population in Boice et al. (2006b) whose referent population included subjects with some direct
association with test stand work but whose job title was other than test stand mechanic. As a
result, it appears that these studies identify TCE exposure potential different for possibly similar
job titles. For example, jobs as instrument mechanics, inspectors, test stand engineers, and
research engineers are identified with medium potential exposure in Zhao et al. (2005). Boice et
al. (2006b) on the other hand included these subjects in the referent population and assumed they
had background exposure. TCE use at SSFL was also widespread and rocket engine cleaning
occurred at other locations besides at test sites (Morgenstern et al., 1999), locations from which
the referent population in Boice et al. (2006b) arose. If referents in Boice et al. (2006b) had
more than background exposure, the bias introduced leads to an underestimation of risk. Third,
Zhao et al. (2005) and Krishnadasan et al. (2007) studies include an examination of incidence,
and are likely to have a smaller bias associated with disease misclassification than Boice et al.
(2006b) who examines only mortality. Fourth, use of cumulative exposure score although still
subject to biases is preferred to qualitative approach for exposure assessment. Last, all three
studies adjusted for potentially confounding factors such as smoking, SES, and other
carcinogenic exposures using different approaches either in the design of the study, such as
Boice et al. (2006b) limitation to only hourly workers, or in the statistical analysis such as Zhao
et al. (2005) and Krishnadansen et al. (2007). For this reason, the large difference in hourly
workers between the UCLA cohort and Boice et al. (2006b) is not likely to greatly impact
observations.
B .3.1.1.2. Blair et al. (1998), Radican et al. (2008).
B.3.1.1.2.1. Radican et al. (2008)) abstract.
OBJECTIVE: To extend follow-up of 14,455 workers from 1990 to 2000, and
evaluate mortality risk from exposure to trichloroethylene (TCE) and other
chemicals. METHODS: Multivariable Cox models were used to estimate relative
risk (RR) for exposed vs. unexposed workers based on previously developed
exposure surrogates. RESULTS: Among TCE-exposed workers, there was no
statistically significant increased risk of all-cause mortality (RR = 1.04) or death
from all cancers (RR = 1.03). Exposure-response gradients for TCE were
relatively flat and did not materially change since 1990. Statistically significant
excesses were found for several chemical exposure subgroups and causes and
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were generally consistent with the previous follow-up. CONCLUSIONS: Patterns
of mortality have not changed substantially since 1990. Although positive
associations with several cancers were observed, and are consistent with the
published literature, interpretation is limited due to the small numbers of events
for specific exposures.
B.3.1.1.2.2. Blair et al. (1998) abstract.
OBJECTIVES: To extend the follow up of a cohort of 14,457 aircraft
maintenance workers to the end of 1990 to evaluate cancer risks from potential
exposure to trichloroethylene and other chemicals. METHODS: The cohort
comprised civilians employed for at least one year between 1952 and 1956, of
whom 5727 had died by 31 December 1990. Analyses compared the mortality of
the cohort with the general population of Utah and the mortality and cancer
incidence of exposed workers with those unexposed to chemicals, while adjusting
for age, sex, and calendar time. RESULTS: In the combined follow up period
(1952-90), mortality from all causes and all cancer was close to expected
(standardized mortality ratios (SMRs) 97 and 96, respectively). Significant
excesses occurred for ischemic heart disease (SMR 108), asthma (SMR 160), and
cancer of the bone (SMR 227), whereas significant deficits occurred for
cerebrovascular disease (SMR 88), accidents (SMR 70), and cancer of the central
nervous system (SMR 64). Workers exposed to trichloroethylene showed non-
significant excesses for non-Hodgkin's lymphoma (relative risk (RR) 2.0), and
cancers of the oesophagus (RR 5.6), colon (RR 1.4), primary liver (RR 1.7),
breast (RR 1.8), cervix (RR 1.8), kidney (RR 1.6), and bone (RR 2.1). None of
these cancers showed an exposure-response gradient and RRs among workers
exposed to other chemicals but not trichloroethylene often had RRs as large as
workers exposed to trichloroethylene. Workers exposed to solvents other than
trichloroethylene had slightly increased mortality from asthma, non-Hodgkin's
lymphoma, multiple myeloma, and breast cancer. CONCLUSION: These
findings do not strongly support a causal link with trichloroethylene because the
associations were not significant, not clearly dose-related, and inconsistent
between men and women. Because findings from experimental investigations and
other epidemiological studies on solvents other than trichloroethylene provide
some biological plausibility, the suggested links between these chemicals and
non-Hodgkin's lymphoma, multiple myeloma, and breast cancer found here
deserve further attention. Although this extended follow up cannot rule out a
connection between exposures to solvents and some diseases, it seems clear that
these workers have not experienced a major increase in cancer mortality or cancer
incidence.
B.3.1.1.2.3. Study description and comment.
This historical cohort study of 14,457 (9,400 male and 3,138 female) civilian personnel
employed at least 1 year between 1942 and 1956 at Hill Air Force Base in Utah examines
mortality to the end of 1982 (Spirtas et aL 1991) to the end of 1990 (Blair etal.. 1998). or to the
end of 2000 (Radican et al., 2008). About half of the cohort was identified with exposure to
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TCE (6,153 white men and 1,051 white women). One-fourth of subjects were born before 1909
with an attained age of 43 years at cohort's identification date of 1952 and whose first exposure
could have been as early as 1939, a cohort considered as a "survivor cohort."
As of December 2008, the end of follow-up in Radican et al. (20081 8,580 deaths
(3,628 in TCE subcohort) were identified, an increase of 2,853 deaths with the additional 8 years
follow-up period compared to Blair et al. (1998) (5,727 total deaths, 2,813 among TCE
subcohort subjects), with a larger proportion deaths among non-TCE exposed subjects (58%) as
of December 2008 compared to the December 2000 (51%). Approximately 50% of
TCE-exposed subjects and 60% of all cohort subjects had died, with mean age of 75 years for
TCE-exposed subjects still alive and >45 years since the cohort's definition (1953 to 1955), a
time period longer than that typically considered for an induction or latent window for detecting
an adverse outcome like cancer. Blair et al. (1998) additionally examined cancer incidence
among white TCE-exposed workers alive on 1-1-1973, a period of 31 years after the cohort's
inception date, to the end of 1990. Incident cancer cases are likely under ascertained for this
reason.
Statistical analyses in Spirtas et al. (1991) and Blair et al. (1998) focus on site-specific
mortality for white subjects or subjects with unknown race who were assumed to as white since
97% of all subjects with known race were white. SMRs are presented with expected numbers of
deaths based upon age-, race-, and year-specific mortality rates of the Utah population (Blair et
al., 1998; Spirtas et al., 1991) or rate ratios for mortality or cancer incidence for the TCE
subcohort from Poisson regression models, adjusting for date of birth, calendar year of death,
and sex where appropriate, and an internal standard of mortality rates of the cohort's
nonchemical exposed subjects (internal referents) (Blair et al., 1998). Blair et al. (1998), in
addition to their presentation in the published papers of risk estimates associated with TCE
exposure, also, presented risk estimates for subjects with an aggregated category of "any solvent
exposure" (ever exposed) and for exposure to 14 solvents. To compare with risk ratios from
Poisson regression models of Blair et al. (1998), Radican et al. (2008) adopted Cox proportional
hazard models to reanalyze mortality observations of follow-up through 1990. For most site-
specific cancers, Radican et al. (2008) did not observe large differences between the Cox hazard
ratio and Poisson rate ratio of Blair et al. (1998), although difference between risk estimates from
Cox proportional hazard and Poisson regression of >20% was observed for kidney cancer
(increased risk estimate) and primary liver cancer (decreased risk estimate). Radican et al.
(2008), furthermore, noted hazard ratios for all subjects were similar to results for white subjects
only; therefore, their analyses of follow-up through 2000 included all subjects.
The original exposure assessment of Stewart et al. (1991) who conducted a detailed
exposure assessment of TCE exposures at Hill Air Force Base was used by Radican et al. (2008),
Blair et al. (1998), and Spirtas et al. (1991). Their study was limited linking subjects with
exposures principally because solvent exposures were associated with work in "shops," but work
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records listed only broad job titles and administrative units. As a result, exposures were
probably substantially misclassified, particularly in "mixed solvent group." TCE was used
principally for degreasing and hand cleaning in work areas during 1955-1968. TCE was the
predominant solvent used in the few available vapor degreasers located in the electroplating
(main hanger), propeller, and engine repair shops before the mid-1950 and, afterwards, as a cold
state solvent, replacing Stoddard solvent. Solvents, notably TCE after 1955, were used primarily
by aircraft mechanics with short but high exposures and sheet metal workers for spot clean
aircraft surfaces. The investigators determined that 32% had "frequent" exposures to peak
concentrations (one or two daily peaks of about 15 minutes to TCE at 200-600 ppm) during
vapor degreasing. Work areas were located in very large buildings with few internal partitions,
which aided dispersion of TCE. While TCE exposures were less controlled in the 1950s, by the
end of 1960s, TCE exposure had been reduced significantly. Only a small number of subjects
with "high" exposure had long-duration exposures, no more than 16%. Few workers were
exposed only to TCE; most had mixed exposures to other chlorinated and nonchlorinated
solvents. Person-years of exposure were computed from date of first exposure, which could have
been as early as 1939, to the end of 1982.
Overall, Blair et al. (1998) and Radican et al. (2008) are studies with approximately half
of the larger cohort identified as having some potential for TCE exposure (the TCE subcohort)
and calculation of cancer risk estimates for TCE exposure, either risk ratios in Blair et al. (1998)
or hazard ratios in Radican et al. (2008), using workers in the cohort without any chemical
exposures as referent population, superior to SMRs of Spirtas et al. (1991) who first reported on
mortality and TCE exposure. Use of an internal referent population of workers from the same
company or plant, but lacking the exposure of interest, is considered to reduce bias associated
with the healthy worker effect. For follow-up in Radican et al. (2008) who examined mortality
45 years after first exposure and likely at the tail of or beyond a window for cancer induction
time, any influence on exposure on disease development or detection times would be diminished
or less evident if exposures like TCE shortened induction time, e.g., if exposure shortened the
natural course of disease development, which would become evident in an unexposed subjects
with longer follow-up periods. The induction time of 35 years in Blair et al. (1998) may also fall
outside a cancer induction window; however, it is more consistent with cancer induction times
observed with other chemical carcinogens such as aromatic amines (Weistenhofer et al., 2008)
and vinyl chloride (Du and Wang, 1998). A strong exposure assessment was performed, but
precision in the exposure assignment was limited by vague personnel data. The cohort had a
modest number of highly exposed (about 100 ppm) subjects, but overall most were exposed to
low concentrations (about 10 ppm) of TCE.
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Radican L, Blair A, Stewart P, Wartenberg D. (2008). Mortality of aircraft maintenance workers exposed to
trichloroethylene and other hydrocarbons and chemicals: extended follow-up. J Occup Environ Med 50:1306-1319.
Blair A, Hartge P, Stewart PA, McAdams M, Lubin J. (1998). Mortality and cancer incidence of aircraft maintenance
workers exposed to trichloroethylene and other organic solvents and chemicals: extended follow-up. Occup Environ Med
55:161-171.
Spirtas R, Stewart PA, Lee JS, Marano DE, Forbes CD, Grauman DJ, Pettigrew HM, Blair A, Hoover RN, Cohen JL. (1991).
Retrospective cohort mortality study of workers at an aircraft maintenance facility. I. Epidemiological results. Br J Ind Med
48:515-530.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Abstract: "...to evaluate cancer risks from potential exposure to trichloroethylene and other
chemicals."
All civilians employed at Hill Air Force Base for >1 yr between 1-1-1952 and 12-3 1-1956; cohort of
14,457 workers identified form earnings records.
TCE subcohort — 7,204 white males and females (50%).
External referents, all civilian cohort — Utah population rates, 1953-1990.
Internal referents, TCE subcohort analysis of mortality (Blair et al . , 1 998); Radican et al. (2008)
and incidence (Blair et al., 1998) — workers without chemical exposures.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality, all civilian cohort and TCE subcohort.
Incidence, TCE subcohort.
Underlying and contributing causes of deaths as coded to ICDA 8.
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CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control
studies
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Detailed records on setting and job activities, worker interviews; work done in large open shops;
shops not recorded in personnel records, link of job with IH data was weak. Limited exposure IH
measurements for TCE between 1960 and 1990. Plant JEM, rank order assignments by history;
determined exposure duration during vapor degreasing tasks about 2,000 ppm-hr and hard degreasing
about 20 ppm-hr. Median exposure were about 10 ppm for rag and bucket (cold degreasing
process); 100-200 ppm for vapor desreasins ( Stewart et al., 1991). Cherrie et al. (2001)
estimated long-term exposure as ~50 ppm with short-term excursion up to ~600 ppm. NRC
(2006) concluded the cohort had a modest number of highly exposed (about 100 ppm) subjects,
but overall most were exposed to low TCE concentrations (about 10 ppm).
97% of cohort traced successfully to 12-3 1-1982.
Yes, all subjects followed minimum of 35 yrs (Blair et al., 1998) or 45 yrs (Radican et al..
2008).
TCE subcohort— 2,813 deaths (39%), 528 cancer deaths, and 549 incident cancers (1973-1990)
(Blair et al.. 1998); 3,628 deaths (50%). 729 cancer deaths (Radican et al.. 2008).
SMR analysis evaluates age, sex, and calendar year (Spirtas et al., 1991).
Date of hire, calendar year of death, and sex in Poisson regression analysis (Blair et al., 1998).
Age, gender, and race (to compare with RR of Blair et al. (1998), or age and gender for follow-up to
2000 in Cox proportional hazard analysis (Radican et al., 2008).
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Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
External analysis is restricted to Caucasian subjects — Life table analysis for mortality (Spirtas et
al.. 1991).
Internal analysis restricted to Caucasian subjects or subject of unknown race assumed to be Caucasian
and followed to 1990 — Poisson regression (Blair et al., 1998) or Cox Proportional Hazard
(Radican et al.. 2008).
Internal analysis— all subjects followed to 2000 (Radican et al., 2008).
Risk ratios from Poisson regression model and hazard ratios from Cox Proportional Hazard model for
exposure rankings but no formal statistical trend test presented in papers.
Adequate.
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B.3.1.1.3. Boiceetal. (1999).
B.3.1.1.3.1. Author's abstract.
OBJECTIVES: To evaluate the risk of cancer and other diseases among workers
engaged in aircraft manufacturing and potentially exposed to compounds
containing chromate, trichloroethylene (TCE), perchloroethylene (PCE), and
mixed solvents. METHODS: A retrospective cohort mortality study was
conducted of workers employed for at least 1 year at a large aircraft
manufacturing facility in California on or after 1 January 1960. The mortality
experience of these workers was determined by examination of national, state,
and company records to the end of 1996. Standardized mortality ratios (SMRs)
were evaluated comparing the observed numbers of deaths among workers with
those expected in the general population adjusting for age, sex, race, and calendar
year. The SMRs for 40 causes of death categories were computed for the total
cohort and for subgroups defined by sex, race, and position in the factory, work
duration, year of first employment, latency, and broad occupational groups.
Factory job titles were classified as to likely use of chemicals, and internal
Poisson regression analyses were used to compute mortality risk ratios for
categories of years of exposure to chromate, TCE, PCE, and mixed solvents, with
unexposed factory workers serving as referents. RESULTS: The study cohort
comprised 77,965 workers who accrued nearly 1.9 million person-years of follow
up (mean 24.2 years). Mortality follow-up, estimated as 99% complete, showed
that 20,236 workers had died by 31 December 1996, with cause of death obtained
for 98%. Workers experienced low overall mortality (all causes of death SMR
0.83) and low cancer mortality (SMR 0.90). No significant increases in risk were
found for any of the 40 specific causes of death categories, whereas for several
causes the numbers of deaths were significantly below expectation. Analyses by
occupational group and specific job titles showed no remarkable mortality
patterns. Factory workers estimated to have been routinely exposed to chromate
were not at increased risk of total cancer (SMR 0.93) or of lung cancer (SMR
1.02). Workers routinely exposed to TCE, PCE, or a mixture of solvents also were
not at increased risk of total cancer (SMRs 0.86, 1.07, and 0.89, respectively), and
the numbers of deaths for specific cancer sites were close to expected values.
Slight to moderately increased rates of non-Hodgkin's lymphoma were found
among workers exposed to TCE or PCE, but none was significant. A significant
increase in testicular cancer was found among those with exposure to mixed
solvents, but the excess was based on only six deaths and could not be linked to
any particular solvent or job activity. Internal cohort analyses showed no
significant trends of increased risk for any cancer with increasing years of
exposure to chromate or solvents.
The results from this large scale cohort study of workers followed up for over
3 decades provide no clear evidence that occupational exposures at the aircraft
manufacturing factory resulted in increases in the risk of death from cancer or
other diseases. Our findings support previous studies of aircraft workers in which
cancer risks were generally at or below expected levels.
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B.3.1.1.3.2. Study description and comment.
This study was conducted on an aircraft manufacturing worker cohort employed at
Lockheed-Martin in Burbank, California with exposure assessment described by Marano et al.
(2000). This large cohort study of 77,965 subject workers with at least 1 year employment on or
after 1-1-1960, examined causes of mortality in the entire cohort, but also by broad job titles and
for selected chemical exposures including TCE. Mortality was assessed as of 12-31-1996, with
subjects lacking death certificates presumed alive at end of follow-up. Exposure assessment
developed using a method of exposure assignment by job categories based on job histories
(Kardex cards) and the judgment of long-term employees. Job histories were not available for
every worker, and, if missing, auxiliary sources of job information were used to broadly classify
workers into various job categories. Only subjects with job histories as recorded on Kardex
cards are included in exposure duration analyses. TCE was used for vapor degreasing on routine
basis prior to 1966 and, given the cohort beginning date of 1960, only a small percentage of the
total cohort was identified as having potential TCE exposure. The investigators determined that
5,443 factory workers had potential TCE exposure. Of these subjects, 3% (2,267/77,965
subjects) had "routine" defined as use of TCE as part of daily job activities and an additional
3,176 subjects (4%) had potential "intermittent" based upon job title and judgment of nonroutine
or nondaily TCE usage and were included in the mortality analysis. No information was
provided on building and working conditions or the frequency of exposure-related tasks, and no
atmospheric monitoring data were available on TCE, although some limited data were available
after 1970 on other solvents such as perchloroethylene, which replaced TCE in 1966 in vapor
degreasing, methylene chloride, and 1,1,1-trichloroethane. Without more information, it is not
possible to determine the quality of some of the TCE assignments. This study had limited ability
to detect exposure-related effects given its use of duration of exposure, a poor exposure metric
given subjects may have differing exposure intensity with similar exposure duration (NRC,
2006). Lacking monitoring information, analyses examining the number of years of routine and
intermittent TCE exposure are likely biased due to exposure misclassification related to inability
to account for changes in process and chemical usage patterns over time. Stewart et al. (1991)
show atmospheric TCE concentrations decreased over time. Similarly, an observation of inverse
relationship between some site-specific causes of death and duration of exposure may be due to
selection bias or to misallocation of person-years of follow-up (NYSDOH, 2006).
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Boice JD, Marano DE, Fryzek JP, Sadler CJ, McLaughlin JK.
Occup Environ Med 56:581-597.
Mortality among aircraft manufacturing workers.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
From abstract: "To evaluate the risk of cancer and other diseases among workers engaged in aircraft
manufacturing and potentially exposed to compounds containing chromate, trichloroethylene (TCE),
perchloroethylene, and mixed solvents."
All workers employed on or after 1-1-1960 for at least 1 yr at Lockheed Martin aircraft
manufacturing factories in California.
Control population: U.S. mortality rates or factory workers no exposed to any solvent (internal
referents).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
ICD code in use at the time of death.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Qualitative. Few exposure measurements existed prior to the late 1970s, a period after TCE had been
discontinued at Lockheed-Martin aircraft manufacturing factories.
Subjects are categorized as potentially TCE exposed received on a routine basis (2,075 subjects),
daily job activity, or routine and intermittent basis (3,016 subjects), nonroutine ornondaily TCE
usage, based on information on Service Record and Permanent Employment Record (Kardex) and
other sources of job history information for subjects lacking Kardex cards.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
This study does not adopt methods to verify vital status of employees. All workers for which death
certificate were not found are assumed to be alive until end of follow-up.
Average follow-up of TCE cohort was 29 yrs.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
1,100 total deaths and 277 cancer deaths in TCE subcohort.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
SMR analysis — age, sex, and calendar-time.
Poisson regression using internal referents — birth date, date first employed,
employment, race, and sex.
date of finishing
SMR for routine TCE exposure subcohort.
Poisson regression for routine and intermittent TCE exposure subcohort.
Duration of exposure for subjects with Kardex cards only —
2-sides test for linear trend.
Adequate.
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B.3.1.1.4. Morgan et al. (1998).
B.3.1.1.4.1. Author's abstract.
We measured mortality rates in a cohort of 20,508 aerospace workers who were
followed up over the period 1950-1993. A total of 4,733 workers had
occupational exposure to trichloroethylene. In addition, trichloroethylene was
present in some of the washing and drinking water used at the work site. We
developed a job-exposure matrix to classify all jobs by trichloroethylene exposure
levels into four categories ranging from "none" to "high" exposure. We calculated
standardized mortality ratios for the entire cohort and the trichloroethylene
exposed subcohort. In the standardized mortality ratio analyses, we observed a
consistent elevation for nonmalignant respiratory disease, which we attribute
primarily to the higher background rates of respiratory disease in this region. We
also compared trichloroethylene-exposed workers with workers in the "low" and
"none" exposure categories. Mortality rate ratios for nonmalignant respiratory
disease were near or less than 1.00 for trichloroethylene exposure groups. We
observed elevated rare ratios for ovarian cancer among those with peak exposure
at medium and high levels] relative risk (RR) = 2.74; 95% confidence interval
(CI) = 0.84-8.99] and among women with high cumulative exposure (RR = 7.09;
95% CI = 2.14-23.54). Among those with peak exposures at medium and high
levels, we observed slightly elevated rate ratios for cancers of the kidney (RR =
1.89; 95% CI = 0.85-4.23), bladder (RR = 1.41; 95% CI = 0.52-3.81), and
prostate (RR = 1.47; 95% CI = 0.85-2.55). Our findings do not indicate an
association between trichloroethylene exposure and respiratory cancer, liver
cancer, leukemia or lymphoma, or all cancers combined.
Erratum:
One of the authors of the article entitled Mortality of aerospace workers exposed
to trichloroethylene, by Robert W. Morgan, Michael A. Kelsh, Ke Zhao, and
Shirley Heringer, published in Epidemiology (1998);9:424-431, informed us of
some errors in one of the tables. In Table 5, the authors had inadvertently included
both genders in counting person-years, rather than presenting gender-specific risk
ratios for prostate and ovarian cancer. In addition, one subject, in the high
trichloroethylene (TCE) exposure category, had been incorrectly classified with a
diagnosis of ovarian cancer, instead of other female genital cancer. The authors
report that correction of these errors did not change the overall conclusions of the
study. The correct estimates of effect for prostate and ovarian cancer are
presented in the Table below.
B.3.1.1.4.2. Study description and comment.
This study of a cohort of 20,508 aircraft manufacturing workers employed for at least 6
months between 1950 and 1985 at Hughes Aircraft in Arizona was followed through 1993 for
mortality. Cause-specific SMRs are resented for the entire cohort and the TCE-subcohort using
U.S. mortality rates from 1950 to 1992 as referents. Additionally, internal cohort analyses fitting
Cox proportional hazards models are presented comparing risks for those with TCE exposure to
B-83
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never-exposed subjects. Morgan et al. (2000, 1998) do not identify job titles of individuals in the
never-exposed group; however, it is assumed these individuals were likely white-collar workers,
administrative staff, or other blue-collar worker with chemical or solvents exposures other than
TCE.
The company conducted a limited semi quantitative assessment of TCE exposure based
on the judgment of long-term employees. Most TCE exposure occurred in vapor degreasing
units between 1952 and 1977. No details were provided on the protocol for processing the jobs
in the work histories into job classifications; no examples were provided. Additionally, no
information is provided other chemical exposures that may also have been used in the different
jobs. Of the 20,508 subjects, 4,733 were identified with TCE exposure. Exposure categories
were assigned to job classifications: high = worked on degreasers (industrial hygiene reported
exposures were >50 ppm); medium = worked near degreasers; and low = work location was
away from degreasers but "occasional contact with (trichloroethylene)." There was also a "no
exposure" category. No data were provided on the frequency of exposure-related tasks. Without
more information, it is not possible to determine the quality of some of these assignments. Only
the high category is an unambiguous setting. Depending on how the degreasers were operated,
operator exposure to TCE might have been substantially >50 ppm. Furthermore, TCE intensity
likely changed over time with changes in degreaser operations and exposure assignment based
on job title only is able to correctly place subjects with a similar job title but held at different
time periods. Furthermore, there are too many possible situations in which an exposure category
of medium or low might be assigned to determine whether the ranking is useful. Therefore, the
medium and low rankings are likely to be highly misclassified. Deficiencies in job rankings are
further magnified in the cumulative exposure groupings. Internal analyses examine TCE
exposed, defined as low and high cumulative exposure, compared to never-TCE exposed
subjects. Low cumulative exposure group includes any workers with the equivalent of up to
5 years of exposure at jobs at low exposure or 1.4 years of medium exposure; all other workers
were placed in the high cumulative exposure grouping. Ambiguity in low and medium job
rankings and the lack of exposure data to define "medium" and "low" precludes meaningful
analysis of cumulative exposure, specifically, and exposure-response, generally.
The development of exposure assignments in this study was insufficient to define
exposures of the cohort and bias related to exposure misclassification is likely great. The
inability to account for changes in TCE use and exposure potential over time introduces bias and
may dampen observed risks. This study had limited ability to detect exposure-related effects
and, overall, limited ability to provide insight on TCE exposure and cancer outcomes.
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Morgan RW, Kelsh MA, Zhao K, Heringer S. (1998). Mortality of aerospace workers exposure to trichloroethylene.
Epidemiol 9:424-431.
Morgan RW, Kelsh MA, Zhao K, Heringer S. (2000). Mortality of aerospace workers exposed to trichloroethylene. Erratum.
Epidemiology 9:424-431.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
"measured mortality rates in a cohort of aerospace workers, comparing TCE workers with workers in
low and none exposure categories."
20,508 male and female workers are identified using company records and who were employed at
plant for at least 6 months between 1-1-1950 and 12-3 1-1985.
TCE subcohort — 4,733 (23%) male and female subjects.
External referents — U.S. population rates, 1950-1992.
Internal referents — Analysis of peak exposure, low or no TCE exposure; analysis of cumulative
exposure, never exposed to TCE. Internal referents are likely white-collar workers, administrative
staff, and blue-collar workers with chemical exposure other than TCE. White-collar and
administrative staff subjects are not representative of blue-collar workers due to SES and sex
differences. Also, the never-TCE exposed blue-collar workers may potentially have other chlorinated
solvents exposures, exposures that may be associated with a similar array of targets as TCE. These
individuals may not be representative of a nonchemical exposed population as that used in Blair et al.
(1998).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality
No, ICD in use at time of death (ICD 7, 8, 9).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Semiquantitative. Limited IH measurements before 1975. Jobs ranked into high, medium, or low
intensity exposure categories; categories are undefined as to TCE intensity. Jobs with high intensity
exposure rating involved work on degreaser machines with TCE exposure equivalent to
50 ppm; assigned exposure score of 9. Job with medium rating were near (distance undefined in
published paper) degreasing area and a score of 4. Jobs with low rating were away (undefined
distance) from degreasing area and assigned score of 1. Cumulative exposure score = £ (duration
exposure x score). Peak exposure defined by job with highest ranking score.
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CATEGORY D: FOLLOW-UP (Cohort)
More than 10% loss to follow-up
>50% cohort with full latency
No, 27 subjects were excluded from analysis due to missing information.
Average 22 yrs of follow-up for TCE subcohort.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control
studies
TCE subcohort— 917 total deaths (19%) of subcohort, 270 cancer deaths.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, race, sex, and calendar year in SMR analysis.
Internal analysis- age (for bladder, prostate, ovarian cancers), and age and sex (liver,
Life table analysis (SMR).
Cox proportional hazards modeling (unexposed subjects as internal referents) — peak
of cumulative exposure (Morgan et al., 1998; EHS, 1997); any TCE exposure
kidney cancers).
and two-levels
(EHS. 1997).
Qualitative presentation, only; no formal statistical test for linear trend.
Adequate.
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B.3.1.1.5. Costa et al. (1989).
B.3.1.1.5.1. Author's abstract.
Mortality in a cohort of 8626 workers employed between 1954 and 1981 in an
aircraft manufacturing factory in northern Italy was studied. Total follow up was
132,042 person-years, with 76% accumulated in the age range 15 to 54. Median
duration of follow up from the date of first employment was 16 years. Vital status
was ascertained for 98.5% of the cohort. Standardized mortality ratios were
calculated based on Italian national mortality rates. Altogether 685 deaths
occurred (SMR = 85). There was a significant excess of mortality for melanoma
(6 cases, SMR = 561). Six deaths certified as due to pleural tumors occurred. No
significant excess of mortality was found in specific jobs or work areas.
B.3.1.1.5.2. Study description and comment.
This study assesses mortality in a small cohort of 8,626 aircraft manufacturing workers
employed between 1954 and the end of follow-up in June, 1981. A period of minimum
employment duration before accumulating person-years was not a prerequisite for cohort
definition. The cohort included employees identified as blue collar workers, technical staff,
administrative clerks, and white-collar workers. Blue-collar workers comprised 7,105 of the
8,626 cohort subjects. Mortality was examined for all workers and included job title of blue
collar workers, technical staff members, administrative clerks, and white-collar workers, not
otherwise specified. No exposure assessment was used and the published paper does not identify
chemical exposures. In fact, Costa et al. (1989) do not even mention TCE in the paper.
Overall, the lack of exposure assessment, the inability to identify TCE as an exposure to
this cohort, and the inclusion of subjects who likely do not have potential TCE exposure are
reasons why this study is not useful for determining whether TCE may cause increased risk of
disease.
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Costas G, Merletti F, Segnan N.
A mortality study in a north Italian aircraft factory. Br J Ind Med 46:738-743.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
The 1st paragraph of the paper identified this study was carried out to investigate an apparently high
number of malignant tumors among employees that were brought to the attention of the local health
authority by staff representative. This study was not designed to examine TCE exposure and cancer
outcomes.
Cohort is defined as all workers every employed between 1-1-1954 and 6-30-1981 (end of follow-up)
at a north Italian aircraft manufacturing factory. Cohort include 8.626 subjects: 950 women
(636 clerks, 3 14 blue-collar workers/technical staff) and 7,676 men (5,625 blue collar workers,
965 technical staff, 571 administrative clerks, and 515 white collar workers).
External referent — Age, year (5-yr periods over 1955-1981)-sex and cause-specific death rates of
Italian population.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
Causes and underlying causes of death coded to ICD rule in effect at the time of death and grouped
into categories consistent with ICD 8th revision.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Exposure is defined as employment in the factory. TCE is not mentioned in published paper and no
exposure assessment was carried out by study investigators.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
Vital status ascertained for 98% of cohort; 2% could not be traced (1% unknown and 1% had
emigrated).
Average mean follow-up: males, 17 yrs; females, 13 yrs.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control
studies
642 total deaths, 168 cancer deaths.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, and calendar year.
SMR.
No.
Adequate.
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B.3.1.1.6. Garabrant et al. (1988).
B.3.1.1.6.1. Author's abstract.
A retrospective cohort mortality study was conducted among men and women
employed for four or more years, between 1958 and 1982, at an aircraft
manufacturing company in San Diego County. Specific causes of death under
investigation included cancer of the brain and nervous system, malignant
melanoma, and cancer of the testicle, which previous reports have suggested to be
associated with work in aircraft manufacturing. Follow-up of the cohort of 14,067
subjects for a mean duration of 15.8 yr from the date of first employment resulted
in successful tracing of 95% of the cohort and found 1,804 deaths through 1982.
Standardized mortality ratios (SMRs) were calculated based on U. S. national
mortality rates and separately based on San Diego County mortality rates.
Mortality due to all causes was significantly low (SMR = 75), as was mortality
due to all cancer (SMR = 84). There was no significant excess of cancer of the
brain, malignant melanoma, cancer of the testicle, any other cancer site, or any
other category of death. Additional analyses of cancer sites for which at least ten
deaths were found and for which the SMR was at least 110 showed no increase in
risk with increasing duration of work or in any specific calendar period. Although
this study found no significant excesses in cause-specific mortality, excess risks
cannot be ruled out for those diseases that have latency periods in excess of 20 to
30 yr, or for exposures that might be restricted to a small proportion of the cohort.
B.3.1.1.6.2. Study description and comment.
This study reported on the overall mortality of a cohort of workers in the aircraft
manufacturing industry in southern California who had worked 1 day at the facility and had at
least 4 years duration of employment. Fifty-four percent of cohort entered cohort at beginning
date (1-1-1958). This is a survivor cohort. This study lacks exposure assessment for study
subjects. The only exposure metric was years of work. Examination of jobs held by 70 study
subjects, no details provided in paper on subject selection criteria, identified 37% as having
possible TCE exposure, but no information was presented on how they were exposed, frequency
or duration of exposure, or job titles associated with exposure. No information is provided on
possible TCE exposure to the remaining -14,000 subjects in this cohort. The exposure
assignment in this study was insufficient to define exposures of the cohort and the frequency of
exposures was likely low. Given the enormous misclassification on exposure, the effect of
exposure would have to be very large to be detected as an overall risk for the population. Null
findings are to be expected due to bias likely associated with a survivor cohort and to exposure
misclassification. Therefore, this study provides little information on whether TCE is related to
disease risk.
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Garabrant DH, Held J, Langholz B, Bernstein L. (1988). Mortality of Aircraft Manufacturing Workers in Southern
California. Am J Ind Med 13:683-693.
Langholz B, Goldstein L. (1996). Risk Set Sampling in Epidemiologic Cohort Studies. Stat Sci 11:35-53.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
"Our objects were to evaluate the oval mortality among the [aircraft manufacturing] workers and to
test the hypotheses that brain tumors, malignant melanoma, and testicular neoplasms are associated
with work in this industry." [Introduction]
This study was not designed to evaluate any specific exposure, but rather employment in aircraft
manufacturing industry.
14,067 males and females working at least 4 yrs with a large aircraft manufacturing company and
who had worked for at least 1 d at a factory in San Diego County, California. Person-year accrued
from the anniversary date of an individual's 4th yr of service or from 1-1-1958 to end of follow-up
12-31-1982.
External referents — age-, race-, sex-, calendar year-, and cause-specific mortality rates of U.S.
population.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality
ICD revision in effect at the date of death. Lymphomas in four groupings: lymphosarcoma and
reticulosarcoma, HD, leukemia and aleukemia, and other.
CATEGORY C: TCE-EXPOSURE CRITERIA
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
ICD revision in effect at the date of death. Lymphomas in four groupings: lymphosarcoma and
reticulosarcoma, HD, leukemia and aleukemia, and other.
Exposure assessment is lacking for all subjects except 70 deaths (14 esophageal and 56 others) who
were included in a nested case-control study. Of the 362 jobs held by these 70 subjects, 37% were
identified as having potential for TCE exposure.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
4.7% with unknown vital status.
Average 16-yr follow-up.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
1,804 deaths (12.8% of cohort), 453 cancer deaths.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, race, sex, and calendar year.
SMR.
No.
SMR analysis, adequate; published paper lacks documentation of nested case-control study of
esophageal cancer.
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B.3.1.2. Cancer Incidence Studies Using Biological Monitoring Databases
Finland and Denmark historically have maintained national databases of biological
monitoring data obtained from workers in industries where toxic exposures are a concern.
Legislation required that employers provide workers exposed to toxic hazards with regular health
examinations, which must include biological monitoring to assess the uptake of toxic chemicals,
including TCE. In Sweden, the only local producer of TCE operated a free exposure-
surveillance program for its customers, measuring U-TCA. These programs used the linear
relationship found for average inhaled TCE vs. U-TCA: TCE (mg/m3) = 1.96; U-TCA (mg/L) =
0.7 for exposures <375 mg/m3 (69.8 ppm) (Ikeda et al., 1972). This relationship shows
considerable variability among individuals, which reflects variation in urinary output and activity
of metabolic enzymes. Therefore, the estimated inhalation exposures are only approximate for
individuals but can provide reasonable estimates of group exposures. There is evidence of
nonlinear formation of U-TCA above about 400 mg/m3 or 75 ppm of TCE. The half-life of
U-TCA is about 100 hours. Therefore, the U-TCA value represents roughly the weekly average
of exposure from all sources, including skin absorption. The Ikeda et al. (1972) relationship can
be used to convert urinary values into approximate airborne concentration, which can lead to
misclassification if tetrachloroethylene and 1,1,1-trichloroethane are also being used because
they also produce U-TCA. In most cases, the Ikeda et al. relationship (1972) provides a rough
upper boundary of exposure to TCE.
B.3.1.2.1. Hansen et al. (2001).
B.3.1.2.1.1. Author's abstract.
Human evidence regarding the carcinogen!city of the animal carcinogen
trichloroethylene (TCE) is limited. We evaluated cancer occurrence among
803 Danish workers exposed to TCE, using historical files of individual air and
urinary measurements of TCE-exposure. The standardized incidence ratio (SIR)
for cancer overall was close to unity for both men and women who were exposed
to TCE. Men had significantly elevated SIRs for non-Hodgkin's lymphoma
(SIR = 3.5; n = 8) and cancer of the esophagus (SIR = 4.2; n = 6). Among women,
the SIR for cervical cancer was significantly increased (SIR = 3.8; n = 4). No
clear dose-response relationship appeared for any of these cancers. We found no
increased risk for kidney cancer. In summary, we found no overall increase in
cancer risk among TCE-exposed workers in Denmark. For those cancer sites
where excesses were noted, the small numbers of observed cases and the lack of
dose-related effects hinder etiological conclusions.
B .3.1.2.1.2. Study description and comment.
This Danish study evaluated cancer incidence in a small cohort of individuals (n = 803)
who had been monitored for TCE exposures in a national surveillance program between 1947
and 1989 for U-TCA or TCE in breath since 1974. In all, 2,397 samples were analyzed for U-
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TCA of workers at 275 companies and 472 breathing zone samples of TCE from workers at 81
companies. Individual workers could not be identified for roughly one-third of the U-TCA
measurements and 50% of breathing zone measurements; many of the individuals most likely
had died prior to 1968, the start of the Central Population Registry from which workers were
identified and follow-up for cancer incidence. A cohort of 658 males and 145 females were
identified from the remaining 1,519 U-TCA and 245 air-TCE measurements. Only two of 803
cohort subjects had both urine and air measurements. Follow-up for cancer incidence ended as
of 12-31-1996.
The retirement and measurement records contained general information about the type of
employer and the subject's job. The subjects in this study came predominantly from the iron and
metal industry with jobs such as metal-product cleaner. Each subject had 1-27 measurements of
U-TCA measurements, an average of 2.2 per subject, going back to 1947. Using the linear
relationship from Ikeda et al. (1972), the historic median exposures estimated from the U-TCA
concentrations were low: 9 ppm for 1947-1964, 5 ppm for 1965-1973, 4 ppm for 1974-1979,
and 0.7 ppm for 1980-1989. However, the distributions were highly skewed. Additionally, 5%
of the cohort had urine or air samples below the limit of detection. Overall, median exposure in
this cohort was 4 ppm and suggests that, in general, workers in a wide variety of industry and job
groups and identified as "exposed" in this study had low TCE intensity exposures. Overall, the
cohort in this study is small, drawn from a wide variety of industries, predominantly degreasing
and metal cleaning, and had generally low exposures (most <20 ppm). The study has a lower
power to examine TCE exposure and cancer for these reasons.
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Hansen J, Raaschou-Nielsen O, Christensen JM, Johansen I, McLaughlin JK, Lipworth L, Blot WJ, Olsen JH. (2001). Cancer
incidence among Danish workers exposed to trichloroethylene. J Occup Environ Med 43:133-139.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
From introduction — A study of incidence was carried out to address shortcomings in earlier TCE
studies related to the lack of direct exposure information and to assessment of mortality as opposed to
incidence.
803 subjects identified from biological monitoring of urine TCA from 1947 to 1989
(1,519 measurements) or breathing zone TCE since 1974 (245 measurements) and who were alive as
of 1968, followed to 1996.
External referents — cancer incidence rates of Danish population (age-, sex-, calendar years-, and site-
specific).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence.
ICD, 7th revision.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Biological marker of TCE in urine or in breath used to assign TCE exposure to cohort subject.
Historic median exposures estimated from the U-TCA were low: 9 ppm for 1947 to 1964, 5 ppm for
1965 to 1973, 4 ppm for 1974 to 1979, and 0.7 ppm for 1980 to 1989. Overall, median TCE
exposure to cohort was 4 ppm (arithmetic mean, 12 ppm).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
No.
Unable to determine given insufficient information in paper; however, text notes follow-up for most
subjects achieved a full latency.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control
studies
128 incident cancers among 804 cohort subjects (15%).
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, and calendar year.
SIR, Life table analysis.
Yes, as dichotomous variable for mean exposure
(<4 ppm, 4+ ppm) and for cumulative exposure.
Adequate.
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B.3.1.2.2. Anttilaetal. (1995).
B.3.1.2.2.1. Author's abstract.
Epidemiologic studies and long-term carcinogenicity studies in experimental
animals suggest that some halogenated hydrocarbons are carcinogenic. To
investigate whether exposure to trichloroethylene, tetrachloroethylene, or
1,1,1-trichloroethane increases carcinogenic risk, a cohort of 2050 male and 1924
female workers monitored for occupational exposure to these agents was followed
up for cancer incidence in 1967 to 1992. The overall cancer incidence within the
cohort was similar to that of the Finnish population. There was an excess of
cancers of the cervix uteri and lymphohematopoietic tissues, however. Excess of
pancreatic cancer and non-Hodgkin lymphoma was seen after 10 years from the
first personal measurement. Among those exposed to trichloroethylene, the
overall cancer incidence was increased for a follow-up period of more than
20 years. There was an excess of cancers of the stomach, liver, prostate, and
lymphohematopoietic tissues combined. Workers exposed to 1,1,1-trichloroethane
had increased risk of multiple myeloma and cancer of the nervous system. The
study provides support to the hypothesis that trichloroethylene and other
halogenated hydrocarbons are carcinogenic for the liver and lymphohematopoietic
tissues, especially for non-Hodgkin lymphoma. The study also documents excess
of cancers of the stomach, pancreas, cervix uteri, prostate, and the nervous system
among workers exposed to solvents.
B .3.1.2.2.2. Study description and comment.
This Finnish study evaluated cancer risk in a small cohort of individuals (2,050 males and
1,924 females) who had been monitored between 1965 and 1982 for exposures to TCE by
measuring their U-TCA. The main source of exposure was identified as degreasing or cleaning
metal surfaces. Some workplaces identified rubber work, gluing, and dry-cleaning. There was
an average of 2.7 measurements per person. Using the Ikeda et al. (1972) conversion
relationship, the exposure for TCE was approximately 7 ppm in 1965, which declined to
approximately 2 ppm in 1982; the 75th percentiles for these dates were 14 and 7 ppm,
respectively. The maximum values for males were approximately 380 ppm during 1965 to 1974
and approximately 96 ppm during 1974 to 1982. Females showed a similar pattern over time but
had somewhat higher exposures than males before the 1970s. Median TCE exposure for females
of 4 ppm compared to 3 ppm for males; maximum values were similar for both sexes. Duration
of exposure was counted from the first measurement of U-TCA, which might underestimate the
length of exposure. Without job histories, the length of exposure is uncertain. Another concern
is the sampling strategy; it was not reported how the workers were chosen for monitoring.
Therefore, it is not clear what biases might be present, especially the possibility of under-
sampling highly exposed workers.
Overall, this TCE exposed cohort drawn from a wide variety of industries was twice the
size of other Nordic biomonitoring studies (Hansen et al., 2001; Axel son et al., 1994) with urine
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TCA measurements from a more recent period, 1965-1982, compared to other Nordic studies of
Danish cohorts, 1947-1980s, or Swedish cohorts, 1955-1975 (Raaschou-Nielsen et al.. 2002:
Hansen et al., 2001; Axel son et al., 1994). Exposures to TCE were generally low, <14 ppm for
the 75th percentile of all measurements, and median TCE exposures decreasing from 7 to 2 ppm
over the 17-year period. The medians are similar to estimated exposures to Danish workers with
biological markers of U-TCA (Hansen et al., 2001; Raaschou-Nielsen et al., 2001). The duration
of exposure was uncertain.
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Anttila A, Pukkala E, Sallmen M, Hernberg S, Hemminki K. (1995). Cancer incidence among Finnish workers exposed to
halogenated hydrocarbons. J Occup Environ Med 37:797-806.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Yes, study aim was to assess cancer incidence among workers biologically monitored for exposure to
TCE, PERC, and 1,1,1-trichloroethane.
3, 976 subjects identified from biological monitoring of urine TCA between 1965 to 1982; PERC in
blood, 1974 to 1983; and, 1,1,1-trichloroethane in blood, 1975 to 1983 (a total of 10.743
measurements). 109 of cohort subjects with TCE poisoning report between 1965 and 1976. Follow-
up for mortality between 1965 and 1991 and for cancer between 1967 and 1992.
TCE subcohort— 3,089 (1,698 males, 1,391 females).
External referents — age-, sex-, calendar year-, and site-specific cancer incidence rates of the Finnish
population.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality and cancer incidence.
ICD, 7th revision.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Biological marker of TCE in urine used to assign TCE exposure for TCE subcohort. There were on
average 2.5 U-TCA measurements per individual. 6% of cohort had measurements for two or all
three solvents. The overall medians of U-TCA for females and males were 8.3 and 6.3 mg/L,
respectively, and before 1970, 10-13 mg/L for females and 13-15 mg/L for males. Using Iked a et
al. (1972) relationship for U-TCA and TCE concentration, median TCE exposures over the
period of study were roughly <4-9 ppm (median, 4 ppm; arithmetic mean, 6 ppm).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
No.
Yes, 18-yr mean follow -up period.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control
studies
208 cancers among 3,089 TCE-exposed subjects (7%).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, and calendar year.
SMR and SIR, Life table analysis.
Yes, U-TCA as dichotomous variable (<6 ppm, 6+ ppm).
Adequate for SIR analysis; details on SMR analysis of TCE subcohort are few.
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B.3.1.2.3. Axelsonetal. (1994).
B.3.1.2.3.1. Author's abstract.
There is limited evidence for mutagenicity and carcinogenicity of
trichloroethylene (TRI) in experimental test systems. Whether TRI is a human
carcinogen is unclear, however. This paper presents an update and extension of a
previously reported cohort of workers exposed to TRI, in total 1670 persons.
Among men (n = 1421), the overall standardized mortality ratio (SMR) and
cancer morbidity ratio (SIR) were close to the expected, with SMR, 0.97; 95%
confidence interval (CI), 0.86 to 1.10; and SIR, 0.96; 95% CI, 0.80 to 1.16,
respectively. The cancer mortality was significantly lower than expected (SMR,
0.65; 95% CI, 0.47 to 0.89), whereas an increased mortality from circulatory
disorders (cardiovascular, cerebrovascular) was of borderline significance (SMR,
1.17; 95% CI, 1.00 to 1.37). No significant increase of cancer of any specific site
was observed, except for a doubled incidence of nonmelanocytic skin cancer
without correlation with the exposure categories. In the small female subcohort
(n = 249), a nonsignificant increase of cancer and circulatory deaths was observed
(SMR, 1.53 and 2.02, respectively). For both genders, however, excess risks were
largely confined to groups of workers with lower exposure levels or short duration
of exposure or both. It is concluded that this study provides no evidence that TRI
is a human carcinogen, i.e., when the exposure is as low as for this study
population.
B .3.1.2.3.2. Study description and comment.
This Swedish study evaluated cancer risk in a small cohort of individuals (1,421 males
and 249 females), who were monitored for U-TCA as part of a surveillance system by the TCE
producer during 1955 to 1975. Both mortality between 1955 and 1986 and cancer morbidity
between 1958 and 1987 are assessed in males only due to the small number of female subjects.
Eighty-one percent of the male subjects had low exposures (<50 mg/L), corresponding to an
airborne concentration of TCE of approximately 20 ppm. There was uncertainty about the
beginning and end of exposure. Exposure was assumed to begin with the first urine sample and
to end in 1979 (the reason for this date is unclear). Because the investigators did not have job
histories, there is considerable uncertainty about the duration of exposure. No information is,
additionally, presented to evaluate if a large proportion of the cohort had a full latency period for
cancer development. Most subjects appear to have had short durations of exposure, but these
might have been underestimated. Another concern is the sampling strategy. It was not reported
how the workers were chosen for monitoring. Therefore, it is not clear what biases could be
present in the data, especially the possibility of under sampling highly exposed workers.
Overall, this study had a small cohort drawn from a wide variety of industries,
predominantly from industries involving degreasing and metal cleaning. Exposure to TCE was
generally low (most <20 ppm). The duration of exposure was uncertain and bias related to under
sampling of higher exposed workers is possible but cannot be evaluated.
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Axelson O, Selden A, Andersson K, Hogstedt C. (1994). Updated and expanded Swedish cohort study on trichloroethylene
and cancer risk. J Occup Environ 36:556-562.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Yes- "This paper present an update and extension of a previously reported cohort of workers
exposure to TCE."
1,670 subjects (1,421 males, 249 females) with records of biological monitoring of urine TCA from
1955 and 1975.
Analysis restricted to 1,421 males.
External referents — age-, sex-, calendar year-, and site-specific mortality or cancer incidence rates of
Swedish population.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence from 1958 to 1987 and all-cause mortality from 1955 to 1986.
ICD, 7th revision.
ICD, 8th revision from 1975 onward for all lympho-hematopoietic system cancers.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Biological marker of TCE in urine used to assign TCE exposure to cohort subject. No extrapolation
of U-TCA data to air-TCE concentration. Roughly % of cohort had U-TCA concentrations
equivalent to <20 ppm TCE.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
No
Insufficient to estimate for full cohort; however, 42% of person years in subjects with 2+ exposure
years also had 10+yrs of latency.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
229 deaths (16% of male subjects).
107 incident cancer cases.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age and calendar year.
SMR — age, sex, and calendar-year.
SIR — analyses restricted to males-
Yes, by three categories of U-TCA
-age and calendar-year.
concentration.
Adequate.
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B.3.1.3. Studies in the Taoyuan Region of Taiwan
B.3.1.3.1. Sung et al. (2008; 2007).
B.3.1.3.1.1. Sung et al.(2008) abstract.
There is limited evidence on the hypothesis that maternal occupational exposure
near conception increases the risk of cancer in offspring. This study is to
investigate whether women employed in an electronics factory increases
childhood cancer among first live born singletons. We linked the databases of
Birth Registration and Labor Insurance, and National Cancer Registry, which
identified 40,647 female workers ever employed in this factory who gave 40,647
first live born singletons, and 47 of them developed cancers during 1979-2001.
Mothers employed in this factory during their periconceptional periods (3 months
before and after conception) were considered as exposed and compared with those
not employed during the same periods. Poisson regression model was constructed
to adjust for potential confounding by maternal age, education, sex, and year of
birth. Based on 11 exposed cases, the rate ratio of all malignant neoplasms was
increased to 2.26 [95% confidence interval (CI), 1.12-4.54] among children
whose mothers worked in this factory during periconceptional periods. The RRs
were associated with 6 years or less (RR=3.05; 95% CI, 1.20-7.74) and 7-9 years
(RR=2.49; 95% CI, 1.26-4.94) of education compared with 10 years or more. An
increased association was also found between childhood leukemia and exposed
pregnancies (RR=3.83; 95% CI, 1.17-12.55). Our study suggests that maternal
occupation with potential exposure to organic solvents during periconception
might increase risks of childhood cancers, especially for leukemia.
B .3.1.3.1.2. Sung et al. (2007) abstract.
Background In 1994, a hazardous waste site, polluted by the dumping of solvents
from a former electronics factory, was discovered in Taoyuan, Taiwan. This
subsequently emerged as a serious case of contamination through chlorinated
hydrocarbons with suspected occupational cancer. The objective of this study was
to determine if there was any increased risk of breast cancer among female
workers in a 23-year follow-up period. Methods A total of 63,982 female
workers were retrospectively recruited from the database of the Bureau of Labor
Insurance (BLI) covering the period 1973-1997; the data were then linked with
data, up to 2001, from the National Cancer Registry at the Taiwanese Department
of Health, from which standardized incidence ratios (SIRs) for different types of
cancer were calculated as compared to the general population. Results There
were a total of 286 cases of breast cancer, and after adjustment for calendar year
and age, the SIR was close to 1. When stratified by the year 1974 (the year in
which the regulations on solvent use were promulgated), the SIR of the cohort of
workers who were first employed prior to 1974 increased to 1.38 (95%
confidence interval, 1.11-1.70). No such trend was discernible for workers
employed after 1974. When 10 years of employment was considered, there was a
further increase in the SIR for breast cancer, to 1.62. Those workers with breast
cancer who were first employed prior to 1974 were employed at a younger age
and for a longer period. Previous qualitative studies of interviews with the
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workers, corroborated by inspection records, showed a short-term high exposure
to chlorinated alkanes and alkenes, particularly trichloroethylene before 1974.
There were no similar findings on other types of cancer. Conclusions Female
workers with exposure to trichloroethylene and/or mixture of solvents, first
employed prior to 1974, may have an excess risk of breast cancer.
B .3.1.3.1.3. Study description and comment.
Sung et al. (2007) examined breast cancer incidence among females in a cohort of
electronic workers with employment at one factory in Taoyuan, Taiwan between 1973 and 1992,
date of factory closure, and followed to 2001. Some female subjects in Sung et al. (2007)
overlap those in Chang et al. (2005; 2003) who included workers from the same factory whose
employment dates were between 1978 and 1997, the closing date of the study a date of vital
status ascertainment. A total of 64,000 females were identified with 63,982 in the analysis after
the exclusion of 15 women with <1 full day of employment and three women with cancer
diagnoses prior to the time of first employment; approximately 6,000 fewer female subjects
compared to Chang et al. (2005) (70,735 females). Cancer incidence between 1979 and 2001 as
identified using the National Cancer Registry which contained 80% of all cancer cases in Taiwan
is examined using life table methods with exposure lag periods of 5-15 years, depending on the
cancer site, and cancer rates from the larger Taiwanese population as referent.
Company employment records were lacking and the cohort was constructed using the
Bureau of Labor Insurance database that contained computer records since 1978 and paper
records for the period 1973-1978. Duration of employment was calculated from the beginning
of coverage of labor insurance and is likely an underestimate. Labor insurance hospitalization
data and a United Labor Association list of names were used to verify cohort completeness.
While these sources may have been sufficient to identified current employees, their ability to
identify former employees may be limited, particularly from the hospitalization data if the
subject's current employer was listed.
This study assumes all employees in the factory were exposed to chlorinated organic
solvent vapors and the primary exposure index was duration of employment at the plant. Most
subjects had employment durations of <1 year (65%). Durations of exposure were likely
underestimated as dates of commencement and termination of insurance coverage were
incomplete, 7.5 and 6%, respectively. There is little to no information on chemical usage and
exposure assignment to individual cohort subjects. As reported in Chang et al. (2005; 2003),
records of the Department of Labor Inspection ad Bureau of International Trade, in addition, to
recall of former industrial hygienists were used to identify chemicals used after 1975 in the
plants. No information is available prior to this date.
Sung et al. (2008) presents an analysis of childhood cancer incidence (1979-2001)
among first liveborn singleton births (1978 and 2001) of female subjects employed at the plant
during a period 3 months before and after beginning of pregnancy, an estimate derived by Sung
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et al. (2008) from the date of birth and estimated length of gestation plus 14 days. Sung et al.
(2007) used Poisson regression methods and cancer incidence among first liveborn births of all
other women in Taiwan in the same time to calculate RRs associated with leukemia risk among
exposed offspring. Poisson models were adjusted for maternal age, maternal educational level,
child's sex, and year of birth. A total of 8,506 first born singleton births among 63,982 female
subjects were identified from the Taiwan Birth Registry database, and 11 cancers, including 6
leukemia cases and no brain/CNS cases identified from the National Cancer Registry database.
Overall, these studies do not provide substantial weight for determining whether TCE
may cause increased risk of disease. The lack of TCE-assessment to individual cohort subjects;
grouping cohort subjects with different exposure potential, both to different solvents and
different intensities; and deficiencies in the record system used to construct the cohort introduce
uncertainty.
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Sung T-I, Chen P-C, Lee L J-H, Lin Y-P, Hsieh G-Y, Wang J-D. (2007). Increased standardized incidence ratio of breast
cancer in female electronics workers. BMC Public Health 7:102. http://www.biotnedcentral.com/content/pdf/1471-2458-7-
102.pdf.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of exposure
and control groups and of cases and controls in case-control
studies is adequate
From abstract "This study is to investigate whether women employed in an electronics factory
increases childhood cancer among first live born singletons." This study was not able to evaluate
TCE exposures uniquely.
63,982 females, some who were also subjects were also in cohort of Chang et al. (2005; 2003)
with 70,735 females.
Cohort initially established using labor insurance records (computer records after 1978 and paper
records from 1973 and 1978) in the absence of company records.
Cohort definition dates are not clearly identified. Cohort identified from records covering period
1973 and 1997 with vital status ascertained as of 2001. Factory closed in 1992.
External referents: age-, calendar-, and sex-specific incidence rates of the Taiwanese general
population.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence as ascertained from National (Taiwan) Cancer Registry (80% of all cancers
reported to Registry).
ICD -Oncology, a supplement to ICD-9.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of JEM
and quantitative exposure estimates
All employees assumed to be potentially exposed to chlorinated organic solvent vapors; study
does not assign potential chemical exposures to individual subjects. No information on specific
chemical exposures or intensity. Limited identification of solvents used in manufacturing
process from the period after 1 975 inferred from records of Department of Labor Inspection,
Bureau of International Trade, and former industrial hygienists recall. No information on solvent
usage was available before 1975.
Exposure index defined as duration of exposure which was likely underestimated. 21% of cohort
with >10 yrs duration of employment and 53% with <1 yr duration.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
No information on loss to follow-up. Subject was assumed disease free at end of follow-up if
lacking cancer diagnosis as recorded in the National Cancer Registry.
No, 57% of cohort employed after November 21, 1978.
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CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers of
total cancer incidence studies; numbers of exposed cases
and prevalence of exposure in case-control studies
1,311 cancer cases.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published paper
Documentation of results
Age-, calendar-, and sex-specific incidence rates.
SIR, analyses include a lag period of 5, 10, or 15 yrs since first employment (as indicated by
labor insurance record).
Cancer incidence examined by duration of employment; however, employment durations were
likely underestimates as dates of commencement and termination dates on of insurance coverage
date were incomplete and misclassification bias is likely present.
Inadequate — analyses that do not include a lag are not presented nor discussed in published
or in supplemental documentation.
paper
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Sung T-I, Wang J-D, Chen P-C. (2008). Increased risk of cancer in the offspring of female electronics workers. Reprod
Toxicol25:115-119.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
From abstract "The study was designed to examine whether breast cancer risk in females was
increased, as had been observed in Chang et al. (2005; 2003) in a cohort with earlier employment
dates." This study was not able to evaluate TCE exposure.
1 1 cancers among 8,506 first born singleton births between 1978 and 2001 in 63,982 female subjects
of Sung et al. (2007). Cancers identified from National Cancer Registry and births identified from
Taiwan Birth Registration database.
External referents: cancer incidence among all other first birth singleton births among Taiwanese
females over the same time period.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence as ascertained from National (Taiwan) Cancer Registry (80% of all cancers
reported to Registry).
ICD-Oncology, a supplement to ICD-9, specific leukemia subtypes not identified in paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
All births were among subjects with employment at factory during a period 3 months before and after
beginning of pregnancy. All mothers were assumed potentially exposed to chlorinated organic
solvent vapors; specific solvents are not identified nor assigned to individual subjects. Limited
identification of solvents used in manufacturing process from the period after 1975 inferred from
records of Department of Labor Inspection, Bureau of International Trade, and former industrial
hygienists recall. No information on solvent usage was available before 1975.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
No information on loss to follow-up for females in Sung et al. (2007).
66% of births would have been 16 yrs of age as of 2001, the date cancer incidence ascertainment
ended.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
1 1 cancer cases among 8,506 first born singleton births.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Maternal age, maternal educational level, child's sex, and child's year of birth.
Poisson regression using childhood cancer incidence among all other first live born children in
Taiwan during same time period.
No.
Yes.
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B.3.1.3.2. Chang et al. (2005; 2003).
B .3.1.3.2.1. Chang et al. (2005) abstract.
A retrospective cohort morbidity study based on standardized incidence ratios
(SIRs) was conducted to investigate the possible association between exposure to
chlorinated organic solvents and various types of cancers in an electronic factory.
The cohort of the exposure group was retrieved from the Bureau of Labor
Insurance (BLI) computer database records dating for 1978 through December 31,
1997. Person-year accumulation began on the date of entry to the cohort, or
January 1, 1979 (whichever came later), and ended on the closing date of the
study (December 31, 1997), if alive without contracting any type of cancers, or
the date of death, or the date of the cancer diagnosis. Vital status and cases of
cancer of study subjects were determined from January 1, 1979 to December 31,
1997 by linking cohort data with the National Cancer Registry Database. The
cancer incidence of the general population was used for comparison. After
adjustment for age and calendar year, only SIR for breast cancer in the exposed
female employees were significantly elevated when compared with the Taiwanese
general population, based on the entire cohort without exclusion. The SIR of
female breast cancer also showed a significant trend of period effect, but no
significant dose-response relationship on duration of employment. Although the
total cancer as well as the cancer for the trachea, bronchus[,] and lung for the
entire female cohort was not significantly elevated, trend analysis by calendar-
year interval suggested an upward trend. However, when duration of employment
or latency was taken into consideration, no significantly elevated SIR was found
for any type of cancer in either male or female exposed workers. In particular, the
risk of female breast cancer was not indicated to be increased. No significant
dose-response relationship on duration of employment and secular trend was
found for the above-mentioned cancers. This study provides no evidence that
exposure to chlorinated organic solvents at the electronics factory was associated
with elevated human cancers.
B .3.1.3.2.2. Chang et al. (2003) abstract.
PURPOSE: A retrospective cohort mortality study based on standardized
mortality ratios (SMRs) was conducted to investigate the possible association
between exposure to chlorinated organic solvents and various types of cancer
deaths. METHODS: Vital status and causes of death of study subjects were
determined from January 1, 1985 to December 31, 1997, by linking cohort data
with the National Mortality Database. Person-year accumulation began on the
date of entry to the cohort, or January 1, 1985 (whichever came later), and ended
on the closing date of the study (December 31, 1997), if alive; or the date of
death. RESULTS: This retrospective cohort study examined cancer mortality
among 86,868 workers at an electronics factory in the northern Taiwan. Using
various durations of employment and latency and adjusting for age and calendar
year, no significantly elevated SMR was found for any cancer in either male or
female exposed workers when compared with the general Taiwanese population.
In particular, the risk of female breast cancer was not found to be increased.
B-lll
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Although ovarian cancer suggested an upward trend when analyzed by length of
employment, ovarian cancer risk for the entire female cohort was not elevated.
CONCLUSIONS: It is concluded that this study provided no evidence that
exposure to chlorinated organic solvents was associated with human cancer risk.
B .3.1.3.2.3. Study description and comment.
Both Chang et al. (2003) and Chang et al. (2005) studied a cohort of 86,868 subjects
employed at an electronics factory between 1985 and 1997, and both administrative and
nonadministrative (blue-collar) workers were included in the cohort. Cancer incidence between
1979 and 1997 was presented by Chang et al. (2005) and cancer mortality from 1985 to 1997 in
Chang et al. (2003). The cohort was predominantly composed of females. The factory operated
between 1968 and 1992, and the inclusion in the cohort of subjects after factory closure is
questionable. Incidence was ascertained from the Taiwan National Cancer Registry, which
contains 80% of all cancer cases in Taiwan (Parkin et al., 2002). The factory could be divided
into three plants by manufacturing process: manufacture of television remote controls,
manufacture of solid state and integrated circuit products, and manufacture of printed circuit
boards. Furthermore, a factory waste disposal site was found to have contaminated the
underground water supply of area communities with organic solvents; however, Chang et al.
(2005) does not provide information on possible exposure to factory employees through
ingestion. The analysis of communities adjacent to the factory is described in Lee et al. (2003).
Company employment records were lacking and the cohort was constructed using the
Bureau of Labor Insurance database that contained computer records since 1978. Labor
insurance hospitalization data and a United Labor Association list of names were used to verify
cohort completeness. While these sources may have been sufficient to identified current
employees, their ability to identify former employees may be limited, particularly from the
hospitalization data if the subject's currently employer was listed.
All employees in the factory were assumed with potential exposure to chlorinated organic
solvent vapors with duration of employment at the factory as the exposure surrogate. Subjects
had varying exposure potentials and employment durations of <1 year (65% of cohort in Chang
et al. (2005)). Durations of exposure were likely underestimated as dates of commencement and
termination of insurance coverage were incomplete, 7.5 and 6%, respectively. Three plants
comprised the factory and with different production processes. A wide variety of organic
solvents were used in each process including dichloromethane, toluene, and methyl ethyl
alcohol, used at all three plants, and perchloroethylene, propanol, and DCE, which were used at
one of the three plants (Chang et al., (2005). Records of the Department of Labor Inspection and
Bureau of International Trade, in addition, to recall of former industrial hygienists were used to
identify chemicals used after 1975 in the plants. No information is available prior to this date.
These sources documented the lack of TCE use between 1975 and 1991 and perchloroethylene
was after 1981. No information was available on TCE and perchloroethylene usage during other
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periods. Given the period of documented lack of TCE usage is before the cohort start date of
1978 and factory closure, there is great uncertainty of TCE exposure to cohort subjects.
Overall, both studies are not useful for determining whether TCE may cause increased
risk of disease. The lack of TCE-assessment to individual cohort subjects and uncertainty of
TCE usage in the factory; potential bias likely introduced through missing employment dates;
and, examination of incidence using broad organ-level categories (i.e., lymphatic and
hematopoietic tissue cancer together) decrease the sensitivity of this study for examining TCE
and cancer. Furthermore, few cancers are expected, 1% of the cohort expected with cancer, and
results in low statistical power from the cohort's young average age of 39 years.
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Chang Y-M, Tai C-F, Yang S-C, Lin R, Sung F-C, Shin T-S, Liou S-H. (2005). Cancer Incidence among Workers Potentially
Exposed to Chlorinated Solvents in An Electronics Factory. J Occup Health 47:171-180.
Chang Y-M, Tai C-F, Yang S-C, Chan C-J, S Shin T-S, Lin RS, Liou S-H. (2003). A cohort mortality study of workers
exposed to chlorinated organic solvents in Taiwan. Ann Epidemiol 13:652-660.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
The study was not designed to uniquely evaluate TCE exposure but rather chlorinated solvents
exposures. From abstract: "... to investigate the possible association between chlorinated organic
solvents and various types of cancer in an electronics factory."
This study is quite limited to meet stated hypothesis by the inclusion of all factory employees in the
cohort and lack of exposure assessment on individual study subjects to TCE, specifically, and to
chlorinated solvents, generally.
n = 86,868 in cohort. Cohort initially established using labor insurance records in the absence of
company records.
Cohort definition dates are not clearly identified. Cohort identified from labor insurance records
covering period 1978 and 1997; yet, plant closed in 1992. All subjects followed through 1997.
Paper states cohort was completely identified; however, former workers who were eligible for cohort
membership may not have been identified if validation sources did not identify former employer.
Duration of employment reconstructed from insurance records: -40% of subjects had employment
durations <3 months, 9% employed >5 yrs, 0.7% employed >10 yrs.
External referents: Age-, calendar-, and sex-specific incidence rates of the Taiwanese general
population.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence as ascertained from National (Taiwan) Cancer Registry (80% of all cancers
reported to Registry) (Chans et al., 2005).
Mortality. ICD revision is not identified other than that used in 1981 (Chang et al., 2003).
ICD-Oncolosv, a supplement to ICD-9 (Chans et al., 2005).
ICD, 9th revision was in effect in 1981, but paper does not identify to which ICD revision used to
assign cause of death (Chang et al., 2003).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
All employees assumed to be potentially exposed to chlorinated organic solvent vapors. No
information on specific chemical exposures or intensity. Limited identification of solvents used in
manufacturing process from the period after 1975 inferred from records of Department of Labor
Inspection, Bureau of International Trade, and former industrial hygienists recall. No information on
solvent usage was available before 1975.
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CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
Other
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
No information on loss to follow-up. Subject was assumed disease free at end of follow-up if lacking
cancer diagnosis as recorded in the National Cancer Registry.
Average 16-yr follow-up (incidence) and 12 yrs (mortality).
Subject's age determined by subtracting year of birth from 1997; however, insurance records did not
contain DOB for 6% of subjects. Furthermore, commencement and termination dates were
incomplete on insurance records, 7 and 6%, respectively.
1,031 cancer cases.
1,357 total deaths (1.6% of cohort), 3 16 cancer deaths.
Age-, calendar-, and sex-specific incidence rates (Chang et al., 2005) or age-, calendar-, and sex-
specific mortality rates (Chang et al., 2003).
SIR (Chang et al.. 2005) and SMR (Chang et al.. 2003).
Cancer incidence and mortality examined by duration of employment; however, employment
durations were likely underestimates as dates of commencement and termination dates on of
insurance coverage date were incomplete and calculated from date on insurance records.
Misclassification bias is likely present.
Adequate.
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B.3.1.4. Studies of Other Cohorts
B.3.1.4.1. Clapp and Hoffman (2008).
B.3.1.4.1.1. Author's abstract.
BACKGROUND: In response to concerns expressed by workers at a public
meeting, we analyzed the mortality experience of workers who were employed at
the IBM plant in Endicott, New York and died between 1969 and 2001. An
epidemiologic feasibility assessment indicated potential worker exposure to
several known and suspected carcinogens at this plant. METHODS: We used the
mortality and work history files produced under a court order and used in a
previous mortality analysis. Using publicly available data for the state of New
York as a standard of comparison, we conducted proportional cancer mortality
(PCMR) analysis. RESULTS: The results showed significantly increased
mortality due to melanoma (PCMR = 367; 95% CI: 119, 856) and lymphoma
(PCMR = 220; 95% CI: 101, 419) in males and modestly increased mortality due
to kidney cancer (PCMR = 165; 95% CI: 45, 421) and brain cancer (PCMR =
190; 95% CI: 52, 485) in males and breast cancer (PCMR = 126; 95% CI: 34,
321) in females. CONCLUSION: These results are similar to results from a
previous IBM mortality study and support the need for a full cohort mortality
analysis such as the one being planned by the National Institute for Occupational
Safety and Health.
B .3.1.4.1.2. Study description and comment.
This proportional cancer mortality ratio study of deaths between 1969 and 2001 among
employees at an IBM facility in Endicott, New York, who were included on the IBM Corporate
Mortality File compared the observed number of site-specific cancer deaths are compared to the
expected proportion, adjusted for age, using 10-year rather than 5-year grouping, and sex, of site-
specific cancer deaths among New York residents during 1979 to 1998. Of the 360 deaths
identified of Endicott employees, 115 deaths were due to cancer, 11 of these with unidentified
site of cancer. Resultant PMRs estimates do not appear adjusted for race nor does the paper
identify whether referent rates excluded deaths among New York City residents or are for New
York deaths. The IBM Corporate Mortality File contained names of employees who had worker
>5 years, who were actively employed or receiving retirement or disability benefits at time of
death, or whose family had filed a claim with IBM for death benefits and Endicott plant
employees were identified using worker employment data from the IBM Corporate Employee
Resource Information System. Study investigators had previously obtained the IBM Corporate
Mortality file through a court order and litigation.
The Endicott plant began operations in 1991 and manufactured a variety of products
including calculating machines, typewriters, guns, printers, automated machines, and chip
packaging. The most recent activities were the production of printed circuit boards. It was
estimated from a National Institute of Occupational Safety and Health (NIOSH) feasibility study
that a larger percentage of the plant's employee were potentially exposure to multiple chemicals,
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including asbestos, benzene, cadmium, nickel compounds, vinyl chloride, tetrachloroethylene,
TCE, PCBs, and o-toluidine. Chlorinated solvents were used at the plant until the 1980s. The
study does not assign exposure potential to individual study subjects.
This study provides little information on cancer risk and TCE exposure given its lack of
worker exposure history information and absence of exposure assignment to individual subjects.
Other limitations in this study which reduces interpretation of the observations included
incomplete identification of deaths, the analysis limited to only vested employees or to those
receiving company death benefits, incomplete identification of all employees at the plant, the
inherent limitation of the PMR method and instability of the effect measure particularly in light
of bias resulting of excesses or deficits in deaths, and observed differences in demographic (race)
between subjects and the referent (New York) population.
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Clapp RW, Hoffman K. (2008)
plant. Environ health 7:13.
Cancer mortality in IBM Endicott plant workers, 1969-2001: an update on a NY production
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
From abstract "... In response to concerns expressed by workers at a public meeting, we analyzed the
mortality experience of workers who were employed at the IBM plant in Endicott, New York and died
between 1969 and 2001."
Deaths among IBM workers identified in IBM Corporate Mortality File; workers with >5 yrs
employment, who were actively employed or receiving retirement or disability benefits at time of
death, or whose family had filed a claim with IBM for death benefits. Expected number of site-
specific cancer deaths calculated from proportion of cancer deaths among New York residents. Paper
does not identify if referent included all New York residents or those living upstate.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
ICD9.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
This study lacks exposure information. TCE and other chemicals were used at the factory and
inclusion on the employee list served as a surrogate for TCE exposure of unspecified intensity and
duration.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
Other
Not able to evaluate given inability to identify complete cohort.
Not able to evaluate given lack of work history records.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
360 deaths, 115 due to cancer, between 1969 and 2001.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age and
gender. No information was available on race and PMRs are unadjusted for race.
PMR.
No.
Yes.
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B.3.1.4.2. ATSDR (2004a).
B.3.1.4.2.1. Author's abstract.
The View-Master stereoscopic slide viewer has been a popular children's
toy since the 1950s. For nearly half a century, the sole U.S. manufacturing
site for the View-Master product was a factory located on Hall Boulevard
in Beaverton, Oregon. Throughout this period, an on-site supply well
provided water for industrial purposes and for human consumption. In
March 1998, chemical analysis of the View-Master factory supply well
revealed the presence of the degreasing solvent trichloroethylene (TCE)
at concentrations as high as 1,670 micrograms per liter (fg/L)—the U.S.
Environmental Protection Agency maximum contaminant level is 5 fg/L.
Soon after the contamination was discovered, the View-Master supply well
was shut down. Up to 25,000 people worked at the plant and may have
been exposed to the TCE contamination. In September of 2001, the
Oregon Department of Human Services (ODHS) entered into a
cooperative agreement with the Agency for Toxic Substances and
Disease Registry (ATSDR) to determine both the need for and the
feasibility of an epidemiological study of the View-Master site. In this
report, ODHS compiles the findings of the feasibility investigation of
worker exposure to TCE at the View-Master factory.
On the basis of the levels of TCE found in the supply well, the past use
of the well as a source of drinking water, and the potential for adverse
health effects resulting from past exposure to TCE, ODHS determined that
the site posed a public health hazard to people who worked at or visited
the plant prior to the discovery of the contamination. Because the use of
the View-Master supply well was discontinued when the contamination
was discovered in March 1998, the View-Master supply well does not
pose a current public health hazard. No other drinking water wells tap into
the contaminated aquifer, and the long-term remediation efforts appear to
be containing the contamination.
ATSDR and ODHS obtained a list of 13,700 former plant workers from
the Mattel Corporation. In collaboration with ATSDR, ODHS conducted a
preliminary analysis of mortality and identified excesses in the proportions
of deaths due to kidney cancer and pancreatic cancer among the factory's
former employees. Although this analysis was limited by the lack of
information about the entire worker population and individual exposures to
TCE, the preliminary findings underscore the need to fully investigate the
impact of TCE exposure on the population of former View-Master workers.
The findings of this feasibility investigation are:
• TCE appears to have been the primary contaminant of the drinking
water at the plant;
• Contamination was likely present for a long period of time
(estimated to have been present in the groundwater since the mid-
1960s);
• A large number were likely exposed to the contamination:
• The primary route of exposure (for the last 18 years the factory
operated) was through contaminated drinking water;
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• Levels of TCE contamination were 300 time the maximum
contaminant levels; and
• A significant portion of the former workers of their next of kin can
indeed be located and invited to participate in a public health
evaluation of their exposures.
Therefore, ODHS recommends further investigation to include the
following:
1. A fate and transport assessment to better establish when TCE reached
the supply well, and to provide a historical understanding of the
concentration of TCE in the well, and
2. Epidemiological studies among former workers to determine their
exposure and whether they have experienced adverse health and
reproductive outcomes associated with TCE exposure at the plant, to
determine the mortality experience of the population, and to document
the cancer incidence in this population.
B .3.1.4.2.2. Study description and comment.
This PMR study of deaths between 1995 and 2001 among 13,697 former employees at a
View-Master toy factory in Beaverton, Oregon contains no exposure information on individual
study subjects. The PMR analysis was conducted as a feasibility study for further epidemiologic
investigations of these subjects by Oregon Department of Health on behalf of ATSDR, and
findings have not been published in the peer-reviewed literature. A former plant owner provided
a listing of former employees; however, employees were not identified using IRS records and the
roster was known to be incomplete. Additionally, work history records were not available and
not information was available on employment length or job title. The goal of the feasibility
analysis was to evaluate ability to identify completeness of death identification using several
sources.
Monitoring of a water supply well in March 1998 showed detectable concentrations of
TCE, and this study assumes all subjects had exposure to TCE in drinking water. TCE had been
used in large quantities for metal degreasing at the factory between 1952 and 1980; this activity
mostly occurred in the paint shop located in one building. At the time metal degreasing ceased,
company records suggested historical use of TCE was up to 200 gallons per month. Historical
practices resulted in releases of hazardous substances at the factory site and former employees
reported waste TCE from the degreased was transported to other sites on the premises, and
discharged to the ground (ATSDR, 2004a). Additionally, chemical spills allegedly occurred in
the paint shop and one report in 1964 of an inspection of the degreaser indicated atmospheric
TCE concentrations above occupational limits. TCE was detected at concentrations between
1,220 and 1,670 |ig/L in four water samples and the Oregon Department of Environmental
Quality estimated the well had been contaminated for over 20 years. Other VOCs besides TCE
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detected in the supply well water in March 1998 included cis-l,2-DCE at levels up to 33 |ig/L
and perchloroethylene at concentrations up to 56 |ig/L. The 160-foot-deep supply well was on
the property since original construction in 1950 and it supplied water for drinking, sanitation, fire
fighting, and industrial use. Connection to municipal water supply occurred in 1956; however,
although municipal water was directed to some parts of the plant, the supply well continued to
serve the facility's needs, including most of the drinking and sanitary water (ATSDR, 2003b).
This study provides little information on cancer risk and TCE exposure given the absence
of monitoring data beyond a single time period, absence of estimated TCE concentrations in
drinking water, and exposure pathways other than ingestion. Other limitation in this study which
reduces interpretation of the observations included incomplete identification of employees with
the result of missing deaths likely, the inherent limitation of the PMR method and instability of
the effect measure particularly in light of bias resulting of excesses or deficits in deaths, and
observed differences in demographic (age and male/female ratio) between subjects and the
referent (Oregon) population.
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ATSDR (Agency for Toxic Substances and Disease Registry). (2004a). Feasibility investigation of worker exposure to
trichloroethylene at the View-Master Factory in Beaverton, Oregon. Final Report. Submitted by Environmental and
Occupational Epidemiology, Oregon Department of Human Services. December 2004.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
The goal of this feasibility investigation for a cohort epidemiologic study of former employees at a
plant manufacturing stereoscopic slide viewers examined the ability to identify former employees and
ascertain vital status.
Name of -13,000 former employee names were provided to ATSDR by the former plant owner.
current list of employees was known to be incomplete. The proportion of site-specific mortality
among workers between 1989 and 2001 was compared to the proportion expected using all death
Oregon for a similar time period.
The
in
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
ICD 9 and ICD 10.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
This study lacks actual exposure information; work history records were not available. TCE was
used at the factory and inclusion on the employee list served as a surrogate for TCE exposure of
unspecified intensity and duration.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
Other
Not able to evaluate given inability to identify complete cohort.
Not able to evaluate given lack of work history records.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
616 deaths between 1989 and 2001.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age and
gender. No information was available on race and PMRs are unadjusted for race.
PMR.
No.
Yes.
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B.3.1.4.3. Raaschou-Nielsen et al. (2003).
B.3.1.4.3.1. Author's abstract.
Trichloroethylene is an animal carcinogen with limited evidence of
carcinogenicity in humans. Cancer incidence between 1968 and 1997 was
evaluated in a cohort of 40,049 blue-collar workers in 347 Danish companies with
documented trichloroethylene use. Standardized incidence ratios for total cancer
were 1.1 (95% confidence interval (CI): 1.04, 1.12) in men and 1.2 (95% CI: 1.14,
1.33) in women. For non-Hodgkin's lymphoma and renal cell carcinoma, the
overall standardized incidence ratios were 1.2 (95% CI: 1.0, 1.5) and 1.2 (95% CI:
0.9, 1.5), respectively; standardized incidence ratios increased with duration of
employment, and elevated standardized incidence ratios were limited to workers
first employed before 1980 for non-Hodgkin's lymphoma and before 1970 for
renal cell carcinoma. The standardized incidence ratio for esophageal
adenocarcinoma was 1.8 (95% CI: 1.2, 2.7); the standardized incidence ratio was
higher in companies with the highest probability of trichloroethylene exposure. In
a subcohort of 14,360 presumably highly exposed workers, the standardized
incidence ratios for non-Hodgkin's lymphoma, renal cell carcinoma, and
esophageal adenocarcinoma were 1.5 (95% CI: 1.2, 2.0), 1.4 (95% CI: 1.0, 1.8),
and 1.7 (95% CI: 0.9, 2.9), respectively. The present results and those of previous
studies suggest that occupational exposure to trichloroethylene at past higher
levels may be associated with elevated risk for non-Hodgkin's lymphoma.
Associations between trichloroethylene exposure and other cancers are less
consistent.
B .3.1.4.3.2. Study description and comment.
Raaschous-Nielsen et al. (2003) examined cancer incidence among a cohort of workers
drawn from 347 companies with documented TCE. Almost half of these companies were in the
iron and metal industry. The cohort was identified using the Danish Supplementary Pension
Fund, which includes type of industry of a company and a history of employees, for the years
1964 to 1997. Altogether, 152,726 workers were identified of whom 39,074 were white-collar
and assumed not to have TCE exposure, 56,970 workers were of unknown status, and 56,578
blue-collar workers, of which 40,049 had been employed at the company for >3 months and are
the basis of the analysis. The cohort was relatively young, 56% were 38 to 57 years old at end of
follow-up, and 29% of subjects were older than 57 years of age. Cancer rates typically increase
with increasing ages; thus, the lower age of this cohort likely limits the ability of this study to
fully examine TCE and cancer, particularly cancers that may be associated with aging. Observed
number of site-specific incident cancers are obtained from 4-1-1968 to the end of 1997 and
compared to expected numbers of site-specific cancers based on incidence rates of the Danish
population.
A separate exposure assessment was conducted using regulatory agency data from 1947
to 1989 (Raaschou-Nielsen et al., 2002). This assessment identified three factors as increasing
potential for TCE exposure, duration of employment, year of first employment, and number of
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employees, to increase the likelihood of cohort subjects as TCE exposed. The percentage of
exposed workers was found to decrease as company size increased: 81% for <50 workers, 51%
for 50-100 workers, 19% for 100-200 workers, and 10% for >200 workers. About 40% of the
workers in the cohort were exposed (working in a room where TCE was used). Smaller
companies had higher exposures. Median exposures to TCE were 40-60 ppm for the years
before 1970, 10-20 ppm for 1970-1979, and approximately 4 ppm for 1980-1989. Additionally,
an assessment of TCA concentrations in urine of Danish workers suggested a similar trend over
time; mean concentrations of 58 mg/L for the period between 1960 and 1964 and 14 mg/L in
sample taken between 1980 and 1985 (Raaschou-Nielsen et al., 2001).
Only a small fraction of the cohort was exposed to TCE. The highest exposures occurred
before 1970 at period in which 21.2% of blue-collar workers had begun employment in a
TCE-using company. The iron and metal industry doing degreasing and cleaning with TCE had
the highest exposures, with a median concentration of 60 ppm and a range up to about 600 ppm.
Overall, strengths of this study include its large numbers of subjects; however, the younger age
of the cohort and the small fraction expected with TCE exposure limit the ability of the study to
provide information on cancer risk and TCE exposure. For these reasons, positive associations
observed in this study are noteworthy.
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Raaschou-Nielsen O, Hansen J, McLaughlin JK, Kolstad H, Christensen JM, Tarone RE, Olsen JH. (2003). Cancer risk
among workers at Danish companies using trichloroethylene: a cohort study. Am J Epidemiol 158:1182-1192.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls in
case-control studies is adequate
This study was designed to evaluate associations observed in Hansen et al. (2001) with TCE
exposure and NHL, esophageal adenocarcinoma, cervical cancer, and liver-biliary tract cancer.
Cohort of 40,049 blue-collar workers employed in 1968 or after with >3 months employment duration
identified by linking 347 companies, who were considered as having a high likelihood for TCE
exposure, with the Danish Supplementary Pension Fund to identify employees and with Danish
Central Population Registry.
External referents are age-, sex-, calendar year-, and site-specific cancer incidence rates of the Danish
population.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence between 4-1-1968 and 12-3 1-1997 as identified from records of Danish Cancer
Registry.
ICD, 7th revision.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Qualitative exposure assessment. A previous industrial hygiene survey of Danish companies
identified several characteristics increase likelihood of TCE exposure-duration of employment, year of
1st employment, and number of employees in company (Raaschou-Nielsen et al., 2002).
Exposure index defined as duration of employment.
Median exposures to TCE were 40-60 ppm for the years before 1970, 10-20 ppm for 1970-1979,
and approximately 4 ppm for 1980-1989. Additionally, an assessment of TCA concentrations in
urine of Danish workers suggested a similar trend over time; mean concentrations of 58 mg/L
for the period between 1960 and 1964 and 14 mg/L in sample taken between 1980 and 1985
(Raaschou-Nielsen et al., 2001).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
Danish Cancer Registry is considered to have a high degree of reporting and accurate cancer
diagnoses.
Yes, average follow-up was 18 yrs.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
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CATEGORY F: PROXY RESPONDENTS
> 10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
3.244 cancers (8% of cohort had developed a cancer over the period from 1968 to 1997).
a large number of subjects, this cohort is of a young age, 29% of cohort was >57 yrs of a|
follow-up.
Although of
l& at end of
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published paper
Documentation of results
Age, sex, and calendar year.
SIR using life-table analysis.
Yes, duration of employment.
Adequate.
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B.3.1.4.4. Ritz(1999a).
B.3.1.4.4.1. Author's abstract.
Data provided by the Comprehensive Epidemiology Data Resource allowed us to
study patterns of cancer mortality as experience by 3814 uranium-processing
workers employed at the Fernald Feed Materials Production Center in Fernald,
Ohio. Using risk-set analyses for cohorts, we estimated the effects of exposure to
trichloroethylene, cutting fluids, and kerosene on cancer mortality. Our results
suggest that workers who were exposed to trichloroethylene experienced an
increase in mortality from cancers of the liver. Cutting-fluid exposure was found
to be strongly associated with laryngeal cancers and, furthermore, with brain,
hemato- and lymphopoietic system, bladder, and kidney cancer mortality.
Kerosene exposure increased the rate of death from several digestive-tract cancers
(esophageal, stomach, pancreatic, colon, and rectal cancers) and from prostate
cancer. Effect estimates for these cancers increased with duration and level of
exposure and were stronger when exposure was lagged.
B .3.1.4.4.2. Study description and comment.
This study of 3,814 white male uranium processing workers employed for at least 3
months between 1-1-1951 and 12-31-1972 at the Fernald Feed Materials Production Center in
Fernald, Ohio, was of deaths as of 1-1-1990. Subjects were part of a larger cohort study of
Fernald workers with potential uranium and products of uranium decay exposures that observed
associations with lung cancer and lymphatic/hematopoietic cancer (Ritz, 1999b). Average length
of follow-up time was 31.5 years. During this period, 1,045 deaths were observed with expected
numbers of deaths based upon age- and calendar-specific U.S. white male mortality rates and
age- and calendar-specific white male mortality rates from the NIOSH Computerized
Occupational Referent Population System (CORPS) (Zahm, 1992). Internal analyses based upon
risk-set sampling and Cox proportional hazards modeling compared workers with differing
exposure intensity rankings (light and moderate) and a category for no- TCE exposure/<2 year
duration TCE exposure.
Fernald produced uranium metal products for defense programs (Hornung et al., 2008).
Subjects had potential exposures to uranium, mainly as insoluble compounds and varying from
depleted to slight enriched, small amounts of thorium, an alpha particle emitter, respiratory
irritants such as tributyl phosphate, ammonium hydroxide, sulfuric acid, and hydrogen fluoride,
TCE, and cutting fluids (Ritz, 1999a, b). Exposure assessment for analysis of chemical
exposures utilized a JEM to assign intensity of TCE, cutting fluids, and kerosene to individual
jobs from the period 1952-1977. Industrial hygienists, a plant foreman, and an engineer during
the late 1970s and early 1980s determined the likelihood of exposure to TCE, cutting fluids, and
kerosene for each job title and plant area. Based on work records, the workforce appeared stable
and 54% were employed >5 years and had held only one job title during employment. Both
intensity or exposure level and duration of exposure in years were used to rank subjects into four
B-129
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categories of no exposure (level 0), light exposure (level 1), moderate exposure (level 2), and
heavy exposure (level 3). Seventy eight percent of the cohort was identified with some potential
for TCE exposure, 2,792 subjects were identified with low TCE exposure (94%), 179 with
moderate exposure (6%), and no subjects were identified with heavy TCE exposure. TCE
exposure was highly correlated with other chemical exposures and with alpha radiation (Hornung
et al., 2008; Ritz, 1999a, b). Fernald subjects had higher exposures to radiation compared to
those of radiation-exposed Rocketdyne workers (Ritz et al.. 2000: Ritz. 1999b: Ritzetal.. 1999).
Atmospheric monitoring information is lacking on TCE exposure conditions as is information on
changes in TCE usage over time. The cohort was identified from company rosters and personnel
records and it is not known whether these were sources for a subject's job title information.
Analysis of TCE exposure carried out using conditional logistic regression adjusting for pay
status, time since first hired, external and internal radiation dose, and previous chemical
exposure. Relative risks for TCE exposure are also presented with a lag time period of 15 years.
Overall, strengths of this study are the long follow-up time and a large percentage of the
cohort who had died by the end of follow-up. TCE exposure intensity is low in this cohort, 94%
of TCE exposed subjects were identified with "light" exposure intensity, and all subjects had
potential for radiation exposure, which was highly correlated with chemical exposures. No
information is presented on the definition of "light" exposure and monitoring data are lacking.
Only 179 subjects were identified with TCE exposure above "light" and the number of cancer
deaths not presented. The published paper reported limited information on site-specific cancer
and TCE exposure; risk estimates are reported for lymphatic and hematopoietic cancers,
esophageal and stomach cancer, liver cancer, prostate cancer and brain cancer. Risk estimates
for bladder and kidney cancer and TCE exposure are found in NRC (2006). Few deaths were
observed with moderate TCE exposure and exposure durations of >2 years: one death due to
lymphatic and hematopoietic cancer, no deaths due to kidney or bladder cancer (as noted in NRC
(2006)), and two liver cancer deaths among these subjects. Low statistical power reflecting few
cases with moderate TCE exposure and multicollinearity of chemical and radiation exposures
greatly limits the support that this study provides in an overall weight-of-evidence analysis.
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Ritz B. (1999a). Cancer mortality among workers exposed to chemicals during uranium processing. J Occup Environ Med
41:556-566.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
The hypothesis in this study was to examine the influence of chemical exposures in the work
environment of the Fernald Feed Materials Production Center (FFMPC) in Fernald, Ohio, on cancer
mortality with a focus on the effects of TCE, cutting fluids, and a combination of kerosene exposure
with carbon (graphite) and other solvents.
3,814 white male subjects identified from company rosters and personnel records, hired between 1951
and 1972 and who were employed continuously for 3 months and monitored for radiation. 2,971
subjects identified as exposed to TCE at "light" and "moderate" exposures. Subjects were identified
in a previous study of cancer mortality and radiation exposure and most subjects had radiation
exposures above 10+mSV(Ritz, 1999b).
External analysis: U.S. white male mortality rates and NIOSH-Computerized Occupational Referent
Population System mortality rates.
Internal analysis: cohort subjects according to level and duration of chemical exposure.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
Vital status searched through Social Security Administration records, before 1979, and National Death
Index for the period 1979-1989.
External analysis: ICDA, 8th revision.
Internal analysis: aggregation of several subsite causes of deaths into larger categories based on ICD,
9th revision.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Semiquantitative approach and development of JEM. JEM developed by expert assessment by plant
employees to classify jobs into four levels of chemical exposures for the period 1952 to 1977.
Intensity using the four-level scale and duration of exposure to TCE, cutting fluids and kerosene were
assigned to individual cohort subjects using JEM. 73% of cohort identified as TCE exposed
(2,971 male with TCE exposure in cohort of 3,814 subjects). Only 4% of TCE -exposed subjects with
exposure identified as "moderate" and no subjects with "high" exposure. High correlation between
TCE and other chemical exposure and radiation exposure.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
Other
All workers without death certificate assumed alive at end of follow-up.
Average follow-up time, 3 1.5 yrs.
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CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
1,045 deaths (27% of cohort), 328 due to cancer. No information on number of all -cancer deaths
among TCE exposed subjects, although reported numbers for specific sites reported by Ritz (1999a)
or NRC (2006): >2-yr exposure duration, hemato- and lymphopoietic cancer (n = 18 with light
exposure, 1 with moderate exposure), esophageal and stomach cancer (n = 15 with light exposure,
0 with moderate exposure), liver cancers (n = 3 with light exposure, 1 with moderate exposure),
kidney and bladder cancers, (n = 7 with light exposure, 0 with moderate exposure) prostate cancers
(n = 10 with light exposure, 1 with moderate exposure), and brain cancers (n = 6 with light exposure,
1 with moderate exposure).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
External analysis: age- and calendar-specific mortality rates for white males.
Internal analysis: pay status, time since first hired, and cumulative time-dependent external- and
internal-radiation doses (continuous); indirect assessment of smoking through examination of smoking
distribution by chemical exposure.
SMR (external analysis) and RR (internal analysis).
Yes, RR presented for exposure to TCE (level 1 and level 2, separately) by duration of exposure.
Adequate.
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B.3.1.4.5. Henschler et al. (1995).
B.3.1.4.5.1. Author's abstract.
A retrospective cohort study was carried out in a cardboard factory in Germany to
investigate the association between exposure to trichloroethene (TRI) and renal
cell cancer. The study group consisted of 169 men who had been exposed to TRI
for at least 1 year between 1956 and 1975. The average observation period was
34 years. By the closing day of the study (December 31, 1992) 50 members of the
cohort had died, 16 from malignant neoplasms. In 2 out of these 16 cases, kidney
cancer was the cause of death, which leads to a standard mortality ratio of
3.28 compared with the local population. Five workers had been diagnosed with
kidney cancer: four with renal cell cancers and one with an urothelial cancer of
the renal pelvis. The standardized incidence ratio compared with the data of the
Danish cancer registry was 7.97 (95% CI: 2.59-18.59). After the end of the
observation period, two additional kidney tumors (one renal cell and one
urothelial cancer) were diagnosed in the study group. The control group consisted
of 190 unexposed workers in the same plant. By the closing day of the study
52 members of this cohort had died, 16 from malignant neoplasms, but none from
kidney cancer. No case of kidney cancer was diagnosed in the control group. The
direct comparison of the incidence on renal cell cancer shows a statistically
significant increased risk in the cohort of exposed workers. Hence, in all types of
analysis the incidence of kidney cancer is statistically elevated among workers
exposed to TRI. Our data suggest that exposure to high concentrations of TRI
over prolonged periods of time may cause renal tumors in humans. A causal
relationship is supported by the identity of tumors produced in rats and a valid
mechanistic explanation on the molecular level.
B .3.1.4.5.2. Study description and comment.
This was a cohort study of workers in a cardboard factory in the area of Arnsberg,
Germany. TCE was used in this area until 1975 for degreasing and solvent needs. Plant records
indicated that 2,800-23,000 L/year was used. Small amounts of tetrachloroethylene and 1,1,1-
trichloroethane were used occasionally, but in much smaller quantities than TCE. TCE was used
in three main areas: cardboard machine, locksmith's area, and electrical workshop. Cleaning the
felts and sieves and cleaning machine parts of grease were done regularly every 2 weeks, in a job
that required 4-5 hours, plus whatever additional cleaning was needed. TCE was available in
open barrels and rags soaked in it were used for cleaning. The machines ran hot (80-120°C) and
the cardboard machine rooms were poorly ventilated and warm (about 50°C), which would
strongly enhance evaporation. This would lead to very high concentrations of airborne TCE.
Cherrie et al. (2001) estimated that the machine cleaning exposures to TCE were >2,000 ppm.
Workers reported frequent strong odors and a sweet taste in their mouths. The odor threshold for
TCE is listed as 100 ppm (ATSDR, 1997c). Workers often left the work area for short breaks "to
get fresh air and to recover from drowsiness and headaches." Based on reports of anesthetic
effects, it is likely that concentrations of TCE exceeded 200 ppm (Stopps and McLaughlin,
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1967). Those reports, the work setting description, and the large volume of TCE used are all
consistent with very high concentrations of airborne TCE. The workers in the locksmith's area
and the electrical workshop also had continuous exposures to TCE associated with degreasing
activities; parts were cleaned in cold dip baths and left on tables to dry. TCE was regularly used
to clean floors, work clothes, and hands of grease, in addition to the intense exposures during
specific cleaning exercises, which would produce a background concentration of TCE in the
facility. Cherrie et al. (2001) estimated the long-term exposure to TCE was approximately 100
ppm.
The subjects in this study clearly had substantial peak exposures to TCE that exceeded
2,000 ppm and probably sustained long-term exposures >100 ppm, which are not confounded by
concurrent exposures to other chlorinated organic solvents.
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Henschler D, Vamvakas S, Lammert M, Dekant W, Kraus B, Thomas B, Ulm K. (1995). Increased incidence of renal cell
tumors in a cohort of cardboard workers exposed to trichloroethene. Arch Toxicol 69:291-299.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
From abstract "... retrospective cohort study was carried out in a cardboard factory I Germany to
investigate the association between exposure to trichloroethene and renal cell cancer."
Employee records were used to identify 183 males employed in a cardboard factory for at least 1 yr
between 1956 and 1975 and with presumed TCE exposure and a control group of 190 male workers at
same factory during the same period of time but in jobs not involving possible TCE exposure.
Mortality rates from German population residing near factory used as referent in mortality analysis.
Renal cancer incidence rates from Danish Cancer Registry used to calculate expected number of
incident cancer. The age-standardized rate in the late 1990s among men in Denmark was 10.6 per
100,000 and in Germany, it was 1.2 per 100,000 (Ferlav etal.. 2004). If these differences in rates
apply when the study was carried out, this would imply that the expect number of deaths would have
been inflated by about 14% (and the rate ratio underestimated by that amount).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Mortality and renal cell cancer incidence.
CATEGORY C: TCE-EXPOSURE CRITERIA
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
ICD-9 for deaths.
Hospital pathology records were used to verify diagnosis of RCC.
Walkthrough survey and interviews with long-term employees were used to identify work areas and
jobs with potential TCE exposure. The workers in the locksmith's area and the electrical workshop
also had continuous exposures to TCE associated with degreasing activities; parts were cleaned in
cold dip baths and left on tables to dry. Cherrie et al. (2001) estimated that the machine cleaning
exposures to TCE were >2,000 ppm with average long-term exposure as 10-225 ppm.
Estimated average chronic exposure to TCE was ~100 ppm to subjects using TCE in cold
degreasing processes.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
14 exposed subjects (8%) were excluded from life-table analysis and no information is presented in
paper on loss-to-follow-up among control subjects.
Median follow-up period was over 30 yrs for both exposed and control subjects.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
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Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
50 total deaths (30%) and 15 cancer death among exposed subjects.
52 deaths (27%) and 15 cancer deaths among control subjects.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age and calendar-year.
SMR and SIR. Analysis excludes person-years of subjects excluded from exposed population with
the number of person-years underestimated and an underestimate of the expected numbers of deaths
and incident renal carcinoma cases.
No.
Adequate.
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B.3.1.4.6. Greenland et al. (1994).
B.3.1.4.6.1. Author's abstract.
To address earlier reports of excess cancer mortality associated with employment
at a large transformer manufacturing plant each plant operation was rated for
seven exposures: Pyranol (a mixture of poly chlorinated biphenyls and
trichlorobenzene), trichloroethylene, benzene, mixed solvents, asbestos, synthetic
resins, and machining fluids. Site-specific cancer deaths among active or retired
employees were cases; controls were selected from deaths (primarily
cardiovascular deaths) presumed to be unassociated with any of the study
exposures. Using job records, we then computed person-years of exposure for
each subject. All subjects were white males. The only unequivocal association
was that of resin systems with lung cancer (odds ratio = 2.2 at 16.6 years of
exposure, P = 0.0001, in a multiple logistic regression including asbestos, age,
year of death, and year of hire). Certain other odds ratios appeared larger, but no
other association was so robust and remained as distinct after considering the
multiplicity of comparisons. Study power was very limited for most associations,
and several biases may have affected our results. Nevertheless, further
investigation of synthetic resin systems of the type used in the study plant appears
warranted.
B .3.1.4.6.2. Study description and comment.
This nested case-control study at General Electric's Pittsfield, Massachusetts, plant was
of deaths reported to the GE pension fund among employees vested in the pension fund. The
cohort from which cases and controls were identified was defined as plant employees who
worked at the facility before 1984; whose date of deaths was between 1969, the date pension
records became available, and 1984; and existence of a job history record. The size of the
underlying employee cohort was unknown because work history records did not exist for a large
fraction of former employees, especially in the earlier years of deaths. All deaths were identified
from records maintained by GE's pension office; other record sources such as the Social Security
Administration and National Death Index were not utilized. Requirements for eligibility or
"vestment" for a pension varied over time, but for most of the study period, required 10-15 years
employment with the company. The analysis was restricted to white males because of few
deaths among females and nonwhite males. A total of 1,911 deaths were identified from pension
records and cases and controls, with 90 deaths excluded as possible cases and controls due to
several reasons. Cases were identified as site-specific deaths and controls were selected from the
remaining noncancer deaths due to circulatory disease, respiratory disease, injury, and other
causes. No information was available on the number of controls selected per case. Controls
were not matched to cases, were slightly older than cases, and were from earlier birth cohorts,
which have a lower job history availability or greater frequency of missing exposure ratings in
work history records (Salvan, 1990). Statistical analysis of the data included covariates for age
and year of death.
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The company's job history record served as the source for exposure rating. The JEM
linked possible exposures to over 1,000 job title from 50 separate departments and 100 buildings.
A categorical ranking was developed for exposure to seven exposures (Pyranol, TCE, benzene,
other solvents, asbestos, resin systems, machining fluids) from 1901 to 1984 based upon on-site
interviews with 18 long-term employees and knowledge of one of the study investigators who
was an industrial hygienist. Two categories were used for potential TCE exposure: Level 1,
duration of indirect exposure (TCE in workplace but does not work directly with TCE) and
Level 2, duration of direct work with TCE, with the continuous exposure scores rescaled to the
97th percentile of controls (Salvan, 1990). Statistical analyses in Greenland et al. (1994)
collapsed these two categories into a dichotomous ranking of no exposure or any exposure. In
many instances, exposure levels were inaccurately estimated and some exposures were highly
correlated (Salvan, 1990). Although of low correlation, TCE exposure was statistically
significantly correlated with exposure to other solvents (r = 0.11), benzene (r = 0.22) and
machining fluids (r = 0.28) (Salvan, 1990). Industrial hygiene monitoring data were not
available before 1978 and limited production and purchase records did not extend far back in
time (Salvan, 1990). TCE was used as a degreaser since the 1930s and discontinued between
1966 and 1975, depending on department. In all, fewer than 10% of jobs were identified as have
TCE exposure potential, primarily through indirect exposure and not directly working with TCE.
In fact, few subjects were identified with as working directly with TCE (Salvan, 1990). It is not
surprising that exposure score distributions were highly skewed towards zero (Salvan, 1990). No
details were provided on the protocol for processing the jobs in the work histories into job
classifications.
Job history information was missing for roughly 35% of the cases and controls,
particularly from subjects with earlier years of death. The highest percentage of missing
information among cases was for leukemia deaths (43% of deaths) and the lowest percentage for
rectal deaths (11%). Moreover, work history records did not exist for a large fraction of former
employees, especially in the earlier years of death. Bias resulting from exposure
misclassification is likely high due to the lack of industrial monitoring to support rankings and
the inability of the JEM to account for changes in TCE exposure concentrations over time.
This study had a number of weaknesses with the likely result of dampening observed
risks. Deaths were underestimated given nonpensioned employees are not included in the
analysis; possible differences in exposure potential between pensioned and nonpensioned
workers may introduce bias, particularly if a subject leaves work as a consequence of a
precondition related to exposure, and would dampen observed associations (Robins, 1987).
Misclassification bias related to exposure is highly likely given missing job history records for
over one-third of deaths, mostly among deaths from the earlier study period, a period when TCE
was used. Salvan (1990) noted "exposure measurements should be regarded as heavily
nondifferentially misclassified relative to the true exposure does" and exposure associations with
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outcomes will be underestimated. For TCE specifically, the development of exposure
assignments in this study was insensitivity to define TCE exposures of the cohort-industrial
hygiene data were not available for the time period of TCE use, exposure rates applied to a job-
building-operation time matrix and may not reflect individual variation, and exposure ratings
obtained by employee interview are subject to subjective assessment and measurement error.
NRC (2006) also noted a low likelihood of exposure potential to subjects in this nested case-
control study. Last, the lymphoma category includes Hodgkin lymphoma, in addition to
traditional NHL forms such as reticulosarcoma and lymphosarcoma. Overall, the sensitivity of
this study for evaluating cancer and TCE exposure is quite limited. The inability of this study to
detect associations for two known human carcinogens, benzene and leukemia and asbestos and
lung cancer, provides ancillary support for the study's low sensitivity and statistical power.
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Greenland S, Salvan A, Wegman DH, Hallock MF, Smith TH. (1994). A case-control study of cancer mortality at the
transformer-assembly facility. Int Arch Occup Environ Health 66:49-54.
Greenland S. (1992). A semi-Bayes approach to the analysis of correlated multiple associations with an application to an
occupational cancer-mortality study. Stat Med 11:219-230.
Salvan A. (1990). Occupational exposure and cancer mortality at an electrical manufacturing plant: A case-control study.
Ph.D. Dissertation, University of California, Los Angeles.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
The study was carried out to reevaluate an earlier observation from a PMR study of GE employment
and excess leukemia and colorectal cancer risks.
Selection of cases and controls is not adequate because only deaths among pensioned workers were
included in the analysis. Also, the size of the underlying cohort was not known and potential for
selection bias is likely given cases and controls are drawn from a select population.
Cases were identified from deaths among white males employed before 1984, who had died between
1969 and 1984, and for whom a job history record was available. Controls selected from noncancer
deaths due to cardiovascular disease, circulatory disease, respiratory disease, injury, or other causes.
Controls are not matched to cases on covariates such as age, or date of hire.
In total, 2,653 subjects were identified as meeting criteria for inclusion in subject, either as a case or
as a control. Job history records were available for 1,714 (512 cases, 1,202 controls) of these subjects
(65%).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Mortality.
CATEGORY C: TCE-EXPOSURE CRITERIA
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
ICD, 8th revision. Lymphomas, Codes 200-202 and includes Hodgkin lymphoma.
Dichotomous ranking, not exposed/exposed, for indirect and direct exposure potential. Most subjects
identified with indirect TCE exposure. The company's job history record served as the source for
exposure rating. The JEM linked possible exposures to over 1,000 job title from 50 separate
departments and 100 buildings. Potential TCE exposure assigned to 10% of all job titles. The seven
exposures were highly correlated. NRC (2006) noted a low likelihood of TCE exposure potential to
subjects in this nested case-control study.
CATEGORY D: FOLLOW-UP (COHORT)
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More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Record study.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
220 of 732 cases and 1,202 or 1,921 possible controls had job history records; job
missing for 35% of all possible cases and controls.
Any potential TCE exposure prevalence among cases:
Laryngeal, pharyngeal cancer, 38%
Liver and biliary passages, 22%
Pancreas, 45%
Lung, 33%
Bladder, 30%
Kidney, 33%
Lymphoma, 27%
Leukemias, 36%
Brain, 31%
Control exposure prevalence, 34%.
Age and year of death. Other unidentified covariates are included if risk estimate
Logistic regression with: (1) dichotomous exposure (Greenland et al., 1994);
(Salvan, 1990); and (3) empirical Bayes models (Greenland, 1992).
history records are
is altered by >20%.
(2) epoch analysis
No.
Adequate.
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B .3.1.4.7. Sinks et al. (1992).
B.3.1.4.7.1. Author's abstract.
A physician's alert prompted us to investigate workers' can cancer risk at a
paperboard printing manufacturer. We conducted a retrospective cohort mortality
study of all 2,050 persons who had worked at the facility for more than 1 day,
calculated standardized incidence ratios (SIRs) for bladder and renal cell cancer,
and conducted a nested case-control study for renal cell cancer. Standardized
mortality ratios (SMRs) from all causes [SMR = 1.0, 95% confidence interval
(CI) = 0.9 - 1.2] and all cancers (SMR = 0.6, 95% CI = 0.3 - 1.0) were not greater
than expected. One bladder cancer and one renal cell cancer were included in the
mortality analysis. Six incident renal cell cancers were observed, however,
compared with less than two renal cell cancers expected (SIR = 3.7, 95% CI = 1.4
- 8.1). Based on a nested case-control analysis, the risk of renal cell carcinoma
was associated with overall length of employment but was not limited to any
single department or work process. Although pigments containing congeners of
dichlorobenzidine and o-toluidine had been used at the plant, environmental
sampling could not confirm any current exposure. Several limitations and a
potential selection bias limit the inferences that can be drawn.
B .3.1.4.7.2. Study description and comment.
Sinks et al. (1992) is the published report of analyses examining morbidity and mortality
among employees at a James River Corporation plant in Newnan, Georgia. This plant
manufactured paperboard (cardboard) packaging. The study was carried out as a NIOSH, Health
Hazard Evaluation to investigate a possible cluster of urinary tract cancers and work in the
plant's Finishing Department (NIOSH, 1992)\. A cohort of 2,050 white and nonwhite, male and
female, subjects were identified from company personnel and death records, considered
complete since 1-1-1957, and were follows for site-specific mortality and cancer morbidity to 6-
30-1988. Records of an additionally 36 subjects were missing hire dates or birth dates, indicated
employment duration of <1 day, and or employment outside the study period and these subjects
were excluded from the analysis. This study suffers from missing information. A large
percentage of personnel records did not identify a subject's race and these subjects were
considered as white in statistical analyses. Additionally, vital status was unknown for
approximately 10% of the cohort. Life-table analyses are based upon U.S. population age-, race-
, sex-, calendar- and cause-specific mortality rates. Expected numbers of incident bladder and
kidney cancers for white males were derived using white male age-specific bladder and renal cell
incidence rates from the Atlanta-SEER registry for the years 1973-1977.
A nested case-control analysis of the incident renal carcinoma cases was also undertaken.
This analysis is based on 6 RCC cases and 48 controls (1:8 matching) who were selected by risk
set sampling of all employees born within 5 years of the case, the same sex as the case, and
having attained the age at which the case was diagnosed or died if date of diagnosis was not
known. A diagnosis of renal carcinoma was confirmed for four of the six cases through
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pathologic examination. Both the nested case-control analysis and the life-table analyses of
morbidity included a renal carcinoma case from the original cluster.
Exposures are poorly defined in this study assessing renal cancer among paper board
printing workers. TCE was mentioned in material-safety data sheets for one or more materials
used by the process but no information was provided regarding TCE usage and use by job title.
It was not possible to assess the degree of contact with TCE or the printing inks which were
identified as containing benzidine. Furthermore, the lack of monitoring data precludes
evaluation of possible exposure intensity. This study is limited for assessing risks associated
with exposures to TCE due to the large percentage of missing information and due to its
exposure assessment approach.
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Sinks T, Lushniak B, Haussler BJ, Sniezek J, Deng J-F, Roper P, Dill P, Coates R.
paperboard printing workers. Epidemiol 3:483-489.
Renal cell cancer among
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
The purpose of the cohort and nested case-control investigations was to determine whether an excess
of bladder or renal cell cancer had occurred among workers in a paperboard packaging plant and, if
so, to determine whether it was associated with any specific exposure or work-related process.
Cohort analysis: 2,050 males and females employed at the plant between 1-1-1957 and 6-30-1988.
External referents for mortality analysis were age-, sex-, race-, and calendar- cause specific mortality
rates of the U.S. population. External referents for morbidity analysis were age-specific bladder and
renal-cell cancer rate for white males from the Atlanta-SEER registry for the years 1973-1977.
Nested case-control analysis: Cases were all subjects with renal cell cancer; eight non-RCC controls
chosen from a risk set of all employees matched to case on date of birth (within 5 yrs), sex and
attained age of cancer diagnosis or death, if diagnosis date unknown.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
ICD revision in effect at the time of death; incident cases of RCC diagnoses confirmed with pathology
reports for four of six cases.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Exposure in cohort analysis defined broadly at level of the plant and, in case-control study,
department worked as identified on company's personnel.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
Yes, 10% of cohort with unknown vital status (n = 204).
P-Y for these workers were censored at the date of last follow-up.
18-yr average follow-up.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Department assignment based on company personnel records.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
141 total deaths (7% of cohort had died by end of follow-up), 16 cancer deaths.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Mortality analysis: Age, race, sex, and calendar year.
Morbidity analysis limited to white males: age.
Nested case-control analysis: Risk set sampling matching controls to
5 yrs), sex, and attained age at diagnosis.
cases on date of birth (within
SIR.
Conditional logistic regression used for nested case-control analysis.
No.
Adequate.
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B.3.1.4.8. Blair et al. (1989).
B.3.1.4.8.1. Author's abstract.
Work history records and fitness reports were obtained for 1767 marine inspectors
of the U.S. Coast Guard between 1942 and 1970 and for a comparison group of
1914 officers who had never been marine inspectors. Potential exposure to
chemicals was assessed by one of the authors (RP), who is knowledgeable about
marine inspection duties. Marine inspectors and noninspectors had a deficit in
overall mortality compared to that expected from the general U.S. population
(standardized mortality ratios [SMRs = 79 and 63, respectively]). Deficits
occurred for most major causes of death, including infectious and parasitic
diseases, digestive and urinary systems, and accidents. Marine inspectors had
excesses of cirrhosis of the liver (SMR = 136) and motor vehicle accidents
(SMR = 107, and cancers of the lymphatic and hematopoietic system (SMR =
157, whereas noninspectors had deficits for these causes of death. Comparison of
mortality rates directly adjusted to the age distribution of the inspectors and
noninspectors combined also demonstrated that mortality for these causes of death
was greater among inspectors than noninspectors (directly adjusted ratio ratios of
190, 145, and 198) for cirrhosis of the liver, motor vehicle accidents, and
lymphatic and hematopoietic system cancer, respectively. The SMRs rose
with increasing probability of exposure to chemicals for motor vehicle accidents,
cirrhosis of the liver, liver cancer, and leukemia, which suggests that contact with
chemicals during inspection of merchant vessels may be involved in the
development of these diseases among marine inspectors.
B .3.1.4.8.2. Study description and comment.
This cohort of 1,767 U.S. Coast Guard male officers and enlisted personnel performing
marine inspection duties between 1942 and 1970 and 1,914 noninspectors matched to inspectors
for registry, rank, and year that rank was achieved examined mortality as of January 1, 1980.
Standardized mortality ratios compared the observed number of site-specific deaths among
marine inspectors (n = 483, 27%) to that expected of the total U.S. white male population and to
standardized mortality ratios of noninspectors (n = 369, 19%). The cohort was predominantly
white (91%), race was unknown for the remaining 8% of subjects, considered in the statistical
analysis as white, with a large percentage (69%) of the marine inspectors having >20-year
employment duration. The minimum latent period was 10 years, calculated from the end date of
cohort identification to the date of vital status ascertainment.
This study lacks exposure information on potential exposures of marine inspectors, who
enter cargo tanks, void spaces, cofferdams, and pump rooms during inspections. TCE is
identified in the paper as a possible exposure along with nine other agents. One authors
acquainted with Coast Guard processes estimated the level of exposure to general chemical
exposures during a marine inspection. A four-point rating scales was developed: nonexposed,
person generally held administrative position; low exposed, assigned to staff with duties that
occasionally required vessel inspections; moderate exposed, assigned to inspection duties that
B-146
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did not regularly include hull structures, and regular inspection of hull structures in geographic
areas where chemicals were not major items of cargo; and high exposed, assigned to subjects
who performed hull inspections at ports were vessels transported chemicals. A cumulative
exposure score was calculated by summing the product of the four-point rating scale and the
duration in each job.
Overall, the exposure assessment in this study is insufficient for examining TCE
exposure and cancer mortality. Furthermore, the few site-specific deaths among marine
inspectors greatly limits statistical power.
B-147
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Blair A, Haas T, Prosser R, Morrissette M, Blackman, Grauman D, van Dusen P, Morgan F.
States Coast Guard marine Inspectors. Arch Environ Health 44:150-156.
Mortality among United
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
The purpose of the cohort study was to examine mortality patterns among Coast Guard marine
inspectors. This study was not designed to examine specific exposures, including TCE.
1,767 U.S. Coast Guard male officers and enlisted personnel performing marine inspections between
1942 and 1970 and 1,914 noninspectors matched to inspectors on registry, rank, and year that rank
was achieved.
External referents: age-specific mortality rates of the U.S. white male population and noninspectors.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
ICDA, 8th revision.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
TCE identified in paper as 1 of 10 potential exposures; however, no exposure assessment to TCE to
individual subjects. Exposure in cohort analysis defined broadly at level of the plant and, in case-
control study, department worked as identified on company's personnel. A cumulative exposure
surrogate developed from duration in each job and a four-point rating scale: nonexposed, person
generally held administrative position; low exposed, assigned to staff with duties that occasionally
required vessel inspections; moderate exposed, assigned to inspection duties that did not regularly
include hull structures, and regular inspection of hull structures in geographic areas where chemicals
were not major items of cargo; and high exposed, assigned to subjects who performed hull inspections
at ports were vessels transported chemicals.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
No
Not reported; minimum latent period was 10 yrs.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
483 deaths among marine inspectors (27% of cohort), 103 cancer deaths.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Mortality analysis: Age,
specific SMR of marine
race, sex, and calendar year. Directly
inspectors to that of noninspectors.
adjusted rate ratios compared cause-
SMR and RR.
Yes, using a ranked cumulative exposure surrogate.
Adequate.
B-149
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B .3.1.4.9. Shannon et al. (1988).
B.3.1.4.9.1. Author's abstract.
A historical prospective study of cancer in lamp manufacturing workers in one
plant was conducted. All men and women who worked for a total of at least
6 months and were employed at some time between 1960 and 1975 were
included. Work histories were abstracted and subjects were divided according to
whether they had worked in the coiling and wire drawing area (CWD). Cancer
morbidity from 1964 to 1982 was ascertained via the provincial registry, and was
compared with the site-specific incidence in Ontario, adjusting for age, sex and
calendar period. Of particular interest were primary breast and gynecological
cancers in women.
The cancers of a priori concern were significantly increased in women in CWD,
but not elsewhere in the plant. The excess was greatest in those with more than
5 yr exposure (in CWD) and more than 15 yr since first working in CWD, with
eight cases of breast and gynecological cancers observed in this category
compared with 2.67 expected. Only three cancers occurred in men in CWD.
Environmental measurements had not been made in the past and little information
was available on substances used in the 1940s and 1950s, the period when the
women with the highest excess began employment. It is known that methylene
chloride and trichloroethylene have been used, but not enough is known about the
dates and patterns.
B .3.1.4.9.2. Study description and comments.
This cohort of 1,770 workers (1,044 females, 826 males) employed >6 months and
working between 1960 and 1975 at a General Electric plant in Ontario, Canada, in the lamp
manufacturing department identified cancer incidence cases from a regional cancer registry from
1964, the first date of high quality information, to 1982. Office workers were included in the
study population. The study was carried out in response to previous reports of excess breast and
gynecological cancer in women employed in the CWD area. SIRs compared the observed
number of site-specific incident cancers to that expected of the Ontario population and supplied
by the regional cancer registry. SIR estimates were calculated for all lamp department workers,
and for two subgroups defined by job title, workers in the coil and wire-drawing area (CWD),
and workers in all other areas. The cohort was successfully traced, with low rates of lost to
follow-up (6% among CWD workers, 7% of all other workers). A total of 98 incident cancer
cases were identified (58 in females, 40 in males) and over half of the incident cancers in females
(n = 31) due to breast and gynecological cancers. The number of incident cancers is likely
underestimated given the 4-year period between cohort identification and the first date of high
quality information in the cancer registry. Additionally, cancer cases among workers who
moved from the province would not be found in the registry, leading to underascertainment of
cases. This is likely a small number given follow-up tracing identified 2% of workers had left
the province.
B-150
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This study lacks exposure information on individual study subjects. Exposures in CWD
were of concern given previous reports. The study lacks exposure monitoring data and potential
exposures in CWD area were identified using purchase records. A number of chemicals were
identified including methylene chloride from 1959 onward and TCE, which records suggested
may have been used beforehand.
Overall, the exposure assessment in this study is insufficient for examining TCE
exposure and cancer mortality. The inclusion of office workers, who likely have low potential
exposure, would introduce a downward bias. Furthermore, the few site-specific deaths among
CWD and all other workers greatly limits statistical power.
B-151
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Shannon HS, Haines T, Bernholz C, Julian JA, Verma DK, Jamieson E, Walsh C.
manufacturing workers. Am J Ind Med 14:281-290.
Cancer morbidity in lamp
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This study was undertaken in response to previous report of apparent excess breast and gynecological
cancers in women employed in the coil and wire drawing area of a lamp manufacturing plant.
Cohort analysis: 1,770 workers (1,044 females, 826 males) in the lamp manufacturing department of a
GE plant in Ontario Province, Canada.
External referents: Age-, sex-, and race-specific site-specific cancer incidence rates for Ontario
Province population.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
Not reported.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
This study does not assign TCE exposure to individual subjects. Job title and work in the CWD
used to assign exposure potential and chemical usage in CWD identified from purchase records.
Methylene chloride used from 1959 onward, with one report from 1955 indicating TCE used as
degreasing solvent.
area
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
No, follow-up was incomplete for 6% of CWD workers and 7% for all other workers.
Not reported
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
98 incident cancer cases
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, race,
sex, and calendar year.
SIR.
No.
Adequate.
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B .3.1.4.10. Shindell and Ulrich (1985).
B.3.1.4.10.1. Author's abstract.
A prospective study was conducted of 2,646 employees who worked three months
or more during the period January, 1957, through July, 1983, in a manufacturing
plant that used trichloroethylene as a degreasing agent throughout the study
period. Ninety-eight percent of the study cohort were traced; they accounted for
16,388 person-years of employment and 38,052 person-years of follow-up.
Mortality experience was found to be generally more favorable than that of the
comparable segment of the U.S. population over the same period of time. For the
white male cohort there were fewer deaths than expected from heart disease,
cancer, and trauma (standard mortality rate for all causes = 0.79, p less than .01).
Reports by current and former employees of health problems requiring medical
treatment showed that there were only one third as many persons with heart
disease or hypertension as were reported in a comparable reference population
studied over the past five years.
B .3.1.4.10.2. Study description and comment.
This study of 2, 546 current and former office and production employees at a
manufacturing plant in northern Illinois compares broad groupings of cause-specific mortality
between 1957 and 1983 to expected number of deaths based on U.S. population mortality rates
for the period. The published paper lacks an assessment of TCE exposure other than noting TCE
was used as a degreasing agent at the plant. No information is presented on quantity used, job
titles with potential exposure, or likely exposure concentrations. Not all study subjects had the
same potential for exposure and the inclusion of office workers who had a very low exposure
potential decreased the study's detection sensitivity. Deaths were identified from company
records or from direct or indirect contact with former employees or next-of-kin for subjects not
known to the company to be deceased instead of using national-based registries such as Social
Security listings or National Death Index for identifying vital status. There were few deaths in
this cohort, a total of 141 among male and female subjects; vital status could not be ascertained
for 52 subjects. The few numbers of cancer deaths (21 total) precluded examination of cause-
specific cancer mortality. Overall, this study provides no information on TCE and cancer; it
lacked exposure assessment to TCE and the few cancer deaths observed greatly limited its
detection sensitivity.
B-154
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Shindell S, Ulrich S.
27:577-579.
A cohort study of employees of a manufacturing plant using trichloroethylene. J Occup Med
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This study was designed to assess mortality patterns of office and production employees at an Illinois
manufacturing plant.
2,646 males and female workers employed from 1-1-1957 to 7-31-1983. Mortality rates of U.S.
population used as referent. The paper lacks information on source for identifying cohort subjects
if company records were complete.
and
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
Not identified.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
The paper does not identify TCE usage other than as a degreaser. Conditions of exposure and jobs
potentially exposure are not identified in paper. This study lacks an assessment of TCE exposure.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
2%.
No information provided in paper.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
This study does not use standard approaches to verify deaths and vital status. Deaths are serf-reported
in response to contact by employer representative. 141 deaths (6%) were reported to employer,
9 deaths lacked a death certificate.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Sex and race.
SMR.
No.
The paper lacks discussion of process used to contact former employees to verify vital status and
methods used to identify subjects.
B-155
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B.3.1.4.11. Wilcosky et al. (1984).
B.3.1.4.11.1. Author's abstract.
Some evidence suggests that solvent exposures to rubber industry workers may be
associated with excess cancer mortality, but most studies of rubber workers lack
information about specific chemical exposure. In one large rubber and tire-
manufacturing plant, however, historical documents allowed a classification of
jobs based on potential exposures to all solvents that were authorized for use in
the plant. A case-control analysis of a 6,678 member cohort compared the solvent
exposure histories of a 20% age-stratified random sample of the cohort with those
of cohort members who died during 1964-1973 for stomach cancer, respiratory
system cancer, prostate cancer, lymphosarcoma, or lymphatic leukemia. Of these
cancers, only lymphosarcoma and lymphatic leukemia showed significant positive
associations with any other potential solvents exposures. Lymphatic leukemia
was especially strongly related to carbon tetrachloride (OR = 1.3, p< .0001) and
carbon disulfide (OR = 8.9, p = .0003). Lymphosarcoma showed similar, but
weaker, association with these two solvents. Benzene, a suspected carcinogen,
was not significantly associated with any of the cancers.
B .3.1.4.11.2. Study description and comment.
Exposure was assessed in this nested case-control study of four site-specific cancers
among rubber workers at a plant in Akron, Ohio through use of a JEM originally used to
examine benzene specifically, but had the ability to assess 24 other solvents, including TCE, or
solvent classes. Exposure was inferred using information on production operations and product
specifications that indicated whether solvents were authorized for use during tire production, and
by process area and calendar year. A subject's work history record was linked to the JEM to
assign exposure potential to TCE. Overall, a low prevalence of TCE exposure, ranging from 9 to
20% for specific cancers was observed among cases.
The JEM was developed originally to assign exposure to benzene and other aromatic
solvents in a nested case-control study of lymphocytic leukemia (Arp et al., 1983). Details of
exposure potential to TCE are not described by either Arp et al. (1983) or Wilcosky et al. (1984).
No data were provided on the frequency of exposure-related tasks. Without more information, it
is not possible to determine the quality of some of the assignments. Similarly, the lack of
industrial hygiene monitoring data precluded validation of the JEM.
Cases of respiratory, stomach and prostate cancers; lymphosarcoma and reticulum cell
sarcoma; and lymphatic leukemia were identified from a previous study, which had observed
associations with these site-specific cancers among a cohort of rubber workers employed at a
large tire manufacturing plant in Akron, Ohio. Statistical power is low in this study, particularly
for evaluation of lymphatic cancer for which there were 9 cases of lymphosarcoma and 10 cases
of lymphatic leukemia. Controls were chosen from a 20% age-stratified random sample of the
cohort. The published paper does not identify if subjects with other diseases associated with
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solvents or TCE were excluded as controls. If no exclusion criteria were adopted, a bias may
have been introduced which would dampen observed associations towards the null.
The few details provided in the paper on exposure assessment and JEM developments,
few details of control selection, the low prevalence of TCE exposure and the few lymphatic
cancer cases greatly limit the ability of this study for assessing risks associated with exposures to
TCE.
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Wilcosky TC, Checkoway H, Marshall EG, Tyroler HA. (1984). Cancer mortality and solvent exposure in the rubber
industry. Am Ind Hyg Assoc J 45:809-811.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This study was identified as "exploratory" to examine several site-specific cancer and specific
solvents, primarily benzene.
Underlying population at risk was a cohort of 6,678 male workers employed in the rubber industry in
1964. Cases are deaths due to respiratory, stomach and prostate cancers; lymphosarcoma; and
lymphatic leukemia observed in the cohort analysis — 30 deaths due to stomach cancer, 333 deaths
from prostate cancer, 9 deaths from lymphosarcoma, and 10 deaths from lymphatic leukemia.
Controls were a 20% age-stratified random sample of the cohort (exclusion criteria not identified in
paper).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
ICDA, 8th revision.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Procedure to assign TCE and other solvent exposures based upon JEM developed originally to assess
benzene and other solvent exposures (Arp et al., 1983). The JEM was linked to a detailed work
history as identified from a subject's personnel record to assign TCE exposure potential. Details of
JEM for TCE not well-described in Wilcosky et al. (1984). Multiple solvent exposures likely
(McMichael etal.. 1976).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Record study with exposure assignment using JEM and personnel records.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
N/A
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
TCE exposure prevalence:
Stomach cancer, five exposed cases (17% exposure prevalence)
Prostate cancer, three exposed cases (9% exposure prevalence)
Lymphosarcoma, three exposed cases (33% exposure prevalence)
Lymphatic leukemia, two exposed cases (20% exposure prevalence).
No information presented in paper on exposure prevalence among control subjects.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age.
Not described in published paper.
No.
Methods and analyses not fully described in published paper.
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B.3.2. Case-Control Studies
B.3.2.1. Bladder Cancer Case-Control Studies
B.3.2.1.1. Pesch et al. (2000a)
B.3.2.1.1.1. Author's abstract.
BACKGROUND: This multicentre population-based case-control study was
conducted to estimate the urothelial cancer risk for occupational exposure to
aromatic amines, polycyclic aromatic hydrocarbons (PAH), and chlorinated
hydrocarbons besides other suspected risk factors. METHODS: In a population-
based multicentre study, 1035 incident urothelial cancer cases and 4298 controls
matched for region, sex, and age were interviewed between 1991 and 1995 for
their occupational history and lifestyle habits. Exposure to the agents under study
was self-assessed as well as expert-rated with two job-exposure matrices and a job
task-exposure matrix. Conditional logistic regression was used to calculate
smoking adjusted odds ratios (OR) and to control for study centre and age.
RESULTS: Urothelial cancer risk following exposure to aromatic amines was
only slightly elevated. Among males, substantial exposures to PAH as well as to
chlorinated solvents and their corresponding occupational settings were associated
with significantly elevated risks after adjustment for smoking (PAH exposure,
assessed with a job-exposure matrix: OR = 1.6, 95% CI: 1.1-2.3, exposure to
chlorinated solvents, assessed with a job task-exposure matrix: OR = 1.8, 95% CI:
1.2-2.6). Metal degreasing showed an elevated urothelial cancer risk among males
(OR = 2.3, 95% CI: 1.4-3.8). In females also, exposure to chlorinated solvents
indicated a urothelial cancer risk. Because of small numbers the risk evaluation
for females should be treated with caution. CONCLUSIONS: Occupational
exposure to aromatic amines could not be shown to be as strong a risk factor for
urothelial carcinomas as in the past. A possible explanation for this finding is the
reduction in exposure over the last 50 years. Our results strengthen the evidence
that PAH may have a carcinogenic potential for the urothelium. Furthermore, our
results indicate a urothelial cancer risk for the use of chlorinated solvents.
B.3.2.1.1.2. Study description and comment.
This multicenter study of urothelial (bladder, ureter, and renal pelvis) and RCC in
Germany included the five regions (West Berlin, Bremen, Leverkusen, Halle, Jena), identified
two case series from participating hospitals, 1,035 urothelial cancer cases and 935 RCC cases
with a single population control series matched to cases by region, sex, and age (1:2 matching
ratio to urothelial cancer cases and 1:4 matching ratio to RCC cases). Findings in Pesch et al.
(2000a) are from analyses of urothelial cancer analysis and Pesch et al. (2000b) from analyses of
RCC. In all, 1,035 (704 males, 331 females) urothelial carcinoma cases were interviewed face-
to-face using with a structured questionnaire in the hospital within 6 months of first diagnosis
and 4,298 randomly selected population controls were interviewed at home. Logistic regression
models were fit separately to for males and females conditional on age (nine 5-year groupings),
study region, and smoking, to examine occupational chemical exposures and urothelial
carcinoma.
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Two general JEMs, British and German, were used to assign exposures based on
subjects' job histories reported in an interview. This approach was the same as that described for
the RCC analysis of Pesch et al. (2000b). Researchers also asked about job tasks associated with
exposure, such as metal degreasing and cleaning, and use of specific agents (organic solvents
chlorinated solvents, including specific questions about carbon tetrachloride, TCE, and
tetrachloroethylene) to evaluate TCE potential using a ITEM. A category of "any use of a
solvent" mixes the large number with infrequent slight contact with the few noted earlier who
have high intensity and prolonged contact. Analyses examining TCE exposure using either the
JEM of ITEM assigned a cumulative TCE exposure index of none to low, medium high and
substantial, defined as the product of exposure probability x intensity x duration with the
following cutpoints: none to low, <30th percentile of cumulative exposure scores; medium, 30th-
<60th percentile; high, 60th-<90th percentile; and, substantial, >90ih percentile. The use of the
German JEM identified approximately twice as many cases with any potential TCE exposure
(44%) compared to the ITEM (22%) and, in both cases, few cases identified with substantial
exposure, 7% by JEM and 5% by JTEM. Pesch et al. (2000a) noted "exposure indices derived
from an expert rating of job tasks can have a higher agent-specificity than indices derived from
job titles." For this reason, the JTEM approach with consideration of job tasks is considered a
more robust exposure metric for examining TCE exposure and urothelial carcinoma due to likely
reduced potential for exposure misclassification compared to TCE assignment using only job
history and title.
While this case-control study includes a region in the North Rhine-Westphalia region
where the Arnsberg area is also located, several other regions are included as well, where the
source of the TCE and chlorinated solvent exposures are expected as much less well defined.
Few cases were identified as having substantial exposure to TCE and, as a result, most subjects
identified as exposed to TCE probably had minimal contact, averaging concentrations of about
10 ppm or less (NRC. 2006).
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Pesch B, Haerting H, Ranft U, Klimpel A, Oelschlagel B, Schill W, and the MURC Study Group. 2000a. Occupational risk
factors for urothelial carcinoma: agent-specific results from a case-control study in Germany. Int J Epidemiol 29:238-247.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and
controls in case-control studies is adequate
Yes, this case-control study was conducted to estimate urothelial carcinoma risk for exposure to
occupational-related agents; chlorinated solvents including TCE were identified as exposures of a priori
interest.
1,035 urothelial (bladder, ureter, renal pelvis) carcinoma cases were identified from hospitals in a five-
region area in Germany between 1991 and 1995. Cases were confirmed histologically. 4,298 population
controls identified from local residency registries in the five-region area were frequency matched to cases
by region, sex and age comprised the control series for both the urothelial carcinoma cases and the RCC
cases, published as Pesch et al. (2000a).
Participation rate: cases, 84%; controls, 71%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for
lymphoma, particularly NHL
Incidence.
No information in paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including
adoption of JEM and quantitative exposure
estimates
A trained interviewer interviewed subjects using a structured questionnaire which covered occupational
history and job title for all jobs held >1 yr, medical history, and personal information. Two general JEMs,
British and German, were used to assign exposures based on subjects' job histories reported in an interview.
Researchers also asked about job tasks associated with exposure, such as metal degreasing and cleaning,
and use of specific agents (organic solvents chlorinated solvents, including specific questions about carbon
tetrachloride, TCE, and tetrachloroethylene) and chemical-specific exposure were assigned using a ITEM.
Exposure index for each subject is the sum over all jobs of duration x probability x intensity. A four
category grouping was used in statistical analyses defined by exposure index distribution of controls: no-
low; medium, 30th percentile; high, 60th percentile; substantial, 90th percentile.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Interviewers carried out face-to-face interview with all cases and controls. All cases were interviewed in
the hospital within 6 months of initial diagnosis. All controls had home interviews.
No, by nature of interview location.
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CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No, all cases and controls were alive at time of interview.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies;
numbers of total cancer incidence studies;
numbers of exposed cases and prevalence of
exposure in case-control studies
JEM: 460 cases with TCE exposure index of medium or higher (44% exposure prevalence among cases),
71 cases with substantial exposure (7% exposure prevalence).
ITEM: 157 cases with TCE exposure index of medium or higher (22% exposure prevalence among cases),
and 36 males assigned substantial exposure (5% exposure prevalence).
No information is presented in paper on control exposure prevalence.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical
analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, study center, and smoking.
Conditional logistic regression.
Yes.
Yes.
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B .3.2.1.2. Siemiatycki et al. (1994), Siemiatycki (1991).
B .3.2.1.2.1. Author's abstract.
A population-based case-control study of the associations between various
cancers and occupational exposures was carried out in Montreal, Quebec, Canada.
Between 1979 and 1986, 484 persons with pathologically confirmed cases of
bladder cancer and 1,879 controls with cancers at other sites were interviewed, as
was a series of 533 population controls. The job histories of these subjects were
evaluated by a team of chemist/hygienists for evidence of exposure to a list of 294
workplace chemicals, and information on relevant non-occupational confounders
was obtained. On the basis of results of preliminary analyses and literature
review, 19 occupations, 11 industries, and 23 substances were selected for in-
depth multivariate analysis. Logistic regression analyses were carried out to
estimate the odds ratio between each of these occupational circumstances and
bladder cancer. There was weak evidence that the following substances may be
risk factors for bladder cancer: natural gas combustion products, aromatic amines,
cadmium compounds, photographic products, acrylic fibers, polyethylene,
titanium dioxide, and chlorine. Among the substances evaluated which showed no
evidence of an association were benzo(a)pyrene, leather dust, and formaldehyde.
Several occupations and industries were associated with bladder cancer, including
motor vehicle drivers and textile dyers.
B.3.2.1.2.2. Study description and comment.
Siemiatycki et al. (1994) and Siemiatycki (1991) reported data from a case-control study
of occupational exposures and bladder cancer conducted in Montreal, Quebec (Canada) and part
of a larger study of 10 other site-specific cancers and occupational exposures. The investigators
identified 617 newly diagnosed cases of primary bladder cancer, confirmed on the basis of
histology reports, between 1979 and 1985; 484 of these participated in the study interview (78%
participation). One control group (n = 1,295) consisted of patients with other forms of cancer
(excluding lung and kidney cancer) recruited through the same study procedures and time period
as the bladder cancer cases. A population-based control group (n = 533, 72% response),
frequency matched by age strata, was drawn using electoral lists and random digit dialing. Face-
to-face interviews were carried out with 82% of all cancer cases with telephone interview (10%)
or mailed questionnaire (8%) for the remaining cases. Twenty percent of all case interviews
were provided by proxy respondents. The occupational assessment consisted of a detailed
description of each job held during the working lifetime, including the company, products, nature
of work at site, job activities, and any additional information that could furnish clues about
exposure from the interviews.
A team of industrial hygienists and chemists blinded to subject's disease status translated
jobs into potential exposure to 294 substances with three dimensions (degree of confidence that
exposure occurred, frequency of exposure, and concentration of exposure). Each of these
exposure dimensions was categorized into none, any, or substantial exposure. Siemiatycki et al.
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(1994) presents observations of analyses examining job title, occupation, and some chemical-
specific exposures, but not TCE. Observations on TCE are found in the original report of
Siemiatycki (1991). Any exposure to TCE was 2% among cases (n = 8) but <1% for substantial
TCE exposure (n = 5); "substantial" is defined as >10 years of exposure for the period up to
5 years before diagnosis. Logistic regression models adjusted for age, ethnicity, SES, smoking,
coffee consumption, and status of respondent (Siemiatycki etal., 1994) or Mantel-Henszel $
stratified on age, family income, cigarette smoking, coffee, and respondent status (Siemiatycki,
1991). Odds ratios for TCE exposure are presented in Siemiatycki (1991) with 90% CIs.
The strengths of this study were the large number of incident cases, specific information
about job duties for all jobs held, and a definitive diagnosis of bladder cancer. However, the use
of the general population (rather than a known cohort of exposed workers) reduced the likelihood
that subjects were exposed to TCE, resulting in relatively low statistical power for the analysis.
The JEM, applied to the job information, was very broad since it was used to evaluate
294 chemicals.
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Siemiatycki J, Dewar R, Nadon L, Gerin M. (1994). Occupational risk factors for bladder cancer: results from a case-control
study in Montreal, Quebec, Canada. Am J Epidemiol 140:1061-1080.
Siemiatycki J. (1991). Risk Factors for Cancer in the Workplace. Baca Raton: CRC Press.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls in
case-control studies is adequate
This population case-control study was designed to generate hypotheses on possible association
between 1 1 site-specific cancers and occupational title or chemical exposures.
617 bladder cancer cases were identified among male Montreal residents between 1979 and 1985 of
which 484 were interviewed.
740 eligible male controls identified from the same source population using random digit dialing or
electoral lists; 533 were interviewed. A second control series consisted of all other cancer controls
excluding lung and kidney cancer cases.
Participation rate: cases, 78%; population controls, 72%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
ICD-O, 188 (malignant neoplasm of bladder).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Unblinded interview using questionnaire sought information on complete job history with supplemental
questionnaire for jobs of a priori interest (e.g., machinists, painters). Team of chemist and industrial
hygienist assigned exposure using job title with a semiquantitative scale developed for 300 exposures,
including TCE. For each exposure, a three-level ranking was used for concentration (low or
background, medium, high) and frequency (percent of working time: low, 1-5%; medium, >5-30%;
and high, >30%).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
82% of all cancer cases interviewed face-to-face by a trained interviewer, 10% telephone interview,
and 8% mailed questionnaire. Cases interviews were conducted either at home or in the hospital; all
population control interviews were conducted at home.
Interviews were unblinded but exposure coding was carried out blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 20% of all cancer cases had proxy respondents.
CATEGORY G: SAMPLE SIZE
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Number of deaths in cohort mortality studies; numbers of
total cancer incidence studies; numbers of exposed cases
and prevalence of exposure in case-control studies
484 cases (78% response), 533 population controls (72%).
Exposure prevalence: Any TCE exposure, 2% cases; Substantial TCE exposure (exposure for >10 yrs
and up to 5 yrs before disease onset), <1% cases.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published paper
Documentation of results
Age, income, index for cigarette smoking, coffee, and respondent status (Siemiatycki, 1991).
Age, ethnicity, SES, smoking, coffee consumption, and status of respondent (Siemiatycki et al.,
1994).
Mantel-Haenszel (Siemiatycki, 1991).
Logistic regression (Siemiatycki etal., 1994).
No.
Yes.
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B.3.2.2. CNS Cancers Case-Control Studies
B.3.2.2.1. De Roos et al. (200T).
B.3.2.2.1.1. Author's abstract.
To evaluate the effects of parental occupational chemical exposures on incidence
of neuroblastoma in offspring, the authors conducted a multicenter case-control
study, using detailed exposure information that allowed examination of specific
chemicals. Cases were 538 children aged 19 years who were newly diagnosed
with confirmed neuroblastoma in 1992-1994 and were registered at any of 139
participating hospitals in the United States and Canada. One age-matched control
for each of 504 cases was selected through random digit dialing. Self-reported
exposures were reviewed by an industrial hygienist, and improbable exposures
were reclassified. Effect estimates were calculated using unconditional logistic
regression, adjusting for child's age and maternal demographic factors. Maternal
exposures to most chemicals were not associated with neuroblastoma. Paternal
exposures to hydrocarbons such as diesel fuel (odds ratio (OR) = 1.5; 95%
confidence interval (CI): 0.8, 2.6), lacquer thinner (OR = 3.5; 95% CI: 1.6, 7.8),
and turpentine (OR = 10.4; 95% CI: 2.4, 44.8) were associated with an increased
incidence of neuroblastoma, as were exposures to wood dust (OR = 1.5; 95% CI:
0.8, 2.8) and solders (OR = 2.6; 95% CI: 0.9, 7.1). The detailed exposure
information available in this study has provided additional clues about the role of
parental occupation as a risk factor for neuroblastoma.
B .3.2.2.1.2. Study description and comment.
De Roos et al. (2001), a large multicenter case-control study of neuroblastoma in
offspring and part of the pediatric collaborative clinical trial groups, the Children's Cancer
Group and the pediatric Oncology Group, examined parental and maternal chemical exposures,
focusing on solvent exposures, expanding the exposure assessment approach of Olshan et al.
(1999) who examined parental occupational title among cases and controls. Neuroblastoma in
patients under the age of 19 years was identified at 1 of 139 participating hospitals in the United
States and Canada from 1992 to 1996. One population control per case s was using a telephone
random digit dialing procedure and matched to the case on date of birth (+6 months for cases 3
years old or younger and +1 year for cases older than 3 years of age). A total of 741 cases and
708 controls were identified with direct interviews by telephone obtained from 538 case mothers
(73% participation), 405 case fathers, 504 control mothers (71% participation), and 304 control
fathers. Mothers served as proxy respondents for paternal information for 67 cases (12%) and
141 controls (28%).
A strength of the study was its use of industrial hygienist review of self-reported
occupational exposure to increase specificity, reduce the number of false-positive information
from self-reported exposures, and to minimize exposure misclassification bias. A parent was
coded as having been exposed to individual chemicals or chemical group (halogenated
hydrocarbons, paints, metals, etc.) if the industrial hygiene review determined probable exposure
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in any job. Individual chemicals in the halogenated hydrocarbons grouping included carbon
tetrachloride, chloroform, Freon, methylene chloride, perchloroethylene and TCE. Typical of
population case-control studies, reported TCE exposure was uncommon among cases and
controls. Only 6 case and 8 control mothers were identified by industrial hygiene review of
occupational information to have probable exposure to halogenated hydrocarbons. The few
numbers prevented examination of specific chemical exposure. Of the 538 cases and
504 controls, paternal exposure to TCE was self-reported for 22 cases (5%) and 12 controls (4%)
were identified with paternal TCE exposure with fewer fathers with probable TCE exposure
confirmed from industrial hygiene expert review, 9 cases (2%) and 7 controls (2%).
Overall, this study has a low sensitivity and statistical power for evaluating parental TCE
exposure and neuroblastoma in offspring due to the low exposure prevalence to TCE. Although
study investigators took effort to reduce false positive reporting, exposure misclassification bias
may still be possible from false negative reporting of occupational information. As discussed by
study authors, job duty information reported by parents was best used to infer exposure to
chemical categories but was not detailed sufficiently to infer specific exposures. The study's
reported risk estimates for TCE exposure are imprecise and do not provide support for or against
an association.
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De Roos AJ, Olshan AF, Teschke K, Poole Ch, Savitz DA, Blatt J, Bondy ML, Pollock BH. (2001). Parental occupational
exposure to chemicals and incidence of neuroblastoma in offspring. Am J Epidemiol 154:106-114.
Olshan AF, De Roos AJ, Teschke K, Neglin JP, Stram DO, Pollock BH, Castleberry RP.
occupation. Cancer Causes Control 10:539-549.
(1999). Neuroblastoma and parental
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of exposure
and control groups and of cases and controls in case-control
studies is adequate
This multicenter population case-control study examined parental chemical-specific
occupational exposures using detailed exposure information.
538 cases of neuroblastoma in children <19 yrs of age and diagnosed between 1992
any of 139 U.S. or Canadian hospitals participating in the Children's Cancer Group
Oncology Group studies.
504 population controls were selected through random digit dialing and matched (1:
on date of birth. Controls could not be located for 34 cases.
538 of 741 potentially eligible cases (73% participation rate).
504 of 681 potentially eligible controls (74% participation rate).
and 1994 at
and Pediatric
1) with cases
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of JEM and
quantitative exposure estimates
Serf-reported exposure to any of 65 chemicals, compounds, or broad categories was obtained
from structured questionnaire. An industrial hygienist confirmed each respondent's serf-
reported chemical exposure responses. Exposures were not assigned using JEM.
TCE exposure examined in analysis as separate exposure and as one of several chemicals in the
broader category of "halogenated hydrocarbons."
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Telephone interview with mother and father of each case and control.
Not identified in paper.
CATEGORY F: PROXY RESPONDENTS
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>10% proxy respondents
No proxy information on maternal exposure; direct interview with mother was obtained for
537 cases and 503 controls.
Analysis of paternal chemical exposures did not include information on paternal exposure from
proxy interviews.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers of total
cancer incidence studies; numbers of exposed cases and
prevalence of exposure in case-control studies
Serf-reported TCE exposure: 22 cases (5% exposure prevalence) and 12 controls (4% exposure
prevalence).
IH-reviewed TCE exposure: 9 cases (2% exposure prevalence) and 7 controls (2% exposure
prevalence).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published paper
Documentation of results
Analyses of maternal and paternal occupational exposure each adjusted for child's age, maternal
race, maternal age, and maternal education.
Separate analyses are conducted for maternal and paternal exposure using logistic regression
methods.
No.
Yes, results are well documented.
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B .3.2.2.2. Heineman et al. (1994).
B.3.2.2.2.1. Author's abstract.
Chlorinated aliphatic hydrocarbons (CAHs) were evaluated as potential risk
factors for astrocytic brain tumors. Job-exposure matrices for six individual
CAHs and for the general class of organic solvents were applied to data from a
case-control study of brain cancer among white men. The matrices indicated
whether the CAHs were likely to have been used in each industry and occupation
by decade (1920-1980), and provided estimates of probably and intensity of
exposure for "exposed" industries and occupations. Cumulative exposure indices
were calculated for each subject.
Associations of astrocytic brain cancer were observed with likely exposure to
carbon tetrachloride, methylene chloride, tetrachloroethylene, and
trichloroethylene, but were strongest for methylene chloride. Exposure to
chloroform or methyl chloroform showed little indication of an association with
brain cancer. Risk of astrocytic brain tumors increase with probability and
average intensity of exposure, and with duration of employment in jobs
considered exposed to methylene chloride, but not with a cumulative exposure
score. These trends could not be explained by exposures to the other solvents.
B .3.2.2.2.2. Study description and comment.
Heineman et al. (1994) studied the association between astrocytic brain cancer (ICD-
9 codes 191, 192, 225, and 239.7) and occupational exposure to chlorinated aliphatic
hydrocarbons. Cases were identified using death certificates from southern Louisiana, northern
New Jersey, and the Philadelphia area. This analysis was limited to white males who died
between 1978 and 1981. Controls were randomly selected from the death certificates of white
males who died of causes other than brain tumors, cerebrovascular disease, epilepsy, suicide, and
homicide. The controls were frequency matched to cases by age, year of death, and study area.
Next-of-kin were successfully located for interview for 654 cases and 612 controls,
which represents 88 and 83% of the identified cases and controls, respectively. Interviews were
completed for 483 cases (74%) and 386 controls (63%). There were 300 cases of astrocytic
brain cancer (including astrocytoma, glioblastoma, mixed glioma with astrocytic cells). The
ascertainment of type of cancer was based on review of hospital records, which included
pathology reports for 229 cases and computerized tomography reports for 71 cases. After
excluding 66 controls with a possible association between occupational exposure to chlorinated
aliphatic hydrocarbons and cause of death (some types of cancer, cirrhosis of the liver), the final
analytic sample consisted of 300 cases and 320 controls.
In the next-of-kin interviews, the work history included information about each job held
since the case (or control) was 15 years old (job title, description of tasks, name and location of
company, kinds of products, employment dates, and hours worked per week). Occupation and
industry were coded based on four-digit Standard Industrial Classification and Standard
Occupational Classification (Department of Commerce) codes. The investigators developed
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matrices linked to jobs with likely exposure to six chlorinated aliphatic hydrocarbons (carbon
tetrachloride, chloroform, methyl chloroform, methylene dichloride, tetrachloroethylene, and
TCE), and to organic solvents (Gomez et al., 1994). This assessment was done blinded to case-
control status. Exposure was defined as the probability of exposure to a substance (the highest
probability score for that substance among all jobs), duration of employment in the exposed
occupation and industry, specific exposure intensity categories, average intensity score (the
three-level semi quantitative exposure concentration assigned to each job multiplied by duration
of employment in the job, summed across all jobs), and cumulative exposure score (weighted
sum of years in all exposed jobs with weights based on the square of exposure intensity [1, 2, 3]
assigned to each job). Secular trends in the use of specific chemicals were considered in the
assignment of exposure potential. Exposures were lagged 10 or 20 years to account for latency.
Thus, this exposure assessment procedure was quite detailed.
The strengths of this case-control study include a large sample size, detailed work
histories including information not just about usual or most recent industry and occupation, but
also about tasks and products for all jobs held since age 15, and comprehensive exposure
assessment and analysis along several different dimensions of exposure. The major limitation
was the lack of direct exposure information and potential inaccuracy of the description of work
histories that was obtained from next-of-kin interviews. The authors acknowledge this limitation
in the report, and in response to a letter by Norman (1968) criticizing the methodology and
interpretation of the study with respect to the observed association with methylene chloride,
Heineman et al. (1994) noted that while the lack of direct exposure information must be
interpreted cautiously, it does not invalidate the results. Differential recall bias between cases
and controls was unlikely because work histories came from next-of-kin for both groups and, the
industrial hygienists made their judgments blinded to disease status. Nondifferential
misclassification is possible due to underreporting of job information by next of kin and would,
on average, attenuate true associations.
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Heineman EF, Cocco P, Gomez MR, Dosemeci M, Stewart PA, Hayes RB, Zahm SH, Thomas TL, Blair A. (1994).
Occupational exposure to chlorinated aliphatic hydrocarbons and risk of astrocytic brain cancer. Am J Ind Med 26:155-169.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Yes, study further examines six specific solvents including TCE in a previous study of brain cancer
which reported association with electrical equipment production and repair.
Brain cancer deaths among white males in southern Louisiana, northern New Jersey, and Philadelphia,
Pennsylvania, were identified using death certificates (n = 741). Controls were randomly selected
(source not identified in paper) among other cause-specific deaths among white male residents of
these areas and matched to cases by age, year of death and study area (n = 741).
Participation rate, 483 of 741 (65% of cases with brain cancer); 386 of 741 controls (52%). Of the
483, 300 deaths were due to astrocytic brain cancer.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
ICD, 9th revision, Codes 191, 192, 225, 239.7.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
The job-exposure-matrix of Gomez et al. (1994) was used to assign potential exposure to six solvents
including TCE.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Interview with next-of-kin but paper does not identify whether telephone or face-to-face.
Interviewer was blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Proxy information was obtained from 100% of cases and controls.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
TCE exposure prevalence: 128 cases (43%) and 125 controls (39%).
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Stratified analysis controlled for age, year of death and study area; employment
occupations was included in addition in logistic regression analyses.
in electronics-related
Stratified analysis using 2x2 tables and logistic regression.
Yes.
Yes.
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B.3.2.3. Colon and Rectal Cancers Case-Control Studies
B .3.2.3.1. Goldberg et al. (2001), Siemiatycki (1991).
B.3.2.3.1.1. Author's abstract.
BACKGROUND: We conducted a population-based case-control study in
Montreal, Canada, to explore associations between hundreds of occupational
circumstances and several cancer sites, including colon. METHODS: We
interviewed 497 male patients with a pathologically confirmed diagnosis of colon
cancer, 1514 controls with cancers at other sites, and 533 population-based
controls. Detailed job histories and relevant potential confounding variables were
obtained, and the job histories were translated by a team of chemists and
industrial hygienists into a history of occupational exposures. RESULTS: We
found that there was reasonable evidence of associations for men employed in
nine industry groups (adjusted odds ranging from 1.1 to 1.6 per a 10-year increase
in duration of employment), and in 12 job groups (OR varying from 1.1 to 1.7). In
addition, we found evidence of increased risks by increasing level of exposures to
21 occupational agents, including polystyrene (OR for "substantial" exposure
(OR(subst) =10.7), polyurethanes (OR(subst) = 8.4), coke dust (OR(subst) = 5.6),
mineral oils (OR(subst) = 3.3), polyacrylates (OR(subst) = 2.8), cellulose nitrate
(OR(subst) = 2.6), alkyds (OR(subst) = 2.5), inorganic insulation dust (OR(subst)
= 2.3), plastic dusts (OR(subst) = 2.3), asbestos (OR(subst) = 2.1), mineral wool
fibers (OR(subst) = 2.1), glass fibers (OR(subst) = 2.0), iron oxides (OR(subst) =
1.9), aliphatic ketones (OR(subst) = 1.9), benzene (OR(subst) = 1.9), xylene
(OR(subst) = 1.9), inorganic acid solutions (OR(subst) = 1.8), waxes, polishes
(OR(subst) = 1.8), mononuclear aromatic hydrocarbons (OR(subst) = 1.6),
toluene (OR(subst) = 1.6), and diesel engine emissions (OR(subst) = 1.5). Not all
of these effects are independent because some exposures occurred
contemporaneously with others or because they referred to a group of substances.
CONCLUSIONS: We have uncovered a number of occupational associations
with colon cancer. For most of these agents, there are no published data to support
or refute our observations. As there are few accepted risk factors for colon cancer,
we suggest that new occupational and toxicologic studies be undertaken focusing
on the more prevalent substances reported herein.
B .3.2.3.1.2. Study description and comment.
Goldberg et al. (2001), and Siemiatycki (1991) reported data from a case-control study of
occupational exposures and colon cancer conducted in Montreal, Quebec (Canada) and part of a
larger study of 10 other site-specific cancers and occupational exposures. The investigators
identified 607 newly diagnosed cases of primary colon cancer (ICD9, 153), confirmed on the
basis of histology reports, between 1979 and 1985; 497 of these participated in the study
interview (81.9% participation). One control group (n = 1,514) consisted of patients with other
forms of cancer (excluding cancers of the lung, peritoneum, esophagus, stomach, small intestine,
rectum, liver and intrahepatic bile ducts, gallbladder and extrahepatic bile ducts and pancreas)
recruited through the same study procedures and time period as the colon cancer cases. A
population-based control group (n = 533, 72% response), frequency matched by age strata, was
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drawn using electoral lists and random digit dialing. Face-to-face interviews were carried out
with 82% of all cancer cases with telephone interview (10%) or mailed questionnaire (8%) for
the remaining cases. Twenty percent of all case interviews were provided by proxy respondents.
The occupational assessment consisted of a detailed description of each job held during the
working lifetime, including the company, products, nature of work at site, job activities, and any
additional information that could furnish clues about exposure from the interviews.
A team of industrial hygienists and chemists blinded to subject's disease status translated
jobs into potential exposure to 294 substances with three dimensions (degree of confidence that
exposure occurred, frequency of exposure, and concentration of exposure). Each of these
exposure dimensions was categorized into none, any, or substantial exposure. Goldberg et al.
(2001) presents observations of analyses examining industries, occupation, and some chemical-
specific exposures, but not TCE. Observations on TCE are found in the original report of
Siemiatycki (1991). Any exposure to TCE was 2% among cases (n = 12) and 1% for substantial
TCE exposure (n = 7); "substantial" is defined as >10 years of exposure for the period up to
5 years before diagnosis.
Logistic regression models adjusted for a number of nonoccupational variables including
age, ethnicity, birthplace, education, income, parent's occupation, smoking, alcohol
consumption, tea consumption, respondent status, heating source and cooking source in
childhood home, consumption of nonpublic water supply, and BMI (Goldberg et al., 2001) or
Mantel-Haenszel $ stratified on age, family income, cigarette smoking, coffee, ethnic origin,
and beer consumption (Siemiatycki, 1991). ORs for TCE exposure are presented in Siemiatycki
(1991) with 90% CIs.
The strengths of this study were the large number of incident cases, specific information
about job duties for all jobs held, and a definitive diagnosis of colon cancer. However, the use of
the general population (rather than a known cohort of exposed workers) reduced the likelihood
that subjects were exposed to TCE, resulting in relatively low statistical power for the analysis.
The JEM, applied to the job information, was very broad since it was used to evaluate
294 chemicals.
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Goldberg MS, Parent M-E, Siemiatycki J, Desy M, Nadon L, Richardson L, Lakhani R, Lateille B, Valois M-F. (2001). A
case-control study of the relationship between the risk of colon cancer in men and exposure to occupational agents. Am J Ind
Med 39:5310-546.
Siemiatycki J.
Risk Factors for Cancer in the Workplace. Baca Raton: CRC Press.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of exposure
and control groups and of cases and controls in case-control
studies is adequate
This population case-control study was designed to generate hypotheses on possible association
between 1 1 site-specific cancers and occupational title or chemical exposures.
607 colon cancer cases were identified among male Montreal residents between 1979 and 1985 of
which 497 were interviewed.
740 eligible male controls identified from the same source population using random digit dialing or
electoral lists; 533 were interviewed. A second control series consisted of all other cancer controls
excluding lung peritoneum and other digestive cancers.
Participation rate: cases, 81.9%; population controls, 72%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
ICD-9, 153 (malignant neoplasm of colon).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of JEM
and quantitative exposure estimates
Unblinded interview using questionnaire sought information on complete job history with
supplemental questionnaire for jobs of a priori interest (e.g., machinists, painters). Team of
chemist and industrial hygienist assigned exposure using job title with a semiquantitative scale
developed for 294 exposures, including TCE. For each exposure, a three-level ranking was used
for concentration (low or background, medium, high) and frequency (percent of working time: low,
1-5%; medium, >5-30%; and high, >30%).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
82% of all cancer cases interviewed face-to-face by a trained interviewer, 10% telephone
interview, and 8% mailed questionnaire. Cases interviews were conducted either at home or in the
hospital; all population control interviews were conducted at home.
Interviews were unblinded but exposure coding was carried out blinded as to case and control
status.
CATEGORY F: PROXY RESPONDENTS
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>10% proxy respondents
Yes, 20% of all cancer cases had proxy respondents.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers of
total cancer incidence studies; numbers of exposed cases
and prevalence of exposure in case-control studies
497 cases (81.9% response), 533 population controls (72%).
Exposure prevalence: Any TCE exposure, 2% cases; substantial TCE exposure (exposure
>10 yrs and up to 5 yrs before disease onset), 1% cases.
for
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published paper
Documentation of results
Age, ethnicity, birthplace, education, income, parent's occupation, smoking, alcohol consumption,
tea consumption, respondent status, heating source and cooking source in childhood home,
consumption of nonpublic water supply, and BMI (Goldberg et al., 2001).
Age, family income, cigarette smoking, coffee, ethnic origin, and beer consumption
(Siemiatvckl 1991).
Mantel-Haenszel (Siemiatycki, 1991).
Logistic regression (Goldberg et al., 2001).
No.
Yes.
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B .3.2.3.2. Dumas et al.(2000), Siemiatycki (1991).
B .3.2.3.2.1. Author's abstract.
In 1979, a hypothesis-generating, population-based case-control study was
undertaken in Montreal, Canada, to explore the association between occupational
exposure to 294 substances, 130 occupations and industries, and various cancers.
Interviews were carried out with 3,630 histologically confirmed cancer cases, of
whom 257 had rectal cancer, and with 533 population controls, to obtain detailed
job history and data on potential confounders. The job history of each subject was
evaluated by a team of chemists and hygienists and translated into occupational
exposures. Logistic regression analyses adjusted for age, education, cigarette
smoking, beer consumption, body mass index, and respondent status were
performed using population controls and cancer controls, e.g., 1,295 subjects with
cancers at sites other than the rectum, lung, colon, rectosigmoid junction, small
intestine, and peritoneum. We present here the results based on cancer controls.
The following substances showed some association with rectal cancer: rubber
dust, rubber pyrolysis products, cotton dust, wool fibers, rayon fibers, a group of
solvents (carbon tetrachloride, methylene chloride, trichloroethylene, acetone,
aliphatic ketones, aliphatic esters, toluene, styrene), polychloroprene, glass fibers,
formaldehyde, extenders, and ionizing radiation. The independent effect of many
of these substances could not be disentangled as many were highly correlated with
each other.
B.3.2.3.2.2. Study description and comment.
Dumas et al. (2000) and Siemiatycki (1991) reported data from a case-control study of
occupational exposures and rectal cancer conducted in Montreal, Quebec (Canada) and part of a
larger study of 10 other site-specific cancers and occupational exposures. The investigators
identified 304 newly diagnosed cases of primary rectal cancers, confirmed on the basis of
histology reports, between 1979 and 1985; 257 of these participated in the study interview
(84.5% response). One control group (n = 1,295) consisted of patients with other forms of
cancer (excluding lung cancer and other intestinal cancers) recruited through the same study
procedures and time period as the rectal cancer cases. A population-based control group
(n = 533), frequency-matched by age strata, was drawn using electoral lists and random digit
dialing (72% response). The occupational assessment consisted of a detailed description of each
job held during the working lifetime, including the company, products, nature of work at site, job
activities, and any additional information that could furnish clues about exposure from the
interviews. The percentage of proxy respondents was 15.2% for cases, 19.7% for other cancer
controls, and 12.6% for the population controls.
A team of industrial hygienists and chemists blinded to subject's disease status translated
jobs into potential exposure to 294 substances with three dimensions (degree of confidence that
exposure occurred, frequency of exposure, and concentration of exposure). Each of these
exposure dimensions was categorized into none, any, or substantial exposure. Any exposure to
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TCE was 5% among cases (n = 12) and 1% for substantial TCE exposure (n = 3); "substantial" is
defined as >10 years of exposure for the period up to 5 years before diagnosis.
Logistic regression models adjusted for age, education, respondent status, cigarette
smoking, beer consumption and BMI (Dumas et al., 2000) or Mantel-Haenszel $ stratified on
age, family income, cigarette smoking, coffee, ethnic origin, and beer consumption (Siemiatycki,
1991). Dumas et al. (2000) presents observations of analyses examining industries, occupation,
and some chemical-specific exposures, including TCE. Observations on TCE from Mantel-
Haenszel analyses are found in the original report of Siemiatycki (1991). ORs for TCE exposure
are presented in Siemiatycki (1991) with 90% CIs and 95% CIs in Dumas et al. (2000).
The strengths of this study were the large number of incident cases, specific information
about job duties for all jobs held, and a definitive diagnosis of rectal cancer. However, the use of
the general population (rather than a known cohort of exposed workers) reduced the likelihood
that subjects were exposed to TCE, resulting in relatively low statistical power for the analysis.
The JEM, applied to the job information, was very broad since it was used to evaluate
294 chemicals.
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Dumas S, Parent M-E, Siemiatycki J, Brisson J. (2000). Rectal cancer and occupational risk factors: a hypothesis-generating,
exposure-based case-control study. Int J Cancer 87:874-879.
Siemitycki J. (1991). Risk Factors for Cancer in the Workplace. Boca Raton: CRC Press.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This population case-control study was designed to generate hypotheses on possible association
between 1 1 site-specific cancers and occupational title or chemical exposures.
304 rectal cancer cases were identified among male Montreal residents between 1979 and 1985 of
which 294 were interviewed.
740 eligible male controls identified from the same source population using random digit dialing or
electoral lists; 533 were interviewed. A second control series consisted of all other cancer controls
excluding lung and other intestinal cancer cases.
Participation rate: cases, 84.5%; population controls, 72%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
ICD-O, 154 (malignant neoplasm of rectum, rectosigmoid junction and anus).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Unblinded interview using questionnaire sought information on complete job history with
supplemental questionnaire for jobs of a priori interest (e.g., machinists, painters). Team of chemist
and industrial hygienist assigned exposure using job title with a semiquantitative scale developed for
294 exposures, including TCE. For each exposure, a three-level ranking was used for concentration
(low or background, medium, high) and frequency (percent of working time: low, 1-5%; medium, >5-
30%; and high, >30%).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
82% of all cancer cases interviewed face to face by a trained interviewer, 10% telephone interview,
and 8% mailed questionnaire. Cases interviews were conducted either at home or in the hospital; all
population control interviews were conducted at home.
Interviews were unblinded but exposure coding was carried out blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 20% of all cancer cases had proxy respondents.
CATEGORY G: SAMPLE SIZE
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Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
294 cases (78% response), 533 population controls (72% response).
Exposure prevalence: Any TCE exposure, 5% cases; substantial TCE exposure (exposure for >10 yrs
and up to 5 yrs before disease onset), 1% cases.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, education, respondent status, cigarette smoking, beer consumption and BMI (Dumas et al..
2000).
Age, family income, cigarette smoking, coffee, ethnic origin, and beer consumption (Siemiatycki,
1991).
Mantel-Haenszel (Siemiatycki, 1991).
Logistic regression (Dumas et al., 2000).
No.
Yes.
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B .3.2.3.3. Fredriksson et al. (1989).
B .3.2.3.3.1. Author's abstract.
A case-control study on colon cancer was conducted encompassing 329 cases and
658 controls. Occupations and various exposures were assessed by questionnaires.
A decreased risk was found in persons with physically active occupations. This
effect was most pronounced in colon descendens and sigmoideum with an odds
ratio (OR) of 0.49 whereas no reduced risk was found for right-sided colon
cancer. Regarding specific jobs, reduced ORs were found for agricultural,
forestry, and saw mill workers and increased OR for railway employees. High-
grade exposure to asbestos or to organic solvents gave a two-fold increased risk.
Regarding exposure to trichloroethylene in general, a slightly increased risk was
found whereas such exposure among dry cleaners gave a 7-fold increase of the
risk.
B.3.2.3.3.2. Study description and comment.
Fredriksson et al. (1989) reported data from a population case-control study of
occupational and nonoccupational exposures and rectal cancer conducted in Urea, Sweden. The
investigators identified 329 diagnosed cases of rectal cancers (ICD 8, 153), between 1980 and
1983, confirmed on the basis of histology reports and alive at the time of data collect between
1984 and 1986; 302 (165 males and 165 females) of these participated in the study interview
(92% response). A population-based control group (n = 658), matched by a 1:2 ratio to cases on
age sex and county residence, was drawn using the Swedish National Population Register list;
623 (306 males and 317 females) returned mailed questionnaires and participated in the study
(95% response).
The occupational assessment consisted of a detailed description of each job held during
the working lifetime, including details on specific occupations and exposures. Occupation
information was provided directly from each case and control given the study's eligibility
requirement of being alive at the time of data collection. A team of experts independently
classified three exposures of interest (asbestos, organic solvents, and impregnating agents) into
two categories, low grade exposure and high grade exposure and other chemical-specific
exposures, including TCE, as either "exposed" or "unexposed." Fredriksson et al. (1989) do not
define these categories nor do they provide information on exposure potential, frequency of
exposure, or concentration of exposure. No information is provided whether experts were
blinded as to disease status.
Statistical analysis examining occupation and agent-specific exposures was carried out
using Mantel-Haenszel ^ stratified on age, sex, and an index of physical activity. Odds ratios
associated with specific chemical exposure are presented with their 95% CIs.
The strengths of this study were its specific information about job duties for all jobs held
and a definitive diagnosis of rectal cancer. However, the study's assignment of exposure
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potential from information using mailed questionnaires is considered inferior to information
obtained directly from trained interviewers and expert assessment because of greater uncertainty
and misclassification (Fritschi et al., 1996). The degree of potential exposure misclassification
bias in this population case-control study of colon cancer is not known. Furthermore, exposure
prevalence to TCE appears low, as judged by the wide CI around the OR. This study is
considered as having decreased sensitivity for examining colon cancer and TCE given the
apparent lower exposure prevalence and likely exposure misclassification bias associated with
mailed questionnaire information.
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Fredriksson M, Bengtsson N-O, Hardell L, Axelson O.
case-control study. Cancer 63:1838-1842.
Colon cancer, physical activity, and occupational exposure. A
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Abstract — to evaluate occupational and nonoccupational exposures as risk factors for colon cancer.
302 (165 males and 165 females) cases participated in study out of 329 eligible cases reported to the
Swedish Cancer Registry between 1980 and 1983, among resident of Umea, Sweden, alive at time of
data collection 1984 and 1986, and with histological-confirmed diagnosis of colon cancer.
623 (306 males and 317 females) identified from Swedish Population Registry and matched for age,
sex, and county of residence.
Participation rate: cases, 92%; population controls, 95%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
ICD-8, 153 (malignant neoplasm of large intestine, except rectum).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Serf-reported information on occupational exposure as obtained from a mailed questionnaire to study
participants. Questionnaire sought information on complete working history, other exposures, and
dietary habits. Procedure for assigning chemical exposures from job title information not described in
paper.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Mailed questionnaire.
No information in published paper.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No proxy respondents, all cases and controls alive at time of data collection.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
302 cases (92% response), 623 population controls (95% response).
Exposure prevalence not calculated, published paper lacks number of TCE exposed cases and controls.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Yes, age, sex, and
index of physical activity.
Mantel-Haenszel.
No.
Yes.
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B.3.2.4. Esophageal Cancer Case-Control Studies
B .3.2.4.1. Parent et al. (2000a), Siemiatycki (1991).
B.3.2.4.1.1. Parent et al. (2000b) abstract.
OBJECTIVES: To describe the relation between oesophageal cancer and many
occupational circumstances with data from a population based case-control study.
METHODS: Cases were 99 histologically confirmed incident cases of cancer of
the oesophagus, 63 of which were squamous cell carcinomas. Various control
groups were available; for the present analysis a group was used that comprised
533 population controls and 533 patients with other types of cancer. Detailed job
histories were elicited from all subjects and were translated by a team of chemists
and hygienists for evidence of exposure to 294 occupational agents. Based on
preliminary results and a review of literature, a set of 35 occupational agents and
19 occupations and industry titles were selected for this analysis. Logistic
regression analyses were adjusted for age, birthplace, education, respondent (self
or proxy), smoking, alcohol, and beta-carotene intake. RESULTS: Sulphuric acid
and carbon black showed the strongest evidence of an association with
oesophageal cancer, particularly squamous cell carcinoma. Other substances
showed excess risks, but the evidence was more equivocal-namely chrysotile
asbestos, alumina, mineral spirits, toluene, synthetic adhesives, other paints and
varnishes, iron compounds, and mild steel dust. There was considerable overlap
in occupational exposure patterns and results for some of these substances may be
mutually confounded. None of the occupations or industry titles showed a clear
excess risk; the strongest hints were for warehouse workers, food services
workers, and workers from the miscellaneous food industry. CONCLUSIONS:
The data provide some support for an association between oesophageal cancer
and a handful of occupational exposures, particularly sulphuric acid and carbon
black. Many of the associations found have never been examined before and
warrant further investigation.
B .3.2.4.1.2. Study description and comment.
Parent et al. (2000b) and Siemiatycki (1991) reported data from a case-control study of
occupational exposures and esophageal cancer conducted in Montreal, Quebec (Canada) and part
of a larger study of 10 other site-specific cancers and occupational exposures. The investigators
identified 129 newly diagnosed cases of primary esophageal cancers, confirmed on the basis of
histology reports, between 1979 and 1985; 99 of these participated in the study interview (76.7%
response). One control group consisted of patients with other forms of cancer recruited through
the same study procedures and time period as the esophageal cancer cases. A population-based
control group (n = 533), frequency-matched by age strata, was drawn using electoral lists and
random digit dialing (72% response). Face-to-face interviews were carried out with 82% of all
cancer cases with telephone interview (10%) or mailed questionnaire (8%) for the remaining
cases. Twenty percent of all case interviews were provided by proxy respondents.
The occupational assessment consisted of a detailed description of each job held during
the working lifetime, including the company, products, nature of work at site, job activities, and
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any additional information that could furnish clues about exposure from the interviews. A team
of industrial hygienists and chemists blinded to subject's disease status translated jobs into
potential exposure to 294 substances with three dimensions (degree of confidence that exposure
occurred, frequency of exposure, and concentration of exposure). Each of these exposure
dimensions was categorized into none, any, or substantial exposure. Any exposure to TCE was
1% among cases (n = 1) and 1% for substantial TCE exposure (n = 1); "substantial" is defined as
>10 years of exposure for the period up to 5 years before diagnosis.
Logistic regression models adjusted for age, education, respondent status, birthplace,
cigarette smoking, beer consumption spirits consumption and beta-carotene intake (Parent et al.,
2000a) or Mantel-Haenszel £ stratified on age, family income, cigarette smoking, coffee, and an
index for alcohol consumption (Siemiatycki, 1991). Parent et al. (2000b) presents observations
of analyses examining industries, occupation, and some chemical-specific exposures, including
solvents, but not TCE. Observations on TCE from Mantel-Haenszel analyses are found in the
original report of Siemiatycki (1991). Odds ratios for TCE exposure are presented in
Siemiatycki (1991) with 90% CIs and 95% CIs in Parent et al. (2000b).
The strengths of this study were the large number of incident cases, specific information
about job duties for all jobs held, and a definitive diagnosis of esophageal cancer. However, the
use of the general population (rather than a known cohort of exposed workers) reduced the
likelihood that subjects were exposed to TCE, resulting in relatively low statistical power for the
analysis. The JEM, applied to the job information, was very broad since it was used to evaluate
294 chemicals.
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Parent M-E, Siemiatycki J, Fritschi L. (2000b). Workplace exposures and oesophageal cancer. Occup Environ Med 57:325-
334.
Siemitycki J. (1991). Risk Factors for Cancer in the Workplace. Boca Raton: CRC Press.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This population case-control study was designed to generate hypotheses on possible association
between 1 1 site-specific cancers and occupational title or chemical exposures.
129 esophageal cancer cases were identified among male Montreal residents between 1979 and 1985
of which 99 were interviewed.
740 eligible male controls identified from the same source population using random digit dialing or
electoral lists; 533 were interviewed. A second control series consisted of all other cancer controls.
Participation rate: cases, 76.7%; population controls, 72%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
ICD-O, 150 (malignant neoplasm of esophagus).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Unblinded interview using questionnaire sought information on complete job history with
supplemental questionnaire for jobs of a priori interest (e.g., machinists, painters). Team of chemist
and industrial hygienist assigned exposure using job title with a semiquantitative scale developed for
294 exposures, including TCE. For each exposure, a three-level ranking was used for concentration
(low or background, medium, high) and frequency (percent of working time: low, 1-5%; medium, >5-
30%; and high, >30%).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
82% of all cancer cases interviewed face-to-face by a trained interviewer, 10% telephone interview,
and 8% mailed questionnaire. Cases interviews were conducted either at home or in the hospital; all
population control interviews were conducted at home.
Interviews were unblinded but exposure coding was carried out blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 20% of all cancer cases had proxy respondents.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
99 cases (76.7% response), 533 population controls (72%).
Exposure prevalence: Any TCE exposure, 1% cases; substantial TCE exposure (exposure for >10 yrs
and up to 5 yrs before disease onset), 1% cases.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, education, respondent status, birthplace, cigarette smoking, beer consumption spirits
consumption, and beta-carotene intake (Parent et al., 2000b).
Age, family income, cigarette smoking, and index for alcohol consumption (Siemiatycki,
Mantel-Haenszel (Siemiatycki, 1991).
Logistic regression (Parent et al., 2000b).
1991).
No.
Yes.
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B.3.2.5. Liver Cancer Case-Control Studies
B.3.2.5.1. Lee et al. (2003).
B.3.2.5.1.1. Author's abstract.
Aims: To investigate the association between cancer mortality risk and exposure
to chlorinated hydrocarbons in groundwater of a downstream community near a
contaminated site. Methods: Death certificates inclusive for the years 1966-97
were collected from two villages in the vicinity of an electronics factory operated
between 1970 and 1992. These two villages were classified into the downstream
(exposed) village and the upstream (unexposed) according to groundwater flow
direction. Exposure classification was validated by the contaminant levels in
49 residential wells measured with gas chromatography/mass spectrometry.
Mortality odds ratios (MORs) for cancer were calculated with cardiovascular-
cerebrovascular diseases as the reference diseases. Multiple logistic regressions
were performed to estimate the effects of exposure and period after adjustment for
age. Results: Increased MORs were observed among males for all cancer, and
liver cancer for the periods after 10 years of latency, namely, 1980-89, and 1990-
97. Adjusted MOR for male liver cancer was 2.57 (95% confidence interval 1.21
to 5.46) with a significant linear trend for the period effect. Conclusion: The
results suggest a link between exposure to chlorinated hydrocarbons and male
liver cancer risk. However, the conclusion is limited by lack of individual
information on groundwater exposure and potential confounding factors.
B .3.2.5.1.2. Study description and comment.
Exposure potential to chlorinated hydrocarbons was assigned in this community case-
control study of liver cancer in males >30 years of age using residency as coded on death
certificates obtained from local household registration offices. No information is available to
assess the completeness of death reporting to the local registration office. Of the 1,333 deaths
between 1966 and 1997 in two villages surrounding a hazardous waste site, an electronics
factory operating between 1970 and 1992 in Taoyuan, Taiwan,3 266 cancer deaths were
identified; 53 liver cancer deaths, 39 stomach cancer deaths, 26 colorectal deaths, and 41 lung
cancer deaths. Controls were identified from 344 deaths due to cardiovascular and
cerebrovascular diseases, without arrhythmia; 286 were included in the statistical analysis.
Residents from a village north and northeast of the plant were considered exposed and residents
living south considered unexposed to chlorinated hydrocarbons. Statistical analyses are limited
to Mantel-Haenszel %2 approaches stratified by sex and age and, for male cases and controls,
logistic regression with age as a covariate. Socioeconomic characteristics were similar between
residents of the two villages (Wang, 2004). The study does not include control for potential
confounding from hepatitis virus; high rates of hepatitis B and C are endemic to Taiwan and
northern Taiwan, the location of this study, has a high prevalence of hepatitis C virus infection
3The factory's workers were subjects in the cohort studies of Chang et al. (2003, 2005) and Sung et al. (2007, 2008).
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(Lee et al., 2003). Confounding would be introduced if the prevalence of hepatitis C differed
between the two villages.
Exposure assessment is quite limited and misclassification bias likely high using
residence address as recorded on the death certificate as a surrogate for consumption of
contaminated drinking water. The paper not only lacks information on intensity and duration of
hydrocarbon exposures to individual cases and controls, but no information is available on an
estimate of the amount of TCE ingested. Information on residence length, population mobility,
and chemical usage at the plant are lacking. Similarly, well water monitoring is sparse, based on
seven chlorinated hydrocarbons monitored over a 7-month period between 1999 and 2000 in
69 groundwater samples from 44 wells to the north and northeast, or downstream from the
factory, and in 5 groundwater samples from 2 wells to the south or upstream from the factory.
Monitoring from other time periods is lacking with no information available to judge if current
monitoring are representative of past concentrations. Median concentrations (ug/L or ppb) and
ranges (ug/L or ppb) for these seven chemicals are identified in the table below. Highest
concentration of contaminants was from wells closest to the factory boundary with
concentrations detected at or close to maximum contaminant levels in wells located 0.5 mile
(1,000 meters) away. A municipal system supplied water to upstream village residents (start date
not identified); however, wells served as source for water to the north or downstream village
residents. The exposure assessment does not consider potential occupational exposure.
Chemical
TCE
Perchloroethylene
cis-l,2-DCE
1 , 1 -Dichloroethane
1,1 -DCE
Vinyl chloride
Downstream
Median
28
o
6
o
3
2
1
0.003
Range
ND-1,791
ND-5,228
ND-1,376
ND-228
ND-1,240
ND-72
Upstream
Median
0.1
0.05
ND
0.05
ND
ND
Range
0.1-0.1
ND-0.1
ND
ND-0.1
ND
ND
ND = not detected
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Lee L J-H, Chung C-W, Ma Y-C, Wang G-S, Chen P-C, Hwang Y-H, Wang J-D. (2003). Increased mortality odds ratio of
male liver cancer in a community contaminated by chlorinated hydrocarbons in groundwater. Occup Environ Med 60:364-
369.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Study hypothesis of investigating cancer mortality risk and exposure to chlorinated hydrocarbons in
groundwater.
Deaths in 1966-1997 identified from local housing registration offices among residents in two villages
were the source for case and control series. The two villages were north (contaminated community)
and south (unexposed) of an electronics factory declared as a hazardous waste site. No information if
all death among residents were reported to registration office.
Cases: 53 liver cancer deaths in males and females, 51 included in statistical analysis (96%); stomach
cancer deaths (n = 39), colon and rectum deaths (n = 26), and lung cancer deaths (n = 41). Paper does
not present numbers of stomach, colo-rectal, and lung cancer deaths used in statistical analyses.
Controls: 344 cardiovascular-cerebrovascular CV-CB disease deaths, 286 CV-CB deaths without
arrhythmia included in statistical analysis (83%).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
ICD-9.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Exposure potential to chlorinated hydrocarbons in drinking water was inferred from residence address
on deaths certificate.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
NA, Record based information.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
NA
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
Liver cancer case exposure prevalence [downstream village resident], 53% (n = 24 males, n = 4
females).
Control exposure prevalence [upstream village resident], 30% (n = 44 males, n = 41 females).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Sex and age (categorical). No control for potential confounding due to hepatitis virus (for liver
cancer) or smoking (for lung cancer analyses).
Mantel-Haenszel %2.
Multiple logistic regressions (males deaths only).
No, MORs presented by time period.
Inadequate, the paper does not discuss mobility patterns of residents, percentage of population who
may have moved from area, or completeness of death ascertainment using certificates obtained from
local housing registration offices.
MOR = mortality odds ratio
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B.3.2.6. Lymphoma Case-Control Studies
B .3.2.6.1. Gold et al. (2011), Purdue et al. (2011)
B.3.2.6.1.1. Gold et al. (2011) abstract.
Objectives Few studies have examined whether exposure to chlorinated solvents
is associated with multiple myeloma. We evaluated associations between multiple
myeloma and occupational exposure to six chlorinated solvents: 1,1,1-
trichloroethane, trichloroethylene (TCE), methylene chloride (DCM),
perchloroethylene, carbon tetrachloride and chloroform. Methods In-person
interviews obtained occupational histories and information on jobs with likely
solvent exposure. We assigned exposure metrics of probability, frequency,
intensity and confidence using job-exposure matrices modified by job-specific
questionnaire information. We used logistic regression to estimate ORs and 95%
CIs for associations between multiple myeloma and ever exposure to each, and
any, chlorinated solvent and analysed whether associations varied by duration and
cumulative exposure. We also considered all occupations that were given the
lowest confidence scores as unexposed and repeated all analyses. Results Risk of
multiple myeloma was elevated for subjects ever exposed to 1,1,1-trichloroethane
(OR (95% CI): 1.8 (1.1 to 2.9)). Ever exposure to TCE or DCM also entailed
elevated, but not statistically significant, risks of multiple myeloma; these became
statistically significant when occupations with low confidence scores were
considered unexposed (TCE: 1.7 (1.0 to 2.7); DCM: 2.0 (1.2 to 3.2)). Increasing
cumulative exposure to perchloroethylene was also associated with increasing
multiple myeloma risk. We observed non-significantly increased multiple
myeloma risks with exposure to chloroform; however, few subjects were exposed.
Conclusions Evidence from this relatively large case-control study suggests that
exposures to certain chlorinated solvents may be associated with increased
incidence of multiple myeloma; however, the study is limited by relatively low
participation (52%) among controls.
B .3.2.6.1.2. Purdue et al. (2011) abstract.
BACKGROUND: Previous epidemiologic findings suggest an association
between exposure to trichloroethylene (TCE), a chlorinated solvent primarily used
for vapor degreasing of metal parts, and non-Hodgkin lymphoma (NHL).
OBJECTIVES: We investigated the association between occupational TCE
exposure and NHL within a population-based case-control study using detailed
exposure assessment methods. METHODS: Cases (n = 1,189; 76% participation
rate) and controls (n = 982; 52% participation rate) provided information on their
occupational histories and, for selected occupations, on possible workplace
exposure to TCE using job-specific interview modules. An industrial hygienist
assessed potential TCE exposure based on this information and a review of the
TCE industrial hygiene literature. We computed odds ratios (ORs) and 95%
confidence intervals (CIs) relating NHL and different metrics of estimated TCE
exposure, categorized using tertiles among exposed controls, with unexposed
subjects as the reference group. RESULTS: We observed associations with NHL
for the highest tertiles of estimated average weekly exposure (23 exposed cases;
OR = 2.5; 95% CI, 1.1-6.1) and cumulative exposure (24 exposed cases; OR =
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2.3; 95% CI, 1.0-5.0) to TCE. Tests for trend with these metrics surpassed or
approached statistical significance (p-value for trend = 0.02 and 0.08,
respectively); however, we did not observe dose-response relationships across the
exposure levels. Overall, neither duration nor intensity of exposure was associated
with NHL, although we observed an association with the lowest tertile of
exposure duration (OR = 2.1; 95% CI, 1.0-4.7). CONCLUSIONS: Our findings
offer additional support for an association between high levels of exposure to
TCE and increased risk of NHL. However, we cannot rule out the possibility of
confounding from other chlorinated solvents used for vapor degreasing and note
that our exposure assessment methods have not been validated.
B .3.2.6.1.3. Gold et al. (2011) study description and comment.
The population case-control study of multiple myeloma in men and women who were
residents of two SEER reporting sites, the Seattle-Puget Sound, Washington region and the
Detroit, Michigan metropolitan area, evaluated occupational risk factors in relation to the risk of
multiple myeloma (MM). Detailed exposure information obtained from job-specific
questionnaires allowed evaluation of association between 1,1, 1-trichloroethane, TCE,
dichloromethane, perchloroethylene carbon tetrachloride, and chloroform. Histologically-
confirmed incident cases of MM (ICD-O-2/3, Codes 9731, 9732) in men and women without a
previous diagnosis of MM, NHL or HIV, between 35 and 74 years of age, and diagnosed
between 2000 and 2002 were eligible as cases, with population controls having Seattle-Puget
Sound, Washington or Detroit, Michigan metropolitan area addresses identified from random
digit dialing if <65 years of age, or by random selection from Medicare or Medicaid files for
controls 65-74 years of age. Controls for this study were the same as those participating in the
population-based case-control study of NHL carried out at the same time in these SEER areas, in
addition, to two other SEER areas. A greater proportion of controls than cases were from
Seattle-Puget Sound area. Face-to-face interviews were completed for 181 cases (71%
participation rate) and 418 (52% participation rate).
In-person interviews were conducted using a computer-assisted interview program with
modules focused specifically on solvent exposures for jobs held >2 years in 20 occupations.
Proxy interviews were not permitted but were allowed to aid in recalling occupational details.
All jobs were coded according to the Standard Occupational Classification system. For each of
the six solvents, exposure metrics of probability, frequency, intensity, and confidence were
assigned by modifying JEMs based on the subjects' answers to the questionnaire's sections on
work history and job module. The JEMs were developed for each decade for specific industries,
occupational and tasks by an industrial hygienist after reviewing published paper and reports on
chlorinated solvents (e.g., 2007 for TCE). The assignment of exposure probability defined as the
theoretical percentage of workers reporting the same information that would have been likely to
have had exposure to the solvent is one strength of the study. For all jobs with probability scores
of at least 1 (>1% of subjects were likely to have had exposure), frequency and intensity scores
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were also assigned, with values of 1, 2, 3, or 4 for each variable. Additionally, depending on the
information source for assigning the probability, frequency, and intensity score, whether from
literature review or self-reported, a confidence level was assigned on a scale of 1-4. Exposure
surrogates developed for each of the six solvents were ever exposed and cumulative exposure,
defined as the sum over all jobs of the product of intensity, exposure duration, and frequency. Of
the 180 cases, 66 (37%) were identified as having been ever exposed to TCE (confidence scores
of 1 or higher) with 24 of the TCE exposed cases (13% of all cases) assigned to the highest
cumulative exposure group. Moreover, roughly one-third of the TCE-exposed cases were
identified as having a low confidence level score (no information was available on probability,
frequency or intensity or contradictory information exists in the literature), suggesting a greater
potential for exposure misclassification bias in TCE assignment.
Association between MM and individual occupational solvents exposure was
assessed using unconditional logistic regression to estimate ORs and 95% CIs. Jobs with
probability score of >2 (>10% subjects in that job were likely to have had TCE exposure)
were defined as ever exposed to TCE. A lag period of 10 years, e.g., summing TCE
exposures up to a period 10 years before disease diagnosis, was also examined in
analyses of cumulative exposure. All statistical models included covariates for sex, age
(three categories), race (four categories), education (three categories), and SEER site.
Each of the continuous exposure metrics was categorized into four groups according to
quartiles of the control exposure distribution. For TCE, cumulative exposure scores were
2,218 ppm-year (median) (range, 1-50,000 ppm-year). Test of trend were conducted
using a linear term for the median duration and cumulative scores among controls in each
category. Gold et al. (2011) further reported findings from sensitivity analyses
considering all cases and controls with confidence scores of 1 as unexposed to address
potential misclassification bias resulting from the identification of unexposed individuals
as exposed. In studies with low exposure prevalences like Gold et al. (2011) this
misclassification bias would diminish observed associations between TCE and multiple
myeloma (Stewart and Correa-Villaseor, 1991).
B .3.2.6.1.4. Purdue et al. (2011) study description and comment.
This population case-control study of NHL in four SEER reporting areas was designed to
investigate the association between NHL and occupational factors and focused on TCE
exposures with a detailed exposure assessment method. Histologically-confirmed incident cases
of NHL in men and women between 20 and 74 years of age, diagnosed between 1998 and 2000,
and without know HIV infection were identified from four SEER reporting areas—the State of
Iowa, the Seattle, Washington and Detroit, Michigan metropolitan areas, and Los Angeles
County, California—with populations controls having addresses in the four SEER reporting
areas identified from random digit dialing for men and women <65 years of age, or by random
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selection from Medicare files, for men and women 65-74 years of age. NHLs were classified
using according to the ICD-O-2 (converted to ICD-O-3, Codes 967-972): B-cell lymphomas,
including small B-cell lymphoma, large diffuse B-cell lymphoma, follicular, or precursor
lymphoblastic leukemia, and T-cell lymphoma, including anaplastic T-cell, N/K, and
lymphoblastic leukemia. Subjects with CLL were ineligible; however, 28 recruited cases of
small lymphocytic lymphoma were later identified by pathology review to be cases of CLL and
were retained because the two diagnoses comprise the same disease. Face-to-face interviews
were completed for 1,321 NHL cases (76% participation rate) and 1,057 controls (52%
participation rate). Of these, 132 cases and 75 controls that were never employed or had
unknown occupation were excluded, leaving 1,189 cases and 982 controls for the analysis.
Subjects provided information on residential and occupation history from a mailed
calendar, with an in-person interview and home visit using a computer-assisted interview
program with modules on solvent exposure, added 1 year after the study's start date. Of the
computer-assisted personal interviews, 682 cases and 640 controls included the solvent-focused
modules. The occupational history gathered information on each job held by the subject for
>1 year since the age of 16. For selected occupations, 1 of 32 job- or industry-specific modules
was administered based on information collected in the occupational histories. The information
collected in the modules included the average frequency of various solvent-related tasks, the
average length of time it took to perform given solvent-related tasks, sensory descriptions,
dermal exposure, work practices, engineering controls, and personal protective equipment use.
Information was also sought from subjects who reported jobs that could involve degreasing on
the usual number of hours per instance spent degreasing, the identity of the chemical used for
degreasing, the percentage of time each chemical was used, whether the degreasing solvent was
heated or at room temperature, and the manner in which parts were cleaned.
The 23 exposure matrices developed by the industrial hygienist using information from
the literature review, including Bakke et al. (2007), the subject's occupational history, and the
information collected in the job modules, an expert industrial hygienist assessed levels of
probability, frequency, and intensity of TCE exposure for each job. The assignment of exposure
probability defined as the theoretical percentage of workers reporting the same information that
would have been likely to have had exposure to the solvent is one strength of this study. For all
jobs with probability scores of at least 1 (>1% of subjects were likely to have had exposure),
frequency and intensity scores were also assigned on a scale of 1-4 for frequency and 1-5 for
intensity. The intensity score also reflected dermal exposure. The job-specified estimates of
frequency and intensity for each subject were integrated to develop several metrics of TCE
exposure. A subject was identified as "unexposed" if all jobs had been assigned an exposure
probability of 0%, "possibly exposed" if one or more jobs had been assigned an exposure
probability of <50% (probability scores of 1, 2, or 3, and "probably exposed" if at least one job
had been assigned an exposure probability of >50% (probability scores of 4 or 5). For subjects
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defined as probably exposed, the following additional exposure metrics were calculated:
exposure duration; cumulative exposure, defined as the sum, across all jobs with exposure
probability scores of 4 or 5, of the product of intensity midpoint, the frequency midpoint, and the
duration in weeks; average week exposure, defined as the cumulative exposure divided by
exposure duration; and average exposure intensity defined as the duration-weighted average
intensity level across all jobs with probability scores of 4 or 5. Of the 1,189 cases, 545 (46%)
were assigned an exposure level of "possible" and 45 cases (4%) an exposure level of
"probable." Among subjects with probable confidence TCE exposure, the median cumulative
exposure score was 150 ppm-year [range, 1->234,000 ppm-year].
Association between NHL and TCE exposure metrics was assessed using unconditional
logistic regression to estimate ORs and 95% CIs. Other than the ever/never analysis, all analyses
include subjects with probable TCE exposure, those with probability scores of 4 or 5. The
observed exposure prevalence among subjects assigned possible exposure, defined as holding a
job with a confidence score of 1, 2, or 3, suggested poor specificity and was inconsistent with the
narrow set of occupational applications for TCE from the literature review. The higher
likelihood for possible exposure misclassification bias and the importance of high specificity
exposure assessment, further analysis of this measure was judged as unlikely to be informative.
All statistical analyses included covariates for age (three categories), sex, race (four categories),
education (three categories) and SEER area. The exposure metrics were categorized using
tertiles among probably exposed controls as cut-points. In addition, ORs and 95% CIs were
reported for exposure defined as the difference between the second and third tertiles among
exposed controls. Test of trend were performed by modeling exposure the exposure metrics as
continuous variables. Last, the association between TCE exposure and specific histologically-
defmed NHL subtypes (diffuse large B-cell, follicular lymphoma, and small lymphocytic
lymphoma/CLL, were reported using polytomous regression to explore possible heterogeneity.
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Gold LS, Stewart PA, Milliken K, Purdue M, Severson R, Seixas N, Blair A, Hartge P, Davis S, Dr Roos AJ. (2011). The
relationship between multiple myeloma and occupational exposure to six chlorinated solvents. Occup Environ Med 68:391-
399. doi:10.1136/oem.2009.054809].
Purdue MP, Bakke B, Stewart P, De Roos AJ, Schenk M, Lynch CF, Bernstein L, Morton LM, Cerhan JR, Severson RK,
Cozen W, Davis S, Rothman N, Martge P, Colt JS. (2011). A case-control study of occupational exposure to trichloroethylene
and non-Hodgkin lymphoma. Environ Health Perspect 119:232-238 doi:10.1289/ehp.!002106 [Online 2 November 2010]
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Study hypotheses of investigating association between TCE exposure and NHL using detailed
exposure assessment methods (Purdue etal.. 2011) and evaluating associations between multiple
myeloma (Gold etal.. 2011) and occupational exposure to six chlorinated solvents: 1,1,1-trichloro-
ethane, methylene chloride, perchloroethylene, carbon tetrachloride, and chloroform.
Cases: 1,321 (2,248 eligible) histologically -confirmed NHL cases in males and females, 20-74 yrs of
age, 1998-2000, and residents of four SEER reporting areas — Iowa, Los Angeles County, California,
Seattle, Washington metropolitan area and Detroit, Michigan metropolitan area (Purdue etal.. 2011):
181 (255 eligible) histologically -confirmed multiple myeloma cases in males and females, 35-74 yrs
of age, 2000-2002, and residents of two SEER reporting areas — Seattle-Puget Sound, Washington
area and Detroit, Michigan metropolitan area (Gold etal.. 2011)
Controls: 1,057 (2,409 eligible) controls identified from random digit dialing (<65 yrs old) or
Medicare file (65-75 yrs old) who were residents in the four SEER areas (Purdue etal.. 2011):
481 (1,133 eligible) controls identified from Purdue et al. (2011) who were 35-74 yrs of age, no
previous diagnosis of HIV, MM, plasmacytoma, or NHL, spoke English, and residents of Seattle-
Puget Sound, Washington area and Detroit, Michigan metropolitan area (Gold et al.).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
NHL and multiple myeloma incidence.
ICD-0-2 [Codes 967-972, NHL; 9731-9732, MM].
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Literature review, exposure matrices occupational histories and information collected in the job
module supported assignment by expert industrial hygienist of probability, frequency, and intensity of
TCE for each iob held >12 months (Purdue et al., 201 1) or >2 vrs (Gold et al., 201 1).
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CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
In-person interview using questionnaire or computer-assisted personal interview (682 of 1,321 cases
and 640 of 1,057 controls in Purdue et al. (2011) with modules for jobs of interest.
Interviewer not blinded. Exposure assessment assigned blinded.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No proxy interviews.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
1,321 cases (76% participation rate); 1,051 controls (52% participation rate) (Purdue et al., 2011). Of
these, 132 cases and 75 controls that were never employed or had unknown occupation were excluded,
leaving 1,189 cases and 982 controls for the analysis.
181 cases (71% participation rate); 1,113 controls (52% participation rate) (Gold etal. 2011).
Exposure prevalence, ever exposed to TCE (>50% of subjects in job probably exposed), 27 (2.8%)
NHL cases; 0.7% of cases in highest cumulative exposure category and 2.3% in highest average
exposure intensity category (Purdue etal.. 2011); ever exposed to TCE (>10% of subjects in job with
probable exposure) (Gold etal.. 2011).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, SEER center, race and education (Gold etal.; Purdue etal.. 2011).
Unconditional logistic regression.
Test for trend performed by modeling the exposure metrics as continuous variable (Purdue et al..
2011) or using median duration and cumulative scores among controls for each exposure category.
Yes, study was well documented with supplemental material on publisher's webpage (Purdue et al..
2011).
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B .3.2.6.2. Cocco et al. (2010).
B .3.2.6.2.1. Author's abstract.
BACKGROUND: Several studies have suggested an association between
occupational exposure to solvents and lymphoma risk. However, findings are
inconsistent and the role of specific chemicals is not known. Objective To
investigate the role of occupational exposure to organic solvents in the aetiology
of B-cell non-Hodgkin's lymphoma (B-NHL) and its major subtypes, as well as
Hodgkin's lymphoma and T-cell lymphoma. METHODS: 2348 lymphoma cases
and 2462 controls participated in a case-control study in six European countries.
A subset of cases were reviewed by a panel of pathologists to ensure diagnostic
consistency. Exposure to solvents was assessed by industrial hygienists and
occupational experts based on a detailed occupational questionnaire. RESULTS:
Risk of follicular lymphoma significantly increased with three independent
metrics of exposure to benzene, toluene and xylene (BTX) (combined p=4 x 10(-
7)) and to styrene (p=l x 10(-5)), and chronic lymphocytic leukaemia (CLL) risk
increased with exposure to solvents overall (p=4 x 10(-6)), BTX (p=5 x 10(-5)),
gasoline (p=8 x 10(-5)) and other solvents (p=2 x 10(-6)). Risk of B-NHL for ever
exposure to solvents was not elevated (OR=1.1, 95% CI 1.0 to 1.3), and that for
CLL and follicular lymphoma was 1.3 (95% CI 1.1 to 1.6) and 1.3 (95% CI 1.0 to
1.7), respectively. Exposure to benzene accounted, at least partially, for the
association observed with CLL risk. Hodgkin's lymphoma and T-cell lymphoma
did not show an association with solvent exposure. CONCLUSION: This analysis
of a large European dataset confirms a role of occupational exposure to solvents
in the aetiology of B-NHL, and particularly, CLL. It is suggested that benzene is
most likely to be implicated, but we cannot exclude the possibility of a role for
other solvents in relation to other lymphoma subtypes, such as follicular
lymphoma. No association with risk of T-cell lymphoma and Hodgkin's
lymphoma was shown.
B.3.2.6.3. Study description and comment.
This population case control study of NHL in the Czech Republic, France, Germany,
Italy, Ireland, and Spain was designed to examine possible personal and occupational risk factors
for lymphoma subtypes as defined using the WHO classification (the Epilymph study).
Observations in German subjects are reported separately in Seidler et al. (2007) (see B.3.2.6.6).
The publication of Cocco et al. (2010) examined solvents and adopted expert assessment to
assign exposure potential to organic solvents, specifically, chlorinated aliphatic hydrocarbons,
benzene, toluene, xylene, gasoline, mineral spirits, styrene, and TCE. Cases of lymphoma in
adults, >17 years of age, and diagnosed in 22 centers in 1998 and 2004 with population controls
selected by sampling from the general population, and matched to cases on sex, age, and
residence area, in Germany and Italy, or matched hospital controls limited to diagnoses other
than cancer, infectious diseases, and immunodeficient diseases in the Czech Republic, France,
Ireland, and Spain. The lymphoma diagnosis was classified according to the 2001 WHO
classification of lymphoma, and slides of about 20% of cases from each center were reviewed
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centrally by a panel of pathologists and reclassified when necessary. Lymphoma cases included
in this study were B-cell lymphomas, including B-cell subtypes, T-cell lymphomas, and Hodgkin
lymphoma. Informed consent was obtained for 2,348 lymphoma cases (88%) and 2,462 controls
(81% hospital controls, 52% population controls) who participated in the study. Most cases were
B-cell lymphomas (n = 1,869) with fewer T-cell (n = 133) and Hodgkin (n = 339) lymphoma.
Trained interviewers administered a structured questionnaire through in-person
interviews with cases and controls to collect information on sociodemographic factors, lifestyle,
health history, and complete work history for all full-time jobs held for >1 year. Special
questionnaire modules for specific occupations gathered additional details on jobs and exposure
of a priori interest. Industrial hygienists in each center reviewed the general and specific
questionnaires and assessed exposure to 43 agents, including organic solvents according to
confidence, intensity, and frequency of exposure. The paper does not report if proxy or next-of-
kin provided information if the case or control was deceased. Confidence represented the degree
of certainty that the worker had been exposed to the agent and was based both on probability of
exposure and on the proportion of workers exposed in a give job, <40% (possible exposure), 40-
90%, (probable exposure), and >90% (certain/definite exposure). Intensity of exposure was
defined as a rank-ordered variable, unexposed (0), low (1), medium (2), high (3), with agent-
specific cut-off points defined based on current threshold limit values, likely half the threshold
limit value (TLV) (low), 51-150% (medium), and >150% (high) (Kiran et al.. 2010). Exposure
frequency expressed the proportion of work time involving contact with the agent: unexposed
(coded as 0), 1-5% of the work time (coded as 1), >5-30% of the work time (coded as 2), and
>30% of the work time (coded as 3). Exposure potential to TCE for cases and controls was
based surrogates for overall exposure and cumulative exposure score. The cumulative exposure
score was the sum over a subjects work history of the product of duration and frequency/3 to the
power of intensity and results in a log distribution of exposure scores. Exposure prevalence to
TCE is low in this study; Cocco et al. (2010) identifies 71 cases of B-cell lymphoma (4%
exposure prevalence) and 117 controls (5% exposure prevalence) with high confidence overall
TCE exposure and of these exposed subjects, 29 cases (2%) and 37 (2%) with a high-confidence,
high-cumulative exposure score.
Association between B-cell lymphoma and B-cell lymphoma subtypes and individual
occupational solvent exposures was assessed using unconditional logistic regression, which
adjusted for age, sex, education, and center. Alcohol and smoking were not included as a
potential confounder as previous analysis of the Epilymph data showed no association (Besson et
al., 2006). Statistical analyses are limited to subjects whose jobs TCE exposure was assessed
with high degree of confidence, defined as >90%of worker exposed in a given job. Lymphoma
subtypes examined included diffuse large B-cell lymphoma, follicular lymphoma, CLL, and
multiple myeloma. There were few cases of T-cell lymphomas with high confidence TCE
exposure; six cases with overall exposure, two of which with high confidence high cumulative
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score. Two-tailed 95% CIs of the OR were calculated with the Wald statistics and trend test
defining cumulative exposure score as a continuous variable using Wald's test for trend. As
common to epidemiological studies, the many statistical analyses and comparisons in Cocco et
al. (2010) increases the potential for false positive errors and Cocco et al. (2010) used Bonferroni
correction of individual CIs and trend tests as an attempt to reduce this type of bias.
This study adopted a detailed exposure assessment, current classification system for
lymphomas, and was of a large number of cases and controls, although exposure prevalence to
TCE was <5%, typical of population case-control studies. This study defines the cumulative
exposure score using a log scale, in addition, to using a rank-order value for intensity instead of a
midpoint of an range of exposure concentrations. Other cohort and case-control studies of TCE
and NHL, e.g., Purdue et al. (2011), define their cumulative exposure score as a product of
intensity, frequency, and duration. Each approach will produce a slightly different rank ordering
(personal communication). In the cumulative exposure formula of Cocco et al. (2010), exposure
duration contributes the greatest weight in light of the formula's treatment of 1/3 the value of
frequency (Cocco et al., 2010). The direction of bias in estimated trends of disease risk by
cumulative exposure depends on the variation of duration, with large variation in durations
between exposure exposures leading to downward bias. Cocco et al. (2010), also, reported ORs
and CIs for high confidence TCE exposure, assigned to a job title when over 90% of workers
were exposed. In comparison, both Purdue et al. (2011) and Gold et al. (2011) defined probable
exposure if at least one job has been assigned an exposure probability of >50%. Any differences
in reported findings between Cocco et al. (2010) and the other NHL studies of Miligi et al.
(2006), Wang et al. (2009), and Purdue et al. (2011) may be due to these differences.
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Cocco P, Mannetje A, Fadda D, Melis M, Becker N, Sanjose S, Foretova L, Marekova J, Staines A, Kleefeld S, Maynadie M,
Nieters A, Brennan P, Boffetta P. (2010). Occupational exposure to solvents and risk of lymphoma subtypes: results from the
Epilymph case-control study. Occup Environ Med 67:341-347.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This study evaluated occupational exposure to organic solvents as risk factors of NHL in a population-
based, case-control study of men and women in six European countries.
2,348 hospital cases of NHL diagnosed between 1998 and 2004 among men and women, >17 yrs of
age, and residents of Czech Republic, France, Germany, Ireland, Italy, and Spain; 2,462 population
and hospital controls, identified from census lists in Germany and Italy or small hospitals as the cases,
in all other countries, and matched to cases on age, sex, and study center.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Lymphoma incidence - B-cell lymphoma (CLL, follicular, and diffuse large B-cell), T-cell lymphoma,
Hodgkin lymphoma, and multiple myeloma. Postransplant lymphoproliferative disorder or
monoclonal gammopathies of undetermined significance were excluded as cases.
WHO classification system
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
All jobs held for >1 yr assigned to standardized occupation (5-digit code). Industrial hygienists at
each center assigned exposure to 43 agents, including TCE and other solvents (benzene, toluene,
xylene, chlorinated aliphatic hydrocarbons, and gasoline) to subjects according to confidence
(possible, probable, certain), intensity (unexposed, low, medium, high), and frequency. Exposure
surrogates for overall exposure and cumulative exposure (low, medium, high).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Face-to-face interview with questionnaire for information about medical history, lifestyle factors,
lifetime occupational history (all jobs held >1 yr) and supplemental modules for specific occupations
to gather additional details on jobs and exposures of a priori interest.
Unblinded interviews. Blinded exposure assessment.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Not reported in published paper.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
2,348 cases (88% participation rate) and 2,462 controls (81% participation rate, hospital controls, 52%
participation rate, population controls).
Exposure prevalence, subjects with high confidence overall TCE exposure, 71 (4%) all B-cell
lymphoma, 6 (7%) T-cell lymphoma, and 48 (6%) NHL (B-cell diffuse and follicular subtypes and
T-cell); subjects with high confidence high cumulative TCE exposure, 29 (2%) all B-cell lymphomas,
2 (2%) T-cell lymphoma, 14 (2%) NHL (B-cell diffuse and follicular subtypes and T-cell).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, education, and center.
Unconditional logistic regression.
Yes, using cumulative exposure defined as low, medium, high.
Yes.
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B .3.2.6.4. Wang et al. (2009).
B .3.2.6.4.1. Author's abstract.
A population-based case-control study involving 601 incident cases of non-
Hodgkin lymphoma (NHL) and 717 controls was conducted in 1996-2000 among
Connecticut women to examine associations with exposure to organic solvents. A
job-exposure matrix was used to assess occupational exposures. Increased risk of
NHL was associated with occupational exposure to chlorinated solvents (odds
ratio (OR) = 1.4, 95% confidence interval (CI): 1.1, 1.8) and carbon tetrachloride
(OR = 2.3, 95% CI: 1.3, 4.0). Those ever exposed to any organic solvent in work
settings had a borderline increased risk of NHL (OR = 1.3, 95% CI: 1.0, 1.6);
moreover, a significantly increased risk was observed for those with average
probability of exposure to any organic solvent at medium-high level (OR =1.5,
95% CI: 1.1, 1.9). A borderline increased risk was also found for ever exposure to
formaldehyde (OR = 1.3, 95% CI: 1.0, 1.7) in work settings. Risk of NHL
increased with increasing average intensity (P = 0.01), average probability (p<
0.01), cumulative intensity (P = 0.01), and cumulative probability (p < 0.01) level
of organic solvent and with average probability level (P = 0.02) and cumulative
intensity level of chlorinated solvent (P = 0.02). Analyses by NHL subtype
showed a risk pattern for diffuse large B-cell lymphoma similar to that for overall
NHL, with stronger evidence of an association with benzene exposure. Results
suggest an increased risk of NHL associated with occupational exposure to
organic solvents for women.
B.3.2.6.4.2. Study description and comment.
This population case-control study of NHL in Connecticut women was designed to
examine possible personal and occupational risk factors for NHL. The publication of Wang et
al. (2009) examined solvent exposure and adopted a JEM to assign exposure potential to nine
chemicals—benzene, formaldehyde, chlorinated solvents, chloroform, carbon tetrachloride,
dichloromethane, methyl chloride, and TCE. Histologically-confirmed incident cases of NHL in
women aged between 21 and 84 years of age and diagnosed in Connecticut between 1996 and
2000 were identified from the Connecticut Cancer Registry, a SEER reporting site, with
population controls having Connecticut address identified from random digit dialing for women
<65 years of age, or by random selection from Centers for Medicare and Medicaid Service files
for women aged >65 years old. Controls were frequency matched to cases within 5-year age
groups. Face-to-face interviews were completed for 601 (72%) cases and 717 controls (69% of
those identified from random digit dialing and 47% identified using Health Care Financing
Administration files).
Trained interviewers administered a structured questionnaire through in-person
interviews with cases and controls to collect information on diet, nutrition, and alcohol intake;
reproductive factors; hair dye use; and lifetime occupational history of all jobs held >1 year.
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Jobs were coded to standardized occupational classification and standardized industry
classification titles and assigned probability and intensity of exposure to formaldehyde and nine
other solvents (benzene, any chlorinated solvents, DCE, chloroform, methylene chloride,
dichloroethane, methyl chloride, TCE, and carbon tetrachloride) using a JEM developed by the
NCI (Dosemeci etal., 1994; Gomez et al., 1994). All jobs held up to a year before cancer
diagnosis were assigned blinded as to disease status potential exposure to each exposure of
interest. Lifetime exposure potential for cases and controls was based on exposure duration and
a weighted score for exposure intensity and probability of each occupational and industry and
defined as a cumulative exposure metric, average metric, or ever/never metric. Of the 601 cases,
77 (13%) were assigned with potential TCE exposure over their lifetime; 8 cases were assigned
potential for high intensity exposure, but with low probability and the 31 cases identified with
medium and high probability of exposure were considered as having low intensity exposure
potential. The low exposure prevalence to TCE, overall, and few subjects identified with
confidence with high TCE exposure intensity or probability implies exposure misclassification
bias is likely, and likely nondifferential, notably for high exposure categories (Dosemeci et al.,
1990).
Association between NHL and individual occupational solvent exposure was assessed
using unconditional logistic regression model which adjusted for age, family history of
hematopoietic cancer, alcohol consumption, and race. Statistical analyses treated exposure
defined as a categorical variable, divided into tertiles based on the distribution of controls, in
logistic regression analyses and as a continuous variable, whenever possible, to test for linear
trend. Polytomous logistic regression was used to evaluate the association between histologic
subtypes of NHL (DLBCL, follicular lymphoma, or CLL/small lymphocytic lymphoma) and
exposure. The largest number of cases was of the cell type DLBCL.
Strength of this study is assignment of TCE exposure potential to individual subjects
using a validated JEM, although uncertainty accompanied exposure assignment and TCE
exposure was largely of low intensity/low probability, and no cases with medium to high
intensity/probability. Resultant misclassification bias would dampen observed associations for
high exposure potential categories. Low prevalence of high intensity TCE exposure would
reduce the study's statistical power.
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Wang R, Zhang Y, Lan Q, Holford TR, Leaderer B, Zahm SH, Boyle P, Dosemeci M, Rothman N, Zhu Y, Qin Q, Zheng T.
(2009). Occupational exposure to solvents and risk of non-Hodgkin lymphoma in Connecticut women. Am J Epidmiol
189:176-185.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This study evaluated multiple potential risk factors of NHL in a population-based case-control study of
Connecticut women. Occupational exposure to TCE was not an a priori hypothesis.
601 (832 eligible) cases of NHL, diagnosed between 1996 and 2000 among women, age 20-84 yrs and
residents of Connecticut and histologically -confirmed, were identified from the Yale Comprehensive
Cancer Center's Rapid Case Ascertainment Shared Resource, a component of the Connecticut Tumor
Registry; 717 (number of eligible controls not identified) population controls were randomly identified
using random digit dialing, if age <65 yrs, or from Medicare and Medicaid Service files, for women
aged >65 yrs old and stratified by sex and 5-yr age groups.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
NHL and chronic lymphatic leukemia incidence.
ICD-O-2 [Codes, M-9590-9642, 9690-9701, 9740-9750].
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
All jobs held for >1 yr were assigned to standardized occupation and industry classifications. Using
JEM of NCI (Dosemeci et al., 1994; Gomez et al., 1994), probability of exposure level (low,
medium and high) and intensity (very low, low, medium, and high) to TCE and other solvents
(benzene, any chlorinated solvents, DCE, chloroform, methylene chloride, dichloroethane, methyl
chloride, carbon tetrachloride, and formaldehyde) was assigned blinded as to case or control status.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Face-to-face interview with questionnaire for detailed information about medical history, lifestyle
factors, education, lifetime occupational history (all jobs held >1 yr).
Unblinded interviews.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
None.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
601 cases (72% participation) and 717 controls (69% participation for random digit dialing controls
and 47% participation for HCFA controls).
Exposure prevalence, ever exposed to TCE, 77 (13%) NHL cases; medium to high TCE intensity, 13
NHL cases (2%); medium to high TCE probability, 34 cases (6%). All 34 cases with medium to high
TCE probability assigned low intensity exposure.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, family history of hematopoietic cancer, alcohol consumption and race.
Unconditional logistic regression.
Yes, by exposure intensity and by exposure probability.
Yes.
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B .3.2.6.5. Costantini et al. (2008), Miligi et al. (2006).
B .3.2.6.5.1. Costantini et al. (2008) abstract.
Background While there is a general consensus about the ability of benzene to
induce acute myeloid leukemia (AML), its effects on chronic lymphoid leukemia
and multiple myeloma (MM) are still under debate. We conducted a population-
based case-control study to evaluate the association between exposure to organic
solvents and risk of myeloid and lymphoid leukemia and MM.
Methods Five hundred eighty-six cases of leukemia (and 1,278 population
controls), 263 cases of MM (and 1,100 population controls) were collected.
Experts assessed exposure at individual level to a range of chemicals.
Results We found no association between exposure to any solvent and AML.
There were elevated point estimates for the associations between medium/high
benzene exposure and chronic lymphatic leukemia (OR: 1.8, 95% CIViO.9-3.9)
and MM (OR: 1.9, 95% CI: 0.9-3.9). Risks of chronic lymphatic leukemia were
somewhat elevated, albeit with wide confidence intervals, from medium/high
exposure to xylene and toluene as well.
Conclusions We did not confirm the known association between benzene and
AML, though this is likely explained by the strict regulation of benzene in Italy
nearly three decades prior to study initiation. Our results support the association
between benzene, xylene, and toluene and chronic lymphatic leukemia and
between benzene and MM with longer latencies than have been observed for
AML in other studies.
B .3.2.6.5.2. Miligi et al. (2006) abstract.
BACKGROUND: A number of studies have shown possible associations between
occupational exposures, particularly solvents, and lymphomas. The present
investigation aimed to evaluate the association between exposure to solvents and
lymphomas (Hodgkin and non-Hodgkin) in a large population-based, multicenter,
case-control study in Italy. METHODS: All newly diagnosed cases of malignant
lymphoma in men and women age 20 to 74 years in 1991-1993 were identified in
8 areas in Italy. The control group was formed by a random sample of the general
population in the areas under study stratified by sex and 5-year age groups. We
interviewed 1428 non-Hodgkin lymphoma cases, 304 Hodgkin disease cases, and
1530 controls. Experts examined the questionnaire data and assessed a level of
probability and intensity of exposure to a range of chemicals. RESULTS: Those
in the medium/high level of exposure had an increased risk of non-Hodgkin
lymphoma with exposure to toluene (odds ratio =1.8; 95% confidence interval =
1.1-2.8), xylene 1.7 (1.0-2.6), and benzene 1.6 (1.0-2.4). Subjects exposed to all 3
aromatic hydrocarbons (benzene, toluene, and xylene; medium/high intensity
compared with none) had an odds ratio of 2.1 (1.1-4.3). We observed an increased
risk for Hodgkin disease for those exposed to technical solvents (2.7; 1.2-6.5) and
aliphatic solvents (2.7; 1.2-5.7). CONCLUSION: This study suggests that
aromatic and chlorinated hydrocarbons are a risk factor for non-Hodgkin
lymphomas, and provides preliminary evidence for an association between
solvents and Hodgkin disease.
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B .3.2.6.5.3. Study description and comment.
This series of papers of a population case-control study of lymphomas in 11 areas in Italy
(Costantini et al., 2008) and occupation examines author's assigned exposure to TCE and other
solvents using job-specific or industry-specific questionnaires and expert rating to cases and
controls. Miligi et al. (2006) reported findings for NHL, a category that included CLL, NHL
subtypes, and Hodgkin lymphoma in eight regions and Constantini et al. (2008) presented
observations for specific leukemia subtypes and multiple myeloma in seven regions
(eight regions for CLL). Exclusion of the regions in the original study does not appear to greatly
reduce study power or to introduce a selection bias. For example, Miligi et al. (2006) included
1,428 of the 1,450 total NHL cases, the largest percentage of all lymphoma subtypes. The
number of other lymphoma subtypes was much smaller compared to NHL; 304 cases of Hodgkin
disease, 586 cases of leukemia, and 263 cases of multiple myeloma. All cases were identified
from participating study centers and controls were randomly selected from the each area's
population using stratified sampling for sex and age.
A face-to-face unblinded interview was conducted primarily at the interviewee's home
with a high proportion of proxy responses among cases (19%) but not controls (5%). Bias is
likely introduced by the lack of blinding of interviewers and from the high proportion of proxy
interviews. A questionnaire was used to obtain information on medical history, lifestyle factors,
occupational exposure, and nonoccupational solvent exposures. Industrial hygiene professionals
assessed the probability and intensity of exposure to individual and classes of solvents using
information provided by questionnaire. Probability was classified into three levels (low,
medium, and high) with a four-category scale for intensity (very low, low, medium, and high).
These qualitative scales lacked information on exposure concentrations and likely introduces
misclassification bias that can either dampen or inflate observed risks given the study's use of
multiple exposure groupings. "Very low level" was used for subjects with occupational
exposure intensities judged to be comparable to the upper end of the normal range for the general
population; "low-level intensity" when workplace exposure was judged to be low because of
control measures but higher than background; "medium exposure" for occupational
environments with moderate or poor control measures; and "high exposure" for workplaces
lacking any control measures. Groupings of "very low/low" and "medium/high" exposure was
used to examine association with NHL. Prevalence of medium to high TCE exposure among
NHL cases was low, 3% for NHL cases and 2% for all leukemia subtypes. Whether temporal
changes in TCE exposure concentrations were considered in assigning level and intensity is not
known. Overall, this study has low sensitivity for examining TCE and lymphoma given the low
prevalence of exposure, particularly to medium to high TCE intensity, the high proportion of
proxy interviews among cases, particularly NHL cases (15%), and qualitative exposure
assessment approach.
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Costantini AS, Benvenuti A, Vineis P, Kriebel D, Tumino R, Ramazzotti V, Rodella S, et al. (2008). Risk of leukemia and
multiple myeloma associated with exposure to benzene and other organic solvents: evidence from the Italian multicenter case-
control study. Am J Ind Med 51:803-811.
Miligi L, Costantini AS, Benvenuti A, Kreibel D, Bolejack V, et al. (2006). Occupational exposure to solvents and the risk of
lymphomas. Epidemiol 17:552-561.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This study evaluated TCE and other solvent exposures and lymphoma in a large population-based,
multicenter, case-control study.
1,732 (2,066 eligible) cases of NHL, chronic lymphatic leukemia, and Hodgkin lymphoma, diagnosed
between 1991 and 1993 among men and women, age 20-74 yrs and residents of eight regions in Italy,
were identified from; 1,530 (2,086 eligible) population controls were randomly selected from
demographic files or from sampling of National Health Service files and stratified by sex and 5-yr age
groups.
586 leukemia and 263 multiple myeloma among men and women, age 20-74 in the period 1991-
1993, from seven regions (eight regions for CLL) in Italy, were identified from hospital or pathology
department records or a regional cancer registry; and 1,100 population controls selected from
demographic files or from sampling of National Health Service files and stratified by sex and 5-yr age
groups.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
NHL and Hodskin lymphoma incidence (Miligi et al., 2006).
Leukemia and multiple myeloma (Costantini et al., 2008).
All NHL cases were defined following NCI Working Formulation Workgroup classification and
Hodgkin lymphomas defined following the Rye classification. NHL diagnosis confirmed for 334 of
1,428 cases (23%).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
IH experts from each region using information collected on questionnaires assigned the probability of
exposure level (low, medium, and high) and intensity (very low, low, medium, and high) to TCE and
other solvents. Exposure was assigned blinded as to case or control status.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
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CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Face-to-face interview with questionnaire for detailed information about medical history, lifestyle
factors, education, occupational history (period is not identified in published paper), and
nonoccupational exposures including solvent exposure.
Unblinded interviews.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
19% of all lymphoma cases and 5% of controls were with proxy respondents (Costantini et al..
2008).
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
1,732 cases (83% participation) and 1,530 controls (73% participation) (Milisi et al., 2006); no
information on participation rate for leukemia or multiple myeloma cases or their controls in
Costantini et al. (2008).
Exposure prevalence, medium to high TCE intensity, 35 NHL cases (3%) (Miligi et al., 2006);
11 leukemia cases (2%), and 5 multiple myeloma cases (2%) (Costantini et al., 2008).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, region, education, and region.
Multiple logistic regressions.
Yes, by exposure intensity and by duration (years) of exposure.
Yes.
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B .3.2.6.6. Seidler et al. (2007).
B .3.2.6.6.1. Author's abstract.
AIMS: To analyze the relationship between exposure to chlorinated and aromatic
organic solvents and malignant lymphoma in a multi-centre, population-based
case-control study. METHODS: Male and female patients with malignant
lymphoma (n = 710) between 18 and 80 years of age were prospectively recruited
in six study regions in Germany (Ludwigshafen/Upper Palatinate,
Heidelberg/Rhine-Neckar-County, Wiirzburg/Lower Frankonia, Hamburg,
Bielefeld/Giitersloh, and Munich). For each newly recruited lymphoma case, a
gender, region and age-matched (+/-1 year of birth) population control was drawn
from the population registers. In a structured personal interview, we elicited a
complete occupational history, including every occupational period that lasted at
least one year. On the basis of job task-specific supplementary questionnaires, a
trained occupational physician assessed the exposure to chlorinated hydrocarbons
(trichloroethylene, tetrachloroethylene, dichloromethane, carbon tetrachloride)
and aromatic hydrocarbons (benzene, toluene, xylene, styrene). Odds ratios (OR)
and 95% confidence intervals (CI) were calculated using conditional logistic
regression analysis, adjusted for smoking (in pack years) and alcohol
consumption. To increase the statistical power, patients with specific lymphoma
subentities were additionally compared with the entire control group using
unconditional logistic regression analysis. RESULTS: We observed a statistically
significant association between high exposure to chlorinated hydrocarbons and
malignant lymphoma (Odds ratio = 2.1; 95% confidence interval 1.1-4.3). In the
analysis of lymphoma subentities, a pronounced risk elevation was found for
follicular lymphoma and marginal zone lymphoma. When specific substances
were considered, the association between trichloroethylene and malignant
lymphoma was of borderline statistical significance. Aromatic hydrocarbons were
not significantly associated with the lymphoma diagnosis. CONCLUSION: In
accordance with the literature, this data point to a potential etiologic role of
chlorinated hydrocarbons (particularly trichloroethylene) and malignant
lymphoma. Chlorinated hydrocarbons might affect specific lymphoma subentities
differentially. Our study does not support a strong association between aromatic
hydrocarbons (benzene, toluene, xylene, or styrene) and the diagnosis of a
malignant lymphoma.
B.3.2.6.6.2. Study description and comment.
This population case-control study of NHL and Hodgkin lymphoma patients in six
Germany regions is part of a larger multiple-center and -country case-control study of lymphoma
and environmental exposures, the EPILYMPH study (see Cocco et al. (2010) in B.3.2.6.3). A
total of 710 cases and 710 controls that were matched to cases on age, sex, and region,
participated in this study. Participation rates were 88% for cases and 44% for controls. Potential
for selection bias may exist given the low control response rate. Strength of this study is the use
of WHO classification scheme for classifying lymphomas and the high percentage of cases with
histologically-confirmed diagnoses. An industrial physician blinded to case and control status
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assigned exposure to specific solvents (i.e., TCE, perchloroethylene, carbon tetrachloride, etc.)
using a JEM developed for the EPILYMPH investigators, a modification of Bolm-Audorff et
al.(1988). Exposure prevalence to TCE among cases was 13%. A cumulative exposure score
was calculated and was the sum for every job held of intensity of solvent exposure, frequency of
exposure, and duration of exposure. High exposure to TCE was defined as >35 ppm-years; 3%
of cases had high cumulative exposure to TCE. Intensity of TCE exposure was assessed on a
semiquantitative scale with the following categories: low intensity, 2.5 ppm (0.5-5); medium
intensity, 25 ppm (>5-50), high intensity, 100 ppm (>50). The frequency of exposure was the
percentage of working time during which the exposure occurred based upon a 40-hour week. A
semiquantitative scale was adopted for frequency of exposure with the following categories: low
frequency, 3% of working time (range, 1-5%), medium frequency, 17.5 % (range, >5-30%),
high frequency, 65% of working time (>30%). A cumulative Prevalence of TCE exposure
among cases was 13% overall with 3% of cases identified with cumulative exposure >35 ppm-
years.
Overall, the use of expert assessment for exposure and WHO classification for disease
coding likely reduce misclassification bias in this study. This population case-control study, like
other population case-control studies of lymphoma and TCE, has a low prevalence of TCE
exposure and limits statistical power to detect risk factors.
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Seidler A, Mohner M, Berger J, Mester B, Deeg E, Eisner G, Neiters A, Becker N. (2007). Solvent exposure and malignant
lymphoma: a population-based case-control study in Germany. J Occup Med Toxicol 2:2. Accessed August 27, 2007,
http://www.occup-tned.eom/content/2/l/2.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This case-control study of NHL and Hodgkin lymphomas was designed to investigate association
between specific exposure and distinct lymphoma classifications which are defined by REAL and
WHO classifications.
812 male and female lymphoma patients between the ages of 18 and 80 yrs were identified from a six
German study regions from 1999 to 2003. 1,602 controls were identified from population registers
and matched (1:1) to cases on sex, region, and age. 710 cases and 710 controls were interviewed.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
NHL and Hodgkin lymphoma incidence.
WHO classification. Diagnosis confirmed by pathological report for 691 cases.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Blinded assignment of intensity and frequency of exposure to specific chlorinated hydrocarbons
(includes TCE) and to aromatic hydrocarbons based upon questionnaire information on complete
occupational history for all jobs of >l-yr duration. Exposure assessment approach based on a
modification of Bolm-Audorffetal. (1988)
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Face-to-face interview with questionnaire for detailed information about medical history, lifestyle
factors, and occupation. Job-task-specific supplementary questionnaire administered to subjects
having held jobs of interest; e.g., painters, metal workers and welders, dry cleaners, chemical workers,
shoemakers and leather workers, and textile workers.
Unblinded interviews.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No information provided in paper.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
710 cases (87.4%) and 710 controls (44.3%).
Exposure prevalence: Any TCE exposure, Cases, 13%, Controls, 15%.
High cumulative exposure (>35 ppm-yr), Cases, 3%, Controls, 1%.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, region, pack years of smoking, and # grams of alcohol
consumed per day.
Conditional logistic regression.
Yes, by ppm-yr as continuous variable.
Yes.
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B .3.2.6.7. Persson and Fredrikson (1999), Persson et al. (1993; 1989).
B .3.2.6.7.1. Author's abstract.
Non-Hodgkin's lymphoma (NHL) has been subject to several epidemiological
studies and various occupational and non-occupational exposures have been
identified as determinants. The present study is a pooled analysis of two earlier
methodologically similar case-referent studies encompassing 199 cases of NHL
and 479 referents, all alive. Exposure information, mainly on occupational agents,
was obtained by mailed questionnaires to the subjects. Exposure to white spirits,
thinner, and aviation gasoline as well as work as a painter was connected with
increased odds ratios, whereas no increased risk was noted for benzene. Farming
was associated with a decreased odds ratio and exposure to phenoxy herbicides,
wood preservatives, and work as a lumberjack showed increased odds ratios.
Moreover, exposure to plastic and rubber chemicals and also contact with some
kinds of pets appeared with increased odds ratios. Office employment and
housework showed decreased odds ratios. This study indicates the importance of
investigating exposures not occurring very frequently in the general population.
Solvents were studied as a group of compounds but were also separated into
various specific compounds. The present findings suggest that the carcinogenic
property of solvents is not only related to the aromatic ones or to the occurrence
of benzene contamination, but also to other types of compounds.
B.3.2.6.7.2. Study description and comment.
The exposure assessment approach of Persson and Fredriksson (1999), a pooled analysis
of NHL cases and referents in Persson et al. (1993), and Persson et al. (1989), was based upon
self-reported information obtain from a mailed questionnaire to cases and controls. Ten of 17
main questions of the detailed multiple-page questionnaire concerned occupational exposure,
with additional questions on specific job and exposure details. These studies of the Swedish
population considered exposure durations of >1 years and those received 5-45 years before NHL
diagnosis for cases and before the point in time of selection for controls. The period of TCE
exposure assessed in the between 1964 and 1986, a time period similar to that of Axelson et al.
(1994). Semiqualitative information about solvent exposure was obtained directly from the
questionnaires. Assignment of exposure potential to individual solvents such as TCE and white
spirit is not described nor does the paper describe whether assignment was done blinded as to
case or control status. A five-category classification for intensity was developed although
statistical analyses grouped the TCE categories as intensity scores of >2 compared to 0/1. TCE
exposure prevalence among cases was 8% (16 of 199) and 7% among referents (32 of 479).
This small study of 199 NHL cases diagnosed between 1964 and 1986 at a regional
Swedish hospital (Orebro) and alive at the time of data acquisition in 1986 was similar in design
to other lymphoma (CLL, multiple myeloma) and occupational studies from these investigators
(Flodin et al., 1987). A series of 479 referents from the same catchment area and from the same
time period, identified previously from the multiple myeloma and CLL studies, served as the
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source for controls in Persson and Fredrikson (1999) for the NHL analysis and in Persson et al.
(1993; 1989) for the Hodgkin lymphoma analysis. Given the study's entrance date as 1964, with
interviews carried out in the 1980s, some cases were deceased with information likely provided
by proxy respondents. The paper does not identify the percentage of deceased cases and the
magnitude of potential bias associated with proxy respondents cannot be determined. Little
information is provided in the published paper on controls; however, the paper notes that 17% of
eligible controls were not able or unwilling to respond to the questionnaire. Case and control
series appear to differ given only subjects 40 to 80 years of age were included in the statistical
analysis. Cases in Perrson et al. (1993) were histologically confirmed diagnosis of NHL; this
was not so for Persson et al. (1989). Misclassification associated with misdiagnosis is not
expected to be large given observation in Perrson et al. (1993) of 2% of lymphoma cases were
misclassified.
Overall, the study's 20-year period between initial case and control identification and
interview suggests some subjects were either survivors or information was obtained from proxy
respondents. In both instances, misclassification bias is likely. No information is provided on
job titles or the nature of TCE exposure, which was defined in the exposure assessment as
"exposed or unexposed." Exposure prevalence to TCE in this study is higher than that found in
community population studies of Miligi et al. (2006), Seidler et al. (2007), and Costantini et al.
(2008).
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Persson B, Fredrikson M. (1999). Some risk factors for non-Hodgkin's lymphoma. Int J Occup Med Environ Health 12:135-
142.
Persson B, Fredriksson M, Olsen K, Boeryd B, Axelson O. (1993). Some occupational exposure as risk factors for malignant
lymphomas. Cancer 72:1773-1778.
Persson B, Dahlander A-M, Fredriksson M, Brage HN, Ohlson C-G, Axelson O. (1989). Malignant lymphomas and
occupational exposures. Br J Ind Med 46:516-520.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and
controls in case-control studies is adequate
These studies of Hodgkin lymphoma and NHL investigated occupational associations. Examination of
TCE is not stated as a priori hypothesis.
Incident NHL and Hodgkin lymphoma cases reported to a regional cancer registry between 1975 and
1984, n = 148 (Persson et al., 1993), or identified from hospital records (Orebro Medical Center
Hospital) for the period 1964 and 1986, n = 175 (Persson et al., 1989). Population controls from the
same geographical area as cases were identified from previous case-control studies of leukemia and
multiple myeloma and matched on age and sex. Analysis of NHL and Hodgkin lymphoma each used the
same set of controls.
Persson and Fredrikson (1999)— 199 cases of NHL, 479 controls.
Persson et al. (1993)— 93 NHL and 3 1 Hodgkin lymphoma (90% participation); 204 controls.
Persson et al. (1989)— 106 NHL and 54 Hodgkin lymphoma (91%); 275 controls.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for
lymphoma, particularly NHL
Incidence.
Classification system not identified in papers.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption
of JEM and quantitative exposure estimates
Self-reported occupational exposures as obtained from a mailed questionnaire.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Mailed questionnaire, only.
N/A
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CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No information provided in paper.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies;
numbers of total cancer incidence studies; numbers
of exposed cases and prevalence of exposure in case-
control studies
Exposure prevalence to TCE
Persson and Fredrikson (1999)— 16 NHL cases (8%) and 32 controls (7%).
Persson et al. (1993)— 8 NHL cases (8%) and 5 Hodgkin lymphoma cases (16%)
Persson et al. (1989)— 8 NHL cases (8%) and 7 Hodgkin lymphoma cases (13%)
; 18 controls (9%).
; 14 controls (5%).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical
analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Cases and controls are matched on age and sex. Statistical analyses do not control
confounders.
Only crude ORs are presented for TCE exposure, although logistic regression was
occupational exposure and NHL/Hodgkin lymphoma.
for other possible
used to examine other
No.
Poor, unable to determine response rate in control population, if controls were similar to cases on
demographic variables such as sex and age, and whether controls were identified from same time period
as cases.
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B .3.2.6.8. Nordstrom et al. (1998).
B .3.2.6.8.1. Author's abstract.
To evaluate occupational exposures as risk factors for hairy cell leukemia (HCL),
a population-based case-control study on 121 male HCL patients and 484 controls
matched for age and sex was conducted. Elevated odds ratio (OR) was found for
exposure to farm animals in general: OR 2.0, 95% confidence interval (CI) 1.2-
3.2. The ORs were elevated for exposure to cattle, horse, hog, poultry and sheep.
Exposure to herbicides (OR 2.9, CI 1.4-5.9), insecticides (OR 2.0, CI 1.1-3.5),
fungicides (OR 3.8, CI 1.4-9.9) and impregnating agents (OR 2.4, CI 1.3-4.6) also
showed increased risk. Certain findings suggested that recall bias may have
affected the results for farm animals, herbicides and insecticides. Exposure to
organic solvents yielded elevated risk (OR 1.5, CI 0.99-2.3), as did exposure to
exhaust fumes (OR 2.1, CI 1.3-3.3). In an additional multivariate model, the ORs
remained elevated for all these exposures with the exception of insecticides. We
found a reduced risk for smokers with OR 0.6 (CI 0.4-1.1) because of an effect
among non-farmers.
B.3.2.6.8.2. Study description and comment.
This population case-control of hairy cell leukemia, a B-cell lymphoid neoplasm and
NHL, examined occupational organic solvent and pesticide exposures among male cases
reported to the Swedish Cancer Registry between 1987 and 1992. A total of 121 cases, including
1 case one case, originally thought to have a diagnosis within the study's window, but latter
learned as in 1993, and four controls per case matched on age and county of residence from the
Swedish Population Registry. Occupational exposure was assessed based upon self-reported
information provided in a mailed questionnaire with telephone follow-up by trained interviewer
blinded to case or control status. Chemical-specific exposures of at least 1-day duration and
occurring 1 year prior to case diagnosis were assigned to study subjects; however, the procedure
for doing this was not described in the paper. Potential for organic solvents exposure included
exposure received during leisure activities and work-related activities. Exposure prevalence to
TCE among cases is 8 and 7% among controls. The low exposure prevalence and study size
limit the statistical power of this study for detecting RRs <2.0.
ORs and 95% CIs are presented for chemical-specific exposures, including TCE, from
logistic regression models in two separate analyses, univariate analysis and multivariate analysis
adjusting for age. The OR for TCE exposure is presented only from univariate analysis. Age
may not greatly confound or bias the observed association; an examination of risk estimates from
univariate and multivariate analyses of the aggregated exposure category for organic solvents
showed similar ORs, indicating age was not a significant source of bias in the statistical analyses
because age was controlled in the study's design, a control was matching to a case on age.
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Nordstrom M, Hardell L, Hagberg H, Rask-Andersen A. (1998). Occupational exposures, animal exposure and smoking as
risk factors for hairy cell leukemia evaluated in a case-control study. Br J Cancer 77:2048-2052.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Abstract — To evaluate occupational exposure as risk factors for hairy cell leukemia.
121 cases of hairy cell leukemia in males reported to the Swedish Cancer Registry between 1987 and
1992.
484 controls (1 :4 matching) identified from Swedish Population Registry and matched for age and
county of residence.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
Not identified in paper, likely ICD-9 (http://www.socialstyrelsen.se/. accessed February 6, 2009).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Serf-reported information on occupational exposure as obtained from a mailed questionnaire to study
participants. Questionnaire sought information on complete working history, other exposures, and
leisure time activities with telephone interview in cases of incomplete information. Paper does not
describe the procedure for assigning chemical exposures from job title information.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Mailed questionnaire.
Follow-up telephone interview and job/exposure coding were done blinded as to case and control
status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Proxy responses: 4%, cases; 1% controls.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control
studies
Ill hairy cell leukemia cases, 400 controls.
Response rate: 91% cases and 83% controls.
Exposure prevalence among cases is 8 and 7% among controls.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Cases and controls are matched for age, sex, and county of residence. Effect measure for TCE
exposure from univariate analysis presented in paper; other possible confounders or covariates not
included in statistical analysis.
Logistic regression.
No.
Yes.
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B .3.2.6.9. Fritschi and Siemiatycki (1996a), Siemiatycki (1991).
B .3.2.6.9.1. Author's abstract.
The known risk factors for lymphoma and myeloma cannot account for the
current incidence rates of these cancers, and there is increasing interest in
exploring occupational causes. We present results regarding lymphoma and
myeloma from a large case-control study of hundreds of occupational exposures
and 19 cancer sites. We examine in more detail those exposures previously
considered to be related to these cancers, as well as exposures which were
strongly related in our initial analyses. Lymphoma was not associated in our data
with exposure to solvents or pesticides, or employment in agriculture or wood-
related occupations, although numbers of exposed cases were sometimes small.
Hodgkin's lymphoma was associated with exposure to fabric dust, and non-
Hodgkin's lymphoma was associated with exposure to copper dust, ammonia and
a number of fabric and textile-related occupations and exposures. Employment as
a sheet metal worker was associated with development of myeloma.
B.3.2.6.9.2. Study description and comment.
This population study of several cancer sites included histologically-confirmed cases of
NHL, Hodgkin lymphoma and myeloma ascertained from 16 Montreal-area hospitals between
1979 and 1985 and part of a larger study of 10 other cancer sites. This study relies on the use of
expert assessment of occupational information on a detailed questionnaire and face-to-face
interview. Fritschi and Siemiatycki (1996a) present observations of analyses examining
industries, occupation, and some chemical-specific exposures, including solvents, but not TCE.
Observations on TCE are found in the original report of Siemiatycki (1991).
A total of 215 NHL cases (83% response) were identified from 19 Montreal-area
hospitals and while this case group is larger than that in Swedish lymphoma case-control studies,
there are fewer NHL cases than other multicenter studies published since 2000. The
533 population controls (72% response), identified through the use of random digit dialing, and
were used for each site-specific cancer case analyses. All controls were interviewed using face-
to-face methods; however, 20% of the NHL cases were either too ill to interview or had died
and, for these cases, occupational information was provided by a proxy respondent. The quality
of interview conducted with proxy respondents was much lower, increasing the potential for
misclassification bias, than that with the subject. The direction of this bias would diminish
observed risk towards the null. Interviewers were unblinded, although exposure assignment was
carried out blinded as to case and control status. The questionnaire sought information on the
subject's complete job history and included questions about the specific job of the employee and
work environment. Occupations considered with possible TCE exposure included machinists,
aircraft mechanics, and industrial equipment mechanics. An additional specialized questionnaire
was developed for certain job title of a prior interest that sought more detailed information on
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tasks and possible exposures. For example, the supplemental questionnaire for machinists
included a question on TCE usage.
A team of industrial hygienists and chemicals assigned exposures blinded based on job
title and other information obtained by questionnaire. A semi quantitative scale was developed
for 294 exposures and included TCE (any, substantial). Any exposure to TCE was 3% among
cases but <1% for substantial TCE exposure; "substantial" is defined as >10 years of exposure
for the period up to 5 years before diagnosis. The TCE exposure frequencies in this study are
lower than those in more recent NHL case-control studies examining TCE. The expert
assessment method is considered a valid and reliable approach for assessing occupational
exposure in community-base studies and likely less biased from exposure misclassification than
exposure assessment based solely on self-reported information (Fritschi etal., 2003; IOM, 2003;
Siemiatvcki et al.. 1997).
Logistic regression models adjusted for age, ethnicity, income, and respondent status
(Fritschi and Siemiatycki, 1996a) or Mantel-Haenszel $ stratified on age, family income, and
cigarette smoking (Siemiatvcki, 1991). Odds ratios for TCE exposure are presented with 90%
CIs in Siemiatycki (1991) and with 95% CIs in Fritschi and Siemiatycki (1996).
The strengths of this study were the large number of incident cases, specific information
about job duties for all jobs held, and a definitive diagnosis of NHL. However, the use of the
general population (rather than a known cohort of exposed workers) reduced the likelihood that
subjects were exposed to TCE, resulting in relatively low statistical power for the analysis. The
JEM, applied to the job information, was very broad since it was used to evaluate 294 chemicals.
Overall, a reasonably good exposure assessment is found in this analysis; however, examination
of NHL and TCE exposure is limited by statistical power considerations related to low exposure
prevalence, particularly for "substantial" exposure. For the exposure prevalence found in this
study to TCE and for NHL, the minimum detectable OR was 3.0 when P = 0.02 and a = 0.05
(one-sided). The low statistical power to detect a doubling of risk and an increased possibility of
misclassification bias associated with case occupational histories resulting from proxy
respondents suggests this study is less sensitive than other NHL case-controls published since
2000 for examining NHL and TCE.
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Fritschi L, Siemiatycki J.
498-503.
Lymphoma, myeloma and occupation: Results of a case-control study. Int J Cancer 67:
Siemitycki J. (1991). Risk Factors for Cancer in the Workplace. J Siemiatycki, Ed. Baca Raton: CRC Press.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This population case-control study of NHL was designed to investigate association between specific
exposure and cancers at 20 sites using expert assessment method for exposure assignment.
258 histologically -confirmed NHL cases were identified among Montreal area males, aged 35-70 yrs,
diagnosed in 16 Montreal hospitals between 1979 and 1985. 740 male population controls were
identified from the same source population using random digit dialing methods.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
NHL.
ICDO-0, 200 and 202, International Statistical Classification of Diseases for Oncology (WHO,
1977).
ICDO-0 is based upon rubrics of ICD, 9th Revision.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Unblinded interview using questionnaire sought information on complete job history with
supplemental questionnaire for jobs of a priori interest (e.g., machinists, painters). Team of chemist
and industrial hygienist assigned exposure using job title with a semiquantitative scale developed for
300 exposures, including TCE. For each exposure, a three-level ranking was used for concentration
(low or background, medium, high) and frequency (percent of working time: low, 1-5%; medium, >5-
30%; and high, >30%).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Yes, 82% of case interviews were face-to-face; 100% of control interviews were with subject.
Interviews were unblinded but exposure coding was carried out blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, -20% of cases had proxy respondents. Interviews were completed with all control subjects.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
215 cases (83% response), 533 population controls (71%).
Exposure prevalence: Any TCE exposure, 3% cases; substantial TCE exposure (exposure for >10 yrs
and up to 5 yrs before disease onset), <1% cases.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, income, index for cigarette smoking (Siemiatycki, 1991).
Age, proxy status, income, ethnicity (Fritschi and Siemiatycki, 1996a).
Mantel-Haenszel (Siemiatycki, 1991).
Unconditional logistic regression (Fritschi and Siemiatycki, 1996a).
No.
Yes.
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B .3.2.6.10. Hardell et al. (1994; 1981).
B.3.2.6.10.1. Author's abstract.
Results on 105 cases with histopathologically confirmed non-Hodgkin's
lymphoma (NHL) and 335 controls from a previously published case-control
study on malignant lymphoma are presented together with some extended
analyses. No occupation was a risk factor for NHL. Exposure to phenoxyacetic
acids yielded, in the univariate analysis, an odds ratio of 5.5 with a 95%
confidence interval of 2.7-11. Most cases and controls were exposed to a
commercial mixture of 2, 4-dichlorophenoxyacetic acid and 2, 4, 5-
trichlorophenoxyacetic acid. Exposure to chlorophenols gave an odds ratio of 4.8
(2.7-8.8) with pentachlorophenol being the most common type. Exposure to
organic solvents yielded an odds ratio of 2.4 (1.4-3.9). These results were not
significantly changed in the multivariate analysis.
Dichlorodiphenyltrichloroethane, asbestos, smoking, and oral snuff were not
associated with an increased risk for NHL. The results regarding increased risk
for NHL following exposure to phenoxyacetic acids, chlorophenols, or organic
solvents were not affected by histopathological type, disease stage, or anatomical
site of disease presentation. Median survival was somewhat longer in cases
exposed to organic solvents than the rest. This was explained by more prevalent
exposure to organic solvents in the group of cases with good prognosis NHL
histopathology.
A number of men with malignant lymphoma of the histiocytic type and
previous exposure to phenoxy acids or chlorophenols were observed and reported
in 1979. A matched case-control study has therefore been performed with cases of
malignant lymphoma (Hodgkin's disease and non-Hodgkin lymphoma). This
study included 169 cases and 338 controls. The results indicate that exposure to
phenoxy acids, chlorophenols, and organic solvents may be a causative factor in
malignant lymphoma. Combined exposure of these chemicals seemed to increase
the risk. Exposure to various other agents was not obviously different in cases and
in controls.
B .3.2.6.10.2. Study description and comment.
Exposure in these case-control studies of histologically-confirmed lymphoma (NHL and
Hodgkin lymphoma) (Hardell et al., 1981) or only the NHL cases only (Hardell et al., 1994) over
a 4-year period, 1974-1978, in Umea, Sweden was assessed based upon information provided in
a self-administered questionnaire. The questionnaire obtained information on a complete
working history over the life of the subjects along with information on various other exposures
and leisure time activities. Organic solvent exposures were examined secondary to this study's
primary hypothesis examining phenoxy acid or chlorophenol exposures and lymphoma. The
extent of recall bias related to self-reported information cannot be determined nor is information
provided in the published papers misclassification bias resulting from next-of-kin interviews.
Occupations were classification according to the Nordic Working Classification system.
Chemical-specific exposures assignment was not described but appears to have been carried out
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blinded as to case or control status. A semi quantitative classification scheme based on intensity
and duration of exposure was used to categorize solvent exposure into two groupings: low
grade—<1 week continuously or <1 month in total—and high grade for all other exposure
scenarios. TCE exposure prevalence is similar in both studies; 4% for cases and 1% for controls.
The low exposure prevalence and small numbers of cases with TCE exposure (n = 4) limits the
statistical power of these analyses and results in wide CIs around the estimated OR for TCE
exposure (95% CI, 1.3-42).
The Rappaport Classification was used to identify NHL and Hodgkin lymphoma cases.
The Rappaport Classification was in widespread use until the 1970s and was based on a cell's
pathologic characteristics. Equivalence of NHL groupings according to Rappaport Classification
system to ICDA-8 groupings, also in use during this time period, is 200 "Lymphosarcoma and
reticulum-cell sarcoma" and 202 "Other neoplasms of lymphoid tissue."
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Hardell L, Eriksson M, Degerman A. (1994). Exposure to phenoxyacetic acids, chlorophenols, or organic solvents in relation
to histopathology, stage, and anatomical localization of non-Hodgkin's lymphoma. Cancer Res 54:2386-2389.
Hardell L, Eriksson M, Lenner P, Lundgren E. (1981). Malignant lymphoma and exposure to chemicals, especially organic
solvents, chlorophenols and phenoxy acids: a case-control study. Br J Cancer 43:169-176.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
NHL cases from a case-control study of lymphoma (NHL and Hodgkin lymphoma) are analyzed
separately to evaluate herbicide and organic solvents exposure.
105 cases of histologically -confirmed NHL among males aged 25-85 yrs admitted to local hospital's
oncology department between 1974 and 1978.
A total of 335 male controls identified from the Swedish Population Registry, for living cases, and
from the Swedish Registry for Causes of Death, for dead cases. Controls matched to cases by age,
residence municipality, and year of death, for dead cases.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
Rappaport Classification; equivalent to ICDA-8 Codes, 200, and 202.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Serf-reported information on occupational exposure as obtained by questionnaire, with a telephone
interview for incomplete or unclear information. Questionnaire sought information on complete
working history, other exposures and leisure time activities. Paper does not describe the procedure for
assigning chemical exposures from job title information.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
No information in paper.
Follow-up telephone interview was done blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No information in paper.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
105 NHL cases, 332 controls.
Response rates could not be calculated given insufficient information in paper.
Prevalence of TCE exposure, 4% cases, 1% controls.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Cases and controls matched on sex, age, place of residence, and vital status. Deceased controls are
matched to deceased cases on year of death.
Mantel-Haenszel stratified by age and vital status.
No.
Yes.
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B.3.2.7. Childhood Leukemia
B.3.2.7.1. Shu et al. (2004; 1999)
B.3.2.7.1.1. Author's abstract.
Ras proto-oncogene mutations have been implicated in the pathogenesis of many
malignancies, including leukemia. While both human and animal studies have
linked several chemical carcinogens to specific ras mutations, little data exist
regarding the association of ras mutations with parental exposures and risk of
childhood leukemia. Using data from a large case control study of childhood
acute lymphoblastic leukemia (ALL; age <15 years) conducted by the Children's
Cancer Group, we used a case-case comparison approach to examine whether
reported parental exposure to hydrocarbons at work or use of specific medications
are related to ras gene mutations in the leukemia cells of children with ALL. DNA
was extracted from archived bone marrow slides or cryopreserved marrow
samples for 837 ALL cases. We examined mutations in K-ras and N-ras genes at
codons 12, 13, and 61 by PCR and allele-specific oligonucleotide hybridization
and confirmed them by DNA sequencing. We interviewed mothers and, if
available, fathers by telephone to collect exposure information. Odds ratios (ORs)
and 95% confidence intervals (CIs) were derived from logistic regression to
examine the association of parental exposures with ras mutations. A total of 127
(15.2%) cases had ras mutations (K-ras 4.7% and N-ras 10.68%). Both maternal
(OR 3.2, 95% CI 1.7-6.1) and paternal (OR 2.0, 95% CI 1.1-3.7) reported use of
mind-altering drugs were associated with N-ras mutations. Paternal use of
amphetamines or diet pills was associated with N-ras mutations (OR 4.1, 95% CI
1.1-15.0); no association was observed with maternal use. Maternal exposure to
solvents (OR 3.1, 95% CI 1.0-9.7) and plastic materials (OR 6.9, 95% CI 1.2-
39.7) during pregnancy and plastic materials after pregnancy (OR 8.3, 95% CI
1.4-48.8) were related to K-ras mutation. Maternal ever exposure to oil and coal
products before case diagnosis (OR 2.3, 95% CI 1.1-4.8) and during the postnatal
period (OR 2.2, 95% CI 1.0-5.5) and paternal exposure to plastic materials before
index pregnancy (OR 2.4, 95% CI 1.1-5.1) and other hydrocarbons during the
postnatal period (OR 1.8, 95% CI 1.0-1.3) were associated with N-ras mutations.
This study suggests that parental exposure to specific chemicals may be
associated with distinct ras mutations in children who develop ALL.
Parental exposure to hydrocarbons at work has been suggested to increase the
risk of childhood leukemia. Evidence, however, is not entirely consistent. Very
few studies have evaluated the potential parental occupational hazards by
exposure time windows. The Children's Cancer Group recently completed a large-
scale case-control study involving 1842 acute lymphocytic leukemia (ALL) cases
and 1986 matched controls. The study examined the association of self-reported
occupational exposure to various hydrocarbons among parents with risk of
childhood ALL by exposure time window, immunophenotype of ALL, and age at
diagnosis. We found that maternal exposure to solvents [odds ratio (OR), 1.8;
95% confidence interval (CI), 1.3-2.5] and paints or thinners (OR, 1.6; 95% CI,
1.2-2.2) during the preconception period (OR, 1.6; 95% CI, 1.1-2.3) and during
pregnancy (OR, 1.7; 95% CI, 1.2-2.3) and to plastic materials during the postnatal
period (OR, 2.2; 95% CI, 1.0-4.7) were related to an increased risk of childhood
ALL. A positive association between ALL and paternal exposure to plastic
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materials during the preconception period was also found (OR, 1.4; 95% CI, 1.0-
1.9). The ALL risk associated with parental exposures to hydrocarbons did not
vary greatly with immunophenotype of ALL. These results suggest that the effect
of parental occupational exposure to hydrocarbons on offspring may depend on
the type of hydrocarbon and the timing of the exposure.
B .3.2.7.1.2. Study description and comment.
Parent hydrocarbon occupational exposure in this case-control study of acute lymphatic
leukemia in children <15 years of age was assessed from telephone questionnaire to mothers and,
whenever available, fathers of cases and controls who were part of the large-scale incidence
study by the Children's Cancer/Oncology Group. A recent paper examines hydrocarbon
exposures and relationship with the ras proto-oncogene (Shu et al., 2004). Nearly 50% of
childhood leukemia cases in the United States were treated by a Children's Cancer Group
hospital or institution and between January 1, 1989 and June 15, 1993, the study period, a total of
2,081 incident childhood leukemia cases were identified with 1,914 interviews with mothers.
Controls were randomly selected using a random digit dialing procedure and matched to cases on
age, race, and geographic location. Using structured questionnaires, parents or a surrogate when
unavailable were asked about job title, industry, duties, starting and stopping date for all jobs
held by the father for >6 months beginning at age 18 years and by the mother for all jobs held at
least 6 months in the period from 2 year prior to the index pregnancy to date of diagnosis of
leukemia case or the reference date of the controls. The questionnaire sought information on
specific exposures to solvents (carbon tetrachloride, TCE, benzene, toluene, and xylene), plastic
materials, paints, pigments or thinners, and oil or coal products. Exposure quantitative was not
possible. Statistical analyses use self-reported exposure to specific hydrocarbons as defined as a
dichotomous variable (yes/no). The potential for misclassification bias is greater with exposure
assessment based upon self-reports compared to that by expert assessment (Teschke et al., 2002).
Exposure information was linked to start and stop data of the relevant job to determine the timing
of exposure related to specific windows of possible susceptibility for ALL. The author's do not
describe jobs associated with possible TCE exposure.
The father's questionnaire was completed for 1,801 of the 2,081 eligible cases and
1,813 of the 2,597 eligible controls. Of the 1,618 matched sets, direct interview with fathers
were obtained for 83% of cases and 68% of controls. Maternal interview were completed for
1,914 of the 2,081 eligible cases (92%). The low prevalence of any exposure to TCE, 1% for
mothers (15 cases of 1,842 matched pairs with maternal exposure information) and 8% for
fathers (136 cases out 1,618 matched pairs), limits the statistical power of this study to detect low
to moderate risk.
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Shu Xo, Perentesis JP, Wen W, Buckley JD, Boyle E, Ross, JA, Robison LL. (2004). Parental exposure to medications and
hydrocarbons and ras mutations in children with acute lymphoblastic leukemia: A report from the Children's Oncology
Group. Cancer Epidemiol Biomarkers Prev 13:1230-1235.
Shu XO, Stewart P, Wen W-Q, Han D, Potter JD, Buckley JD, Heineman E, Robison LL. (1999). Parental occupational
exposure to hydrocarbons and risk of acute lymphocytic leukemia in offspring. Cancer Epidemiol Markers Prev 8:783-291.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Shu et al. (2004; 1999) examine possible association with a number of maternal and paternal
exposures among cases and controls identified from the Children's Cancer/Oncology Group. The
Children's Cancer/Oncology Group is an association of > 120 centers in the United States, Canada, and
Australia who collaboratively carry out research on risk factors and treatment of childhood cancers.
848 children with acute lymphatic leukemia of ages 0-9 yrs of age at diagnosis from 1980 to 1993 and
<14 yrs old at diagnosis between 1994 and 2000 were identified from cancer care centers in Quebec,
Canada.
Controls are concurrently identified from population, from 1980 to 1993, from family allowance files
and from 1994 to 2000, from universal health insurance files; and, matched (1:1 matching ratio) to
cases on sex and age at the time of diagnosis (calendar date).
Participation rates- 93.1% cases (790 of 849 eligible cases); 86.2% controls (790 of 916 eligible
controls).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Childhood leukemia incidence.
ICD, 9th revision, Code 204.0.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Telephone interviews of mothers of cases and controls using structured questionnaire were
administered to obtain information on general risk factors and potential confounders. Questionnaire
also sought information on a complete job history, for the mother from 18 yrs of age to the end of
pregnancy and included for each job, job title, dates of employment, type of industry, and location of
employer. Statistical analyses based on serf -reported occupational exposure to hydrocarbons as
defined by broad groups and individual hydrocarbons.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
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CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Telephone interview, >99% response.
Telephone interviews were not blinded, but exposure assignment and coding was carried out blinded
to case and control status by chemists and industrial hygienists.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
100% of cases and controls had maternal history provided by direct interview with mothers.
13% of cases and 30% of controls had paternal information provided by proxy respondent (e.g.,
through maternal interview).
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
15 cases (2% exposure prevalence) and 9 controls (1% exposure prevalence) with maternal TCE
exposure.
136 cases (8% exposure prevalence) and 104 controls (13% exposure prevalence) with paternal TCE
exposure.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Child's age at time of diagnosis, sex, and calendar year of diagnosis, maternal age and level of
schooling.
Conditional logistic regression
By two time periods; 2 yrs before pregnancy up to birth, during specific pregnancy period.
By level of exposure; Level 1 (some exposure) compared to no exposure, and Level 2 (greater
exposure potential) compared to no exposure.
Yes.
Yes.
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B .3.2.7.2. Costas et al. (2002), MDPH (1997b).
B.3.2.7.2.1. Author's abstract.
A 1981 Massachusetts Department of Public Health study confirmed a childhood
leukemia cluster in Woburn, Massachusetts. Our follow-up investigation attempts
to identify factors potentially responsible for the cluster. Woburn has a 130-year
industrial history that resulted in significant local deposition of tannery and
chemical manufacturing waste. In 1979, two of the city's eight municipal drinking
water wells were closed when tests identified contamination with solvents
including trichloroethylene. By 1986, 21 childhood leukemia cases had been
observed (5.52 expected during the seventeen year period) and the case-control
investigation discussed herein was begun. Nineteen cases and 37 matched
controls comprised the study population. A water distribution model provided
contaminated public water exposure estimates for subject residences. Results
identified a non-significant association between potential for exposure to
contaminated water during maternal pregnancy and leukemia diagnosis, (odds
RATIOS.33, 95% CI 0.73-94.67). However, a significant dose-response
relationship (P<0.05) was identified for this exposure period. In contrast, the
child's potential for exposure from birth to diagnosis showed no association with
leukemia risk. Wide confidence intervals suggest cautious interpretation of
association magnitudes. Since 1986, expected incidence has been observed in
Woburn including 8 consecutive years with no new childhood leukemia
diagnoses.
B.3.2.7.2.2. Study description and comment.
Exposure in this case-control study of childhood leukemia over a 20-year period in
Woburn, Massachusetts was assessed based upon the potential for a residence at the time of
diagnosis to receive water from wells G and H, wells with a hydraulic mixing model of Murphy
(Murphy, 1990), which described the town's water distribution system. Monitoring of wells G
and H in 1979 showed the presence of several VOCs; TCE and perchloroethylene (PERC) were
found to exceed drinking water guidelines, at 267 and 21 ppb, respectively. Low levels of other
contaminates were detected including chloroform, 1,2-DCE methyl chloroform,
trichlorotrifluoroethane, and inorganic arsenic. The Murphy model described the water flow
through Woburn during the lifetime of wells G and H. The model uses data describing the
physical layout of Woburn's municipal water system and information regarding the pumping
cycles of wells G and H and other active uncontaminated wells that supplied the municipal water
system. Model accuracy showed distribution of water from wells G and H to a block area with
predicted mixture concentrations with an average error within 10% of the know concentration.
Nearly 70% of the model predictions were within 20% of the know validation concentrations.
An exposure value for cases and controls by exposure period was the sum of the model-predicted
water concentration for each residence in Woburn as assigned to a hydrologically-distinct area
along the water distribution network. Both cumulative and average exposure estimates were
derived using the model.
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Costas K, Knorr RS, Condon SK. (2002). A case-control study of childhood leukemia in Woburn, Massachusetts: the
relationship between leukemia incidence and exposure to public drinking water. Sci Total Environ 300:23-25.
Massachusetts Department of Public Health (MDPH). (1997b). Woburn Childhood Leukemia Follow-up Study. Volumes I
and II. Final Report.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Yes, "this follow-up investigation attempts to identify factors potentially responsible for the leukemia
cluster in Woburn, MA" and the primary exposure of concern for investigation is "the potential
consumption of contaminated water from Wells G and H by Woburn residents."
21 cases of leukemia diagnosed in children <19 yrs between 1969 and 1989 who were residents of
Woburn Massachusetts. Cases diagnosed from 1982 and latter were provided by the Massachusetts
Cancer Registry. Cases diagnosed prior to 1982 were identified from local pediatric health
professionals and by contacting all greater-Boston childhood oncology centers that treated children
with leukemia.
Two controls for each case were randomly selected from Woburn Public School records on a
geographically basis and matched to cases on race, sex and date of birth (± 3 months).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Childhood leukemia incidence.
ICD-O (Acute Lymphatic Leukemia, Acute Myelogenous Leukemia, and Chronic Myelogenous
Leukemia).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
In-person interviewers with mothers and fathers of cases and controls using questionnaire to gather
information regarding demographics, residential information for the mother and child, occupational
history, maternal medical and reproductive history, child's medical history, and lifestyle questions.
The father's questionnaire contained questions concerning military and occupational history and also
included duplicate questions on maternal occupational history, child's medical history, and lifestyle
habits.
A hydraulic mixing computer model describing Woburn' s water distribution system was utilized to
assign an exposure index expressed as cumulative number of months a household received
contaminated drinking water from Wells G and H.
Exposure Index = fraction of time during month when water from Wells G and H reached the user area
+ fraction of water from Wells G and H supplied to user area.
No quantitative measures of TCE and other volatile organic solvents concentrations were included in
hydraulic mixing model.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
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CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Personal interviews with cases and controls; 19 of 21 cases (91%) and 38 of possible 54 controls
(70%) were interviewed.
Interviewers were not blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
One parent interviewed for 21% of cases and 1 1% of controls.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
Participation rates- 93.1%cases (790 of 849 eligible cases); 86.2% controls (790 of 916 eligible
controls).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Composite covariates used to control for SES, maternal smoking during pregnancy, maternal age at
birth of child, and maternal alcohol consumption during pregnancy.
Conditional logistic regression.
Yes.
Yes and includes information in MDPH Final Report (1997b).
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B .3.2.7.3. McKinney et al. (1991).
B.3.2.7.3.1. Author's abstract.
OBJECTIVE—TO determine whether parental occupations and chemical and other
specific exposures are risk factors for childhood leukemia. DESIGN—Case-
control study. Information on parents was obtained by home interview.
SETTING—Three areas in north England: Copeland and South Lakeland (west
Cumbria); Kingston upon Hull, Beverley, East Yorkshire, and Holderness (north
Humberside), and Gateshead. SUBJECTS—109 children aged 0-14 born and
diagnosed as having leukemia or non-Hodgkin's lymphoma in study areas during
1974-88. Two controls matched for sex and date and district of birth were
obtained for each child. MAIN OUTCOME MEASURES-Occupations of
parents and specific exposure of parents before the children's conception, during
gestation, and after birth. Other adults living with the children were included in
the postnatal analysis. RESULTS—Few risk factors were identified for mothers,
although preconceptional association with the food industry was significantly
increased in case mothers (odds ratio 2.56; 95% confidence interval 1.32 to 5.00).
Significant associations were found between childhood leukemia and reported
preconceptional exposure of fathers to wood dust (2.73, 1.44 to 5.16), radiation
(3.23, 1.36 to 7.72), and benzene (5.81, 1.67 to 26.44); ionizing radiation alone
gave an odds ratio of 2.35 (0.92 to 6.22). Raised odds ratios were found for
paternal exposure during gestation, but no independent postnatal effect was
evident. CONCLUSION—These results should be interpreted cautiously because
of the small numbers, overlap with another study, and multiple exposure of some
parents. It is important to distinguish periods of parental exposures; identified risk
factors were almost exclusively restricted to the time before the child's birth.
B.3.2.7.3.2. Study description and comment.
A population case-control study of ALL and NHL in children of <14 years of age and
residing in three areas in the United Kingdom was carried out to identify possible risk factors for
the region's observed increased background childhood leukemia rates. The Sellafield nuclear
reprocessing plant was located in one of the areas and one hypothesis was an examination of
parental radiation exposure and childhood lymphoma. Unblinded face-to-face interviews with
cases, identified from regional tumor registries, and controls, identified using regional birth
registers, used a structured questionnaire to ascertain a complete history of employment and
exposure to specific substances and radiation from both child's biological parents, preferred,
although, in the absence of one parent, surrogate information by the other parent was obtained
from the date of first employment to end of the study period or, if earlier, the date the parent
ceased seeing the child. The questionnaire additionally sought information on maternal and
paternal exposure to 22 known chemical carcinogens. McKinney et al. (1991) noted that
exposures were highly correlated. Information on job title and industry as reported in the
questionnaire was coded independently by experts to occupational groupings and titles using a
national classification scheme from the Office of Population Census and Surveys and is a
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strength of this study. The category of metal refining industry and occupations was one of nine
occupational groups identified a priori for hypothesis testing. Statistical analyses are based on
exposure as defined by industry, occupational title, or chemical-specific exposure.
Interviewers with one or both parents were carried out for 109 of 151 eligible cases
(72%) and with 206 of 269 eligible controls (77%), and the low exposure prevalence; no
information was presented on the number of surrogate interviews, or, where only one parent
responded for both parents. The low prevalence of TCE exposure, five discordant pairs (one
subject with exposure and the matched subject without exposure) identified with maternal TCE
exposure and 16 discordant pairs with paternal preconceptional TCE exposure, greatly limited
the statistical power of this study.
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McKinney PA, Alexander FE, Cartwright RA, Parker L. (1991). Parental occupations of children with leukemia in west
Cumbria, north Humberside, and Gateshead. BMJ 302:681-687.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This study examines a number of risk factors (specific chemicals and occupational groups) as possibly
associated with the high background rate of acute lymphatic leukemia and NHL in children <14 yrs in
the three regions. 22 individual chemicals and 7 occupational groups for a priori hypothesis testing.
151 case children identified from two tumor registries (Yorkshire and Northern Region). No
information provided in paper on reporting accuracy of these registries. 269 population controls
identified from District health authority birth registers and matched to cases on age, sex, and region of
residency at time of case diagnosis.
Participation rates- 72% of cases (n = 109) and 77% of controls (n = 206).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Childhood leukemia incidence.
No information provided in published paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Face-to-face interviews of mothers of cases and controls using structured questionnaire were
administered to obtain information on general risk factors and potential confounders. Questionnaire
also sought information on a maternal and paternal complete job history, from first employment to end
of study and included for job title, dates of employment, and industry. Questionnaire administered to
both parents, and, if one parent was unavailable, information was provided by proxy. Questionnaire
also sought information on 22 specific chemicals. Expert assignment of occupation based upon
National classification system. Statistical analyses industry of employment, job or occupation, and
specific exposures.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
No, face-to-face interview with 72% of case parents and 77% of control parents.
Face-to-face interviews were not blinded. Expert assignment of occupation was carried out blinded.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No information provided in paper on percentage of proxy interviews.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
Exposure prevalence to TCE — maternal exposure, 2 cases (2%)
exposure, 9 cases (9%) and 7 controls (4%).
and 3 controls (2%); paternal
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Cases and control matched on age, sex, and region of residency
at time of case diagnosis.
Discordant pair analysis.
No.
Limited reporting of ORs for job title and occupations.
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B .3.2.7.4. Lowengart et al. (1987)
B.3.2.7.4.1. Author's abstract.
A case-control study of children of ages 10 years and under in Los Angeles
County was conducted to investigate the causes of leukemia. The mothers and
fathers of acute leukemia cases and their individually matched controls were
interviewed regarding specific occupational and home exposures as well as other
potential risk factors associated with leukemia. Analysis of the information from
the 123 matched pairs showed an increased risk of leukemia for children whose
fathers had occupational exposure after the birth of the child to chlorinated
solvents [odds ratio (OR) = 3.5, P = .01], spray paint (OR = 2.0, P = .02), dyes or
pigments (OR = 4.5, P = .03), methyl ethyl ketone (CAS: 78-93-3; OR = 3.0, P =
.05), and cutting oil (OR = 1.7, P = .05) or whose fathers were exposed during the
mother's pregnancy with the child to spray paint (OR = 2.2, P = .03). For all of
these, the risk associated with frequent use was greater than for infrequent use.
There was an increased risk of leukemia for the child if the father worked in
industries manufacturing transportation equipment (mostly aircraft) (OR = 2.5, P
= .03) or machinery (OR = 3.0, P = .02). An increased risk was found for children
whose parents used pesticides in the home (OR = 3.8, P = .004) or garden (OR =
6.5, P = .007) or who burned incense in the home (OR = 2.7, P = .007). The risk
was greater for frequent use. Risk of leukemia was related to mothers'
employment in personal service industries (OR = 2.7, P = .04) but not to specified
occupational exposures. Risk related to fathers' exposure to chlorinated solvents,
employment in the transportation equipment-manufacturing industry, and parents'
exposure to household or garden pesticides and incense remains statistically
significant after adjusting for the other significant findings.
B.3.2.7.4.2. Study description and comment.
Self-assessed parental exposure to chemical classes and to individual chlorinated solvents
was assigned in this case-control study of leukemia in children <10 years old using information
obtained through telephone interviews with mothers and fathers of cases and controls.
Interviews were carried out for 79% of case mothers (159 or 202 cases) and 81% (124 of 154)
case fathers. The number of potential controls was not identified in the paper, although it was
reported that interviews were carried out for 136 referent mothers and 87 referent fathers.
Mothers served as proxy respondents for paternal exposures in roughly 20% of cases and 30% of
controls. The complete occupational history was sought for the period 1 year before the case
diagnosis date, if the case was older than 2 years, 6 months before the diagnosis date, if the case
was between the ages of 1 and 2 years, and the same as the date of diagnosis of the case was <1
year old. Questions on specific occupational exposures such as solvents or degreasers, metals,
and other categories were included on the questionnaire, with self-reported information used to
assign exposure potential. Exposure is defined only as a dichotomous variable (yes/no). In this
study using a matched-pair design in the statistical analyses, there were six case-control pairs of
paternal cases but not controls and three case-control pairs with paternal controls but not cases
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with TCE exposure before pregnancy or during pregnancy. Few mothers reported exposure to
chlorinated solvents. A strength of the study is the ability to examine exposure at a number of
developmental periods, preconception, during pregnancy, and postnatal. Misclassification bias is
likely strong in this study, introduced through the large number of proxy respondents and
exposure assessment based upon self-reported information. Misclassification resulting from
proxy information will dampen observed risks, where as misclassification of self-reported
exposures may bias observed risks in either direction. For this reason and because of the low
prevalence of exposure nature of exposure assessment approach, this study provides little
information on childhood leukemia risks and TCE exposure.
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Lowengart RA, Peters JM, Cicioni C, Buckley J, Bernstein L, Preston-Martin S, Rappaport E. (1987). Childhood leukemia
and parents' occupational and home exposures. JNCI 79:39-46.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This case-control study of children <10 yrs of age was conducted to identify possible risk factors of
childhood leukemia. TCE exposure was one of many occupational exposures assessed in this study.
202 cases of acute lymphatic leukemia in children <10 yrs of age at time of diagnosis from 1980
through 1984 were identified from the Los Angeles County Cancer Surveillance Program, a
population-based cancer registry. Controls were identified from among friends of cases with
additional controls selected using random digit dialing from the same population as cases and were
matched to cases on age, sex, race, and Hispanic origin.
123 cases (61% response rate) and 123 controls (not able to calculate response rate since number of
possible controls not identified in paper).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence.
Not identified in paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Telephone questionnaire sought information on maternal and paternal preconception, pregnancy, and
postnatal (up to 1 yr before case diagnosis) exposures, including a full occupational history (job title,
employers, and dates of employments) and on the child's exposure from birth to 1 yr before case
diagnosis. Parents also provide serf-reported information on specific exposures or occupational
activities. Occupations grouped according to hydrocarbon exposure potential using definition of Zack
et al. (1980).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Telephone interview with 159 of 202 (79%) case mothers and 124 of 202 case fathers (61%). Of
controls, interviews were obtained from 136 mothers (65 friends of cases, 71 population controls) and
87 fathers.
Unblinded interviews.
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CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 19% of paternal exposure information on cases was provided by the mother. 43 of 130 control
mothers provided information on paternal exposures (33%).
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
Paternal TCE exposure
1 yr before pregnancy, 1/0 discordant pairs
During pregnancy, 6/3 discordant pairs
After delivery 8/3 discordant pairs.
No information is provided in paper on maternal TCE exposure.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, race, and Hispanic origin.
Discordant pair analysis.
No.
Yes.
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B.3.2.8. Melanoma Case-Control Studies
B .3.2.8.1. Fritschi and Siemiatycki (1996b), Siemiatycki (1991).
B.3.2.8.1.1. Author's abstract.
OBJECTIVES: Associations between occupational exposures and the occurrence
of cutaneous melanoma were examined as part of a large population based case-
control study of 19 cancer sites. METHODS: Cases were men aged 35 to 70 years
old, resident in Montreal, Canada, with a new histologically confirmed cutaneous
melanoma (n = 103). There were two control groups, a randomly selected
population control group (n = 533), and a cancer control group (n = 533)
randomly selected from among subjects with other types of cancer in the large
study. Odds ratios for the occurrence of melanoma were calculated for each
exposure circumstance for which there were more than four exposed cases (85
substances, 13 occupations, and 20 industries) adjusting for age, ethnicity, and
number of years of schooling. RESULTS: Significantly increased risk of
melanoma was found for exposure to four substances (fabric dust, plastic dust,
trichloroethylene, and a group containing paints used on surfaces other than metal
and varnishes used on surfaces other than wood), three occupations (warehouse
clerks, salesmen, and miners and quarrymen), and two industries (clothing and
non-metallic mineral products). CONCLUSIONS: Most of the occupational
circumstances examined were not associated with melanoma, nor is there any
strong evidence from previous research that any of those are risk factors. For the
few occupational circumstances which were associated in our data with
melanoma, the statistical evidence was weak, and there is little or no supporting
evidence in the scientific literature. On the whole, there is no persuasive evidence
of occupational risk factors for melanoma, but the studies have been too small or
have involved too much misclassification of exposure for this conclusion to be
definitive.
B .3.2.8.1.2. Study description and comment.
Fritschi and Siemiatycki (1996b) and Siemiatycki (1991) reported data from a case-
control study of occupational exposures and melanoma conducted in Montreal, Quebec (Canada)
and part of a larger study of 10 other site-specific cancers and occupational exposures. The
investigators identified 124 newly diagnosed cases of melanoma (ICD-O, 172), confirmed on the
basis of histology reports, between 1979 and 1985; 103 of these participated in the study
interview (83.1% participation). One control group (n = 533) consisted of patients with other
forms of cancer recruited through the same study procedures and time period as the melanoma
cancer cases. A population-based control group (n = 533, 72% response), frequency matched by
age strata, was drawn using electoral lists and random digit dialing. Face-to-face interviews
were carried out with 82% of all cancer cases with telephone interview (10%) or mailed
questionnaire (8%) for the remaining cases. Twenty percent of all case interviews were provided
by proxy respondents. The occupational assessment consisted of a detailed description of each
job held during the working lifetime, including the company, products, nature of work at site, job
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activities, and any additional information that could furnish clues about exposure from the
interviews.
A team of industrial hygienists and chemists blinded to subject's disease status translated
jobs into potential exposure to 294 substances with three dimensions (degree of confidence that
exposure occurred, frequency of exposure, and concentration of exposure). Each of these
exposure dimensions was categorized into none, any, or substantial exposure. Fritschi and
Siemiatycki (1996b) present observations of logistic regression analyses examining industries,
occupation, and some chemical-specific exposures, but not TCE. Observations on TCE from
Mantel-Haenszel analyses are found in the original report of Siemiatycki (1991). Any exposure
to TCE was 6% among cases (n = 8) and 4% for substantial TCE exposure (n = 4); "substantial"
is defined as >10 years of exposure for the period up to 5 years before diagnosis.
Logistic regression models adjusted for age, ethnic origin, SES, Quetlet as an index of
body mass, and respondent status (Fritschi and Siemiatycki, 1996b) or Mantel-Haenszel ^
stratified on age, family income, cigarette smoking, Quetlet, ethnic origin, and respondent status
(Siemiatycki, 1991). Odds ratios for TCE exposure are presented with 90% CIs in Siemiatycki
(1991) and 95% CIs in Fritschi and Siemiatycki (1996b).
The strengths of this study were the large number of incident cases, specific information
about job duties for all jobs held, and a definitive diagnosis of melanoma. However, the use of
the general population (rather than a known cohort of exposed workers) reduced the likelihood
that subjects were exposed to TCE, resulting in relatively low statistical power for the analysis.
The JEM, applied to the job information, was very broad since it was used to evaluate
294 chemicals.
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Fritschi L, Siemiatycki J. (1996b). Melanoma and occupation: Results of a case-control study. 1996. Occup Environ Med
53:168-173.
Siemiatycki J. (1991). Risk Factors for Cancer in the Workplace. J Siemiatycki, Ed. Boca Raton: CRC Press.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This population case-control study was designed to generate hypotheses on possible association
between 1 1 site-specific cancers and occupational title or chemical exposures.
124 melanoma cases were identified among male Montreal residents between 1979 and 1985 of which
103 were interviewed.
740 eligible male controls identified from the same source population using random digit dialing or
electoral lists; 533 were interviewed. A second control series consisted of other cancer cases
identified in the larger study (n = 533).
Participation rate: cases, 83.1%; population controls, 72%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
ICD-O, 172 (malignant neoplasm of skin).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Unblinded interview using questionnaire sought information on complete job history with
supplemental questionnaire for jobs of a priori interest (e.g., machinists, painters). Team of chemist
and industrial hygienist assigned exposure using job title with a semiquantitative scale developed for
294 exposures, including TCE. For each exposure, a three-level ranking was used for concentration
(low or background, medium, high) and frequency (percent of working time: low, 1-5%; medium, >5-
30%; and high, >30%).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
82% of all cancer cases interviewed face-to-face by a trained interviewer, 10% telephone interview,
and 8% mailed questionnaire. Cases interviews were conducted either at home or in the hospital; all
population control interviews were conducted at home.
Interviews were unblinded but exposure coding was carried out blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 20% of all cancer cases had proxy respondents.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
99 cases (76.7% response), 533 population controls (72%).
Exposure prevalence: Any TCE exposure, 8% cases (n = 8); substantial TCE exposure (exposure for
>10 yrs and up to 5 yrs before disease onset), 4% cases (n = 4).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, education, and ethnic origin (Fritschi and Siemiatycki, 1996b).
Age, family income, cigarette smoking, and ethnic origin (Siemiatycki, 1991).
Mantel-Haenszel (Siemiatycki, 1991).
Logistic regression (Fritschi and Siemiatycki, 1996b).
No.
Yes.
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B.3.2.9. Pancreatic Cancer Case-Control Studies
B .3.2.9.1. Kernan et al. (1999).
B.3.2.9.1.1. Author's abstract.
Background The relation between occupational exposure and pancreatic cancer is
not well established. A population-based case-control study based on death
certificates from 24 U.S. states was conducted to determine if occupations/
industries or work-related exposures to solvents were associated with pancreatic
cancer death.
Methods The cases were 63,097 persons who died from pancreatic cancer
occurring in the period 1984±1993. The controls were 252,386 persons who died
from causes other than cancer in the same time period.
Results Industries associated with significantly increased risk of pancreatic cancer
included printing and paper manufacturing; chemical, petroleum, and related
processing; transport, communication, and public service; wholesale and retail
trades; and medical and other health-related services. Occupations associated with
significantly increased risk included managerial, administrative, and other
professional occupations; technical occupations; and sales, clerical, and other
administrative support occupations.
Potential exposures to formaldehyde and other solvents were assessed by using a
job exposure matrix developed for this study. Occupational exposure to
formaldehyde was associated with a moderately increased risk of pancreatic
cancer, with ORs of 1.2, 1.2, 1.4 for subjects with low, medium, and high
probabilities of exposure and 1.2, 1.2, and 1.1 for subjects with low, medium, and
high intensity of exposure, respectively.
Conclusions The findings of this study did not suggest that industrial or
occupational exposure is a major contributor to the etiology of pancreatic cancer.
Further study may be needed to confirm the positive association between
formaldehyde exposure and pancreatic cancer.
B .3.2.9.1.2. Study description and comment.
Kernan et al. (1999) reported data from a case-control study of occupational exposures
and pancreatic cancer, coding usual occupation as noted on death certificates to assign potential
TCE exposure to cases and controls. Deaths from pancreatic cancer from 1984 to 1993 were
identified from 24 U.S. state and frequency-matched to nonpancreatitis or other pancreatic
disease deaths by state, race, sex, and age (5-year groups); 63,097 pancreatic cancer deaths (case
series) and 252,386 controls were selected for analysis.
Exposure assessment in this study group occupational (n = 509) and industry (n = 231)
codes into 16 broad occupational and 20 industrial categories. Additionally, a JEM of Gomez et
al. (1994) was applied to develop exposure surrogates for 11 chlorinated hydrocarbons, including
TCE, and two larger groupings, all chlorinated hydrocarbons and organic solvents. A qualitative
surrogate (ever exposed/never exposed) for TCE exposure is developed and no information is
provided on death certifications on employment duration to examine exposure-response patterns.
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Kernan et al. (1999) report mortality ORs from logistic regression for TCE exposure intensity
and probability of exposure.
Overall, this is a large study that examined specific exposures using a generic JEM.
Errors resulting from exposure misclassification are likely, not only introduced by the generic
JEM, but through the use of usual occupation as coded on death certificates, which may not fully
represent an entire occupational history.
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Kernan GJ, Ji B-T, Dosemeci M, Silverman DT, Balbus J, Zahm SH. (1999). Occupational risk factors for pancreatic cancer:
A case-control study based on death certificates from 24 U.S. states. Am J Ind Med 36:260-270.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This population case-control study was designed to generate hypotheses on possible association
between pancreatic cancers and occupational title or chemical exposures.
63,097 pancreatic cancer cases were identified using death certificates from 24 U.S. states between
1984 and 1993.
63,097 noncancer, nonpancreatitis or other pancreatic disease deaths (controls) identified from the
same source population and frequency -matched to cases by state, race, sex, and age (1:4 matching).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Mortality.
ICD-9, 157 (malignant neoplasm of pancreas).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Usual occupation coded on death certificate coded to 1980 U.S. census classification system for
occupation and industry. 509 occupation codes and 23 1 industry codes grouped into 16 broad
occupational and 20 industrial categories based on similarity of occupational exposures. JEM of
Gomez et al. (1994) used to assign exposure surrogates for 1 1 chlorinated hydrocarbons, including
TCE, and two broad categories, chlorinated hydrocarbons and organic solvents.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
This study did not use interviews, information reported on death certificate used to infer potential
exposure.
No interviews were conducted in this study.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
Exposure prevalence: Any TCE exposure (Low intensity exposure or higher), 14% cases (n = 9,068);
High TCE exposure, 2% cases (n = 1,271).
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, metropolitan status, region of residence,
and marital status.
Logistic regression.
No.
Yes.
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B.3.2.10. Prostatic Cancer Case-Control Studies
B.3.2.10.1. Aronson et al. (1996), Siemiatycki (1991).
B.3.2.10.1.1. Author's abstract.
A population-based case-control study of cancer and occupation was carried out
in Montreal, Canada. Between 1979 and 1986, 449 pathologically confirmed
cases of prostate cancer were interviewed, as well as 1,550 cancer controls and
533 population controls. Job histories were evaluated by a team of
chemist/hygienists using a checklist of 294 workplace chemicals. After
preliminary evaluation, 17 occupations, 11 industries, and 27 substances were
selected for multivariate logistic regression analyses to estimate the odds ratio
between each occupational circumstance and prostate cancer with control for
potential confounders. There was moderate support for risk due to the following
occupations: electrical power workers, water transport workers, aircraft
fabricators, metal product fabricators, structural metal erectors, and railway
transport workers. The following substances exhibited moderately strong
associations: metallic dust, liquid fuel combustion products, lubricating oils and
greases, and polyaromatic hydrocarbons from coal. While the population
attributable risk, estimated at between 12% and 21% for these occupational
exposures, may be an overestimate due to our method of analysis, even if the true
attributable fraction were in the range of 5-10%, this represents an important
public health issue.
B.3.2.10.1.2. Study description and comment.
Aronson et al. (1996) and Siemiatycki (1991) reported data from a case-control study of
occupational exposures and prostate cancer conducted in Montreal, Quebec (Canada) and was
part of a larger study of 10 other site-specific cancers and occupational exposures. The
investigators identified 557 newly diagnosed cases of prostate cancer (ICD-O, 185), confirmed
on the basis of histology reports, between 1979 and 1985; 449 of these participated in the study
interview (80.6% participation). One control group consisted of patients with other forms of
cancer recruited through the same study procedures and time period as the prostate cancer cases.
A population-based control group (n = 533, 72% response), frequency-matched by age strata,
was drawn using electoral lists and random digit dialing. Face-to-face interviews were carried
out with 82% of all cancer cases with telephone interview (10%) or mailed questionnaire (8%)
for the remaining cases. Twenty percent of all case interviews were provided by proxy
respondents. The occupational assessment consisted of a detailed description of each job held
during the working lifetime, including the company, products, nature of work at site, job
activities, and any additional information that could furnish clues about exposure from the
interviews.
A team of industrial hygienists and chemists blinded to subject's disease status translated
jobs into potential exposure to 294 substances with three dimensions (degree of confidence that
exposure occurred, frequency of exposure, and concentration of exposure). Each of these
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exposure dimensions was categorized into none, any, or substantial exposure. Aronson et al.
(1996) presents observations of logistic regression analyses examining industries, occupation,
and some chemical-specific exposures, but not TCE. Observations on TCE from Mantel-
Haenszel analyses are found in the original report of Siemiatycki (1991). Any exposure to TCE
was 2% among cases (n = 11) and <2% for substantial TCE exposure (n = 7); "substantial" is
defined as >10 years of exposure for the period up to 5 years before diagnosis.
Logistic regression models adjusted for age, education, and ethnicity (Aronson et al.,
1996) or Mantel-Haenszel £ stratified on age, family income, cigarette smoking, coffee, and
ethnic origin (Siemiatycki, 1991). Odds ratios for TCE exposure are presented with 90% CIs in
Siemiatycki (1991) and 95% CIs in Aronson et al. (1996).
The strengths of this study were the large number of incident cases, specific information
about job duties for all jobs held, and a definitive diagnosis of prostate cancer. However, the use
of the general population (rather than a known cohort of exposed workers) reduced the likelihood
that subjects were exposed to TCE, resulting in relatively low statistical power for the analysis.
The JEM, applied to the job information, was very broad since it was used to evaluate
294 chemicals.
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Aronson KJ, Siemiatycki J, Dewar R, Gerin M. (1996). Occupational risk factors for prostate cancer: Results from a case-
control study in Montreal, Canada. Am J Epidemiol 143:363-373.
Siemitycki J. (1991). Risk Factors for Cancer in the Workplace. J Siemiatycki, Ed. Boca Raton: CRC Press.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This population case-control study was designed to generate hypotheses on possible association
between 1 1 site-specific cancers and occupational title or chemical exposures.
557 prostate cancer cases were identified among male Montreal residents between 1979 and 1985 of
which 449 were interviewed.
740 eligible male controls identified from the same source population using random digit dialing or
electoral lists; 533 were interviewed. A second control series consisted of other cancer cases
identified in the larger study.
Participation rate: cases, 83.1%; population controls, 72%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
ICD-O, 185 (malignant neoplasm of prostate).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Unblinded interview using questionnaire sought information on complete job history with
supplemental questionnaire for jobs of a priori interest (e.g., machinists, painters). Team of chemist
and industrial hygienist assigned exposure using job title with a semiquantitative scale developed for
294 exposures, including TCE. For each exposure, a three-level ranking was used for concentration
(low or background, medium, high) and frequency (percent of working time: low, 1-5%; medium, >5-
30%; and high, >30%).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
82% of all cancer cases interviewed face-to-face by a trained interviewer, 10% telephone interview,
and 8% mailed questionnaire. Cases interviews were conducted either at home or in the hospital; all
population control interviews were conducted at home.
Interviews were unblinded but exposure coding was carried out blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 20% of all cancer cases had proxy respondents.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
449 cases (80.6% response), 533 population controls (72%).
Exposure prevalence: Any TCE exposure, 2% cases (n = 1 1); substantial TCE exposure (exposure for
>10 yrs and up to 5 yrs before disease onset), <2% cases (n = 7).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, ethnic origin, SES, Ouetlet as an index of body mass, and respondent status (Aronson
1996).
Age, family income, cigarette smoking, ethnic origin, and respondent status (Siemiatycki,
et al..
1991).
Mantel-Haenszel (Siemiatycki, 1991).
Logistic regression (Aronson et al.. 1996).
No.
Yes.
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B.3.2.11. RCC Case-Control Studies—Arnsberg Region of Germany
A series of studies (including Henschler et al. (1995), discussed in cohort study section)
have been conducted in an area with a long history of TCE use in several industries. The main
importance of these studies is that there is considerable detail on the nature of exposures, which
made it possible to estimate the order of magnitude of exposure even though there were no direct
measurements.
B.3.2.11.1. Bruning et al. (2003).
B.3.2.11.1.1. Author's abstract.
BACKGROUND: German studies of high exposure prevalence have been
debated on the renal carcinogenicity of trichloroethylene (TRI). METHODS: A
consecutive hospital-based case-control study with 134 renal cell cancer (RCC)
cases and 401 controls was conducted to reevaluate the risk of TRI in this region
which were estimated in a previous study. Exposure was self-assessed to compare
these studies. Additionally, the job history was analyzed, using expert-based
exposure information. RESULTS: The logistic regression results, adjusted for
age, gender, and smoking, confirmed a TRI-related RCC risk in this region. Using
the database CAREX for a comparison of industries with and without TRI
exposure, a significant excess risk was estimated for the longest held job in TRI-
exposing industries (odds ratio (OR) 1.80, 95% confidence interval (CI) 1.01-
3.20). Any exposure in "metal degreasing" was a RCC risk factor (OR 5.57, 95%
CI 2.33-13.32). Self-reported narcotic symptoms, indicative of peak exposures,
were associated with an excess risk (OR 3.71, 95% CI 1.80-7.54).
CONCLUSIONS: The study supports the human nephrocarcinogenicity of
tri chl oroethy 1 ene.
B.3.2.11.1.2. Study description and comment.
This study is a second case-control follow-up of renal cell cancer in the Arnsberg area of
Germany, which was intended to deal with some of the methodological issues present in the two
earlier studies. The major advantage of studies in the Arnsberg area is the high prevalence of
exposure to TCE because of the large number of companies doing the same kind of industrial
work. An interview questionnaire procedure for self-assessment of exposures similar to the one
used by Vamvakas et al. (1998) was used to obtain detailed information about solvents used, job
tasks, and working conditions, as well as the occurrence of neurological symptoms. The industry
and job title information in the subjects' job histories were also analyzed by two schemes of
expert-rated exposure assignments for broad groups of jobs. The CAREX database from the
European Union, for industry categories, and the British JEM developed by Pannett et al. (1985),
for potential exposure to chemical classes or specific chemical, but not TCE, was adopted in an
attempt to obtain a potentially less biased assessment of exposures.
Exposure prevalences for employment in industries with potential TCE and
perchloroethylene exposures was high in both cases (87%) and controls (79%) using the CAREX
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approach, but much lower using the JEM approach for potential exposure to degreasing agents
(12% cases, 9% controls), self-reported exposure to TCE (18% cases, 10% controls), and TCE
exposure with any symptom occurrence (14% cases, 4% controls). Both the CAREX and British
JEM rating approaches are very broad and they have potentially high rates of misclassification of
exposure intensity in job groupings and industry groupings. In an attempt to avoid reporting
biases associated with the legal proceeding for compensation, analyses were conducted on self-
reported exposure to selected agents (yes or no). The regional use of TCE and perchloroethylene
(tetrachloroethylene) were so widespread that most individuals recognized the local
abbreviations. If individuals claimed to be exposed when they were not, it would reduce the
finding of a relationship if one existed. Similarly, subjects were grouped by frequency of
perceived symptoms (any, less than daily, daily) associated with TCE or perchloroethylene
exposure. Overreporting would also introduce misclassification and reduce evidence of any
relationship. Self-reporting of exposure to chemicals in case-control studies, generally, is
considered unreliable since, within the broad population, workers rarely know specific chemicals
to which they have potential exposure. However, in cohort studies and case-control studies in
which one industry dominates a local population such as in this study, this is less likely because
the numbers of possible industries and job titles are much smaller than in a broad population.
The Arnsberg area studies focused on a small area where one type of industry was very
prevalent, and that industry used primarily just two solvents: TCE and tetrachloroethylene. As a
result, it was common knowledge among the workers what solvent an individual was using, and,
for most, it was TCE. Self-reported TCE exposure is considered to be less biased compared to
possible misclassification bias associated with using the CAREX exposure assessment approach
which identified approximately 90% of all cases as holding a job in an industry using TCE or
perchloroethylene (see above discussion).
Some subjects in Briining et al. (2003) are drawn from the underlying Arnsberg
population as studied by Vamvakas et al. (1998) (reviewed below) and TCE exposures to these
subjects would be similar—substantial, sustained high exposures to TCE at 400-600 ppm during
hot dip cleaning and >100 ppm overall. However, the larger ascertainment area outside the
Arnsberg region for case and control identification may have resulted in a lower exposure
prevalence compared to Vamvakas et al. (1998).
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Briining T, Pesch B, Wiesenhiitter B, Rabstein S, Lammert M, Baumiiller A, Bolt H. (2003). Renal cell cancer risk and
occupational exposure to trichloroethylene: results of a consecutive case-control study in Arnsberg, Germany. Am J Ind Med
23:274-285.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
From abstract — study aim was to "reevaluate the risk of TRI in this region which were estimated in a
previous study."
162 RCC cases identified from September 1999 to April 2000 and who had undergone nephrectomy
between 1992 and 2000 (a time period preceding that adopted in Vamvakas et al, (1998)1) from a
regional hospital urology department in Arnsberg, Germany; 134 of the recruited cases were
interviewed. 401 hospital controls were interviewed between 1999 and 2000 from local surgery
departments or geriatric departments and frequency matched to cases by sex and age.
134 of 162 (83%) cases; response rate among controls could not be calculated lacking information on
the number of eligible controls.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
N/A
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Face-to-face interview with subjects or their next of kin using a structured questionnaire with
questions to obtain information on a complete job history by job title, supplemental information on job
tasks with suspected exposure to specific agents, medical history, and personal habits. Questionnaires
also sought serf-reported information on duration and frequency of exposure to TCE and
perchloroethylene, and, for these individuals, frequency of narcotic symptoms as a marker of high
peak exposure.
Jobs titles were coded according to a British classification of occupations and industries with potential
chemical-specific exposures identified for each occupation using CAREX, a carcinogen exposure
database or the British JEM of Pannett et al. (1985) for chemical groupings (e.g., degreasing agents,
organic solvents).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
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CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
100% of cases or their NOK and 100% controls with face-to-face interviews.
No information on whether interviewers were blinded.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 17% of case interviews with next-of-kin; all controls were alive at time of interview.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancers in incidence studies; numbers of
exposed cases and prevalence of exposure in case-
control studies
CAREX Job-exposure-matrix
1 17 cases with TCE exposure (87% exposure prevalence among cases).
3 16 controls with TCE exposure (79% exposure prevalence among controls).
Self-reported TCE exposure
25 cases with TCE exposure (18% exposure prevalence among cases).
38 controls with TCE exposure (9.5% exposure prevalence among controls).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, and tobacco smoking.
Conditional logistic regression.
Yes, duration of exposure as 4 categories (no, <10 yrs, 10-<20 yrs, and 20+ yrs).
Yes.
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B.3.2.11.2. Pesch et al. (2000b).
B .3.2.11.2.1. Author's abstract.
BACKGROUND: This case-control study was conducted to estimate the renal
cell cancer (RCC) risk for exposure to occupation-related agents, besides other
suspected risk factors. METHODS: In a population-based multicentre study, 935
incident RCC cases and 4298 controls matched for region, sex, and age were
interviewed between 1991 and 1995 for their occupational history and lifestyle
habits. Agent-specific exposure was expert-rated with two job-exposure matrices
and a job task-exposure matrix. Conditional logistic regression was used to
calculate smoking adjusted odds ratios (OR). RESULTS: Very long exposures in
the chemical, rubber, and printing industries were associated with risk for RCC.
Males considered as 'substantially exposed to organic solvents' showed a
significant excess risk (OR = 1.6, 95% CI: 1.1-2.3). In females substantial
exposure to solvents was also a significant risk factor (OR = 2.1, 95% CI: 1.0-
4.4). Excess risks were shown for high exposure to cadmium (OR = 1.4, 95% CI:
1.1-1.8, in men, OR = 2.5, 95% CI: 1.2-5.3 in women), for substantial exposure
to lead (OR = 1.5, 95% CI : 1.0-2.3, in men, OR = 2.6, 95% CI: 1.2-5.5, in
women) and to solder fumes (OR = 1.5, 95% CI: 1.0-2.4, in men). In females, an
excess risk for the task 'soldering, welding, milling' was found (OR = 3.0, 95% CI
: 1.1-7.8). Exposure to paints, mineral oils, cutting fluids, benzene, polycyclic
aromatic hydrocarbons, and asbestos showed an association with RCC
development.
CONCLUSIONS: Our results indicate that substantial exposure to metals and
solvents may be nephrocarcinogenic. There is evidence for a gender-specific
susceptibility of the kidneys.
B.3.2.11.2.2. Study description and comment.
This multicenter study of RCC and bladder cancer and in Germany, which included the
Arnsberg region plus four others, identified two case series from participating hospitals, 1,035
urothelial cancer cases and 935 RCC cases with a single population control series matched to
cases by region, sex, and age (1:2 matching ratio to urothelial cancer cases and 1:4 matching
ratio to RCC cases). A strength of the study was the high percentage of interviews with RCC
cases within 2 months of diagnosis (88.5%), reducing bias associated with proxy or next-of-kin
interview, and few cases diagnoses confirmed by sonography only (5%). In all, 935 (570 males,
365 females) RCC cases were interviewed face-to-face with a structured questionnaire.
Two general JEMs, British and German, were used to assign exposures based on
subjects' job histories reported in an interview. Researchers also asked about job tasks
associated with exposure, such as metal degreasing and cleaning, and use of specific agents
(organic solvents chlorinated solvents, including specific questions about carbon tetrachloride,
TCE, and tetrachloroethylene) to evaluate TCE potential using a ITEM. A category of "any use
of a solvent" mixes the large number with infrequent slight contact with the few noted earlier
who have high intensity and prolonged contact. Analyses examining TCE exposure using either
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the JEM of ITEM assigned a cumulative TCE exposure index of none to low, medium high and
substantial, defined as the product of exposure probability x intensity x duration with the
following cutpoints: none to low, <30th percentile of cumulative exposure scores; medium, 30th-
<60th percentile; high, 60th-<90th percentile; and, substantial, >90th percentile. The use of the
German JEM identified approximately twice as many cases with any potential TCE exposure
(42%) compared to the ITEM (17%) and, in both cases, few cases identified with substantial
exposure, 6% by JEM and 3% by JTEM. Pesch et al. (2000b) noted "exposure indices derived
from an expert rating of job tasks can have a higher agent-specificity than indices derived from
job titles." For this reason, the JTEM approach with consideration of job tasks is considered as a
more robust exposure metric for examining TCE exposure and RCC due to likely reduced
potential for exposure misclassification compared to TCE assignment using only job history and
title.
While this case-control study includes the Arnsberg area, several other regions are
included as well, where the source of the TCE and chlorinated solvent exposures are much less
well defined. Few cases were identified as having substantial exposure to TCE and, as a result,
most subjects identified as exposed to TCE probably had minimal contact, averaging
concentrations of about 10 ppm or less (NRC, 2006).
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Pesch B, Haerting J, Ranft U, Klimpet A, Oelschagel, Schill W, and the MURC Study Group. (2000b). Occupational risk
factors for renal cell carcinoma: agent-specific results from a case-control study in Germany. Int J Epidemiol 29:1014-1024.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This case-control study was conducted to estimate RCC risk for exposure to occupational-related
agents; chlorinated solvents including TCE were identified as exposures of a priori interest.
935 RCC cases were identified from hospitals in a five-region area in Germany between 1991 and
1995. Cases were confirmed histologically (95%) or by sonography (5%) and selected without age
restriction. 4,298 population controls identified from local residency registries in the five-region area
were frequency matched to cases by region, sex, and age.
Participation rate: cases, 88%; controls, 71%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
N/A
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
A trained interviewer interviewed subjects using a structured questionnaire which covered
occupational history and job title for all jobs held longer than 1 yr, medical history, and personal
information. Two general JEMs, British and German, were used to assign exposures based on
subjects' job histories reported in an interview. Researchers also asked about job tasks associated with
exposure, such as metal degreasing and cleaning, and use of specific agents (organic solvents
chlorinated solvents, including specific questions about carbon tetrachloride, TCE, and
tetrachloroethylene) and chemical-specific exposure were assigned using a ITEM. Exposure index for
each subject is the sum over all jobs of duration x probability x intensity. A four category grouping
was used in statistical analyses defined by exposure index distribution of controls: no-low; medium,
30th percentile; high, 60th percentile; substantial, 90th percentile.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Interviewers carried out face-to-face interview with all cases and controls. All cases were interviewed
in the hospital; 88.5% of cases were interviewed within 2 months after diagnosis. All controls had
home interviews.
No, by nature of interview location.
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CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancers in incidence studies; numbers of
exposed cases and prevalence of exposure in case-
control studies
JEM: 391 cases with TCE exposure index of medium or higher (42% exposure prevalence among
cases).
ITEM: 172 cases with TCE exposure index of medium or higher (18% exposure prevalence among
cases).
No information is presented in paper on control exposure prevalence.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, study center, and smoking.
Conditional
logistic regression.
Yes.
Yes.
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B .3.2.11.3. Vamvakas et al. (1998).
B .3.2.11.3.1. Author's abstract.
A previous cohort-study in a cardboard factory demonstrated that high and
prolonged occupational exposure to trichloroethene (C2HC13) is associated with
an increased incidence of renal cell cancer. The present hospital-based
case/control study investigates occupational exposure in 58 patients with renal
cell cancer with special emphasis on C2HC13 and the structurally and
lexicologically closely related compound tetrachloroethene (C2C14). A group of
84 patients from the accident wards of three general hospitals in the same area
served as controls. Of the 58 cases, 19 had histories of occupational C2HC13
exposure for at least 2 years and none had been exposed to C2C14; of the 84
controls, 5 had been occupationally exposed to C2HC13 and 2 to C2C14. After
adjustment for other risk factors, such as age, obesity, high blood pressure,
smoking and chronic intake of diuretics, the study demonstrates an association of
renal cell cancer with long-term exposure to C2HC13 (odds ratio 10.80; 95% CI:
3.36-34.75).
B.3.2.11.3.2. Study description and comment.
In a follow-up to Henschler et al. (1995) (discussed below), a case-control study was
conducted in the Arnsberg region of Germany where there has long been a high prevalence of
small enterprises manufacturing small metal parts and goods, such as nuts, lamps, screws, and
bolts. Both cases and controls were identified from hospital records; cases from of a large
regional hospital in North Rhine Wetphalia during the period 1987 and 1992 and controls who
were admitted to accident wards during 1993 at three other regional hospitals. Control selection
was carried out independent of cases demographic risk factors (i.e., controls were not matched to
cases). Controls may not be fully representative of the case series (NRC, 2006); they were
selected from a time period after case selection, which may introduce bias if TCE use changes
over time resulted in decreased potential for exposure among controls, and use of accident ward
patients may be representative of the target population.
Exposures to TCE resulted from dipping metal pieces into vats, with room temperatures
up to 60°C, and placing the wet parts on tables to dry. Some work rooms were noted to be small
and poorly ventilated. These conditions are likely to result in high inhalation exposure to TCE
(100-500 ppm). Cherrie et al. (2001) estimated the long-term exposures to be approximately
100 ppm. Some of the cases included in this study were also pending legal compensation. As a
result, there had been considerable investigation of the exposure situation by occupational
hygienists from the Employer's Liability Insurance Association and occupational physicians,
including walk-through visits and interviews of long-term employees. The legal action could
introduce a bias, a tendency to overreport some of the subjective reports by the subjects.
However, the objective working conditions were assessed by knowledgeable professionals, who
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corroborated the presence of the poorly controlled hot dip tanks, extensive use of TCE for all
types of cleaning, and the process descriptions.
NRC (2006) discussed a number of criticisms in the literature on Vamvakas et al. (1998)
by Green and Lash (1999), Cherrie et al. (2001), and Mandel (2001) and noted the direction of
possible bias would be positive or negative depending on the specific criticism. Overall, cases in
this study substantial, sustained exposures to high concentrations of TCE at 400-600 ppm during
hot dip cleaning and >100 ppm overall and observations can inform hazard identification
although the magnitude of observed association is uncertain give possible biases.
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Vamvakas S, Briining T, Thomasson B, Lammert M, Baumiiller A, Bolt HM, Dekant W, Birner G, Henschler D, Ulm K.
(1998). Renal cell cancer risk and occupational exposure to trichloroethylene: results of a consecutive case-control study in
Arnsberg, Germany. Am J Ind Med 23:274-285.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls in
case-control studies is adequate
Yes. From introduction — study aim was designed to investigate further the role of occupation
exposure to TCE/perchloroethylene in the formation of renal cancer.
73 RCC cases that had undergone nephrectomy between December 1987 and May 1992 from a
hospital urology department in Arnsberg, Germany were contacted by mail; 58 of the recruited cases
were. 112 controls identified from accident wards of three area hospitals were interviewed during
1993. Controls underwent abdominal sonography to exclude kidney cancer.
62 of 73 (85%) cases and 84 of 112 (75%) of controls participated in study.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
N/A
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Face-to-face interview with subjects or, if deceased, with their next of kin or former colleagues using a
structured questionnaire with questions to obtain information on job tasks with selected exposure to
specific agents and to serf-reported selected exposures. A supplemental questionnaire on job
conditions was administered to subjects reporting exposure to TCE and perchloroethylene. Subjects
with TCE exposures were primarily exposed through degreasing operations in small businesses. Serf-
reported TCE exposure was ranked using a semiquantitative scale based upon total exposure time and
frequency/duration of self-reported acute prenarcotic symptoms. Cherrie et al. (2001) estimated
that the machine cleaning exposures to TCE were ~400-600 ppm, with long-term average TCE
exposure as ~100 ppm.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Personal physicians interviewed 100% of cases or their NOK/former colleague and 100% controls.
Interviewers were not blinded nor was developments of exposure assessment semiquantitative scale.
CATEGORY F: PROXY RESPONDENTS
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>10% proxy respondents
No information provided in paper on number of cases with NOK interviews or interviews with
colleagues; all controls were alive and interviewed by their personal physician.
former
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancers in incidence studies; numbers of
exposed cases and prevalence of exposure in case-
control studies
19 cases with TCE or perchloroethylene exposure (33% exposure prevalence) and 1 control with
perchloroethylene exposure.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, obesity, high blood pressure, smoking, and diuretic use.
Mantel-Haenszel %2-
Yes, semiquantitative scale of 4 categories (no, +, ++, +++).
No information on number of eligible controls or number interviews with case NOK or former
colleagues.
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B.3.2.12. RCC Case-Control Studies—Arve Valley Region of France
A case-control study was conducted in the Arve Valley to examine the a priori hypothesis
of an association with RCC and TCE exposure. The Arve Valley, like the Arnsburg Region in
Germany, has a long history of TCE use in the screw-cutting industry. The Arve Valley, situated
in the Rhone-Alpes region of eastern France is a major metalworking sector with around
800 small and medium-sized firms specializing in "screw-cutting" or the machining of small
mechanical parts from bars, in small, medium, and large series on conventional automatic lathes
or by digital control. This industry evolved around the time of World War I from the region's
expertise in clock-making. A major point of this study is that it was designed as a follow-up
study to the German renal cell cancer case-control studies but in a different population with
similar exposure patterns and with high prevalence of exposure to TCE. For this reason, there is
considerable detail on the nature of exposure, which made it possible to estimate the order of
magnitude of exposure, even though there were not direct measurements.
B .3.2.12.1. Charbotel et al.(2009), Charbotel et al. (2007) Charbotel et al. (2006).
B.3.2.12.1.1. Charbotel et al. (2009) abstract.
Abstract Background- Several studies have investigated the association between
trichloroethylene (TCE) exposure and renal cell cancer (RCC) but findings were
inconsistent. The analysis of a case control study has shown an increased risk of
RCC among subjects exposed to high cumulative exposure. The aim of this
complementary analysis is to assess the relevance of current exposure limits
regarding a potential carcinogenic effect of TCE on kidney.
Methods- Eighty-six cases and 316 controls matched for age and gender were
included in the study. Successive jobs and working circumstances were described
using a detailed occupational questionnaire. An average level of exposure to TCE
was attributed to each job period in turn. The main occupational exposures
described in the literature as increasing the risk of RCC were assessed as well as
non-occupational factors. A conditional logistic regression was performed to test
the association between TCE and RCC risk. Three exposure levels were studied
(average exposure during the eight-hour shift): 35 ppm, 50 ppm and 75 ppm.
Potential confounding factors identified were taken into account at the threshold
limit of 10% (p = 0.10) (body mass index [BMI], tobacco smoking, occupational
exposures to cutting fluids and to other oils).
Results- Adjusted for tobacco smoking and BMI, the odd-ratios associated with
exposure to TCE were respectively 1.62 [0.77-3.42], 2.80 [1.12-7.03] and 2.92
[0.85-10.09] at the thresholds of 35 ppm, 50 ppm and 75 ppm. Among subjects
exposed to cutting fluids and TCE over 50 ppm, the OR adjusted for BMI,
tobacco smoking and exposure to other oils was 2.70 [1.02-7.17].
Conclusion- Results from the present study as well as those provided in the
international literature suggest that current French occupational exposure limits
for TCE are too high regarding a possible risk of RCC.
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B.3.2.12.1.2. Charbotel et al. (2007) abstract.
Background: We investigated the association between exposure to
trichloroethylene (TCE) and mutations in the von Hippel-Lindau (VHL) gene and
the subsequent risk for renal cell carcinoma (RCC).
Methods: Cases were recruited from a case-control study previously carried out in
France that suggested an association between exposures to high levels of TCE and
increased risk of RCC. From 87 cases of RCC recruited for the epidemiological
study, 69 were included in the present study. All samples were evaluated by a
pathologist in order to identify the histological subtype and then be able to focus
on clear cell RCC. The majority of the tumor samples were fixed either in
formalin or Bouin's solutions. The majority of the tumors were of the clear cell
RCC subtype (48 including 2 cystic RCC). Mutation screening of the 3 VHL
coding exons was carried out. A descriptive analysis was performed to compare
exposed and non exposed cases of clear cell RCC in terms of prevalence of
mutations in both groups.
Results: In the 48 cases of RCC, four VHL mutations were detected: within exon
1 (c.332G>A, p.Serl 11 Asn), at the exon 2 splice site (c.463+lG>C and
c.463+2T>C) and within exon 3 (c.506T>C, p.Leu!69Pro). No difference was
observed regarding the frequency of mutations in exposed vs. unexposed groups:
among the clear cell RCC, 25 had been exposed to TCE and 23 had no history of
occupational exposure to TCE. Two patients with a mutation were identified in
each group.
Conclusion: This study does not confirm the association between the number and
type of VHL gene mutations and exposure to TCE previously described.
B.3.2.12.1.3. Charbotel et al. (2006) abstract.
Background: We investigated the association between exposure to
trichloroethylene (TCE) and mutations in the von Hippel-Lindau (VHL) gene and
the subsequent risk for renal cell carcinoma (RCC).
Methods: Cases were recruited from a case-control study previously carried out in
France that suggested an association between exposures to high levels of TCE and
increased risk of RCC. From 87 cases of RCC recruited for the epidemiological
study, 69 were included in the present study. All samples were evaluated by a
pathologist in order to identify the histological subtype and then be able to focus
on clear cell RCC. The majority of the tumor samples were fixed either in
formalin or Bouin's solutions. The majority of the tumors were of the clear cell
RCC subtype (48 including 2 cystic RCC). Mutation screening of the 3 VHL
coding exons was carried out. A descriptive analysis was performed to compare
exposed and non-exposed cases of clear cell RCC in terms of prevalence of
mutations in both groups.
Results: In the 48 cases of RCC, four VHL mutations were detected: within exon
1 (c.332G>A, p.Serl 11 Asn), at the exon 2 splice site (c.463+lG>C and
c.463+2T>C) and within exon 3 (c.506T>C, p.Leu!69Pro). No difference was
observed regarding the frequency of mutations in exposed vs. unexposed groups:
among the clear cell RCC, 25 had been exposed to TCE and 23 had no history of
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occupational exposure to TCE. Two patients with a mutation were identified in
each group.
Conclusion: This study does not confirm the association between the number and
type of VHL gene mutations and exposure to TCE previously described.
To test the effect of the exposure to trichloroethylene (TCE) on renal cell cancer
(RCC) risk, a case-control study was performed in the Arve Valley (France), a
geographic area with a high frequency and a high degree of such exposure. Cases
and controls were selected from various sources: local general practitioners and
urologists practicing in the area and physicians (urologists and oncologists) from
other hospitals of the region who might treat patients from this area. Blinded
telephone interviews with cases and controls were administered by a single
trained interviewer using occupational and medical questionnaires. The analysis
concerned 86 cases and 316 controls matched for age and gender. Three
approaches were developed to assess the link between TCE exposure and RCC:
exposure to TCE for at least one job period (minimum 1 year), cumulative dose
number of ppm of TCE per job period multiplied by the number of years in the
job period) and the effect of exposure to peaks. Multivariate analysis was
performed taking into account potential confounding factors. Allowing for
tobacco smoking and Body Mass Index, a significantly 2-fold increased risk was
identified for high cumulative doses: odds ratio (OR) = 2.16 (1.02-4.60). A dose-
response relationship was identified, as was a peak effect; the adjusted OR for
highest class of exposure-plus-peak being 2.73 (1.06-7.07). After adjusting for
exposure to cutting fluids the ORs, although still high, were not significant
because of lack of power. This study suggests an association between exposures
to high levels of TCE and increased risk of RCC. Further epidemiological studies
are necessary to analyze the effect of lower levels of exposure.
B.3.2.12.1.4. Study description and comment.
Cases in the population-based, case-control study were obtained retrospectively from
regional medical practitioners or from teaching hospitals from 1993 to 2002, and prospectively
from 2002 to mid-2003. One case was excluded from analysis because it was not possible to
find a control subject. Controls were either selected from the same urology practice as cases or,
for cases selected from teaching hospitals, from among patients of the case's general practitioner.
Telephone interviews of 87 RCC cases and 316 controls matched for age and sex by a trained
interviewer were used to obtain information on occupational and medical history for the case-
control analysis of Charbotel et al. (2006). Of the 87 RCC cases, 67 cases provided consent for
mutational analysis of which 48 cases were diagnosed with clear cell RCC, suitable for
mutational analysis of the VHL gene (Charbotel et al., 2007). Tissue samples were paraffin-
embedded or frozen tissues and ability to fully sequence the VHL gene depended on type of the
fixative procedure; only 26 clear cell RCC cases (34% of 73 clear cell RCC cases in the case-
control study) could full sequencing of the VHL gene occur.
Two occupational questionnaires were administered to both cases and controls, a
questionnaire developed specifically to evaluate jobs and exposure potential in the screw-cutting
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industry and a more general one for any other jobs. Interviewers were essentially blinded to
subject status as case or control for the occupational questionnaires given the medical
questionnaire was administered afterwards (Fevotte et al., 2006). The medical questionnaire
included familial kidney disease and medical history, BMI, and history of smoking. A
task/TCE-Exposure Matrix was designed using information obtained from questionnaires and
routine atmospheric monitoring of workshops or biological monitoring (U-TCA) of workers
carried out since the 1960s. Questionnaires were used to elicit from each subject the main tasks
associated with each job, working conditions, activities, or jobs that might involve TCE
exposures and possible exposure to other occupational risk factors for RCC.
The JEM linked to corresponding TCE-exposure levels using available industrial hygiene
monitoring data on atmospheric TCE levels and from biological measurement on workers.
Estimates reflected task duration, use of protective equipment, and distance from TCE source, as
well, as both dermal and inhalation exposure routes. Estimated TCE intensities for jobs
associated with open cold degreasing were 15-18 ppm, 120 ppm for jobs working near open hot
degreasing machines, with up to 300 ppm for work directly above tank and for job and intensities
of 300-600 ppm for emptying, cleaning, and refilling degreasers. Eight local physicians with
knowledge of working conditions corroborated the working conditions for individual job periods
after 1980 in screw-cutting shops. Overall, there was good agreement (72%) between physician
and the JEM. Three exposure surrogates were assigned to each case and control: TWA
exposure (Charbotel et al., 2009), cumulative exposure (Charbotel et al., 2006), and cumulative
exposure with and without peak exposure (Charbotel et al., 2006).
An 8-hour TWA exposure concentration was developed for each job period from 1924 to
2003 and was the product of the task-specific estimated TCE intensity and duration of task. A
subject's lifetime 8-hour TWA was the sum of each job period specific estimated TWA.
Exposure peak, daily exposure reaching >200 ppm for at least 15 minutes, was assessed as an
additive factor and was defined by frequency (seldom exposed, few times yearly to frequently
exposure, few time weekly).
Over the study period, 19% (295 of 1,486) job periods were assessed as having TCE
exposure with an 8-hour TWA of <35 ppm for 72% of exposed jobs and >75 ppm for 5% of
exposed jobs. Exposure prevalence to TCE peaked in the 1970s with roughly 20% of job periods
with TCE exposure and 8% of subjects identified with >75 ppm. By the 1990s, exposure
prevalence had not only decreased to 7% but also exposure intensity, only 5% of job periods
with >75 ppm.
Cumulative TCE exposure was the sum of 8-hour TWAs overall job periods with
statistical analysis using four categories: no, low, medium, and high. These were defined as low,
5-150 ppm-years; medium, 155-335 ppm-year; and high, >335 ppm-years (HSIA, 2005).
Analyses were also carried out examining peak exposure, classified as yes/no and without
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assignment of quantitative level, as additional exposure to average TCE concentration;
33 subjects were exposed to peaks and very few to high peaks.
The high exposure prevalence and strong approach for exposure assessment provides
Charbotel et al. (2009; 2006) more statistical power and ability to assess association of RCC and
TCE exposure. However, the low participation rate, inability to fully sequence the VHL gene in
all clear cell RCC cases, the lower background prevalence of mutations (15% in this study
compared to roughly 50% in other series) in Charbotel et al. (2007) suggest a relative
insensitivity of assay used and lack of a positive control limits the mutational analysis. These
methodological limitations introduce bias with greater uncertainties for evaluating consistency of
findings with somatic VHL mutations observed in other TCE-exposed RCC cases (Brauch et al.,
1999; Briming et al., 1997b). TCE exposure prevalence (>5 ppm-year) in Charbotel et al. (2006)
was 43% among cases and is higher than that observed in other population-based case-control
studies of RCC and TCE (e.g., Pesch et al., 2000a). While some subjects had jobs with
exposures to high concentrations of TCE during the 1970s and 1980s, a large percentage of jobs
were to TCE concentrations of <35 ppm (8-hour TWA). Jobs with high TCE concentrations also
were identified as having frequent exposure to peak TCE concentrations, particularly before
1980. Peak TCE estimates in this study were judged to be lower than those in German studies of
the Arnsberg region (Vamvakas et al., 1998; Henschler et al., 1995) but higher than those of Hill
Air Force Base civilian workers (Blair etal., 1998; Stewart et al., 1991) due to a lower frequency
of degreasing tasks in Blair et al. (1998) cohort and to slower technological changes in
degreasing process in the French case-control study (Fevotte et al., 2006).
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Charbotel B, Fevotte J, martin JL, Bergeret A. (2009). Cancer du rein et expositions au trichloroethylene: les valeurs limites
d'exposition professionnelle fracaises en vigueur sont-elles adaptees. Rev Epidemiol Sante Publique 57:41-47.
Charbotel B, Fevotte J, Hours M, Martin J-L, Bergeret A. (2006). Case-control study on renal cell cancer and occupational
exposure to trichloroethylene. Part II: Epidemiological Aspects. Ann Occup Hyg 50:777-787.
Fevotte J, Charbotel B, Muller-Beaute P, Martin J-L, Hours, Bergeret A. (2006). Case-control study on renal cell cancer and
occupational exposure to trichloroethylene. Part I: Exposure assessment. Ann Occup Hyg 50:765-775.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Yes. From abstract — study aim was to "test the effect of TCE exposure on renal cell cancer."
117 cases of RCC patients were identified retrospectively from 1993 to June 2002, and prospectively
from June 2002 to June 2003 from patients of urology practices and hospital urology and oncology
departments in the region of Arve Valley, France. 404 controls were identified from the same urology
practice or from the same general practitioner, for cases identified from hospital records and matched
on residency in the geographic study area at time of case diagnosis, sex, and year of birth. Controls
sought medical treatment for conditions other than kidney or bladder cancer. Case definition included
clear cell and other subtypes of RCC including chromophil, chromophobe and collecting duct
carcinomas.
87 or 1 17 (74%) cases and 3 16 of 404 (78%) controls participated in study.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
N/A
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CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Occupational questionnaires sought information for each study subject a complete job history and was
followed-up with either a questionnaire specific for jobs and exposures in the screw-cutting industry or
a General Occupational Questionnaire, whichever was more applicable to subject. Questionnaires also
sought self-reported information on potential TCE exposures. A medical questionnaire seeking
information on medical history and familial kidney disease was administered after occupational
questionnaires.
Jobs titles were coded according to standardized classification of occupations and 1,486 job periods
grouped into three categories (screw-cutting, nonscrew-cutting but job with possible TCE exposure,
and no TCE exposure). An estimated 8-hr TWA was assigned to each job and job period using a
JTEM.
RCC and TCE was examined using three exposure approaches: exposure to at least 5 ppm for at least
one job period (minimum 1 yr), cumulative dose or £ (TCE ppm per job x years) using quantitative
ranking levels (no exposure, low, medium, and high), and potential for peak defined as any exposure
200+ ppm. TCE concentrations associated with quantitative ranking are low, 5-150 ppm-yrs;
medium, 155-335 ppm-yrs; high, >335 ppm-yrs.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Telephone interviews were conducted by a trained interviewer.
The paper notes interviewers were blinded "as far as possible" since medical questionnaire was
administered after the occupational questionnaires.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 22% of cases were dead at time of interview compared to 7% of controls.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancers in incidence studies; numbers of
exposed cases and prevalence of exposure in case-
control studies
37 cases with TCE exposure (43% exposure prevalence), 1 10 controls with TCE exposure (35%
exposure prevalence).
16 cases with high level confidence TCE exposure (27% exposure prevalence), 37 controls with high
level confidence TCE exposure (16%).
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, tobacco smoking, andBMI (Charbotel et al., 2006).
Age, sex tobacco smoking, BMI, and exposure to cutting or petroleum oils (Charbotel
2009).
et al..
Conditional logistic regression on matched pairs.
Yes, cumulative exposure as four categories (no, low, medium and high exposure) and cumulative
exposure plus peaks.
Yes.
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B.3.2.13. RCC Case-Control Studies in Other Regions
B.3.2.13.1. Moore et al. (2010)
B.3.2.13.1.1. Author's abstract.
Trichloroethylene (TCE) is a suspected renal carcinogen. TCE-associated renal
genotoxicity occurs predominantly through glutathione S-transferase (GST)
conjugation and bioactivation by renal cysteine beta-lyase (CCBL1). We
conducted a case-control study in Central Europe (1,097 cases and 1,476 controls)
specifically designed to assess risk associated with occupational exposure to TCE
through analysis of detailed job histories. All jobs were coded for
organic/chlorinated solvent and TCE exposure (ever/never) as well as the
frequency and intensity of exposure based on detailed occupational
questionnaires, specialized questionnaires, and expert assessments. Increased risk
was observed among subjects ever TCE exposed [odds ratio (OR) = 1.63; 95%
confidence interval (95% CI), 1.04-2.54]. Exposure-response trends were
observed among subjects above and below the median exposure [average intensity
(OR = 1.38; 95% CI, 0.81-2.35; OR = 2.34; 95% CI, 1.05-5.21; P(trend) = 0.02)].
A significant association was found among TCE-exposed subjects with at least
one intact GSTT1 allele (active genotype; OR = 1.88; 95% CI, 1.06-3.33) but not
among subjects with two deleted alleles (null genotype; OR = 0.93; 95% CI, 0.35-
2.44; P(interaction) = 0.18). Similar associations for all exposure metrics
including average intensity were observed among GSTTl-active subjects (OR =
1.56; 95% CI, 0.79-3.10; OR = 2.77; 95% CI, 1.01-7.58; P(trend) = 0.02) but not
among GSTT1 nulls (OR = 0.81; 95% CI, 0.24-2.72; OR= 1.16; 95% CI, 0.27-
5.04; P(trend) = 1.00; P(interaction) = 0.34). Further evidence of heterogeneity
was seen among TCE-exposed subjects with >or=l minor allele of several
CCBLl-tagging single nucleotide polymorphisms: rs2293968, rs2280841,
rs2259043, and rs941960. These findings provide the strongest evidence to date
that TCE exposure is associated with increased renal cancer risk, particularly
among individuals carrying polymorphisms in genes that are important in the
reductive metabolism of this chemical, and provides biological plausibility of the
association in humans.
B.3.2.13.1.2. Study description and comment.
The hospital case-control study of kidney cancer in men and women who were residents
in areas of the sevens study centers evaluated nonoccupational and occupational risk factors and
included a detailed exposure assessment for chlorinated organic solvents, including TCE.
Histologically-confirmed incident cases of RCC (ICD-O-2, Code C.64) between 20 and 79 years
of age and diagnosed between 1999 and 2003 at seven participating hospitals were eligible as
cases, with hospital in-patient or out-patient controls admitted to the same hospital centers but
with non-tobacco-related conditions, excluding genitourinary cancers, and frequency-matched to
cases by sex and age, and by study center. The final study population included 1,097 cases and
1,476 controls for a participation rate, depending on study center of 90-98% and 90-96% for
cases and controls, respectively. As part of the study, blood samples obtained from 925 cases
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and 1,192 controls were assayed for deletion of the GSTT1 polymorphism and genetic variation
across the renal cysteine p-lyase (CCBL1) gene.
Face-to-face interviews were conducted using standard questionnaires that asked about
lifestyle habits and personal, familial medical history, and for each job held >1 year. For specific
jobs or industries with likely exposure to know or suspected occupational carcinogens of interest,
a specialized occupation questionnaires were used to gather more detailed information. For
every job in a subject's work history, an exposure assessment team from each center, with
extensive knowledge of industries in the region and blinded to case or control status, evaluated
the frequency and intensity of exposure to organic and chlorinated solvents based on the general
and job-specific questionnaires. The general category of aliphatic chlorinate organic solvents
included perchloroethylene, methylene chloride, carbon tetrachloride, 1,1, 1-trichloroethane, and
TCE. Subjects identified as exposed to organic solvents were reevaluated by the team at a later
date to confirm assignment as an attempt to reduce exposure misclassification. The reevaluation
was performed blinded to case and controls status. For each exposed job, the frequency,
intensity, and confidence of exposure to TCE, organic solvents, and chlorinated solvents. While
TCE exposure was correlated with both chlorinated solvents and organic solvents exposure, it
was not associated with other co-exposures. Exposure frequency was coded into three
categories, representing the average percentage of a working day exposure was likely (1-4.9, 5-
30, >30%), with midpoint weights for cumulative exposure calculations of 0.025, 0.175, and
0.50, respectively, and assuming a log-normal exposure distribution. TCE intensity was also
coded into three categories (0-<5, 5-50, >50 ppm) with midpoint weights for cumulative
exposure calculations of 2.5, 25, and 75 ppm, respectively. Exposure surrogates developed
included cumulative exposure, the product of the midpoints for intensity and frequency and
multiplied by duration. Average exposure intensity was a second exposure surrogate and defined
as the quotient of cumulative exposure and duration. Last, confidence of exposure that
represented the expected percentage of workers that would be exposed in that job was
categorized as possible (<40%), probable (40-89%), or definite (>90%). Among subjects with
probable exposure (high confidence TCE exposure), the median intensity score was 0.076 ppm
[25th and 75th percentile range among cases, 0.83-7.25 ppm] and median cumulative exposure
scores were 1.58 (25th and 75th percentiles, 0.77-2.87 ppm-year) and 1.95 ppm-years (25th and
75th percentiles, 0.83-7.25 ppm-year) among cases and controls, respectively.
Association between RCC and organic solvents, chlorinated solvents, and TCE exposure
for jobs with any confidence level and for holding a job with probable or definite exposure was
assessed using unconditional logistic regression to estimate ORs and 95% CIs. All statistical
models included covariates for sex, age, and study center. Analyses were also modeled to
account for a 20-year lag. Almost all TCE exposure occurred at least 20 years before RCC onset
and Moore et al. (2010) did not report these findings as OR estimates were similar to those from
the models using unlagged exposure surrogate.
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The strong exposure approach in Moore et al. (2010) and examination of exposure
probability or confidence are strengths of the study. TCE used did not appear widespread as
exposure prevalence was low, 6 % of cases had held a job of any exposure probability, compared
to 29% of cases identified with any exposure to organic solvents. The percentage of cases was
even lower, 4%, for higher confidence TCE exposure. Additionally, evaluation of GST
polymorphisms provides assessment of susceptibility factors.
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Moore LE, Buffetta P, Karami S, Brennan P, Stewart PS, Hung R, et al. (2010). Occupational trichloroethylene exposure and
renal carcinoma risk: Evidence of genetic susceptibility by reductive metabolism gene variants. Cancer Res 20:6527-6536.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Study hypotheses of investigating risk association with occupation TCE exposure and kidney
(excluding pelvis) cancers through analysis of job histories and use of detailed exposure assessment
method.
Cases: 1,097 histologically -confirmed RCC cases in males and females, 20-79 yrs of age, 1999-2003,
identified through seven hospital centers in four countries (Czech Republic, Poland, Romania, Russia).
Controls: 1,476 in-patient or out-patient hospital controls admitted to same hospital as case with
nontobacco-related conditions and frequency matched to cases by sex and age, and by study center.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
RCC incidence.
ICD-0-2 [Codes C.54].
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Job-specific questionnaire for job >1 year. Exposure assessment team from each center with
knowledge of region's industries to assess frequency, intensity and confidence of exposure to TCE and
organic solvent group (perchloroethylene, methylene chloride, carbon tetrachloride, and 1,1,1-
trichloroethane). Exposure surrogates of frequency (three categories based on percentage of day),
intensity (three groups), cumulative exposure (product of intensity, duration, frequency), and average
exposure intensity (cumulative exposure score divided by the number of years exposed). Exposure
confidence score (possible, probably, definite) defined as percentage of workers exposed at a job.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
In-person interview using questionnaire.
No information in published paper if interviewers were blinded. Exposure assessment assigned
blinded.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
No proxy interviews.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
Cases: 90-99% participation rate; Controls: 90-96% participation rate.
Exposure prevalence, ever exposed to TCE (6% of cases holding TCE job, any confidence level;
of cases with probable or definite exposure).
4%
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, and center. Place of residence, tobacco smoking, BMI, and hypertension also examined but
did not alder OR estimate by >10%, and thus, were not included in final models.
Unconditional logistic regression.
Test for trend reported for years, hours, cumulative and average intensity of exposure.
Yes, study was well documented with supplemental material available on publisher's webpage.
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B .3.2.13.2. Parent et al. (2000a), Siemiatycki (1991).
B .3.2.13.2.1. Author's abstract.
BACKGROUND: Little is known about the role of workplace exposures on the
risk of renal cell cancer. METHODS: A population-based case-control study was
undertaken in Montreal to assess the association between hundreds of
occupational circumstances and several cancer sites, including the kidney. A total
of 142 male patients with pathologically confirmed renal cell carcinoma, 1900
controls with cancer at other sites and 533 population-based controls were
interviewed. Detailed job histories and relevant data on potential confounders
were obtained. A group of chemists-hygienists evaluated each job reported and
translated them into a history of occupational exposures using a checklist of 294
substances. Multivariate logistic regression models using either population, cancer
controls, or a pool of both groups were used to estimate odds ratios. RESULTS:
There were some indications of excess risks among printers, nursery workers
(gardening), aircraft mechanics, farmers, and horticulturists, as well as in the
following industries: printing-related services, defense services, wholesale trade,
and retail trade. Notwithstanding the low precision of many of the odds ratio
estimates, the following workplace exposures showed some evidence of excess
risk: chromium compounds, chromium (VI) compounds, inorganic acid solutions,
styrene-butadiene rubber, ozone, hydrogen sulphide, ultraviolet radiation, hair
dust, felt dust, jet fuel engine emissions, jet fuel, aviation gasoline, phosphoric
acid and inks. CONCLUSIONS: For most of these associations there exist no, or
very little, previous data. Some associations provide suggestive evidence for
further studies.
B.3.2.13.2.2. Study description and comment.
This population case-control study of histologically-confirmed kidney cancer among
males who resided in the Montreal Metropolitan area relies on the use of expert assessment of
occupational information on a detailed questionnaire and face-to-face interview and was part of a
larger study of 10 other site-specific cancers and occupational exposures (Parent et al., 2000a:
Siemiatycki, 1991). Interviewers were unblinded, although exposure assignment was carried out
blinded as to case and control status. The questionnaire sought information on the subject's
complete job history and included questions about the specific job of the employee and work
environment. Occupations considered with possible TCE exposure included machinists, aircraft
mechanics, and industrial equipment mechanics. An additional specialized questionnaire was
developed for certain job title of a prior interest that sought more detailed information on tasks
and possible exposures. For example, the supplemental questionnaire for machinists included a
question on TCE usage. A team of industrial hygienists and chemicals assigned exposures
blinded based on job title and other information obtained by questionnaire. A semiquantitative
scale was developed for 300 exposures and included TCE (any, substantial). Parent et al.
(2000a) presents observations of analyses examining job title, occupation, and some chemical -
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specific exposures, but not TCE. Observations on TCE are found in the original report of
Siemiatycki (1991). Any exposure to TCE was 3% among cases but <1% for substantial TCE
exposure; "substantial" is defined as >10 years of exposure for the period up to 5 years before
diagnosis. The TCE exposure frequencies in this study are lower than those in Briining et al.
(2003) and Charbotel et al. (2006), studies conducted in geographical areas with a high
prevalence of industries using TCE. The expert assessment method is considered a valid and
reliable approach for assessing occupational exposure in community-base studies and likely less
biased from exposure misclassification than exposure assessment based solely on self-reported
information (Fritschi et al., 2003; IOM, 2003; Siemiatycki et al., 1987). For example, Dewar et
al. (1994) examine sensitivity of JEM of Siemiatycki et al. (1987) to exposure assessment by
chemists and industrial hygienists using interview information and evaluation of job histories.
Specific solvents are not examined, although, a sensitive 84% and specificity of 97% was found
for the JEM for general solvent exposure.
This population study of several cancer sites included histologically-confirmed cases of
kidney cancer (ICD-O 189, malignant neoplasm of kidney and other and unspecified urinary
organs) ascertained from 16 Montreal-area hospitals between 1979 and 1985. A total of
227 eligible kidney cancer cases were identified were identified from 19 Montreal-area hospitals;
177 cases participated in the study (78% response). One control group (n = 1,295) consisted of
patients with other forms of cancer (excluding lung cancer and other intestinal cancers) recruited
through the same study procedures and time period as the rectal cancer cases. A population-
based control group (n = 533), frequency matched by age strata, was drawn using electoral lists
and random digit dialing. All controls were interviewed using face-to-face methods; however,
20% of the all cancer cases in the larger study were either too ill to interview or had died and, for
these cases, occupational information was provided by a proxy respondent. The quality of
interview conducted with proxy respondents was much lower, increasing the potential for
misclassification bias, than that with the subject. The direction of this bias would diminish
observed risk towards the null.
Statistical analysis are considered valid; logistic regression model, which included terms
for respondent status, age, smoking, and BMI in Parent et al. (2000a) and Mantel-Haenszel ^
stratified on age, family income, cigarette smoking, and ethnic origin in Siemiatycki (1991).
Odds ratios are presented with 90% CIs in Siemiatycki (1991) and 95% CIs in Parent et al.
(2000a).
Overall, exposure assessment in this study adopted a superior approach, using expert
knowledge and use of a JEM. However, examination of NHL and TCE exposure is limited by
statistical power considerations related to low exposure prevalence, particularly for "substantial"
exposure. For the exposure prevalence found in this study to TCE and for kidney cancer, the
minimum detectable OR was 3.0 when p = 0.02 and a = 0.05 (one-sided). The low statistical
power to detect a doubling of risk and an increased possibility of misclassification bias
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associated with case occupational histories resulting from proxy respondents suggests a
decreased sensitivity in this study for examining kidney cancer and TCE.
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Parent M-E, Hua Y, Siemiatycki J. (2000a). Occupational risk factors for renal cell carcinoma in Montreal. Am J Ind Med
38:609-618.
Siemiatycki J. (1991). Risk Factors for Cancer in the Workplace. Baca Raton: CRC Press.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This population case-control study was designed to generate hypotheses on possible association
between 1 1 site-specific cancers and occupational title or chemical exposures.
277 kidney cancer cases were identified among male Montreal residents between 1979 and 1985 of
which 177 (147 RCCs) were interviewed.
740 male population controls were identified from the same source population using random digit
dialing; 533 were interviewed. A second control series consisted of all other cancer controls excluding
lung and bladder cancer cases.
Participation rate: cases, 78%; population controls, 72%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence.
ICD 189 (malignant neoplasm of the kidney and other and unspecified urinary organs)
(Siemiatvckl 1991).
ICD 189.0, RCC (Parent et al.. 2000a).
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Unblinded interview using questionnaire sought information on complete job history with
supplemental questionnaire for jobs of a priori interest (e.g., machinists, painters). Team of chemist
and industrial hygienist assigned exposure using job title with a semiquantitative scale developed for
300 exposures, including TCE. For each exposure, a three-level ranking was used for concentration
(low or background, medium, high) and frequency (percent of working time: low, 1-5%; medium, >5-
30%; and high, >30%).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
100% of cases and controls were interviewed face-to-face by a trained interviewer. Cases interviews
were conducted either at home or in the hospital; all population control interviews were conducted at
home.
Interviews were unblinded but exposure coding was carried out blinded as to case and control status.
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CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 16% of cases, 13% of population controls, and 22% of cancer controls had
proxy respondents.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancers in incidence studies; numbers of
exposed cases and prevalence of exposure in case-
control studies
177 cases (78% response), 533 population controls (72%).
Exposure prevalence: Any TCE exposure, 2% cases; substantial TCE exposure
and up to 5 yrs before disease onset), 1% cases.
(exposure for >10 yrs
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, income, index for cigarette smoking (Siemiatycki, 1991).
Age, smoking, BMI, and proxy status (Parent et al., 2000b).
Mantel-Haenszel (Siemiatycki, 1991).
Logistic regression (Parent et al., 2000a).
No.
Yes.
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B .3.2.13.3. Dosemeci et al. (1999).
B .3.2.13.3.1. Author's abstract.
BACKGROUND: Organic solvents have been associated with renal cell cancer;
however, the risk by gender and type of solvents is nuclear. METHODS: We
evaluated the risk of renal cell carcinoma among men and women exposed to all
organic solvents-combined, all chlorinated aliphatic hydrocarbons (CAHC)-
combined, and nine individual CAHC using a priori job exposure matrices
developed by NCI in a population-based case-control study in Minnesota, U.S.
We interviewed 438 renal cell cancer cases (273 men and 165 women) and 687
controls (462 men and 225 women). RESULTS: Overall, 34% of male cases and
21% of female cases were exposed to organic solvents in general. The risk of
renal cell carcinoma was significantly elevated among women exposed to all
organic solvents combined (OR = 2.3; 95% CI = 1.3-4.2), to CAHC combined
(OR = 2.1; 95% CI = 1.1-3.9), and to trichloroethylene (TCE) (OR = 2.0; 95% CI
= 1.0-4.0). Among men, no significant excess risk was observed among men
exposed to any of these nine individual CAHCs, all CAHCs-combined, or all
organic solvents-combined. DISCUSSION: These observed gender differences in
risk of renal cell carcinoma in relation to exposure to organic solvents may be
explained by chance based on small numbers, or by the differences in body fat
content, metabolic activity, the rate of elimination of xenobiotics from the body,
or by differences in the level of exposure between men and women, even though
they have the same job title.
B.3.2.13.3.2. Study description and comment.
Dosemeci et al. (1999) reported data from a population-based case-control study of the
association between occupation exposures and renal cancer risk. The investigators identified
newly diagnosed patients with histologically confirmed RCC from the Minnesota Cancer
Surveillance System from July 1, 1988 to December 31, 1990. The study was limited to white
cases, and age and gender-stratified controls were ascertained using random digit dialing (for
subjects ages 20-64) and from Medicare records (for subjects 65-85 years). Of the 796 cases
and 796 controls initially identified, 438 cases (273 men, 165 women) and 687 controls
(462 men, 225 women) with complete personal interviews were included in the occupational
analysis.
Data were obtained using in-person interviews that included demographic variables,
residential history, diet, smoking habits, medical history, and drug use. The occupational history
included information about the most recent and usual industry and occupation (coded using the
standard industrial and occupation codes, Department of Commerce), job activities, hire and
termination dates, and full/part time status. A JEM developed by the NCI (Gomez etal., 1994)
was used with the coded job data assign occupational exposure potential for 10 chlorinated
aromatic hydrocarbons and organic solvents, and includes TCE.
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Dosemeci et al. (1999) adopted logistic regression methods to evaluate renal cancer and
occupational exposures. Odds ratios were adjusted for age, smoking, hypertension, and use of
drugs for hypertension, and BMI.
Strengths of this study include the use of incident cases of renal cancer from a defined
population area, with confirmation of the diagnosis using histology reports. The occupation
history was based on usual and most recent job, in combination with a relatively focused JEM.
In contrast to the type of exposure assessment that can be conducted in cohort studies within a
specific workplace; however, exposure measurements, based on personal or workplace
measurement, were not used, and a full lifetime job history was not obtained.
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Dosemeci M, Cocco P, Chow W-H. (1999). Gender differences in risk of renal cell carcinoma and occupational exposures to
chlorinated aliphatic hydrocarbons. Am J Ind Med 36:54-59.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Yes. From abstract — study aim was to evaluate effect of organic solvents on RCC risk using a priori
JEMs.
796 white males and females identified through the Minnesota Cancer Surveillance System with
histological confirmed RCC between July 1, 1988 and December 31, 1990. Interviews were obtained
for 690 subjects, of which 241 were with next-of-kin and excluded; 438 cases (273 males and
165 females) were included in analysis. 707 white population controls identified through random digit
dialing, and matched to cases, aged 20-65 yrs old, by age and sex using a stratified random sample or,
for cases aged 65-85, from Health Care Financing Administration list. 687 controls (462 males and
225 females) are included in the analysis.
Participation rate: cases, 87%; controls, 86%.
Occupational analysis: cases, 55%, controls 83%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Incidence
N/A
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
A trained interviewer blinded to case and control status interviewed subjects at home using a
questionnaire which covered occupational, residential, and medical histories; demographic information;
and personal information. Occupational history included self-reporting of the most recent job and usual
occupation and industry, employment dates, and focused on 13 specific occupations or industries.
Occupation and industry were coded according to a standard occupational classification or standard
industrial classification with potential chemical-specific exposures to TCE and eight other chlorinated
hydrocarbons identified using the JEM of Dosemeci et al. (1999) and Gomez et al. (1994).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
All cases and controls had face-to-face interviews.
Yes, interviewers were blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
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>10% proxy respondents
No, subjects with next-of-kin interviews were excluded from the analysis.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancers in incidence studies; numbers of
exposed cases and prevalence of exposure in case-
control studies
55 cases with TCE exposure (13% exposure prevalence among cases).
69 controls cases with TCE exposure (10% exposure prevalence among controls).
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, smoking, BMI, and hypertension/ use of diuretics/use of anti-hypertension drugs.
Logistic regression.
No.
Yes.
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B.3.2.14. Other Cancer Site Case-Control Studies
B.3.2.14.1. Siemiatycki (1991), Siemiatycki et al. (1987).
B.3.2.14.1.1. Author's abstract.
A multi-cancer site, multi-factor, case-referent study was undertaken to generate
hypotheses about possible occupational carcinogens. About 20 types of cancer
were included. Incident cases among men aged 35-70 years and diagnosed in any
of the major Montreal hospitals were eligible. Probing interviews were carried out
for 3,726 eligible cases. The interview was designed to obtain detailed lifetime
job histories and information on potential confounders. Each job history was
reviewed by a team of chemists who translated it into a history of occupational
exposures. These occupational exposures were then analyzed as potential risk
factors in relation to the sites of cancer included. For each site of cancer
analyzed, referents were selected from among the other sites in the study. The
analysis was carried out in stages. First a Mantel-Haenszel analysis was
undertaken of all cancer-substance associations, stratifying on a limited number of
covariates, and, then, for those associations which were noteworthy in the initial
analysis, a logistic regression analysis was made taking into account all potential
confounders. This report describes the fieldwork and analytical methods.
B.3.2.14.1.2. Study description and comment.
Siemiatycki (1991) reported data from a case-control study of occupational exposures
and several site-specific cancers, including lung and pancreas, conducted in Montreal, Quebec
(Canada). Other cases included in this study were cancers of the bladder, colon, rectum,
esophagus prostate, and lymphatic system (NHL); a description of the other case series are found
in other sections in this appendix. The investigators identified 1,082 newly diagnosed cases of
lung cancer (ICD-O, 162) and 165 newly diagnosed cases of pancreatic cancer (ICD-O, 157),
confirmed on the basis of histology reports, between 1979 and 1985; 857 lung cancer (79.2% )
and 117 pancreatic cancer cases (70.7%) participated in the study interview. One control group
consisted of patients with other forms of cancer recruited through the same study procedures and
time period as the melanoma cancer cases. The control series for lung cancer cases excluded
other lung cancer cases; the control series for pancreatic cancer cases excluded all lung cancer
cases. Additionally, a population-based control group (n = 533, 72% response), frequency-
matched by age strata, was drawn using electoral lists and random digit dialing. Face-to-face
interviews were carried out with 82% of all cancer cases with telephone interview (10%) or
mailed questionnaire (8%) for the remaining cases. Twenty percent of all case interviews were
provided by proxy respondents. The occupational assessment consisted of a detailed description
of each job held during the working lifetime, including the company, products, nature of work at
site, job activities, and any additional information that could furnish clues about exposure from
the interviews.
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A team of industrial hygienists and chemists blinded to subject's disease status translated
jobs into potential exposure to 294 substances with three dimensions (degree of confidence that
exposure occurred, frequency of exposure, and concentration of exposure). Each of these
exposure dimensions was categorized into none, any, or substantial exposure. Any exposure to
TCE was 2% among cases (n = 21 lung cancer cases, 2 pancreatic cancer cases) and 1% for
substantial TCE exposure (n = 9 lung cancer cases); "substantial" is defined as >10 years of
exposure for the period up to 5 years before diagnosis. None of the pancreatic cancer cases was
identified with "substantial" exposure to TCE.
Mantel-Haenszel $ analyses examined occupation exposures and lung cancer stratified
on age, family income, cigarette smoking, ethnic origin, alcohol consumption, and respondent
status or pancreatic cancer stratified on age, income, cigarette smoking, and respondent status
(Siemiatycki, 1991). Odds ratios for TCE exposure in Siemiatycki (1991) are presented with
90% CIs.
The strengths of this study were the large number of incident cases, specific information
about job duties for all jobs held, and a definitive diagnosis of cancer. However, the use of the
general population (rather than a known cohort of exposed workers) reduced the likelihood that
subjects were exposed to TCE, resulting in relatively low statistical power for the analysis. The
JEM, applied to the job information, was very broad since it was used to evaluate 294 chemicals.
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Siemitycki J. (1991). Risk Factors for Cancer in the Workplace. J Siemiatycki, Ed. Boca Raton: CRC Press.
Siemiatycki J, Wacholder S, Richardson L, Dewar R, Gerin M. (1987). Discovering carcinogens in the occupational
environment. Scand J Work Environ Health 13:486-492.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and
controls in case-control studies is adequate
This population case-control study was designed to generate hypotheses on possible association between
1 1 site-specific cancers and occupational title or chemical exposures.
1,082 lung cases were identified among male Montreal residents between 1979 and 1985 of which
857 were interviewed; 165 cases were identified among male Montreal residents between 1979 and 1985 of
which 1 17 were interviewed.
740 eligible male controls identified from the same source population using random digit dialing or
electoral lists; 533 were interviewed. A second control series consisted of other cancer cases identified in
the larger study.
Participation rate: lung cancer cases, 79.2 %, pancreatic cancer cases, 70.7%; population controls, 72%.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for
lymphoma, particularly NHL
Incidence.
ICD-O, 122 (malignant neoplasm of trachea, bronchus and lung).
ICD-O, 157 malignant neoplasm of pancreas.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption
of JEM and quantitative exposure estimates
Unblinded interview using questionnaire sought information on complete job history with supplemental
questionnaire for jobs of a priori interest (e.g., machinists, painters). Team of chemist and industrial
hygienist assigned exposure using job title with a semiquantitative scale developed for 294 exposures,
including TCE. For each exposure, a three-level ranking was used for concentration (low or background,
medium, high) and frequency (percent of working time: low, 1-5%; medium, >5-30%; and high, >30%).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
82% of all cancer cases interviewed face-to-face by a trained interviewer, 10% telephone interview, and 8%
mailed questionnaire. Cases interviews were conducted either at home or in the hospital; all population
control interviews were conducted at home.
Interviews were unblinded but exposure coding was carried out blinded as to case and control status.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Yes, 20% of all cancer cases had proxy respondents.
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies;
numbers of total cancer incidence studies; numbers
of exposed cases and prevalence of exposure in
case-control studies
857 lung cancer cases (79.2% response), 1 17 pancreatic cancer cases (70.7% response); 533 population
controls (72% response).
Exposure prevalence: Any TCE exposure, 2% cancer cases (n = 21 lung cancer cases and 2 pancreatic
cancer cases); substantial TCE exposure (exposure for >10 yrs and up to 5 yrs before disease onset), 1%
lung cancer cases (n = 9), no pancreatic cancer cases assigned "substantial" TCE exposure.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical
analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Lung cancer — age, family income, cigarette smoking, ethnic origin, alcohol consumption, and respondent
status.
Pancreatic cancer — age, income, cigarette smoking, and respondent status.
Mantel-Haenszel (Siemiatycki, 1991).
No.
Yes.
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B.3.3. Geographic-Based Studies
B.3.3.1. Coyle et al. (2005)
B.3.3.1.1. Author's abstract.
Purpose. To investigate the role of environment in breast cancer development, we
conducted an ecological study to examine the association of releases for selected
industrial chemicals with breast cancer incidence in Texas.
Methods. During 1995-2000, 54,487 invasive breast cancer cases were reported
in Texas. We identified 12 toxicants released into the environment by industry
that: (1) were positively associated with breast cancer in epidemiological studies,
(2) were Environmental Protection Agency (EPA) Toxics Release Inventory
(TRI) chemicals designated as carcinogens or had estrogenic effects associated
with breast cancer risk, and (3) had releases consistently reported to EPA TRI for
multiple Texas counties during 1988-2000. We performed univariate, and
multivariate analyses adjusted for race and ethnicity to examine the association of
releases for these toxicants during 1988-2000 with the average annual age-
adjusted breast cancer rate at the county level.
Results. Univariate analysis indicated that formaldehyde, methylene chloride,
styrene, tetrachloroethylene, trichloroethylene, chromium, cobalt, copper, and
nickel were positively associated with the breast cancer rate. Multivariate
analyses indicated that styrene was positively associated with the breast cancer
rate in women and men (b = 0.219, p =0.004), women (b = 0.191, p=0.002), and
women J 50 years old (b = 0.187, p=0.002).
Conclusion. Styrene was the most important environmental toxicant positively
associated with invasive breast cancer incidence in Texas, likely involving
women and men of all ages. Styrene may be an important breast carcinogen due
to its widespread use for food storage and preparation, and its release from
building materials, tobacco smoke, and industry.
B .3.3.1.2. Study description and comment.
Residential address in 254 Texas counties at time of cancer diagnosis was the exposure
surrogate in this ecologic study of invasive breast cancer in over a 5-year period (1995-2000).
Incident breast cancer cases in males and females were identified from Texas Cancer Registry.
During the 5-year period, 54,487 cases were diagnosed, of which 53,910 were in females (99%).
The association between median average annual age-adjusted breast cancer rates for women and
men, all women, women <50 years old, and women >50 years old and 12 hazardous air
pollutants identified as exposures of interested were examined using nonparametric tests (Mann-
Whitney U test) and linear regression analyses. The 12 hazardous air pollutants were: carbon
tetrachloride, formaldehyde, methylene chloride, styrene, perchloroethylene, TCE, arsenic,
cadmium, chromium, cobalt, copper, and nickel. On-site atmospheric release data on individual
hazardous air pollutants was identified from EPA's Toxics Release Inventory (TRI) for a 13-year
period, 1998-2000 with an exposure surrogate as the annual total release in pounds/year for the
12 hazardous air pollutants.
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Coyle et al. (2005) compared average annual age-adjusted breast cancer rate for counties
reporting a release to that rate for non-reporting counties using Mann-Whitney U test.
Additionally, multiple linear regression analyses was used to determine the association of the
average annual age-adjusted breast cancer rates with the 12 hazardous air pollutants, adjusting
for race and ethnicity when associated with the study's outcome variable.
While this study provides insight on cancer rates in studied population, TCE and other
hazardous air pollutant exposures are poorly defined and the exposure surrogate unable to
distinguish subjects more with higher exposure potential from those with low or minimal
exposure potential. Some information may be provided through examination of inter-county
release rates; however, no information is provided by Coyle et al. (2005). Furthermore, the
ecologic design of the study does not address residential history or other information on an
individual-subject level and is subject to bias from "ecologic fallacy" or improper inference
about individual-level associations based on aggregate-level analysis. Overall, this study is not
able to identify risk factors (etiologic exposures), has low sensitivity for examining TCE, and
provides little weight in an overall weight of evidence evaluation of TCE and cancer.
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Coyle YM, Hynan LS, Euhus DM, Minhajuddin ATM. (2005). An ecological study of the association of environmental
chemicals on breast cancer incidence in Texas. Breast Cancer Res Treat.92:107-114.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Hypothesis of this study was to evaluate breast risks in Texas counties
and hazardous air pollutants.
Cases are incident breast cancers in males and females over a 5-yr period (1995-2000) in subjects
residing in Texas and reported to the Texas Cancer Registry.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence.
Not identified in paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Residence in Texas county as time of diagnosis is exposure surrogate. Annual release by county of
12 hazardous air pollutants (carbon tetrachloride, formaldehyde, methylene chloride, styrene,
perchloroethylene, TCE, arsenic, cadmium, chromium, cobalt, copper, and nickel) are obtained from
EPA's TRI database.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
54,487 incident breast cancer cases in males and females.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, and race/ethnicity.
Mann- Whitney U test (nonparametric) to compared average
between counties reported hazardous air pollutant release to
Linear logistic regression
annual age-adjusted breast cancer rate
that for non-reporting counties.
No.
Yes.
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B.3.3.2. Morgan and Cassady (2002)
B .3.3.2.1. Author's abstract.
In response to concerns about cancer stemming from drinking water contaminated
with ammonium perchlorate and trichloroethylene, we assessed observed and
expected numbers of new cancer cases for all sites combined and 16 cancer types
in a California community (1988 to 1998). The numbers of observed cancer cases
divided by expected numbers defined standardized incidence ratios (SIRs) and
99% confidence intervals (CI). No significant differences between observed and
expected numbers were found for all cancers (SIR, 0.97; 99% CI, 0.93 to 1.02),
thyroid cancer (SIR, 1.00; 99% CI, 0.63 to 1.47), or 11 other cancer types.
Significantly fewer cases were observed than expected for cancer of the lung and
bronchus (SIR, 0.71; 99% CI, 0.61 to 0.81) and the colon and rectum (SIR, 0.86;
0.74 to 0.99), whereas more cases were observed for uterine cancer (SIR, 1.35;
99% CI, 1.06 to 1.70) and skin melanoma (SIR, 1.42; 99% CI, 1.13 to 1.77).
These findings did not identify a generalized cancer excess or thyroid cancer
excess in this community.
B.3.3.2.2. Study description and comment.
Residential address in 13 census tracts in Redlands (San Bernardino County, California)
at time of cancer diagnosis was the exposure surrogate in this ecologic study of cancer incidence
over a 10-year period (1988-1998). Seventeen cancers in adults (all cancers, bladder, brain and
other nervous system, breast [females only], cervix, colon and rectum, Hodgkin lymphoma,
kidney and renal pelvis, leukemia [all], liver and bile duct, lung and bronchus, NHL, melanoma,
ovary, prostate, thyroid and uterus) and three site-specific incident cancers in children under
15 years of age (leukemia [all], brain/CNS, and thyroid) were identified from the Desert Sierra
Cancer Surveillance Program, a regional cancer registry reporting to the California Cancer
Registry, with expected numbers of site-specific cancer using age-race annual site-specific
cancer incidence rates between 1988 and 1992 to 1990 census-reported information on
population size and demographics. The use of the Desert Sierra Cancer Surveillance Program
rates which include the studied population would inflate the number of site-specific cancer
expected; however, the potential magnitude of bias is likely minimal given the Redlands
populations was estimated as 2% of the total population of the regional cancer registries
ascertainment area (Morgan and Cassady, 2002). This is a record-based study and information
on personal habits and potential risk factors other than race, sex, and age are lacking for
individual subjects.
Morgan and Cassidy (2002) identified TCE and perchlorate from drinking water as
exposures of interest. Limited monitoring data from the 1,980 identified TCE concentrations in
Redlands wells as between 0.09 and 97 ppb TCE and drinking water concentrations as below the
maximum contaminant level (5 ppb) since 1991. The paper lacks information if water
monitoring represented wells in the 13-census tract study area. Furthermore, the paper does not
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include information on water treatment and distribution networks to provide an estimate of TCE
concentration in finished tap water to individual homes. These authors noted their inability to
identify higher or lower exposed subjects, as well, as minimally exposed subjects as a source of
uncertainty. No data are presented on perchlorate concentrations in well or drinking water. The
assumption of residence in 13 census tracts is insufficient as a surrogate of potential exposure to
TCE and perchlorate in the absence of exposure modeling and data on water distribution
patterns. Exposure misclassification bias is highly likely and of a nondifferential nature which
would dampen observed associations.
While this study provides insight on cancer rates in studied population, TCE exposure is
poorly defined and the exposure surrogate unable to distinguish subjects more with higher
exposure potential from those with low or minimal exposure potential. Furthermore, the
ecologic design of the study does not address residential history or other information on an
individual-subject level and is subject to bias from "ecologic fallacy" or improper inference
about individual-level associations based on aggregate-level analysis. Morgan and Cassidy
(2002) furthermore discuss the relatively high education and income levels in the Redlands
population compared with the average for the referent population may lead to lower tobacco use
and higher than average access to health care, biases that would dampen risks for lung and other
tobacco-related cancers, but may also increase risks for colon and cervical cancers. Overall, this
study is not able to identify risk factors (etiologic exposures), has low sensitivity for examining
TCE, and provides little weight in an overall weight of evidence evaluation of TCE and cancer.
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Morgan JW, Cassady RE. (2002). Community cancer assessment in response to long-time exposure to perchlorate and
trichloroethylene in drinking water. J Occup Environ Med 44:616-621.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Hypothesis of this study was to evaluate cancer risks in a California community, not to evaluate TCE
and cancer explicitly.
Cases are incident cancers over a 10-yr period (1988-1989) in subjects residing in 13 Redlands
(California) census tracts at time of diagnosis. 17 site-specific cancers are identified in adults and
3 site-specific cancers in children <15 yrs old. Cancer cases identified from Desert Sierra Cancer
Surveillance Program (DSCSP), a regional cancer registry.
Annual age-race-site specific cancer rates from DSCSP for 1988 and 1992 and age-race-sex specific
population estimates for 1990.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence.
Not identified in paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Residence in a 13 -census tract area of Redlands, California is exposure surrogate. No data are
presented on TCE or perchlorate concentrations in treated drinking water supplied to residents.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
3,098 incident cancers, the largest number from 536 breast cancer and fewest number from Hodgkin
disease.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, and race/ethnicity.
SIR with indirect standardization of estimated expected
population growth; 90% CIs presented in tables.
numbers of site-specific cancers adjusted for
No.
Yes.
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B.3.3.3. Cohn et al. (1994b)
B .3.3.3.1. Author's abstract.
A study of drinking water contamination and leukemia and non-Hodgkin's
lymphoma (NHL) incidence (1979-1987) was conducted in a 75-town study area.
Comparing incidence in towns in the highest trichloroethylene (TCE) stratum (>5
microg/L) to towns without detectable TCE yielded an age-adjusted rate ratio
(RR) for total leukemia among females of 1.43 (95% CI 1.07-1.90). For females
under 20 years old, the RR for acute lymphocytic leukemia was 3.26 (95% CI
1.27-8.15). Elevated RRs were observed for chronic myelogenous leukemia
among females and for chronic lymphocytic leukemia among males and females.
NHL incidence among women was also associated with the highest TCE stratum
(RR = 1.36; 95% CI 1.08-1.70). For diffuse large cell NHL and non-Burkitt's
high-grade NHL among females, the RRs were 1.66 (95% CI 1.07-2.59) and 3.17
(95% CI 1.23-8.18), respectively, and 1.59 (95% CI 1.04-2.43) and 1.92 (95% CI
0.54-6.81), respectively, among males. Perchloroethylene (PCE) was associated
with incidence of non-Burkitt's high-grade NHL among females, but collinearity
with TCE made it difficult to assess relative influences. The results suggest a link
between TCE/PCE and leukemia/NHL incidence. However, the conclusions are
limited by potential misclassification of exposure due to lack of individual
information on long-term residence, water consumption, and inhalation of
volatilized compounds.
B.3.3.3.2. Study description and comment.
This expanded study of a previous analysis of TCE and perchloroethylene in drinking
water in a 27-town study area (Fagliano et al., 1990)examined leukemia and NHL incidence
from 1979 to 1987 in residents and TCE and other VOCs in drinking water delivered to 75
municipalities. Exposure estimates were developed from data generated by a mandatory
monitoring program for 4 trihalomethane chemicals and 14 other volatile organic chemicals in
1984-1985 for public water supplies and from historical monitoring data conducted in 1978-
1984 by the New Jersey Department of Environmental Protection and Energy and the New
Jersey Department of Health, which was the mean of monthly averages for this period. The
average and maximum concentration of TCE and other chemicals were estimated by considering
together, for the period prior to 1985, details of the distribution system size, well or surface water
use, patterns of water purchases among systems, and significant changes in water supply, and for
years after 1985, samples of finished water from the plant and samples taken from the
distribution system under the assumption of homogeneous mixing. The number of distribution
system samples for each supply varied from 2 to 50. Additionally, a dilution factor assuming
complete mixing was used to adjust for water purchased from another source. A single summary
average and maximum concentration for each contaminate for a municipality was assigned to all
cases residing in that municipality at the time of cancer diagnosis. Concentrations of TCE and
perchloroethylene were highly correlated (r = 0.63). A ranking of municipalities was the same
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when using average or maximum concentration and the maximum concentration of TCE or
perchloroethylene used in statistical analyses was grouped into three strata: <0.1 (referent group),
0.1-5, >5-20, and >20 ppb.
Incident cases of NHL and forms of leukemia reported to the New Jersey State Cancer
Registry were identified from 1979 and 1987. Incidence rate ratios were estimated using Poisson
regression models fitted to age- and sex-specific numbers of cases by exposure strata and the
stratum-specific population. Statistical treatment considered exposure to other drinking water
contaminants, atmospheric emissions of hazardous air pollutants as reported to U.S. EPA's TRI
by municipality and two socioeconomic variables measured as municipal—average annual
household income and percentage of high school graduates. None of the water trihalomethane or
VOCs other than perchloroethylene was shown to be associated with childhood leukemia or
adult lymphomas. Furthermore, neither average income, education, nor TRI release data were
associated with NHL or leukemia except in one exception, TRI release was shown to modify the
effects of TCE and high-grade non-Burkett's lymphoma in females.
This ecological study is subject to known biases and confounding as introduced through
its study design (NRC, 1997). Exposure estimates are crude (averages), do not consider
individual differences in drinking water patterns, and assigns group exposure levels to all
subjects without consideration of residential history. Potential for misclassification bias is likely
great in this study as is the potential for bias. This study does attempt to examine three possible
confounding exposures, although these are crudely defined, and some potential for residual
confounding is possible given the study's use of aggregated data.
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Cohn P, Klotz J, Bove F, Berkowitz M, Fagliano J. (1994b). Drinking water contamination and the incidence of leukemia and
non-Hodgkin's lymphoma. Environ Health Perspect 102:556-561.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This study was designed to further examine drinking water contaminates and lymphoma; a previous
study of TCE and perchloroethylene in drinking water found a statistically significant association with
leukemia among females residing in a 27-town study area (Fagliano et al., 1990).
Incident cases of various forms of leukemia (all leukemia, acute lymphocytic, chronic lymphocytic,
acute myelogenous, chronic myelogenous, other specified and unspecified leukemia) and NHL (total,
low-grade, intermediate-grade [total and diffuse large cell a B-cell lymphoma], high-grade including
non-Burkett's lymphoma) from 1979 to 1987 are identified from New Jersey State Cancer Registry.
Subjects grouped in lowest exposure category are referents.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence.
Not identified in paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Average and maximum concentration of TCE and other chemicals were estimated by considering
together, for the period prior to 1985, details of the distribution system size, well or surface water use,
patterns of water purchases among systems, and significant changes in water supply, and for years
after 1985, samples of finished water from the plant and samples taken from the distribution system
under the assumption of homogeneous mixing. No difference in municipality ranking by average or
maximum concentration.
Three grouped categories of maximum concentration in statistical analysis are <0. 1 (referent), 0. 1-5,
>5 ppb (U.S. EPA Maximum Contaminant Level for TCE and perchloroethylene).
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
1,190 leukemia cases (663 males, 527 females), 119 cases assigned >5.0 ppb TCE.
1,658 NHL cases (841 males, 817 females), 165 cases assigned >5.0 ppb TCE.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age and sex.
Poisson regression fitted to the age-and sex-specific count of cases in towns grouped by exposure
strata and weighted by the logarithm of the strata-specific population.
Yes.
Yes.
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B.3.3.4. Vartiainen et al. (1993)
B.3.3.4.1. Author's abstract.
Concentrations up to 212 ug/1 of trichloroethene (TCE) and 180 ug/1 of
tetrachloroethene (TeCE) were found in the drinking water from two villages in
Finland. To evaluate a possible exposure, urine sample from m95 and 21
inhabitants in these villages and from two control groups of 45 and 15 volunteers
were collected. Dichloroacetic acid (DCA) and trichloroacetic acid (TCA), the
metabolites of TCE and TeCE, were also analyzed. The individuals using
contaminated water in one of the villages excreted TCE an average 19 ug/d (<1 -
110 ug/d) and in the other 7.9 ug/d (<1 - 50 ug/d), while the controls excreted an
average 2.0 ug/d (<1 - 6.4 ug/d) or 4.0 ug/d (<1 - 13 ug/d). No increased
incidence rates were found in the municipalities in question for total cancer, liver
cancer, non-Hodgkin's lymphomas, Hodgkin's disease, multiple myeloma, or
leukemia.
B.3.3.4.2. Study description and comment.
This published study of two separate analyses: (1) urinary biomonitoring of 106 subjects
from two Finish municipalities, Hausjarvi and Hattula and (2) calculation of total cancer and
site-specific cancer incidence between 1953 and 1991 in Hausjarvi and Hattula residents.
Limited exposure monitoring data are presented in the paper. TCE concentrations in drinking
water from Oitti are lacking other than noting TCE and perchloroethylene were 100-200 ug/L in
1992. TCE concentrations in drinking water from Hattula were <10 ug/L in December 1991;
however, samples (number unknown) taken 6 months later contained 212 and 66 ug/L TCE.
These two municipalities discontinued use of these sources for drinking water in August 1992.
Cancer incidence for six sites (all cancers, liver cancer, NHL, Hodgkin lymphoma,
multiple myeloma, and leukemia) between 1953 and 1991 in Hausjarvi and Hattula residents was
obtained from the Finnish Cancer Registry. A total of 1,934 cancers were observed during the
study period. Standardized incidence ratios for each municipality were calculated using site-
specific cancer incidence rates from the Finnish population for the entire time period and for
three shorter periods, 1953-1971, 1972-1981, and 1982-1991. The paper does not identity the
source for or size of Hausjarvi and Hattula population estimates and if temporal changes in
population estimates were considered in the statistical analysis. This study, using record
systems, did not include information obtained directly from subjects and lacks information on
personal and lifestyle factors that may introduce bias or confounding.
This study provides little information in an overall weight-of-evidence analysis on cancer
risks and TCE exposure. A major limitation is its lack of exposure assessment to TCE and
perchloroethylene. While this study provides some information on cancer incidence in the two
towns over a 40-year period, this study is not able to identify potential risk factors and exposures.
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Vartiainen T, Pukkala E, Rienoja T, Strandman T, Kaksonen K. (1993). Population exposure to tri- and tetrachloroethene
and cancer risk: two cases of drinking water pollution. Chemosphere 27:1171-1181.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Study aim was: (1) to determine if residents of two villages in Finland had exposure to TCE and
perchloroethylene as indicated from urinary biomonitoring; (2) identify biomarker for low -level
exposure; and (3) to determine cancer incidence in Hausjarvi and Hattula, two municipalities in
Finland. This study could not identify potential risk factors.
Cancer incidence cases identified from Finnish Cancer Registry.
Site-specific cancer rates for the Finnish population was used a referent.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence.
Not identified in paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Residence in two municipalities is the exposure surrogate in this ecologic study. The paper lacks
exposure assessment to TCE and perchloroethylene in drinking water in Hausjarvi and Hattula.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
3,846 cancer cases; 1,942 from Hausjarvi and 1,904 from Hattula.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age and sex.
SIR with cancer incidence
rates in Finnish population
as referent.
No.
Cancer incidence analysis
is not well documented.
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B.3.3.5. Mallin (1990)
B .3.3.5.1. Author's abstract.
Cancer maps from 1950 through 1979 revealed areas of high mortality from
bladder cancer for both males and females in several northwestern Illinois
counties. In order to further explore this excess, a bladder cancer incidence study
was conducted in the eight counties comprising this region. Eligible cases were
those first diagnosed with bladder cancer between 1978 and 1985. Age adjusted
standardized incidence ratios were calculated for each county and for 97 zip codes
within these counties. County results revealed no excesses. Zip code results
indicated elevated risks in a few areas, but only two zip codes had significantly
elevated results. One of these zip codes had a significant excess in males
(standardized incidence ratio = 1.5) and females (standardized incidence ratio =
1.9). This excess was primarily confined to one town in this zip code, in which
standardized incidence ratios were significantly elevated in males (1.7) and
females (2.6). Further investigation revealed that one of four public drinking
water wells in this town had been closed due to contamination; two wells were
within a half mile (0.8 km) of a landfill site that had ceased operating in 1972.
Tests of these two wells revealed traces of trichloroethylene, tetrachloroethylene,
and other solvents. Further investigation of this cluster is discussed.
B.3.3.5.2. Study description and comment.
This ecologic study of bladder cancer incidence and mortality among white residents in
nine Illinois counties between 1978 and 1985 was carried out to further investigate a previous
finding of elevated bladder cancer mortality rates in some counties. The study lacks exposure
assessment to subjects and potential sources of exposure was examined in a post hoc manner in
one case only, for a community with an observed elevated bladder cancer incidence. The limited
exposure examination focused on groundwater contamination and proximity of Superfund sites
to the community, lacked assignment of exposure surrogates to individual study subjects, and
findings are difficult to interpret given the lack of exposure assessment for the other eight
counties.
Histologically-confirmed incident bladder cancer cases were identified from hospital
records in eight of the nine counties. Since the nine-county area bordered on neighboring states
of Wisconsin and Iowa, incident bladder cancer cases were also ascertained from the Wisconsin
Cancer Reporting System and Iowa's State Health Registry. No information is provided in the
paper on completeness of ascertainment of bladder cancer cases among residents or on the source
for identifying bladder cancer deaths. Expected numbers of incident cancers calculated using
age-specific rates for white males and females from the SEER program (incidence) or the U.S.
population (mortality), and the census data on population estimates for the nine-county area.
Statistical analyses adopt indirect standardization methods to calculate SMR and SIRs for a
community and SIRs for individual postal zip codes. The use of records and absence of
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information collected from subject personal interviews precluded examination of possible
confounders other than age and race.
This ecological study is subject to known biases and confounding as introduced through
its study design (NRC, 1997). Ecological studies like this study are subject to bias known as
"ecological fallacy" since variables of exposure and outcome measured on an aggregate level
may not represent association at the individual level. Consideration of this bias is important for
diseases with more than one risk factor, such as the site-specific cancers evaluated in this
assessment. Lack of information on smoking is another uncertainty. While this study provides
insight on bladder cancer rates in the studied communities, it does not provide any evidence on
cancer and TCE exposure. For this reason, this study provides little weight in an overall weight-
of-evidence analysis.
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Mallin K.
Investigation of a bladder cancer cluster in Northwestern Illinois. Amer J Epidemiol 132:896-8106.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
The hypothesis of study was to "further exposure a previous finding of bladder cancer excess in
several northwestern Illinois counties." (from abstract).
Incident cancer cases diagnosed between 1978 and 1985 were identified in residents in nine
northwestern Illinois counties from the Illinois Cancer Registry, the Wisconsin Cancer Reporting
System or the Iowa State Health Registry. Source for deaths in subjects residing at the time of death
in the 9 counties was not identified in the published paper.
Expected number of bladder cancer derived using: (1) SEER age-race-sex specific incidence rates and
(2) age-race-sex specific mortality rates of the U.S. population for 1978-1981 and for 1982-1985 and
census estimates of population for each county or postal zip code area.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence and mortality.
Not identified in paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
This is a health survey and lacks exposure assessment to communities and to individual subjects.
Monitoring of volatile organic chemicals including TCE in two municipal drinking water wells for
1982-1988 in a community with elevated bladder cancer rates was identified in paper; TCE
concentrations were <15 ppb. It is not known whether monitoring data are representative of exposure
to study subjects.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
712 bladder cancer incident cases and 222 bladder cancer deaths among white males and female
residents in nine northwestern Illinois counties.
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age and sex.
SIR with cancer incidence rates from
referents.
SEER program and mortality rates of U.S.
population as
No.
Yes.
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B.3.3.6. Isacson et al. (1985)
B.3.3.6.1. Author's abstract.
With data from the Iowa Cancer Registry, age-adjusted sex-specific cancer
incidence rates for the years 1969-1981 were determined for towns with a
population of 1,000-10,000 and a public water supply from a single stable ground
source. These rates were related to levels of volatile organic compounds and
metals found in the finished drinking water of these towns in the spring of 1979.
Results showed association between 1,2 dichloroethane and cancers of the colon
and rectum and between nickel and cancers of the bladder and lung. The effects
were most clearly seen in males. These associations were independent of other
water quality and treatment variables and were not explained by occupational or
other sociodemographic features including smoking. Because of the low levels of
the metals and organics, the authors suggest that they are not causal factors, but
rather indicators of possible anthropogenic contamination of other types. The data
suggest that water quality variables other than chlorination and trihalomethanes
deserve further consideration as to their role in the development of human cancer.
B.3.3.6.2. Study description and comment.
This ecologic study of cancer incidence at six sites (bladder, breast, colon, lung, prostate,
rectum) and chlorinated drinking water uses monitoring data from finished public drinking water
supplies to infer exposure to residents of Iowa towns of 1,000-10,000 population sizes. Towns
were included if they received water from a single major source (surface water, wells of <150
feet depth, or wells >50 feet depth) prior to 1965. Water monitoring for VOCs, trace elements,
and heavy metals was carried in Spring, 1979, as part of a larger nationwide collaborative study
of bladder cancer and artificial sweeteners (Hoover and Strasser, 1980), and samples analyzed
using proton-induced x-ray emission for trihalomethanes, TCE, perchloroethylene, 1,2-
dichloroethane, 1,1,1-trichloroethane, carbon tetrachloride, 1,2-DCE, and 43 inorganic elements.
1,1,1-Trichloroethane was the most frequently detected VOC in both surface and groundwater;
TCE, perchloroethylene, and 1,2-dichloroethane were more frequently detected in shallow wells
than in deep (>150 feet) wells.
Cancer incidence was obtained for the period 1969 and 1981 with age-adjusted site-
specific cancer incidence rates for males and females calculated separately for four VOCs
(1,2-dichloroethane, TCE, perchloroethylene, and 1,1,1-trichloroethane) in finished groundwater
supplies using the direct standardization method. Using the address at the time of diagnosis,
each cancer patient was classified into one of two groups: (1) residing within the city limits and,
thus, drinking the municipality's water; or (2) residing outside the city limits and consuming
water from a private source. Age-adjusted incidence rates are reported by group study town into
two TCE water concentrations categories of <0.15 and >0.15 ug/L.
This ecological study on drinking water exposure and cancer provides little information
in a weight-of-evidence analysis of TCE and cancer. Exposure estimates are crude (averages),
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do not consider individual differences in drinking water patterns or other sources of exposure,
and assigns group exposure levels to all subjects. Potential for misclassification bias is likely
great in this study, likely of a nondifferential nature, and dampen observations.
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Isacson P, Bean JA, Splinter R, Olson DB, Kohler J. (1985). Drinking water and cancer incidence in Iowa. III. Association
of cancer with indices of contamination. Amer J Epidemiol 121:856-869.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This ecological study was designed to examine consistency with the hypothesis of an association
between cancer and chlorinated water through examination of other water contaminants besides water
chlorination byproducts and trihalomethanes.
Subjects are incident cases of cancer of the bladder, breast, prostate, lung rectum, and stomach
reported to the Iowa Cancer Registry between 1969 and 1981 and, who resided in towns with a 1970
population of 1,000-10,000 and a public drinking water supply coming solely from a single major
source (wells) prior to 1965.
Age-adjusted site-specific incidence rates are calculated using the direct method and the 1970 Iowa
population.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence.
Not identified in paper.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
As part of another epidemiologic study on water chlorination and bladder cancer, finished drinking
water samples from treatment plant were collected in Iowa municipalities with populations of 1,000 or
larger in Spring 1979 and analyzed using proton induced x-ray emission for 4 trihalomethanes
(chloroform, chlorodibromomethane, bromoform, dibromochloromethane), 7 VOCs (TCE,
perchloroethylene, 1,1,1-trichloroethane, carbon tetrachloride, 1,2-dichloroethane, and cis- andtrans-
1,2-DCE) and 43 inorganic elements, including metals. The predominant contaminant was 1,1,1-tri-
chloroethane; detectable levels of TCE were found in approximately 20% of sampled municipalities.
Study towns were ranked into two categories of TCE in finished water, <0. 15 ug/L and >0. 15 ug/L in
the statistical analysis.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
1 1 ,09 1 cancer cases of which -20% of cases resided in municipality with finished water TCE
concentration of >0. 15 ug/L.
Bladder, 852 cases
Breast (female), 1,866 cases
Colon, 2,032 cases
Lung 1,828 cases
Prostate, 1,823 cases
Rectum, 824 cases
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age and sex.
Age-adjusted site-specific mortality rates calculated using direct standardization method and 1970
Iowa population.
No.
Yes.
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B.3.3.7. Studies in the Endicott Area of New York
A series of health statistics reviews and exposure studies have been conducted in an area
with a history of VOCs, including TCE, detected in municipal wells used to supply drinking
water to residents of Endicott, Broome County, New York. These studies were carried out by
staff the NYS DOH with support from the ATSDR. Early health surveys examined cancer
incidence among Broome County residents between 1976 and 1980 or 1981 and 1990, with
focused analyses of cancer incidence among residents of Endicott Village and other nearby
towns, childhood leukemia in the Town of Union and possible etiologic factors, and adult
leukemia deaths and employment in the shoe and boot manufacturing industry (NYSDOH, 2005;
Forand, 2004). Two recent studies focused on cancer incidence or birth outcomes among Village
of Endicott residents living in a geographically defined area with VOC exposure potential as
documented from indoor and soil vapor monitoring (ATSDR, 2008b, 2006a).
The Village of Endicott is a mixed residential, commercial, and industrial community
with a rich industrial heritage, and a number of VOCs were used at industrial locations in and
around Endicott, as well as been disposed at area landfills (ATSDR, 2006b). Three wells
provide drinking water to the Village of Endicott: Ranney, which supplied most of the water
used by the Endicott Municipal Water Works since it was first placed in service in 1950; and,
South Street, where two wells resided. The Endicott Municipal Water Supply operates on a grid-
water system, neighborhoods closest to the wells are usually supplied at a greater rate from
nearby wells as compared to wells farther away (ATSDR, 2006b).
Routine monitoring of the Ranney well in the early 1980s detected VOCs at levels above
New York State drinking water guidelines (ATSDR, 2006b). A groundwater-contaminated
plume northwest of the Ranney Well was found in a lower aquifer from which the municipal
drinking supply is drawn. Several sources were initially recognized as contributing to
contamination of the wellfield with a supplemental remedial investigation concluding that the
Endicott Village Landfill was the source of the VOCs in the Endicott Wellfield water supply
(ATSDR, 2006b). Groundwater samples collected from monitoring wells installed during
previous investigations, wells installed as part of the supplemental remedial investigation, the
Purge well, and the Ranney well contained many VOCs. Remediation efforts starting in the
1980s have reduced contamination in this well to current maximum contaminant levels. Water
monitoring of the South Street wells (wells 5 and 28) has been carried out for VOCs since 1980
and 1981, respectively (ATSDR, 2006b). Detection limits for VOCs from the South Street wells
varied from 0.5 to 1.0 ug/L; 1,1-dichloroethane had the highest detection frequency, in 44% of
all samples, and TCE was detected in 3 of 116 samples obtained between 1980 and 2004
(ATSDR, 2006b).
An upper aquifer with a contaminant plume containing VOCs was also identified and
sampling data indicated that there were multiple sources of vapor contamination, including a
former IBM facility located in the Village (NYSDEC. 2008: U.S. EPA. 2005d). This
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groundwater contaminant plume flows directly beneath the center of the Village of Endicott and
serves as a source of soil vapor contamination. Findings of a 2002 investigation indicated that
vapor migration had resulted in detectable levels of contaminants in indoor air structures,
including locations in the Village of Endicott and Town of Union. Of soil gas and indoor air
monitoring at >300 properties in an area south of the IBM Endicott facility, TCE was the most
commonly found contaminant in indoor air, at levels ranging from 0.18 to 140 ug/m3 (NYSDEC,
2008). This area is identified as the Eastern study area in the health statistics review of ATSDR
(2008b, 2006a). Other contaminants besides TCE detected in soil gas and indoor air less
frequently and at lower levels included tetrachloroethylene, cis-l,2-dichloroethene, 1,1,1-tri-
chloroethane, 1,1-DCE, 1,1-dichloroethane, andFreon 113. Vapor-intrusion contamination was
also identified in a neighborhood adjacent to the Eastern area, call the Western study in the
health statistic review, and perchloroethylene and its degradation byproducts were detected by
vapor monitoring. Perchloroethylene levels generally ranged from 0.1 to 3.5 ug/m3 of air
(ATSDR. 2006a).
B.3.3.7.1. ATSDR (2008b, 2006a)
B.3.3.7.1.1. ATSDR (2006a) executive summary.
Background The New York State Department of Health (NYS DOH) conducted
this Health Statistics Review because of concerns about health issues associated
with environmental contamination in the Endicott area. Residents in the Endicott
area may have been exposed to volatile organic compounds (VOCs) through a
pathway known as soil vapor intrusion. Groundwater in the Endicott area is
contaminated with VOCs as a result of leaks and spills associated with local
industry and commercial businesses. In some areas of Endicott, VOC
contamination from the groundwater has contaminated the adjacent soil vapor
which has migrated through the soil into structures through cracks in building
foundations (soil vapor intrusion). Trichloroethene (TCE), tetrachloroethene
(PCE) and several other VOCs have been found in the soil vapor and in the indoor
air of some structures.
Conclusions This health statistics review was conducted because of concerns that
exposure to VOCs through vapor intrusion may lead to adverse health effects.
Although this type of study cannot prove whether there is a causal relationship
between VOC exposure in the study area and the increased risk of several health
outcomes observed, it does serve as a first step in providing guidance for further
health studies and interventions. The elevated rates of several cancers and birth
outcomes observed will be evaluated further to try to identify additional risk
factors which may have contributed to these adverse health outcomes.
Limitations in the current study included limited information about the levels
of VOCs in individual homes, the duration of the exposure, the amount of time
residents spent in the home each day and the multiple exposures and exposure
pathways that likely existed among long term residents of the Endicott area. In
addition, personal information such as medical history; dietary and lifestyle
choices such as smoking and drinking; and occupational exposures to chemicals
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were not examined. Future evaluations of cancer and birth defects and VOC
exposures in the area should take these factors into account. The small population
size of the study area also limited the ability to detect meaningful elevations or
deficits in disease rates, especially for certain rare cancers and birth outcomes.
This study represents the first step in a step-wise approach to addressing
health outcome concerns related to environmental contamination in Endicott, NY.
Follow-up will consist of further reviewing of the cancer and birth outcome data
already collected. Additional efforts will include reviewing individual case
records of kidney and testicular cancers, heart defects, Down syndrome and term
low birth weight births. In addition, we will review spontaneous fetal deaths
among residents of the area. The information gained, along with the results of this
Health Statistics Review, will be used to assess if a follow up epidemiologic study
is feasible. Any follow-up study should be capable of accomplishing one of two
goals: either to advance the scientific knowledge about the relationship between
VOC exposure and health outcomes; or as part of a response plan to address
community concerns. While not mutually exclusive, the distinction between these
goals must be considered when developing a follow-up approach. Any plans for
additional study will need to address other risk factors for these health outcomes
such as smoking, occupation and additional information on environmental
exposures. As in the past, NYS DOH will solicit input from the community.
B .3.3.7.1.2. ATSDR (2008b) executive summary.
This follow-up investigation was conducted to address concerns and to provide
more information related to elevated cancers and adverse birth outcomes
identified in the initial health statistics review entitled "Health Statistics Review:
Cancer and Birth Outcome Analysis, Endicott Area, Town of Union, Broome
County, New York" (2006a).
The initial health statistics review was carried out to address concerns about
health issues among residents in the Endicott area who may have been exposed to
volatile organic compounds (VOCs) through a pathway known as soil vapor
intrusion. The initial health statistics review reported a significantly elevated
incidence of kidney and testicular cancer among residents in the Endicott area. In
addition, elevated rates of heart defects and low birth weight births were
observed. The number of term low birth weight births, a subset of low birth
weight births, and the number of small for gestational age (SGA) births were also
significantly higher than expected.
The purpose of this follow-up investigation was to gather more information
and conduct a qualitative examination of medical and other records of individuals
identified with adverse birth outcomes and cancers found to be significantly
elevated. Quantitative analyses were also carried out for two additional birth
outcomes, conotruncal heart defects (specific defects of the heart's outflow
region), and spontaneous fetal deaths (stillbirths), and for cancer incidence
accounting for race.
Cancer Incidence Adjusting for Race: Because a higher percentage of the
population in the study area was white compared to the comparison population,
we examined the incidence of cancer among whites in the study area compared to
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the incidence in the white population of New York State, excluding New York
City. Cancer incidence among whites was evaluated for the years 1980-2001.
Results: Limiting the analysis of cancer to only white individuals had little effect
on overall cancer rates or standardized incidence ratios compared to those of the
entire study area population analyzed previously. The only difference was the
lung cancer which had been borderline non-significantly elevated was not
borderline significantly elevated.
Cancer Case Record Review: We reviewed medical and other records of
individuals with kidney and testicular cancers to try to determine smoking,
occupational and residential histories. A number of preexisting data sources were
used including: hospital medical records; cancer registry records; death
certificates; newspaper obituaries; Motor Vehicle records; and city and telephone
directories. Results: The case record review did not reveal any unusual patterns in
terms of age, gender, year of diagnosis, cell type, or mortality rate among
individuals with kidney or testicular cancer. There was some evidence of an
increased prevalence of smoking among those with kidney cancer and some
indication that several individuals diagnosed with testicular and kidney cancer
may have been recent arrivals to the study area.
Conclusions/Recommendations: The purpose of the additional analyses
reported in the draft for public comment follow-up report was to provide
information on certain cancers and reproductive outcomes which were elevated in
the initial health statistics review. Although these additional analyses could not
determine whether there was a causal relationship between VOC exposures in the
study area and the increased risk of several health outcomes that were observed,
they did provide more information to help guide additional follow-up. The March
2007 public comment report provided a list of follow-up options for consideration
and stated, "Although an analytical (case-control) epidemiologic study of cancer
or birth defects within this community is not recommended at this time, we
describe several follow up options for discussion with the Endicott community. A
case-control study would be the preferable method for progressing with this type
of investigation, but the potentially exposed population in the Endicott area is too
small for conducting a study that would be likely to be able to draw strong
conclusions about potential health risks.
Alternative follow-up options were discussed at meetings with Endicott
stakeholders and were the subject of responses to comments on the draft report.
From these discussions and written responses, NYS DOH has noted community
interest in two possible options for future activities: a health statistics review
based on historic outdoor air emissions modeling, and a multi-site epidemiologic
study examining cancer outcomes in communities across the state with VOC
exposures similar to Endicott. NYS DOH has considered these comments and
examined whether these options would be able to accomplish one of two goals:
either to advance the scientific knowledge about the relationship between VOC
exposure and health outcomes or to be part of a response plan to address
community concerns.
An additional health statistics review using historic outdoor air emission
modeling results to identify and study a larger population of residents potentially
exposed to TCE is not likely to meet either of these goals at this time. Because of
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the limitations of the health statistics review for drawing conclusions about cause
and effect, conducting an additional health statistics review is not likely to
increase our understanding of whether exposures in the Endicott area are linked to
health outcomes. Limitations with the available historic outdoor air data also
would make it difficult to accurately define the appropriate boundaries for the
exposure area. ATSDR historic outdoor air emissions modeling activity was
unable to model TCE due to a lack of available records.
A multi-site epidemiologic study of health outcomes in communities across
the state with VOC exposures similar to Endicott offers some promise of meeting
the goal of advancing the scientific knowledge about the relationship between
VOC exposures and health outcomes. The community has indicated its preference
that such a study focus on cancer outcomes. Given the complex issues involved in
conducting such a study (e.g., tracking down cases or their next of kin after many
years, participants' difficulty in accurately remembering possible risk factors from
many years ago, and the long time period between exposure to a carcinogen and
the onset of cancer), we do not consider a multisite case-control study of cancer as
the best option at this time. An occupational cancer study is a better option than a
community-based study because it can better incorporate information about past
workplace exposures and could use corporate records to assist in finding
individual employees many years after exposure.
Heart defects have been associated with TCE exposure in other studies. Given
the shorter latency period, and thus the shorter time period in which other risk
factors could come into play, a multi-site study of heart defects has some merit as
a possible option. Currently, NYS DEC and NYS DOH are investigating many
communities around New York State which could have VOC exposure patterns
similar to Endicott, and thus could be included in such a multi-site epidemiologic
study. However, in most of these communities exposure information sufficient to
identify a study population is not yet available. NYS DOH will continue to
evaluate these areas as additional exposure information becomes available, with
the goal of identifying other communities for possible inclusion in a multi-site
epidemiologic study of heart defects.
NYS DOH will continue to keep the Endicott community and stakeholders
informed about additional information regarding other communities with
exposures similar to those that occurred in the Endicott area. NYS DOH staff will
be available as needed to keep interested Endicott area residents up-to-date on the
feasibility of conducting a multi-site study that includes the Endicott area.
B .3.3.7.1.3. Study description and comment.
Health statistics review conducted by NYS DOH because of concerns about possible
exposures to VOCs in Endicott area groundwater and vapor intrusion into residences examined
cancer incidence between 1980 and 2001 and birth outcomes among residents living in a study
area defined by soil vapor sampling and exposure modeling. The reviews were supported by
ATSDR and conclusions presented in final reports (ATSDR, 2008b, 2006a) have received
external comment, but the studies have not been published in the open peer-reviewed literature.
Testing of soil gas and indoor air of >300 properties, including 176 residences (location not
identified) for VOCs detected TCE levels ranging from 0.18 to 140 ug/m3; other VOCs less
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commonly detected included perchloroethylene, 1,1-dichloroethane, 1,1-DCE, 1,2-DCE, vinyl
chloride, 1,1,1-trichloroethane, methylene chloride, and Freon 113. A model was developed to
predict VOC presence in soil vapor based on measured results ("Groupwater Vapor Project
Endicott New York: Summary of findings, working draft. Cited in ATSDR," 2006). Subsequent
sampling and data collection verified this model. Initial study area boundaries were determined
based on the extent of the probable soil vapor contamination >10 ug/m3 of VOCs as defined by
the model. Contour lines of modeled VOC soil vapor contamination levels, known as isopleths,
were mapped using a GIS. This study area is referred to as the Eastern study area in ATSDR
(2008b, 2006a). Additional sampling west of the initial study area identified further
contamination with the contaminant in this area primarily identified as perchloroethylene at
levels ranging from 0.1 to 3.5 ug/m3 in an area referred to as the Western study area (ATSDR,
2008b, 2006a). The source of perchloroethylene contamination was not known. A digital map
of the 2000 Census block boundaries was overlaid on these areas of contamination. The study
areas were then composed of a series of blocks combined to conform as closely to the areas of
soil vapor contamination as possible.
Incident cancer cases for 18 sites, including cancer in children <19 years, between 1980
and 2001 and obtained from the New York State Cancer Registry and addresses were geocoded
to identify cases residing in the study area. The observed numbers of site-specific cancers were
compared to that expected calculated using age-, sex-, and year-specific cancer incidence rates
for New York State exclusive of New York City and population estimates from 1980, 1990, and
2000 Censuses. Expected numbers of site-specific cancer did not include adjustment for race in
(ATSDR, 2006a): however, race was examined in the 2008 follow-up, study which compared
cancer incidence among the white residents in the study area to that of whites in New York State
(ATSDR, 2008b). Over the 22-year period, a total of 347 incident cancers were observed among
residents in the study area, 339 of these were in white residents. Less than six cases of cancers in
children <19 years old were identified and ATSDR (2006a) did not present a SIR for this
grouping, similar to their treatment of other site-specific cancers with less than six observed
cases.
The follow-up analysis by ATSDR (2008b) reviewed medical records of kidney and
testicular cancer cases for smoking and occupational and residential histories, and restricted the
statistical analysis to white residents, given the few numbers of observed cancers in the small
population of nonwhite residents. Limiting the analysis to only white individuals in the study
area had little effect on overall cancer rates or SIR estimates (ATSDR, 2006a). As observed in
ATSDR (2006a), statistically significant excess risks were observed for kidney cancer in both
sexes and testicular cancer in males. In addition, lung cancer estimate risks in males and in
males and females were of the same magnitude in both analyses, but CIs excluded a risk of 1.0 in
the ATSDR (2008b) analyses, which adjusted for race. Review of medical records for the
15 kidney and 6 testicular cancer cases provided limited information about personal exposures
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and potential risk factors because of incomplete reporting in records. The record review did not
reveal any unusual patterns in either kidney or testicular cancer in terms of age, year of
diagnosis, anatomical site, cell type, or mortality rate. Occupational history suggested possible
workplace chemical exposure for roughly half of the 13 kidney cancer cases and none of the
testicular cancer cases whose medical records included occupational history. For smoking, half
of the nine kidney cancer cases and some (number not identified) of the three testicular cancer
cases with such information in medical records were current or former smokers; smoking habits
were not reported for the other cases. Last, examination of city and phone directories revealed
that while half the kidney cancer cases as long term Endicott residents, several cases of testicular
cancer were among residents who recently moved into the Endicott area.
These health surveys are descriptive; they provide evidence of cancer rates in a
geographical area with some documented exposures to several VOCs including TCE, but are
unable to identify possible etiologic factors for the observed elevations in kidney, testicular, or
lung cancers. The largest deficiency is the lack of exposure assessment, notably historical
exposure, to individual subjects. Review of city and phone directories suggests some kidney and
testicular cancer cases were among recently-arrived residents, a finding inconsistent with a
cancer latent period; however, of greater importance is the finding of cancers among subjects
with long residential history. On the other hand, the population in the study areas has declined
over the past 20 years (ATSDR, 2006a) and residents who may have moved from the study area
were not included, introducing potential bias if cancer risks differed in these individuals. The
medical history review suggests several risk factors, including smoking and occupational
exposure, as important to kidney and testicular cancer observations. Lacking information for all
subjects, there is uncertainty regarding the additive effect of other potential risk factors such as
smoking to residential exposures. For this reason, while excesses in several incident cancers are
observed in these reports, potential etiological risk factors are ill-defined, and the weight these
studies contribute in the overall weight-of-evidence analysis is limited.
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ATSDR (Agency for Toxic Substances and Disease Registry). (2006a). Health Consultation. Cancer and Birth Outcome
Analysis, Endicott Area, Town of Union, Broome County, New York. Health Statistics Review. Atlanta, GA: U.S.
Department of Health and Human Services, Public Health Service, Agency for Toxic Substances and Disease Registry. May
26, 2006.
ATSDR (Agency for Toxic Substances and Disease Registry). (2008b). Health Consultation. Cancer and Birth Outcome
Analysis, Endicott Area, Town of Union, Broome County, New York. Health Statistics Review Follow-Up. Atlanta, GA: U.S.
Department of Health and Human Services, Public Health Service, Agency for Toxic Substances and Disease Registry. May
15, 2008.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
This health statistics review examined incidence for 18 types of cancer in residents living in the
Village of Endicott at the time of diagnosis. This study was not designed to identify possible etiologic
factors.
Subjects are incident cases of cancer of the 18 types of cancers including childhood cancer (all cancers
in children <19 yrs of age) reported to the New York Cancer Registry between 1980 and 2001 among
residents in two areas of the Village of Endicott, New York.
The expected number of cancer cases for the period was calculated using cancer incidence rates for
New York State exclusion of New York City and population estimates from 1980, 1990, and 2000
Censuses.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence.
ICD 9th Revision.
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CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
This geographic-based study does not develop quantitative estimates of exposure, rather study
boundaries are defined using soil gas and indoor air monitoring data and computer modeling.
Testing of soil gas and indoor air of >300 properties, including 176 residences (location not identified)
in the Eastern study area for VOCs detected TCE levels ranging from 0. 18 to 140 ug/m3; other VOCs
less commonly detected included perchloroethylene, 1,1-dichloroethane, 1,1-DCE, 1,2-DCE, vinyl
chloride, 1,1,1-trichloroethane, methylene chloride, andFreon 113. A model was developed to predict
VOC presence in soil vapor based on measured results ("Groupwater Vapor Project, Endicott
New York: Summary of findings, working draft. Cited in ATSDR," 2006). Subsequent
sampling and data collection verified this model. Initial study area boundaries were determined based
on the extent of the probable soil vapor contamination >10 ug/m3 of VOCs as defined by the model.
Additional sampling west of the initial study area identified further contamination with the
contaminant in this area primarily identified as perchloroethylene at levels ranging from 0. 1 to
3.5 ug/m3 in an area referred to as the Western study area.
The study areas were then composed of a series of blocks combined to conform as closely to the areas
of soil vapor contamination as possible.
Cancer incident cases in residents at the time of diagnosis in the two areas were included in the study.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
No information.
No information.
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Record study.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
Record study.
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
347 total cancers in males and females among an estimated population size of 3,540 (1980)-3,002
(2000).
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CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age and sex (ATSDR. 2006a).
Age, sex, and race (ATSDR. 2008b).
Medical record review of 15 kidney and 6 testicular cancer cases provided limited information on
smoking, work history, and residential history for a small percentage of these cases (AT SDR,
2008b).
No.
Yes.
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B.3.3.8. Studies in Arizona
B .3.3.8.1. Studies of West Central Phoenix Area, Maricopa County, Arizona.
B .3.3.8.1.1. Aickin et al. (1992), Aickin (2004).
B.3.3.8.1.1.1. Aickin et al. (1992) author's abstract.
Reports of a suspected cluster of childhood leukemia cases in West Central
Phoenix have led to a number of epidemiological studies in the geographical area.
We report here on a death certificate-based mortality study, which indicated an
elevated rate ratio of 1.95 during 1966-1986, using the remainder of the Phoenix
standard metropolitan statistical area (SMSA) as a comparison region. In the
process of analyzing the data from this study, a methodology for dealing with
denominator variability in a standardized mortality ratio was developed using a
simple linear Poisson model. This new approach is seen as being of general use in
the analysis of standardized rate ratios (SRR), as well as being particularly
appropriate for cluster investigations.
B.3.3.8.1.1.2. Aickin (2004) author's abstract.
BACKGROUND AND OBJECTIVES: Classical statistical inference has attained
a dominant position in the expression and interpretation of empirical results in
biomedicine. Although there have been critics of the methods of hypothesis
testing, significance testing (P-values), and confidence intervals, these methods
are used to the exclusion of all others. METHODS: An alternative metaphor and
inferential computation based on credibility is offered here. RESULTS: It is
illustrated in three datasets involving incidence rates, and its advantages over both
classical frequentist inference and Bayesian inference, are detailed.
CONCLUSION: The message is that for those who are unsatisfied with classical
methods but cannot make the transition to Bayesianism, there is an alternative
path.
B.3.3.8.1.1.3. Study description and comment.
This study by staff of Arizona Department of Health Services of leukemia mortality or
incidence rates among children <19 years old living at the time a death in West Central Phoenix
in Maricopa County assume residence in the defined geographical area as a surrogate of
undefined exposures. Aickin et al. (2004) adopted a classical statistical approach, linear Poisson
regression, to estimate age-, sex- and calendar year adjusted RRs for leukemia mortality between
1966 and 1986 among children <19 years old living in the study area at the time of death.
Leukemia mortality rates for the rest of Maricopa County, excluding the study area and three
additional geographic areas previously identified with hazardous waste contamination, were
selected as the referent (Aickin etal., 1992). Aickin (2004) adopted inferential or Bayesian
approaches to test whether childhood leukemia incidence between 1966 and 1986 would confirm
the mortality analysis observation.
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Both studies use residence at time of diagnosis or death in the study area, West Central
Phoenix, Arizona, as the exposure surrogate; specific exposures such as drinking water
contaminants are not examined nor is information on parental factors considered in the analysis.
Some information on potential exposures in the community-at-large may be obtained from
reports prepared by the AZ DHS of epidemiologic investigations of cancer mortality rates among
residents of this area. Aickin et al. (1992) is the published finding on childhood leukemia. Past
exposure to the population of West Central Phoenix to environmental contaminants has been
difficult to quantify because of a paucity of environmental monitoring data (ADHS, 1990).
Community concerns about the environment focused on TCE found in drinking water in late
1981: air pollution, from benzene emission from a nearby major gasoline storage and
distribution facility, and pesticide residues. Two wells that occasionally supplemented the water
supply in West Central Phoenix were closed after TCE was detected at the wellhead. The levels
of TCE measured at the time contamination was detected were 8.9 and 29.0 ppb (report does not
identify the number of samples nor concentration ranges). The period over which contaminated
water had been supplied from these wells was not known nor whether significant exposure to the
population occurred after mixing with surface water. Other compounds identified in the
contaminated plume besides TCE included 1,1-DCE, trans-1,2-DCE, chloroform, and chromium.
The exposure assessment in the AZ DHS reports is inadequate to describe exposure potential to
TCE to subjects of Aickin et al. (1992) and Aickin (2004). Moreover, potential etiologic factors
for the observed elevated estimated RR for childhood leukemia bases are not examined. While
these studies support an inference of elevated childhood leukemia rates in residents of West
Central Phoenix, these studies provide little information on childhood leukemia and TCE
exposure and contribute little weight in the overall weight-of-evidence analysis of cancer and
TCE.
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Aickin M, Chapin CA, Flood TJ, Englender SJ, Caldwell GG. (1992). Assessment of the spatial occurrence of childhood
leukemia mortality using standardized rate ratios with a simple linear Poisson model. Int J Epidemiol 21:649-655.
Aickin M. (2004). Bayes without priors. J Clin Epidemiol 57:4-13.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Aickin et al. (1992) illustrated a methodologic approach to reduce variability in rate ratios from
small-sized populations. Childhood leukemia mortality in a geographically -defined area in central
Phoenix, Arizona, was the case study adopted to illustrate methodologic approach. The analysis was
not designed to examine possible etiologic factors.
The purpose of Aickin (2004) "was to determine whether a 1.95 standardized mortality ratio [19] for
leukemia in West Central Phoenix (compared to the remainder of Maricopa County) would be
confirmed in an incidence study" [p. 8].
Leukemia deaths among children <19 yrs of age between the years 1966 and 1986 and with addresses
on death certificates in the geographically -defined study area were identified from Arizona death
tapes.
Referent group is childhood leukemia mortality rate of all other Maricopa residents excluding the
study area and three other areas with identified hazardous waste contamination (Aickin et al..
1992).
Incident cases of childhood leukemia (<19 yrs) among residents living in study area were identified
from the Arizona Cancer Registry and from cancer registry and medical record reviews at 13 area
hospitals (APRS. 1990).
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer mortality (Aickin et al., 1992).
Cancer incidence (Aickin, 2004).
Mortality— ICD 7, ICDA 8, ICD 9 (Flood, 1988).
Incidence— ICD-O.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Residence in geographical area is a surrogate of undefined exposures; possible exposures are not
identified in the paper.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
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>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Record study.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
38 childhood leukemia deaths over a period of 21 yrs.
49 childhood leukemia incident cases over a period of 21 yrs.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, and year (1966-1 969, 1979-1981, 1982-1986).
Poisson regression using 1970, 1980, and 1985 population estimates from U.S. Bureau of the Census.
No.
Yes.
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B .3.3.8.2. Studies in Tucson, Pima County, Arizona.
B .3.3.8.2.1. Arizona Department of Health Services (1995,1990).
B.3.3.8.2.1.1. Arizona Department of Health Services (1990) author's summary.
In 1986, responding to community concerns about possible past exposure to low
levels of trichloroethylene in drinking water, a committee appointed by the
Director of the Arizona Department of health Services recommended that the
incidence of childhood leukemia and testicular cancer be studied in the population
residing in the Tucson Airport Area (TAA). The study reported here was
designed to count all cancer cases occurring in 0-19 year-old Pima County
residents, and all testicular cancer cases in Pima County residents of all ages,
during the 1970-1986 time period. Based on the incidence rates in the remainder
of Pima County, approximately seven cases of childhood leukemia and
approximately eight cases of testicular cancer would have been expected in the
TAA. Eleven cases of leukemia (SIR= 1.50, 95% C.I. 0.76-2.70) and six cases of
testicular cancer (SIR = 0.78, 95% C.I. 0.32-1.59) were observed. Statistical
analyses showed that the incidence rates of these cancers were not significantly
elevated. Additionally, it was determined that the rates of other childhood cancers
in the TAA, grouped as lymphoma, brain/CNS and other, were not significantly
elevated. The childhood leukemia, childhood cancer, and testicular cancer rates
in Pima County were comparable to rates in other states and cities participating in
the National Cancer Institute's Surveillance Epidemiology and End Results
Program.
B.3.3.8.2.1.2. Arizona Department of Health Services (1995) author's summary.
In 1986, responding to community concerns about possible past exposure to low
levels of trichloroethylene in drinking water, a committee appointed by the
Director of the Arizona Department of health Services recommended that the
incidence of childhood leukemia and testicular cancer be studied in the population
residing in the Tucson Airport Area (TAA). The study reported here was
designed to count all cancer cases occurring in 0-19 year-old Pima County
residents, and all testicular cancer cases in Pima County residents of all ages,
during the 1986-1991 time period. Based on the incidence rates in the remainder
of Pima County, approximately 3 cases of childhood leukemia and 4 cases of
testicular cancer would have been expected in the TAA. Three cases of leukemia
(SIR = .80; 95% C.I. 0.31-2.05) and 4 cases of testicular cancer (SIR = .93; 95%
C.I. 0.37-2.35) were observed. Statistical analyses showed that the incidence
rates of these cancers were not significantly elevated. Additionally, results
indicate no statistically elevated incidence rates of childhood lymphoma,
brain/CNS, and other childhood cancers, for ages 0-19, in the TAA. No
consistent pattern of disease occurrence was observed when comparing the past
incidence and mortality studies conducted by ADHS in the TAA with this present
study regarding disease categories.
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B .3.3.8.2.1.3. Study description and comment.
These reports by staff of AZ DHS of cancer incidence among children <19 years old and
of testicular cancer incidence among males living at the time a diagnosis in 1970-1986 or 1987-
1991 in the Tucson International Airport Area (TAA) of southwest Tucson (APRS. 1995. 1990)
compared to incidence rates for the rest of Pima County were conducted in response to
community concerns about cancer and possible past exposure to low levels of TCE in drinking
water. In contrast to studies in West Central Phoenix, findings from the 1990 and 1995 AZ DHS
studies in Tucson have not been published in the peer-reviewed literature. Childhood cancers
included were leukemia, brain/CSN, lymphoma, and a broad category of all other cancers
diagnosed in children <19 years old. The Arizona Cancer Registry and reviews of medical
records of 10 Pima county hospitals served as sources for identifying incident cases. The study
area was defined as a geographical area overlaying a plume of contaminated groundwater and
was comprised of five census tracts. The approximate areas boundaries are Ajo Way (north),
Los Reales Road (south), Country Club Road (east), and the Santa Cruz River (west). Adjacent
census tracts in Pima County were aggregated into four separate study areas and incident cancer
rates during the 1970-1986 time period (APRS. 1990) or 1987-1991 (APRS. 1995) of the
aggregated four-area census tract, excluding the TAA area, were used to calculate expected
numbers of cancers using the indirect standardization method and population estimates from
1960, 1970, 1975, 1980, and 1985 (APRS. 1990) or 1990 (APRS. 1995) of the U.S. Bureau of
Census. A secondary analysis of AZ DHS (1990) compared the incidence rate of childhood
leukemia and testicular cancer among Pima County residents to that reported to the SEER for a
similar time period.
These studies assume residence in the defined geographical area as a surrogate of
undefined exposures. The reports do not identify specific exposures for the individual subjects
and some information on exposures in the community-at-large may be obtained from Public
Health Assessments of the Tucson International Airport Area Superfund Site prepared by the
AZ DHS for the AT SDR (2001. 2000). The TAA site includes one main contaminated
groundwater plume with smaller areas of groundwater contamination located east of the main
plume. Insufficient data existed to evaluate groundwater contamination prior to 1981. Studies
conducted by AZ DHS in 1981-1982 showed TCE concentrations of >5 ppb, the maximum
contaminant level, in the main groundwater plume with TCE detected in some municipal
drinking water wells at concentrations of up to 239 ppb. An ATSDR health assessment
conducted in 1988 indicated that soil and groundwater in the Main Plume had been contaminated
by chromium and VOCs such as TCE and DCE (ATSDR, 2000). Sampling of private wells from
1981 through 1994 identified both drinking and irrigation private wells in and near the TAA with
TCE concentrations ranging from nondetected to 120 ppb. Concentrations of other VOCs and
chromium from the 1980s are not presented in the ATSDR reports. Besides groundwater, areas
of contaminated soil and sediment have also been identified as part of the site. The "Three
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Hangars" area of the airport was found to contain poly chlorinated biphenyls in drainage areas
with migration off-site into residential neighborhoods (ATSDR, 2001). The exposure
assessment in these studies is inadequate to describe exposure to TCE. The studies provide little
information on cancer risks and TCE exposure and carry little weight in the overall weight-of-
evidence analysis.
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AZ DHS (Arizona Department of Health Services). (1990). The incidence of childhood leukemia and testicular cancer in Pima
County: 1970-1986. Prepared by the Arizona Department of Health Services, Division of Disease Prevention, Office of Risk
Assessment and Investigation, Office of Chronic Disease Epidemiology. September 17,1990.
AZ DHS (Arizona Department of Health Services). (1995). Update of the incidence of childhood leukemia and testicular
cancer in Southwest Tucson, 1987-1991. Prepared by the Arizona Department of Health Services, Office of Risk Assessment
and Investigation, Disease Prevention Services. June 6,1995.
Description
CATEGORY A: STUDY DESIGN
Clear articulation of study objectives or hypothesis
Selection and characterization in cohort studies of
exposure and control groups and of cases and controls
in case-control studies is adequate
Yes, from ADHS (1990), "1) To determine whether there was an elevated incidence of leukemia or
other cancers among children residing in the Tucson Airport Area (TAA) and 2) To determine whether
there was an elevated incidence of testicular cancer in males in the TAA."
From ADHS (1995), "The objective of this study is to determine whether the incidence rates of
childhood leukemia (ages 0-19) and testicular cancer in males of all ages were significantly elevated
in the TAA when compared to the rest of Pima County for the years 1987 through 1991."
Cases are identified from the Arizona Cancer Registry and review of medical records at 10 Pima
County hospitals. The referent is incidence rates for the remaining population of Pima County,
excluding the study area.
CATEGORY B: ENDPOINT MEASURED
Levels of health outcome assessed
Changes in diagnostic coding systems for lymphoma,
particularly NHL
Cancer incidence.
ICD-O and ICD-9 or equivalent codes from ICDA-8, ICD-7, HICDA, or SNODO.
CATEGORY C: TCE-EXPOSURE CRITERIA
Exposure assessment approach, including adoption of
JEM and quantitative exposure estimates
Residence in geographical area is a surrogate of undefined exposures; possible exposures are not
identified in the paper.
CATEGORY D: FOLLOW-UP (COHORT)
More than 10% loss to follow-up
>50% cohort with full latency
CATEGORY E: INTERVIEW TYPE
<90% face-to-face
Blinded interviewers
Record study.
CATEGORY F: PROXY RESPONDENTS
>10% proxy respondents
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CATEGORY G: SAMPLE SIZE
Number of deaths in cohort mortality studies; numbers
of total cancer incidence studies; numbers of exposed
cases and prevalence of exposure in case-control studies
ADHS (1990), 31 childhood cancers — 11 leukemia cases, 2 lymphoma, 3 CNS/Brain, and 15 other,
and 6 testicular cancers.
ADHS (1995), 1 1 childhood cancers — 3 leukemia, 1 lymphoma, 2 CNS/Brain, and 5 other, and 4
testicular cancers.
CATEGORY H: ANALYSIS
Control for potential confounders in statistical analysis
Statistical methods
Exposure-response analysis presented in published
paper
Documentation of results
Age, sex, and year.
SIRs calculated using indirect standardization.
No.
Yes.
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C. META-ANALYSIS OF CANCER RESULTS FROM
EPIDEMIOLOGICAL STUDIES
C.I. METHODOLOGY
An initial review of the epidemiological studies indicated some evidence for associations
between TCE exposure and NHL and cancers of the kidney and liver (see Section 4.1). To
investigate further these possible associations, we performed meta-analyses of the
epidemiological study results for these three cancer types. There was suggestive evidence for
some other cancer types, as well; however, fewer TCE studies reported RR estimates for these
other site-specific cancers, and meta-analysis was not attempted for these cancer types (see
Section 4.1). In addition, at the request of our Science Advisory Board (SAB, 2011), we
conducted a meta-analysis of lung cancer in the TCE cohort studies to address the issue of
smoking as a possible confounder in the kidney cancer studies (see Section 4.4.2.3).
Meta-analysis provides a systematic way to combine study results for a given effect
across multiple (sufficiently similar) studies. The resulting summary (weighted average)
estimate is a quantitatively objective way of reflecting results from multiple studies, rather than
relying on a single study, for instance. Combining the results of smaller studies to obtain a
summary estimate also increases the statistical power to observe an effect, if one exists.
Furthermore, meta-analyses typically are accompanied by other analyses of the epidemiological
studies, including analyses of publication bias and investigations of possible factors responsible
for any heterogeneity across studies.
Given the diverse nature of the epidemiological studies for TCE, random-effects models
were used for the primary analyses, and fixed-effect analyses were conducted for comparison.
Both approaches combine study results (in this case, RR estimates) weighted by the inverse
variance; however, they differ in their underlying assumptions about what the study results
represent and how the variances are calculated. For a random-effects model, it is assumed that
there is true heterogeneity across studies and that both between-study and within-study
components of variation need to be taken into account; this was done using the methodology of
DerSimonian and Laird (1986). For a fixed-effect model, it is assumed that the studies are all
essentially measuring the same thing and all of the variance is within-study variance; thus, for
the fixed-effect model, the RR estimate from each study is simply weighted by the inverse of the
(within-study) variance of the estimate.
Studies for the meta-analyses were selected as described in Appendix B, Section B.2.9.
Because each of the cancer types being evaluated is considered rare in the populations being
studied (all have lifetime risks <10%, and all but lung cancer have lifetime risks <3%), the
different measures of RR (e.g., ORs, risk ratios, and rate ratios) are good approximations of each
C-l
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other (Rothman and Greenland, 1998) and are included together as RR estimates in the meta-
analyses. (In addition, the meta-analyses of lung cancer and liver cancer comprised only cohort
studies and, thus, no ORs were included in those analyses.) The general approach for selecting
RR estimates was to select the reported RR estimate that best reflected an RR for TCE exposure
vs. no TCE exposure (overall effect). When multiple estimates were available for the same study
based on different subcohorts with different inclusion criteria, the preference for overall
exposure was to select the RR estimate that represented the largest population in the study, while
trying to minimize the likelihood of TCE exposure misclassification. A subcohort with more
restrictive inclusion criteria was selected if the basis was to reduce exposure misclassification
(e.g., including only subjects with more probable TCE exposure), but not if the basis was to
reflect subjects with greater exposure (e.g., routine vs. any exposure).
When available, RR estimates from internal analyses were selected over standardized
incidence or mortality ratios (SIRs, SMRs) and adjusted RR estimates were generally selected
over crude estimates. Incidence estimates would normally be preferred to mortality estimates;
however, for the two studies providing both incidence and mortality results, incidence
ascertainment was for a substantially shorter period of time than mortality follow-up, so the
endpoint with the greater number of cases was used to reflect the results that had better case
ascertainment. Furthermore, RR estimates based on exposure estimates that discounted an
appropriate lag time prior to disease onset were typically preferred over estimates based on
unlagged exposures, although few studies reported lagged results.
For separate analyses, an RR estimate for the highest exposure group was selected from
studies that presented results for different exposure groups. Exposure groups based on some
measure of cumulative exposure were preferred, if available; however, duration was often the
sole exposure metric used.
Sensitivity analyses were generally done to investigate the impact of alternate selection
choices, as well as to estimate the impact of study findings that were not reported. Specific
selection choices are described in the following subsections detailing the actual analyses.
The meta-analysis calculations are based on (natural) logarithm-transformed values.
Thus, each RR estimate was transformed to its natural logarithm (referred to here as "log RR,"
the conventional terminology in epidemiology), and either an estimate of the SE of the log RR
was obtained, from which to estimate the variance for the weights, or an estimate of the variance
of the log RR was calculated directly. If the reported 95% CI limits were proportionally
symmetric about the observed RR estimate (i.e., UCL/RR ~ RR/LCL), then an estimate of the SE
of the log RR estimate was obtained using the formula
\log(UCL)-log(LCL)-\
SL = -, (Eq. C-l)
3.92
C-2
-------
where UCL is the upper confidence limit and LCL is the lower confidence limit (for 90% CIs,
the divisor is 3.29) (Rothman and Greenland, 1998). In all of the TCE cohort studies reporting
SMRs or SIRs as the overall RR estimates, reported CIs were calculated assuming the number of
deaths (or cases) is approximately Poisson distributed. In such cases, the CIs are not
proportionally symmetric about the RR estimate (unless the number of deaths is fairly large), and
the SE of the log RR estimate was estimated as the inverse of the square root of the observed
number of deaths (or cases) (Breslow and Day, 1987). In some case-control studies, no overall
OR was reported, so a crude OR estimate was calculated as OR = (a/b)/(c/d), where a, b, c, and d
are the cell frequencies in a 2 x 2 table of cancer cases vs. TCE exposure, and the variance of the
log OR was estimated using the formula
Var [log (OR)] = - + 1 + - + 1, (Eq. C-2)
L J a b c d
in accordance with the method proposed by Woolf (1955), as described by Breslow and Day
(1980).
The analyses that were performed for this assessment include:
• meta-analyses to obtain overall summary estimates of RR (denoted RRm),
• heterogeneity analyses,
• analyses of the influence of single studies on the summary estimates,
• analyses of the sensitivity of the summary estimates to alternate study inclusion
selections or to alternate selections of RR estimates from a study,
• publication bias analyses,
• meta-analyses to obtain summary estimates for the highest exposure groups in studies
that provide data by exposure group, and
• consideration of some potential sources of heterogeneity across studies.
The analyses were conducted using Microsoft Excel spreadsheets and the software package
Comprehensive Meta-Analysis, Version 2 (© 2006, Biostat, Inc.). Funnel plots and cumulative
analyses plots were generated using the Comprehensive Meta-Analysis software, and forest plots
were created using SAS, Version 9.2 (© 2002-2008, SAS Institute Inc.).
The heterogeneity (or homogeneity) analysis tests the hypothesis that the study results are
homogeneous (i.e., that all of the RR estimates are estimating the same population RR and the
total variance is no more than would be expected from within-study variance). Heterogeneity
was assessed using the statistic Q described by DerSimonian and Laird (1986). The (^-statistic
C-3
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represents the sum of the weighted squared differences between the summary RR estimate
(obtained under the null hypothesis [i.e., using a fixed-effect model]) and the RR estimate from
each study, and, under the null hypothesis, Q approximately follows a ^ distribution with
degrees of freedom equal to the number of studies minus one. However, this test can be under-
powered when the number of studies is small, and it is only a significance test (i.e., it is not very
informative about the extent of any heterogeneity). Therefore, the / value (Higgins et al., 2003)
was also considered, f = 100% x (Q - df)IQ, where Q is the (^-statistic and dfis the degrees of
freedom, as described above. This value estimates the percentage of variation that is due to
study heterogeneity. Typically, I2 values of 25, 50, and 75% are considered low, moderate, and
high amounts of heterogeneity, respectively. For a negative value of (Q - df), I2 is set to 0%,
indicating no observable heterogeneity.
Subgroup analyses were sometimes conducted to examine whether or not the combined
RR estimate varied significantly between different types of studies (e.g., case-control vs. cohort
studies). In such subgroup analyses of categorical variables (e.g., study design), ANOVA was
used to determine if there was significant heterogeneity between the subgroups. Applying
ANOVA tO meta-analySeS With tWO Subgroups (df = 1), ^between subgroups = Coverall - (Gsubgroupl +
<2subgrouP2) = -z-value2, where Qovera\\ is the (^-statistic calculated across all of the studies and
Gsubgroupi and 2subgrouP2 are the (^-statistics calculated within each subgroup.
Publication bias is a systematic error that occurs if statistically significant studies are
more likely to be submitted and published than nonsignificant studies. Studies are more likely to
be statistically significant if they have large effect sizes (in this case, RR estimates); thus, an
upward bias would result in a meta-analysis if the available published studies have higher effect
sizes than the full set of studies that were actually conducted. One feature of publication bias is
that smaller studies tend to have larger effect sizes than larger studies, since smaller studies need
larger effect sizes in order to be statistically significant. Thus, many of the techniques used to
analyze publication bias examine whether or not effect size is associated with study size.
Methods used to investigate potential publication bias for this assessment included funnel plots,
which plot effect size vs. study size (actually, SE vs. log RR here); the "trim and fill" procedure
of Duval and Tweedie (2000), which imputes the "missing" studies in a funnel plot (i.e., the
studies needed to counterbalance an asymmetry in the funnel plot resulting from an ostensible
publication bias) and recalculates a summary effect size with these studies present; forest plots
(arrays of RRs and CIs by study) sorted by precision (i.e., SE) to see if effect size shifts with
study size; Begg and Mazumdar rank correlation test (Begg and Mazumdar, 1994), which
examines the correlation between effect size estimates and their variances after standardizing the
effect sizes to stabilize the variances; Egger's linear regression test (Egger et al., 1997), which
tests the significance of the bias reflected in the intercept of a regression of effect size/SE on
1/SE; and cumulative meta-analyses after sorting by precision to assess the impact on the
summary effect size estimate of progressively adding the smaller studies.
C-4
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C.2. META-ANALYSIS FOR NHL
C.2.1. Overall Effect of TCE Exposure
C.2.1.1. Selection of RR Estimates
The selected RR estimates for NHL associated with TCE exposure from the selected
epidemiological studies are presented in Table C-l for cohort studies and in Table C-2 for case-
control studies. Some of the more recent case-control studies classified NHLs along the lines of
the recent World Health Organization/Revised European-American Classification of Lymphoid
Neoplasms (WHO/REAL) classification system (Harris et al., 2000), which recognizes
lymphocytic leukemias and multiple myelomas (plasma cell myelomas) as (non-Hodgkin)
lymphomas; however, most of the available TCE studies reported NHL results according to the
International Classification of Diseases (ICD), Revisions 7, 8, and 9, using a traditional
definition of NHL that excluded lymphocytic leukemias and multiple myelomas and focused on
ICD-7, -8, -9 codes 200 + 202. For consistency of endpoint in the NHL meta-analyses, RR
estimates for ICD 200 + 202 were selected, wherever possible; otherwise, estimates for the
classification(s) best approximating this traditional definition of NHL were selected. In addition,
many of the studies provided RR estimates only for males and females combined, and we are not
aware of any basis for a sex difference in the effects of TCE on NHL risk; thus, wherever
possible, RR estimates for males and females combined were used. The only study of much size
(in terms of number of NHL cancer cases) that provided results separately by sex was Raaschou-
Nielsen et al. (2003). This study reports an insignificantly higher SIR for females (1.4, 95% CI:
0.73, 2.34) than for males (1.2, 95% CI: 0.98, 1.52).
C-5
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Table C-l. Selected RR estimates for NHL associated with TCE exposure (overall effect) from cohort studies
Study
Anttila et al.
(1995)
Axelson et al.
(1994)
Boice et al.
(1999)
Greenland et al.
(1994)
Hansen et al.
(2001)
Morgan et al.
(1998)
Raaschou-
Nielsen et al.
(2003)
Radican et al.
(2008)
Zhao et al.
(2005)
RR
1.81
1.52
1.19
0.76
3.1
1.01
1.24
1.36
1.44
95%
LCL
0.78
0.49
0.83
0.24
1.3
0.46
1.01
0.77
0.90
95%
UCL
3.56
3.53
1.65
2.42
6.1
1.92
1.52
2.39
2.30
RRtype
SIR
SIR
SMR
Mortality
OR
SIR
SMR
SIR
Mortality
hazard
ratio
Mortality
RR
logRR
0.593
0.419
0.174
-0.274
1.13
0.00995
0.215
0.307
0.363
SE (log RR)
0.354
0.447
0.267
0.590
0.354
0.333
0.104
0.289
0.239
Alternate RR
estimates (95% CI)
None
1.36(0.44,3.18)
with estimated
female contribution
to SIR added (see
text)
1.19(0.65, 1.99) for
potential routine
exposure
None
None
1.36(0.35,5.21)
unpublished RR for
ICD 200 (see text)
1.5(1.2, 2.0) for
subcohort with
expected higher
exposures
None
Incidence RR: 0.77
(0.42, 1.39)
Boice 2006 SMR for
ICD-9 200 + 202:
0.21(0.01, 1.18)
Comments
ICD-7 200 + 202.
ICD-7 200 and 202. Results reported separately;
combined assuming Poisson distribution. Results
reported for males only, but there was a small female
component to the cohort.
ICD-9 200 + 202. For any potential exposure.
ICD-8 200-202. Nested case-control study.
ICD-7 200 + 202. Male and female results reported
separately; combined assuming Poisson distribution.
ICD 200 + 202. Results reported by Mandel et al.
(2006). ICD Revision 7, 8, or 9, depending on year of
death.
ICD-7 200 + 202.
ICD-8,-9 200 + 202; ICD-10 C82-C85. Time variable
= age; covariates = sex and race. Referent group is
workers with no chemical exposures.
All lymphohematopoietic cancer (ICD-9 200-208), not
just 200 + 202. Males only; adjusted for age, SES,
time since first employment. Mortality results reflect
more exposed cases (33) than do incidence results
(17). Overall RR estimated by combining across
exposure groups (see text). Boice et al. (2006b) cohort
overlaps Zhao et al. (2005) cohort; just 1 exposed
death for ICD 200 + 202; 9 for 200-208 vs. 33 in Zhao
et al. (2005).
C-6
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Table C-2. Selected RR estimates for NHL associated with TCE exposure from case-control studies3
Study
Cocco et al.
(2010)
Hardell et al.
(1994)
Miligi et al.
(2006)
Nordstrom et al.
(1998)
Persson and
Frederikson
(1999)
Purdue et al.
(2011)
Siemiatycki
(1991)
Wang et al.
(2009)
RR
0.8
7.2
0.93b
1.5
1.2
1.4
1.1
1.2
95%
LCL
0.5
1.3
0.67b
0.7
0.5
0.8
0.5
0.9
95%
UCL
1.1
42
1.29b
3.3
2.4
2.4
2.5
1.8
logRR
-0.223
1.97
-0.0726
0.405
0.182
0.336
0.0953
0.182
SE (log RR)
0.201
0.887
0.168
0.396
0.400
0.280
0.424
0.177
NHL type
NHL
NHL
NHL + CLL
Hairy cell
leukemia
NHL
NHL
NHL
NHL
Comments
Grouping consistent with traditional NHL definition provided by
author (see text). High-confidence subgroup. Adjusted for age,
sex, center, and education.
Rappaport classification system. Males only; controls matched for
age, place of residence, vital status.
NCI Working Formulation. Crude OR; overall adjusted OR not
presented.
Hairy cell leukemia specifically. Males only; controls matched for
age and county; analysis controlled for age.
Classification system not specified. Controls selected from same
geographic areas; OR stratified on age and sex.
ICD-O-3 codes 967-972. Probable-exposure subgroup. Adjusted
for age, sex, SEER center, race, and education.
ICD-9 200 + 202. SE and 95% CI calculated from reported 90%
CIs; males only; adjusted for age, income, and cigarette smoking
index.
ICD-O M-9590-9595, 9670-9688, 9690-9698, 9700-9723.
Females only; adjusted for age, family history of
lymphohematopoietic cancers, alcohol consumption, and race.
aThe RR estimates are all ORs for incident cases.
bAs calculated by U.S. EPA.
C-7
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Most of the selections in Tables C-l and C-2 should be self-evident, but some are
discussed in more detail here, in the order the studies are presented in the tables. For Axelson et
al. (1994), in which a small subcohort of females was studied but only results for the larger male
subcohort were reported, the reported male-only results were used in the primary analysis;
however, an attempt was made to estimate the female contribution to an overall RR estimate for
both sexes and its impact on the meta-analysis. Axelson et al. (1994) reported that there were no
cases of NHL observed in females, but the expected number was not presented. To estimate the
expected number, the expected number for males was multiplied by the ratio of female-to-male
person-years in the study and by the ratio of female-to-male age-adjusted incidence rates for
NHL.4 The male results and the estimated female contribution were then combined into an RR
estimate for both sexes assuming a Poisson distribution, and this alternate RR estimate for the
Axelson et al. (1994) study was used in a sensitivity analysis.
For Boice et al. (1999), results for "any potential exposure" were selected for the primary
analysis, because this exposure category was considered to best represent overall TCE exposure,
and results for "potential routine exposure," which was characterized as reflecting workers
assumed to have received more cumulative exposure, were used in a sensitivity analysis.
The Greenland et al. (1994) study is a case-control study nested within a worker cohort,
and we treat it here as a cohort study (see Appendix B, Section B.2.9.1). Greenland et al. (1994)
report results only for all lymphomas, including Hodgkin lymphoma (ICD-8 201).
For Morgan et al. (1998), the reported results did not allow for the combination of
ICD 200 and 202, so the SMR estimate for the combined 200 + 202 grouping was taken from the
meta-analysis paper of Mandel et al. (2006), who included one of the investigators from the
Morgan et al. (1998) study. RR estimates for overall TCE exposure from internal analyses of the
Morgan et al. (1998) cohort data were available from an unpublished report (EHS, 1997) (the
published paper only presented the internal analyses results for exposure subgroups), but only for
ICD 200; from these, the RR estimate from the Cox model that included age and sex was
selected, because those are the variables deemed to be important in the published paper (Morgan
et al., 1998). Although the results from internal analyses are generally preferred, in this case, the
SMR estimate was used in the primary analysis and the internal analysis RR estimate was used in
a sensitivity analysis because the latter estimate represented an appreciably smaller number of
deaths (3, based on ICD 200 only) than the SMR estimate (9, based on ICD 200 + 202).
4Person-years for men and women <79 years were obtained from Axelson et al. (1994): 23516.5 and 3691.5,
respectively. Lifetime age-adjusted incidence rates for NHL for men and women were obtained from the National
Cancer Institute's 2000-2004 SEER-17 (Surveillance Epidemiology and End Results from 17 geographical areas)
database (http://seer.cancer.gov/statfacts/html/nhl.html'): 23.2/100,000 and 16.3/100,000, respectively. The
calculation for estimating the expected number of cases in females in the cohort assumes that the males and females
have similar TCE exposures and that the relative distributions of age-related incidence risk for the males and
females in the Swedish cohort are adequately represented by the ratios of person-years and U.S. lifetime incidence
rates used in the calculation.
C-8
-------
For Raaschou-Nielsen et al. (2003), results for the full cohort were used for the primary
analysis and results for the subcohort with expected higher exposure levels (> 1-year duration of
employment and year of 1st employment before 1980) were used in a sensitivity analysis.
Raaschou-Nielsen et al. (2003), in their Table 3, also present overall results for NHL with a lag
time of 20 years; however, they use a definition of lag that is different from a lagged exposure in
which exposures prior to disease onset are discounted and it is not clear what their lag time
actually represents5, thus these results were not used in any of the meta-analyses for NHL.
For Radican et al. (2008), the Cox model hazard ratio from the 2000 follow-up was used.
In the Radican et al. (2008) Cox regressions, age was the time variable, and sex and race were
covariates. It should also be noted that the referent group is composed of workers with no
chemical exposures, not just no exposure to TCE.
For Zhao et al. (2005), RR estimates were only reported for ICD-9 200-208 (all
lymphohematopoietic cancers), and not for 200 + 202 alone. Given that other studies have not
reported associations between leukemias and TCE exposure, combining all lymphohematopoietic
cancers would dilute any NHL effect, and the Zhao et al. (2005) results are expected to be an
underestimate of any TCE effect on NHL alone. Another complication with the Zhao et al.
(2005) study is that no results for an overall TCE effect are reported. We were unable to obtain
any overall estimates from the study authors, so, as a best estimate, the results across the
"medium" and "high" exposure groups were combined, under assumptions of group
independence, even though the exposure groups are not independent (the "low" exposure group
was the referent group in both cases). Zhao et al. (2005) present RR estimates for both incidence
and mortality; however, the time frame for the incidence accrual is smaller than the time frame
for mortality accrual and fewer exposed incident cases (17) were obtained than deaths (33).
Thus, because better case ascertainment occurred for mortality than for incidence, the mortality
results were used for the primary analysis, and the incidence results were used in a sensitivity
analysis. A sensitivity analysis was also done using results from Boice et al. (2006b) in place of
the Zhao et al. (2005) RR estimate. The cohorts for these studies overlap, so they are not
independent studies and should not be included in the meta-analysis concurrently. Boice et al.
(2006b) report an RR estimate for an overall TCE effect for NHL alone; however, it is based on
far fewer cases (1 death in ICD-9 200 + 202; 9 deaths for 200-208) and is an SMR rather than an
internal analysis RR estimate, so the Zhao et al. (2005) estimates are preferred for the primary
analysis.
For the case-control studies, the main issue was the NHL classifications. Cocco et al.
(2010) present results for NHLs classified according to the WHO/REAL classification system
(i.e., including lymphocytic leukemias and multiple myelomas). For this meta-analysis, we were
able to obtain results for a grouping of lymphomas generally consistent with the traditional
5In their Methods section, Raaschou-Nielsen et al. (2003) define their lag period as the period "from the date of first
employment to the start of follow-up for cancer".
C-9
-------
definition of NHL (T-cell lymphomas and B-cell lymphomas, excluding Hodgkin lymphomas,
CLLs, multiple myelomas, and unspecified lymphomas) from Dr. Cocco (personal
communication from Pierluigi Cocco, University of Cagliari, Italy, to Cheryl Scott U.S. EPA,
19 March 2011; see Section 4.6.1.2). The results used in the meta-analyses are for the high-
confidence subgroup, which included workers with jobs with a "certain" probability of exposure
and >90% of workers exposed (5.5% of cases).
Hardell et al. (1994) used the Rappaport classification system, which, according to
Weisenburger (1992) is consistent with the traditional definition of NHL.
Miligi et al. (2006) include CLLs in their NHL results, consistent with the current
WHO/REAL classification. Also, Miligi et al. (2006) do not report an overall adjusted RR
estimate, so a crude estimate of the OR was calculated for the two TCE exposure categories
together vs. no TCE exposure.
The Nordstrom et al. (1998) study was a case-control study of hairy cell leukemias, so
only results for hairy cell leukemia were reported. Hairy cell leukemias are a subgroup of NHLs
under current classification systems, but they were not included in the traditional definition of
NHL.
Persson and Frederikson (1999) did not report the classification system used.
According to Schenk et al.(2009), Purdue et al. (2011) used ICD-O-3 codes 967-972,
which are generally consistent with the traditional definition of NHL. The results used in the
meta-analyses are for the probable-exposure subgroup, which includes workers with at least one
job assigned an exposure probability of >50% (3.8% of cases).
According to Zhang et al. (2004). Wang et al. (2009) used ICD-O-2 codes M-9590-9595,
9670-9688, 9690-9698, 9700-9723, which are consistent with the traditional definition of NHL
(i.e., ICD-7, -8, -9 codes 200 + 202).
No alternate RR estimates were considered for any of the case-control studies of NHL.
For the Cocco et al. (2010) and Purdue et al. (2011) studies, the RR estimates used are for a
higher confidence subgroup. No overall results for the full studies were presented to use as
alternative estimates. Results for lower confidence subgroups were presented separately, but no
attempt was made to combine the results across confidence groups because these results were not
independent, as they relied on the same referent groups.
An alternate analysis was done including only the studies for which RR estimates for the
traditional definition of NHL were available. In this analysis, Miligi et al. (2006), Nordstrom et
al.(1998), Persson and Frederikson (1999), and Greenland et al. (1994) were omitted and the
Boice et al. (2006b) cohort study was used instead of Zhao et al. (2005).
C.2.1.2. Results of Meta-Analyses
Results from some of the meta-analyses that were conducted on the epidemiological
studies of TCE and NHL are summarized in Table C-3. The summary estimate (RRm) from the
C-10
-------
primary random-effects meta-analysis of the 17 studies was 1.23 (95% CI: 1.07, 1.42) (see
Figure C-l). No single study was overly influential; removal of individual studies resulted in
RRm estimates that ranged from 1.18 (with the removal of Hansen et al. (2001)) to 1.27 (with the
removal of Miligi et al. (2006) or Cocco et al. (2010)) and were all statistically significant (all
with/? < 0.02). Removal of Hardell et al. (1994), whose RR estimate is a relative outlier (see
Figure C-l), only decreased the RRm estimate to 1.21 (95% CI: 1.07, 1.38), since this study does
not contribute a lot of weight to the meta-analysis. Removal of studies other than Hansen et al.
(2001) resulted in RRm estimates that were all >1.20.
TCE Exposure and Non Hoclykin Lymphoma
Study Relative Risk and 95% CI
A n+±H a " 1 (""iCiK"' M,
AnTtl I at ( 1 y fe'D ,f > Ur
Axslson (1©Q*4) 0
Orssnland (IQQ1^) nln
U a n« o n t'^nfl i"", ' Fl
riciRsen tzuu i j u
Morgan (1998) • I]
Raaschou-Nielsen (2003) • CU
Radiean (2008s ---; D - - -
Zhao (2005) -- -0
Coeeo (2010) ~ D
1 1 - rHiili ifinO-l^ !
narusit ^ i^a'Hj I
Miligi (2006) -O- -
Nordstrom (1998) • -0
D o c ^i 'i finf*fi\ n
r 8 FSSQ n&r T© Q © TlrSO R lt 1 w c'y ) U
Purdue (201 1) . --- Q
"™* " ' +• • L ' if '1 QD 1 *i ' ' n.
Wang (2009) ~-d
OVERALL , I -• \
0.1 1 10
RR
ID "1
.O 1
1.19
0~^lH
,f O
3 in
. IU
1.01
1.24
1.38
1.44
0.80
0.90
1.50
. —_
1 .^.U
1.40
1 m
I . IU
1.20
1.23
0
LCL
.<' O
0 ."40
0.83
O.^^
I .^5U
0.43
1.01
0.77
0.90
0.50
0.70
0.70
n fin
u.ou
0.80
D ^50
0.90
1.07
UCL
j_ —jn
•J.DO
1.65
6m
. IU
1.92
1.52
2.39
2.30
1.10
1.30
3.30
j~, ,j-i
*i.HU
2.40
f^ f~f\
1.80
1.42
Figure C-l. Meta-analysis of NHL and overall TCE exposure. Rectangle sizes
reflect relative weights of the individual studies. The bottom diamond represents
the summary RR estimate.
Similarly, the RRm estimate was not highly sensitive to alternate RR estimate selections.
Use of the six alternate selections, individually, resulted in RRm estimates that ranged from
1.20 to 1.28 (see Table C-3) and were all statistically significant (all with/? < 0.03).
Nor was the RRm estimate highly sensitive to restriction of the meta-analysis to only
those studies for which RR estimates for the traditional definition of NFIL were available. An
alternate analysis which omitted Miligi et al. (2006) (which included CLLs), Nordstrom et al.
(1998) (which was a study of hairy cell leukemias), Persson and Frederikson (1999) (for which
C-ll
-------
the classification system not specified), and Greenland et al. (1994) (which included Hodgkin
lymphomas) and which included Boice et al. (2006b) instead of Zhao et al. (2005) (which
included all lymphohematopoietic cancers) yielded an RRm estimate of 1.27 (95% CI: 1.05,
1.55).
There was some heterogeneity apparent across the 17 studies, although it was not
statistically significant (p = 0.16). The /2-value (see Section C. 1) was 26%, suggesting low-to-
moderate heterogeneity. This small amount of heterogeneity is also indicated by the finding that
the RRm estimate from the fixed-effect analysis was slightly different from that of the random-
effects model (1.21 vs. 1.23) and had a slightly narrower 95% CI (1.08-1.35 vs. 1.07-1.42). In
addition, nonsignificant heterogeneity was apparent in each of the meta-analyses with alternate
RR selections—/"-values ranged from 0.09 to 0.17 and /-values ranged from 25 to 34%.
To investigate the heterogeneity, subgroup analyses were done examining the cohort and
case-control studies separately. With the random-effects model (and tau-squared not pooled
across subgroups), the resulting RRm estimates were 1.33 (95% CI: 1.13, 1.58) for the cohort
studies and 1.11 (95% CI: 0.89, 1.38) for the case-control studies. There was residual
heterogeneity in each of the subgroups, but in neither case was it statistically significant, f-
values were 12% for the cohort studies, suggesting low heterogeneity, and 27% for the case-
control studies, suggesting low-to-moderate heterogeneity. The difference between the RRm
estimates for the cohort and case-control subgroups was not statistically significant. Some
thought was given to further analyses to investigate the source(s) of the heterogeneity, such as
qualitative tiering or subgroups based on likelihood for correct exposure classification or on
likelihood for higher vs. lower exposures across the studies. Ultimately, these approaches were
rejected because in many of the studies, it was difficult to judge (and weight) the extent of
exposure misclassification or the degree of TCE exposure with any precision. In other words,
there was inadequate information to reliably assess either the extent to which each study
accurately classified exposure status or the relative TCE exposure levels and prevalences of
exposure to different levels across studies. See Section C.2.3 for a qualitative discussion of some
potential sources of heterogeneity.
C-12
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Table C-3. Summary of some meta-analysis results for TCE (overall) and NHL
Analysis
All studies
Cohort
Case-control
Alternate RR
selections3
Alternate
analysis;
traditional
definition of
NHL only
Number of
studies
17
9
8
17
17
17
17
17
17
13
Model
Random
Fixed
Random
Fixed
Random
Fixed
Random
Random
Random
Random
Random
Random
Random
RRm
estimate
1.23
1.21
1.33
1.31
1.11
1.07
1.20
1.22
1.23
1.24
1.25
1.28
1.27
95%
LCL
1.07
1.08
1.13
1.14
0.89
0.90
1.03
1.03
1.07
1.07
1.08
1.09
1.05
95% UCL
1.42
1.35
1.58
1.51
1.38
1.28
1.39
1.43
1.42
1.44
1.44
1.49
1.55
Heterogeneity
Not significant
(P = 0.16)
f = 26%
Not significant
(p = 0.34)
I2 = 12%
Not significant
(p = 0.22)
I2 = 27%
Not significant
(p = 0.11)
72 = 31%
Not significant
(p = 0.09)
f = 34%
Not significant
(p = 0.16)
I2 = 25%
Not significant
(p = 0.16)
I2 = 26%
Not significant
(p = 0.17)
I2 = 25%
Not significant
(p = 0.09)
f = 34%
Not significant
(p = 0.054)
f = 42%
Comments
Statistical significance of RRm not dependent on individual
studies.
Not significant difference between CC and cohort studies
(p = 0.19).
Not significant difference between CC and cohort studies
(p = 0.08).
With estimated Zhao et al. (2005) overall RR for incidence
rather than mortality.
With Boice et al. (2006b) study rather than Zhao et al.
(2005).
With estimated female contribution to Axelson et al. (1994).
With Boice et al. (1999) potential routine exposure SMR.
With Morgan et al. (1998) unpublished RR.
With Raaschou-Nielsen et al. (2003) subgroup expected to
have higher exposures
Omitting Miligi et al. (2006). Nordstrom et al. (1998).
Persson and Frederikson (1999). and Greenland et al.
(1994), and including Boice et al. (2006b) instead of Zhao et
al. (2005).
C-13
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TableC-3. Summary of some meta-analysis results for TCE (overall) and NHL (continued)
Analysis
Highest
exposure groups
Number of
studies
13
Model
Random
Fixed
RRm
estimate
1.43
1.43
95%
LCL
1.13
1.16
95% UCL
1.82
1.75
Heterogeneity
Not significant
(p = 0.30)
I2 = 14%
Comments
Statistical significance not dependent on single study.
See Table C-5 for results with alternate RR selections.
""Changing the primary analysis by one alternate RR each time; more details on alternate RR estimates in text.
C-14
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As discussed in Section C.I, publication bias was examined in several different ways.
The funnel plot in Figure C-2 suggests some relationship between RR estimate and study size (if
there were no relationship, the studies would be symmetrically distributed around the summary
RR estimate rather than veering towards higher RR estimates with increasing SEs), although the
observed asymmetry is highly influenced by the Hardell et al. (1994) study, which is a relative
outlier and which contributes little weight to the overall meta-analysis, as discussed above. The
Begg and Mazumdar (1994) rank correlation test and Egger et al.'s (1997) linear regression test
were not statistically significant (the one-tailed/"-values were 0.18 and 0.07, respectively); it
should be noted, however, that both of these tests have low power. The trim-and-fill procedure
of Duval and Tweedie (2000) yielded a summary RR estimate (under the random-effects model)
of 1.15 (95% CI: 0.97, 1.36) when the four studies deemed missing from the funnel plot were
filled into the meta-analysis (these studies are filled in so as to counter-balance the apparent
asymmetry of the more extreme values in the funnel plot). Eliminating the Hardell et al. (1994)
study made little difference to the results of the publication bias analyses. The results of a
cumulative meta-analysis, incorporating studies with increasing SE one at a time, are depicted in
Figure C-3. This procedure is a transparent way of examining the effects of including studies
with increasing SE. The figure shows that the summary RR estimate is 1.16 after inclusion of
the four largest (i.e., most precise) studies, which constitute about 50% of the weight. The RRm
estimate decreases to 1.10 with the inclusion of the next most precise study, which contributes
another 9% of the total weight. The RRm estimate increases to 1.22 with inclusion of the 6 next
most precise studies; this summary estimate represents 11 of the 17 studies and about 87% of the
weight. Adding in the 6 least precise studies (13% of the weight) barely increases the RRm
estimate further. In summary, there is some evidence of potential publication bias in this data
set. It is uncertain, however, that this reflects actual publication bias rather than an association
between effect size and SE resulting for some other reason, e.g., a difference in study
populations or protocols in the smaller studies. Furthermore, if there is publication bias in this
data set, it does not appear to account completely for the findings of an increased NHL risk.
C-15
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Funnel Plot of Standard Error by Log rate ratio
o
ft
•p
0.0
0.2
0.4
0.6
0.8
1.0
O
o
o
o
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Log rate ratio
Figure C-2. Funnel plot of SE by log RR estimate for TCE and NHL studies.
C-16
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TCE and Non-Hodgkin Lymphoma
Study name
Cumulative statistics
Lower Upper
Point limit limit p-Value
Cumulative rate ratio (95%CI)
Raaschou-Nelsen 2003
Miligi 2006
Wang 2009
Boice 1999 any
Cocco2010
Zhao 2005 mort
Purdue 2011
Radcan2006
Morgan 1996
Anttila 1995
Hansen2001
Nordstrom 1998
Persson&Fredikson 1999
Siemiatycki 1991
Axelson 1994
Greenland 1994
Handell 1994
1.240 1.0152
1.106 0.8412
1.153 0.9785
1.163 1.0103
1.096 0.9355
1.124 0.9682
1.142 0.9959
1.156 1.0212
1.152 1.0223
1.167 1.0374
1.221 1.0378
1.227 1.0506
1.222 1.0555
1.215 1.0575
1.218 1.0667
1.210 1.0627
1.233 1.0676
1.233 1.0676
1.5146
1.4588
1.3577
1.3383
1.2846
1.3054
1.3100
1.3062
1.2976
1.3123
1.4338
1.4330
1.4148
1.3958
1.3910
1.3767
1.4247
1.4247
0.035
0.466
0.089
0.036
0.256
0.125
0.057
0.022
0.020
0.010
0.016
0.010
0.007
0.006
0.004
0.004
0.004
0.004
0.5
randomeffects model; cimlative analysis, sorted bySE
Figure C-3. Cumulative meta-analysis of TCE and NHL studies,
progressively including studies with increasing SEs.
C.2.2. NHL Effect in the Highest Exposure Groups
C.2.2.1. Selection of RR Estimates
The selected RR estimates for NHL in the highest TCE exposure categories, for studies
that provided such estimates, are presented in Table C-4. All eight cohort studies (but not the
nested case-control study of Greenland et al. (1994) and five of the eight case-control studies did
report NHL risk estimates categorized by exposure level. As in Section C.2.1.1 for the overall
risk estimates, estimates to best correspond to NHL as represented by ICD-7, -8, and -9 200 and
202 were selected, and, wherever possible, RR estimates for males and females combined were
used.
C-17
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Table C-4. Selected RR estimates for NHL risk in highest TCE exposure groups
Study
Anttila et al.
(1995)
Axelson et al.
(1994)
Boice et al.
(1999)
Hansen et al.
(2001)
Morgan et al.
(1998)
Raaschou-
Nielsen et al.
(2001)
RR
1.4
6.25
1.62
2.7
0.81
1.6
95%
LCL
0.17
0.16
0.82
0.56
0.1
1.1
95%
UCL
5.04
34.83
3.22
8.0
6.49
2.2
Exposure
category
100+ umol/L
U-TCA3
>2 yrs exposure
and 100+ mg/L
U-TCA
>5 yrs exposure
> 1,080 months
x mg/m3
High
cumulative
exposure score
>5 yrs in
subcohort with
expected higher
exposure, levels
logRR
0.336
1.83
0.482
0.993
-0.211
0.470
SE (log
RR)
0.707
1.00
0.349
0.577
1.06
0.183
Alternate RR
estimates (95% CI)
none
5.62(0.14,31.3)
with estimated
female contribution
added (see text)
None
3.7(1.0, 9.5) for
>75 months
exposure duration
2.9 (0.79, 7.5) for
>19 mg/m3 mean
exposure
1.31(0.28, 6.08) for
med/high peak vs.
low/no
1.45(0.99, 2.05) for
>5 yrs in full cohort,
both sexes combined
Comments
SIR. ICD200 + 202.
SIR. ICD200 + 202. Results reported for males
only, but there was a small female component to
the cohort.
Mortality RR. ICD200 + 202. For potential
routine or intermittent exposure. Adjusted for
date of birth, dates 1st and last employed, race, and
sex. Referent group is workers not exposed to any
solvent.
SIR. ICD200 + 202. Expo sure -group results
presented only for males. Female results
estimated and combined with male results
assuming Poisson distribution (see text).
Mortality RR. ICD 200 only. Adjusted for age
and sex.
SIR. ICD 200 + 202.
C-18
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Table C-4. Selected RR estimates for NHL risk in highest TCE exposure groups (continued)
Study
Radican et al.
(2008)
Zhao et al.
(2005)
Cocco et al.
(2010)
Miligi et al.,
(2006)
Purdue et al.
(2011)
Siemiatycki
(1991)
Wang et al.
(2009)
RR
1.41
1.30
0.7
1.2
3.3
0.8
2.2
95%
LCL
0.71
0.52
0.4
0.7
1.1
0.2
0.9
95%
UCL
2.81
3.23
1.3
2.0
10.1
3.3
5.4
Exposure
category
>25 unit-yrs
High
exposure
score
High
cumulative
exposure
Med/high
exposure
intensity
Cumulative
exposure
>234,000 ppm
x hrs
Substantial
Medium-high
intensity
logRR
0.337
0.262
-0.357
0.182
1.194
-0.223
0.788
SE (log
RR)
0.350
0.466
0.301
0.268
0.566
0.719
0.457
Alternate RR
estimates (95%
CI)
Blair et al. (1998)
0.97 (0.42, 2.2)
incidence RR
Incidence RR:
0.20 (0.03, 1.46)
None
1.0(0.5, 2.6) for
med/high intensity
and>15 yrs
2.3(1.0, 5.0) for
highest exposure
tertile
(>1 12,320 ppm x
hrs)
None
None
Comments
Mortality hazard ratio. ICD200 + 202. Male and
female results presented separately and combined (see
text). Cox regression time variable = age; covariate =
race. Referent group is workers with no chemical
exposures.
Mortality RR. Results for all lymphohematopoietic
cancer (ICD-9 200-208), not just 200 + 202. Males
only; adjusted for age, SES, time since first
employment. Mortality results reflect more exposed
cases (six in high-exposure group) than do incidence
results (one in high-exposure group).
Incidence OR. Grouping consistent with traditional
NHL definition provided by author (see text). High-
confidence subgroup. Adjusted for age, sex, center,
and education.
Incidence OR. NHL + CLL (see Section C.2.1.1).
Adjusted for age, sex, education, and area.
Incidence OR. ICD-O-3 codes 967-972. Probable-
exposure subgroup. Adjusted for age, sex, SEER
center, race, and education.
Incidence OR. NHL. SE and 95% CI calculated from
reported 90% CIs. Males only; adjusted for age,
income, and cigarette smoking index.
Incidence OR. NHL. Females only; adjusted for age,
family history of lymphohematopoietic cancers,
alcohol consumption, and race.
aMean personal TCA in urine. 1 umol/L = 0.1634 mg/L.
C-19
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As above for the overall TCE effect, for Axelson et al. (1994), in which a small subcohort
of females was studied but only results for the larger male subcohort were reported, the reported
male-only high-exposure group results were used in the primary analysis; however, an attempt
was made to estimate the female contribution to a high-exposure group RR estimate for both
sexes and its impact on the meta-analysis. To estimate the expected number in the highest
exposure group for females, the expected number in the highest exposure group for males was
multiplied by the ratio of total female-to-male person-years in the study and by the ratio of
female-to-male age-adjusted incidence rates for NHL. The RR estimate for both sexes was used
as an alternate RR estimate for the Axelson et al. (1994) study in a sensitivity analysis.
For Boice et al. (1999), only results for workers with "any potential exposure" (rather
than "potential routine exposure") were presented by exposure category, and the referent group is
workers not exposed to any solvent.
For Hansen et al. (2001), exposure group data were presented only for males. To
estimate the female contribution to a highest exposure group RR estimate for both sexes, it was
assumed that the expected number of cases in females had the same overall-to-highest-exposure-
group ratio as in males. The RR estimate for both sexes was then calculated assuming a Poisson
distribution, and this estimate was used in the primary analysis. Hansen et al. (2001) present
results for three exposure metrics; the cumulative exposure metric was preferred for the primary
analysis, and results for the other two metrics were used in sensitivity analyses.
For Morgan et al. (1998), results did not allow for the combination of ICD 200 and 202,
so the highest exposure group RR estimate for ICD 200 only was used. The primary analysis
used results for the cumulative exposure metric, and a sensitivity analysis was done with the
results for the peak exposure metric.
For Radican et al. (2008), it should be noted that the referent group is composed of
workers with no chemical exposures, not just no exposure to TCE. In addition, results for
exposure groups (based on cumulative exposure scores) were reported separately for males and
females and were combined for this assessment using inverse-variance weighting, as in a fixed-
effect meta-analysis. Radican et al. (2008) present only mortality hazard ratio estimates by
exposure group; however, in an earlier follow-up of this same cohort, Blair et al. (1998) present
both incidence and mortality RR estimates by exposure group. The mortality RR estimate based
on the more recent follow-up by Radican et al. (2008) (17 deaths in the highest exposure group)
was used in the primary analysis, while the incidence RR estimate based on similarly combined
results from Blair et al. (1998) (nine cases) was used as an alternate estimate in a sensitivity
analysis. Radican et al. (2008) also present results for categories based on frequency and pattern
of exposure; however, subjects weren't distributed uniquely across the categories (the numbers
of cases across categories exceeded the total number of cases); thus, it was difficult to interpret
these results and they were not used in a sensitivity analysis.
C-20
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For Zhao et al. (2005). RR estimates were only reported for ICD-9 200-208 (all
lymphohematopoietic cancers), and not for 200 + 202 alone. Given that other studies have not
reported associations between leukemias and TCE exposure, combining all lymphohematopoietic
cancers would dilute any NHL effect, and the Zhao et al. (2005) results are expected to be an
underestimate of any TCE effect on NHL alone. Zhao et al. (2005) present RR estimates for
both incidence and mortality in the highest exposure group; however, the time frame for the
incidence accrual is smaller than the time frame for mortality accrual and fewer incident cases
(1) were obtained than deaths (6), so the mortality results were used for the primary analysis to
reflect the better case ascertainment in the mortality data, and the incidence results were used in
a sensitivity analysis.
Cocco et al. (2010) present exposure group results only for their high-confidence
subgroup, which included workers with jobs with a "certain" probability of exposure and >90%
of workers exposed (5.5% of cases). Results for a grouping of lymphomas generally consistent
with the traditional definition of NHL (T-cell lymphomas and B-cell lymphomas, excluding
Hodgkin lymphomas, CLLs, multiple myelomas, and unspecified lymphomas) were kindly
provided by Dr. Cocco (personal communication from Pierluigi Cocco, University of Cagliari,
Italy, to Cheryl Scott U.S. EPA. 19 March 2011: see Section 4.6.1.2).
Miligi et al. (2006) include CLLs in their NHL results, consistent with the current
WHO/REAL classifications. Miligi et al. (2006) report RR estimates for medium and high
exposure intensity overall and by duration of exposure; however, there was incomplete
information for the duration breakdowns (e.g., a case missing), so the RR estimate for med/high
exposure intensity overall was used in the primary analysis, and the RR estimate for med/high
exposure for >15 years was used in a sensitivity analysis.
Purdue et al. (2011) used ICD-O-3 codes 967-972, generally consistent with a traditional
definition of NHL. These investigators present exposure group results only for their probable-
exposure subgroup, which included workers with jobs with an assigned probability of exposure
of >50% (3.8% of cases). The exposure groups are cumulative exposure tertiles, with cutpoints
determined from the exposure distribution in the probably exposed controls. The highest
exposure tertile was further subdivided using the intra-category median. The highest exposure
group from the subdivided highest exposure tertile was used for the primary analysis (four
cases), and the results for the complete highest tertile were used in a sensitivity analysis (nine
cases).
Wang et al. (2009) used ICD-O-2 codes (M-9590-9595, 9670-9688, 9690-9698, 9700-
9723), consistent with the traditional definition of NHL (i.e., ICD-7, -8, -9 codes 200 + 202).
Wang et al. (2009) present exposure-group (low or medium/high intensity) results cross-
categorized by exposure probability (low and medium/high). The medium and high exposure-
intensity category was used as the highest exposure group, although all of the subjects with
medium and high exposure intensity were in the low exposure-probability category.
C-21
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C.2.2.2. Results of Meta-Analyses
Results from the meta-analyses that were conducted for NHL in the highest exposure
groups are summarized at the bottom of Table C-3 and reported in more detail in Table C-5. The
summary RR estimate from the primary random-effects meta-analysis of the 13 studies with
results presented for exposure groups was 1.43 (95% CI: 1.13, 1.82) (see Figure C-4). No single
study was overly influential; removal of individual studies resulted in RRm estimates that were all
statistically significant (all with/? < 0.025) and that ranged from 1.38 (with the removal of Purdue
et al. [(2011)1) to 1.57 (with the removal of Cocco et al. (2010)). In addition, the RRm estimate
was not highly sensitive to alternate RR estimate selections. Use of the nine alternate selections,
individually, resulted in RRm estimates that were all statistically significant (all with/? < 0.025)
and all in the narrow range from 1.40 to 1.49 (see Table C-5).
There was some heterogeneity apparent across the 13 studies, although it was not
statistically significant (p = 0.30). The /2-value was 14%, suggesting low heterogeneity. This
small amount of heterogeneity is also indicated by the finding that the RRm estimate from the
fixed-effect analysis had a slightly narrower 95% CI (1.16-1.75 vs. 1.13-1.82), although the RRm
estimates themselves were essentially identical. In addition, nonsignificant heterogeneity was
apparent in each of the meta-analyses with alternate RR selections—/^-values ranged from 0.12 to
0.37 and /2-values ranged from 9 to 33%.
C-22
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Table C-5. Summary of some meta-analysis results for TCE (highest exposure groups) and NHL
Analysis
All studies (13)
Cohort studies (8)
Case-control
studies (5)
Alternate RR
selections3
(all studies)
Model
Random
Fixed
Random
Fixed
Random
Fixed
Random
Random
Random
Random
Random
Random
Random
Random
Random
RRm estimate
1.43
1.43
1.60
1.60
1.29
1.18
1.40
1.40
1.41
1.43
1.43
1.44
1.44
1.45
1.49
95% LCL
1.13
1.16
1.24
1.24
0.76
0.84
1.11
1.09
1.05
1.13
1.15
1.12
1.14
1.14
1.14
95% UCL
1.82
1.75
2.08
2.08
2.20
1.64
1.75
1.80
1.88
1.80
1.78
1.85
1.83
1.86
1.93
Heterogeneity
NS(/? = 0.30)
/ = 14%
None observable
(random = fixed)
NS (p = 0.08)
/ = 53%
NS(/? = 0.33)
/= 11%
NS (p = 0.25)
/ = 19%
NS(/? = 0.12)
/ = 33%
NS(p = 0.32)
/ = 13%
NS(p = 0.37)
f = 9%
NS (p = 0.29)
/ = 16%
NS(/? = 0.32)
/ = 13%
NS (p = 0.25)
/ = 19%
NS(/? = 0.17)
/ = 27%
Comments
Statistical significance not dependent on single study.
Not significant difference between CC and cohort studies
(p = 0.47).
Not significant difference between CC and cohort studies
(p = 0.15).
With Raaschou-Nielsen et al. (2003) full cohort instead of
subgroup expected to have higher exposures.
With Blair et al. (1998) incidence RR instead of Radican et
al. (2008) mortality hazard ratio.
With Zhao et al. (2005) incidence.
With estimated female contribution for Axelson et al.
(1994).
With Purdue et al. (2011) highest cumulative exposure
tertile
With Miligi et al. (2006) with >15 yrs.
With Morgan et al. (1998) peak.
With Hansen et al. (2001) mean exposure.
With Hansen et al. (2001) duration.
"Changing the primary analysis by one alternate RR estimate each time.
CC: case-control; NS: not statistically significant
C-23
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TCE Exposure and Non-Hodgkin Lymphoma - highest exposure groups
Study Relative Risk and 95% CI RR
Buice (1999)
Raaschou-Nielsen (2003)
Radican (2008)
Cocco(201D) ; — IH —
Purdue (2011)
Wang (2009)
OVERALL
— D — 1.62
— I I— 1.60
D 1.40
0.70
0.1 1 10
LCL
0.17
0.16
0.82
Q.1Q
1.10
0.71
0.52
0.40
1.10
0.20
0.90
1.13
UCL
5.D4
34.33
3.22
6.4S
2.20
2.80
3.23
1.30
10.10
3.30
5.40
1.82
Figure C-4. Meta-analysis of NHL and TCE exposure—highest exposure
groups. Rectangle sizes reflect relative weights of the individual studies. The
bottom diamond represents the RRm estimate.
To investigate the heterogeneity, subgroup analyses were done examining the cohort and
case-control studies separately. With the random-effects model (and tau-squared not pooled
across subgroups), the resulting RRm estimates were 1.60 (95% CI: 1.24, 2.08) for the cohort
studies and 1.29 (95% CI: 0.76, 2.20) for the case-control studies. There was no residual
heterogeneity in the cohort subgroup (I2 = 0%). Heterogeneity remained in the case-control
subgroup, but it was not statistically significant (p = 0.08)—the /2-value was 53%, suggesting
moderate heterogeneity. The difference between the RRm estimates for the cohort and case-
control subgroups was not statistically significant. As with the meta-analysis for overall TCE
exposure in Section C.2.1.2, no further attempt was made to quantitatively investigate possible
sources of heterogeneity; see Section C.2.3 for a qualitative discussion of some potential sources
of heterogeneity. It is, however, noted that the RR estimate from Axel son et al. (1994) appears
to be a relative outlier at the high end (see Figure C-4). Removal of this study does not eliminate
the heterogeneity, however, because the study carries little weight. Similarly, removal of the
study with the next largest RR estimate (Purdue et al., 2011), whose removal results in the lowest
RRm estimate in the analyses of study influence (see above) does not eliminate the
heterogeneity. On the other hand, removal of the study with the lowest RR estimate (Cocco et
C-24
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al., 2010), which also has a substantial amount of weight and whose removal results in the
highest RRm estimate in the analyses of study influence (see above), eliminates all of the
heterogeneity. This suggests that the result from Cocco et al. (2010) for the highest exposure
group might be an outlier, but it is unclear what about the study might account for this result
being inordinately low.
C.2.3. Discussion of NHL Meta-Analysis Results
The meta-analyses of the overall effect of TCE exposure on NHL suggest a small,
statistically significant increase in risk. The summary estimate from the primary random-effects
meta-analysis of the 17 studies was 1.23 (95% CI: 1.07, 1.42). This result was not overly
influenced by any single study, nor was it overly sensitive to individual RR estimate selections or
to restricting the analysis to only those studies for which RR estimates based on the traditional
definition of NHL were available, and in all of the influence and sensitivity analyses, the RRm
estimate was statistically significantly increased. Thus, the finding of an increased risk of NHL
associated with TCE exposure, though the increased risk is not large in magnitude, is robust.
There is some evidence of potential publication bias in this data set; however, it is
uncertain that this is actually publication bias rather than an association between SE and effect
size resulting for some other reason (e.g., a difference in study populations or protocols in the
smaller studies). Furthermore, if there is publication bias in this data set, it does not appear to
account completely for the finding of an increased NHL risk. For example, using the trim-and-
fill procedure of Duval and Tweedie (2000) to impute the values from the four 'missing' studies
that would balance the funnel plot yields an RRm estimate of 1.15 (95% CI: 0.97, 1.36).
Although there was some heterogeneity across the 17 studies, it was not statistically
significant (p = 0.16). The /-value was 26%, suggesting low-to-moderate heterogeneity.
Similarly, when subgroup analyses were done of cohort and case-control studies separately, there
was some observable heterogeneity in each of the subgroups, but it was not statistically
significant in either case, /-values were 12% for the cohort studies, suggesting low
heterogeneity, and 27% for the case-control studies, suggesting low-to-moderate heterogeneity.
In the subgroup analyses, the increased risk of NHL was strengthened in the cohort study
analysis and nearly eliminated in the case-control study analysis, although the subgroup RRm
estimates were not statistically significantly different. Study design itself is unlikely to be an
underlying cause of heterogeneity and, to the extent that it may explain some of the differences
across studies, is more probably a surrogate for some other difference(s) across studies that may
be associated with study design. Furthermore, other potential sources of heterogeneity may be
masked by the broad study design subgroupings. The true source(s) of heterogeneity across
these studies is an uncertainty. As discussed above, further quantitative investigations of
heterogeneity were ruled out because of database limitations. A qualitative discussion of some
potential sources of heterogeneity follows.
C-25
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Study differences in exposure assessment approach, exposure prevalence, average
exposure intensity, and NHL classification are possible sources of heterogeneity. Many studies
included TCE assignment from information on job and task exposures, e.g., a JEM (Radican et
al.. 2008: Boice et al.. 2006b: Miligi et al.. 2006: Zhao et al.. 2005: Boiceetal.. 1999: Morgan et
al.. 1998: Siemiatvcki, 1991): (Purdue et al.. 2011: Cocco et al.. 2010: Wang et al.. 2009). or
from an exposure biomarker in either breath or urine (Hansen et al., 2001: Anttila et al., 1995:
Axel son et al., 1994). Three case-control studies relied on self-reported exposure to TCE
(Persson and Fredrikson, 1999: Nordstrom et al., 1998: Hardell et al., 1994). Misclassification is
possible with all exposure assessment approaches. No information is available to judge the
degree of possible misclassification bias associated with a particular exposure assessment
approach; it is quite possible that in some cohort studies, in which past exposure is inferred from
various data sources, exposure misclassification may be as great as in population-based or
hospital-based case-control studies. Approaches based upon JEMs can provide order-of-
magnitude estimates that are useful for distinguishing groups of workers with large differences in
exposure; however, smaller differences usually cannot be reliably distinguished (NRC, 2006).
Biomonitoring can provide information on potential TCE exposure in an individual, but the
biomarkers used aren't necessarily specific for TCE and they reflect only recent exposures.
General population studies have special problems in evaluating exposure, because the
subjects could have worked in any job or setting that is present within the population (NRC,
2006: 'tMannetieetal..20Q2: McGuire et al.. 1998: Nelson etal.. 1994: Copeland et al.. 1977).
Low exposure prevalence in the case-control studies may be another source of heterogeneity.
Prevalence of TCE exposure among cases in the case-control studies was low, ranging from 3 in
Siemiatycki (1991) to 13% in Wang et al. (2009). However, prevalence of high TCE exposure in
these case-control studies was even rarer—3% of all cases in Miligi et al. (2006), 2% in Wang et
al. (2009) and Cocco et al. (2010) (high-confidence assessments; personal communication from
Pierluigi Cocco, University of Cagliari, Italy, to Cheryl Scott, U.S. EPA, 19 March 2011: see
Section 4.6.1.2), 1% (with probable exposure) in Purdue et al. (2011), and <1% in Siemiatycki
(1991). Low exposure prevalence may be one of the underlying characteristics differentiating
the case-control and cohort studies and explaining some of the heterogeneity across the studies.
Study differences in NHL groupings and in NHL classification schemes are another
potential source of heterogeneity in the meta-analysis, although restricting the meta-analysis to
only those studies for which RR estimates based on the traditional NHL definition were available
did not eliminate all heterogeneity. All studies included a broad but sometimes slightly different
group of lymphosarcoma, reticulum-cell sarcoma, and other lymphoid tissue neoplasms, with the
exception of the Nordstrom et al. (1998) case-control study, which examined hairy cell leukemia,
now considered a (non-Hodgkin) lymphoma, and the Zhao et al. (2005) cohort study, which
reported only results for all lymphohematopoietic cancers, including nonlymphoid types.
Persson and Fredrikson (1999) do not identify the classification system for defining NHL, and
C-26
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Hardell et al. (1994) define NHL using the Rappaport classification system. Miligi et al. (2006)
used the NCI Working Formulation and also considered CLLs as (non-Hodgkin) lymphomas.
Cocco et al. (2010) used the WHO/REAL classification system, which reclassifies lymphocytic
leukemias and NHLs as lymphomas of B-cell or T-cell origin and considers CLLs and multiple
myelomas as (non-Hodgkin) lymphomas; however, results were obtained generally consistent
with the traditional NHL definition from Dr. Cocco, although lymphomas not otherwise
specified were excluded. Wang et al. (2009) defined NHL using ICD-O-2 codes (M-9590-9595,
9670-9688, 9690-9698, 9700-9723), which is consistent with the traditional definition of NHL
(i.e., ICD-7, -8, -9 codes 200 + 202). Purdue et al. (2011) used ICD-O-3 codes 967-972, which
is generally consistent with the traditional definition of NHL, although this grouping doesn't
include the malignant lymphomas of unspecified type coded as M-9590-9599. The cohort
studies [except for Zhao et al. (2005)1 and the case-control study of Siemiatycki (1991) have
some consistency in coding NHL, with NHL defined as lymphosarcoma and reticulum-cell
sarcoma (ICD code 200) and other lymphoid tissue neoplasms (ICD 202) using the ICD
Revisions 7, 8, or 9. Revisions 7 and 8 are essentially the same with respect to NHL; under
Revision 9, the definition of NHL was broadened to include some neoplasms previously
classified as Hodgkin lymphomas (Banks, 1992).
Thirteen of the 17 studies categorized results by exposure level. Different exposure
metrics were used, and the purpose of combining results across the different highest exposure
groups was not to estimate an RRm associated with some level of exposure, but rather to see the
impacts of combining RR estimates that should be less affected by exposure misclassification.
In other words, the highest exposure category is more likely to represent a greater differential
TCE exposure compared to people in the referent group than the exposure differential for the
overall (typically any vs. none) exposure comparison. Thus, if TCE exposure increases the risk
of NHL, the effects should be more apparent in the highest exposure groups. Indeed, the RRm
estimate from the primary meta-analysis of the highest exposure group results was 1.43 (95% CI:
1.13, 1.82), which is greater than the RRm estimate of 1.23 (95% CI: 1.07, 1.42) from the overall
exposure analysis. The statistical significance of the increased RR estimate for the highest
exposure groups was not dependent on any single study, nor was it sensitive to individual RR
estimate selections. The robustness of this finding lends substantial support to a conclusion that
TCE exposure increases the risk of NHL.
Although there was some heterogeneity apparent across the 13 highest-exposure-group
studies, it was not statistically significant (p = 0.30). The /2-value was 14%, suggesting low
heterogeneity. When subgroup analyses were done examining the cohort and case-control
studies separately, there was no residual heterogeneity in the cohort subgroup (I2 = 0%).
Heterogeneity remained in the case-control subgroup, but it was not statistically significant
(p = 0.08)—the /2-value was 53%, suggesting moderate heterogeneity. In the subgroup analyses,
the increased risk of NHL was strengthened in the cohort study analysis and reduced in the case-
C-27
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control study analysis, although the subgroup RRm estimates were not statistically significantly
different. As with the meta-analysis for overall TCE exposure discussed above, no further
attempt was made to quantitatively investigate potential sources of heterogeneity. It is, however,
noted that removal of the Cocco et al. (2010) study, whose removal had the greatest impact in the
analyses of study influence (RRm = 1.57, 95% CI: 1.27, 1.95), eliminates all of the
heterogeneity, suggesting that the RR estimate for the highest exposure group from that study is
a relative outlier.
C.3. META-ANALYSIS FOR KIDNEY CANCER
C.3.1. Overall Effect of TCE Exposure
C.3.1.1. Selection of RR Estimates
The selected RR estimates for kidney cancer associated with TCE exposure from the
epidemiological studies are presented in Table C-6 for cohort studies and in Table C-7 for case-
control studies. The majority of the cohort studies reported results for all kidney cancers,
including cancers of the renal pelvis and ureter (i.e., ICD-7 180; ICD-8 and -9 189.0-189.2;
ICD-10 C64-C66), whereas the majority of the case-control studies focused on RCC, which
comprises roughly 85% of kidney cancers. Where both all kidney cancer and RCC were
reported, the primary analysis used the results for RCC, because RCC and the other forms of
kidney cancer are very different cancer types and it seemed preferable not to combine them; the
results for all kidney cancers were then used in a sensitivity analysis. The preference for the
RRC results alone is supported by the results in rodent cancer bioassays, where TCE-associated
rat kidney tumors are observed in the renal tubular cells (Section 4.4.5), and in metabolism
studies, where the focus of studies for the GSH conjugation pathway (considered the primary
metabolic pathway for kidney toxicity) is in renal cortical and tubular cells (Sections 3.3.3.3 and
4.4.6).
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Table C-6. Selected RR estimates for kidney cancer associated with TCE exposure (overall effect) from
cohort studies
Study
Anttila et al.
(1995)
Axelson et al.
(1994)
Boice et al.
(1999)
Greenland et al.
(1994)
Hansen et al.
(2001)
Morgan et al.
(1998)
Raaschou-
Nielsen et al.
(2001)
Radican et al.
(2008)
Zhao et al.
(2005)
RR
0.87
1.16
0.99
0.99
1.1
1.14
1.20
1.18
1.7
95%
LCL
0.32
0.42
0.4
0.30
0.3
0.51
0.94
0.47
0.38
95%
UCL
1.89
2.52
2.04
3.32
2.8
2.58
1.50
2.94
7.9
RRtype
SIR
SIR
SMR
Mortality
OR
SIR
Mortality
RR
SIR
Mortality
hazard
ratio
Mortality
RR
logRR
-0.139
0.148
-0.010
-0.010
0.095
0.134
0.182
0.166
0.542
SE (log RR)
0.408
0.408
0.378
0.613
0.500
0.415
0.115
0.468
0.775
Alternate RR
estimates (95% CI)
None
1.07 (0.39, 2.33)
with estimated
female contribution
to SIR added (see
text)
None
None
None
Published SMR
1.32 (0.57, 2.6)
1.20(0.98, 1.46) for
ICD-7 180
1.4(1.0, 1.8) for
subcohort with
expected higher
exposures
None
Incidence RR: 2.0
(0.47, 8.2)
Mortality RR no lag:
0.89 (0.22, 3.6)
Incidence RR no
lag: 2. 1(0.56, 8.1)
Boice et al. (2006b)
SMR: 2.22 (0.89,
4.57)
Comments
ICD-7 180.
ICD-7 180. Results reported for males only, but
there was a small female component to the cohort.
ICD-9 189.0-189.2. For potential routine exposure.
Results for any potential exposure not reported.
Nested case-control study. ICD-8 codes not
specified, presumably all of 189.
ICD-7 180. Male and female results reported
separately; combined assuming Poisson distribution.
ICD-9 189.0-189.2. Unpublished RR, adjusted
for age and sex (see text).
RCC.
ICD-8, -9 189.0, ICD-10 C64. Time variable =
age; covariates = sex and race. Referent group is
workers with no chemical exposures.
ICD-9 189. Males only. Adjusted for age, SES,
time since first employment, exposure to other
carcinogens. 20-yr lag. Mortality results reflect
same number exposed cases (10 with no lag) as
do incidence results, so no reason to prefer
mortality results, but they are used in primary
analysis to avoid appearance of "cherry -picking."
Overall RR estimated by combining across
exposure groups (see text). Boice et al. (2006b)
cohort overlaps Zhao et al. (2005) cohort; just
seven exposed deaths.
C-29
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Table C-7. Selected RR estimates for RCC associated with TCE exposure from case-control studies3
Study
Briining et al.
(2003)
Charbotel et al.
(2006)
Dosemeci et al.
(1999)
Moore et al.
(2010)
Pesch et al.
(2000b)
Siemiatycki
(1991)
RR
estimate
2.47
1.88
1.30
2.05
1.24b
0.8
95% LCL
1.36
0.89
0.9
1.13
1.03b
0.3
95% UCL
4.49
3.98
1.9
3.73
1.49b
2.2
logRR
0.904
0.631
0.262
0.718
0.215
-0.223
SE (log RR)
0.305
0.382
0.191
0.305
0.094
0.524
Alternate RR
estimates (95%
CI)
1.80(1.01,3.20)
for longest job
held in industry
with TCE
exposure
1.64(0.95,2.84)
for full study
1.68(0.97,2.91)
for full study with
10-yr lag
None
1.63(1.04,2.54)
for all subjects
1.13 (0.98,1.30)b
with German
JEM
None
Comments
Self-assessed exposure. Adjusted for age, sex, and
smoking.
Subgroup with good level of confidence about
exposure assessment. Matched on sex, age. Adjusted
for smoking, BMI.
Adjusted for age, sex, smoking, hypertension, and/or
use of diuretics and/or anti-hypertension drugs, BMI.
Subgroup with high-confidence assessments. Adjusted
for age, sex, and center.
With ITEM. Crude OR calculated from data provided
in personal communication (see text).
"Kidney cancer." SE and 95% CI calculated from
reported 90% CIs. Males only; adjusted for age,
income, and cigarette smoking index.
aThe RR estimates are all ORs for incident cases.
bAs calculated by U.S. EPA.
C-30
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As for NHL, many of the studies provided RR estimates only for males and females
combined, and we are not aware of any basis for a sex difference in the effects of TCE on kidney
cancer risk; thus, wherever possible, RR estimates for males and females combined were used.
Of the three larger (in terms of number of cases) studies that did provide results separately by
sex, Dosemeci et al. (1999) suggest that there may be a sex difference for TCE exposure and
RCC (OR = 1.04 [95% CI: 0.6, 1.7] in males and 1.96 [95% CI: 1.0, 4.0] in females), while
Raaschou-Nielsen et al. (2003) report the same SIR (1.2) for both sexes and crude ORs
calculated from data from the Pesch et al. (2000b) study (provided in a personal communication
from Beate Pesch, Forschungsinstitut fur Arbeitsmedizin [BGFA], to Cheryl Scott U.S. EPA,
21 February 2008) are 1.28 for males and 1.23 for females. Radican et al. (2008) and Hansen et
al. (2001) also present some results by sex, but both of these studies have too few cases to be
informative about a sex difference for kidney cancer.
Most of the selections in Tables C-6 and C-7 should be self-evident, but some are
discussed in more detail here, in the order the studies are presented in the tables. For Axelson et
al. (1994), in which a small subcohort of females was studied but only results for the larger male
subcohort were reported, the reported male-only results were used in the primary analysis;
however, as for NHL, an attempt was made to estimate the female contribution to an overall RR
estimate for both sexes and its impact on the meta-analysis. Axelson et al. (1994) reported
neither the observed nor the expected number of kidney cancer cases for females. It was
assumed that none was observed. To estimate the expected number, the expected number for
males was multiplied by the ratio of female-to-male person-years in the study and by the ratio of
female-to-male age-adjusted incidence rates for kidney cancer.6 The male results and the
estimated female contribution were then combined into an RR estimate for both sexes assuming
a Poisson distribution, and this alternate RR estimate for the Axelson et al. (1994) study was
used in a sensitivity analysis.
For Boice et al. (1999), only results for "potential routine exposure" were reported for
kidney cancer. Boice et al. (1999) report in general that the SMRs for workers with any potential
exposure "were similar to those for workers with daily potential exposure."
In their published paper, Morgan et al. (1998) present only SMRs for overall TCE
exposure, although the results from internal analyses are presented for exposure subgroups. RR
estimates for overall TCE exposure from the internal analyses of the Morgan et al. (1998) cohort
data were available from an unpublished report (EHS, 1997); from these, the RR estimate from
6Person-years for men and women <79 years were obtained from Axelson et al. (1994): 23516.5 and 3691.5,
respectively. Lifetime age-adjusted incidence rates for cancer of the kidney and renal pelvis for men and women
were obtained from the National Cancer Institute's 2000-2004 SEER-17 (Surveillance Epidemiology and End
Results from 17 geographical locations) database (http://seer.cancer.gov/statfacts/html/kidrp.html'): 17.8/100,000
and 8.8/100,000, respectively. The calculation for estimating the expected number of cases in females in the cohort
assumes that the males and females have similar TCE exposures and that the relative distributions of age-related
incidence risk for the males and females in the Swedish cohort are adequately represented by the ratios of person-
years and U.S. lifetime incidence rates used in the calculation.
C-31
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the Cox model that included age and sex was selected, because those are the variables deemed to
be important in the published paper. The internal analysis RR estimate was preferred for the
primary analysis, and the published SMR result was used in a sensitivity analysis.
Raaschou-Nielsen et al. (2003) reported results for RCC and renal pelvis/ureter
separately. As discussed above, RCC estimates were used in the primary analysis, and the
results for both kidney cancer categories were combined (across sexes as well), assuming a
Poisson distribution, and used in a sensitivity analysis. In another sensitivity analysis, results for
RCC from the subcohort with expected higher exposure levels (> 1-year duration of employment
and year of 1st employment before 1980) were used. Raaschou-Nielsen et al. (2003), in their
Table 3, also present the overall results for RCC and for renal pelvis/ureter cancer with a lag time
of 20 years; however, they use a definition of lag that is different from a lagged exposure in
which exposures prior to disease onset are discounted and it is not clear what their lag time
actually represents7; thus, as for NHL, these results were not used in any of the meta-analyses for
kidney cancer.
For Radican et al. (2008), the Cox model hazard ratio from the 2000 follow-up was used.
In the Radican et al. (2008) Cox regressions, age was the time variable, and sex and race were
covariates. It should also be noted that the referent group is composed of workers with no
chemical exposures, not just no exposure to TCE.
For Zhao et al. (2005), no results for an overall TCE effect are reported. We were unable
to obtain any overall estimates from the study authors, so, as a best estimate, as was done for
NHL, the results across the "medium" and "high" exposure groups were combined, under
assumptions of group independence, even though the exposure groups are not independent (the
"low" exposure group was the referent group in both cases). Unlike for NHL, adjustment for
exposure to other carcinogens made a considerable difference, so Zhao et al. (2005) also present
kidney results with this additional adjustment, with and without a 20-year lag. Estimates of RR
with this additional adjustment were selected over those without. In addition, a 20-year lag
seemed reasonable for kidney cancer, so the lagged estimates were preferred to the unlagged;
unlagged estimates were used in sensitivity analyses. Zhao et al. (2005) present RR estimates for
both incidence and mortality. Unlike for NHL, the number of exposed incident cases (10 with no
lag) was identical to the number of deaths, so there was no reason to prefer the mortality results
over the incidence results. (In fact, there were more exposed incident cases [10 vs. 7] after
lagging.) However, the mortality results, which yield a lower RR estimate, were selected for the
primary analysis to avoid any appearance of "cherry-picking," and incidence RR estimates were
used in sensitivity analyses. A sensitivity analysis was also done using results from Boice et al.
(2006b) in place of the Zhao et al. (2005) RR estimate. The cohorts for these studies overlap, so
they are not independent studies and should not be included in the meta-analysis concurrently.
7 In their Methods section, Raaschou-Nielsen et al. (2003) define their lag period as the period "from the date of first
employment to the start of follow-up for cancer".
C-32
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Boice et al. (2006b) report results for an overall TCE effect for kidney cancer; however, the
results are SMR estimates rather than internal comparisons and are based on fewer exposed
deaths (7), so either Zhao et al. (2005) estimate is preferred over the Boice et al. (2006b)
estimate.
Regarding the case-control studies, for B riming et al. (2003), the results based on self-
assessed exposure were preferred because, although TCE exposure was probably under-
ascertained with this measure, there were greater concerns about the result based on the alternate
measure reported—longest-held job in an industry with TCE exposure. Even though this study
was conducted in the Arnsberg region of Germany, an area with high prevalence of exposure to
TCE, the exposure prevalence in both cases (87%) and controls (79%) seemed inordinately high,
and this for not just any job in an industry with TCE exposure, but for the longest-held job.
Furthermore, Table V of Briining et al. (2003), which presents this result, states that the result is
for longest-held job in industries with TCE or tetrachloroethylene exposure. Additionally, some
of the industries with exposure to TCE presented in Table V have many jobs that would not
entail TCE exposure (e.g., white-collar workers), so the assessment based on industry alone
likely has substantial misclassification. Both of these—inclusion of tetrachloroethylene and
exposure assessment by industry—could result in overstating TCE exposure prevalence. Results
based on the longest-held-job measure were used in a sensitivity analysis.
For Charbotel et al. (2006), results from the analysis that considered "only job periods
with a good level of confidence for TCE exposure assessment" [Table 7 of Charbotel et al.
(2006)] were preferred, as these estimates would presumably be less influenced by exposure
misclassification. Estimates from the full study analysis were used in a sensitivity analysis.
Results for exposure with a 10-year lag are also provided in an unpublished report (Charbotel et
al., 2005): however, lagged results are presented only for the full study and, thus, were similarly
used in a sensitivity analysis.
Likewise, for Moore et al. (2010), results from the analysis that considered high-
confidence assessments only were preferred. Here, the definition of TCE exposure was
restricted to jobs classified as having probable or certain exposure (i.e., at least 40% of workers
with that job were expected to be exposed), so these estimates should be less influenced by
exposure misclassification. The RR estimate from the analysis of all subjects was used in a
sensitivity analysis.
For Pesch et al. (2000b), TCE results were presented for two different exposure
assessments. Estimates using the ITEM approach were preferred because they seemed to
represent a more comprehensive exposure assessment (see Appendix B, Section B.2.4); estimates
based on the JEM approach were used in a sensitivity analysis. Furthermore, results were
presented only by exposure category, with no overall RR estimate reported. Case and control
numbers for the different exposure categories were kindly provided by Dr. Pesch (personal
communication from Beate Pesch, BGFA, to Cheryl Scott, U.S. EPA, 21 February 2008), and we
C-33
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calculated crude overall ORs for males and females combined for each exposure assessment
approach.
C.3.1.2. Results of Meta-Analyses
Results from some of the meta-analyses that were conducted on the epidemiological
studies of TCE and kidney cancer are summarized in Table C-8. The summary estimate from the
primary random-effects meta-analysis of the 15 studies was 1.27 (95% CI: 1.13, 1.43) (see
Figure C-5). As shown in Figure C-5, the analysis was dominated by two (contributing over
65% of the weight) or three (about 75% of the weight) large studies. No single study was overly
influential; removal of individual studies resulted in RRm estimates that were all statistically
significant (all with/? < 0.005) and that ranged from 1.24 (with the removal of (Briining etal.,
2003)1 to 1.30 (with the removal of Raaschou-Nielsen et al. (2003)).
Similarly, the RRm estimate was not highly sensitive to alternate RR estimate selections.
Use of the 13 alternate selections, individually, resulted in RRm estimates that were all
statistically significant (all with/? < 0.0005) and that ranged from 1.21 to 1.32 (see Table C-8).
In fact, as can be seen in Table C-8, all but two of the alternates had negligible impact. The
Zhao et al. (2005). Axelson et al. (1994). Morgan et al. (1998). Briining et al. (2003). Charbotel
et al. (2006), and Moore et al. (2010) original values and alternate selections were associated
with very little weight and, thus, had little influence in the RRm. The Raaschou-Nielsen et al.
(2003) all-kidney-cancer value carried more weight, but the alternate RR estimate was identical
to the original, although with a narrower CI, and thus did not alter the RRm. Only the Raaschou-
Nielsen et al. (2003) high-exposure-subcohort alternate and the Pesch et al. (2000b) alternate
(with the JEM exposure assessment approach instead of the ITEM approach) had much impact,
resulting in RRm estimates of 1.32 (95% CI: 1.17, 1.49) and 1.21 (95% CI: 1.09, 1.34),
respectively. As noted above, the ITEM approach is preferred; thus, the lower RRm estimate
obtained with the JEM alternate is considered clearly inferior. The JEM approach takes jobs into
account but not tasks; thus, it is expected to have greater potential for exposure misclassification.
Indeed, a comparison of exposure prevalences for the two approaches suggests that the JEM
approach is less discriminating about exposure; 42% of cases were defined as TCE-exposed
under the JEM approach, but only 18% of cases were exposed under the JTEM approach. On the
other hand, the higher RRm estimate obtained with the Raaschou-Nielsen et al. (2003) high-
exposure-subcohort alternate is consistent with an expectation that the subgroup has higher
exposures and less exposure misclassification.
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Table C-8. Summary of some meta-analysis results for TCE (overall) and kidney cancer
Analysis
All studies
Cohort
Case-control
Alternate RR
selections3
Highest exposure
groups
Number of
studies
15
9
6
15
15
15
15
15
15
15
15
15
15
10
13
13
Model
Random
Fixed
Random
Fixed
Random
Fixed
Random
Random
Random
Random
Random
Random
Random
Random
Random
Random
Random
Random
Random
RRm estimate
1.27
1.27
1.16
1.16
1.48
1.36
1.27-1.28
1.29
1.27
1.28
1.27
1.32
1.26
1.28
1.27
1.21
1.64
1.58
1.47-1.60
95% LCL
1.13
1.13
0.96
0.96
1.15
1.17
1.13-1.14
1.15
1.13
1.14
1.13
1.17
1.12
1.14
1.13
1.09
1.31
1.28
1.20-1.29
95% UCL
1.43
1.43
1.40
1.40
1.91
1.39
1.42-1.43
1.45
1.43
1.43
1.42
1.49
1.41
1.43
1.43
1.34
2.04
1.96
1.79-1.98
Heterogeneity
None observable
(fixed = random)
None observable
Not significant
(p = 0.14)
None observable
None observable
None observable
None observable
None observable
None observable
None observable
None observable
None observable
None observable
None observable
None observable
See Table C-10
Comments
Statistical significance not dependent on
single study. No apparent publication bias.
Not significant difference between CC and
cohort studies (p = 0. 12).
Not significant difference between CC and
cohort studies (p = 0.19).
With 3 different alternates from Zhao et al.
(2005) (see Table C-6).
With Boice et al. (2006b) study rather than
Zhao et al. (2005).
With estimated female contribution to
Axelsonetal. (1994).
With Morgan et al. (1998) published SMR.
With Raaschou-Nielsen et al. (2003) all
kidney cancer.
With Raaschou-Nielsen et al. (2003) high-
exposure subcohort.
With Bruning et al. (2003) longest job held in
industry with TCE.
With Charbotel et al. (2006) full study, with
and without 10-yr lag.
With Moore et al. (2010) full study.
With Pesch et al. (2000b) JEM.
Using RR = 1 for Anttila et al. (1995).
Axelson et al. (1994). and Hansen et al.
(2001) (see text).
Using RR = 1 for Anttila et al. (1995).
Axelson et al. (1994). and Hansen et al.
(2001) and various alternate RR selection
results (see Table C-10)a.
"Changing the primary analysis by one alternate RR each time.
C-35
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Study
Anttila (1995) •
.e son ( . ;
Boies ( 199y )
Greenland (1994)
u ^onn i "\
neinsen (^.uui}
Morgan (1998) ,
R a aschou- Nielsen (2003)
Radican (2008J
Zhao (2005 j .
Bruning (2003)
Charbotel (2006)
Dosemeci (1999)
Mo ore (20 10)
Peseh (2000)
r" • ^ i • inn*^
OVERALL •
f
0.1
TCE Exposure and Kidney Cancer
Relative Risk and 95% CI RR
1-— 0.87
I 1 1 P
•^—T-| i . i o
\ u.yk
\- 0.99
* i- 1 i n
( |. i . i u
: l- 1.14
j-iZ3-- 1.20
1 1.18
. I 1.70
: 0 2.47
1 1 .88
i D 1.30
-• B 2.05
'-1 1- 1.24
In rtn
U .oU
! rt-'-l 1.27
1 10
LCL
0.32
0_1^
."TA.
U.-4U
0.30
_ ^«
U.dU
0.51
0.94
0.47
0.38
1.36
0.89
0.90
1.13
1.00
n •'Sn
1.13
UCL
1.89
*- - - 4
3,32
^ Qn
i.ou
2.58
1.50
2.94
7.00
4.49
3.98
1.90
3.73
1.50
? ^n
1.43
Figure C-5. Meta-analysis of kidney cancer and overall TCE exposure.
Random-effects model; fixed-effect model same. Rectangle sizes reflect
relative weights of the individual studies. The summary estimate is in the
bottom row, represented by the diamond.
There was no apparent heterogeneity across the 15 studies (i.e., the random-effects model
and the fixed-effect model gave the same results {phetem = 0.67; / = 0%]). Nonetheless, subgroup
analyses were done examining the cohort and case-control studies separately. With the random-
effects model (and tau-squared not pooled across subgroups), the resulting RRm estimates were
1.16 (95% CI: 0.96, 1.40) for the cohort studies and 1.48 (95% CI: 1.15, 1.91) for the case-
control studies. There was no heterogeneity in the cohort subgroup (p = 0.998; / = 0%). There
was heterogeneity in the case-control subgroup, but it was not statistically significant (p = 0.14)
and the / -value of 41% suggests that the extent of the heterogeneity in this subgroup was low-to-
moderate. Nor was the difference between the RRm estimates for the cohort and case-control
subgroups statistically significant under either the random-effects model or the fixed-effect
model. Further quantitative investigations of heterogeneity were not pursued because of
database limitations and, in any event, there is no evidence for heterogeneity of study results in
this database. A qualitative discussion of some potential sources of heterogeneity across studies
is nonetheless included in Section C.3.3.
As discussed in Section C.I, publication bias was examined in several different ways.
The funnel plot in Figure C-6 shows little relationship between RR estimate and study size, and,
indeed, none of the other tests performed found any evidence of publication bias. The trim-and-
C-36
-------
fill procedure of Duval and Tweedie (2000), for example, determined that no studies were
missing from the funnel plot (i.e., there was no asymmetry to counterbalance). Similarly, the
results of a cumulative meta-analysis, incorporating studies with increasing SE one at a time,
shows no evidence of a trend of increasing effect size with addition of the less precise studies.
Including the three most precise studies, reflecting 75% of the weight, the RRm goes from
1.24 to 1.22 to 1.23. The addition of the Moore et al. (2010) study brings the RRm to 1.26 and
the weight to 79% and further addition of the Briining et al. (2003) study increases the RRm to
1.38 and the weight to 83%. After the addition of the next six studies, the RRm stabilizes at
about 1.28, and further addition of the four least precise studies has little impact.
Funnel Plot of Standard Error by Log risk ratio
0.0
0.2
Standard $rror
0.4
0.6
0.8
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Log risk ratio
Figure C-6. Funnel plot of SE by log RR estimate for TCE and kidney
cancer studies.
C-37
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C.3.2. Kidney Cancer Effect in the Highest Exposure Groups
C.3.2.1. Selection of RR Estimates
The selected RR estimates for kidney cancer in the highest TCE exposure categories, for
studies that provided such estimates, are presented in Table C-9. Five of the nine cohort studies
and five of the six case-control studies reported kidney cancer risk estimates categorized by
exposure level. As in Section C.3.1.1 for the overall risk estimates, estimates for RCC were
preferentially selected when presented, and, wherever possible, RR estimates for males and
females combined were used.
Three of the nine cohort studies (Hansen et al., 2001; Anttila et al., 1995; Axel son et al.,
1994) did not report kidney cancer risk estimates categorized by exposure level even though
these same studies reported such estimates for selected other cancer sites. To address this
reporting bias, attempts were made to obtain the results from the primary investigators, and,
failing that, an alternate analysis was performed in which null estimates (RR = 1.0) were
included for all three studies. This alternate analysis was then used as the main analysis, e.g., the
basis of comparison for the sensitivity analyses. For the SE (of the log RR) estimates for these
null estimates, SE estimates from other sites for which highest-exposure-group results were
available were used. For Anttila et al.(1995), the SE estimate for liver cancer in the highest
exposure group was used, because liver cancer and kidney cancer had similar numbers of cases
in the overall study (5 and 6, respectively). For Axelson et al. (1994), the SE estimate for NHL
in the highest exposure group was used, because NHL and kidney cancer had similar numbers of
cases in the overall study (5 and 6, respectively). For Hansen et al. (2001), the SE estimate for
NHL in the highest exposure group was used, because NHL was the only cancer site of interest
in this assessment for which highest-exposure-group results were available.
For Boice et al. (1999), only results for workers with "any potential exposure" (rather
than "potential routine exposure") were presented by exposure category, and the referent group is
workers not exposed to any solvent.
For Morgan et al. (1998), the primary analysis used results for the cumulative exposure
metric, and a sensitivity analysis was done with the results for the peak exposure metric.
C-38
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Table C-9. Selected RR estimates for kidney cancer risk in highest TCE exposure groups
Study
Anttila et al.
(1995)
Axelson et al.
(1994)
Boice et al.
(1999)
Hansen et al.
(2001)
Morgan et al.
(1998)
Raaschou-
Nielsen et al.
(2003)
Radican et al.
(2008)
RR
0.69
1.59
1.7
1.11
95%
LCL
0.22
0.68
1.1
0.35
95%
UCL
2.12
3.71
2.4
3.49
Exposure
category
100+ umol/L
U-TCA a
>2-yr exposure
and 100+ mg/L
U-TCA
>5 yrs exp
> 1,080 months x
mg/m3
High cumulative
exposure score
>5 yrs in
subcohort with
expected higher
exposure levels
>25 unit-yrs
logRR
-0.371
0.464
0.531
0.104
SE
(logRR)
0.578
0.433
0.183
0.582
Alternate RR
estimates (95% CI)
1.0 assumed
1.0 assumed
None
1.0 assumed
1.89(0.85, 4.23) for
med/highpeakvs.
low/no
1.6 (1.1, 2.2) for
>5 yrs in total cohort
1.4 (0.99, 1.9)
ICD-7 180
>5 yrs in total cohort
Blair et al. (1998)
incidence RR
0.9 (0.3, 3.2)
Comments
Reported high exposure group results for some
cancer sites but not kidney.
Reported high exposure group results for some
cancer sites but not kidney.
Mortality RR. ICD-9 189.0-189.2. For
potential routine or intermittent exposure.
Adjusted for date of birth, dates 1st and last
employed, race, and sex. Referent group is
workers not exposed to any solvent.
Reported high exposure group results for some
cancer sites but not kidney.
Mortality RR. ICD-9 189.0-189.2. Adjusted
for age and sex.
SIR. RCC.
Mortality hazard ratio. ICD-8, -9 189.0, ICD-
10 C64. Male and female results presented
separately and combined (see text). Referent
group is workers with no chemical exposures.
C-39
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Table C-9. Selected RR estimates for kidney cancer risk in highest TCE exposure groups (continued)
Study
Zhao et al.
(2005)
Briining et al.
(2003)
Charbotel et al.
(2006)
RR
7.40
2.69
3.34
95%
LCL
0.47
0.84
1.27
95%
UCL
116
8.66
8.74
Exposure
category
High exposure
score
>20 yrs serf-
assessed exposure
High cumulative
dose
logRR
2.00
0.990
1.21
SE
(logRR)
1.41
0.595
0.492
Alternate RR
estimates (95% CI)
Mortality RR: 1.82
(0.09, 38.6)
Incidence RR no lag:
7.71(0.65,91.4)
Mortality RR no lag:
0.96 (0.09, 9.91)
Boiceetal. (2006b)
mortality RR: 2. 12
(0.63, 7. 11) for
>5 yrs as test stand
mechanic; 3.13
(0.74, 13. 2) for
>4 test-yr engine
flush
None
3.80(1.27, 11.40) for
high cum + peaks.
Full study, high cum:
2.16(1.02,4.60)
+ peaks: 2.73 (1.06,
7.07)
Full study with 10-yr
lag, high cum: 2.16
(1.01,4.65)
+ peaks: 3. 15 (1.19,
8.38)
Full study, additional
adjustment, high
cum: 1.96 (0.71,
5.37)
+ peaks:
2.63 (0.79, 8.83)
Comments
Incidence RR. ICD-9 189. Males only.
Adjusted for age, SES, time since first
employment, exposure to other carcinogens.
20-yr lag. Incidence results reflect more
exposed cases (4 with no lag) than do mortality
results (3), so they are used in primary
analysis.
Incidence OR. RCC. Adjusted for age, sex,
and smoking.
Incidence OR. RCC. In subgroup with good
level of confidence for TCE exposure.
Adjusted for smoking and BMI. Matched on
sex and age. Alternate full study estimates
(without lag) with additional adjustment were
also adjusted for exposure to cutting fluids and
other petroleum oils.
C-40
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Table C-9. Selected RR estimates for kidney cancer risk in highest TCE exposure groups (continued)
Study
Moore et al.
Q01Q)
Pesch et al.
(2000b)
Siemiatycki
(1991)
RR
2.23
1.4
0.8
95%
LCL
1.07
0.9
0.2
95%
UCL
4.64
2.1
3.4
Exposure
category
>1.58ppm x yrs
Substantial
Substantial
logRR
0.802
0.336
-0.233
SE (log RR)
0.374
0.219
0.736
Alternate RR
estimates (95%
CI)
2.02(1.14,3.59)
for all subjects
1.2(0.9, 1.7) for
JEM
None
Comments
Incidence OR. Subgroup with high-confidence
assessments. Adjusted for age, sex, and
center.
Incidence OR. RCC. ITEM approach.
Adjusted for age, study center, and smoking.
Sexes combined.
Incidence OR. Kidney cancer. SE and 95%
CI calculated from reported 90% CIs. Males
only; adjusted for age, income, and cigarette
smoking index.
aMean personal TCA in urine. 1 umol/L = 0.1634 mg/L.
C-41
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For Raaschou-Nielsen et al. (2003), results for RCC in the highest duration subgroup
from the subcohort with expected higher exposure levels (> 1-year duration of employment and
year of 1st employment before 1980) were preferred for the highest-exposure-group analyses.
Results for RCC in the highest duration subgroup from the whole cohort were combined across
sexes, assuming a Poisson distribution, and used in a sensitivity analysis. Also, for the whole
cohort, results for RCC and renal pelvis/ureter cancers in the highest duration group were
combined (across sexes as well), assuming a Poisson distribution, and used in an additional
sensitivity analysis.
For Radican et al. (2008), it should be noted that the referent group is workers with no
chemical exposures, not just no TCE exposure. In addition, results for exposure groups (based
on cumulative exposure scores) were reported separately for males and females and were
combined for this assessment using inverse-variance weighting, as in a fixed-effect meta-
analysis. Radican et al. (2008) present only mortality hazard ratio estimates by exposure group;
however, in an earlier follow-up of this same cohort, Blair et al. (1998) present both incidence
and mortality RR estimates by exposure group. The mortality RR estimate based on the more
recent follow-up by Radican et al. (2008) (six deaths in the highest exposure group) was used in
the primary analysis, while the incidence RR estimate based on similarly combined results from
Blair et al. (1998) (four cases) was used as an alternate estimate in a sensitivity analysis.
Radican et al. (2008) also present results for categories based on frequency and pattern of
exposure; however, subjects weren't distributed uniquely across the categories (the numbers of
cases across categories exceeded the total number of cases); thus, it was difficult to interpret
these results and they were not used in a sensitivity analysis.
Zhao et al. (2005) present kidney cancer RR estimates adjusted for exposure to other
carcinogens, because, unlike for NHL, this adjustment made a considerable difference.
Estimates of RR with this additional adjustment were selected over those without. Furthermore,
the kidney results were presented with and without a 20-year lag. A 20-year lag seemed
reasonable for kidney cancer, so the lagged estimates were preferred to the unlagged; unlagged
estimates were used in sensitivity analyses. In addition, the incidence results reflect more cases
(4 with no lag) in the highest exposure group than do the mortality results (3), so the incidence
result (with the 20-year lag) was used for the primary analysis, and the unlagged incidence result
and the mortality results were used in a sensitivity analysis. Sensitivity analyses were also done
using results from Boice et al. (2006b) in place of the Zhao et al. (2005) RR estimate. The
cohorts for these studies overlap, so they are not independent studies. Boice et al. (2006b) report
mortality RR estimates for kidney cancer by years worked as a test stand mechanic, a job with
potential TCE exposure, and by a measure that weighted years with potential exposure from
engine flushing by the number of flushes each year. No results were presented for a third metric,
years worked with potential exposure to any TCE, because the Cox proportional hazards model
C-42
-------
did not converge. The Boice et al. (2006b) estimates are adjusted for years of birth and hire and
for hydrazine exposure.
For Charbotel et al. (2006), results from the analysis that considered "only job periods
with a good level of confidence for TCE exposure assessment" [Table 7 of Charbotel et al.
(2006)] were preferred, as these estimates would presumably be less influenced by exposure
misclassification. Additionally, the high cumulative dose results were preferred, but the results
for high cumulative dose + peaks were included in a sensitivity analysis. Exposure group results
with a 10-year lag are provided in an unpublished report (Charbotel et al., 2005): however,
lagged results are presented only for the full study and, thus, were used in sensitivity analyses.
Estimates from the full study analysis (without the lag) that were further adjusted for exposure to
cutting fluids and other petroleum oils were also used in sensitivity analyses.
Similarly, for Moore et al. (2010), results from the analysis that considered high-
confidence assessments only were preferred. Here the definition of TCE exposure was restricted
to jobs classified as having probable or certain exposure (i.e., at least 40% of workers with that
job were expected to be exposed), so these estimates should be less influenced by exposure
misclassification. Estimates from the analysis of all subjects were used in a sensitivity analysis.
The highest exposure group was reported as >1.58 ppm x years; however, this value is not based
on continuous exposure estimates but rather calculated from midpoints of estimated ranges
corresponding to categorical groups, i.e., cumulative exposure = categorical intensity weight
(ppm) x categorical frequency weight x duration (years).
For Pesch et al. (2000b), TCE results were presented for two different exposure
assessments. As discussed above, estimates using the ITEM approach were preferred because
they seemed to represent a more comprehensive exposure assessment; estimates based on the
JEM approach were used in a sensitivity analysis.
C.3.2.2. Results of Meta-Analyses
Results from the meta-analyses that were conducted for kidney cancer in the highest
exposure groups are summarized at the bottom of Table C-8 and reported in more detail in
Table C-10. The RRm estimate from the random-effects meta-analysis of the 10 studies with
results presented for exposure groups was 1.64 (95% CI: 1.31, 2.04). The RRm estimate from
the primary random-effects meta-analysis with null RR estimates (i.e., 1.0) included for Anttila
et al. (1995), Axelson et al. (1994), and Hansen et al. (2001) to address reporting bias (see above)
was 1.58 (95% CI: 1.28, 1.96) (see Figure C-7). The inclusion of these three additional studies
contributed just over 7% of the total weight. As with the overall kidney cancer meta-analyses,
the meta-analyses of the highest exposure groups were dominated by two studies (Raaschou-
Nielsen et al., 2003; Pesch et al., 2000b), which provided about 60% of the weight. No single
study was overly influential; removal of individual studies resulted in RRm estimates that were
C-43
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all statistically significant (all with/? < 0.005) and that ranged from 1.52 [with the removal of
Raaschou-Nielsen et al. (2003)] to 1.64 [with the removal of Pesch et al. (2000b)].
Similarly, the RRm estimate was not highly sensitive to alternate RR estimate selections.
Use of the 18 alternate selections, individually, resulted in RRm estimates that were all
statistically significant (all with/? < 0.0005) and that ranged from 1.47 to 1.60, with all but two
of the alternate selections yielding RRm estimates in the narrow range of 1.54-1.60 (see
Table C-10). The lowest RRm estimates, 1.47 in both cases, were obtained when the alternate
selections involved the two large studies. One of the alternate selections was for Raaschou-
Nielsen et al. (2003), with a highest-exposure-group estimate for all kidney cancer in the total
cohort, rather than RCC in the subcohort expected to have higher exposure levels. The latter
value is strongly preferred because, as discussed above, the subcohort is likely to have less
exposure misclassification. Furthermore, RCC is very different from other types of kidney
cancer, and TCE, if an etiological factor, may not be etiologically associated with all kidney
cancers, so using the broad category may dilute a true association with RCC, if one exists. The
other alternate selection with a considerable impact on the RRm estimate was for Pesch et al.
(2000b), with the highest exposure group result based on the JEM exposure assessment
approach, rather than the ITEM approach. As discussed above, the ITEM approach is preferred
because it seemed to be a more comprehensive and discriminating approach, taking actual job
tasks into account, rather than just larger job categories. Thus, although results with these
alternate selections are presented for comprehensiveness and transparency, the primary analysis
is believed to reflect better the potential association between kidney cancer (in particular, RCC)
and TCE exposure.
Other than a negligible amount of heterogeneity observed in the sensitivity analysis with
the Pesch et al. (2000b) JEM alternate discussed above (/ = 0.64%), there was no observable
heterogeneity across the studies for any of the meta-analyses conducted with the highest
exposure groups, including those in which RR values for Anttila et al.(1995), Axelson et al.
(1994), and Hansen et al. (2001) were assumed. No subgroup analyses (e.g., cohort vs. case-
control studies) were done with the highest exposure group results.
C-44
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Table C-10. Summary of some meta-analysis results for TCE (highest exposure groups) and kidney cancer
Analysis
Analysis based on
reported results
Primary analysis
Alternate RR
selections3
Model
Random
Random
Random
Random
Random
Random
Random
Random
Random
Random
RRm estimate
1.64
1.58
1.57
1.60
1.47, 1.55
1.56-1.58
1.58-1.59
1.59
1.54-1.58
1.47
95% LCL
1.31
1.28
1.27
1.29
1.20, 1.25
1.26-1.28
1.28-1.29
1.29
1.24-1.27
1.20
95% UCL
2.04
1.96
1.95
1.98
1.80, 1.91
1.93-1.96
1.95-1.96
1.95
1.90-1.95
1.79
Heterogeneity
None observable
(fixed = random)
None observable
None observable
None observable
None observable
None observable
None observable
None observable
None observable
Not significant (p
= 0.44)
Comments
Includes assumed values for Anttila et al. (1995).
Axelson et al. (1994). and Hansen et al. (2001) (see
text).
Statistical significance not dependent on single study.
With Blair et al. (1998) incidence RR instead of
Radican et al. (2008) mortality hazard ratio.
With Morgan et al. (1998) peak metric.
With Raaschou-Nielsen et al. (2003) >5 yrs in total
cohort for all kidney cancer and for RCC, respectively.
With Zhao et al. (2005) incidence unlagged and
mortality with and without lag.
With Boice et al. (2006b) alternates for Zhao et al.
(2005).
With Moore et al. (2010) full study.
With Charbotel et al. (2006) high cumulative dose +
peaks in subgroup; and high cumulative dose and high
cumulative dose + peaks in full study with and without
10-yr lag and with and without additional adjustment
for exposure to cutting fluids and other petroleum oils.
With Pesch et al. (2000b) JEM.
""Changing the primary analysis by one alternate RR each time.
C-45
-------
TCE Exposure and Kidney Cancer - highest exposure groups
Study Relative Risk and 95% Cl RR
i
Raaschou-Nielsen (2003)
Charbotel (2006)
Moore (2010)
Pesch (2000)
OVERALL
— d — 1.40
h^H 1.58
I I
0.1 1 10
LCL
0.22
0.68
1.10
0.35
0.47
0.84
1.27
1.D7
0.90
0.20
0.25
0.14
0.32
1.28
UCL
2.12
3.71
2.40
3.49
116.0
8.66
8.74
4.64
2.10
3.40
4.00
7.10
3.10
1.96
Figure C-7. Meta-analysis of kidney cancer and TCE exposure—highest
exposure groups, with assumed null RR estimates for Anttila et al. (1995),
Axelson et al. (1994), and Hansen et al. (2001) (see text). Random-effects
model; fixed-effect model same. Rectangle sizes reflect relative weights of the
individual studies. The summary estimate is in the bottom row, represented by
the diamond.
C.3.3. Discussion of Kidney Cancer Meta-Analysis Results
For the most part, the meta-analyses of the overall effect of TCE exposure on kidney
cancer suggest a small, statistically significant increase in risk. The summary estimate from the
primary random-effects meta-analysis of the 15 studies was 1.27 (95% CI: 1.13, 1.43). Although
the analysis was dominated by 2-3 large studies that contribute 65-75% of the weight, the
summary estimate was not overly influenced by any single study, nor was it overly sensitive to
individual RR estimate selections. The largest downward impacts were from the removal of the
Briining et al. (2003) study, resulting in an RRm estimate of 1.24 (95% CI: 1.10, 1.40), and from
the substitution of the Pesch et al. (2000b) ITEM RR estimate with the RR estimate based on the
JEM approach, resulting in an RRm estimate of 1.21 (95% CI: 1.09, 1.34). Thus, the finding of
an increased risk of kidney cancer associated with TCE exposure is robust. Furthermore, there is
no evidence of publication bias in this data set.
In addition, there was no heterogeneity observed across the results of the 15 studies.
When subgroup analyses were done of cohort and case-control studies separately, there was
C-46
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some observable heterogeneity among the case-control studies, but it was not statistically
significant (p = 0.14) and the /-value of 41% suggested the extent of the heterogeneity was low-
to-moderate. The increased risk of kidney cancer was strengthened in the case-control study
analysis and weakened in the cohort study analysis, but the difference between the two RRm
estimates was not statistically significant. One difference between the case-control and cohort
studies is that the case-control studies were of RCC and almost all of the cohort studies were of
all kidney cancers, including renal pelvis. As discussed above, RCC is very different from other
types of kidney cancer, and TCE, if an etiological factor, may not be etiologically associated
with all kidney cancers, so using the broad category may dilute a true association with RCC, if
one exists.
With respect to the nonsignificant heterogeneity in the six case-control studies, these
studies differ in TCE exposure potential to the underlying population from which case and
control subjects were identified, and this may be a source of some heterogeneity. Prevalence of
exposure to TCE among cases in these studies was 27% in Charbotel et al. (2006) (for high-
level-of-confidence jobs), 18% in Briining et al. (2003) (for self-assessed exposure), 18% in
Pesch et al. (2000b), 13% in Dosemeci et al. (1999), 3.6% in Moore et al. (2010) (for high-
confidence jobs), and 1% in Siemiatycki (1991). Both Briining et al. (2003) and Charbotel et al.
(2006) are studies designed specifically to assess RCC and TCE exposure. These studies were
carried out in geographical areas with both a high prevalence and a high degree of TCE
exposure. Some information is provided in these and accompanying papers to describe the
nature of exposure, making it possible to estimate the order of magnitude of exposure, even
though there were no direct measurements (Fevotte et al., 2006; Briming et al., 2003; Cherrie et
al., 2001). The Charbotel et al. (2006) study was carried out in the Arve Valley region in France,
where TCE exposure was through metal-degreasing activity in small shops involved in the
manufacturing of screws and precision metal parts (Fevotte et al., 2006). Industrial hygiene data
from shops in this area indicated high intensity TCE exposures of >100 ppm, particularly from
exposures from hot degreasing processes. Considering exposure only from the jobs with a high
level of confidence about exposure, 18% of exposed cases were identified with high cumulative
exposure to TCE. The source population in the Briining et al. (2003) study includes the
Arnsberg region in Germany, which also has a high prevalence of TCE exposure. A large
number of small companies used TCE in metal degreasing in small workrooms. Subjects in this
study also described neurological symptoms previously associated with higher TCE intensities.
While subjects in the Briining et al. (2003) study had potential high TCE exposure intensity,
average TCE exposure in this study is considered lower than that in the Charbotel et al. (2006)
study because the base population was enlarged beyond the Arnsberg region to areas which did
not have the same focus of industry.
Siemiatycki (1991), Dosemeci et al. (1999), and Pesch et al. (2000b) are population-
based studies. Sources of exposure to TCE and other chlorinated solvents are much less well
C-47
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defined in these studies, and most subjects identified with TCE exposure probably had minimal
contact; estimated average concentrations to exposed subjects were of about 10 ppm or less
(NRC, 2006). Pesch et al. (2000b) includes the Arnsberg area and four other regions. Neither
Dosemeci et al. (1999) nor Siemiatycki (1991) describe the nature of the TCE exposure. TCE
exposure potential in these two studies is likely lower than in the other studies and closer to
background. Furthermore, the use of generic job-exposure-matrices for exposure assessment in
these studies may result in a greater potential for exposure misclassification bias.
Moore et al. (2010) is a hospital-based study which identified subjects from four Eastern
and Central European countries with high kidney cancer rates (Czech Republic, Poland, Russia,
and Romania). In their exposure assessment, Moore et al. (2010) accounted for the likelihood of
TCE exposure, defined as possible, probable, or definite exposure. This likely increased
exposure potential in their subgroup of high-confidence TCE assessments, which was restricted
to subjects with probable or definite exposure. Although their semi-quantitative exposure
assessment most probably improved exposure rankings, TCE exposure potential is likely lower
in their study than in Briining et al. (2003) and Charbotel et al. (2006), given the many jobs and
industries included.
Ten of the 15 studies categorized results by exposure level. Three other studies reported
results for other cancer sites by exposure level, but not kidney cancer; thus, to address this
reporting bias, null values (i.e., RR estimates of 1.0) were used for these studies. Different
exposure metrics were used in the various studies, and the purpose of combining results across
the different highest exposure groups was not to estimate an RRm associated with some level of
exposure, but rather to see the impacts of combining RR estimates that should be less affected by
exposure misclassification. In other words, the highest exposure category is more likely to
represent a greater differential TCE exposure compared to people in the referent group than the
exposure differential for the overall (typically any vs. none) exposure comparison. Thus, if TCE
exposure increases the risk of kidney cancer, the effects should be more apparent in the highest
exposure groups. Indeed, the RRm estimate from the primary meta-analysis of the highest
exposure group results was 1.58 (95% CI: 1.28, 1.96), which is greater than the RRm estimate of
1.27 (95% CI: 1.13, 1.43) from the overall exposure analysis. This result for the highest
exposure groups was not overly influenced by any single study, nor was it overly sensitive to
individual RR estimate selections. Heterogeneity was not observed in any of the analyses, with
the exception of some negligible heterogeneity (/ = 0.64%) in one sensitivity analysis. The
robustness of this finding lends substantial support to a conclusion that TCE exposure increases
the risk of kidney cancer.
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C.4. META-ANALYSIS FOR LIVER CANCER
C.4.1. Overall Effect of TCE Exposure
CALL Selection of RR Estimates
The selected RR estimates for liver cancer associated with TCE exposure from the
epidemiological studies are presented in Table C-l 1. There were no case-control studies for
liver cancer and TCE exposure that were selected for inclusion in the meta-analysis (see
Appendix B, Section B.2.9), so all of the relevant studies are cohort studies. All of the studies
reported results for liver cancers plus cancers of the gall bladder and extrahepatic biliary
passages (i.e., ICD-7 155.0 + 155.2; ICD-8 and -9 155 + 156). Three of the studies also report
results for liver cancer alone (ICD-7 155.0; ICD-8 and -9 155). For the primary analysis, results
for cancers of the liver, gall bladder, and biliary passages combined were selected, for the sake of
consistency, since these were reported in all of the studies. An alternate analysis was also done
using results for liver cancer alone for the three studies that reported them and the combined liver
cancer results for the remainder of the studies.
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Table C-ll. Selected RR estimates for liver cancer associated with TCE exposure (overall effect) from cohort
studies
Study
Anttila et al.
(1995)
Axelson et al.
(1994)
Boice et al.
(1999)
Greenland et al.
(1994)
Hansen et al.
(2Q01)
Morgan et al.
(1998)
Raaschou-Nielsen
et al. (2003)
Radican et al.
(2008)
Boice et al.
(2006b)
RR
1.89
1.41
0.81
0.54
2.1
1.48
1.35
1.12
1.28
95% LCL
0.86
0.38
0.45
0.11
0.7
0.56
1.03
0.57
0.35
95%UCL
3.59
3.60
1.33
2.63
5.0
3.91
1.77
2.19
3.27
RRtype
SIR
SIR
SMR
Mortality OR
SIR
SMR
SIR
Mortality
hazard ratio
SMR
logRR
0.637
0.344
-0.616
-0.616
0.742
0.393
0.300
0.113
0.247
SE (log RR)
0.333
0.5
0.5
0.810
0.447
0.495
0.132
0.343
0.5
Alternate RR
estimates (95% CI)
2.27(0.74, 5.29) for
155.0 alone
1.34(0.36,3.42)
with estimated
female contribution
to SIR added (see
text)
0.54(0.15, 1.38) for
potential routine
exposure
None
None
Published SMR
0.98(0.36,2.13)
1.28(0.89, 1.80) for
ICD-7 155.0
1.25 (0.31, 4. 97) for
ICD-8, -9 155.0
1 .0 assumed for
Zhao et al. (2005)
Comments
ICD-7 155.0 + 155.1; combined assuming Poisson
distribution.
ICD-7 155. Results reported for males only, but there
was a small female component to the cohort.
ICD-9 155 + 156. For any potential exposure.
ICD-8 155 + 156. Nested case-control study.
ICD-7 155. Male and female results reported separately;
combined assuming Poisson distribution.
ICD-9 155 + 156. Unpublished RR, adjusted for age and
sex (see text).
ICD-7 155.0 + 155.1. Results for males and females and
different liver cancer types reported separately;
combined assuming Poisson distribution.
ICD-8, -9 155 + 156, ICD-10 C22-C24. Time variable =
age; covariates = sex, race. Referent group is workers
with no chemical exposures.
ICD-9 155 + 156. Boice et al. (2006b) used in lieu of
Zhao et al. (2005) because Zhao et al. (2005) do not
report liver cancer results. Boice et al. (2006b) cohort
overlaps Zhao et al. (2005) cohort.
C-50
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As for NHL and kidney cancer, many of the studies provided RR estimates only for
males and females combined, and we are not aware of any basis for a sex difference in the
effects of TCE on liver cancer risk; thus, wherever possible, RR estimates for males and females
combined were used. The only study of much size (in terms of number of liver cancer cases)
that provided results separately by sex was Raaschou-Nielsen et al. (2003). The results of this
study suggest that liver cancer risk in females might be slightly higher than the risk in males, but
the number of female cases is small (primary liver cancer SIR: males 1.1 [95% CI: 0.74, 1.64;
27 cases], females 2.8 [95% CI: 1.13, 5.80; 7 cases]; gallbladder and biliary passage cancers SIR:
males 1.1 [95% CI: 0.61, 1.87; 14 cases]; females 2.8 [1.28, 5.34; 9 cases]). Radicanetal.
(2008) report hazard ratios for liver/biliary passage cancers combined of 1.36 (95% CI: 0.59,
3.11; 28 deaths) for males and 0.74 (95% CI: 0.18, 2.97; 3 deaths) for females, but these results
are based on fewer cases, especially in females.
Most of the selections in Table C-l 1 should be self-evident, but some are discussed in
more detail here, in the order the studies are presented in the table. For Axelson et al. (1994), in
which a small subcohort of females was studied but only results for the larger male subcohort
were reported, the reported male-only results were used in the primary analysis; however, as for
NHL and kidney cancer, an attempt was made to estimate the female contribution to an overall
RR estimate for both sexes and its impact on the meta-analysis. Axelson et al. (1994) reported
that there were no cases of liver cancer observed in females, but the expected number was not
presented. To estimate the expected number, the expected number for males was multiplied by
the ratio of female-to-male person-years in the study and by the ratio of female-to-male age-
adjusted incidence rates for liver cancer.8 The male results and the estimated female contribution
were then combined into an RR estimate for both sexes assuming a Poisson distribution, and this
alternate RR estimate for the Axelson et al. (1994) study was used in a sensitivity analysis.
For Boice et al. (1999), results for "any potential exposure" were selected for the primary
analysis, because this exposure category was considered to best represent overall TCE exposure,
and results for "potential routine exposure," which was characterized as reflecting workers
assumed to have received more cumulative exposure, were used in a sensitivity analysis. To
estimate the SE (log RR) for the primary RR selection, it was assumed that the number of
exposed cases (deaths) was 15. The actual number was not presented, but 15 was the number
that allowed us to reproduce the reported CIs. The number suggested by exposure level in Boice
8Person-years for men and women <79 years were obtained from Axelson et al. (1994): 23,516.5 and 3,691.5,
respectively. Lifetime age-adjusted incidence rates for liver cancer for men and women were obtained from the
National Cancer Institute's 2000-2004 SEER-17 (Surveillance Epidemiology and End Results from 17 geographical
areas) database (http://seer.cancer.gov/statfacts/html/livibd.html'): 9.5/100,000 and 3.4/100,000, respectively. The
calculation for estimating the expected number of cases in females in the cohort assumes that the males and females
have similar TCE exposures and that the relative distributions of age-related incidence risk for the males and
females in the Swedish cohort are adequately represented by the ratios of person-years and lifetime U.S. incidence
rates used in the calculation.
C-51
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et al. (1999) Table 9 is 13; however, it may be that exposure level data were not available for all
of the cases.
In their published paper, Morgan et al. (1998) present only SMRs for overall TCE
exposure, although the results from internal analyses are presented for exposure subgroups. RR
estimates for overall TCE exposure from the internal analyses of the Morgan et al. (1998) cohort
data were available from an unpublished report (EHS, 1997): from these, the RR estimate from
the Cox model that included age and sex was selected, because those are the variables deemed to
be important in the published paper. The internal analysis RR estimate was preferred for the
primary analysis, and the published SMR result was used in a sensitivity analysis.
Raaschou-Nielsen et al. (2003) reported results for primary liver cancer (ICD-7 155.0),
gallbladder and biliary passage cancers (ICD-7 155.1), and unspecified liver cancers (ICD-7 156)
separately. As discussed above, RR estimates for cancers of the liver, gall bladder, and biliary
passages combined were preferred for the primary analysis; thus, the results for primary liver
cancer and gallbladder/biliary passage cancers were combined (across sexes as well), assuming a
Poisson distribution. The results for primary liver cancer only (similarly combined across sexes)
were used in an alternate analysis. The results for unspecified liver cancers (ICD-7 156) were
not included in any analyses because, under the ICD-7 coding, 156 can include secondary liver
cancers. Raaschou-Nielsen et al. (2003), in their Table 3, also present overall results for primary
liver cancer and gallbladder/biliary passage cancers with a lag time of 20 years; however, they
use a definition of lag that is different from a lagged exposure in which exposures prior to
disease onset are discounted and it is not clear what their lag time actually represents9, thus, as
for NHL and kidney cancer, these results were not used in any of the meta-analyses for liver
cancer. In addition, results for the subcohort with expected higher exposure levels were not
provided for liver cancer, so no alternate analysis was done based on the subcohort.
For Radican et al. (2008), the Cox model hazard ratio from the 2000 follow-up was used.
In the Radican et al. (2008) Cox regressions, age was the time variable, and sex and race were
covariates. It should also be noted that the referent group is composed of workers with no
chemical exposures, not just no exposure to TCE.
Zhao et al. (2005) did not present RR estimates for liver cancer; thus, results from Boice
et al. (2006b) were used in the primary analysis. The cohorts for these studies overlap, so they
are not independent studies. Zhao et al. (2005), however, was our preferred study for NHL and
kidney cancer results; thus, in a sensitivity analysis, a null value (RR = 1.0) was assumed for
Zhao et al. (2005) to address the potential reporting bias. The SE estimate for kidney cancer
(incidence with 0 lag) was used as the SE for the liver cancer. (It is not certain that there was a
reporting bias in this case. In the "Methods" section of their paper, Zhao et al. [(2005) list the
9In their Methods section, Raaschou-Nielsen et al. (2003) define their lag period as the period "from the date of first
employment to the start of follow-up for cancer".
C-52
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cancer sites examined in the cohort, and liver was not listed; it is not clear if the list of sites was
determined a priori or post hoc.)
Also, on the issue of potential reporting bias, the Siemiatycki (1991) study should be
mentioned. This study was a case-control study for multiple cancer sites, but only the more
common sites, in order to have greater statistical power. Thus, NHL and kidney cancer results
were available, but not liver cancer results. Because no liver results were presented for any of
the chemicals, this is not a case of reporting bias.
C.4.1.2. Results of Meta-Analyses
Results from some of the meta-analyses that were conducted on the epidemiological
studies of TCE and liver cancer are summarized in Table C-12. The RRm from the primary
random-effects meta-analysis of the nine studies was 1.29 (95% CI: 1.07, 1.56) (see Figure C-8).
As shown in Figure C-8, the analysis was dominated by one large study (contributing about 53%
of the weight). That large study was critical in terms of the statistical significance of the RRm
estimate. Without the large Raaschou-Nielsen et al. (2003) study, the RRm estimate decreases
somewhat and is no longer statistically significant (RRm = 1.22; 95% CI: 0.93, 1.61). No other
single study was overly influential; removal of any of the other individual studies resulted in
RRm estimates that were all statistically significant (all with/? <0.03) and that ranged from 1.24
[with the removal of Anttila et al. (1995)1 to 1.39 [with the removal of Boice et al. (1999)].
As discussed in Section C.4.1.1, all of the nine studies presented results for liver and gall
bladder/biliary passage cancers combined, and these results were the basis for the primary
analysis discussed above. An alternate analysis was performed substituting, simultaneously,
results for liver cancer alone for the three studies for which these were available. The RRm
estimate from this analysis was slightly lower than the one based entirely on results from the
combined cancer categories and was just short of statistical significance (1.25; 95% CI: 0.99,
1.57). This result was driven by the fact that the RR estimate from the large Raaschou-Nielsen et
al. (2003) study decreased from 1.35 for liver and gall bladder/biliary passage cancers combined
to 1.28 for liver cancer alone.
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Table C-12. Summary of some meta-analysis results for TCE and liver cancer
Analysis
All studies
(all cohort
studies)
All studies; liver
cancer only, when
available
Alternate RR
selections3
Highest exposure
groups
Number of
studies
9
9
9
9
9
9
6
8
7-8
Model
Random
Fixed
Random
Random
Random
Random
Random
Random
Random
Random
RRm estimate
1.29
1.29
1.25
1.28
1.34
1.29
1.26
1.32
1.28
1.24-1.26
95% LCL
1.07
1.07
0.99
1.06
1.09
1.07
1.05
0.93
0.93
0.88-0.91
95% UCL
1.56
1.56
1.57
1.55
1.63
1.55
1.52
1.86
1.77
1.73-1.82
Heterogeneity
None observable
(fixed = random)
None observable
None observable
None observable
None observable
None observable
None observable
None observable
None observable
Comments
Statistical significance not dependent on
single study, except for Raaschou-Nielsen
et al. (2003). without which/) = 0.15. No
apparent publication bias.
Used RR estimates for liver cancer alone
for the three studies that presented these;
remaining RR estimates are for liver and
gall bladder/biliary passage cancers.
With RR = 1 assumed for Zhao et al.
(2005) in lieu of Boice et al. (2006b) (see
text).
With Boice et al. (1999) potential routine
exposure rather than any potential
exposure.
With estimated female contribution to
Axelson et al. (1994).
With Morgan et al. (1998) published
SMR.
Primary analysis. Using RR = 1 for
Hansen et al. (2001) and Zhao et al.
(2005) (see text).
Using alternate selections for Morgan et
al. (1998) and Raaschou-Nielsen et al.
(2003) and excluding Axelson et al.
(1994) (see text).3
""Changing the primary analysis by one alternate RR each time.
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TCE Exposure and Liver Cancer
Study Relative Risl
i
Boice (1999) — Q-
Raaschou-Nielsen (2003)
OVERALL
1
and95%CI RR LCL
0.81 0.46
-I h 1.35 1.03
h^H 1.29 1.07
0.1 1 10
UCL
3.59
3.60
1.33
3.27
2.63
5.00
3.91
1.77
2.19
1.56
Figure C-8. Meta-analysis of liver cancer and TCE exposure. Random-effects
model; fixed-effect model same. Rectangle sizes reflect relative weights of the
individual studies. The summary estimate is in the bottom row, represented by the
diamond.
Similarly, the RRm estimate was not highly sensitive to other alternate RR estimate
selections. Use of the 4 other alternate selections, individually, resulted in RRm estimates that
were all statistically significant (all with/? < 0.02) and that ranged from 1.26 to 1.34 (see
Table C-12). In fact, as can be seen in Table C-12, only one of the alternates had notable impact.
The Boice et al. (2006b), Morgan et al. (1998), and Axelson et al. (1994) original values and
alternate selections were associated with very little weight and, thus, have little influence in the
RRm. Using the Boice et al. (1999) alternate RR estimate based on potential routine exposure
rather than any potential exposure increased the RRm slightly from 1.29 to 1.34. The alternate
Boice et al. (1999) RR estimate is actually smaller than the original value (0.54 vs. 0.81);
however, use of the more restrictive exposure metric captures fewer liver cancer deaths, causing
the weight of that study to decrease from almost 14% to about 4.1%.
There was no apparent heterogeneity across the nine studies (i.e., the random-effects
model and the fixed-effect model gave the same results [/ = 0%]). Furthermore, all of the liver
cancer studies were cohort studies, so no subgroup analyses examining cohort and case-control
studies separately, as was done for NHL and kidney cancer, were conducted. No alternate
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quantitative investigations of heterogeneity were pursued because of database limitations and, in
any event, there is no evidence of heterogeneity of study results in this database.
As discussed in Section C.I, publication bias was examined in several different ways.
The funnel plot in Figure C-9 shows little relationship between RR estimate and study size, and,
indeed, none of the other tests performed found any evidence of publication bias. The trim-and-
fill procedure of Duval and Tweedie (2000), for example, suggested that no studies were missing
from the funnel plot (i.e., there was no asymmetry to counterbalance). Similarly, the results of a
cumulative meta-analysis, incorporating studies with increasing SE one at a time, shows no
evidence of a trend of increasing effect size with addition of the less precise studies. The
Raaschou-Nielsen et al. (2003) study contributes about 53% of the weight. Including the two
next most precise studies, the RRm goes from 1.35 to 1.10 to 1.25 and the weight to 75%. With
the addition of the next two most precise studies, the RRm estimate goes to 1.23 and then 1.29.
Further addition of the four least precise studies leaves the RRm essentially unchanged.
Funnel Plot of Standard Error by Log risk ratio
0.0
0.2
0.4
Standard Srror
0.6
0.8
1.0
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Log risk ratio
Figure C-9. Funnel plot of SE by log RR estimate for TCE and liver cancer
studies.
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C.4.2. Liver Cancer Effect in the Highest Exposure Groups
C.4.2.1. Selection of RR Estimates
The selected RR estimates for liver cancer in the highest TCE exposure categories, for
studies that provided such estimates, are presented in Table C-13. Six of the nine cohort studies
reported liver cancer risk estimates categorized by exposure level. As in Section C.4.1.1 for the
overall risk estimates, estimates for cancers of the liver and gall bladder/biliary passages
combined were preferentially selected, when presented, for the sake of consistency, and,
wherever possible, RR estimates for males and females combined were used.
Two of the nine cohort studies (Zhao et al., 2005; Hansen et al., 2001) did not report liver
cancer risk estimates categorized by exposure level, even though these same studies reported
such estimates for selected other cancer sites. To address this reporting bias (as discussed above,
Zhao et al. (2005) did not present any liver results, and it is not clear if this was actual reporting
bias or an a priori decision not to examine liver cancer in the cohort), attempts were made to
obtain the results from the primary investigators, and, failing that, alternate analyses were
performed in which null estimates (RR = 1.0) were included for both studies. This alternate
analysis was then used as the main analysis, e.g., the basis of comparison for the sensitivity
analyses. For the SE (of the log RR) estimates for the null estimates, SE estimates from other
sites for which highest-exposure-group results were available were used. For Hansen et al.
(2001), the SE estimate for NHL in the highest exposure group was used, because NHL was the
only cancer site of interest in this assessment for which highest-exposure-group results were
available. For Zhao et al. (2005), the SE estimate for kidney cancer in the highest exposure
group (incidence with 0 lag) was used. (Note that Boice et al. (2006b), who studied a cohort that
overlapped that of Zhao et al. (2005), also did not present liver cancer results by exposure level.)
C-57
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Table C-13. Selected RR estimates for liver cancer risk in highest TCE exposure groups
Study
Anttila et al.
(1995)
Axelson et al.
(1994)
Boice et al.
(1999)
Hansen et al.
(2001)
Morgan et al.
(1998)
Raaschou-
Nielsen et al.
(2001)
Radican et al.
(2008)
Zhao et al.
(2005)
RR
2.74
3.7
0.94
1.19
1.2
1.49
95%
LCL
0.33
0.09
0.36
0.34
0.7
0.67
95%
UCL
9.88
21
2.46
4.16
1.9
3.34
Exposure
category
100+ umol/L
U-TCA a
100+ mg/L
U-TCA
>5 yrs exposure
>1,080 months x
mg/m3
High cumulative
exposure score
>5yrs
>25 unit-yrs
High exposure
score
logRR
1.008
1.308
-0.062
0.174
0.182
0.399
SE (log
RR)
0.707
1.000
0.490
0.639
0.243
0.411
Alternate RR
estimates (95%
CI)
None
Exclude study
None
1.0 assumed
0.98 (0.29, 3.35)
for med/high peak
vs. low/no
1.1(0.5,2.1)
ICD-7 155.0
(liver only)
None (see text)
1.0 assumed
Comments
SIR. ICD-7 155.0 (liver only).
SIR. ICD-7 155. 0 cases observed in highest
exposure group (i.e., >2 yrs and 100+ U-TCA),
so combined with <2 yrs and 100+ subgroup and
females, estimating the expected numbers (see
text).
Mortality RR. ICD-9 155 + 156. For potential
routine or intermittent exposure. Adjusted for
date of birth, dates 1st and last employed, race,
and sex. Referent group is workers not exposed
to any solvent.
Reported high exposure group results for some
cancer sites but not liver.
Mortality RR. ICD-9 155 + 156. Adjusted for
age and sex.
SIR. ICD-7 155.0 +155.1. Male and female
results presented separately and combined
assuming a Poisson distribution.
Mortality hazard ratio. ICD-8, -9 155 + 156,
ICD-10 C22-C24. Male and female results
presented separately and combined (see text).
Time variable = age, covariate = race. Referent
group is workers with no chemical exposures.
No liver results reported.
"Mean personal TCA in urine. 1 umol/L = 0.1634 mg/L.
C-58
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For Axelson et al. (1994), there were no liver cancer cases in the highest exposure group
(>2 years and 100+ mean U-TCA level), so no log RR and SE (log RR) estimates were available
for the meta-analysis. Instead, the <2 and >2 years results were combined, assuming expected
numbers of cases were proportional to person-years, and 100+ U-TCA (with any exposure
duration) was used as the highest exposure category. The female contribution to the expected
number was also estimated, again assuming proportionality to person-years, and adjusting for the
difference between female and male age-adjusted liver cancer incidence rates. The estimated RR
and SE values for the combined exposure times and sexes were used in the primary analysis. In
an alternate analysis, the Axelson et al. (1994) study was excluded altogether, because we
estimated that <0.2 cases were expected in the highest exposure category, suggesting that the
study had low power to detect an effect in the highest exposure group and would contribute little
weight to the meta-analysis.
For Boice et al. (1999), only results for workers with "any potential exposure" were
presented by exposure category, and the referent group is workers not exposed to any solvent.
For Morgan et al. (1998), the primary analysis used results for the cumulative exposure metric,
and a sensitivity analysis was done with the results for the peak exposure metric. For Raaschou-
Nielsen et al. (2003), unlike for NHL and RCC, liver cancer results for the subcohort with
expected higher exposure levels were not presented, so the only highest-exposure-group results
were for duration of employment in the total cohort. Results for cancers of the liver and gall
bladder/biliary passages combined were used for the primary analysis and results for liver cancer
alone in a sensitivity analysis.
For Radican et al. (2008), it should be noted that the referent group is workers with no
chemical exposures, not just no TCE exposure. Furthermore, results for exposure groups (based
on cumulative exposure scores) were reported separately for males and females and were
combined for this assessment using inverse-variance weighting, as in a fixed-effect meta-
analysis. In addition to results for biliary passage and liver cancer combined, Radican et al.
(2008) present results for liver only by exposure group; however, there were no liver cancer
deaths in females and the number expected was not reported, so no alternate analysis for the
highest exposure groups with an RR estimate from Radican et al. (2008) for liver cancer only
was conducted. Radican et al. (2008) present only mortality hazard ratio estimates by exposure
group; however, in an earlier follow-up of this same cohort, Blair et al. (1998) present both
incidence and mortality RR estimates by exposure group. As with the Radican et al. (2008) liver
cancer only results, however, there were no incident cases for females in the highest exposure
group in Blair et al. (1998) (and the expected number was not reported). Additionally, there
were more biliary passage/liver cancer deaths (31) in Radican et al. (2008) than incident cases
(13) in Blair et al. (1998) overall and in the highest exposure group (14 vs. 4). Thus, we elected
to use only the Radican et al. (2008) mortality results from this cohort and not to include an
alternate analysis based on incidence results from the earlier follow-up. Radican et al. (2008)
C-59
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also present results for categories based on frequency and pattern of exposure; however, subjects
weren't distributed uniquely across the categories (the numbers of cases across categories
exceeded the total number of cases), thus it was difficult to interpret these results and they were
not used in a sensitivity analysis.
C.4.2.2. Results of Meta-Analyses
Results from the meta-analyses that were conducted for liver cancer in the highest
exposure groups are summarized at the bottom of Table C-12. The RRm estimate from the
random-effects meta-analysis of the six studies with results presented for exposure groups was
1.32 (95% CI: 0.93, 1.86). As with the overall liver cancer meta-analyses, the meta-analyses of
the highest exposure groups were dominated by one study (Raaschou-Nielsen et al., 2003),
which provided about 52% of the weight. The RRm estimate from the primary random-effects
meta-analysis with null RR estimates (i.e., 1.0) included for Hansen et al. (2001) and Zhao et al.
(2005) to address (potential) reporting bias (see above) was 1.28 (95% CI: 0.93, 1.77) (see
Figure C-10). The inclusion of these two additional studies contributed about 10% of the total
weight. No single study was overly influential (removal of individual studies resulted in
nonsignificant RRm estimates that ranged from 1.23 to 1.36), and the RRm estimate was not
highly sensitive to alternate RR estimate selections (RRm estimates with alternate selections
ranged from 1.24 to 1.26, all nonsignificant; see Table C-12). In addition, there was no
observable heterogeneity across the studies for any of the meta-analyses conducted with the
highest exposure groups (/ = 0%). However, none of the RRm estimates was statistically
significant.
C-60
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TCE Exposure and Liver Cancer - highest exposure groups
Study Relative Risk and 95% Cl RR LCL UCL
i
Raaschou-Nielien (2003)
OVERALL 1-
0.1 1
H | — 1.20 0.70 1.90
- — | 1.2S 0.93 1.77
10
Figure C-10. Meta-analysis of liver cancer and TCE exposure—highest
exposure groups, with assumed null RR estimates for Hansen et al. (2001)
and Zhao et al. (2005) (see text). Random-effects model; fixed-effect model
same. Rectangle sizes reflect relative weights of the individual studies. The
summary estimate is in the bottom row, represented by the diamond.
Furthermore, most of the RRm estimates for the highest exposure groups were less than
the significant RRm estimate for an overall effect on liver cancer (1.29; 95% CI: 1.07, 1.56; see
Section C.4.2.2 and Table C-12). This contradictory result is driven by the fact that the RR
estimate for the highest exposure group was less than the overall RR estimate for Raaschou-
Nielsen et al. (2003), which contributes the majority of the weight to the meta-analyses. The
liver cancer results are relatively underpowered with respect to numbers of studies and number
of cases, and the Raaschou-Nielsen et al. (2003) study, which dominates the analysis, uses
duration of employment as an exposure-level surrogate for liver cancer, and duration of
employment is a notoriously weak exposure metric10. Thus, the contradictory finding that most
of the RRm estimates for the highest exposure groups were less than the RRm estimate for an
10Moreover, this study is prone to misclassifying some of the subjects with longer durations of employment as
having lesser durations of employment due to the fact that employment information prior to 1964 was not available
and, thus, employment prior to 1964 was not included in the calculations of duration of employment. For example,
17 of the 27 primary liver cancer cases in men were observed in men first employed before 1970 and some of these
might have occurred in men first employed before 1964. Thus, some of the 18 cases with durations of employment
reported as < 5 years may actually have had durations >5 years and hence may have belonged in the highest
exposure group.
C-61
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overall effect does not rule out an effect of TCE on liver cancer; however, it certainly does not
provide additional support for such an effect.
€.4.3. Discussion of Liver Cancer Meta-Analysis Results
For the most part, the meta-analyses of the overall effect of TCE exposure on liver (and
gall bladder/biliary passages) cancer suggest a small, statistically significant increase in risk.
The summary estimate from the primary random-effects meta-analysis of the nine (all cohort)
studies was 1.29 (95% CI: 1.07, 1.56). The analysis was dominated by one large study that
contributed about 53% of the weight. When this study was removed, the RRm estimate
decreased somewhat and was no longer statistically significant (RRm = 1.22; 95% CI: 0.93,
1.61). The summary estimate was not overly influenced by any other single study, nor was it
overly sensitive to individual RR estimate selections. The next largest downward impacts were
from the removal of the Anttila et al. (1995) study, resulting in an RRm estimate of 1.24
(95% CI: 1.02, 1.51), and from the substitution of the Morgan et al. (1998) unpublished RR
estimate (EHS, 1997) with the published SMR estimate (Morgan et al., 1998), resulting in an
RRm estimate of 1.26 (95% CI: 1.05, 1.52). Substituting the RR estimates for liver/gall
bladder/biliary passage cancers with those of liver cancer alone for the three studies that
provided these results yielded an RRm estimate of 1.25 (95% CI: 0.99, 1.57). There was no
evidence of publication bias in this data set, and there was no observable heterogeneity across the
study results.
Six of the nine studies provided liver cancer results by exposure level. Two other studies
reported results for other cancer sites by exposure level, but not liver cancer; thus, to address this
reporting bias, null values (i.e., RR estimates of 1.0) were used for these studies. Different
exposure metrics were used in the various studies, and the purpose of combining results across
the different highest exposure groups was not to estimate an RRm associated with some level of
exposure, but rather to see the impacts of combining RR estimates that should be less affected by
exposure misclassification. In other words, the highest exposure category is more likely to
represent a greater differential TCE exposure compared to people in the referent group than the
exposure differential for the overall (typically any vs. none) exposure comparison. Thus, if TCE
exposure increases the risk of liver cancer, the effects should be more apparent in the highest
exposure groups. However, the RRm estimate from the primary meta-analysis of the highest
exposure group results (and most of the RRm estimates from the sensitivity analyses) was less
than the RRm estimate from the overall exposure analysis. This anomalous result is driven by
the fact that for Raaschou-Nielsen et al. (2003), which contributes the majority of the weight to
the meta-analyses, the RR estimate for the highest exposure group, although >1, was less than
the overall RR estimate.
Thus, while there is the suggestion of an increased risk for liver cancer associated with
TCE exposure, the statistical significance of the overall summary estimate is dependent on one
C-62
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study, which provides the majority of the weight in the meta-analyses. Removal of this study
yields an RRm estimate that is decreased somewhat but is still >1; however, it becomes
nonsignificant (p = 0.15). Furthermore, meta-analysis results for the highest exposure groups
yielded generally lower RRm estimates than for an overall effect. These results do not rule out
an effect of TCE on liver cancer, because the liver cancer results are relatively underpowered
with respect to numbers of studies and number of cases and the overwhelming study in terms of
weight uses the weak exposure surrogate of duration of employment for categorizing exposure
level; however, at present, there is only modest support for such an effect.
C.5. META-ANALYSIS FOR LUNG CANCER
C.5.1. Overall Effect of TCE Exposure
C.5.1.1. Selection of RR Estimates
Although there was no general indication of an increased risk of lung cancer associated
with TCE exposure in the epidemiologic literature, the Science Advisory Board recommended a
meta-analysis for lung cancer to more exhaustively examine the issue of smoking as a possible
confounder in the kidney cancer studies (SAB, 2011). Only the cohort studies were considered
for the meta-analysis because these provide a consistent group of studies to compare RRm
estimates for kidney cancer to those for lung cancer and the cohort studies are the studies of
concern for potential confounding since the kidney cancer results from these studies were not
adjusted for smoking. The selected RR estimates for lung cancer from the nine cohort studies
are presented in Table C-14. All of the studies, with the possible exception of Greenland et al.
(1994), reported cancers of the lung and bronchus combined. Some also included cancer of the
trachea; however, this is a rare tumor (<0.1% of tumors) (Macchiarini, 2006) and so its inclusion
is negligible.
As for NHL and kidney and liver cancer, many of the studies provided RR estimates only
for males and females combined, and we are not aware of any basis for a sex difference in the
effects of TCE on lung cancer risk; thus, wherever possible, RR estimates for males and females
combined were used. The only two studies of much size (in terms of number of lung cancer
cases) that provided results separately by sex were Raaschou-Nielsen et al. (2003) and Radican
et al. (2008). The results from Raaschou-Nielsen et al. (2003) suggest that lung cancer RR in
females might be slightly higher than the RR in males (SIR: males 1.4 [95% CI: 1.3, 1.5;
559 cases], females 1.9 [95% CI: 1.5, 2.4; 73 cases]), but the difference narrows when a 20-year
lag is taken into account (males 1.4 [95% CI: 1.2, 1.6; 202 cases], females 1.6 [95% CI: 1.0, 2.3;
26 cases]). Radican et al. (2008) report hazard ratios for lung cancer of 0.91 (95% CI: 0.67,
1.24; 155 deaths) for males and 0.53 (95% CI: 0.27, 1.07; 11 deaths) for females, but these
results are based on fewer cases, especially in females.
C-63
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Table C-14. Selected RR estimates for lung (& bronchus) cancer associated with TCE exposure (overall effect)
from cohort studies
Study
Anttila et al.
(1995)
Axelson et al.
(1994)
Boice et al.
(1999)
Greenland et al.
(1994)
Hansen et al.
(2001)
Morgan et al.
(1998)
Raaschou-
Nielsen et al.
(2001)
Radican et al.
(2008)
Zhao et al.
(2005)
RR
0.92
0.69
0.76
1.01
0.8
1.14
1.43
0.83
1.04
95%
LCL
0.59
0.31
0.66
0.69
0.5
0.90
1.32
0.63
0.81
95%
UCL
1.35
1.30
0.87
1.47
1.3
1.44
1.55
1.08
1.34
RRtype
SIR
SIR
SMR
OR
SIR
SMR
SIR
Mortality
hazard ratio
RR
logRR
-0.0834
-0.371
-0.274
0.00995
-0.223
0.133
0.358
-0.186
0.0392
SE (log RR)
0.2
0.333
0.0705
0.193
0.243
0.119
0.0398
0.138
0.128
Alternate RR
estimates (95%
CI)
None
None
0.76 (0.60, 0.95)
for potential
routine exposure
None
None
Published SMR
1.10(0.89,1.34)
None
None
1.27 (0.88, 1.83)
for incidence.
1.24 (0.92, 1.63)
for Boice et al.
(2006b) mortality.
Comments
Results reported for males only, but there was a
small female component to the cohort.
For any potential exposure.
Nested case-control study.
Male and female results reported separately;
combined assuming Poisson distribution.
Unpublished RR, adjusted for age and sex (see
text).
Time variable = age; covariates = sex, race.
Referent group is workers with no chemical
exposures.
Mortality
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Most of the selections in Table C-14 should be self-evident, but some are discussed in
more detail here, in the order the studies are presented in the table. For Axelson et al. (1994), in
which a small subcohort of females was studied but only results for the larger male subcohort
were reported, only the reported male results were used. Unlike for NHL and kidney and liver
cancer, no attempt was made to estimate the female contribution to an overall RR estimate for
both sexes and its impact on the meta-analysis because, unlike for those other cancer types, the
meta-analysis for lung cancer was not done to test a null hypothesis of no effect, but rather to
investigate whether or not smoking might be confounding the kidney cancer results. An
association of TCE exposure and lung cancer might indicate a confounding effect of smoking (or
a causal association with lung cancer), but a finding of no association would essentially rule out
a confounding effect of smoking, since smoking is such a strong risk factor for lung cancer.
Axelson et al. (1994) reported neither the number of lung cancers observed in females nor the
number expected. To test a null hypothesis of no effect, one might conservatively assume none
was observed and estimate the number expected, as was done for kidney cancer; however, since
that is not the hypothesis here, we chose not to make any assumptions or estimates for the female
component of the cohort.
For Boice et al. (1999), results for "any potential exposure" were selected for the primary
analysis, because this exposure category was considered to best represent overall TCE exposure,
and results for "potential routine exposure," which was characterized as reflecting workers
assumed to have received more cumulative exposure, were used in a sensitivity analysis. The
number of cases (deaths) with "any potential exposure" was not presented, but a value of 200
allowed us to reproduce the reported CIs. The number suggested by exposure level in Boice et
al. (1999) Table 9 is 173; however, it may be that exposure level data were not available for all
of the cases. Because the exact number is unknown but is a large number, consistent with CIs
that are proportionally symmetric, the SE (log RR) was calculated as from symmetric CIs (see
Section C.I).
In their published paper, Morgan et al. (1998) present only SMRs for overall TCE
exposure, although the results from internal analyses are presented for exposure subgroups. RR
estimates for overall TCE exposure from the internal analyses of the Morgan et al. (1998) cohort
data were available from an unpublished report (EHS, 1997); from these, the RR estimate from
the Cox model that included age and sex was selected, because those are the variables deemed to
be important in the published paper. The internal analysis RR estimate was preferred for the
primary analysis, and the published SMR result was used in a sensitivity analysis.
Raaschou-Nielsen et al. (2003) reported results for lung cancer for both sexes combined
in the text. In their Table 3, Raaschou-Nielsen et al. (2003) also present overall results for lung
cancer with a lag time of 20 years; however, they use a definition of lag that is different from a
lagged exposure in which exposures prior to disease onset are discounted and it is not clear what
C-65
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their lag time actually represents11, thus, these results were not used in any of the meta-analyses
for lung cancer. In addition, results for the subcohort with expected higher exposure levels were
not provided for lung cancer, so no alternate analysis was done based on the subcohort.
For Radican et al. (2008), the Cox model hazard ratio from the 2000 follow-up was used.
In the Radican et al. (2008) Cox regressions, age was the time variable, and sex and race were
covariates. It should also be noted that the referent group is composed of workers with no
chemical exposures, not just no exposure to TCE.
Zhao et al. (2005) do not report results for an overall TCE effect. Therefore, as for NHL
and kidney cancer, the results across the "medium" and "high" exposure groups were combined,
under assumptions of group independence, even though the exposure groups are not independent
(the "low" exposure group was the referent group in both cases). Zhao et al. (2005) present RR
estimates for both incidence and mortality; however, the time frame for the incidence accrual is
smaller than the time frame for mortality accrual and fewer exposed incident cases (49) were
obtained than deaths (95). Thus, because better case ascertainment occurred for mortality than
for incidence, the mortality results were used for the primary analysis, and the incidence results
were used in a sensitivity analysis. A sensitivity analysis was also done using results from Boice
et al. (2006b) in place of the Zhao et al. (2005) RR estimate. The cohorts for these studies
overlap, so they are not independent studies and should not be included in the meta-analysis
concurrently. Boice et al. (2006b) report an RR estimate for an overall TCE effect for lung
cancer mortality; however, it is based on fewer deaths (51) and is an SMR rather than an internal
analysis RR estimate, so the Zhao et al. (2005) mortality estimate is preferred for the primary
analysis.
C.5.1.2. Results of Meta-Analyses
Results from some of the meta-analyses that were conducted on the epidemiological
studies of TCE and lung cancer are summarized in Table C-15. The RRm from the fixed-effect
meta-analysis of the nine studies was 1.16 (95% CI: 1.09, 1.23) (see Figure C-l 1). As shown in
Figure C-l 1, the analysis was dominated by one large study [Raaschou-Nielsen et al. (2003),
contributing about 58% of the weight]. The RR estimate from that large study was higher than
the RR estimates from all of the other studies and, with its relatively narrow CI, was largely
inconsistent with the results of the other studies, in particular that of the next largest study (Boice
(1999), contributing about 18% of the weight). While the RR estimate of Raaschou-Nielsen et
al. (2003) was statistically significantly elevated, that of Boice et al. (1999) was statistically
significantly decreased. This heterogeneity of study results is corroborated by a statistically
significant p-value for the test of heterogeneity (p < 10"8) and an /-value of 90%, indicating a
high amount of heterogeneity. Because of this heterogeneity, the appropriateness of conducting
nln their Methods section, Raaschou-Nielsen et al. (2003) define their lag period as the period "from the date of first
employment to the start of follow-up for cancer".
C-66
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any meta-analysis without attempting to explain the heterogeneity is arguable, but a fixed-effect
meta-analysis is clearly improper (see Section C.I).
TCE Exposure and Lung Cancer - fixed-effect model
Study Relative Risk
Boice(1999) Q
Morgan (1998)
Raaschou-Nielien (2003)
Radican(2008) — |—
Zhao (2005)
OVERALL ,
and95WCI RR
0.76
H — 1.14
n 1.43
0.83
| 1.04
4^ 1-16
I ' ' I
0.1 1 10
LCL
0.59
0.31
0.66
0.69
0.50
0.90
1.32
0.63
0.81
1.09
UCL
1.35
1.30
0.87
1.47
1.30
1.44
1.55
1.08
1.34
1.23
Figure C-ll. Meta-analysis of lung cancer and TCE exposure—fixed-effect
model. Rectangle sizes reflect relative weights of the individual studies. The
summary estimate is in the bottom row, represented by the diamond.
The RRm from the primary random-effects meta-analysis of the nine studies was 0.96
(95% CI: 0.76, 1.21) (see Figure C-12). As shown in Figure C-12, because the random-effects
model takes both between-study and within-study variation into account in the study weight, and
because the between-study variation is fairly substantial for these studies, study size has minimal
impact on study weight. The relative weights for the nine studies range from 6.7 to 13.9% in the
random-effects meta-analysis; thus, no single study dominates the analysis in terms of weight.
The most influential single study is nonetheless the largest study, Raaschou-Nielsen et al. (2003)
(2003), because it also has an RR estimate well above the others, and its removal from the
analysis reduces the RRm estimate to 0.90 (95% CI: 0.79, 1.04). In contrast, removal of Boice et
al. (1999), the study with the lowest RR estimate, increases the RRm estimate to 1.01 (95% CI:
0.82, 1.24). Removal of any of the other individual studies resulted in RRm estimates that were
all nonsignificantly decreased and that ranged from 0.93 [with the removal of Morgan et al.
(1998)1 to 0.98 [with the removal of Axelson et al. (19941 Hansen et al. (200IX or Radican et al.
C-67
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(2008)]. Use of the four alternate selections, individually, resulted in RRm estimates that were
all nonsignificant and that fell in a narrower range—0.96 to 0.98 (see Table C-15).
TCE Exposure and Lung Cancer - random-effects model
Study Relative Risk and 95% Cl RR
Anttila (1995) C
A--r irr .., finnd1! I"!
Boice(1999) Q
Greenland (1994) — |
1 I n n rj^ n f^fff"! 1 ^ l~l
Morgan (1998)
Raaschou-Nielien (2003)
Radican(2008) — Q-
D.92
n fio
u.uy
0.76
] — 1.01
n en
U.OU
-Q— 1.14
n 1.43
0.83
Zh a o(2005) — Q— 1.04
OVERALL , |-^
H 0-96
I ' ' I
0.1 1 10
LCL
0.59
0.31
o.ee
0.69
Off)
.ou
0.90
1.32
0.63
0.81
0.76
UCL
1.35
1.30
0.87
1.47
1 ^n
I . JU
1.44
1.55
1.08
1.34
1.21
Figure C-12. Meta-analysis of lung cancer and TCE exposure—random-
effects model. Rectangle sizes reflect relative weights of the individual studies.
The summary estimate is in the bottom row, represented by the diamond.
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Table C-15. Summary of some meta-analysis results for TCE and lung cancer
Analysis
All studies
(all cohort
studies)
Alternate RR
selections3
Highest exposure
groups
Number of
studies
9
9
9
9
9
6
6
Model
Random
Fixed
Random
Random
Random
Random
Random
Random
RRm estimate
0.96
1.16
0.98
0.98
0.97
0.96
0.96
0.92-0.98
95% LCL
0.76
1.09
0.78
0.77
0.78
0.76
0.72
0.67-0.75
95% UCL
1.21
1.23
1.25
1.24
1.20
1.20
1.27
1.25-1.30
Heterogeneity
Significant
(p < iO"8)
/ = 90%
Because of
significant
heterogeneity,
fixed-effect model
not appropriate
Significant
(P < 10'8)
/ = 90%
Significant
(P < 10'8)
/ = 90%
Significant
(P < 10'7)
/ = 85%
Significant
(P < lO'8)
/ = 90%
Significant
Comments
Nonsignificance of RRm not dependent
on any single study.
No apparent publication bias.
Significant elevation in RRm dependent
on single study, Raaschou-Nielsen et al.
(2003). without which the RRm would be
significantly decreased (RRm =0.87,
p = 0.004).
With Zhao et al. (2005) incidence instead
of mortality.
With Boice et al. (2006b) instead of Zhao
et al. (2005).
With Boice et al. (1999) potential routine
exposure rather than any potential
exposure.
With Morgan et al. (1998) published
SMR.
See Table C-17 for details.
Using alternate selections (see text).3
""Changing the primary analysis by one alternate RR each time.
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As discussed above, there was significant heterogeneity across the nine studies. All of
the lung cancer studies were cohort studies, so no subgroup analyses examining cohort and case-
control studies separately, as was done for NHL and kidney cancer, were conducted. In addition,
no alternate quantitative investigations of heterogeneity were pursued because our goal here was
to investigate lung cancer risks as an indication of possible confounding of the kidney cancer
results by smoking, not to do an all-encompassing meta-analysis of lung cancer. The majority of
the studies have nonsignificant RR estimates for lung cancer that fall near or <1. The relative
outliers are the significantly increased RR estimate from Raaschou-Nielsen et al. (2003) and the
significantly decreased RR estimate from Boice et al. (1999). The Raaschou-Nielsen et al.
(2003) study considered a lot of different job titles and the RR estimate could reflect a TCE
effect or exposure to other chemicals that are lung carcinogens. Alternatively, because the study
is an SMR study of largely blue-collar workers and the comparison population is the general
Danish population, the elevated RR estimate could reflect small differences in smoking rates
between those two populations. However, if the observed increase is attributable to smoking, it's
not enough of an effect to explain the increased RR estimate for RCC in the same study because
smoking is a much stronger risk factor for lung cancer than for RCC, whereas the increased RR
estimate for lung cancer in the study was relatively small (Raaschou-Nielsen et al., 2003): see
also Section 4.4.2.3). It is unclear why the Boice et al. (1999) study reports a significantly
decreased RR estimate. In any event, there is no increase in the RRm estimate for all nine
studies from the random-effects model, suggesting that there is no confounding of the overall
RRm for kidney cancer by smoking, in particular for the cohort studies.
As discussed in Section C.I, publication bias was examined in several different ways, and
there is no indication of publication bias for these lung cancer studies (results not shown). If
anything, the relationship between study size and RR estimate is the opposite of what would be
expected if publication bias were occurring because the one large study is the only study with a
significantly increased RR estimate and incorporating studies with increasing SE one at a time,
generally shows a decrease in effect size with addition of the less precise studies.
C.5.2. Lung Cancer Effect in the Highest Exposure Groups
C.5.2.1. Selection of RR Estimates
The selected RR estimates for lung cancer in the highest TCE exposure categories, for
studies that provided such estimates, are presented in Table C-16. Six of the nine cohort studies
reported lung cancer risk estimates categorized by exposure level. As in Section C.5.1.1 for the
overall risk estimates, RR estimates for males and females combined were used, wherever
possible.
Three of the nine cohort studies (Axelson et al., 1994): (Hansen et al., 2001): (Zhao et al.,
2005) did not report lung cancer risk estimates categorized by exposure level, even though these
same studies reported such estimates for selected other cancer sites. Unlike for the other cancer
C-70
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types, we did not attempt to address the issue of unreported results by including RR estimates of
1 for the missing estimates. This is because, as discussed in Section C.5.1.1 above with respect
to estimate a female contribution to the Axelson et al. (1994) study, unlike for the other cancer
types, we are not testing a null hypothesis of no effect for lung cancer but rather investigating
whether smoking might be a confounder in the kidney cancer studies. Thus, we would not want
to bias the RRm estimate toward 1 in this case by including estimates of 1 for missing RR
values.
For Boice et al. (1999), only results for workers with "any potential exposure" were
presented by exposure category, and the referent group is workers not exposed to any solvent.
For Morgan et al. (1998), the primary analysis used results for the cumulative exposure
metric, and a sensitivity analysis was done with the results for the peak exposure metric.
For Raaschou-Nielsen et al. (2003), unlike for NHL and RCC, lung cancer results for the
subcohort with expected higher exposure levels were not presented, so the only highest-
exposure-group results were for duration of employment in the total cohort. Results for males
and females combined were estimated assuming a Poisson distribution.
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Table C-16. Selected RR estimates for lung cancer risk in highest TCE exposure groups
Study
Anttila et al.
(1995)
Boice et al.
(1999)
Morgan et al.
(1998)
Raaschou-
Nielsen et al.
(2003)
Radican et al.
(2008)
Zhao et al.
(2005)
RR
0.83
0.64
0.96
1.4
0.90
1.0
95%
LCL
0.33
0.46
0.72
1.2
0.63
0.68
95%
UCL
1.71
0.89
1.29
1.6
1.27
1.53
Exposure
category
100+ umol/L
U-TCA a
>5 yrs exposure
High
cumulative
exposure score
>5yrs
>25 unit-yrs
High exposure
score
logRR
-0.186
-0.446
-0.041
0.336
-0.105
0.020
SE
(logRR)
0.378
0.168
0.149
0.070
0.179
0.207
Alternate RR
estimates (95% CI)
None
None
1.07(0.82, 1.40) for
medium/high peak vs.
low/no
None
0.8(0.4, 1.7) for Blair
et al. (1998) incidence
1.1(0.60, 2.06) for
Zhao et al. (2005)
incidence.
Boice etal. (2006b):
0.80(0.46, 1.41) for
>4 yrs with any
potential exp;
0.86(0.56, 1.33) for
>5 yrs test stand
mechanic,
0.76(0.42, 1.36) for
>4 test-yrs.
Comments
SIR.
Mortality RR. For any potential exposure.
Adjusted for date of birth, dates 1st and last
employed, race, and sex. Referent group is
workers not exposed to any solvent.
Mortality RR. Adjusted for age and sex.
SIR. Male and female results presented
separately and combined assuming a Poisson
distribution.
Mortality hazard ratio. Male and female results
presented separately and combined (see text).
Time variable = age, covariate = race. Referent
group is workers with no chemical exposures.
Mortality RR. Males only. Adjusted for time
since 1st employment, SES, age.
"Mean personal TCA in urine. 1 umol/L = 0.1634 mg/L.
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For Radican et al. (2008), it should be noted that the referent group is workers with no
chemical exposures, not just no TCE exposure. Furthermore, results for exposure groups (based
on cumulative exposure scores) were reported separately for males and females and were
combined for this assessment using inverse-variance weighting, as in a fixed-effect meta-
analysis. Radican et al. (2008) present only mortality hazard ratio estimates by exposure group;
however, in an earlier follow-up of this same cohort, Blair et al. (1998) present both incidence
and mortality RR estimates by exposure group. There were no incident cases for females in the
highest exposure group in Blair et al. (1998) (and the expected number was not reported); thus,
for the same reasons we didn't use RR estimates of 1 for unreported RR estimates in the Axelson
et al. (1994), Hansen et al. (2001), and Zhao et al. (2005) studies discussed above, the male-only
results were used for the RR estimate without attempting to approximate a contribution to the RR
estimate from the females in the cohort. Radican et al. (2008) also present results for categories
based on frequency and pattern of exposure; however, subjects weren't distributed uniquely
across the categories (the numbers of cases across categories exceeded the total number of
cases); thus, it was difficult to interpret these results and they were not used in a sensitivity
analysis.
Unlike for kidney cancer, Zhao et al. (2005) present lung cancer RR estimates only for
unlagged exposures. The mortality results reflect more cases (33) in the highest exposure group
than do the incidence results (14), so the mortality RR estimate was used for the primary
analysis, and the incidence estimate was used in a sensitivity analysis. Sensitivity analyses were
also done using results from Boice et al. (2006b) in place of the Zhao et al. (2005) RR estimate.
The cohorts for these studies overlap, so they are not independent studies. Boice et al. (2006b)
report mortality RR estimates for lung cancer by years worked with any potential exposure, years
worked as a test stand mechanic, a job with potential TCE exposure, and by a measure that
weighted years with potential exposure from engine flushing by the number of flushes each year.
The Boice et al. (2006b) estimates are adjusted for years of birth and hire and for hydrazine
exposure.
C.5.2.2. Results of Meta-Analyses
Results from the meta-analyses that were conducted for lung cancer in the highest
exposure groups are summarized at the bottom of Table C-15 and reported in more detail in
Table C-17. The RRm estimate from the random-effects meta-analysis of the six studies with
results presented for exposure groups was 0.96 (95% CI: 0.72, 1.27). As with the overall results
for lung cancer, the highest-exposure-group results exhibited significant heterogeneity, with the
largest study (Raaschou-Nielsen et al., 2003) having a statistically significantly increased RR
estimate and the next largest (Boice et al., 1999) having a statistically significantly decreased RR
estimate (see Figure C-13). The remaining four studies all had nonsignificant RR estimates
closer to 1. Nonsignificance of the RRm estimate was not dependent on any single study,
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although removing Raaschou-Nielsen et al. (2003) decreased the RRm estimate to 0.86 and
removing Boice et al. (1999) increased the RRm estimate to 1.07. The RRm estimate was not
highly sensitive to alternate RR estimate selections. Use of the six alternate selections,
individually, resulted in RRm estimates that were all nonsignificant and that ranged from 0.92 to
0.98 (see Table C-17). As with the primary analysis, significant heterogeneity was observed for
all of the meta-analyses with alternate selections (see Table C-17).
The RRm estimate from the primary analysis of the highest exposure groups was the
same as that for the overall TCE analysis (0.96), indicating no evidence of an exposure-response
relationship and confirming the absence of evidence of an increased risk of lung cancer
associated with TCE exposure from these studies as a whole.
C.5.3. Discussion of Lung Cancer Meta-Analysis Results
Significant heterogeneity was observed in the lung cancer results (for both overall TCE
exposure and for the highest exposure groups) from the different studies, and there was no clear
explanation for the source(s) of the heterogeneity, as discussed in Section C.5.1.2. Nonetheless,
we conducted (random-effects) meta-analyses of the lung cancer results with the goal of
addressing the question of whether or not there was evidence of an association between TCE
exposure and lung cancer that might suggest that smoking could be confounding the kidney
cancer results, in particular in the cohort studies, which did not adjust for smoking.
Both the overall and highest-exposure-group analyses yielded nonsignificant RRm
estimates of 0.96 for lung cancer. Influence analyses and sensitivity analyses using alternate RR
estimate selection for various studies similarly found no evidence of an association between TCE
exposure and lung cancer from these studies as a whole. This finding suggests that there is no
confounding of the overall RRm for kidney cancer by smoking, in particular from the cohort
studies (see Section 4.4.2.3 for a more comprehensive discussion of the issue of potential
confounding of the kidney cancer results by smoking).
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Table C-17. Summary of some meta-analysis results for TCE (highest exposure groups) and lung cancer
Analysis
Primary analysis
Alternate RR
selections3
Model
Random
Fixed
Random
Random
Random
Random
RRm estimate
0.96
1.15
0.95
0.98
0.96
0.92-0.93
95% LCL
0.72
1.03
0.70
0.75
0.71
0.67-0.69
95% UCL
1.27
1.27
1.29
1.29
1.30
1.25
Heterogeneity
Significant
(p < 0.0002)
/ = 80%
Because of
significant
heterogeneity,
fixed-effect model
not appropriate
Significant
(p < 0.0003)
/ = 79%
Significant
(p = 0.0003)
/ = 79%
Significant
(p = 0.0002)
/ = 79%
Significant
(p < 0.0002)
/ = 81%
Comments
Nonsignificance of RRm not dependent on any single
study.
Significant elevation in RRm dependent on single
study, Raaschou-Nielsen et al. (2003). without which
the RRm would be nonsignificantly decreased (RRm
= 0.86,^ = 0.07).
With Blair et al. (1998) incidence RR instead of
Radican et al. (2008) mortality hazard ratio.
With Morgan et al. (1998) peak metric.
With Zhao et al. (2005) incidence.
With Boice et al. (2006b) alternates for Zhao et al.
(2005) (see text).
""Changing the primary analysis by one alternate RR each time.
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TCE Exposure and Lung Cancer - highest exposure groups
Study Relative Risk and 95% Cl RR
Anttila (1995)
Boice(1999)
Morgan (1998)
Raaschou-Nielien (2003)
0.83
LCL
0.33
UCL
1.71
Radican(2008)
0.64 0.46 0.89
0.96 0.72 1.29
1.40 1.20 1.60
0.90 0.63 1.27
1.00 0.68 1.53
0.96 0.72 1.27
Figure C-13. Meta-analysis of lung cancer and TCE exposure—highest
exposure groups. Random-effects model. Rectangle sizes reflect relative
weights of the individual studies. The summary estimate is in the bottom row,
represented by the diamond.
C.6. DISCUSSION OF STRENGTHS, LIMITATIONS, AND UNCERTAINTIES IN
THE META-ANALYSES
Meta-analysis provides a systematic way of objectively and quantitatively combining the
results of multiple studies to obtain a summary effect estimate. Use of meta-analysis can help
risk assessors avoid some of the potential pitfalls in overly relying on a single study or in making
more subjective qualitative judgments about the apparent weight of evidence across studies.
Combining the results of smaller studies also increases the statistical power to observe an effect,
if one exists. In addition, meta-analysis techniques assist in systematically investigating issues
such as potential publication bias and heterogeneity in a database.
While meta-analysis can be a useful tool for analyzing a database of epidemiological
studies, the analysis is limited by the quality of the input data. If the individual studies are
deficient in their abilities to observe an effect (in ways other than low statistical power, which
meta-analysis can help ameliorate), the meta-analysis will be similarly deficient. A critical step
in the conduct of a meta-analysis is to establish eligibility criteria and clearly and transparently
identify all relevant studies for inclusion in the meta-analysis. For the TCE database, a
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comprehensive qualitative review of available studies was conducted and eligible studies were
identified, as described in Appendix B, Section B.2.9.
Identifying all relevant studies may be hampered if publication bias has occurred.
Publication bias is a systematic error that can arise if statistically significant studies are more
likely to be published than nonsignificant studies. This can result in an upward bias on the effect
size measure (i.e., the RR estimate). To address this concern, potential publication bias was
investigated for the databases for which meta-analyses were undertaken. For the studies of
kidney cancer and liver cancer, there was no evidence of publication bias. For the studies of
NHL, there was some evidence of potential publication bias. It is uncertain whether this reflects
actual publication bias or rather an association between SE and effect size (as discussed in
Section C. 1, a feature of publication bias is that smaller studies tend to have larger effect sizes)
resulting for some other reason, e.g., a difference in study populations or protocols in the smaller
studies. Furthermore, if there is publication bias in this data set, it may be creating an upward
bias on the RR estimate, but this bias does not appear to account completely for the finding of an
increased NHL risk (see Section C.2.1.2).
Another concern in meta-analyses is heterogeneity across studies. Random-effects
models were used for the primary meta-analyses in this assessment because of the diverse nature
of the individual studies. When there is no heterogeneity across the study results, the random-
effects model will give the same result as a fixed-effect model. When there is heterogeneity, the
random-effects model estimates the between-study variance. Thus, when there is heterogeneity,
the random-effects model will generate wider CIs and be more "conservative" than a fixed-effect
model. However, if there is substantial heterogeneity, it may be inappropriate to combine the
studies at all. In cases of significant heterogeneity, it is important to try to investigate the
potential sources of the heterogeneity.
For the studies of kidney and liver cancer, there was no apparent heterogeneity across the
study results (i.e., random- and fixed-effects models gave identical summary estimates). For the
NHL studies, there was heterogeneity, but it was not statistically significant (p = 0.16). The
/ -value was 26%, suggesting low-to-moderate heterogeneity. When subgroup analyses were
done for the cohort and case-control studies separately, there was some heterogeneity in both
groups, but in neither case was it statistically significant. Further attempts to quantitatively
investigate the heterogeneity were not pursued because of limitations in the database. The
sources of heterogeneity are an uncertainty in the database of studies of TCE and NHL. Some
potential sources of heterogeneity, which are discussed qualitatively in Section C.2.3, include
differences in exposure assessment or in the intensity or prevalence of TCE exposures in the
study population and differences in NHL classification.
The joint occurrence of heterogeneity and potential publication bias in the database of
studies of TCE and NHL raises special concerns. Because of the heterogeneity, a random-effects
model should be used if these studies are to be combined; yet, the random-effects model gives
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relatively large weight to small studies, which could exacerbate the potential impacts of
publication bias. For the NHL studies, the summary RR estimates from the random-effects and
fixed-effect models are not very different (RRm = 1.23 [95% CI: 1.07, 1.42] and 1.21 [95% CI:
1.08, 1.35], respectively); however, the CI for the fixed-effect estimate does not reflect the
between-study variance and is, thus, overly narrow.
Heterogeneity was statistically significant for the lung cancer studies (p < 10"8) and the
/-value was 90%, indicating that the amount of heterogeneity was high. Nonetheless, (random-
effects) meta-analyses were conducted for the purpose of investigating the potential for smoking
to be confounding the kidney cancer results (see Sections C.5 and 4.4.2.3).
C.7. CONCLUSIONS
The strongest finding from the meta-analyses was for TCE and kidney cancer. The
summary estimate from the primary random-effects meta-analysis of the 15 studies was
RRm = 1.27 (95% CI: 1.13, 1.43). There was no apparent heterogeneity across the study results
(i.e., fixed-effect model gave same summary estimate), and there was no evidence of potential
publication bias. The summary estimate was robust across influence and sensitivity analyses; the
estimate was not markedly influenced by any single study, nor was it overly sensitive to
individual RR estimate selections. The findings from the meta-analyses of the highest exposure
groups for the studies that provided kidney cancer results categorized by exposure level were
similarly robust. The summary estimate was RRm = 1.58 (95% CI: 1.28, 1.96) for the 13 studies
included in the analysis. There was no apparent heterogeneity in the highest-exposure-group
results, and the estimate was not markedly influenced by any single study, nor was it overly
sensitive to individual RR estimate selections. In sum, these robust results support a conclusion
that TCE exposure increases the risk of kidney cancer.
The meta-analyses of the overall effect of TCE exposure on NHL also suggest a small,
statistically significant increase in risk. The summary estimate from the primary random-effects
meta-analysis of the 17 studies was 1.23 (95% CI: 1.07, 1.42). This result was not overly
influenced by any single study, nor was it overly sensitive to individual RR estimate selections.
There is some evidence of potential publication bias in the NHL study data set; however, it is
uncertain that this is actually publication bias rather than an association between SE and effect
size resulting for some other reason, e.g., a difference in study populations or protocols in the
smaller studies. Furthermore, if there is publication bias, it does not appear to account
completely for the findings of an increased NHL risk. There was some heterogeneity across the
results of the 17 studies, but it was not statistically significant (p = 0.16). The /2-value was 26%,
suggesting low-to-moderate heterogeneity. The source(s) of this heterogeneity remains an
uncertainty. The summary estimate from the meta-analysis of the highest exposure groups for
the 13 studies which provided NHL results categorized by exposure level was RRm = 1.43
(95% CI: 1.13, 1.82). The statistical significance of the increased RR estimate for the highest
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exposure groups was not dependent on any single study, nor was it sensitive to individual RR
estimate selections. Although there was some heterogeneity across the 13 highest-exposure-
group studies, it was not statistically significant (p = 0.30) and the /2-value was 14%, suggesting
that the amount of heterogeneity was low. Furthermore, the heterogeneity is dependent on a
single study, Cocco et al. (2010), suggesting that the RR estimate for the highest exposure group
from that study is a relative outlier. Overall, the robustness of the finding of an increased NHL
risk for the highest exposure groups strengthens the more moderate evidence from the meta-
analyses for overall effect.
The meta-analyses of the overall effect of TCE exposure on liver (and gall bladder/biliary
passages) cancer also suggest a small, statistically significant increase in risk, but the study
database is more limited. The summary estimate from the primary random-effects meta-analysis
of the nine (all cohort) studies was 1.29 (95% CI: 1.07, 1.56). The analysis was dominated by
one large study that contributed about 53% of the weight. When this study was removed, the
RRm estimate decreased somewhat and was less precise (RRm = 1.22; 95% CI: 0.93, 1.61). The
summary estimate was not overly influenced by any other single study, nor was it overly
sensitive to individual RR estimate selections. There was no evidence of publication bias in this
data set, and there was no observable heterogeneity across the study results. However, the
findings from the meta-analyses of the highest exposure groups for the studies that provided liver
cancer results categorized by exposure level do not add support to the overall effect findings.
The summary estimate was RRm = 1.28 (95% CI: 0.93, 1.77) for the eight studies included in the
analysis, which is slightly lower than the summary estimate for the overall effect. This
contradictory result is driven by the fact that the RR estimate for the highest exposure group in
the individual study which contributes the majority of the weight to the meta-analyses, although
>1, was less than the overall RR estimate for the same study. In sum, these results do not rule
out an effect of TCE on liver cancer, because the liver cancer results are relatively underpowered
with respect to numbers of studies and number of cases and the overwhelming study in terms of
weight uses the weak exposure surrogate of duration of employment for categorizing exposure
level; however, at present, there is only modest support for an increased risk of liver cancer.
Meta-analyses were also conducted for lung cancer with the goal of addressing the question of
whether or not there was evidence of an association between TCE exposure and lung cancer that
might suggest that smoking could be confounding the kidney cancer results, in particular in the
cohort studies, which did not adjust for smoking. Both the overall and highest-exposure-group
random-effects meta-analyses yielded a nonsignificant RRm estimate of 0.96 for lung cancer.
Influence analyses and sensitivity analyses using alternate RR estimate selection for various
studies similarly found no evidence of an association between TCE exposure and lung cancer
from these studies as a whole. This finding suggests that there is no confounding of the overall
RRm for kidney cancer by smoking (see Section 4.4.2.3 for a more comprehensive discussion of
the issue of potential confounding of the kidney cancer results by smoking).
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D. NEUROLOGICAL EFFECTS OF TCE
D.I. HUMAN STUDIES ON THE NEUROLOGICAL EFFECTS OF TCE
There is an extensive body of evidence in the literature on the neurological effects caused
by exposure to TCE in humans. The primary functional domains that have been studied and
reported are trigeminal nerve function and nerve conductivity (latency), psychomotor effects,
including RTs (simple and choice), visual and auditory effects, cognition, memory, and
subjective neurological symptoms, such as headache and dizziness. This section discusses the
primary studies presented for each of these effects. Summary tables for all of the human TCE
studies are at the end of this section.
D.I.I. Changes in Nerve Conduction
There is strong evidence in the literature that exposure to TCE results in impairment of
trigeminal nerve function in humans exposed occupationally, by inhalation, or environmentally,
by ingestion. Functional measures such as the blink reflex and masseter reflex tests were used to
determine if physiological functions mediated by the trigeminal nerve were significantly
impacted. Additionally, TSEPs were also measured in some studies to ascertain if nerve activity
was directly affected by TCE exposure.
D.I.1.1. Blink Reflex and Masseter Reflex Studies—Trigeminal Nerve
Barret et al. (1984) conducted a study on 188 workers exposed to TCE occupationally
from small and large factories in France (type of factories not disclosed). The average age of the
workers was 41 (SD not provided, but authors noted 14% <30 years and 25% >50 years) and the
average exposure duration was 7 hours/day for 7 years. The 188 workers were divided into high-
and low-exposure groups for both TCE exposure measured using detector tubes and TCA levels
measured in urine. There was no unexposed control population, but responses in the high-
exposure group were compared response in the low-exposure group. TCE exposure groups were
divided into a low-exposure group (<150 ppm; n = 134) and a high-exposure group (>150 ppm;
n = 54). The same workers (n = 188) were also grouped by TCA urine measurements such that a
high exposure was >100 mg TCA/g creatinine. Personal factors including age, tobacco use, and
alcohol intake were also analyzed. No mention was made regarding whether or not the
examiners were blind to the subjects' exposure status. Complete physical examination including
testing visual performance (acuity and color perception), evoked trigeminal potential latencies
and audiometry, facial sensitivity, reflexes, and motoricity of the masseter muscles. %2 analysis
was used to examine distribution of the different groups for comparing high and low exposed
workers followed by one way ANOVA. Overall, 22/188 workers (11.7%) experienced
trigeminal nerve impairment (p < 0.01) as measured by facial sensitivity, reflexes (e.g., jaw,
D-l
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corneal, blink) and movement of the masseter muscles. When grouped by TCE exposure,
12/54 workers (22.2%) in the high-exposure group (>150 ppm) and 10/134 workers (7.4%) in the
low-exposure group had impaired trigeminal nerve mediated responses. When grouped by the
presence of TCA in the urine, 41 workers were now in the high TCA group and 10/41 workers
(24.4%) experienced trigeminal nerve impairment in comparison to the 12/147 (8.2%) in the low
TCA (<100 mg TCA/g creatinine) group. Statistically significant results were also presented for
the following symptoms based on TCE and TCA levels: trigeminal nerve impairment (p < 0.01),
asthenia (p < 0.01), optic nerve impairment (p < 0.001), and dizziness (0.05
-------
student's t-test was used for testing the difference between the group means for the blink reflex
component latencies. Because of the variability of R2 responses, this study focused primarily on
the Rl response latencies. Highly significant differences in the conduction latency means of the
blink reflex components for the TCE exposed population vs. control population were observed
when comparing means for the right and left side Rl to the controls. The mean Rl blink reflex
component latency for the exposed group was 11.35 ms, SD = 0.74 ms, 95% CI: 11.03-11.66.
The mean for the controls was 10.21 ms, SD = 0.78 ms, 95% CI: 9.92-10.51; (p < 0.001). The
study was well conducted with consistency of methods, and statistically significant findings for
trigeminal nerve function impairment resulting from environmental exposures to TCE.
However, the presence of other solvents in the well water, self selection of subjects involved in
litigation, and incomplete characterization of exposure present problems in drawing a clear
conclusion of TCE causality or dose-response relationship.
Kilburn and Warshaw (1993a) conducted an environmental study on 544 Arizona
residents exposed to TCE in well water. TCE concentrations were 6-500 ppb and exposure
ranged from 1 to 25 years. Subjects were recruited and categorized in three groups. Exposed
group 1 consisted of 196 family members with cancer or birth defects. Exposed group 2
consisted of 178 individuals from families without cancer or birth defects; and exposed group 3
included 170 parents whose children had birth defects and rheumatic disorders. Well water was
measured from 1957 to 1981 by several governmental agencies and average annual TCE
exposures were calculated and then multiplied by each individual's years of residence for
170 subjects. A referent group of histology technicians (n = 113) was used as a comparison for
the blink reflex test. For this test, recording electrodes were placed over the orbicularis oculi
muscles (upper and lower) and the blink reflex was elicited by gently tapping the glabeela
(located on the mid-frontal bone at the space between the eyebrows and above the nose). A two-
sided Student's t-test and linear regression were used for statistical analysis. Significant
increases in the Rl component of the blink reflex response was observed in the exposed
population as compared to the referent group. The Rl component measured from the right eye
appeared within 10.9 ms in TCE-exposed subjects, whereas in referents, this component
appeared 10.2 ms after the stimulus was elicited, indicating a significant delay (p < 0.008) in the
reflex response. Similarly, delays in the latency of appearance for the Rl component were also
noted for the left eye but the effect was not statistically significant (p = 0.0754). This study
shows statistically significant differences in trigeminal nerve function between subjects
environmentally exposed and nonexposed to TCE. This is an ecological study with TCE
exposure inferred to subjects by residence in a geographic area. Estimates of TCE
concentrations in drinking water to individual subjects are lacking. Additionally, litigation is
suggested and may introduce a bias, particularly if no validity tests were used.
Kilburn (2000a, 2002b) studied 236 residents (age range: 18-83 years old) lived nearby
manufacturing plants (e.g., microchip plants) in Phoenix, Arizona. Analysis of the groundwater
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in the residential area revealed contamination with many VOCs including TCE. Concentrations
of TCE in the well water ranged from 0.2 to > 10,000 ppb and the exposure duration varied
between 2 and 37 years. Additional associated solvents included dichloroethane (DCE),
perchloroethylene, and vinyl chloride. A group-match design was used to compare the
236 TCE-exposed residents to 161 unexposed regional referents and 67 referents in Northeastern
Phoenix in the blink reflex test. The blink reflex response was recorded from surface electrodes
placed over the location of the orbicularis oculi muscles. The reflex response was elicited by
gently tapping the left and right supraorbital notches with a small hammer. The Rl component
of the blink reflex response was measured for both the left and right eye. Statistically significant
increases in latency time for the Rl component was observed for residents exposed to TCE in
comparison to the control groups. In unexposed individuals, the Rl component occurred within
13.4 ms from the right eye and 13.5 ms from the left eye. In comparison, the residents near the
manufacturing plant had latency times of 14.2 ms (p < 0.0001) for the right eye and 13.9 ms
(p < 0.008) for the left eye. This study shows statistically significant differences between
environmentally exposed and unexposed populations for trigeminal nerve function, as a result of
exposures to TCE. This is an ecological study with TCE exposure potential to subjects inferred
by residence in a geographic area. Estimates of TCE concentrations in drinking water to
individuals are lacking. Additionally, litigation is suggested and may introduce a bias,
particularly if no validity tests were used.
Feldman et al. (1992) evaluated the blink reflex in 18 subjects occupationally exposed to
neurotoxic chemicals (e.g., degreasers, mechanics, and pesticide sprayers among many others).
Eight of the subjects were either extensively (n = 4) or occupationally (n = 4) exposed to TCE.
The remaining subjects (n = 10) were exposed to other neurotoxic chemicals, but not TCE.
Quantitative exposure concentration data were not reported in the study, but TCE exposure was
characterized as either "extensive" or "occupational." Subjects in the "extensive" exposure
group were chronically exposed (>1 year) to TCE at least 5 days/week and for at >50% of the
workday (n = 3) or experienced a direct, acute exposure to TCE for >15 minutes (n = 1).
Subjects in the "occupational" group were chronically exposed (>1 year) to TCE for 1-3
days/week and for >50% of the workday. The blink reflex responses from the TCE-exposed
subjects were compared to a control group consisting of 30 nonexposed subjects with no noted
neurological disorders. Blink reflex responses were measured using surface electrodes over the
lower lateral portion of the orbicularis oculi muscle. Electrical shocks with durations of 0.05 ms
were applied to the supraorbital nerve to generate the Rl and R2 responses. All of the subjects
that were extensively exposed to TCE had significantly increased latency times in the appearance
of the Rl component (no/?-value listed) and for three subjects, this increased latency time
persisted for at least 1 month and up to 20 years postexposure. However, none of the subjects
occupationally exposed to TCE had changes in the blink reflex response in comparison to the
control group. In comparing the remaining neurotoxicant-exposed subjects to the TCE-exposed
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individuals, the sensitivity, or the ability of a positive blink reflex test to identify correctly those
who had TCE exposure was 50%. However, in workers with no exposure to TCE, 90%
demonstrated a normal Rl latency.
Mixed results were obtained in a study by Ruijten et al. (1991) on 31 male printing
workers exposed to TCE. The mean age was 44; mean exposure duration was 16 years and had
at least 6 years of TCE exposure. The control group consisted of 28 workers with a mean age
45 years. Workers in the control group were employed at least 6 years in print factories (similar
to TCE-exposed), had no exposure to TCE, but were exposed to "turpentine-like organic
solvents." TCE exposure potential was inferred from historical monitoring of TCE at the plant
using gas detection tubes. These data indicated TCE concentrations in the 1960s of around
80 ppm, mean concentration of 70 ppm in the next decade, with measurements from 1976 and
1981 showing a mean concentration of 35 ppm. The most recent estimate of TCE concentrations
in the factory was 17 ppm (stable for 3 years) at the time of the report. The authors calculated
that mean cumulative TCE exposure would be 704 ppm x years worked in factory. The masseter
and blink reflexes were measured to evaluate trigeminal nerve function in TCE-exposed and
control workers. For measurement of the masseter reflex, surface electrodes were attached over
the right masseter muscle (over the cheek area). A gentle tap on a roller placed under the
subject's chin was used to elicit the masseter reflex. For measurement of the blink reflex,
surface electrodes were placed on the muscle near the upper eyelid. Electrical stimulation of the
right supraorbital nerve was used to generate the blink reflex. There was a significant increase in
the latency of the masseter reflex to appear for the TCE-exposed workers (p < 0.05). However,
there was no significant change in the blink reflex measure between TCE-exposed workers and
control. Although no change in the blink reflex measures were observed between the two
groups, it should be noted that the control group was exposed to other volatile organic solvents
(not specified) and this VOC exposure could be a possible confounder for determination of
TCE-induced effects.
There are two studies that reported no effect of TCE exposure on trigeminal nerve
function (Rasmussen et al., 1993a: El Ghawabi et al., 1973). El Ghawabi et al. (1973) conducted
a study on 30 money printing shop workers occupationally exposed to TCE. Metabolites of total
TCA and TCOH were found to be proportional to TCE concentrations up to 100 ppm
(550 mg/m3). Controls were 20 age- and SES-matched nonexposed males and 10 control
workers not exposed to TCE. Trigeminal nerve involvement was not detected, but the authors
failed to provide details as to how this assessment was made. It is mentioned that each subject
was clinically evaluated and trigeminal nerve involvement may have been assessed through a
clinical evaluation. As a result, the conclusions of this study are tempered since the authors did
not provide details as to how trigeminal nerve function was evaluated in this study.
Rasmussen et al. (1993a) conducted an historical cohort study on 99 metal degreasers.
Subjects were selected from a population of 240 workers from 72 factories in Denmark. The
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participants were divided into three groups based on solvent exposure durations where low
exposure was up to 0.5 years, medium was 2.1 years and high was 11.0 years (mean exposure
duration). Most of the workers (70/99) were primarily exposed to TCE with an average exposure
duration of 7.1 years for 35 hours/week. TCA and TCOH levels were measured in the urine
samples provided by the workers and mean TCA levels in the high group was 7.7 mg/L and was
as high as 26.1 mg/L. Experimental details of trigeminal nerve evaluation were not provided by
the authors. It was reported that 1/21 people (5%) in the low-exposure group, 2/37 (5%) in the
medium-exposure group, and 4/41 (10%) in the high-exposure group experienced abnormalities
in trigeminal nerve sensory function. No linear association was seen on trigeminal nerve
function (Mantel-Haenzel test for linear association,/? = 0.42). However, the trigeminal nerve
function findings were not compared to a control (no TCE exposure) group and it should be
noted that some of the workers (29/99) were not exposed to TCE.
D.l.1.2. TSEP Studies—Trigeminal Nerve
In a preliminary study, Barret et al. (1982) measured TSEPs) in 11 workers that were
chronically exposed to TCE. Nine of these workers were suffering effects from TCE
intoxication (changes in facial sensitivity and clinical changes in trigeminal nerve reflexes), and
two were TCE-exposed without exhibiting any clinical manifestations from exposure. A control
group of 20 nonexposed subjects of varying ages were used to establish the normal response
curve for the trigeminal nerve function. In order to generate a TSEP, a surface electrode was
placed over the lip and a voltage of 0.05 ms in duration was applied. The area was stimulated
500 times at a rate of 2 times/second. TSEPs were recorded from a subcutaneous electrode
placed between the international CZ point (central midline portion of the head) and the ear. In
8 of the 11 workers, an increased voltage ranging from a 25 to a 45 volt increase was needed to
generate a normal TSEP. Two of the 11 workers had an increased latency of appearance for the
TSEP and 3 workers had increases in TSEP amplitudes. The preliminary findings indicate that
TCE exposure results in abnormalities in trigeminal nerve function. However, the study does not
provide any exposure data and lacks information with regards to the statistical treatment of the
observations.
Barret et al. (1987) conducted a study on 104 degreaser machine operators in France
(average age = 41.6 years; range = 18-62 years) who were highly exposed to TCE with an
average exposure of 7 hours/day for 8.23 years. Although TCE exposure concentrations were
not available, urinary concentrations of TCOH and TCA were measured for each worker. A
control group consisting of 52 subjects without any previous solvent exposure and neurological
deficits was included in the study. Trigeminal nerve symptoms and TSEPs were collected for
each worker. Trigeminal nerve symptoms were clinically assessed by examining facial
sensitivity and reflexes dependent on this nerve such as the jaw and blink reflex. TSEPs were
elicited by electrical stimulation (70-75 V for 0.05 ms) of the nerve using an electrode on the lip
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commissure. Eighteen out of 104 TCE-exposed machine operators (17.3%) had trigeminal nerve
symptoms. The subjects that experienced trigeminal nerve symptoms were significantly older
(47.8 years vs. 40.5; p < 0.001). Both groups had a similar duration of exposure with a mean of
9.2 years in the sensitive group and 7.8 years in the nonsensitive group. Urinary concentrations
of TCOH and TCA were also statistically similar although the levels were slightly higher in the
sensitive group (245 vs. 162 mg/g creatinine for TCOH; 131 vs. 93 mg/g creatinine for TCA).
However, in the same group, 40/104 subjects (38.4%) had an abnormal TSEP. Abnormal TSEPs
were characterized as potentials that exhibited changes in latency and/or amplitude that were at
least 2.5 times the SD of the normal TSEPs obtained from the control group. Individuals with
abnormal TSEP were significantly older (45 vs. 40.1 years;/? < 0.05) and were exposed to TCE
longer (9.9 vs. 5.6 years;/? < 0.01). Urinary concentrations TCOH and TCA were similar
between the groups with sensitive individuals having average metabolite levels of 195 mg
TCOH/g creatinine and 98.3 mg TCA/g creatinine in comparison to 170 mg TCOH/g creatinine
and 96 mg TCA/g creatinine in nonsensitive individuals. When a comparison was made between
workers that had normal TSEP and no trigeminal symptoms and workers that had an abnormal
TSEP and experienced trigeminal symptoms, it was found that in the sensitive individuals
(abnormal TSEP and trigeminal symptoms) there was a significant increase in age (48.5 vs.
39.5 years old,/? < 0.01), duration of exposure (11 vs. 7.5 years,/? < 0.05) and an increase in
urinary TCA (313 vs. 181 mg TCA/g creatinine). No significant changes were noted in urinary
TCOH, but the levels were slightly higher in sensitive individuals (167 vs. 109 mg TCOH/g
creatinine). Overall, it was concluded that abnormal TSEPs were recorded in workers who were
exposed to TCE for a longer period (average duration 9.9 years). This appears to be a well-
designed study with statistically significant results reported for abnormal trigeminal nerve
response in TCE exposed workers. Exposure assessment to TCE is by exposure duration and
mean urinary TCOH and TCA concentrations. TCE concentrations to exposed subjects as
measured by atmospheric or personal monitoring are lacking.
Mhiri et al. (2004) measured TSEPs from 23 phosphate industry workers exposed to TCE
for 6 hours/day for at least 2 years while cleaning tanks. Exposure assessment was based on
measurement of urinary metabolites of TCE, which were performed 3 times/worker, and air
measurements. Blood tests and hepatic enzymes were also collected. The mean exposure
duration was 12.4 ± 8.3 years (exposure duration range = 2-27 years). Although TCE exposures
were not provided, mean urinary concentrations of TCOH, TCA, and total trichlorides were
79.3 ± 42, 32.6 ± 22, and 111.9 ± 55 mg/g urinary creatinine, respectively. The control group
consisted of 23 unexposed workers who worked in the same factory without being exposed to
any solvents. TSEPs were generated from a square wave pulses (0.1 ms in duration) delivered
through a surface electrode that was placed 1 cm under the corner of the mouth. The responses
to the stimuli (TSEPs) were recorded from another surface electrode that was placed over the
contralateral parietal area of the brain. The measured TSEP was divided into several
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components and labeled according to whether it was: (1) a positive (P) or negative (N) potential
and (2) the placement of the potential in reference to the entire TSEP (e.g., PI is the first positive
potential in the TSEP). TSEPs generated from the phosphate workers that were ±2.5 times the
SD from the TSEPs obtained from the control group were considered abnormal. Abnormal
TSEP were observed in six workers with clinical evidence of trigeminal involvement and in nine
asymptomatic workers. Significant increases in latency were noted for all TSEP potentials (Nl,
PI, N2, P2, N3,/> < 0.01) measured from the phosphate workers. Additionally, significant
decreases in the PI (p < 0.02) and N2 (p < 0.05) amplitudes were observed. A significant
positive correlation was demonstrated between duration of exposure and the N2 latency
(p < 0.01) and P2 latency (p < 0.02). Only one subject had urinary TCE metabolite levels over
tolerated limits. TCE air contents were over tolerated levels, ranging from 50 to 150 ppm (275-
825 mg/m3). The study is well presented with statistically significant results for trigeminal nerve
impairment resulting from occupational exposures to TCE. Exposure potential to TCE is defined
by urinary biomarkers, TCA, TTCs, and TCOH. The study lacks information on atmospheric
monitoring of TCE in this occupational setting.
D.I.1.3. Nerve Conduction Velocity Studies
Nerve conduction latencies were also studied in two occupational studies by Triebig et al.
(1983; 1982) using methods for measurement of nerve conduction that differ from most
published studies, but the results indicate a potential impact on nerve conduction following
occupational TCE exposure. There was no impact seen on latencies in the 1982 study, but a
statistically significant response was observed in the latter study. The latter study, however, is
confounded by multiple solvent exposures.
In Triebig et al. (1982), 24 healthy workers (20 males, 4 females) were exposed to TCE
occupationally at three different plants. The ages ranged from 17 to 56 years, and length of
exposure ranged from 1 to 258 months (mean 83 months). TCE concentrations measured in air
at work places ranged from 5 to 70 ppm (27-385 mg/m3). A control group of 144 healthy,
complaint-free individuals were used to establish 'normal' responses on the nerve conduction
studies. The matched control group consisted of 24 healthy nonexposed individuals (20 males,
4 females), chosen to match the subjects for age and sex. TCA, TCE, and TCOH were measured
in blood, and TCE and TCA were measured in urine. Nerve conduction velocities were
measured for sensory and motor nerve fibers using the following tests: MCVMAx (U): Maximum
NLG of the motor fibers of the N. ulnaris between the wrist joint and the elbow; dSCV (U):
Distal NLG of mixed fibers of the N. ulnaris between finger V and the wrist joint; pSCV (U):
Proximal NLG of sensory fibers of the N. medianus between finger V and Sulcus ulnaris; and
dSCV (M): Distal NLG of sensory fibers of the N. medianus between finger III and the wrist
joint. Data were analyzed using parametric and nonparametric tests, rank correlation, linear
regression, with 5% error probability. Results show no statistically significant difference in
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nerve conduction velocities between the exposed and unexposed groups. This study has
measured exposure data, but exposures/responses are not reported by dose levels.
Triebig et al. (1983) has a similar study design to the previous study (Triebig et al., 1982)
in the tests used for measurement of nerve conduction velocities, and in the analysis of blood and
urinary metabolites of TCE. However, in this study, subjects were exposed to a mixture of
solvents, including TCE, specifically "ethanol, ethyl acetate, aliphatic hydrocarbons (gasoline),
methyl ethyl ketone (MEK), toluene, and trichloroethene." The exposed group consists of
66 healthy workers selected from a population of 112 workers. Workers were excluded based on
polyneuropathy (n = 46) and alcohol consumption (n = 28). The control group consisted of
66 healthy workers with no exposures to solvents. Subjects were divided into three exposure
groups based on length of exposure, as follows: 20 employees with "short-term exposure" (7-
24 months); 24 employees with "medium-term exposure" (25-60 months); and 22 employees
with "long-term exposure" (>60 months). TCA, TCE, and TCOH were measured in blood, and
TCE and TCA were measured in urine. Subjects were divided into exposure groups based on
length of exposures, and results were compared for each exposure group to the control group. In
this study, there was a dose-response relationship observed between length of exposure to mixed
solvents and statistically significant reduction in nerve conduction velocities observed for the
medium and long-term exposure groups for the ulnar nerve (NCV). Interpretation of this study is
limited by the mixture of solvent exposure, with no results reported for TCE alone.
D.1.2. Auditory Effects
There are three large environmental studies reported that assessed the potential impact of
TCE exposures through groundwater ingestion on auditory functioning. They present mixed
results. All three studies were conducted on the population in the TCE Subregistry from the
National Exposure Registry (NER) developed by the ATSDR. The two studies conducted by
Burg et al. (1999; 1995) report an increase in auditory effects associated with TCE exposure, but
the auditory endpoints were self reported by the population, as opposed to testing of measurable
auditory effects in the subject population. The third of these studies, reported by ATSDR (2002),
conducted measurements of auditory function on the subject population, but failed to
demonstrate a positive relationship between TCE exposure and auditory effects. Results from
these studies strongly suggest that children <9 years old are more susceptible to hearing
impairments from TCE exposure than the rest of the general population. These studies are
described below.
Burg et al. (1995) conducted a study on registrants in the National Health Interview
Survey (NHIS) TCE subregistry of 4,281 (4,041 living and 240 deceased) residents
environmentally exposed to TCE via well water in Indiana, Illinois, and Michigan. Morbidity
baseline data were examined from the TCE Subregistry from the NER developed by the ATSDR.
Participants were interviewed in the NHIS, which consists of 25 questions about health
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conditions. Data were self reported via face-to-face interviews. Neurological endpoints were
hearing and speech impairments. This study assessed the long-term health consequences of
long-term, low-level exposures to TCE in the environment. The collected data were compared to
the NHIS, and the National Household Survey on Drug Abuse. Poisson Regression analysis
model was used for registrants >19 years old. The statistical analyses performed treated the
NHIS population as a standard population and applied the age- and sex-specific period
prevalence and prevalence rates obtained from the NHIS data to the corresponding age- and sex-
specific denominators in the TCE Subregistry. This one-sample approach ignored sampling
variability in the NHIS data because of the large size of the NHIS database when compared to
the TCE Subregistry data file. A binomial distribution was assumed in estimating SEs for the
TCE Subregistry data. Weighted age- and sex-specific period prevalence and prevalence rates
by using the person-weights were derived for the TCE Subregistry. These "standard" rates were
applied to the corresponding TCE Subregistry denominators to obtain expected counts in each
age and sex combination. In the NHIS sample, 18% of the subjects were nonwhite. In the TCE
Subregistry sample, 3% of the subjects were nonwhite. Given this discrepancy in the proportion
of nonwhites and the diversity of races reported among the nonwhites in the TCE Subregistry,
the statistical analyses included 3,914 exposed white TCE registrants who were alive at baseline.
TCE registrants that were <9 years old had a statistically significant increase in hearing
impairment as reported by the subjects. The RR in this age group for hearing impairments was
2.13. The RR decreased to 1.12 for registrants aged 10-17 years and to <0.32 for all other age
groups. As a result, the effect magnitude was lower for children 10-17 years and for all other
age groups. The study reports a dose-response relationship, but the hearing effects are self-
reported, and exposure data are modeled estimates.
Burg and Gist (1999) reported a study conducted on the same Subregistry population
described for Burg et al. (1995). It investigated intrasubregistry differences among 3,915 living
members of the National Exposure Registry's Trichloroethylene Subregistry (4,041 total living
members). The participants' mean age was 34 years (SD = 19.9 years), and included children in
the registry. All registrants had been exposed to TCE through domestic use of contaminated well
water. All were Caucasian. All registrants had been exposed to TCE though domestic use of
contaminated well water; there were four exposure subgroups, each divided into quartiles:
(1) maximum TCE measured in well water, exposure subgroups include 2-12, 12-60, and 60-
800 ppb; (2)cumulative TCE exposure subgroups include <50, 50-500, 500-5,000, and
>5,000 ppb; (3) cumulative chemical exposure subgroups include TCA, DCE, DCA, in
conjunction with TCE, with the same exposure Categories as in # 2; and (4) duration of exposure
subgroups include <2, 2-5, 5-10, and >10 years; 2,867 had TCE exposure of <50 ppb; 870 had
TCE exposure of 51-500 ppb; 190 had TCE exposure of 501-5,000 ppb; and 35 had TCE
exposure >5,000 ppb. The lowest quartile was used as a control group. Interviews included
occupational, environmental, demographic, and health information. A large number of health
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outcomes were analyzed, including speech impairment and hearing impairment. Statistical
methods used include Logistic Regression and ORs. The primary purpose was to evaluate the
rate of reporting health-outcome variables across exposure categories. The data were evaluated
for an elevation of the risk estimates across the highest exposure categories or for a dose-
response effect, while controlling for potential confounders. Estimated prevalence ORs for the
health outcomes, adjusted for the potential confounders, were calculated by exponentiating the
p-coefficients from the exposure variables in the regression equations. The SE of the estimate
was used to calculate 95% CIs. The referent group used in the logistic regression models was the
lowest exposure group. The results variables were modeled as dichotomous, binary dependent
variables in the regression models. Nominal, independent variables were modeled, using dummy
variables. The covariables used were sex, age, occupational exposure, education level, smoking
history, and the sets of environmental subgroups. The analyses were restricted to persons >19
years old when the variables of occupational history, smoking history, and education level were
included. When the registrants were grouped by duration of exposure to TCE, a statistically
significant association (adjusted for age and sex) between duration of exposure and reported
hearing impairment was found. The prevalence ORs were 2.32 (95% Cl: 1.18, 4.56) (>2-
<5 years); 1.17 (95% Cl: 0.55, 2.49) (>5-<10 years); and 2.46 (95% Cl = 1.30, 5.02) (>10 years).
Higher rates of speech impairment (although not statistically significant) were associated with
maximum and cumulative TCE exposure, and duration of exposure. The study reports dose-
response relationships, but the effects are self reported, and exposure data are estimates. No
information was reported on presence or absence of additional solvents in drinking water.
ATSDR (2002) conducted a follow-up study to the TCE subregistry findings (Burg and
Gist 1999; Burg et al., 1995) and focused on the subregistry children. Of the 390 subregistry
children (<10 years old at time of original study), 116 agreed to participate. TCE exposure
ranged from 0.4 to 5,000 ppb from the drinking water. The median TCE exposure for this
subgroup was estimated to be 23 ppb per year of exposure. To further the hearing impairments
reported in Burg et al. (1999; 1995), comprehensive auditory tests were conducted with the 116
children and compared to a control group of 182 children that was age-matched. The auditory
tests consisted of a hearing screening (typanometry, pure tone and distortion product otoacoustic
emissions [DPOAE]) and a more in-depth hearing evaluation for children that failed the initial
screening. Ninety percent of the TCE-exposed children passed the typanometry and pure tone
tests, and there were no significant differences between control and TCE-exposed groups.
Central auditory processing tests were also conducted and consisted of a test for acoustic reflexes
and a screening test for auditory processing disorders (SCAN). The acoustic reflex tested the
ipsilateral and contralateral auditory pathway at 1,000 Hz for each ear. In this test, each subject
hears the sound frequency and determines if the sound causes the stapedius muscle to tighten the
stapes (normal reflex to noise). Approximately 20% of the children in the TCE subregistry and
5-7% in the controls exhibited an abnormal acoustic reflex, and this increased abnormality in the
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test was a significant effect (p = 0.003). No significant effects were noted in the SCAN tests.
The authors concluded that the significant decrease in the acoustic reflex for the TCE subregistry
children is reflective of potential abnormalities in the middle ear, which may reflect
abnormalities in lower brainstem auditory pathway function. Lack of effects with the pure tone
and typanometry tests suggests that the cochlea is not affected by TCE exposure.
Although auditory function was not directly measured, Rasmussen et al. (1993c) used a
psychometric test to measure potential auditory effects of TCE exposure in an environmental
study. Results from 96 workers exposed to TCE and other solvents were presented in this study.
The workers were divided into three exposure groups: low, medium, and high. Details of the
exposure groups and exposure levels are provided in Table 4-22 [under study description of
Rasmussen et al. (1993c)1. Three auditory-containing tasks were included in this study, but only
the acoustic motor function test could be used for evaluation of auditory function. In the
acoustic motor function test, high and low frequency tones were generated and heard through a
set of earphones. Each individual then had to imitate the tones by knocking on the table using
the flat hand for a low frequency and using a fist for a high frequency. A maximal score of 8
could be achieved through this test. The tones were provided in either a set of one or three
groups. In the one group acoustic motor function test, the average score for the low-exposure
group was 4.8 in comparison to 2.3 in the high-exposure group. Similar decrements were noted
in the 3-group acoustic motor function test. A significant association was reported for TCE
exposure and performance on the one group acoustic motor function test (p < 0.05) after
controlling for confounding variables.
D.I.3. Vestibular Effects
The data linking acute TCE exposure with transient impairment of vestibular function are
quite strong based on human chamber studies, occupational exposure studies, and laboratory
animal investigations. It is clear from the human literature that these effects can be caused by
exposures to TCE, as they have been reported extensively in the literature.
The earliest reports of neurological effects resulting from TCE exposures focused on
subjective symptoms, such as headaches, dizziness, and nausea. These symptoms are subjective
and self-reported, and, therefore, offer no quantitative measurement of cause and effect.
However, there is little doubt that these effects can be caused by exposures to TCE, as they have
been reported extensively in the literature, resulting from occupational exposures (Liu et al.,
1988; Rasmussen and Sabroe, 1986; Smith, 1970; Grand]ean et al., 1955), environmental
exposures (Hirsch et al., 1996), and in chamber studies (Stewart et al., 1970; Kvlin et al., 1967).
These studies are described below in more detail.
Grandjean et al. (1955) reported on 80 workers exposed to TCE from 10 different
factories of the Swiss mechanical engineering industry. TCE air concentrations varied from 6 to
1,120 ppm (33-6,200 mg/m3) depending on time of day and proximity to tanks, but mainly
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averaged between 20 and 40 ppm (100-200 mg/m3). Urinalysis (TCA) varied from 30 mg/L to
300 mg/L. This study does not include an unexposed referent group, although prevalences of
self-reported symptoms or neurological changes among the higher-exposure group are compared
to the lower-exposure group. Workers were classified based on their exposures to TCE and there
were significant differences (p = 0.05) in the incidence of neurological disorder between
Groups I (10-20 ppm), II (20-40 ppm; 110-220 mg/m3), and III (>40 ppm; 220 mg/m3). Thirty-
four percent of the workers had slight or moderate psycho-organic syndrome; 28% had
neurological changes. Approximately 50% of the workers reported incidences of vertigo and
30% reported headaches (primarily an occasional and/or minimal disorder). Based on TCA
eliminated in the urine, results show that subjective, vegetative, and neurological disorders were
more frequent in Groups II (40-100 mg/L) and III (101-250 mg/L) than in Group I (10-
39 mg/L). Statistics do support a dose-effect relationship between neurological effects and TCE
exposure, but exposure data are questionable.
Liu et al. (1988) evaluated the effects of occupational TCE exposure on 103 factory
workers in Northern China. The workers (79 men, 24 women) were exposed to TCE during
vapor degreasing production or operation. An unexposed control group of 85 men and
26 women was included for comparison. Average TCE exposure was mostly at <50 ppm
(275 mg/m3). The concentration of breathing zone air during entire shift was measured by
diffusive samplers placed on the chest of each worker. Subjects were divided into three exposure
groups; 1-10 ppm (5.5-55 mg/m3), 11-50 ppm (60-275 mg/m3), and 51-100 ppm (280-
550 mg/m3). Results were based on a self-reported subjective symptom questionnaire. The
frequency of subjective symptoms, such as nausea, drunken feeling, light-headedness, floating
sensation, heavy feeling of the head, forgetfulness, tremors and/or cramps in extremities, body
weight loss, changes in perspiration pattern, joint pain, and dry mouth (all >3 times more
common in exposed workers); reported as 'prevalence of affirmative answers', was significantly
greater in exposed workers than in unexposed (p < 0.01). "Bloody strawberry jam-like feces"
was borderline significant in the exposed group and "frequentflatus" was statistically
significant. Dose-response relationships were established (but not statistically significant) for
symptoms. Most workers were exposed at <10 ppm, and some at 11-50 ppm. The differences
in exposure intensity between men and women was of borderline significance (0.05
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hours/week; 57% TCE and 37% 1,1,1-trichloroethane); (2) currently working with other solvents
(n = 131; petroleum, gasoline, toluene, xylene); (3) previously (1-5 years.) worked with
chlorinated or other solvents (n = 66); and (4) never worked with organic solvents (n = 94). A
dose-response relationship was observed between exposure to chlorinated solvents and chronic
neuropsychological symptoms including vestibular system effects such as dizziness (p < 0.005),
and headache (p < 0.01). The authors indicated that TCE exposure resulted in the most overall
symptoms. Significant associations were seen between previous exposure and consumption of
alcohol with chronic neuropsychological symptoms. Results are confounded by exposures to
additional solvents.
Smith (1970) conducted an occupational study on 130 workers (108 males, 22 females)
exposed to TCE (industry not reported). The control group consisted of 63 unexposed men
working at the same factories matched by age, marital status, and other nonspecified criteria. A
referent group was included and consisted of 112 men and women exposed to low concentration
of lead and matched to the TCE exposed group in age and sex distribution. Seventy-three out of
130 workers (56.2%) reported dizziness and 23 workers reported having headaches (17.7%).
The number of complaints reported by subjects was greater for those with >60 mg/L TCA than
for those with <60 mg/L TCA. There was no difference in the number of symptoms reported
between those with shorter durations of exposure and those with longer durations of exposure.
No statistics were reported.
Hirsch et al. (1996) evaluated the vestibular effects of an environmental exposure to TCE
in Roscoe, Illinois residents. A medical questionnaire was mailed to 103 residents of Roscoe
with 100% response. These 103 and an additional 15 residents, not previously surveyed, brought
the subject population to 118 residents. During the course of testing, 12 subjects (young children
and uncooperative patients) were excluded bringing the total number of subjects to 106, all of
whom were in the process of taking legal action against the company whose industrial waste was
assumed to be the source of the polluting TCE. This was a case series report with no controls.
Random testing of the wells between 1983 and 1984 revealed groundwater in wells to have
levels of TCE between 0 and 2,441 ppb. The distance of residence from contaminated well was
used to estimate exposure level. Sixty-six subjects (62%) complained of headaches at the time of
evaluation. Diagnosis of TCE-induced cephalagia was considered credible for 57 patients
(54%). Forty-seven of these had a family history of headaches. Retrospective TCE level of well
water or well's distance from the industrial site analysis did not correlate with the occurrence of
possibly-TCE induced headaches. This study shows a general association between headaches
and exposure to TCE in drinking water wells. There were no statistics to support a dose-
response relationship. All subjects were involved in litigation.
Stewart et al. (1970) evaluated vestibular effects in 13 subjects who were exposed to TCE
vapor 100 ppm (550 mg/m3) and 200 ppm (1,100 mg/m3) for periods of 1 hour to a 5-day work
week. Experiments 1-7 were for a duration of 7 hours with a mean TCE concentration of 198-
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200 ppm (1,090-1,100 mg/m3). Experiments 8 and 9 exposed subjects to 190-202 ppm (1,045-
1,110 mg/m3) TCE for a duration of 3.5 and 1 hour, respectively. Experiment 10 exposed
subjects to 100 ppm (550 mg/m3) TCE for 4 hours. Experiments 2-6 were carried out with the
same subjects over 5 consecutive days. Gas chromatography of expired air was measured.
There were no self controls. Subjects reported symptoms of lightheadedness, headache, eye,
nose, and throat irritation. Prominent fatigue and sleepiness by all were reported >200 ppm
(1,100 mg/m3). There were no quantitative data or statistics presented regarding dose and effects
of neurological symptoms.
Kylin et al. (1967) exposed 12 volunteers to 1,000 ppm (5,500 mg/m3) TCE for 2 hours
in a 1.5 x 2 x 2 meters chamber. Volunteers served as their own controls since 7 of the 12 were
pretested prior to exposure and the remaining 5 were post-tested days after exposure. Subjects
were tested for optokinetic nystagmus, which was recorded by electronystogmography, that is,
"the potential difference produced by eye movements between electrodes placed in lateral angles
between the eyes." Venous blood was also taken from the volunteers to measure blood TCE
levels during the vestibular task. The authors concluded that there was an overall reduction in
the limit ("fusion limit") to reach optokinetic nystagmus when individuals were exposed to TCE.
Reduction of the "fusion limit" persisted for up to 2 hours after the TCE exposure was stopped
and the blood TCE concentration was 0.2 mg/100 mL.
D.1.4. Visual Effects
Kilburn (2000a, 2002b) conducted an environmental study on 236 people exposed to
TCE in groundwater in Phoenix, Arizona. Details of the TCE exposure and population are
described earlier in Section D. 1.1.1 (see Kilburn, 2000a, 2002b). Among other neurological
tests, the population and 161 nonexposed controls was tested for color discrimination using the
desaturated Lanthony 15-hue test, which can detect subtle changes in color vision deficiencies.
Color discrimination errors were significantly increased in the TCE exposed population
(p < 0.05) with errors scores averaging 12.6 in the TCE exposed in comparison to 11.9 in the
control group. This study shows statistically significant differences in visual response between
exposed and nonexposed subjects exposed environmentally. Estimates of TCE concentrations in
drinking water to individual subjects are lacking.
Reif et al. (2003) conducted a cross sectional environmental study on 143 residents of the
Rocky Mountain Arsenal community of Denver whose water was contaminated with TCE and
related chemicals from nearby hazardous waste sites between 1981 and 1986. The residents
were divided into three groups based on TCE exposure with the lowest exposure group at
<5 ppb, the medium exposure group at 5-15 ppb and the high-exposure group defined as
>15 ppb TCE. Visual performance was measured by two different contrast sensitivity tests
(C and D) and the Benton visual retention test. In the two contrast sensitivity tests, there was a
20-22% decrease in performance between the low and high TCE exposure groups and
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approached statistical significance (p = 0.06 or 0.07). In the Benton visual retention test, which
measures visual perception and visual memory, scores, dropped by 10% from the lowest
exposure to the highest TCE exposure group and was not statistically significant. It should be
noted that the residents were potentially exposed to multiple solvents including TCE and a
nonexposed TCE group was not included in the study. Additionally, modeled exposure data are
only a rough estimate of actual exposures, and possible misclassification bias associated with
exposure estimation may limit the sensitivity of the study.
Rasmussen et al. (1993c) conducted a cross-sectional study on 96 metal workers, working
in degreasing at various factories in Denmark (industries not specified) with chlorinated solvents.
These subjects were identified from a larger cohort of 240 workers. Details of the exposure
groups and TCE exposure levels are presented in Section D. 1.1.1 [under Rasmussen et al.
(1993a)1. Neuropsychological tests including the visual gestalts (test of visual perception and
retention) and the stone pictures test (test of visual learning and retention) were administered to
the metal workers. In the visual gestalts test, cards with a geometrical figure containing four
items were presented and workers had to redraw the figure from memory immediately (learning
phase) after presentation and after 1 hour (retention phase). In the learning phase, the figures
were redrawn until the worker correctly drew the figure. The number of total errors significantly
increased from the low group (3.4 errors) to the high-exposure group (6.5 errors;/? = 0.01)
during the learning phase (immediate presentation). Similarly, during the retention phase of this
task (measuring visual memory), errors significantly increased from an average of 3.2 in the low
group to 5.9 in the high group (p < 0.001). In the stone pictures test, slides of 10 stones
(different shapes and sizes) were shown and the workers had to identify the 10 stones out of a
lineup of 25 stones. There were no significant changes in this task, but the errors increased from
4.6 in the low-exposure group to 6.3 in the high-exposure group during the learning phase of this
task. Although this study identifies visual performance deficits, a control group (no TCE
exposure) was not included in this study and the presented results may actually underestimate
visual deficits from TCE exposure.
Troster and Ruff (1990) presented case studies conducted on two occupationally exposed
workers to TCE and included a third case study on an individual exposed to 1,1,1-
trichloroethane. Case #1 was exposed to TCE (concentration unknown) for 8 months and Case
#2 was exposed to TCE over a 3-month period. Each patient was presented with a visual-spatial
task (Ruff-Light Trail Learning test as referenced by the authors). Both of the individuals
exposed to TCE were unable to complete the visual-spatial task and took the maximum number
of trials (10) to attempt to complete the visual task. A control group of 30 individuals and the
person exposed to 1,1,1-trichloroethane were able to complete this task accordingly. The lack of
quantitative exposure data and a small sample size severely limits the study and does not allow
for statistical comparisons.
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Vernon and Ferguson (1969) exposed eight male volunteers (ages 21-30 years) to 0, 100,
300, and 1,000 ppm TCE for 2 hours. Each individual was exposed to all TCE concentrations
and a span of at least 3 days was given between exposures. The volunteers were presented with
six visuo-motor tests during the exposure sessions. When the individuals were exposed to
1,000 ppm TCE (5,500 mg/m3), significant abnormalities were noted in depth perception as
measured by the Howard-Dolman test (p < 0.01), but no effects on the flicker fusion frequency
test (threshold frequency at which the individual sees a flicker as a single beam of light) or on the
form perception illusion test (volunteers presented with an illusion diagram). This is one of the
earliest chamber studies of TCE. This study included only healthy young males, is of a small
size, limiting statistical power, and reports mixed results on visual testing following TCE
exposure.
D.1.5. Cognition
There is a single environmental study in the literature that presents evidence of a negative
impact on intelligence resulting from TCE exposure. Kilburn and Warshaw (1993a) (study
details in Section D.I. 1.1) evaluated the effects on cognition for 544 Arizona residents exposed
to TCE in well water. Subjects were recruited and categorized into three groups. Exposed
Group 1 consisted of 196 family members with cancer or birth defects. Exposed Group 2
consisted of 178 individuals from families without cancer or birth defects; and exposed Group 3
included 170 parents whose children had birth defects and rheumatic disorders. Sixty-eight
referents were used as a comparison group for the clinical memory tests. Several cognitive tests
were administered to these residents in order to test memory recall skills and determine if TCE
exposure resulted in memory impairment. Working or short-term memory skills were tested by
asking each individual to recall two stories immediately after presentation (verbal recall) and
also draw three diagrams immediately after seeing the figures (visual recall). Additionally, a
digit span test where increasing numbers of digits were presented and then the subject had to
recall the digits was conducted to the extent of the short-term memory. Exposed subjects had
lower intelligence scores and there were significant impairments in verbal recall (p = 0.001),
visual recall (p = 0.03) and with the digit span test (p = 0.07). Significant impairment in short-
term memory as measured by three different cognitive test was correlated with TCE exposure.
Lower intelligence scores (p = 0.0001) as measured by the Culture Fair IQ test may be a possible
confounder in these findings. Additionally, the large range of TCE concentrations (6-500 ppb)
and exposure durations (1 to 25 years) and overall poor exposure characterization precludes a
NOAEL/LOAEL from being estimated from this study on cognitive function.
Rasmussen et al. (1993c, 1993d) and Troster and Ruff (1990) present results of positive
findings in occupational studies for cognitive effects of TCE. Rasmussen et al. (1993c) reported
an historical cohort study conducted on 96 metal degreasers, identified 2 years previously and
were selected from a population of 240 workers from 72 factories in Denmark. They reported
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psychoorganic syndrome, a mild syndrome of dementia characterized by cognitive impairment,
personality changes, and reduced motivation, vigilance, and initiative, was increased in the three
exposure groups. The medium- and high-exposure groups were compared with the low-exposure
group. Neuropsychological tests included WAIS (original version, Vocabulary, Digit Symbol,
Digit Span), SRT, Acoustic-motor function (Luria), Discriminatory attention (Luria), Sentence
Repetition, Paced Auditory Serial Addition Test (PASAT), Text Repetition, Rey's Auditory
Verbal Learning, Visual Gestalts, Stone Pictures (developed for this study, nonvalidated),
revised Santa Ana, Luria motor function, and Mira. The prevalence of psychoorganic syndrome
was 10.5% in low-exposure group; 38.9% in medium-exposure group; 63.4% in high-exposure
group, (x2 trend analysis: low vs. medium exposure x2 = 11.0,/> < 0.001; low vs. high exposure
x2 = 19.6, p < 0.001.) Psychoorganic syndrome increased with age (p < 0.01). Age was strongly
correlated with exposure.
Rasmussen et al. (1993d) used a series of cognitive tests to measure effects of
occupational TCE exposure. Short-term memory and retention following an latency period of
one hour was evaluated in several tests including a verbal recall (auditory verbal learning test),
visual gestalts, visual recall (stone pictures), and the digit span test. Significant cognitive
performance decreases were noted in both short-term memory and memory retention. In the
verbal recall test, immediate memory and learning were significantly decreased (p = 0.03 and
0.04, respectively). No significant effects were noted for retention following a 1-hour latency
period was noted. Significant increases in errors were noted in both the learning (p = 0.01) and
memory (p < 0.001) phases for the visual gestalts test. No significant effects were found in the
visual recall test in either the learning or memory phases or in the digit span test. As a result,
there were some cognitive deficits noted in TCE-exposed individuals as measured through
neuropsychological tests.
Troster and Ruff (1990) provides additional supporting evidence in an occupational study
for cognitive impairment, although the results reported in a qualitative fashion are limited in their
validity. In the two case studies that were exposed to TCE, there were decrements (no statistical
analysis performed) in cognitive performance as measured in verbal and visual recall tests that
were conducted immediately after presentation (learning phase) and 1 hour after original
presentation (retention/memory phase).
Triebig et al. (1977c) presents findings of no impairment of cognitive ability resulting
from TCE exposure in an occupational setting. This study was conducted on eight subjects
occupationally exposed to TCE. Subjects were seven men and one woman with an age range
from 23 to 38 years. Measured TCE in air averaged 50 ppm (260 mg/m3). Length of
occupational exposure was not reported. There was no control group. Results were compared
after exposure periods, and compared to results obtained after periods removed from exposure.
TCA and TCE metabolites in urine and blood were measured. The testing consisted of the
Syndrome Short Test, which consists of nine subtests through which amnesic and simple
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perceptive and cognitive functional deficits are detected; the "Attention Load Test" or "d2 Test"
from Brickenkamp is a procedure that measures attention, concentration, and stamina; number
recall test; letter recall test; the "Letter Reading Test;" and "Word Reading Test." Data were
assessed using Wilcoxon and Willcox nonparametric tests. Due to the small sample size, a
significance level of 1% was used. The concentrations of TCE, TCOH, and TCA in the blood
and total TCE and total TCA elimination in the urine were used to assess exposure in each
subject. The mean values observed were 330 mg TCOH and 319 mg TCA/g creatinine,
respectively, at the end of a work shift. The psychological tests showed no statistically
significant difference in the results before or after the exposure-free time period. The small
sample size may limit the sensitivity of the study.
Salvini et al. (1971), Gamberale et al. (1976), and Stewart et al. (1970) reported positive
findings for the impairment of cognitive function following TCE exposures in chamber studies.
Salvini et al. (1971) reported a controlled exposure study conducted on six male university
students. TCE concentration was 110 ppm (550 mg/m3) for 4-hour intervals, twice per day.
Each subject was examined on two different days, once under TCE exposure, and once as self
controls, with no exposure. Two sets of tests were performed for each subject corresponding to
exposure and control conditions. The test battery included a perception test with tachistoscopic
presentation, the Wechsler memory scale test, a CRT test, and a manual dexterity test.
Statistically significant results were observed for perception tests learning (p < 0.001), mental
fatigue (p < 0.01), subjects (p < 0.05); and CRT learning (p < 0.01), mental fatigue (p < 0.01),
subjects (p < 0.05). This is controlled exposure study with measured dose (110 ppm; 600
mg/m3) and clear, statistically significant impact on neurological functional domains. However,
it only assesses acute exposures.
Gamberale et al. (1976) reported a controlled exposure study conducted on 15 healthy
men aged 20-31 years old, employed by the Department of Occupational Medicine in
Stockholm, Sweden. Controls were within subjects (15 self-controls), described above. Test
used included RT addition and short-term memory using an electronic panel. Subjects also
assessed their own conditions on a 7-point scale. Researchers used a repeated measures
ANOVA for the four performance tests based on a 3 x 3 Latin square design. In the short-term
memory test (version of the digit span test), a series of numbers lasting for 1 second was
presented to the subject. The volunteer then had to reproduce the numerical sequence after a
latency period (not specified). No significant effect on the short-term memory test was observed
with TCE exposure in comparison to air exposure. Potential confounders from this study include
repetition of the same task for all exposure conditions, volunteers served as their own controls,
and TCE exposure preceded air exposure in two of the three exposure experimental designs.
This is a well controlled study of short term exposures with measured TCE concentrations and
significant response observed for cognitive impairment.
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Additional qualitative support for cognitive impairment is provided by Stewart et al.
(1970). This was a controlled exposure study conducted on 13 subjects in 10 experiments, which
consisted of 10 chamber exposures to TCE vapor of 100 ppm (550 mg/m3) and 200 ppm
(1,100 mg/m3) for periods of 1 hour to a 5-day work week. Experiments 1-7 were for 7 hours
with a mean TCE concentration of 198-200 ppm (1,090-1,100 mg/m3). Experiments 8 and
9 exposed subjects to 190-202 ppm (1,045-1,110 mg/m3) TCE for a duration of 3.5 and 1 hour,
respectively. Experiment 10 exposed subjects to 100 ppm (550 mg/m3) TCE for 4 hours.
Experiments 2-6 were carried out with the same subjects over 5 consecutive days. Gas
chromatography of expired air was measured. There were no self controls. All had normal
neurological tests during exposure, but 50% reported greater mental effort was required to
perform a normal modified Romberg test on more than one occasion. There were no quantitative
data or statistics presented regarding dose and effects of neurological symptoms.
Two chamber studies conducted by Triebig et al. (1977a: 1976) report no impact of TCE
exposure on cognitive function. Triebig et al. (1976) was a controlled exposure study conducted
on seven healthy male and female students (four females, three males) exposed for 6 hours/day
for 5 days to 100 ppm (550 mg/m3 TCE). The control group was seven healthy students (four
females, three males) exposed to hair care products. This was assumed as a zero exposure, but
details of chemical composition were not provided. Biochemical and psychological testing was
conducted at the beginning and end of each day. Biochemical tests included TCE, TCA, and
TCOH in blood. Psychological tests included the d2 test, which was an attention load test; the
short test [as characterized in the translated version of Treibig (1976)] is used to record patient
performance with respect to memory and attention; daily Fluctuation Questionnaire measured the
difference between mental states at the start of exposure and after the end of exposure is
recorded; The MWT-A is a repeatable short intelligence test; Culture Fair Intelligence Test
(CFT-3) is a nonverbal intelligence test that records the rather "fluid" part of intelligence, that is,
finding solution strategies; Erlanger Depression Scale. Results were not randomly distributed.
The median was used to describe the mean value. Regression analyses were conducted. In this
study the TCE concentrations in blood reported ranged from 4 to 14 ug/mL. A range of 20-
60 ug/mL was obtained for TCA in the blood. There was no correlation seen between exposed
and unexposed subjects for any measured psychological test results. The biochemical data did
demonstrate subjects' exposures. This is a well-controlled study with excellent exposure data,
although the small sample size may have limited sensitivity.
Triebig et al. (1977a) is an additional report on the seven exposed subjects and seven
controls evaluated in Triebig et al. (1976). Additional psychological testing was reported. The
testing included the Syndrome Short Test, which consists of nine subtests, described above.
Statistics were conducted using Whitney Mann. Results indicated the anxiety values of the
placebo random sample group dropped significantly more during the course of testing (p < 0.05)
than those of the active random sample group. No significantly different changes were obtained
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with any of the other variables. Both of these studies were well controlled with excellent
exposure data, which may provide some good data for establishing a short-term NOAEL. The
small sample size may have limited the sensitivity of the study.
Additional reports on the impairment of memory function as a result of TCE exposures
have been reported, and provide additional evidence of cognitive impairment. The studies by
Chalupa et al. (I960). Rasmussen et al. (1993d. 1993c: 1986), and Troster and Ruff (1990) report
impairment of memory resulting from occupational exposures to TCE. Kilburn and Warshaw
(1993 a) and Kilburn (2002b, a) report impairment of memory following environmental
exposures to TCE. Salvini et al. (1971) reports impairment of memory in a chamber study,
although Triebig et al. (1976) reports no impact on memory following TCE exposure in a
chamber study.
D.1.6. Psychomotor Effects
There is evidence in the literature that TCE can have adverse psychomotor effects in
humans. The effects of TCE exposure on psychomotor response have been studied primarily as
the impact on RTs, which provide a quantitative measure of the impact TCE exposure has on
motor skills. Studies on motor dyscoordination resulting from TCE exposure are more
subjective, but provide additional evidence that TCE may cause adverse psychomotor effects.
These studies are described below.
D.l.6.1. RT
There are several reports in the literature that report an increase in RTs following
exposures to TCE. The best evidence for TCE exposures causing an increase in CRTs comes
from environmental studies by Kilburn (2002b, 2002a), Kilburn and Warshaw (1993a), Reif et
al. (2003), and Kilburn and Thornton (1996), which were all conducted on populations which
were exposed to TCE through groundwater contaminated as the result of environmental spills.
Kilburn (2002b, 2002a) (study details described in Section D.I.I) evaluated reaction times in a
Phoenix, Arizona population exposed to TCE through groundwater. Volunteers were tested for
response rates in the SRT and two CRT tests. Various descriptive statistics were used, as well as
analysis of covariance (ANCOVA) and a step-wise adjustment of demographics. The principal
comparison, between the 236 exposed persons and the 161 unexposed regional controls, revealed
significant differences (p < 0.05) indicating that SRTs and CRTs were delayed. Balance was
also abnormal with excessive sway speed (eyes closed), but this was not true when both eyes
were open. This study shows statistically significant differences in psychomotor responses
between exposed and nonexposed subjects exposed environmentally. However, it is limited by
poor exposure characterization.
Kilburn and Warshaw (1993a) (study details described in Section D.I. 1.1) evaluated
reaction times in 170 Arizona residents exposed to TCE in well water. A referent group of
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68 people was used for comparison. TCE concentration was from 6 to 500 ppb and exposure
ranged from 1 to 25 years. SRT was determined by presenting the subject a letter on a computer
screen and measuring the time (in milliseconds [msec]) that it took for the person to type that
letter. SRT significantly increased from 281 ± 55 to 348 ± 96 msec in TCE-exposed individuals
(p < 0.0001). Similar increases were reported for CRT where subjects were presented with two
different letters and required to make a decision as to which letter key to press. CRT of the
exposed subjects was 93 msec longer in the third trial (p < 0.0001) than referents. It was also
longer in all trials, and remained significantly different after age adjustment. This study shows
statistically significant differences for neurological test results between subjects environmentally
exposed and nonexposed to TCE, but is limited by poor exposure data on individual subjects
given the ecological design of this study. Additionally, litigation is suggested and may introduce
a bias, particularly if no validity tests were used.
Kilburn and Thornton (1996) conducted an environmental study that attempts to use
reference values from two control groups in assessing neurological responses for chemically
exposed subjects using neurophysiological and neuropsychological testing on three groups.
Group A included randomly selected registered voters from Arizona and Louisiana with no
exposure to TCE: n = 264 unexposed volunteers aged 18-83 years. Group B included volunteers
from California n = 29 (17 males and 12 females) who were used to validate the equations;
group C included those exposed to TCE and other chemicals residentially for >5 years n = 237.
Group A was used to develop the regression equations for SRT and CRT. A similarly selected
comparison group B was used to validate the equations. Group C, the exposed population, was
submitted to SRT and CRT tests (n = 237) and compared to the control groups. All subjects
were screened by a questionnaire. Reaction speeds were measured using a timed computer
visual-stimulus generator. No exposure data were presented. The Box-Cox transformation was
used for dependent variables and independent variables. They evaluated graphical methods to
study residual plots. Cook's distance statistic was used as a measure of influence to exclude
outliers with undue influence and none of the data were excluded. Lack-of-fit test was
performed on Final model and F statistic was used to compare estimated error to lack-of-fit
component of the model's residual sum of squared error. Final models were validated using
group B data and paired t-test to compare observed values for SRT and CRT. F statistic was
used to test the hypothesis that parameter estimates obtained with group B were equal to those of
Group A, the model. The results are as follows: Group A: SRT = 282 ms; CRT = 532 ms.
Group B: SRT = 269 ms; CRT = 531 ms. Group C: SRT = 334 ms; CRT = 619 ms. TCE
exposure produced a step increase in reaction times (SRT and CRT). The coefficients from
Group A were valid for group B. The predicted value for SRT and for CRT, plus 1.5 SDs
selected 8% of the model group as abnormal. The model produced consistent measurement
ranges with small numerical variation. This study is limited by lack of any exposure data, and
does not provide statistics to demonstrate dose-response effects.
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Kilburn (2002b, 2002a) conducted an environmental study on 236 residents chronically
exposed to TCE-associated solvents in the groundwater resulting from a spill from a microchip
plant in Phoenix, Arizona. Details of the TCE exposure and population are described earlier in
Section D. 1.1.1 (see Kilburn, 2002b, 2002a). The principal comparison, between the 236
exposed persons and the 161 unexposed regional controls, revealed significant differences
indicating that SRTs and choice reaction times (CRTs) were increased. SRTs significantly
increased from 283 ± 63 msec in controls to 334 ±118 msec in TCE exposed individuals
(p < 0.0001). Similarly, CRTs also increased from 510 ± 87 to 619 ± 153 msec with exposure to
TCE (p < 0.0001). This study shows statistically significant differences in psychomotor
responses as measured by reaction times between TCE-exposed and nonexposed subjects.
Estimates of TCE concentrations in drinking water to individual subjects were not reported in the
paper. Since the TCE exposure ranged from 0.2 to >10,000 ppb in well water, it is not possible
to determine a NOAEL for increased reaction times through this study. Additionally, litigation is
suggested and may introduce a bias, particularly if no validity tests were used.
Reif et al. (2003) conducted a cross sectional study on 143 residents of the Rocky
Mountain Arsenal (RMA) community of Denver exposed environmentally to drinking water
contaminated with TCE and related chemicals from nearby hazardous waste sites between 1981
and 1986. The referent group was at the lowest estimated exposure concentration (<5 ppb). The
socioeconomic profile of the participants closely resembled those of the community in general.
A total of 3393 persons was identified through the census, from which an age-
and gender-stratified sample of 1267 eligible individuals who had lived at their
current residence for at least 2 years was drawn. Random selection was then used
to identify 585 persons from within the age-gender strata, of whom 472 persons
aged 2-86 provided samples for biomonitoring. Neurobehavioral testing was
conducted on 204 adults who lived in the RMA exposure area for a minimum of
2 years. Among the 204 persons who were tested, 184 (90.2%) lived within the
boundaries of the LWD and were originally considered eligible for the current
analysis. Therefore, participants who reported moving into the LWD after 1985
were excluded from the total of 184, leaving 143 persons available for study.
An elaborate hydraulic simulation model (not validated) was used in conjunction with a
GIS to model estimates of residential exposures to TCE. The TCE concentration measured in
community wells exceeded the maximum contaminant level of 5 ppb in 80% of cases.
Approximately 14% of measured values exceeded 15 ppb. Measured values were used to model
actual exposure estimates based on distance of residences from sampled wells. The estimated
exposure for the high-exposure group was >15 ppb; the estimate for the low-exposure referent
group was <5 ppb. The medium exposure group was estimated at exposures 5< x <15 ppb TCE.
The test battery consisted of the Neurobehavioral Core Test Battery (NCTB), which consists of
seven neurobehavioral tests including SRT. Results were assessed using the Multivariate Model.
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Results were statistically significant (p < 0.04) for the SRT tests. The results are confounded by
exposures to additional solvents and modeled exposure data, which while highly technical, are
still only a rough estimate of actual exposures, and may limit the sensitivity of the study.
Gamberale et al. (1976) conducted a controlled exposure (chamber) study on 15 healthy
men aged 20-31 years old, employed by the Department of Occupational Medicine in
Stockholm, Sweden. Controls were within subjects (15 self-controls). Subjects were exposed to
TCE for 70 minutes via a breathing valve to 540 mg/m3 (97 ppm), 1,080 mg/m3 (194 ppm), and
to ordinary atmospheric air (0 ppm). Sequence was counterbalanced between the three groups,
days, and exposure levels. Concentration was measured with a gas chromatographic technique
every third minute for the first 50 minutes, then between tests thereafter. Tests used were RT
addition, SRT, CRT and short-term memory using an electronic panel. Subjects also assessed
their own conditions on a 7 point scale. The researchers performed Friedman two-way analysis
by ranks to evaluate differences between the 3 conditions. The results were nonsignificant when
tested individually, but significant when tested on the basis of six variables. Nearly half of the
subjects could distinguish exposure/nonexposure. Researchers performed ANOVA for the four
performance tests based on a 3 x 3 Latin square design with repeated measures. In the RT-
addition test, the level of performance varied significantly between the different exposure
conditions (F[2.24] = 435;p < 0.05) and between successive measurement occasions
(F[2.24] = 19.25; p < 0.001). The level of performance declined with increased exposure to
TCE, whereas repetition of the testing led to a pronounced improvement in performance as a
result of the training effect. No significant interaction effects were observed between exposure
to TCE and training. This is a good study of short-term exposures with measured TCE
concentrations and significant response observed for RT.
Gun et al. (1978) conducted an occupational study on eight TCE-exposed workers who
operated degreasing baths in two different plants. Four female workers were exposed to TCE
only in one plant and four female workers were exposed to TCE and nonhalogenated
hydrocarbon solvents in the second plant. The control group (n = 8) consisted of four female
workers from each plant who did not work near TCE. Each worker worked two separate 4-hour
shifts daily, with one shift exposed to TCE and the second 4-hour shift not exposed. Personal air
samples were taken continuously over separate 10-minute sessions. Readings were taken every
30 seconds. Eight-choice reaction times were carried out in four sessions; at the beginning and
end of each exposure to TCE or TCE + solvents; a total of 40 RT trials were completed. TCE
concentrations in the TCE only plant 1 (148-418 ppm [800-2,300 mg/m3]) were higher than in
the TCE + solvent plant 2 (3-87 ppm [16-480 mg/m3]). Changes in CRTs were compared to
level of exposure. The TCE only group showed a mean increase in RT, with a probable
cumulative effect. In the TCE + solvent group, mean RT shortened in Session 2, then increased
to be greater than at the start. Both control groups showed a shortening in mean CRT in
Session 2, which was sustained in Sessions 3 and 4 consistent with a practice effect. This is a
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study with well-defined exposures and reports of cause and effect (TCE exposure on RT);
however, no statistics were presented to support the conclusions or the significance of the
findings, and the small sample size is a limitation of the study.
D.l.6.2. Muscular Dyscoordination
Effects on motor dyscoordination resulting from TCE exposure have been reported in the
literature. These impacts are subjective, but may provide additional evidence that TCE can cause
adverse psychomotor effects. There are three reports summarized below that suggest that
muscular dyscoordination resulted from TCE exposure, although all three have significant
limitations due to confounding factors. Rasmussen et al. (1993a) presented findings on muscular
dyscoordination as it relates to TCE exposure. This was a historical cohort study conducted on
96 metal degreasers, identified 2 years previously. Subjects were selected from a population of
240 workers from 72 factories in Denmark. Although the papers report a population of
99 participants, tabulated results were presented for a total of only 96. No explanation was
provided for this discrepancy. These workers had chronic exposure to fluorocarbon (CFC113)
(n = 25) and mostly TCE (n = 70; average duration: 7.1 years). There were no external controls.
The range of working full-time degreasing was 1 month to 36 years. Researchers collected data
regarding the workers' occupational history, blood and urine tests, as well as biological
monitoring for TCE and TCE metabolites. A chronic exposure index (CEI) was calculated based
on number of hours/week worked with solvents multiplied by years of exposure multiplied by
45 weeks/year. No TCE air concentrations were reported. Participants were categorized into
three groups: (1) "Low exposure:" n = 19, average full-time exposure = 0.5 years; (2) "Medium
exposure:" n = 36, average full-time exposure = 2.1 years; or (3) "High exposure:" n = 41,
average full-time exposure =11 years. The mean TCA level in the "high" exposure group was
7.7 mg/L (max = 26.1 mg/L). TWA measurements of CFC113 levels were 260-420 ppm (U.S.
and Danish TLV was 500 ppm). A significant trend of dyscoordination from low to high solvent
exposure was observed (p = 0.003). This study provides evidence of causality for muscular
dyscoordination resulting from exposure to TCE, but no measured exposure data were reported.
Additional evidence of the psychomotor effects caused by exposure to TCE is presented
in Gash et al. (2008) and Troster and Ruff (1990). There are, however, significant limitations
with each of these studies. In Gash et al. (2008), the researchers evaluated the clinical features of
1 Parkinson's disease patient, identified in a Phase 1 clinical trial study, index case, and an
additional 29 coworkers of the patient, all with chronic occupational exposures to TCE. An
additional 2 subjects with Parkinson's disease were included, making the total of 3 Parkinson's
disease patients, and 27 non-Parkinson's coworkers making up the study population. Coworkers
for the study were identified using a mailed questionnaire to 134 former coworkers. No details
were provided in the paper on selection criteria for the 134 former coworkers. Of the 134 former
workers sent questionnaires, 65 responded. Twenty-one self-reported no symptoms, 23 endorsed
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1-2 symptoms, and 21 endorsed >3 more signs of parkinsonism. Fourteen of the 21 with three
or more signs and 13 of the 21 without any signs agreed to a clinical exam; this group comprises
the 27 additional workers examined for parkinsonian symptoms. No details were provided on
nonresponders. All subjects were involved in degreasing with long-term chronic exposure to
TCE through inhalation and dermal exposure (14 symptomatic: age range = 31-66 years,
duration of employment range: 11-35 years) (13 asymptomatic: age range = 46-63 years,
duration of employment range: 8-33 years). The data were compared between groups and with
data from 110 age-matched controls. Exposure to TCE is self-reported and based on job
proximity to degreasing operations. The paper lacks any description of degreasing processes
including TCE usage and quantity. Mapping of work areas indicated that workers with
Parkinson's disease worked next to the TCE container, and all symptomatic workers worked
close to the TCE container. Subjects underwent a general physical exam, neurological exam and
Unified Parkinson's Disease Rating Scale (UPDRS), timed motor tests, occupational history
survey, and mitochondrial neurotoxicity. ANOVA analysis was conducted, comparing
symptomatic vs. nonsymptomatic workers, and comparing symptomatic workers to age-matched,
nonexposed controls. No description of the control population (n = 110), nor how data were
obtained for this group, was presented. The symptomatic non-Parkinson's group was
significantly slower in fine motor hand movements than age-matched nonsymptomatic group
(p < 0.001). The symptomatic group was significantly slower (p < 0.0001) than age-matched
unexposed controls as measured in fine motor hand movements on the Movement Analysis
Panel. All symptomatic workers had positive responses to 1 or more questions on UPDRS Part
II (diminished activities of daily life), and/or deteriorization of motor functions on Part III. The
fine motor hand movement times of the asymptomatic TCE-exposed group were significantly
slower (p < 0.0001) than age-matched nonexposed controls. Also, in TCE-exposed individuals,
the asymptomatic group's fine motor hand movements were slightly faster (p < 0.01) than those
of the symptomatic group. One symptomatic worker had been tested 1 year prior and his
UPDRS score had progressed from 9 to 23. Exposures are based on self-reported information,
and no information on the control group is presented. One of the Parkinson's disease patients
predeceased the study and had a family history of Parkinson's disease.
Troster and Ruff (1990) reported a case study conducted on two occupationally exposed
workers to TCE. Patients were exposed to low levels of TCE. There were two groups of n = 30
matched controls (all age and education matched) whose results were compared to the
performance of the exposed subjects. Exposure was described as "Unknown amount of TCE for
8 months." Assessment consisted of the San Diego Neuropsychological Test Battery (SDNTB)
and "1 or more of Thematoc Apperception Test (TAT), Minnesota Multiphasic Personal
Inventory (MMPI), and Rorschach. Medical examinations were conducted, including
neurological, CT scan, and/or chemo-pathological tests, and occupational history was taken, but
not described. There were no statistical results reported. Results were reported for each test, but
D-26
-------
no tests of significance were included; therefore, the authors presented their conclusions for each
"case" in qualitative terms, as such: Case 1: Intelligence "deemed" to drop from premorbid
function at 1 year and 10 months after exposure. Impaired functions improved for all but reading
comprehension, visuospatial learning and categorization (abstraction). Case 2: Mild deficits in
motor speed, but symptoms subsided after removal from exposure.
D.1.7. Summary Tables
The following Tables (D-l through D-3) provide a detailed summary of all of the
neurological studies conducted with TCE in humans. Tables D-l and D-2 summarize each
individual human study where there was TCE exposure. Table D-l consists of studies where
humans were primarily or solely exposed to TCE. Table D-2 contains human studies where
there was a mixed solvent exposure and TCE was one of the solvents in the mixture. For each
study summary, the study population, exposure assessment, methods, statistics, and results are
provided. Table D-3 indicates the neurological domains that were tested from selected
references (primarily from Table D-l).
D-27
-------
Table D-l. Epidemiological studies: Neurological effects of TCE
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Barret et al.
(19841
188 workers exposed
to TCE occupationally
from small and large
factories in France
(type of factories not
disclosed); average age
= 41; 6yrs average
exposure time.
The workers were
divided into high- and
low-exposure groups
for both TCE and
urinary TCA. No
control group was
mentioned.
Review of medical
records and analysis of
TCE atmospheric levels
(detector tubes) and
level of urinary
metabolites
measurement (TCA).
TCE exposure groups
included high-exposure
group (>150 ppm;
n = 54) and low-
exposure group
<150ppm;n= 134).
Personal factors
including age, tobacco
use, and alcohol intake
were also analyzed;
Exposure duration =
7 hrs/d for 7 yrs; no
mention was made
regarding whether or not
the examiners were
blind to the subjects'
exposure status.
Complete physical
examination including
testing visual
performance (acuity and
color perception),
evoked trigeminal
potential latencies and
audiometry, facial
sensitivity, reflexes, and
motoricity of the
masseter muscles.
X2 examined
distribution of the
different groups for
comparing high
and low exposed
workers, one way
ANOVA, Mann
Whitney U, and t-
test for analyzing
personal factors.
Symptoms for which TCE role is statistically
significant include the following: trigeminal nerve
impairment was reported in 22.2% (n = 12) of
workers in the high-exposure group for TCE,
7.4% (n = 10) in the low-exposure group for TCE,
24.4% (n = 10) in the high-exposure group for
TCA, and 8.2% (n = 12) in the low-exposure
group for TCA.
TCE results
Trigeminal
nerve
Impairment
asthenia
Optic nerve
impairment
Headache
Dizziness
High
dose%
22.2
18.5
14.8
20.3
13
Low
dose%
7.4
4.5
0.75
19.4
4.5
0.01
0.01
0.001
NS
0.05
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Barret et al.
(19871
104 workers highly
exposed to TCE during
work as degreaser
machine operators in
France. Controls:
52 healthy,
nonexposed controls of
various ages who were
free from neurological
problems.
Urinary analysis
determined TCE and
TCA rates. The average
of the last five
measurements were
considered indicative of
the average level of past
exposure. Mean
exposure 8.2 yrs,
average daily exposure
7 hrs/d. Mean age
41.6 yrs.
Evoked trigeminal
potentials were studied
while eyes closed and
fully relaxed. Also,
physical exams with
emphasis on nervous
system, a clinical study
of facial sensitivity, and
of the reflexes
depending on the
trigeminal nerve were
systematically
performed. Normal
latency and amplitude
values for TSEP
obtained from data from
control population.
Normal response
characterized from four
main peaks, alternating
from negative to
positive, respective
latency of 12.8 ms
(SD = 0.6), 19.5 ms
(SD= 1.3), 27.6ms
(SD= 1.6), and 36.8ms
(SD = 2.2), mean
amplitude of response is
2.5 uv (SD = 0.5 uv).
Pathological responses
were results 2.5 SDs
over the normal value.
Student's t-test and
one-way ANOVA
used as well as
nonparametric tests,
Mann-Whitney U
test, and Kruskal-
Wallistest. Also
decision matrix and
the analysis of the
receiver operating
curve to appreciate
the accuracy of the
TSEP method. The
distribution of the
different
populations was
compared by a %2
test.
Dizziness (71.4%), headache (55.1%), asthenia
(46.9%), insomnia (24.4%), mood perturbation
(20.4%), and sexual problems (12.2%) were
found. Symptomatic patients had significantly
longer exposure periods and were older than
asymptomatic patients. 17.3% of patients had
trigeminal nerve symptoms. Bilateral
hypoesthesia with reflex alterations in nine cases.
Hypoesthesia was global and predominant in the
mandibular and maxillary nerve areas. Several
reflex abolitions were found without facial palsy
and without convincing hypoesthesia in nine
cases. Cornea! reflexes were bilaterally abolished
in five cases as were naso-palpebral reflexes in
six cases; length of exposure positively correlated
with functional manifestations (p < 0.01);
correlation between symptoms and exposure
levels was nonsignificant; 40 (38.4%) subjects
had pathological response to TSEP with
increased latencies, amplitude, or both; of these,
28 had normal clinical trigeminal exam and
12 had abnormal exam. TSEP was positively
correlated with length of exposure (p < 0.01) and
with age (p < 0.05), but not with exposure
concentration; trigeminal nerve symptoms
(n = 18) were positively correlated with older age
(p< 0.001).
D-29
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Barret et al.
(1982)
Burg et al.
(1995)
Study population
1 1 workers with
chronic TCE exposure;
9 were suffering
effects of solvent
intoxication; 2 were
work place controls.
Control group was
20 unexposed subjects
of all ages.
From an NHIS TCE
subregistry of 4,281
(4,041 living and
240 deceased)
residents
environmentally
exposed to TCE via
well water in Indiana,
Illinois, and Michigan;
compared to NHIS
registrants.
Exposure assessment
and biomarkers
Selected following
clinical evaluations of
their facial sensitivity
and trigeminal nerve
reflexes; exposures
verified by urinalysis.
Presence of TCE and
TCA found. (Exposure
rates not reported.)
Morbidity baseline data
were examined from the
TCE Subregistry from
the NER developed by
the ATSDR; were
interviewed in the
NHIS.
Tests used
Somatosensory evoked
potential (SEP)
following stimulation of
the trigeminal nerve
through the lip
alternating right and left
by a bipolar surface
electrode utilizing
voltage, usually 75-
80 V, just below what is
necessary to stimulate
the orbicularis oris
muscle. Duration was
approximately 0.05 ms
stimulated 500 times
(2x/sec).
Self report via face-to-
face interviews —
25 questions about
health conditions; were
compared to data from
the entire NHIS
population; neurological
endpoints were hearing
and speech
impairments.
Statistics
SEP recordings
illustrated from
trigeminal nerve
graphs.
Poisson Regression
analysis model used
for registrants >19
years old.
Maximum
likelihood
estimation and
likelihood ratio
statistics and Wald
CI; TCE subregistry
population was
compared to larger
NHIS registry
population.
Results
Three pathological abnormalities present in
exposed (TCE intoxicated) workers: (1) in eight
workers, higher voltage required to obtain normal
response; (2) excessive delay in response
observed twice; and (3) excessive graph
amplitude noted in three cases. One subject
exhibited all three abnormalities. Correlation
was reported between clinical observation and
test results. Most severe SEP alternations
observed in subjects with the longest exposure to
TCE (although exposure levels or exposure
durations are not reported). No statistics
presented.
Speech impairments showed statistically
significant variability in age-specific risk ratios
with increased reporting for children <9 yrs old
(RR: 2.45, 99% CI: 1.31, 4.58) and for registrants
>35 yrs old (data broken down by 10-yr ranges).
Analyses suggest a statistically significant
increase in reported hearing impairments for
children <9 yrs old (RR: 2.13, 99% CI: 1.12,
4.06). It was lower for children 10-17 yrs old
(RR: 1.12, 99% CI: 0.52, 2.44) and <0.32 for all
other age groups.
D-30
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Burg and
Gist (19991
Study population
4,041 living members
of the National
Exposure Registry's
TCE Subregistry; 97%
white; mean age 34 yrs
(SD= 19.9 yrs.);
divided in four groups
based on type and
duration of exposure;
analysis reported only
for 3,9 15 white
registrants; lowest
quartile used as control
group.
Exposure assessment
and biomarkers
All registrants exposed
to TCE though domestic
use of contaminated
well water; four
exposure Subgroups,
each divided into
quartiles: (1) Maximum
TCE measured in well
water, exposure
subgroups: 2-12, 12-60,
and 60-800 ppb;
(2) Cumulative TCE
exposure subgroups:
<50, 50-500, 500-
5,000, and >5,000 ppb;
(3) Cumulative chemical
exposure subgroups:
include TCA, DCE,
DCA, in conjunction
with TCE, with the same
exposure categories as
in # 2; and (4) Duration
of exposure subgroups:
<2, 2-5, 5-10, and
>10 yrs; 2,867 had TCE
exposure of <50 ppb;
870 had TCE exposure
of 5 1-500 ppb; 190 had
TCE exposure of 50 1-
5,000 ppb; 35 had TCE
exposure >5,000 ppb.
Tests used
Interviews
(occupational,
environmental,
demographic, and
health information); a
large number of health
outcomes were
analyzed, including
speech and hearing
impairment.
Statistics
Logistic Regression,
ORs; lowest quartile
used as reference
population.
Results
When the registrants were grouped by duration of
exposure to TCE, a statistically significant
association (adjusted for age and sex) between
duration of exposure and reported hearing
impairment was found. The prevalence ORs
were 2.32 (95% Cl: 1.18, 4.56) (>2-<5 yrs);
1.17 (95% Cl: 0.55, 2.49) (>5-<10 yrs); and
2.46 (95% Cl:1.30, 5.02) (>10 yrs). Higher rates
of speech impairment (not statistically
significant) were associated with maximum and
cumulative TCE exposure, and duration of
exposure.
D-31
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Buxton and
Hayward
(1967)
This was a case study
on four workers
exposed to very high
concentrations of TCE,
which resulted from an
industrial accident. No
controls were
evaluated.
Case 1 was a 44-yr-old
man exposed for
10 min; Case 2 was a
39-yr-old man exposed
for 30 min; Case 3 was a
43-yr-old man exposed
for 2.5 hrs; Case 4 was a
39-yr-old man exposed
for 4 hrs. TCE
concentrations were not
reported.
Clinical evaluations
were conducted by a
physician when patients
presented with
symptoms: numbness
of face, ocular pain,
enlarged right blind
spot, nausea, loss of
taste, headache,
dizziness, unsteadiness,
facial diplesia, loss of
gag and swallowing
reflex, absence of
cornea! reflex, and
reduction of trigeminal
response.
There was no
statistical
assessment of
results presented.
Case 1 exhibited headaches and nausea for
48 hrs, but had a full recovery. Case 2 exhibited
nausea and numbness efface, but had a full
recovery. Case 3 was seen and treated at a
hospital with numbness efface, insensitivity to
pin prick over the trigeminal distribution, ocular
pain, enlarged right blind spot, nausea, and loss
of taste. No loss of mental faculty was observed.
Case 4 was seen and treated for headache,
nausea, dizziness, unsteadiness, facial diplesia,
loss of gag and swallowing reflex, facial
analgesia, absence of cornea! reflex, and
reduction of trigeminal response. The patient
died and was examined postmortem. There was
demyelination of the 5th cranial nerve evident.
Chalupa et al.
(1960)
This was a case study
conducted on
22 patients with acute
poisoning caused by
carbon monoxide and
industrial solvents. Six
subjects were exposed
to TCE (doses not
known). Average age
3 8 years.
No exposure data were
reported.
Medical and
psychological exams
were given to all
subjects. These
included EEGs,
measuring middle
voltage theta activity of
5-6 sec duration.
Subjects were tested for
memory disturbances.
No statistics were
performed.
80% of those with pathological EEG displayed
memory loss; 30% of those with normal EEGs
displayed memory loss. Pathology and memory
loss were most pronounced in subjects exposed to
carbon monoxide.
D-32
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
El Ghawabi
et al. (19731
30 money printing
shop workers
occupationally exposed
to TCE; Controls: 20
age and SES matched
nonexposed males and
10 control workers not
exposed to TCE but
exposed to inks used in
printing.
Air samples on
30 workers. Mean TCE
air concentrations
ranged from 41 to
163 ppm throughout the
Intalgio process
Colorimetric
determination of both
TCA and TTCs in urine
with Fujiware reaction.
Inquiries about
occupational, past and
present medical
histories, and family
histories in addition to
age and smoking habits.
EKGs were performed
on 25 of the workers.
Lab investigations
included complete
blood and urine
analysis, and routine
liver function tests.
Descriptive
statistics and central
tendency evaluation
for metabolites; no
statistics reported
for neurological
symptoms.
Most frequent symptoms: prenarcotic headache
(86 vs. 30% for controls), dizziness (67 vs. 6.7%
for controls), and sleepiness (53 vs. 6% for
controls) main presenting symptoms in addition
to suppression of libido. Trigeminal nerve
involvement was not detected. The concentration
of TTCs increased toward mid-week and was
stationary during the last 2 working d.
Metabolites of total TCA and TCOH are only
proportional to TCE concentrations up to 100
ppm.
Feldman et
al. (1988)
21 Massachusetts
residents with alleged
chronic exposure to
TCE in drinking water;
27 laboratory controls.
TCE in residential well
water was 30-80 times
greater than U.S. EPA
maximum contaminant
level; maximum
reported concentration
was 267 ppb; other
solvents also present.
Blink reflex used as an
objective indicator of
neurotoxic effects of
TCE; clinical
neurological exam,
EMGs to evaluate blink
reflex, nerve conduction
studies, and extensive
neuropsychological
testing.
Student's t-test used
for testing the
difference between
the group means for
the blink reflex
component
latencies.
Highly significant differences in the conduction
latency means of the blink reflex components for
the TCE exposed population vs. control
population, when comparing means for the right
and left side Rl to the controls (p < 0.001). The
mean Rl blink reflex component latency for the
exposed group was 11.35 ms, SD = 0.74 ms, 95%
CI: 11.03-11.66. The mean for the controls was
10.21 ms, SD = 0.78 ms, 95% CI: 9.92-10.51;
p < 0.001. Suggests a subclinical alteration of the
trigeminal nerve function due to chronic,
environmental exposure to TCE.
D-33
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Feldman et
al. (1992)
18 workers
occupationally exposed
to TCE; 30 laboratory
controls.
Reviewed exposure
histories of each worker
(job type, length of
work) and audited
medical records to
categorize into three
exposure categories:
"extensive,"
"occasional," and
"chemical other than
TCE."
Blink reflexes using
TECA 4 EMG.
Non-Gaussian
distribution and
high coefficient of
variance data were
log-transformed and
then compared to
the log-transformed
control mean
values. MRVwas
calculated by
subtracting the
subjects value (x)
from the control
group mean (M),
and the difference is
divided by the
control group SD.
The "extensive" group revealed latencies >3 SDs
above the nonexposed group mean on Rl
component of blink reflex; none of the
"occasional" group exhibited such latencies;
however, two of them demonstrated evidence of
demyelinating neuropathy on conduction velocity
studies; the sensitivity, or the ability of a positive
blink reflex test to correctly identify those who
had TCE exposure, was 50%. However, the
specificity was 90%, which means that of those
workers with no exposure to TCE, 90%
demonstrated a normal Kl latency. Subclinical
alteration of the Vth cranial nerve due to chronic
occupational exposure to TCE is suggested.
Gash et al.
(2008)
30 Parkinson's disease
patients and 27 non-
Parkinson coworkers
exposed to TCE; no
unexposed controls.
Mapping of work areas.
General physical exam,
neurological exam and
UPDRS, timed motor
tests, and occupational
history survey;
mitochondria!
neurotoxicity;
Questionnaire mailed to
134 former non-
Parkinson's workers,
(14 symptomatic of
parkinsonism: age
range = 31-66 yrs,
duration of employment
range: 11-35 yrs)
(13 asymptomatic: age
range = 46-63 yrs,
duration of employment
range: 8-33 yrs).
Workers' raw scores
given; ANOVA
comparing
symptomatic vs.
nonsymptomatic
workers.
Symptomatic non-Parkinson's group was
significantly slower in fine motor hand
movements than age-matched nonsymptomatic
group (p < 0.001). All symptomatic workers had
positive responses to one or more questions on
UPDRS Part I and Part II, and/or had signs of
parkinsonism on Part III One symptomatic
worker had been tested 1 yr prior and his UPDRS
score had progressed from 9 to 23.
D-34
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Grandjean et
al. (1955)
80 workers employed
in 10 different factories
of the Swiss
mechanical
engineering industry
exposed to TCE, 7 of
whom stopped
working with TCE
from 3 wks to 6 yrs
prior; no unexposed
control group.
Vapors were collected in
ethylic alcohol 95%.
Volume of air was
checked using a
flowmeter, and
quantitatively measured
according to the method
ofTruhaut(1951).
which is based on a
colored reaction
between TCE and the
pyridine in an alkaline
medium (with
modifications). Urine
analysis of TCA levels;
TCE air concentrations
varied from 6 to
1,120 ppm depending on
time of day and
proximity to tanks, but
mainly averaged
between 20 and 40 ppm.
Urinalysis varied from
30 to 300 mg/L; Could
not establish a
relationship between
TCE eliminated through
urine and TCE air
levels. Four exposure
groups estimated based
on air sampling data.
Medical exam,
including histories;
Blood and biochemical
tests, and psychiatric
exam. Psychological
exam; Meggendorf,
Bourdon, Rorschach,
Jung, Knoepfel's
"thirteen mistakes" test,
and Bleuler's test.
Coefficient of
determination,
Regression
coefficient.
Men working all day with TCE showed, on
average, larger amounts of TCA than those who
worked part time with TCE. Relatively high
frequency of subjective complaints, alterations of
the vegetative nervous system, and neurological
and psychiatric symptoms: 34% had slight or
moderate psycho-organic syndrome; 28% had
neurological changes. There is a relationship
between the frequency of those alterations and
the degree of exposure to TCE. There were
significant differences (p = 0.05) in the incidence
of neurological disorder between Groups I and
III, while between Groups II and III, there were
significant differences (p = 0.05) in vegetative
and neurological disorders. Based on TCA
eliminated in the urine, results show that
subjective, vegetative, and neurological disorders
were more frequent in Groups II and III than in
Group I. Statistical analysis revealed the
following significant differences (p < 0.01):
subjective disorders between I and II; vegetative
disorders between I and II and between I and III;
neurological disorders between I and (II and III).
Vegetative, neurological, and psychological
symptoms increased with the length of exposure
to TCE. The following definite differences were
shown by statistical analysis (p < 0.03):
vegetative disorders between I and IV;
neurological disorders between I and II and
between I and IV; and psychological disorders
between I and III and between I and IV.
D-35
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Gun, et al.
(19781
Eight exposed: four
female workers from
one plant exposed to
TCE and four female
workers from another
plant exposed to TCE
nonhalogenated
hydrocarbon solvent
used in degreasing;
control group (n = 8)
consisted of four
female workers from
each plant who did not
work near TCE.
Air sampled
continuously over
separate 10 min
durations drawn into a
Davis Halide Meter.
Readings taken every
30 sec; ranged from 3 to
419ppm.
Eight-choice reaction
times carried out in four
sessions; 40 RT trials
completed.
Variations in RT by
level of exposure;
ambient air
exposure TCE
concentrations and
mean air TCE
values.
TCE only group had consistently high mean
ambient air TCE levels (which exceeded the 1978
TLV of 100 ppm) and showed a mean increase in
RT, with a probable cumulative effect. In TCE +
solvent group, ambient TCE was lower (did not
exceed 100 ppm) and mean RT shortened in
Session 2, then rose subsequently to be greater
than at the start. Both control groups showed a
shortening in mean CRT in Session 2, which was
sustained in Sessions 3 and 4 consistent with a
practice effect. No statistics were provided.
Hirsch et al.
(1996)
106 residents of
Roscoe, a community
in Illinois on the Rock
River, in direct
proximity to an
industrial plant that
released an unknown
amount of TCE into
the River. All
involved in litigation.
Case series report; no
unexposed controls.
Random testing of the
wells between 1983 and
1984 revealed
groundwater in wells to
have levels of TCE
between 0 and
2,441 ppb; distance of
residence from well
used to estimate
exposure level.
Medical, neurologic,
and psychiatric exams
and histories. For those
who complained of
headaches, a detailed
headache history was
taken, and an extensive
exam of nerve-threshold
measurements of toes,
fingers, face, olfactory
threshold tests for
phenylethyl methylethyl
carbinol, brain map,
Fast Fourier Transform
(FFT), P300 cognitive
auditory evoked
response, EEG, visual
evoked response,
Somato sensory Evoked
Potential, BAER,
MMPI-II, MCMI-II,
and Beck Depression
Inventory were also
given.
Student t-test, %2
analysis,
nonparametric t-test
and ANOVA,
correlating all
history, physical
exam findings, test
data, TCE levels in
wells, and distance
from plant.
66 subjects (62%) complained of headaches,
Diagnosis of TCE-induced cephalagia was
considered credible for 57 patients (54%).
Retrospective TCE level of well water or well's
distance from the industrial site analysis did not
correlate with the occurrence of possibly-TCE
induced headaches. Studies that were not
statistically significant with regard to possible
TCE-cephalalgia included P300, FFT, VER,
BAER, MMPI, MCMI, Beck Depression
Inventory, SSER, and nerve threshold
measurements. Headache might be associated
with exposure to TCE at lower levels than
previously reported. Headaches mainly occurred
without sex predominance, gradual onset,
bifrontal, throbbing, without associated features;
No quantitative data were presented to support
statement of headache in relation to TCE
exposure levels, except for incidences of
headache reporting and measured TCE levels in
wells.
D-36
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Kilburn and
Thornton
(1996)
Study population
Group A: Randomly
selected registered
voters from Arizona
and Louisiana with no
exposure to TCE:
n = 264 unexposed
volunteers aged 18-83:
Group B volunteers
from California n = 29
17 males and
12 females to validate
the equations; Group C
exposed to TCE and
other chemicals
residentially for >5 yrs
n = 237.
Exposure assessment
and biomarkers
No exposure or
groundwater analyses
reported.
Tests used
Reaction speed using a
timed computer visual-
stimulus generator;
Compared groups to
plotted measured SRT
and CRT questionnaire
to eliminate those
exposed to possibly
confounding chemicals.
Statistics
Box-Cox
transformation for
dependent and
independent
variables.
Evaluated graphical
methods to study
residual plots.
Cooks distance
statistic measured
influence of outliers
examined. Lack-of-
fit test performed on
Final model and
F statistic to
compare estimated
error to lack-of-fit
component of the
model's residual
sum of squared
error. Final models
were validated
using Group B data
and paired t-test to
compare observed
values for SRT and
CRT. F statistic to
test hypothesis that
parameter estimates
obtained with
Group B were equal
to those of the
model.
Results
Group A: SRT = 282 ms, CRT = 532 ms.
Group B: SRT = 269 ms, CRT = 531 ms.
Group C: SRT = 334 ms, CRT = 619 ms.
Lg(SRT) = 5.620,80 = 0.198.
Regression equation for Lg(CRT) = 6.094389 +
0.0037964 x age. TCE exposure produced a step
increase in SRT and CRT, but no divergent lines.
Coefficients from Group A were valid for
Group B. Predicted value for SRT and for CRT,
plus 1.5 SDs. selected 8% of the model group as
abnormal.
D-37
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Kilburn and
Warshaw
(1993a)
Kilburn
(2002b. a)
Study population
Well water exposed
subjects to 6-500 ppb
of TCE for 1-25 yrs;
544 recruited test
subjects; Group 1
= 196 exposed family
members of subjects
with cancer or birth
defects; Group 2 = 178
from exposed families
without cancer or birth
defects; Group 3
= 170 exposed parents
whose children had
birth defects and
rheumatic disorders;
Controls: 68 referents
and 113 histology
technicians (HTs)
without environmental
exposure to TCE.
236 residents
chronically exposed to
TCE and associated
solvents, including
DCE,
perchloroethylene, and
vinyl chloride, in the
environment from a
Exposure assessment
and biomarkers
Well water was
measured from 1957 to
1981 by several
governmental agencies,
and average annual TCE
exposures were
calculated and then
multiplied by each
individual's years of
residence for
170 subjects.
Exposure estimate based
on groundwater plume
based on contour
mapping; concentrations
between 0.2 and 10,000
ppb of TCE over a 64
km2 area; additional
Tests used
Neurobehavioral
testing — augmented
NET; Eye Closure and
Blink using EMG.
Neuropsychological
(NFS) test— portions of
Wechsler's Memory
Scale, and WAIS and
embedded figures test,
grooved pegboard, Trail
Making A and B,
POMS, and Culture Fair
Test. Neurophysio-
logical (NPH) testing —
simple visual RT, body
balance apparatus,
cerebellar function,
proprioception, visual,
associative links and
motor effector function.
SRT, CRT, balance
sway speed (with eyes
open and eyes closed),
color errors, blink reflex
latency, Supra orbital
Statistics
Two sided student t-
test with ap< 0.05.
Linear regression
coefficients to test
how demographic
variables or other
factors may
contribute.
Descriptive
statistics;
ANCOVA; step-
wise adjustment of
demographics.
Results
Exposed subjects had lower intelligence scores
and more mood disorders.
NPH: Significant impairments in sway speed
with eyes open and closed, blink reflex latency
(R-l), eye closure speed, and two choice visual
RT.
NPS: Significant impairments in Culture Fair
(intelligence) scores, recall of stories, visual
recall, digit span, block design, recognition of
fingertip numbers, grooved pegboard, and Trail
Making A and B.
POMS: All subtests, but the fatigue, were
elevated. Mean speeds of sway were greater with
eyes open atp < 0.0001) and with eyes closed
p < 0.05) in the exposed group compared to the
combined referents. The exposed group mean
SRT was 67 msec longer than the referent group
(p < 0.0001). CRT of the exposed subjects was
93 msec longer in the third trial (p < 0.0001) than
referents. It was also longer in all trials, and
remained significantly different after age
adjustment. Eye closure latency was slower for
both eyes in the exposed and significantly
different (p < 0.0014) on the right compared to
the HT referent group.
The principal comparison, that was between the
236 exposed persons and the 161 unexposed
regional controls, revealed 13 significant
differences (p < 0.05). SRTs and CRTs were
delayed. Balance was abnormal with excessive
sway speed (eyes closed), but this was not true
when both eyes were open.
D-38
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Kilburn
(2002b. a)
Study population
nearby microchip
plant, some involved in
litigation, prior to 1983
and those who lived in
the area between 1983
and 1993, during
which time dumping of
chlorinated solvents
had supposedly ceased
and clean-up activities
had been enacted;
Controls: 67 referents
from northeast
Phoenix, who had
never resided near the
two plants (mean
distance = 2,000 m,
range = 1,400-3,600 m
from plants) and
161 regional referents
from Wickenburg,
Arizona up-wind of
Phoenix, recruited via
random calls made to
numbers on voter
registration rolls,
matched to exposed
subjects by age and
years of education,
records showed no
current or past water
contamination in the
areas.
Exposure assessment
and biomarkers
associated solvents,
including DCE,
perchloroethylene, and
vinyl chloride, No air
sampling.
Tests used
tap (left and right),
Culture Fair A,
Vocabulary, Pegboard,
Trail Making A and B,
Immediate verbal recall,
POMS; pulmonary
function. The same
examiners who were
blinded to the subjects'
exposure status
examined the Phoenix
group, but the
Wickenburg referents'
status was known to the
examiners. Exact order
or timing of testing not
stated.
Statistics
Results
Color discrimination errors were increased. Both
right and left blink reflex latencies (R-l) were
prolonged. Scores on Culture Fair 2A,
vocabulary, grooved pegboard (dominant hand),
trail making A and B, and verbal recall (i.e.,
memory) were decreased in the exposed subjects.
Litigation is suggested but not stated and study
paid by lawyers. Litigation status may introduce
a bias, particularly if no validity tests were used.
D-39
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Kilburn
(2QQ2b)
236 residents exposed
environmentally from
a nearby microchip
plant (exact number of
litigants not stated);
156 individuals
exposed for > 10 yrs
compared to 80
individuals <10 yrs of
exposure; Controls:
58 nonclaimants in
3 areas within
exposure zone
(Zones A, B, and C).
No discussion of
exposure assessment
methods and results.
Solvents included TCE,
DCE, perchloroethylene.
and vinyl chloride;
concluded exposure is
primarily due to
groundwater plume
rather than air releases.
SRT, CRT, Balance
sway speed (with eyes
open and eyes closed),
color errors, blink reflex
latency, Supra orbital
tap (left and right),
Culture Fair A,
Vocabulary, Pegboard,
Trail Making A and B,
immediate verbal recall,
POMS.
Descriptive
statistics, regression
analysis. Similar
study to the one
reported above with
the exception of
looking at the
effects of duration
of residence,
proximity to the
microchip plant,
and being involved
in litigation.
Insignificant effects of longer duration of
residence. No effect of proximity and litigation.
Effects of longer duration of residence modest
and insignificant. No effect of proximity. No
litigation effect. Zone A: 100 clients were not
different from the nine nonclients. Zone B:
nonclients were more abnormal in color different
than clients and right-sided blink was less
abnormal in nonclients. Zone C: 9 of the
13 measurements were not significantly different.
26 of the original 236 subjects re-tested in 1999:
maintained impaired levels of functioning and
mood. No tests of effort and malingering used,
limiting interpretations. Again, no tests of effort
and malingering were used, thus limiting
interpretation. Litigation is suggested but not
stated and study paid by lawyers. Litigation
status may introduce a bias, particularly if no
validity tests were used.
D-40
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Landrigan et
al. (19871
Study population
13 Pennsylvania
residents exposed
through drinking and
bathing water
contaminated by
approximately
1,900 gallon TCE spill;
February 1980: Nine
workers exposed to
TCE while degreasing
metal in pipe
manufacturing plant
and nine unexposed
controls (mean ages
were 42.7 exposed and
46.4-yr old unexposed;
mean durations of
employment = 4.4 yrs,
exposed, and 9.4 yrs,
unexposed. May 1980:
10 exposed workers
and same 9 unexposed
worker controls from
February monitoring.
Exposure assessment
and biomarkers
Community evaluation:
Nov 1979—
questionnaires on TCE
and other chemical
exposures, and
occurrence of signs and
symptoms of exposure
to TCE, morning urine
samples, urine samples
analyzed
coloreimetrically for
TTCs.
Occupational
evaluations (In
workers): breathing-
zone air samples( mean
205 mg/m3; 37 ppm);
medical evaluations,
pre- and postshift spot
urine samples in
February and again in
May, mid- and postshift
venous blood samples
during the May survey.
Tests used
Community evaluation,
occupational
evaluations; urine
evaluations for TCE
metabolites;
questionnaires to
evaluate neurologic
effects and symptoms;
ISO concentrations;
map of TCE in
groundwater.
Statistics
Descriptive
statistics
Results
Community evaluation: No urinary TCA detected
in community population except for one resident
also working at plant and one resident with no
exposure.
Occupational evaluation: Range 117-357 mg/m3-
(21-64 ppm).
February: Airborne exposures exceeded NIOSH
limit by up to 222 mg/m3 (40 ppm) (NIOSH
TWA < 135 mg/m3). (24 ppm). Short-term
exposure exceeded NIOSH values of 535 mg/m3
(96 ppm) by up to 1,465 mg/m3 (264 ppm).
Personal breathing zone of other workers within
recommended limits (0.5-125 mg/m3) (0.1-
23 ppm). Seven exposed workers reported acute
symptoms, including fatigue, light-headedness,
sleepiness, nausea, and headache, consistent with
TCE exposure; No control workers reported such
symptoms; Prevalence of one or more symptoms
78% in exposed worker group, 0% in control
worker group. Symptoms decreased after
recommendations were in place for 3 months
(May testing) for reduced exposures.
D-41
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Liu et al.
(19881
103 workers from
factories in Northern
China, exposed to TCE
(79 men, 24 women),
during vapor
degreasing production
or operation. The
unexposed control
group included 85 men
and 26 women.
Exposed to TCE, mostly
at <50 ppm;
concentration of
breathing zone air
during entire shift
measured by diffusive
samplers placed on the
chest of each worker;
divided into three
exposure groups; 1-10,
11-50, and 51-100 ppm.
Also, hematology,
serum biochemistry,
sugar, protein, and
occult blood in urine
were collected.
Serf-reported subjective
symptom questionnaire.
Prevalence of
affirmative
answers = total
number of
affirmative answers
divided by (number
of respondents x
number of
questions); X2.
Dose-response relationship established in
symptoms such as nausea, drunken feeling, light-
headedness, floating sensation, heavy feeling of
the head, forgetfulness, tremors and/or cramps in
extremities, body weight loss, changes in
perspiration pattern, joint pain, and dry mouth
(all >3 times more common in exposed workers);
"bloody strawberry jam-like feces" was
borderline significant in the exposed group and
"frequentflatus" was statistically significant.
Exposure ranged up to 100 ppm; however, most
workers were exposed <10 ppm, and some at 11-
50 ppm. Contrary to expectations, production
plant men had significantly higher levels of
exposure (24 had levels of 1-10 ppm, 15 had
levels of 11-50 ppm, 4 had levels of 51-100
ppm) than degreasing plant men (31 had levels of
1-10 ppm, 2 had levels of 11-50 ppm, 0 had
levels of 51-100 ppm); p < 0.05 by y2 test. No
significant difference (p > 0.10) was found in
women workers. The differences in exposure
intensity between men and women was of
borderline significance (0.05
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
McCunney
(19881
This is a case study
conducted on three
young white male
workers exposed to
TCE in degreasing
operations. There
were no controls
included. Case 1 was a
25-yr-old male, Case 2
was a 28-yr-old white
male, Case 3 was a 45-
yr-old white male.
Case 1: TCE in air at the
work place was
measured at 25 ppm, but
his TCA in urine was
measured at 210 mg/L.
This is likely due to
dermal exposure while
cleaning metal rods in
TCE. Case 2: no TCE
exposure data presented,
TCA at 9 mg/L after
6 months; Case 3: no
TCE exposure data
presented.
Clinical evaluation of
loss of balance, light
headedness, resting
tremor, blurred vision,
and dysdiadochokinesia,
change in demeanor and
loss of coordination,
cognitive changes were
noted, as well as
depression; CT scan,
EEG, nerve
conductivity, and visual
and somatosensory
evoked response.
Neurological exams
included sensitivity to
pinprick over the face.
Ophthalmic evaluation.
There were no
statistical analyses
of results presented.
Case 1 was a 25-yr-old male, who presented with
a loss of balance, light headedness, resting
tremor, blurred vision, and dysdiadochokinesia.
The subject had been in a car accident and
suffered head injuries. He later returned with a
change in demeanor and loss of coordination. He
showed a normal CT scan, EEG, nerve
conductivity, and visual and somatosensory
evoked response. Neurological exams revealed
reduced sensitivity to pinprick over the face, deep
tendon reflexes were reduced, and mild to
moderate cognitive changes were noted, as well
as depression. Ophthalmic evaluation was
normal. He was removed from the TCE exposure
and appeared to recover.
Case 2 was a 28-yr-old white male who presented
with numbness and shooting pains in fingers. He
exhibited anorexia, and tiredness. He worked in
a degreasing operation for a jeweler using open
containers filled with TCE in a small,
unventilated room. There were no exposure data
provided, but his TCA was 9 mg/L at 6 months
after exposure. He had been hospitalized with
hepatitis previously. No neurological tests were
administered.
Case 3 was a 45-yr-old white male who presented
with numbness in hands and an inability to sleep.
He exhibited slurred speech. He was positive for
blood in stool, but had a history of duodenal
ulcers.
D-43
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Mhiri et al.
(20041
23 phosphate industry
workers exposed to
TCEfor6hrs/dforat
least 2 yrs while
cleaning walls to be
painted; controls:
23 unexposed workers
from the department of
neurology.
Measurement of urinary
metabolites of TCE
were performed 3 times/
worker. Blood tests and
hepatic enzymes were
also collected.
TSEPs recorded using
Nihon-KohdenEMG-
evoked potential
system; baseline clinical
evaluations regarding
facial burn or
numbness, visual
disturbances,
restlessness,
concentration difficulty,
fatigue, mood changes,
assessment of cranial
nerves, quality of life;
biological tests
described under
biomarkers.
Paired or unpaired
Student's t-test as
appropriate, p-
value setat<0.05.
Spearman rank-
correlation
procedure was used
for correlation
analysis.
Abnormal TSEP were observed in six6 workers
with clinical evidence of Trigeminal involvement
and in nine asymptomatic workers. A significant
positive correlation between duration of exposure
and the N2 latency (p < 0.01) and P2 latency (p
< 0.02) was observed. Only one subject had
urinary TCE metabolite levels over tolerated
limits. TCE air contents were over tolerated
levels, ranging from 50 to 150 ppm.
Mitchell and
Parsons-
Smith (1969)
This was a case study
of one male patient,
age 33 yrs,
occupational exposed
to TCE during
degreasing. There
were no controls.
No exposure data were
presented.
Trigeminal nerve, loss
of taste, X-rays of the
skull, EEC,
hemoglobin, and
Wassermann reaction.
No statistics
provided.
The patient had complete analgesia in the right
trigeminal nerve and complete loss of taste;
patient complained of loss of sensation on right
side efface and uncomfortable right eye, as well
as vertigo and depression. X-rays of the skull,
EEG, hemoglobin, and Wassermann reaction
were all normal.
Nagaya et al.
(1990)
84 male workers ages
18-61 yrs (mean
36.2 yrs) constantly
using TCE in their
jobs. Duration of
employment (i.e.,
exposure) 0.1-34.0 yrs,
(mean 6.1 yrs; SD =
5.9). Controls: 83 age-
matched office
workers and students
with no exposure.
Workers exposed to
about 22-ppm TCE in
air. Serum dopamine-
(3-hydroxylase (DBH)
activity levels measured
from blood. U-TTCs
also measured.
Blood drawn during
working time and DBH
activities were
analyzed; spot urine
collected at time of
blood sampling and U-
TTC determined by
alkaline-pyridine
method.
Student's t-test and
linear correlation
coefficient. Results
of U-TTC presented
by age groups: <25;
26-40; and >41 yrs.
A slight decrease in serum DBH activity with age
was noted in both groups. Significant inverse
correlation of DBH activity and age was found in
workers (r = -0.278, O.OK/? < 0.02), but not in
controls (r = -0.182, 0.05
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Reifetal.
(20031
143 residents of the
Rocky Mountain
Arsenal community of
Denver whose water
was contaminated with
TCE and related
chemicals from nearby
hazardous waste sites
between 1981 and
1986; referent group at
lowest concentration
(<5 ppb).
Hydraulic simulation
model used in
conjunction with a GIS
estimated residential
exposures to TCE;
approximately 80% of
the sample exposed to
TCE exceeding
maximum contaminant
level of 5 ppb and
approximately 14%
exceeded 15 ppb. High
exposure group
>15 ppb, low-exposure
referent group <5 ppb,
medium exposure group
5
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Rasmussen
and Sabroe
(19861
368 metal workers
working in degreasing
at various factories in
Denmark (industries
not specified) with
chlorinated solvents;
94 controls randomly
selected semiskilled
metal workers from
same area; mean age:
37.7 yrs (range: 17-
65+yrs). Total
443 men; 19 women.
Questionnaire:
categorized in four
groups; three exposure
groups plus control: (1)
currently working with
chlorinated solvents
(n= 171; average.
duration: 7.3 yrs,
16.5 hrs/wk; 57% TCE
and 37% 1,1,1-
trichloroethane);
(2) currently working
with other solvents
(n= 131; petroleum,
gasoline, toluene,
xylene); (3) previously
(1-5 yrs) worked with
chlorinated or other
solvents (n = 66); and
(4) never worked with
organic solvents
(n=94).
Questionnaire: 74 items
about
neuropsychological
symptoms (memory,
concentration,
irritability, alcohol
intolerance, sleep
disturbance, fatigue).
%2; ORs; t-test;
logistic regression.
Neuropsychological symptoms significantly more
prevalent in the chlorinated solvents-exposed
group; TCE caused the most "inconveniences and
symptoms;" dose-response between exposure to
chlorinated solvents and chronic
neuropsychological symptoms (memory
\p < 0.001], concentration \p < 0.02], irritability
\p < 0.004], alcohol intolerance \p < 0.004],
forgetfulness \p < 0.001], dizziness \p < 0.005],
and headache [p < 0.01]). Significant
associations between previous exposure and
consumption of alcohol with chronic
neuropsychological symptoms.
D-46
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Rasmussen et
al. (1993d)
96 Danish workers
involved in metal
degreasing with
chlorinated solvents,
mostly TCE (n = 70);
(industries not
specified), age range:
19-68 yrs; no external
controls.
Chronic exposure to
TCE (n = 70); CFC
(n=25);HC(n= 1);
average duration:
7.1 yrs; range of full-
time degreasing:
1 month to 36 yrs;
occupational history,
blood and urinary
metabolites (TCA);
biological monitoring
for TCE and TCE
metabolites; CEI
calculated based on
number of hrs/wk
worked with solvents x
yr of exposure x 45 wks
peryr; three groups:
(1) low exposure:
n = 19, average full-time
exposure 0.5 yr;
(2) medium exposure:
n = 36, average full-time
exposure 2.1 yrs; and
(3) high exposure:
n = 41, average full-time
exposure 11 yrs. Mean
TCA in high exposure
group = 7.7 mg/L
(maximum =
26.1 mg/L); TWA
measurements of
CFC113 levels: 260-
420 ppm (U.S. and
Danish TLV is
500 ppm).
Medical interview,
neurological exam,
neuropsychological
exam. Tests: WAIS:
Vocabulary, Digit
Symbol; SRT, acoustic-
motor function,
discriminatory attention,
Sentence Repetition,
Paced Auditory Serial
Addition Test, Text
Repetition, Rey's
Auditory Verbal
Learning, visual gestalt,
Stone Pictures
(developed for this
study, nonvalidated),
revised Santa Ana,
Luria motor function,
Mira; Blind study.
Fisher's exact test,
y2 trend test, t-test,
ANOVA, logistic
regression, ORs, y2
goodness-of-fit test.
Confounders
examined: age,
primary intellectual
level,
arteriosclerosis,
neurological/
psychiatric disease,
alcohol abuse, and
present solvent
exposure.
After adjusting for confounders, the high-
exposure group had significantly increased risk
for psychoorganic syndrome following exposure
(OR: 11.2); OR for medium exposed group = 5.6;
Significant increase in risk with age and with
decrease in WAIS Vocabulary scores.
Prevalence of psychoorganic syndrome: 10.5% in
low-exposure group, 38.9 in medium exposure
group, 63.4% in high-exposure group; no
significant interaction between age and solvent
exposure.
D-47
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Rasmussen et
al. (1993c)
Study population
96 Danish workers
involved in metal
degreasing with
chlorinated solvents
(industries not
specified), age range:
19-68 yrs; no external
controls.
Exposure assessment
and biomarkers
Chronic exposure to
TCE (n = 70); CFC
(n=25);HC(n= 1);
average duration:
7.1 yrs); range of full-
time degreasing:
1 month to 36 yrs;
occupational history,
blood and urinary
metabolites (TCA);
biological monitoring
for TCE and TCE
metabolites; CEI
calculated based on
number of hrs/wk
worked with solvents x
yr of exposure x 45 wks
peryr; three groups:
(1) low exposure:
n = 19, average full-time
exposure 0.5 yr;
(2) medium exposure:
n = 36, average full-time
exposure 2.1 yrs; and
(3) high exposure:
n = 41, average full-time
exposure 1 1 yrs. Mean
TCA in high-exposure
group = 7.7 mg/L
(maximum =
26.1 mg/L); TWA
measurements of
CFC1 13 levels: 260-
420 ppm (U.S. and
Danish TLV is
500 ppm).
Tests used
WAIS (original
version): Vocabulary,
Digit Symbol, Digit
Span; SRT, Acoustic-
motor function (Luria),
Discriminatory attention
(Luria), Sentence
Repetition, PASAT,
Text Repetition, Rey's
Auditory Verbal
Learning, Visual
Gestalts, Stone Pictures
(developed for this
study, nonvalidated),
revised Santa Ana,
Luria motor function,
Mira; Blind study.
Statistics
Linear regression
analysis;
Confounding
variables analyzed:
age, primary
intellectual
function, word
blindness,
education,
arteriosclerosis,
neurological/
psychiatric disease,
alcohol use, present
solvent exposure.
Results
Dose response with 9 of 15 tests; controlling for
confounds, significant relationship of exposure
was found with Acoustic-motor function
(p < 0.001), PASAT (p < 0.001), Rey AVLT
(p < 0.001), vocabulary (p < 0.001), and visual
gestalts (p < 0.001); significant age effects.
D-48
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Rasmussen et
al. Q993a)
96 Danish workers
involved in metal
degreasing with
chlorinated solvents
(industries not
specified), age range:
19-68 yrs; no external
controls.
Chronic exposure to
TCE (n = 70); CFC
(n=25);HC(n= 1);
average duration:
7.1 yrs); range of full-
time degreasing:
1 month to 36 yrs;
occupational history,
blood and urinary
metabolites; biological
monitoring for TCE and
TCE metabolites; CEI
calculated based on
number of hrs/wk
worked with solvents x
yr of exposure x 45 wks
peryr; 3 groups: (1) low
exposure: n= 19,
average full-time
exposure 0.5 yr;
(2) medium exposure:
n = 36, average full-time
exposure 2.1 yrs; and
(3) high exposure:
n = 41, average full-time
exposure 11 yrs. Mean
TCA in high-exposure
group = 7.7 mg/L
(maximum =
26.1 mg/L); TWA
measurements of
CFC113 levels: 260-
420 ppm (U.S. and
Danish TLV is
500 ppm).
Medical interview,
clinical neurological
exam,
neuropsychological
exam.
Multiple regression;
Fisher's exact test;
Mantel-Haenzel test
for linear
association.
Significant dose response between exposure and
motor dyscoordination remained after controlling
for confounders; bivariate analysis showed
increased vibration threshold with increased
exposure, but with multivariate analysis, age was
a significant factor for the increase.
D-49
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Ruijten et al.
(1991)
Study population
3 1 male printing
workers exposed to
TCE. Mean age
44 yrs; Mean duration
16 yrs; Controls: 28;
mean age 45 yrs.
Exposure assessment
and biomarkers
Relied on exposure data
from past monitoring
activities conducted by
plant personnel using
gas detection tubes.
Estimated 17 ppmfor
past 3 yrs, 35 ppm for
preceding 8 yrs, and
70 ppm before that.
Individual cumulative
exposure was calculated
as time spent in different
exposure periods and the
estimated exposure in
those periods. Mean
cumulative exposure
= 704 ppm x yrs
(SD 583, range: 160-
2, 150 ppm xyrs.
Tests used
General questionnaire,
cardiotachogram
recorded on ink writer
to measure autonomic
nerve function,
including forced
respiratory sinus
arrhythmia (FRSA),
muscle heart reflex
(MHR), resting
arrhythmia. Trigeminal
nerve function
measured using
masseter reflex and
blink reflex;
electrophysiological
testing of peripheral
nerve functioning using
motor nerve conduction
velocity of the peroneal
nerve.
Statistics
Combined Z score
= individual Z
scores of the FRSA
and MHR;
ANCOVA to
calculate difference
between exposed/
nonexposed
workers.
Cumulative
exposure effect
calculated by
multiple linear
regression analysis.
Controlled for age,
alcohol
consumption, and
nationality by
including them as
covariables.
Quetelet-index
included for
autonomic nerve
parameters; Body
length and skin
temperature used
for all peripheral
nerve functions;
one-sided
significance level of
5% used. Non-
normal distributions
were log or square
root transformed.
Results
Slight reduction in Sural nerve conduction
velocity was found and a prolongation of the
Sural refractory period. Latency of the masseter
reflex had increased. No prolongation of the
blink reflex was found; no impairment of
autonomic or motor nerve function were found.
Long-term exposure to TCE at threshold limit
values (approximately 35 ppm) may slightly
affect the trigeminal and sural nerves.
D-50
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Smith (1970)
130 (108 males,
22 females); controls:
63 unexposed men
working at the same
factory matched by
age, marital status.
TCA metabolite levels
in urine were measured:
60.8% had levels up to
20 mg/L, and 82.1% had
levels up to 60 mg/L.
Cornell Medical Index
Questionnaire
(Psychiatric section),
Heron's Personality
Questionnaire, Fluency
Test, 13-Mistake Test,
Serial Sevens, Digit
Span, General
Knowledge Test, tests
of memory.
Descriptive
statistics.
Of the 130 subjects exposed, 27% had no
complaints of symptoms, 74.5% experienced
fatigue, 56.2% dizziness, 17.7% headache, 25.4%
GI problems, 7.7% autonomic effects, and 24.9%
had other symptoms. The number of complaints
reported by subjects were statistically significant
between those with <20 mg/L TCA (M =
1.8 complaints) and those >60 mg/L (M =
2.7 complaints). Each group, however, had a
similar proportion of subjects who reported
having only 'slight' symptoms. The total time of
continuous exposure to TCE (ranging from <1 yr
to >10 yrs) appeared to have little influence on
frequency of symptoms. No results of the tests
were reported. Author postulates that symptom
assessment raises the possibility of "errors of
subjective judgment."
Triebig et al.
(1977c)
This study was
conducted on eight
subjects occupationally
exposed to TCE.
Subjects were seven
men and one woman
with an age range of
23-3 8 yrs. There was
no control group.
Measured TCE in air
averaged 50 ppm
(260mg/m3). Length of
occupational exposure
was not reported.
Results were compared
after exposure periods,
and compared to results
obtained after periods
removed from exposure.
TCA and TCE
metabolites in urine and
blood were measured.
Psychological tests
included d2, MWT-A,
and short test.
Wilcoxon and
Willcox
nonparametric tests.
Due to the small
sample size, a
significance level of
1% was used.
Mean values observed were 330-mg TCOH and
319-mg TCA/g creatinine, respectively, at the
end of a work shift. The psychological tests
showed no statistically significant difference in
the results before or after the exposure-free time
period.
D-51
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Triebig
(1982)
Study population
This study was
conducted on
24 healthy workers (20
males, 4 females)
exposed to TCE
occupationally at three
different plants. The
ages were 17-56 yrs;
length of exposure
ranged from 1 to
258 months (mean
83 months). A control
group of 144 controls
used to establish
'normal' responses on
the nerve conduction
studies. The matched
control group consisted
of 24 healthy
nonexposed
individuals (20 males,
4 females), chosen to
match the subjects for
age and sex.
Exposure assessment
and biomarkers
Length of exposure
ranged from 1 to
258 months (mean
83 months). TCE
concentrations measured
in air at work places
ranged from 5 to
70ppm. TCA, TCE,
and TCOH were
measured in blood, and
TCE and TCA were
measured in urine.
Tests used
Nerve conduction
velocities were
measured for sensory
and motor nerve fibers
using the following
tests: MCVMAx(U):
Maximum NLG of the
motor fibers of the N.
ulnaris between the
wrist joint and the
elbow; dSCV (U),
pSCV (U), and dSCV
(M).
Statistics
Data were analyzed
using parametric
and nonparametric
tests, rank
correlation, and
linear regression,
with 5% error
probability.
Results
Results show no statistically significant
difference in nerve conduction velocities between
the exposed and unexposed groups. This study
has measured exposure data, but
exposures/responses were not reported by dose
levels.
D-52
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Triebig
(1981)
Study population
The exposed group
consists of 66 healthy
workers selected from
a population of
112 workers. Workers
were excluded based
on polyneuropathy
(n = 46) and alcohol
consumption (n = 28).
The control group
consisted of 66 healthy
workers with no
exposures to solvents.
Exposure assessment
and biomarkers
Subjects were exposed
to a mixture of solvents,
including TCE,
specifically "ethanol,
ethyl acetate, aliphatic
hydrocarbons (gasoline),
MEK, toluene, and
trichloroethene."
Subjects were divided
into three exposure
groups based on length
of exposure, as follows:
20 employees with
"short-term exposure"
(7-24 months);
24 employees with
"medium-term
exposure" (25-60
months); and
22 employees with
"long-term exposure"
(over 60 months). TCA,
TCE, and TCOH were
measured in blood, and
TCE and TCA were
measured in urine.
Tests used
Nerve conduction
velocities were
measured for sensory
and motor nerve fibers
using the following
tests: MCVMAx(U):
Maximum NLG of the
motor fibers of the N.
ulnaris between the
wrist joint and the
elbow; dSCV (U),
pSCV (U), and dSCV
(M).
Statistics
Data were analyzed
using parametric
and nonparametric
tests, rank
correlation, and
linear regression,
with 5% error
probability.
Results
There was a dose-response relationship observed
between length of exposure to mixed solvents
and statistically significant reduction in nerve
conduction velocities observed for the medium
and long-term exposure groups for the NCV.
D-53
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Troster and
Ruff (1990)
Three occupationally
exposed workers to
TCEorTCA: two
patients acutely
exposed to low levels
of TCE and one patient
exposed to TCA;
Controls: two groups
of n= 30 matched
controls (all age and
education matched).
"Unknown amount of
TCE for 8 months."
SDNTB," lor more
of:" TAT, MMPI,
Rorschach, and
interviewing
questionnaire, medical
examinations (including
neurological, CT scan,
and/or chemo-
pathological tests and
occupational history).
Not reported.
Case 1: Intelligence "deemed" to drop from
premorbid function at 1 yr and 10 mo after
exposure. Impaired functions improved for all
but reading comprehension, visuospatial learning,
and categorization (abstraction). Case 2: Mild
deficits in motor speed, verbal learning, and
memory; "marked" deficits in visuospatial
learning; good attention; diagnosis of mild
depression and adjustment disorder, but
symptoms subsided after removal from exposure.
Case 3: Manual dexterity and logical thinking
borderline impaired; no emotional changes,
cognitive function spared, diagnosis of
somatoform disorder.
D-54
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
White et al.
(19971
Group 1:
28 individuals in
Massachusetts exposed
to contaminated well
water; source: tanning
factory and chemical
plant; age range: 9-
55 yrs. Group 2:
12 individuals in Ohio
exposed to
contaminated well
water; source:
degreasing; age range:
12-68 yrs. Group 3:
20 individuals in
Minnesota exposed to
contaminated well
water; n = 14 for nerve
conduction studies and
n = 6 for
neuropsychological
testing; source:
ammunition plant; age
range: 8-62 yrs. No
controls.
Group 1: two wells
tested in 1979: 267 ppb
TCE,21ppb
tetrachloroethylene,
12 ppb chloroform,
29 ppb dichloro-
ethylene, 23 ppb
trichlorotrifluoro-
ethane; 2 yrs average
TCE 256 ppb for well
G, and 111 ppb for well
H. Group 2:13 wells
with 1,1,1-
trichloroethane (up to
2,569 ppb) and TCE (up
to 760 ppb); blood
analysis of individuals
2 yrs after end of
exposure and soon after
exposure showed
normal or mild
elevations of TCE,
elevations of 1,1,1-
trichloroethane,
ethylbenzene, and
xylenes. Group 3: mean
TCE for one well
261 ppb; 1,1-DCE
9.0 ppb; and 1,2-DCE
107 ppb.
Occupational and
environmental
questionnaire,
neurological exam,
neuropsychological
exam: WAIS-R, WISC-
R, WMS, WMS-R,
Wisconsin Card
Sorting, COWAT,
Boston Naming, Boston
Visuospatial
Quantitative Battery,
Milner Facial
Recognition Test, Sticks
Visuospatial Orientation
Task, Word triads,
Benton Visual
Retention Test, Santa
Ana, Albert's Famous
Faces, Peabody Picture
Vocabulary Test,
WRAT, POMS, MMPI,
Trail-making,
Fingertapping, Delayed
Recognition Span Test;
Neurophysiological
exam: eyeblink, evoked
potentials, nerve
conduction; and other:
EKG, EEC, medical
tests.
Data shown in
proportion in three
communities,
clinical diagnostic
categories, analysis
of central
tendencies, and
descriptive
statistics.
Group 1: Some individuals with subclinical
peripheral neuropathy; 92.8% with reflex
abnormalities; 75% total diagnosed with
peripheral neuropathy; 88.9% with impairment in
at least one memory test. Impairments: attention
and executive function in 67.9%; motor function
in 60.71%, visuospatial in 60.71%, and mild to
moderate encephalopathy in 85.7%.
Group 2: 25% with abnormal nerve conduction,
Impairments: attention and executive function in
83.33%, memory in 58.33%, and language/verbal
in 50%.
Group 3: 35.7% with peripheral neuropathy;
neuropsychological: all six tested had memory
impairment, attention and executive function
impairment, three had manual motor slowing.
Participants younger at time of exposure with
wider range of deficits. Language deficits in
younger, but not in older, participants.
D-55
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Winneke
(1982)
Study population
This is a review article
presenting multiple
studies that evaluated
neurological effects of
TCE, and other
solvents. Only the
TCE results are
summarized herein.
Experiment 1 :
18 subjects (results
taken from Schlipkoter
et al. [(197411 and
summary is based on
information from
Winneke (1982)1)
Experiment 2:
12 subjects (results
taken from Winneke et
al. (1978: 1976)1 and
summary is based on
information from
Winneke (1982)])
Exposure assessment
and biomarkers
Experiment 1 : Subjects
were exposed to 50 ppm
TCEfor3.5hrs.
Experiment 2:
Comparative study of
effects from (a) 50 ppm
TCEfor3.5hrsand(b)
0.76 mL/kg ethanol.
Tests used
For both experiments 1
and 2: critical flicker
fusion, sustained
attention task, auditory
evoked potentials
Statistics
No statistical details
were reported.
Results
Significant decrease (p < 0.05) in auditory
evoked potentials in individuals (experiments 1
and 2) exposed to 50 ppm TCE. No significant
effects were noted in the critical flicker fusion or
the sustained attention tasks.
D-56
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
ATSDR
(2002)
Study population
116 children from
registry of
14 hazardous waste
sites with TCE in
groundwater; under
10 yrs of age at time of
registry; control
population (n = 177);
communities with no
evidence of TCE in
groundwater
(measured below
maximum contaminant
level); matched by age
and race; there were
other chlorinated
solvents present in the
exposed group wells.
Exposure assessment
and biomarkers
Exposures were
modeled using tap water
TCE concentrations and
GIS for spatial
interpolation, and
LaGrange for temporal
interpolation to estimate
exposures from
gestation to 1990 across
the area of subject
residences, modeled
data were used to
estimate lifetime
exposures (ppb-yrs) to
TCE in residential wells;
three exposure level
groups; control = 0 ppb;
low exposure-group = 0
<23 ppb-yrs; and high-
exposure group =
>23 ppb-yrs;
confounding exposure
was a concern.
Tests used
Fisher Logemann test;
OSME-R; CSP;
D-COME-T; hearing
screening; DPOAE;
SCAN.
Statistics
Screening results as
binary variables
using logistic
regression within
SAS; independent
variables included
exposure measures,
age, gender, case
history; %2 test,
Fisher's exact test,
t-tests, linear
models.
Results
Exposed children had higher abnormalities for D-
COME-T (p < 0.002), CSP (p < 0.008),
velopharyngeal function (p < 0.04), high palatal
arch (p < 0.04), and abnormal outer ear cochlear
function. No difference observed in exposed and
nonexposed populations for speech or hearing
function. No difference found in OSH function.
D-57
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Epidemiological studies: controlled exposure studies; neurological effects of trichloroethylene
Gamberale et
al. (19761
Konietzko et
al. (1975)
15 healthy men aged
20-3 1-yr old employed
by the Department of
Occupational Medicine
in Stockholm, Sweden;
Controls: Within
Subjects (15 serf-
controls).
This is a controlled
exposure study
conducted on
20 healthy male
students and scientific
assistants with a mean
age of 27.2 yrs.
Exposed for TCE
70 min via a breathing
valve to 540 mg/m3
(97 ppm), 1,080 mg/m3
(194 ppm), and during
ordinary atmospheric
air. Sequence was
counterbalanced
between the three
groups, days, and
exposure levels.
Concentration was
measured with a gas
chromatographic
technique every third
min for the 1st 50 min,
then between tests
thereafter.
Subjects were exposed
to a constant TCE
concentration of
95.3 ppm (520 mg/m3)
for up to 12 hrs, and
blood concentrations of
TCE were also analyzed
at hourly intervals.
RT addition, SRT, CRT
and short-term memory
using an electronic
panel. Subjects also
assessed their own
conditions on a 7-point
scale.
Evaluated for changes
in alpha waves (<14 Hz)
in the EEG recordings;
EEG recordings were
performed hourly for a
period of 1 min with the
eyes closed. This was
used as a potential
measure of
psychomotor
disturbance.
Friedman two-way
analysis by ranks to
evaluate difference
between three
conditions,
nonsignificant when
tested individually,
but significant when
tested on the basis
of six variables.
Nearly half of the
subjects could
distinguish
exposure/nonexposu
re. ANOVAfor
four performance
tests based on a 3 x
3 Latin square
design with
repeated measures.
In the RT-addition test, the level of performance
varied significantly between the different
exposure conditions (F[2.24] = 4.35;;? < 0.051)
and between successive measurement occasions
(tF[2.24] = 19.25;;? < 0.001). The level of
performance declined with increased exposure to
TCE, whereas repetition of the testing led to a
pronounced improvement in performance as a
result of the training effect. No significant
interaction effects between exposure to TCE and
training.
The alpha segment increased over time of
exposure (from 0800 to 0900 and 1,000 hrs
[military time]) (p = 0.05). There were no
significant differences for the other time spans or
for other parameters. Subjects with highest and
lowest TCE blood levels, <2 and >5 iig/mL, were
compared to determine if they showed different
responses, but in no case were the differences
statistically different.
D-58
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Kylin et al.
(1967)
Salvini et al.
(1971)
Study population
12 subjects exposed to
l,OOOppmTCEfor
2hrsinal. 5x2x2
meters chamber;
2 subjects were given
alcohol (0.7 gm of
body weight); controls:
7 of the 12 were tested
some days prior to
exposure and 5 of the
12 were tested some
days after exposure.
This is a controlled
exposure study
conducted on six male
university students.
Each subject was
examined on
2 different d, once
under TCE exposure,
and once as serf
controls, with no
exposure.
Exposure assessment
and biomarkers
l,OOOppmofTCEwas
blown into a chamber
via an infusion unit and
vaporizing system.
Ostwald's distribution
factor for TCE— the
quotient of the amount
of solvent in the blood
by the amount of
alveolar air.
TCE concentration was
110 ppm for 4-hr
intervals, twice per day.
0-ppm control exposure
for all as serf controls.
Tests used
Optokinetic nystagmus;
venus blood and
alveolar air specimens
were taken at various
times after exposure and
analyzed in a gas
chromatograph with a
flame ionization
detector.
Two sets of tests were
performed for each
subject corresponding to
exposure and control
conditions. Perception
test with tachistoscopic
presentation, Wechsler
memory scale, CRT
test, and manual
dexterity test.
Statistics
Ostwald's
distribution factor
for TCE (the
quotient of the
amount of solvent
in the blood in mg/L
by the amount of
the alveolar air in
mg/L) = 9.7;
significant
relationship
between TCE in air
and blood (0.88).
ANOVA
Results
"A number" of subjects showed reduction in
Fusion limit although more pronounced in the
two subjects who consumed alcohol. "Others,"
however, showed little if any effect. No
statistics.
A decrease in function for all measured effects
was observed. Statistically significant results
were observed for perception tests learning
(p < 0.001), mental fatigue (p < 0.01), subjects
(p < 0.05); and CRT learning (p < 0.01), mental
fatigue (p < 0.01), subjects (p < 0.05).
D-59
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Stewart et al.
(1970)
Study population
13 subjects in
10 experiments
Exposure assessment
and biomarkers
Ten chamber exposures
to TCE vapor (100 ppm
and 200 ppm) for
periods of 1 h to a 5 -d
work week.
Experiments 1-7 were
for a duration of 7 hrs
with a mean TCE
concentration of 198-
200 ppm. Experiments
8 and 9 exposed subjects
to 202 ppm TCE for a
duration of 3.5 and 1 hr,
respectively.
Experiment 10 exposed
subjects to 100 ppm
TCE for 4 hrs.
Experiments 2-6 were
carried out with the
same subjects over
5 consecutive d; gas
chromatography of
expired air; no serf
controls.
Tests used
Physical examination
1 hr prior to exposure.
Blood analysis for
complete blood cell
count, sedimentation
rate, total serum lipid,
total serum protein,
serum electrophoresis,
serum glutamic
oxaloacetic
transaminase, and
serum glutamic pyruvic
transaminase. 24-hr
urine collection for
urobilinogen, TCA and
TCE. Also a
preexposure
expirogram, tidal
volume measurement,
and an alveolar breath
sample for TCE; short
neurological exam
including modified
Romberg test, heel-to-
toe test, finger-to-nose
test.
Statistics
Descriptive
statistics.
Results
Ability to perceive TCE odor diminished as
duration of expo increased; 40% had dry throat
after 30-min exposure; 20% reported eye
irritation. Urine specimens showed progressive
increase in amounts of TCE metabolites over the
five consecutive exposures. Concentrations of
TCA and TCE decreased exponentially after last
exposure, but were still present in abnormal
amounts in urine specimens 12 d after exposure.
Loss of smelling TCE: >1 hr = 33%; >2
hrs = 80%; >6.5 hrs = 100%. Symptoms of
lightheadedness, headache, and eye, nose, and
throat irritation. Prominent fatigue and
sleepiness by all after 200 ppm. These symptoms
may be of clinical significance. All had normal
neurological tests during exposure, but 50%
reported greater mental effort was required to
perform a normal modified Romberg test on
more than one occasion.
D-60
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Triebig
(1976)
Study population
This was a controlled
exposure study
conducted on seven
healthy male and
female students (four
females, three males).
The control group was
seven healthy students
(four females, three
males).
Exposure assessment
and biomarkers
Subjects exposed for
6 hrs/d for 5 d to 100
ppm (550 mg/m3 TCE).
Controls were exposed
in chamber to zero TCE.
Biochemical tests
included TCE, TCA,
and TCOH in blood. In
this study, the TCE
concentrations in blood
reported ranged from 4
to 14 ug/mL. A range
of 20-60 ug/mL was
obtained for TCA in the
blood.
Tests used
Psychological tests
were: the d2 test was an
attention load test; the
short test is used to
record patient
performance with
respect to memory and
attention; daily
Fluctuation
Questionnaire measured
the difference between
mental states at the start
of exposure and after
the end of exposure is
recorded; the MWT-A
is a repeatable short
intelligence test; the
Freiburg Personality
Inventory is a test for 12
independent personality
traits; CFT-3 is a
nonverbal intelligence
test; Erlanger
Depression Scale.
Statistics
Regression analyses
were conducted.
Results
There was no correlation seen between exposed
and unexposed subjects for any measured
psychological test results. The biochemical data
did demonstrate that exposed subjects' exposures.
D-61
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Triebig et al.
(1977a)
This was a controlled
exposure study
conducted on seven
healthy male and
female students (four
females, three males)
The control group was
seven healthy students
(four females, three
males).
Subjects exposed for
6 hrs/d for 5 d to
100 ppm (550 mg/m3
TCE). Controls were
exposed in chamber to
zero TCE. Biochemical
tests included TCE,
TCA, and TCOH in
blood. In this study, the
TCE concentrations in
blood reported ranged
from 4 to 14 ug/mL. A
range of 20-60 ug/mL
was obtained for TCA in
the blood.
The testing consisted of:
the Syndrome Short
Test; the "Attention
Load Test" or "d2
Test;" Number recall
test, letter recall test,
The "Letter Reading
Test," "Word Reading
Test," Erlanger
Depression Scale. Scale
for Autonomic
Dysfunction, Anxiety
Scale, Pain Short Scale,
and Information on
Daily Fluctuations.
Statistics were
conducted using
Whitney Mann.
Results indicated the anxiety values of the
placebo random sample group dropped
significantly more during the course of testing (p
< 0.05) than those of the active random sample
group. No significantly different changes were
obtained with any of the other variables.
Vernon and
Ferguson
(1969)
Eight male volunteers
age range 21-30; self
controls: 0 dose.
TCE administered as
Trilene air-vapor
mixtures through
spirometers
administered at random
concentrations of 0, 100.
300, or 1,000 ppm of
TCE for 2 hrs at a time,
during which testing
took place.
Concentrations were
measured with a halide
meter. Medical history,
exam including CBC,
urinalysis, BUN, and
SCOT.
Flicker Fusion with
Krasno-Ivy Flicker
Photometer, Howard-
Dolman depth
perception apparatus,
Muller-Lyer two-
dimensional illusion,
groove-type steadiness
test, Purdue Pegboard,
Written "code
substitution," blood
studies.
ANOVAs,
Dunnett's test.
TCE did not produce any appreciable effects at
lower concentrations. Compared to controls,
participants exposed to 1,000 ppm of TCE had
adverse effects on the Howard-Dolman,
steadiness, and part of the pegboard, but no
effects on Flicker Fusion, from perception or
code substitution. No appreciable changes in
CBC, urinalysis, SCOT, or BUN.
D-62
-------
Table D-l. Epidemiological studies: neurological effects of TCE (continued)
Reference
Study population
Exposure assessment
and biomarkers
Tests used
Statistics
Results
Windemuller
and Ettema
(19781
Pilot study: 24 healthy
male volunteers; age
range = 19-26 yr, four
groups with six
volunteers in each:
(1) control;
(2) exposed to TCE;
(3) exposed to alcohol;
and (4) exposed to
TCE and alcohol; final
study: 15 other
volunteers, each
exposed to all four
conditions.
Chamber study; Group 1
no exposure; Group 2
TCE exposure: 2.5 hrs
with 200 ppm; Group 3
alcohol exposure: 0.35
g/kg body weight;
Group 4 TCE and
alcohol: same as above
levels. Blood alcohol
levels taken with
breathalyzer; exhaled air
sampled for levels of
TCE and TCOH; TCE
exposure: average
measured TCE in
exhaled air = 29 ug/L
(SD = 3); TCE and
alcohol exposure
average measured TCE
in exhaled air = 63 ug/L
(SD = 12).
Binary Choice Task
(Visual); Pursuit Rotor;
Recording of heart rate,
sinus arrhythmia,
breathing rate;
Questionnaire (15 items
on subjective feelings).
K-sample trend test;
two-tailed
Wilcoxon test.
Pilot study: no systematic effect of exposure on
test perform. Alcohol group had higher heart rate
than TCE group, and TCE and alcohol group;
minimal effect of mental load on heart rate; sinus
arrhythmia suppressed as mental load increased
with higher suppression in exposed groups (all 3)
compared to controls (differences possibly due to
existing group differences); Final Study: pursuit-
rotor task "somewhat impaired by exposure
condition;" authors acknowledge possibility of
sequence effects; no significant difference
between conditions on questionnaire responses;
performing mental tasks resulted in higher heart
rate in the TCE + alcohol condition than in
Alcohol alone condition; Mental load suppressed
sinus arrhythmia, especially in TCE + alcohol
condition; Conclusion: TCE and alcohol together
impair mental capacity more than each one alone.
NIOSH = National Institute of Occupational Safety and Health
D-63
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Albers et al.
(19991
30 railroad workers with
toxic encephalopathy;
involved in litigation;
long-term exposure to
solvents (n= 20yrs.;
range = 10-29 yrs.);
Historical controls
matched by gender, age,
and body mass.
Most common solvents
included TCE,
trichloroethane,
perchloroethylene; respirator
not typically used.
Neurologic exams (cranial
nerves, motor function,
alternate motion range,
subjective sensory function,
Romberg test, reflexes),
occupational history,
medical history, sensory
and motor nerve conduction
studies (NCS).
Log transformations
of amplitude data;
Mann-Whitney
U Test for NCS ;t-
test; simple linear
regression and
stepwise regression
for dose response.
Three workers met clinical
polyneuropathy criteria; NCS values
not influenced by exposure duration
or job title; no significant difference
in NCS between presence or absence
of polyneuropathy symptoms,
disability status, severity or type of
encephalopathy, or prior
polyneuropathy diagnosis.
Antti-Poika
(1982)
87 patients (painters, paint
and furniture factory
workers, carpet and
laundry workers)
diagnosed 3-9 yrs prior
with chronic solvent
exposure (mean age
38.6 yrs).
Control: 29 patients with
occupational asthma.
Mean duration of exposure
10.4 yrs; solvents: TCE,
perchloroethylene, solvent
mixture; based on patients'
and/or employers' reports;
Nine worksites visited for
environmental measures;
biological measures at
One worksite; exposure
classified as low, moderate,
or high.
Interview, neurologic exam,
EEC,
electroneuromyographs,
psychological examination
(intellectual, short-term
memory, sensory and motor
functions).
Correlation
coefficients for
prognosis and
factors influencing
diagnosis.
Reported symptoms: fatigue,
headaches, memory disturbances,
pain, numbness, paresthesias;
1st exam: 87 patients with objective
and subjective neurological signs,
61 with psychological disturbance,
58 abnormal EEG, 25 clinical
abnormalities, 57 PNS symptoms;
69 patients had neurophysiological
or psychological disturbances
identified by neurologist in only
4 patients; 2nd exam: 42 with clinical
neurological signs, 21 patients
deteriorated, 23 improved, 43 same;
poor correlation between prognosis
of examinations; no significant
correlation between prognosis and
age, sex, exposure duration and
level, alcohol use, or other diseases.
D-64
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Aratani et al.
(1993)
437 exposed workers from
various industries (not
specified); 394 males,
43 females and
1,030 male clerical
workers as controls; age
range: 16-72 yrs.
Exposed to Thinner, G/5100.
TCE, xylene, toluene,
methylchloride, and
gasoline.
Vibrometer (VPT); urinary
metabolites.
Spearman
correlation.
Positive correlations between age
and VPT 7; between job experience
and VPT; urinary metabolites not
significantly correlated with VPT;
no dose-effect for subjective
symptoms and neurological signs.
Binaschi and
Cantu (1983)
35 patients with
occupational exposure to
organic solvents; Industry
not specified; no controls.
Occupational history
provided by patients;
descriptions of jobs and
conditions provided by
employer; workplace
observations. Some
available measurements of
solvents in air; 9 patients
exposed to TCE; 11 exposed
to toluene and xylene;
15 exposed to mixtures of
solvents; all exposures
described to be under TLV-
TWA, but short exposure
might have exceeded
ACGIH limit for short time.
Examination of provoked
and spontaneous vestibular
symptoms; pure tone
threshold measurement;
EEG; psychiatric interviews
and psychiatric history;
prevalence of 37 psychiatric
symptoms.
Not stated.
All patients had subjective
symptoms (fatigue, psychic
disturbances, dizziness, vegetative
symptoms, vertigo); vestibular
system affected in most cases, with
lesions in nucleo-reticular substance
and brain stem; EEG change with
diffuse and focal slowing; 71% of
patients had mild neurasthenic
symptoms (fatigue, emotional
instability, memory and
concentration difficulties).
Bowler et al.
(1991)
67 former
microelectronics workers
exposed to multiple
organic solvents; controls
(n = 157) were recruited
from the same region;
67 pairs were matched on
the basis of age, sex,
ethnicity, educational
level, sex, and number of
children.
Serf-report and work history
from microelectronics
workers. Exposures and
risks were estimated.
Solvents include TCE, TCA,
benzene, toluene, methylene
chloride, and n-hexane.
California
Neuropsychological
Screening Battery.
t-test for matched
pairs; Wilcoxon
Signed Rank test.
Exposed workers performed
significantly worse on tests of
attention, verbal ability, memory,
visuospatial, visuomotor speed,
cognitive flexibility, psychomotor
speed, and RT; no significant
differences in mental status, visual
recall, learning, and tactile function.
D-65
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Colvin et al.
(1993)
Study population
Final sample: 67 workers
(43 exposed;
24 unexposed) in a paint
manufacturing plant
employed there for at least
5 yrs; all black males;
exclusion criteria:
encephalopathy, head
injury with >24 hrs
unconsciousness,
psychotropic medication,
alcohol/drug dependence
history, epilepsy, mental
illness.
Exposure assessment and
biomarkers
Chronic exposure was
assessed through self-
reported detailed work
history for each worker; past
and current industrial
hygiene measurements of
solvent levels in air; "total
cumulative expo" in the
factory and "average lifetime
exposures" were calculated;
visitations to establish areas
with "homogeneous
exposure;" All exposures
below the ACGIH limit.
Solvents include MEK,
benzene, TCE, methyl
isobutyl ketone, toluene,
butyl acetate, xylene,
cellosolve acetate,
isophorone, and white
spirits.
Tests used
Work and personal history
interview; brief
neurological evaluation,
WHO Neurobehavioral
Core Test Battery (all tests
except POMS); Computer-
administered tests: RT,
Fingertapping, Continuous
Performance Test,
Switching attention, Pattern
Recognition Test, Pattern
Memory; UNISA
Neuropsychological
Assessment Procedure:
Four word memory test,
Paragraph memory,
Geometric Shape drawing;
symptom and health
questionnaires.
Statistics
Division into
exposed and
unexposed;
Student's t-test;
Multiple linear
regression.
Results
Exposed group performed worse
than unexposed on 27/33 test results;
only significant difference was on
latency times of two switching
attention tests; no difference in
subjects' symptom reporting
between groups when questions
analyzed separately or analyzed as a
group; Average lifetime exposure
was a significant predictor for
continuous performance latency
time, Switching attention latency
time, mean RT, pattern memory;
fine visuomotor tracking speed
significantly associated with
cumulative exposure; effects of
exposure concluded to be "relatively
mild" and subclinical.
D-66
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Daniell et al.
(1999)
89 retired male workers
(62-74 yrs old) with prior
long-term exposure to
solvents including
67 retired painters and
22 aerospace
manufacturing workers.
Controls: 126 retired
carpenters with minimal
solvent exposure.
Chronic occupational
exposure; structured clinical
interview about past and
present exposure to solvents;
Cumulative Exposure Index
was constructed. Solvents
not specified.
Psychiatric interview;
questionnaires; physical
exam; blood cell counts,
chemistry panel, blood lead
levels, Neuropsychological:
BDI, verbal fluency test.
WAIS-R: Vocabulary,
Similarities, Block Design,
Digit Span, Digit Symbol;
Wisconsin Card Sorting;
verbal aphasia screening
test, Trails A and B,
Fingertapping; WMS-R:
logical memory and visual
subtests; Rey Auditory
Verbal Learning; Benton
Visual Retention test; d2
test; Stroop; Grooved
pegboard; SRT.
OR, logarithmic
transformation of
non-Gaussian data,
standardization of
test scores,
ANCOVA, Multiple
Linear regression;
Kruskal Wallis test
for differences in
blood lead
concentration.
CEI was similar for painters and
aerospace workers. Painters
reported greater alcohol use than
carpenters; painters also had lower
scores on WAIS-R Vocabulary
subtest. Controlling for age,
education, alcohol use, and
vocabulary score, painters
performed worse on motor, memory.
and reasoning ability tests; painters
reported more symptoms of
depression and neurological
symptoms; painters more likely to
have more abnormal test scores
(OR: 3.1) as did aerospace workers
(OR: 5.6); no dose effect with
increasing exposure and
neuropsychological tests.
Donoghue et
al. (1995)
16 patients diagnosed with
organic-solvent-induced
toxic encephalopathy with
various occupations
compared to age-stratified
normal groups (n = 38);
average age: 43 yrs
(range = 31-58);
exclusion criteria: diabetes
mellitus, ocular disease
impairing vision, visual
acuity with existing
refractive correction of
less than 4/6, abnormal
direct ophthalmoscopic
exam.
Average exposure duration
was 19 yrs (range = 5-
36 yrs); Solvents include
TCE, MEK, toluene,
thinners, unidentified
hydrocarbons.
Visual acuity measured
with a 4-m optotype chart;
Contrast sensitivity
measured with Vistech
VCTS 6,500 chart;
monocular thresholds, pupil
diameter.
test-
Six participants (37.5%) with
abnormal contrast sensitivity; two of
the six (33%) had monocular
abnormalities; abnormalities
occurred at all tested spatial
frequencies; significant difference
between groups at 3, 6, and 12 cpd
frequencies.
D-67
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Elofsson et al.
(1980)
Epidemiologic study of
car or industrial spray
painters (male) exposed
long-term to low levels of
organic solvents (n = 80);
two groups of matched
controls; 80 nonexposed
male industrial workers in
each control group.
Long term, low-level expo to
multiple solvents. Assessed
by interviews, on-the-job
measurements, and a 1955
workshop model. Blood
analysis: mean values were
within normal limits for both
groups. Exposed group had
significantly higher values
for alkaline phosphates,
hemoglobin, hematocrit, and
erythrocytes; early exposure
TLVs in Sweden were
significantly lower; solvents
include TCE, TCA,
methylene chloride, and
others.
Serf-administered
psychiatric questionnaires,
Eysenck's Personality
Inventory, psychosocial
structured interview,
Comprehensive
Psychopathological Rating
Scale; visual evoked
responses; EEG;
Electroneurography;
Vibration Sense Threshold
estimations; Neurological
exam.
Calculation of
z values; Pearson
correlation; Multiple
Regression
Analysis.
Significant differences between
controls and exposed in symptoms
of neurasthenic syndrome, in RT,
manual dexterity, perceptual speed,
and short-term memory; no
significant differences on verbal,
spatial, and reasoning ability; some
differences on EEG, VER,
ophthalmologic, and CT.
Gregersen
(1988)
Workers exposed to
organic solvents (paint,
lacquer, photogravure, and
polyester boat industries).
Controls: warehousemen
electricians; 1st follow-up
5.5yrs after initial
evaluation (59 exposed,
30 unexposed);
2nd follow-up: 10.6 yrs
after initial evaluation
(53 exposed,
30 unexposed controls).
1s follow-up: data about
working conditions,
materials and exposure in
prior 5 yrs used for exposure
index; 2nd follow-up: nine
questions asking about
exposure to solvents in the
prior 5 yrs; TCE, toluene,
styrene, white spirits.
1s follow-up: structured
interviews on occupational,
social, medical history;
clinical exam, neurological
exam; 2nd follow-up: mailed
questionnaire (49 follow-up
issues to 1st follow-up).
Wilcoxon-Mann-
Whitney tests;
Kruskal-Wallis test;
2; Spearman Rank
Partial Correlation
Coefficient.
More acute neurotoxic symptoms in
exposed group at both follow-ups,
but fewer symptoms at 2nd follow-up
than at 1st follow-up; at both follow-
ups exposed participants had more
encephalopathy symptoms,
especially memory and
concentration; no encephalopathy
symptoms in control group;
symptoms and signs of peripheral,
sensory, and motor neuropathy
significantly worse in participants
still exposed. Exposure index
showed dose-effect with memory
and concentration. Both follow-ups:
improvement in acute symptoms;
aggravation in CNS; more
symptoms of peripheral nervous
system and social consequences.
D-68
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Juntunen et al.
(1980)
37 patients with suspected
organic solvent poisoning
(mean age = 40.1 yrs);
selection based on
pneumoencephalography;
no controls.
Patients were exposed to
carbon disulphide (n = 6),
TCE (5), styrene (1), thinner
(2), toluene (1),
methanol (1), and carbon
tetrachloride (2), and
mixtures (19). Exposure
was assessed by patients'
and employers' reports and
measurements of air
concentrations when
available.
Neurologic examination,
pneumoencephalographic
exam, EEG, tests assessing
intelligence, memory and
learning, motor function,
and personality.
Descriptive
Statistics.
Clinical neurological findings of
slight psychoorganic alterations,
cerebellar dysfunction, and
peripheral neuropathy; 63% had
indication of brain atrophy; 23 of the
28 patients examined with
electroneuromyography showed
signs of peripheral neuropathy; 94%
had personality changes, 80% had
psychomotor deficits, 69% had
impaired memory, and 57% had
intelligence findings; no dose-effect
found.
Juntunen et al.
(19821
80 (41 women, 39 men)
Finnish patients diagnosed
3-9 yrs prior with chronic
solvent exposure (mean
age = 38.6 yrs); 31 had
slight neurological signs;
no controls.
Assessed by patients'
occupational history,
employers' workplace
description, observations and
data collected at workplace,
environmental
measurements, biological
tests; TCE,
perchloroethylene, or mixed
solvent exposures.
Neurologic examination;
EEG and ENMG; tests of
intellectual function,
memory, learning,
personality, and
psychomotor performance.
, Maxwell-Stuart,
correlation and
multiple linear
regression analyses.
Significant correlations between
prognosis of disturbances in gait
(p < 0.05) and station and length of
follow-up, duration and level of
exposure and multiplying the two;
no gender effects. Common
subjective symptoms; headaches,
fatigue, and memory problems.
Impairment in fine motor skills, gait,
and cerebellar functions. Subjective
symptoms decreased during follow-
up, but clinical signs increased.
D-69
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Laslo-Baker et
al. (2004)
32 mothers with
occupational exposure to
organic solvents during
pregnancy and their
children (3-9 yrs of age);
included if exposure
started in 1st trimester and
lasted for at least 8 wks of
pregnancy (32 mother-
child pairs). Controls:
32 unexposed control
mothers matched on age,
child age, child sex, SES,
and reported cigarette use
and their children
(32 mother-child pairs).
Exposure information
collected at 3 times:
(1) during pregnancy;
(2) when contacted for study
participation later in
pregnancy; and (3) at time of
assessment. Information
collected included types of
solvent, types of setting,
duration of exposure during
pregnancy, use of protection,
symptoms, and ventilation.
Solvents include toluene
(n = 12 women),
xylene (10), ethanol (7),
acetone (6), methanol (5),
TCE (3), etc. (a total of
78 solvents were reported).
Children: Wechsler
Preschool and Primary
Scale of Intelligence,
WISC, Preschool Language
Scale, Clinical Evaluations
of Language Fundamentals,
Beery-Buktenica
Developmental test of
Visuo-Motor Integration,
Grooved Pegboard Test,
Child Behavior Checklist
(Parent Version), Connor's
Rating Scale-Revised
(Parent Version),
Behavioral Style
Questionnaire; Mothers:
WASI.
Power analysis,
Multiple linear
regression.
Verbal IQ was lower (104) in
children exposed in utero vs.
unexposed children controls (110);
Children did not differ between
groups in birth weight, gestational
age, or developmental milestones;
Children in the exposed group had
significantly lower VIQ (108) and
Full IQ (108) than controls (VIQ
= 116 and Full IQ = 114; No
significant difference in PIQ;
Performance on expressive
language, total language, and
receptive language was significantly
worse in children from exposed
group.
Lee et al.
(1998)
40 Korean female shoe
factory workers employed
there for at least 5 yrs;
cases with head injury,
neurological or
psychological disorder, or
hearing or visual
impairment were
excluded. Controls:
28 (housekeepers); no in-
plant controls available.
Four workers wore passive
personal air samplers for a
full 8-hr shift. Detected
solvents: toluene, methyl
ethyl ketone, w-hexane, c-
hexane, cyclohexane, DCE,
TCE, benzene, and xylene.
In frame-making, air
concentration of solvents
was 0.46-0.71 ppm. In
adhesive process, solvent air
concentrations were 1.83-
2.39 ppm; three exposure
indices were calculated:
current exposures, exposure
duration (yrs), and
Cumulative Exposure
Estimate (CEE) (yrs x
average exposures).
Questionnaire;
Neurobehavioral Core Test
Battery (includes POMS,
SRT, Santa Ana Dexterity
test, Digit Span, Benton
Visual Retention Test,
Pursuit aiming motor
steadiness test); POMS was
excluded because of
cultural inapplicability.
Multivariate
ANOVA for tests
with 2 outcomes;
ANOVA for tests
with 1 outcome;
education was
adjusted in analyses.
Significant differences between
groups based on exposure index.
Differences in performance between
controls and participants on Santa
Ana were found only in the CEE
(participants performed worse).
CEE is a more sensitive measure of
exposure to organic solvents.
D-70
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Lindstrom
(1973)
Lindstrom
(1980)
Study population
168 male workers with
suspected occupational
exposure to solvents.
Group I with solvent
poisoning (n = 42).
Group II with solvent
exposure, undergoing
mandatory periodic health
check (n = 126). Control:
50 healthy nonexposed
male volunteers working
in a viscose factory.
Group IV: 50 male
workers with carbon
disulfide poisoning.
56 male workers
diagnosed with
occupational disease
caused by solvents.
Controls: 98 styrene-
exposed workers;
43 nonexposed
construction workers.
Exposure assessment and
biomarkers
44 exposed to TCE, 8 to
tetrachloroethylene, 26 to
toluene, 25 to toluene and
xylene, 44 to thinners, 21 to
"miscellaneous;" solvent-
exposed group had an
average of 6 yrs of exposure;
CS2 group had average of
9 yrs of exposure.
Chronic "excessive"
exposure: mean duration of
exposure = 9.1 yrs (SD =
8.3); exposed to halogenated
and aromatic hydrocarbons,
paint solvents, alcohols, and
aliphatic hydrocarbons (TCE
n = 14). Individual exposure
levels estimated as TWAs,
based on information
provided by subjects,
employer, or workplace
measurements, were
categorized as low
(3 patients), intermediate
(26 patients), and high
(27 patients).
Tests used
WAIS: Similarities, Picture
Completion, Digit Symbol;
Bourdon- Wiersma
vigilance test, Santa Ana,
Rorschach Inkblot test,
Mira test.
WAIS subtests:
Similarities, Digit Span,
Digit Symbol, Picture
Completion, Block Design;
WMS subtests: Visual
Reproduction; Benton
Visual Retention test;
Symmetry Drawing; Santa
Ana Dexterity test; Mira
test.
Statistics
Student's t-test.
Factor analysis;
Student's t-test;
Multivariate
Discriminant
analysis.
Results
The solvent-exposed group and CS2
group had significantly worse
"psychological performances" than
controls; greatest differences in
sensorimotor speed and
psychomotor function; solvent-
exposed and CS2 groups had
deteriorated visual accuracy.
Significant decline in visuomotor
performance and freedom from
distractibility (attention) in the
solvent-exposed participants;
significant relationship between
duration of solvent exposure and
visuomotor performance; solvent
exposure level was not significant;
psychological test performance of
styrene-exposed control was only
slightly different from nonexposed
controls.
D-71
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Lindstrom et
al. (1982)
86 patients with prior
diagnosis of solvent
intoxication (mean age
38.6 yrs); 40 male, 46
female; 52 exposed to
mixed solvents; 21
exposed to TCE or
perchloroethylene; 13
exposed to both; results at
follow-up compared to
those at initial diagnosis.
Mean duration of exposure
10.4 yrs; solvents: TCE,
perchloroethylene, solvent
mixture; based on patients'
and/or employers' reports.
Intellectual Function: from
WAIS - Similarities, Block
Design, Picture
Completion; Short Term
Memory: from WMS -
Digit Span, Logical
Memory, Visual
Reproduction; Benton
Visual Retention test;
Sensory and Motor
Functions: Bourdon
Wiersma Vigilance Test,
Symmetry Drawing, Santa
Ana Dexterity test, Mira
test.
Frequency
distributions,
Student's t-test for
paired data,
stepwise linear
regression.
All patients grouped together
regardless of types of past solvent
exposure; on follow-up, significant
learning effects for similarities when
compared to results at initial
diagnosis; group mean for
intellectual functioning increased; no
significant change in memory test
results; group means for sensory and
motor tasks were lower; prognosis
was better for longer follow-up and
younger age and poorer for users of
medicines with neurological effects.
Marshall et al.
(1997)
All singleton births in
1983-1986 in 188 New
York State counties (total
number not specified);
473 CNS-defect births and
3,305 musculoskeletal-
defect births; controls:
12,436 normal births.
Exclusion criteria:
Trisomy 13, 18, or 21,
birth weight of <1,000 g,
sole diagnosis of
hydrocephaly or
microencephalopathy, hip
subluxation.
Information on inactive
waste sites was examined,
including air vapor, air
particulates, groundwater
exposure via wells, and
groundwater exposure, via
basements; exposure was
categorized as "high,"
"medium," "low," or
unknown based on
probability of exposure;
proximity to waste sites was
also considered; Most
common solvents: TCE,
toluene, xylenes,
tetrachloroethene, 1,1,1-
trichloroethane; Most
common metals found lead,
mercury, cadmium,
chromium, arsenic, and
nickel.
OR, Fisher's exact
test, x2,
unconditional
logistic regression.
13 CNS cases and 351 controls with
potential exposures; crude OR.
When controlling for mother's
education, prenatal care, and
exposure to a TCE facility, OR was
0.84; CNS and solvents OR: 0.8;
CNS and metals OR: 1.0,
musculoskeletal defects and solvents
OR: 0.9, musculoskeletal defects
and pesticides OR: 0.8; higher risk
for CNS defects when living close to
solvent-emitting facilities.
D-72
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Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
McCarthy and
Jones (1983)
384 industrial workers
with solvent poisoning;
103 operated degreasing
baths, 62 maintained
degreasing baths, 37 used
TCE in portable form,
37 miscellaneous; no
controls.
Individuals poisoned with
TCE, perchloroethylene, and
methylchloroform were
examined retrospectively;
medical record review; 288
exposed to TCE, 44 to
perchloroethylene, 52 to
1,1,1 -trichloroethane.
Symptoms reported in
occupational/medical
records from industrial
poisoning incidents; data
from 1961 to 1980 on
demographics, occupation,
work process, type of
industry, if incident caused
fatality.
17 fatality cases, with 10 in confined
spaces; most common symptoms
include effects on CNS;
gastrointestinal and respiratory
symptoms; no strong evidence for
cardiac and hepatic toxicity; no
change in affected number of
workers in 1961 to 1980; greatest
effect due to narcotic properties.
Mergler et al.
(1991)
54 matched pairs;
Matching on the basis of
age, sex, ethnicity,
educational level, sex, and
number of children taken
fromlSO former
microelectronics workers
exposed to multiple
organic solvents and
control population of
157 recruited from the
same region.
Average duration of
employment: 6.1 yrs (range:
1-15 yrs); information about
products used and chemical
make-up from employer;
chemicals:
chlorofluorocarbons,
chlorinated hydrocarbons,
glycol ethers, isopropanol,
acetone, toluene, xylene, and
ethyl alcohol.
Sociodemographic
questionnaire; monocular
examination of visual
function: Far visual acuity
using a Snellen chart, near
visual acuity using a
National Optical Visual
Chart, color vision using
Lanthony D-15, near
contrast sensitivity using
Vistech grating charts.
Signed-rank
Wilcoxon test;
Mann-Whitney; %2
test for matched
pairs; Multiple
Regression;
Stepwise regression.
Significant difference in near
contrast sensitivity: 75% of exposed
workers with poorer contrast
sensitivity at most frequencies than
the matched controls (no difference
in results based on smoking, alcohol
use, and near visual acuity loss).
Significant differences on near
visual acuity, color vision, and rates
of acquired dyschromatopsia for one
eye only. No difference between
groups in near or far visual acuity.
D-73
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Morrow et al.
(1989)
22 male patients with
exposure to multiple
organic solvents;
4 involved in litigation.
Exclusion: neurologic or
psychiatric disorder prior
to assessment, alcohol
consumption more than
two drinks/d. Average yrs
education 12 (range: 10-
16 yrs); average age 38
yrs (range: 27-61);
compared to responses of
WWII prisoner of war
(POW) population with
posttraumatic stress
disorder (PTSD).
Exposure assessed with
questionnaire (duration, type
of solvents, weeks since last
exposure, cases of excessive
exposure); Average
exposure duration = 7.3 yrs
(range: 2 months-19 yrs);
average wks since last
exposure was 19.8 (range:
1-84 wks); 28% had at least
one instance of excessive
exposure.
Exposure questionnaire,
Group form of the MMPI.
Stepwise multiple
regression.
All profiles valid; 90% with at least
two elevated scales above T score of
70 (clinically significant); highest
elevations on scales 1, 2, 3, and 8;
only one case within normal limits;
when compared to a group of
nonpsychiatric patients, exposed
patients had more elevations,
although both groups have physical
complaints. When compared with
WWII POW (1/2 diagnosed with
PTSD) with similar SES and
education, both groups have similar
profiles; no age effects found;
significant positive correlation
between scale 8 and duration of
exposure; no significant difference
based on time since last exposure or
on experiencing excessive exposure.
Morrow et al.
(1992)
Nine men and three
women occupationally
exposed to multiple
organic solvents with
CNS complaints; all met
criteria for mild toxic
encephalopathy; exposed
group average age was
47 yrs; Controls:
19 (healthy male
volunteers); 26 psychiatric
controls (male patients
with chronic
schizophrenia) average
age unexposed controls:
34 yrs; average age
schizophrenic patients:
36 yrs.
Exposure assessed with
occupational and
environmental exposure
questionnaire; mean duration
of exposure = 3 yrs (range =
<1 d-30 yrs); average time
between last exposure and
assessment was 2 yrs (range;
2 months-10 yrs); solvents
toluene, TCE.
Auditory event-related
potentials under the oddball
paradigm: counting and
CRT tasks.
Repeated measures
ANOVA.
Exposed patients had significant
delays in N250 and P300 compared
to normal controls and in P300
compared to psychiatric controls.
Exposed patients had higher
amplitudes forNlOO, P200, and
N250; no difference in P300
amplitude between groups; for the
exposed group, P300 positively
correlated with exposure duration;
findings indicate that solvent
exposure affects neural networks.
D-74
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Seppalainen
and Antti-
Poika (1983)
87 patients with solvent
poisoning (40 male and 47
female) with occupational
exposure to solvents;
follow-up 3-9 yrs after
initial diagnosis; mean age
at diagnosis 38.6 (range:
20-59 yrs); no control
population.
Chronic exposure with
average duration of 10.7 yrs
(range: 1-33); patients were
exposed to TCE(n = 21),
perchloroethylene (n = 12),
mixtures of solvents
(n = 53), mixtures and TCE
or perchloroethylene
(n=13). Exposure of
54 patients stopped after
diagnosis, 33 continued to be
exposed; at follow-up, only
5 working with potential of
some exposure.
EEC using 10/20 system
with 25-30 min of
recording, 3 min
hyperventilation and
intermittent photic
stimulation; ENMG.
2, hypergeometric
distribution,
McNemar test.
Significantly more ENMG
abnormalities at follow-up than at
initial diagnosis. Most common
finding: slight polyneuropathy; 43%
showed improved ENMG, 33% had
deteriorated, and 18 points, with
similar ENMG findings (six normal
at both exams); at follow-up, slow-
wave abnormalities decreased and
paroxysmal abnormalities increased;
41 with improved EEG, 28 with
similar EEG (19 had normal EEG at
diagnosis), and 18 with deteriorated
EEG; EEG pattern of change
compared to external head injuries.
D-75
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Shlomo et
al.(2002)
Male industrial workers;
mercury exposure group
(n = 40); average age 49.7
(±6.4) yrs; chlorinated
hydrocarbons exposure
group (n = 37) average
age 46.0 (±4.73); controls,
unexposed (n = 36)
average age 49.8 (±5.8),
matched by age;
(industries not specified).
Interview and record review;
urine samples collected at
end of work shift prior to
testing and tested for
mercury and TCA;
chlorinated hydrocarbons:
TCE(n=7),
perchloroethylene (n = 8),
trichloroethane (n = 22).
Mean duration of CH
exposure 15.8 (±7.2) yrs.
Mean duration of mercury
exposure 15.5 (±6.4) yrs.
Air sampling: mercury:
0.008 mg/m3 (TLV = 0.025);
TCE:98ppm(TLV = 350);
perchloroethylene: 12.7 ppm
(TLV = 25); and
trichloroethane: 14.4 ppm
(TLV = 200). Blood levels:
mercury (B-hg) 0.5 g%
(±0.3); TCA urine levels: 1-
80% of Biologic Exposure
Index (BEI); CH urine
levels: 0.11-0.2 of BEL
Medical history,
Neurological tests assessing
cranial nerves and
cerebellar function;
Otoscopy, review of
archival data from pure-
tone audiometric tests;
Auditory brain stem
responses (ABR).
Student's t-test,
proportions test.
Significant differences between
exposed and controls: 33.8% of CH-
exposed workers with abnormal
IPL I-III; 18% of controls; authors
suggest ABRs are sensitive for
detecting subclinical CNS effects of
CH and mercury.
D-76
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Till et al.
(200 lb)
The children of mothers
who had contacted a
Canadian pregnancy risk
counseling program
during pregnancy and
reported occupational
exposure to solvents
(n = 33); children age
range: 3-7 yrs; Mothers'
occupations: lab
technicians, factory
workers, graphic
designers, artists, and dry
cleaning. Controls:
28 matched on age,
gender, parental SES, and
ethnicity; children of
mothers exposed to
nonteratogenic agents.
Structured questionnaire
about exposure; method:
weight assigned to each
exposure parameter (length
of exposure, frequency of
exposure, symptoms); sum
of scores for each parameter
used as exposure index;
median split used to
categorize in low (n = 19)
and high (n = 14) exposures;
solvents include benzene,
toluene, methane, ethane,
TCE, methyl chloride, etc.
NEPSY: Visual Attention,
Statue, Tower, Body Part
Naming, Verbal Fluency,
Speeded Naming,
Visuomotor Precision,
Imitating Hand Positions,
Block Construction, Design
Copying, Arrows; Peabody
Picture Vocabulary Test;
WRAVMA Pegboard test;
Child Behavior Checklist
(Parent form); Continuous
Performance Test.
Mantel Haenszel
test, t-test,
ANCOVA,
Hierarchical
multiple linear
regression.
Lower composite neurobehavioral
scores as exposure increased after
adjusting for demographics in
receptive language, expressive
language, graphomotor ability.
Significantly more exposed children
rated with mild-severe problems.
No significant difference between
groups in attention, visuo-spatial
ability, and fine-motor skills. Mean
difference on broad- and narrow-
band scales of Child Behavior
Checklist scores not significant.
Till et al.
(200 Ib)
Children of mothers who
had contacted a Canadian
pregnancy risk counseling
program during pregnancy
and reported occupational
exposure to solvents
(n = 32); children age
range: 3-7 yrs. Mothers'
occupations: lab
technicians, factory
workers, graphic
designers, artists, and dry
cleaning. Controls:
27 matched on age,
gender, parental SES, and
ethnicity; children of
mothers exposed to
nonteratogenic agents.
Structured questionnaire
about exposure; method:
weight assigned to each
exposure parameter (length
of exposure, frequency of
exposure, symptoms); sum
of scores for each parameter
used as exposure index;
median split used to
categorize in low (n = 19)
and high (n = 14) exposures;
solvents include benzene,
toluene, methane, ethane,
TCE, methyl chloride, etc.
Minimalist test to assess
color vision; Cardiff Cards
to assess visual acuity.
Independent
samples t-tests,
Mantel Haenszel
Chi test; Wilcoxon-
Mann-Whitney test;
Kruskal-Wallis %2.
Significantly higher number of
errors on red-green and blue-yellow
discrimination in exposed children
compared to controls; exposed
children had poorer visual acuity
than controls. No significant dose-
response relationship between
exposure index and color
discrimination and visual acuity.
D-77
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Till et al.
(2005)
21 infants (9 male,
12 female) of mothers
who contacted a Canadian
pregnancy risk counseling
program and reported
occupational exposure to
solvents (occupations:
factory, laboratory, dry
cleaning. Controls: 27
age-matched infants (17
male, 10 female) of
mothers contacted the
program due to exposure
during pregnancy to
nonteratogenic
substances).
Structured questionnaire
about exposure; method:
weight assigned to each
exposure parameter (length
of exposure, frequency of
exposure, symptoms); sum
of scores for each parameter
used as exposure index;
median split used to
categorize in low and high
exposures; exposure groups:
(1) aliphatic and/or aromatic
hydrocarbons (n = 9);
(2) alcohols (n = 3);
(3) multiple solvents (n = 6);
and (4) perchloroethylene,
(n = 3); mean duration of
exposure during pregnancy
27.2 wks. (SD 7.93,
range = 12-40); solvents
include benzene, toluene,
methane, ethane, TCE,
methyl chloride, etc.
1st visit: Sweep VEP to
assess contrast sensitivity
and grating acuity; 2nd visit
(2 wks after 1st): Transient
VEPs to assess chromatic
and achromatic
mechanisms;
ophthalmological exam,
physical and neurological
exam; testers masked to
exposure status of infant.
Median split;
Multiple Linear
Regression; %2, t-
test, Mann-Whitney
U test, Multivariate
ANCOVA, Pearson
correlation, Logistic
Regression.
Significant decline of contrast
sensitivity in low and intermediate
spatial frequencies in exposed
infants when compared with
controls. Significant effect of
exposure level on grating acuity,
26.3% of exposed (but 0% of
controls) with abnormal VEP to red-
green onset stimulus. No
differences between groups in
latency and amplitude of chromatic
and achromatic response.
Valic et al.
f!997)
138 occupationally
exposed and
100 unexposed controls.
Exclusion criteria:
congenital color vision
loss, severe ocular
disease, significant vision
impairment, tainted
glasses or contact lenses,
diabetes mellitus,
neurological disease, prior
severe head or eye
injuries, alcohol abuse,
medication impairing
color vision.
Solvents: TCE,
perchloroethylene, toluene,
xylene; historical data on
duration of exposure
protective equipment use,
subjective evaluation of
exposure, nonoccupational
solvent exposure, solvent-
related symptoms at work,
alcohol and smoking, drug
intake. Mean urinary levels
of TCA: 1.55 (± 1.75) mg/L.
Lanthony D15.
Polytomous logistic
regression.
Significant effect of age in exposed
group; with alcohol of <250 g/wk no
significant correlation between color
confusion and solvent exposure.
Significant interaction between
solvent exposure and alcohol intake.
Color Confusion Index significantly
higher in exposed group with
alcohol use of >250 g/wk.
D-78
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Windham et
al. (2006)
Children born in 1994 in
San Francisco Bay Area
with ASDs (n = 284) and
controls (n = 657),
matched on basis of
gender and month of birth.
Birth addresses were
geocoded and linked to
hazardous air pollutant
database; exposure levels
assigned for 19 chemicals;
chemicals were grouped
based on mechanistic and
structural properties;
Summary index scores were
calculated; risk of ASD
calculated in upper quartiles
of groups or individual
chemical concentrations;
adjustment for demographic
factors.
Archival data.
Pearson correlation,
Logistic Regression.
Elevated adjusted ORs for ASD (by
50%) in top quartile of chlorinated
solvents, but not for aromatic
solvents; AOR for TCE in 4th
quartile = 1.47; lessened when
adjusted for metals; correlation
between hydrocarbon and metals
exposures; when adjusted, increased
risk for metals (in 3rd quartile = 1.95;
in 4th quartile = 1.7). Contributing
compounds: mercury, cadmium,
nickel, TCE, vinyl chloride. Results
interpreted to suggest relationship
between autism and estimated metal
and solvent concentrations in air
around place of birth residence.
D-79
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Epidemiological studies: controlled exposure studies; neurological effects of trichloroethylene/mixed solvents
Levy et al.
(1981)
Nine participants (eight
males and one female)
recruited through
newspaper ad; 8 hrs
fasting before testing; no
control.
Experiment 1: alcohol
consumption (three doses) —
blood alcohol levels were
measured with breath
analyzer pre- (multiple
baselines) and post-test
(multiple).
Experiment 2: CH
administered orally over
2 min in either 500 or
1,500 mg dose; multiple
baseline smooth pursuit eye
movement (SPEM) tests and
multiple posttests after
exposure; no control dose
administered.
SPEM tests of following a
sinusoidally oscillated
target at 0.4 Hz; eye
movements were recorded
through electrodes at each
eye.
t-tests; ANOVA.
Experiment 1 : prealcohol all
subjects had intact SPEM; no
significant effect for 1.5 mL/kg of
alcohol; significant decline in SPEM
at 2.0 and 3.0 mL/kg alcohol;
significant dose-effect.
Experiment 2: at 500 mg CH, no
significant change in pursuit was
noted; at 1,500 mg CH, qualitative
disruptions in pursuit in all
participants (4); at 500 mg,
participants observed to be drowsy.
When number reading was added
SPEM impairment was 'attenuated'
in both alcohol and CH conditions.
D-80
-------
Table D-2. Epidemiological studies: neurological effects of TCE/mixed solvents (continued)
Reference
Study population
Exposure assessment and
biomarkers
Tests used
Statistics
Results
Stopps and
McLaughlin
(1967)
Chamber study using two
healthy male volunteers
exposed to Freon-113;
one volunteer exposed to
TCE; No control.
Exposure booth was
constructed; TCE in air: TCE
concentrations: 100, 200,
300, or 400 ppm (1965 TLV:
100 ppm for 8-hr exposure)
in ascending and descending
order; total time in chamber:
2.75 hrs; Freon-113
concentrations: 1,500, 2,500,
3,500, or 4,500 ppm (1965
TLV: 1,000 ppm for 8-hr
exposure), duration 1.5 hrs;
TCE and Freon-113:
(1) reduction of weight of
compound during exposure
was calculated;
(2) continuous air sampling
in the chamber; and (3) gas
chromatography on air
captured in bottles sealed in
the chamber; no control dose
given.
Crawford Small Parts
Dexterity Test, Necker
Cube Test, Card Sorting,
Card Sorting with an
Auxiliary Task, Dial
Display (TCE participant
only); Short Employment
Test-Clerical (Freon-113
participants only).
Descriptive statistics
for air measurement
plots by % of TCE
change in groups.
No TCE effect at 100 ppm, but test
performance deteriorated with
increase of TCE concentration. No
effect of Freon-113 on psycho mo tor
function at 1,500 ppm, deterioration
at 2,500 ppm, as concentration
increased, performance deteriorated.
D-81
-------
Table D-3. Literature review of studies of TCE and domains assessed with neurobehavioral/neurological
methods
Authors
ATSDR
Barret et al.
Barret et al.
Barret et al.
Burg et al.
Burg and Gist
El Ghawabi et al.
Feldman et al.
Feldman et al.
Gamberale et al.
Gash et al.
Grandjean et al.
Gun et al.
Hirsch et al.
Kilbum and
Thornton
Kilbum and
Warshaw
Kilbum
Kilbum
Konietzko et al.
Kylin, et al.
Landrigan, et al.
Liu, et al.
Mhiri et al.
Nagaya et al.
Year
(2QQ3a)
(1984)
(1987)
(1982)
(1995)
(1999)
(1973)
(1988)
(1992)
(1976)
(2008)
(1955)
(1978)
(1996)
(1996)
(1993a)
(2002a)
(2002b)
(1975)
(1967)
(1987)
(1988)
(2004)
(1990)
Study
type
E
0
0
o
E
E
0
E
O
C
O
0
0
E
E
E
E
E
C
C
0
o
o
0
Participants no.
(N = exposed
C = nonexposed)
N=116,C=177
N=188
N=104,C = 52
N= 11,C = 2
N = 4,281
N= 3,915
N=30,C = 30
N = 21,C = 27
N= 18, C = 30
N=15
N=30
N=80
N = 8, C = 8
N= 106
N = 237, C = 264
N=544, C= 181
N = 236, C = 228
N = 236,C = 58
N = 20
N= 12
Residents and 12 W
N= 103, C= 111
N = 23, C = 23
N = 84, C = 83
Dur
C
C
C
C
C
C
C
C
A,C
A
C
C
C
C
C
C
C
C
A
A
A,C
C
A
C
PM/RT
ne
ne
ne
ne
ne
ne
ne
ne
ne
V
V
ne
V
ne
V
V
ne
(-)
ne
V
ne
ne
ne
ne
VM
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
V
ne
ne
ne
ne
ne
ne
ne
ne
Cogn
ne
ne
ne
ne
ne
ne
ne
ne
ne
V
ne
ne
V
ne
V
V
V
ne
ne
ne
V
ne
ne
ne
M&L
ne
ne
ne
ne
ne
ne
ne
ne
ne
(-)
ne
ne
ne
ne
ne
V
ne
ne
ne
ne
ne
V
ne
ne
M&P
ne
ne
V
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
V
ne
(-)
ne
ne
ne
ne
ne
ne
Sympf
ne
H,D
H, D, S, I
ne
ne
ne
H, S
ne
ne
ne
M,N
ne
ne
H
ne
M
M
ne
M
ne
H,D
D,N
ne
ne
Sentt
A
T,N,V
T,N
T
A,N
A,N
(-)
T
T,N
ne
N
N
ne
ne
T,N
B
ne
N
N
ne
N
T
N
Resp
ne
ne
ne
ne
V
V
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
Dose effect
vv
urinary
metabolites
V
ne
V
V
V
V
VV
V
ne
ne
ne
ne
v,vv
ne
ne
ne
ne
ne
ne
V
ne
W
W
v,vv
V
TCE levels
0 -> 23 ppb in
dg water
ISOppm
ne
ne
ne
4 gps: 2-
75,000 ppb
165 ppm
ne
ne
540-1, 080 mg3
ne
6-1, 120 ppm
3^1 8 ppm
0-2,441 ppb
ne
6-500 ppb
6-500 ppb
0.2-1, 000 ppb
953 ppm
1,000 ppm
>1 83,000 ppb
1-100 ppm
ne
22 ppm
D-82
-------
Table D-3. Literature review of studies of TCE and domains assessed with neurobehavioral/neurological
methods (continued)
Authors
Rasmussen and
Sabroe
Rasmussen et al.
Rasmussen et al.
Rasmussen et al.
Reifetal.
Ruijten et al.
Smith
Stewart et al
Triebig et al.
Triebig et al.
Triebig et al.
Triebig et al.
Triebig et al.
Troster and Ruff
Vemon and
Ferguson
Windemuller and
Ettema
Winneke
Year
(1986)
(1993d)
(1993c)
(1993a)
(2003)
(1991)
(1970)
(1970)
(1976)
(1977a)
(1977c)
(1982)
(1983)
(1990)
(1969)
(1978)
(1982)
Study
type
O
O
0
O
E
0
O
c
c
c
O
0
O
0
c
c
O
Participants no. (N
= exposed
C = nonexposed)
N = 240, C = 350
N=96
N=96
N=99
N=143
N=31,C = 28
N= 130, C = 63
N=13
N = 7, C = 7
N = 7, C = 7
N=8
N = 24, C = 24
N = 66, C = 66
N=3,C = 60
N=8
N=39
Not reported
Dur
C
C
C
c
c
c
c
A
A
A
A,C
C
C
A
A
A
ne
PM/RT
ne
ne
ne
V
V
V
ne
ne
ne
ne
ne
ne
ne
V
V
V
(-)
VM
ne
ne
V
ne
V
ne
ne
ne
ne
ne
V
ne
ne
V
V
ne
(-)
Cogn
ne
V
V
ne
ne
ne
ne
V
V
V
V
ne
ne
V
ne
ne
ne
M&L
ne
ne
ne
ne
ne
ne
ne
V
V
V
ne
ne
V
ne
ne
ne
M&P
V
ne
ne
ne
V
ne
ne
ne
V
V
ne
ne
ne
V
ne
ne
ne
Sympf
H,D, I, M
ne
ne
ne
M
ne
H,D
H
(-)
M
ne
ne
N,H
ne
ne
ne
ne
Sentt
ne
ne
ne
N
M
ne
N
ne
ne
(-)
ne
N
N
N
N
ne
ne
Resp
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
ne
Dose effect
vv
urinary
metabolitesV
ne
w
w
w
w
ne
v,w
V
v,w
v,w
V
v,w
V
ne
w
ne
ne
TCE levels
ne
ne
ne
ne
5-15 ppb
17-70 ppm
ne
100-202 ppm
0-1 00 ppm
0-1 00 ppm
50 ppm
5-70 ppm
10-600 mg/m3
ne
0-1, 000 ppm
200 ppm
50 ppm
fH = Headaches; D = Dizziness; I = Insomnia; S = Sex Probls; M = Mood; N = Neurological.
f f A = Audition; B = Balance; V = Vision; T = Trigeminal nerve; N = Other Neurological.
Study: C = Chamber; E = Environmental; O = Occupational.
Duration: A = Acute, C = Chronic.
V = positive findings; (-) = findings not significant; ne = not examined or reported; Dur = duration; PM/RT = psychomotor/reaction time; VM = visuo-motor; Cogn = cognitive;
M&L = memory and learning; M&P = mood and personality; Symp = symptoms; Sen = sensory; Resp = respiratory
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D.2. CNS TOXICITY IN ANIMAL STUDIES FOLLOWING TCE EXPOSURE
In vivo studies in animals and in vitro models have convincingly demonstrated that TCE
produces functional and physiological neurological changes. Overall, these effects collectively
indicate that TCE has CNS depressant-like effects at lower exposures and causes anesthetic-like
effects at high exposures. Studies of TCE toxicity in animals have generally not evaluated
whether or not adverse effects seen acutely persist following exposure or whether there are
permanent effects of exposure. Exceptions to the focus on acute impairment while under TCE
intoxication include studies of hearing impairment and histopathological investigations focused
primarily on specific neurochemical pathways, hippocampal development, and demyelination.
These persistent TCE effects are discussed initially followed by the results of studies that
examined the acute effects of this agent. Summary tables for all of the animal studies are at the
end of this section.
D.2.1. Alterations in Nerve Conduction
There is little evidence that TCE disrupts trigeminal nerve function in animal studies.
Two studies demonstrated that TCE produces morphological changes in the trigeminal nerve at a
dose of 2,500 mg/kg-day for 10 weeks (1992: 1991). However, dichloroacetylene, a degradation
product formed during the volatilization of TCE was found to produce more severe
morphological changes in the trigeminal nerve and at a lower dose of 17 mg/kg-day (Barret et
al.. 1992: Barret etal.. 1991). Only one study (Albee et al.. 2006) evaluated the effects of TCE
on trigeminal nerve function, and a subchronic inhalation exposure did not result in any
significant functional changes. A summary of these studies is provided in Table D-4.
Barret et al. (1992: 1991) conducted two studies evaluating the effects of both TCE and
dichloroacetylene on trigeminal nerve fiber diameter and internodal length as well as several
markers for fiber myelination. Female Sprague-Dawley rats (n = 7/group) were dosed with
2,500 mg/kg TCE or 17 mg/kg-day dichloroacetylene by gavage for 5 days/week for 10 weeks.
These doses were selected based upon the ratio of the LD50 values (dose at which there is 50%
lethality) for these two agents. Two days after administration of the last dose, a morphometric
approach was used to study the diameter of teased fibers from the trigeminal nerve. The fibers
were classified as Class A or Class B and evaluated for internode length and fiber diameter.
TCE-dosed animals only exhibited changes in the smaller Class A fibers where internode length
increased marginally (<2%) and fiber diameter increased by 6%. Conversely, dichloroacetylene-
treated rats exhibited significant and more robust decreases in internode length and fiber
diameter in both fiber classes A and B. Internode length decreased 8% in Class A fibers and 4%
in Class B fibers. Fiber diameter decreased 10% in Class A fibers and 6% in Class B fibers.
Biochemical data are presented for fatty acid composition from total lipid extractions from the
trigeminal nerve. These two studies identify a clear effect of dichloroacetylene on trigeminal
nerve fibers, but the effect by TCE is quite limited.
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Albee et al. (2006) evaluated the effects of a subchronic inhalation TCE exposure in F344
rats (10/sex/group). Rats were exposed to 0, 250, 800, and 2,500 ppm TCE for 6 hours/day,
5 days/week for 13 weeks. At the 11th week of exposure, rats were surgically implanted with
epidural electrodes over the somatosensory and cerebellar regions, and TSEPs were collected 2-
3 days following the last exposure. TSEPs were generated using subcutaneous needle electrodes
to stimulate the vibrissal pad (area above the nose). The resulting TSEP was measured with
electrode previously implanted over the somatosensory region. The TCE exposures were
adequate to produce permanent auditory impairment, even though TSEPs were unaffected.
While TCE appears to be negative in disrupting the trigeminal nerve, the TCE breakdown
product, dichloroacetylene, does impair trigeminal nerve function.
Albee et al. (1997) reported that dichloroacetylene disrupted trigeminal nerve
somatosensory evoked potentials in F344 male rats. The subjects were exposed to a mixture of
300 ppm dichloroacetylene, 900 ppm acetylene, and 170 ppm TCE for a single 2.25-hour period.
This dichloroacetylene was generated by decomposing TCE in the presence of potassium
hydroxide and stabilizing with acetylene. A second treatment group was exposed to a 175 ppm
TCE/1,030 ppm acetylene mix with no potassium hydroxide present. Therefore, no
dichloroacetylene was present in the second treatment group, providing an opportunity to
determine the effects on the trigeminal nerve somatosensory evoked potential in the absence of
dichloroacetylene. Evoked potentials from the dichloroacetylene/TCE/acetylene-exposed rats
were about 17% smaller measured between peaks I and II and 0.13 msec slower in comparison to
the preexposure measurements. Neither latency nor amplitude of this potential changed
significantly between the pre- and postexposure test in the air-exposed animals (control). The
dichloroacetylene-mediated evoked potential changes persisted at least until day 4 postexposure.
No changes in evoked potentials were observed in the 175 ppm TCE/1,030 ppm acetylene mix
group. It is noteworthy that dichloroacetylene treatment produced broader evidence of toxicity
as witnessed by a persistent drop in body weight among subjects over the 7-day postexposure
measuring period. In light of the differences observed between the effects of TCE and
dichloroacetylene on the trigeminal nerve, it would be instructive to calculate the dose of TCE
that would be necessary to produce comparable tissue levels of dichloroacetylene produced in
the Albee et al. (1997) study.
Kulig (1987) also measured peripheral (caudal nerve) nerve conduction time in male
Wistar rats and failed to show an effect of TCE with exposures as high as 1,500 ppm for
16 hours/day, 5 days/week for 18 weeks.
D.2.2. Auditory Effects
D.2.2.1. Inhalation
The ability of TCE to disrupt auditory function and produce inner ear histopathology
abnormalities has been demonstrated in several studies using a variety of test methods. Two
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different laboratories have identified NOAELs for auditory function of 1,600 ppm following
inhalation exposure for 12 hours/day for 13 weeks in Long-Evans rats (n = 6-10) (Rebert et al.,
1991) and 1,500 ppm in Wistar-derived rats (n = 12) exposed by inhalation for 18 hours/day,
5 days/week for 3 weeks (Jaspers et al., 1993). The LOAELs identified in these and similar
studies are 2,500-4,000-ppm TCE for periods of exposure ranging from 4 hours/day for 5 days
to 12 hours/day for 13 weeks (e.g., Albee et al.. 2006: Boves et al.. 2000: Muijser et al.. 2000:
Fechter et al.. 1998: Crofton and Zhao. 1997: Rebert etal.. 1995: Crofton et al.. 1994: Rebert et
al.. 1993). Rebert et al. (1993) estimated acute blood TCE levels associated with permanent
hearing impairment at 125 ug/mL by methods that probably underestimated blood TCE values
(rats were anaesthetized using 60% carbon dioxide). A summary of these studies is presented in
Table D-5.
Rebert et al. (1991) evaluated auditory function in male Long-Evans rats (n = 10) and
F344 rats (n = 4-5) by measuring brainstem auditory-evoked responses (BAERs) following
stimulation with 4-, 8-, and 16-kHz sounds. The Long-Evans rats were exposed to 0, 1,600, or
3,200 ppm TCE, 12 hour/day for 12 weeks and the F344 rats were exposed to 0, 2,000, or
3,200 ppm TCE, 12 hours/day for 3 weeks. BAERs were measured every 3 weeks during the
exposure and then for an additional 6 weeks following the end of exposure. For the F344 rats,
both TCE exposures (2,000 and 3,200 ppm) significantly decreased BAER amplitudes at all
frequencies tested. In comparison, Long-Evans rats exposed to 3,200 ppm TCE also had
significantly decreased BAER amplitude, but exposure to 1,600 ppm did not significantly affect
BAERs at any stimulus frequency. These data suggest a LOAEL of 2,000 ppm for the F344 rats
and a NOAEL of 1,600 ppm for the Long-Evans rats. In subsequent studies, Rebert et al. (1995:
1993) again demonstrated TCE significantly decreases BAER amplitudes and significantly
increases the latency of the initial peak (identified as PI).
Jaspers et al. (1993) exposed Wi star-derived WAG-Rii/MBL rats (n = 12) to 0, 1,500 and
3,000 ppm TCE exposure for 18 hours/day, 5 days/week for 3 weeks. Auditory function for each
frequency was assessed by reflex modification (recording the decibel threshold required to
generate a startle response from the rat). Three tones (5, 20, and 35 kHz) were used to test
auditory function. The startle measurements were made prior to exposure and at 1, 3, 5, and
6 weeks after exposure. A selective impairment of auditory threshold for animals exposed to
3,000-ppm TCE was observed at all postexposure times at 20 kHz only. No significant effects
were noted in rats exposed to 1,500 ppm TCE. This auditory impairment was persistent up
through 6 weeks after exposure, which was the last time point presented. There was no
impairment of hearing at either 5 or 25 kHz for animals exposed to 1,500 or 3,000 ppm TCE.
This study indicates TCE selectively produces a persistent mid-frequency hearing loss and
identifies a NOAEL of 1,500 ppm. Similarly, Crofton et al. (1994) exposed male Long-Evans
rats (n = 7-8) to 3,500 ppm TCE, 8 hours/day for 5 days. Auditory thresholds were determined
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by reflex modification audiometry 5-8 weeks after exposure. TCE produced a selective
impairment of auditory threshold for mid frequency tones, 8 and 16 kHz.
Muijser et al. (2000) evaluated the ability of TCE to potentiate the damaging effect of
noise on hearing. Wistar rats (n = 8 per group) were exposed by inhalation to 0 or 3,000 ppm
TCE alone for 18 hours/day, 5 days/week for 3 weeks (no noise) or in conjunction with 95-dB
broad band noise. The duration of noise exposure is not specified, but presumably was also
18 hours/day, 5 days/week for 3 weeks. Pure tone auditory thresholds were determined using
reflex modification audiometry 1 and 2 weeks following the exposures. Significant losses in
auditory sensitivity were observed for rats exposed to noise alone at 8, 16, and 20 kHz, for rats
exposed to TCE alone at 4, 8, 16, and 20 kHz and for combined exposure subjects at 4, 8, 16, 20,
and 24 kHz. The loss of hearing sensitivity at 4 kHz is particularly striking for the combined
exposure rats, suggesting a potentiation effect at this frequency. Impairment on this auditory test
suggests toxicity at the level of the cochlea or brainstem.
Fechter et al. (1998) exposed Long-Evans rats inhalationally to 0 or 4,000 ppm TCE
6 hours/day for 5 days. Three weeks later, auditory thresholds were assessed by reflex
modification audiometry (n = 12), and then 5-7 weeks later, cochlear function was assessed by
measuring compound action potentials (CAPs) and the cochlear microphonic response (n = 3-
10). Cochlear histopathology was assessed at 5-7 weeks (n = 4) using light microscopy. Reflex
modification thresholds were significantly elevated at 8 and 18 kHz, as were CAP thresholds.
The growth of the Nl evoked potential was reduced in the TCE group, and they failed to show
normal Nl amplitudes even at supra-threshold tone levels. There was no effect on the sound
level required to elicit a cochlear microphonic response of 1 uV. Histological data suggest that
TCE produces a loss of spiral ganglion cells.
Albee et al. (2006) exposed male and female F344 rats to TCE at 250, 800, or 2,500 ppm
for 6 hours/day, 5 days/week, for 13 weeks. At 2,500 ppm TCE, mild frequency-specific hearing
deficits were observed, including elevated tone-pip auditory brainstem response thresholds.
Focal loss of hair cells in the upper basal turn of the cochlea was observed at 2,500 ppm; this was
apparently based upon midmodiolar sections, which lack power in quantification of hair cell
death. Except for the cochleas of rats at 2,500 ppm, no treatment-related lesions were noted
during the neuro-histopathologic examination. The NOAEL for this study was 800 ppm based
on ototoxicity at 2,500 ppm.
The relationship between dose and duration of exposure with respect to producing
permanent auditory impairment was presented in Crofton and Zhao (1997) and again in Boyes et
al. (2000). The LOAELs identified in Long-Evans rats (n = 10-12) were 6,000 ppm for a 1-day
exposure, 3,200 ppm per day for both the 1- and 4-week exposures, and 2,400 ppm per day for
the 13-week exposure. It was estimated from these data that the LOAEL for a 2-year long
exposure would be 2,100 ppm. Auditory thresholds were determined for a 16-kHz tone 3-
5 weeks after exposure using reflex modification audiometry. Results replicated previous
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findings of a hearing loss at 16 kHz for all exposure durations. One other conclusion reached by
this study is that TCE concentration and not concentration x duration of exposure is a better
predictor of auditory toxicity. That is, the notion that total exposure represented by the function,
concentration (C) x time (t), or Haber's law, is not supported. Therefore, higher exposure
concentrations for short durations are more likely to produce auditory impairment than are lower
concentrations for more protracted durations when total dosage is equated. Thus, consideration
needs to be given not only to total C x t, but also to peak TCE concentration.
Crofton and Zhao (1997) also presented a BMD for which the calculated dose of TCE
would yield a 15-dB loss in auditory threshold. This BMR was selected because a 15-dB
threshold shift represents a significant loss in threshold sensitivity for humans. The benchmark
concentrations for a 15-dB threshold shift are 5,223 ppm for 1 day, 2,108 ppm for 5 days, 1,418
ppm for 20 days, and 1,707 ppm for 65 days of exposure. While more sensitive test methods
might be used and other definitions of a benchmark effect chosen with a strong rationale, these
data provide useful guidance for exposure concentrations that do yield hearing loss in rats.
These data demonstrate that the ototoxicity of TCE was less than that predicted by a strict
concentration x time relationship. These data also demonstrate that simple models of
extrapolation (i.e., C x t = k, Haber's Law) overestimate the potency of TCE when extrapolating
from short-duration to longer-duration exposures. Furthermore, these data suggest that, relative
to ambient or occupational exposures, the ototoxicity of TCE in the rat is a high-concentration
effect; however, the selection of a 15-dB threshold for detecting auditory impairment along with
tests at a single auditory frequency may not capture the most sensitive reliable measure of
hearing impairment.
With the exception of a single study performed in the Hartley guinea pig (n = 9-10)
(Yamamura et al., 1983), there are no data in other laboratory animals related to TCE-induced
ototoxicity. Yamamura et al. (1983) exposed Hartley guinea pigs to TCE at doses of 6,000,
12,000, and 17,000 ppm for 4 hours/day for 5 days and failed to show an acute impairment of
auditory function. However, despite the negative finding in this study, it should be considered
that auditory testing was performed in the middle of a laboratory and not in an audiometric sound
attenuating chamber. The influence of extraneous and uncontrolled noise on cochlear
electrophysiology is marked and assesses auditory detection thresholds in such an environment
unrealistic. Although the study has deficiencies, it is important to note that the guinea pig has
been reported to be far less sensitive than the rat to the effects of ototoxic aromatic hydrocarbons
such as toluene.
It may be helpful to recognize that the effects of TCE on auditory function in rats are
quite comparable to the effects of styrene (e.g., Campo et al., 2006; Crofton et al., 1994; Pryor et
al., 1987), toluene (e.g., Campo et al., 1999; Pryor et al., 1983), ethylbenzene (e.g, Fechter et al.,
2007: Cappaert et al., 2000: Cappaert et al., 1999), and/>-xylene (e.g., Gagnaire et al., 2001:
Pryor etal., 1987). All of these aromatic hydrocarbons produce reliable impairment at the
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peripheral auditory apparatus (inner ear), and this impairment is associated with death of sensory
receptor cells, the outer hair cells. In comparing potency of these various agents to produce
hearing loss, it appears that TCE is approximately equipotent to toluene and less potent than, in
order, ethylbenzene, />-xylene, and styrene. Occupational epidemiological studies do appear to
identify auditory impairments in workers who are exposed to styrene (Morata et al., 2002;
Morioka et al., 2000; Sliwihska-Kowalska et al., 1999) and those exposed to toluene (Morata et
al., 1997; Abbate et al., 1993), particularly when noise is also present.
D.2.2.2. Oral and Injection Studies
No experiments were identified in which auditory function was assessed following TCE
administration by either oral or injection routes.
D.2.3. Vestibular System Studies
The effect of TCE on vestibular function was evaluated by either: (1) promoting
nystagmus (vestibular system dysfunction) and comparing the level of effort required to achieve
nystagmus in the presence and absence of TCE or (2) using an elevated beam apparatus and
measuring the balance. Overall, it was found that TCE disrupts vestibular function as presented
below. A summary of these studies is found in Table D-6.
Tham et al. (1984: 1979) demonstrated disruption in the stimulated vestibular system in
rabbits and Sprague-Dawley rats during i.v. infusion with TCE. It is difficult to determine the
dosage of TCE necessary to yield acute impairment of vestibular function since testing was
performed under continuing infusion of a lipid emulsion containing TCE, and therefore, blood
TCE levels were increasing during the course of the study. Tham et al. (1979), for example,
infused TCE at doses of 1-5 mg/kg-minute reaching arterial blood concentrations as high as
100 ppm. They noted increasing numbers of rabbits experiencing positional nystagmus as blood
TCE levels increased. The most sensitive rabbit showed nystagmus at a blood TCE
concentration of about 25 ppm. Similarly, the Sprague-Dawley rats also experienced increased
nystagmus with a threshold effect level of 120 ppm as measured in arterial blood (Tham et al.,
1984). Animals demonstrated a complete recovery in vestibular function when evaluated for
nystagmus within 5-10 minutes after the i.v. infusion was stopped.
Niklasson et al. (1993) showed acute impairment of vestibular function in male and
female pigmented rats during acute inhalation exposure to TCE (2,700-7,200 ppm) and to
trichloroethane (500-2,000 ppm). Both of these agents were able to promote nystagmus during
optokinetic stimulation in a dose related manner. While there were no tests performed to assess
persistence of these effects, Tham et al. (1984: 1979) did find complete recovery of vestibular
function in rabbits (n = 19) and female Sprague-Dawley rats (n = 11) within minutes of
terminating a direct arterial infusion with TCE solution.
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The finding that TCE can yield transient abnormalities in vestibular function is not
unique. Similar impairments have been shown for toluene, styrene, along with trichloroethane
(Niklasson et al., 1993) and by Tham et al. (1984) for a broad range of aromatic hydrocarbons.
The concentration of TCE in blood at which effects were observed for TCE (0.9 mM/L) was
quite close to that observed for most of these other vestibulo-active solvents.
D.2.4. Visual Effects
Changes in visual function have also been demonstrated in animal studies following acute
(Boyes et al., 2005a: Boyes et al., 2003) and subchronic exposure (Blain et al., 1994). Summary
of all TCE studies evaluating visual effects in animals can be found in Table D-6. In these
studies, the effect of TCE on visual-evoked responses to patterns (Boyes et al., 2005a: Boyes et
al.. 2003: RebertetaL 1991) or a flash stimulus (Blain etal.. 1994: Rebertetal.. 1991) were
evaluated. Overall, the studies demonstrated that exposure to TCE results in significant changes
in the visual evoked response, which is reversible once TCE exposure is stopped. Only one
study (Rebert et al., 1991) did not demonstrate changes in visual system function with a
subchronic TCE exposure, but visual testing was conducted 10 hours after each exposure.
Boyes et al. (2005a: 2003) found significant reduction in the VEP acutely while Long-
Evans male rats were being exposed to TCE concentrations of 500, 1,000, 2,000, 3,000, 4,000,
and 5,000 ppm for intervals ranging from 4 to 0.5 hours, respectively. In both instances, the
degree of effect correlated more with brain TCE concentrations than with duration of exposure.
Boyes et al. (2003) exposed adult, male Long-Evans rats to TCE in a head-only exposure
chamber while pattern onset/offset VEPs were recorded. Exposure conditions were designed to
provide C x t products of 0 ppm/hour (0 ppm for 4 hours) or 4,000 ppm/hour created through
four exposure scenarios: 1,000 ppm for 4 hours; 2,000 ppm for 2 hours; 3,000 ppm for 1.3 hours;
or 4,000 ppm for 1 hour (n = 9-10/concentration). Blood TCE concentrations were assessed by
GC with ECD, and brain TCE concentrations were estimated using a PBPK model. The
amplitude of the VEP frequency double component (F2) was decreased significantly (p < 0.05)
by exposure. The mean amplitude (± SEM in uV) of the F2 component in the control and
treatment groups measured 4.4 ± 0.5 (0 ppm/4 hours), 3.1 ± 0.5 (1,000 ppm/4 hours), 3.1 ±
0.4 (2,000 ppm/2 hours), 2.3 ± 0.3 (3,000 ppm/1.3 hours), and 1.9 ± 0.4 (4,000 ppm/1 hour). A
PBPK model was used to estimate the concentrations of TCE in the brain achieved during each
exposure condition. The F2 amplitude of the VEP decreased monotonically as a function of the
estimated peak brain concentration but was not related to the area under the curve of the brain
TCE concentration. These results indicate that an estimate of the brain TCE concentration at the
time of VEP testing predicted the effects of TCE across exposure concentrations and duration.
In a follow-up study, Boyes et al. (2005a) exposed Long-Evans male rats (n = 8-
10/concentration) to TCE exposures of 500 ppm for 4 hours, 1,000 ppm for 4 hours, 2,000 ppm
for 2 hours, 3,000 ppm for 1.3 hours, 4,000 ppm for 1 hour, and 5,000 ppm for 0.8 hour. VEP
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recordings were made at multiple time points, and their amplitudes were adjusted in proportion
to baseline VEP data for each subject. VEP amplitudes were depressed by TCE exposure during
the course of TCE exposure. The degree of VEP depression showed a high correlation with the
estimated brain TCE concentration for all levels of atmospheric TCE exposure.
This transient effect of TCE on the peripheral visual system has also been reported by
Blain (1994) in which New Zealand albino rabbits were exposed by inhalation to 350 and
700 ppm TCE 4 hours/day, 4 days/week for 12 weeks. ERGs and OPs were recorded weekly
under mesopic conditions. Recordings from the 350 and 700 ppm exposed groups showed a
significant increase in the amplitude of the a- and b-waves (ERG). The increase in the a-wave
was dose related increasing 30% at the low dose and 84% in the high dose. For the b-wave, the
lower exposure dose yielded a larger change from baseline (52%) than did the high dose (33%).
The amplitude of the OPs was significantly decreased at 350 ppm (57%) and increased at 700
ppm (117%). The decrease in the OPs shown in the low-dose group appears to be approximately
25% from 9 to 12 weeks of exposure. These electroretinal changes were reversed to the baseline
value within 6 weeks after the inhalation stopped.
Rebert et al. (1991) evaluated VEPs (flash evoked potentials and pattern reversal evoked
potentials) in male Long-Evans rats that received 1,600 or 3,200 ppm TCE for 3 weeks 12
hours/day. No significant changes in flash evoked potential measurements were reported
following this exposure paradigm. Limited shifts in pattern reversal VEPs were reported during
subchronic exposure, namely a reduction in the N1-P1 response amplitude that reached statistical
significance following 8, 11, and 14 weeks of exposure. The drop in response amplitude ranged
from approximately 20% after 8 weeks to nearly 50% at week 14. However, this potential
recovered completely during the recovery period.
D.2.5. Cognitive Function
There have been a number of reports (e.g., Kishi et al., 1993; Kulig, 1987; Kj ell strand et
al., 1980) showing alteration in performance in learning tasks such as a change in speed to
complete the task, but little evidence that learning and memory function are themselves impaired
by exposure. Table D-7 presents the study summaries for animal studies evaluating cognitive
effects following TCE exposure. Such data are important in efforts to evaluate the functional
significance of decreases in myelinated fibers in the hippocampus reported by Isaacson et al.
(1990) and disruption of long-term potentiation discovered through in vitro testing (Ohta et al.,
2001) since the hippocampus has been closely tied to memory formation.
Kjellstrand et al. (1980) exposed Mongolian gerbils (n = 12/sex) to 900 ppm TCE by
inhalation for 9 months. Inhalation was continuous except for 1-2 hours/week for cage cleaning.
Spatial memory was tested using the radial arm maze task. In this task, the gerbils had to visit
each arm of the maze and remember which arm was visited and unvisited in selecting an arm to
visit. The gerbils received training and testing in a radial arm maze starting after 2 months of
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TCE exposure. There was no effect of TCE on learning or performance on the radial arm maze
task.
Kishi et al. (1993) acutely exposed Wistar rats to TCE at concentrations of 250, 500,
1,000, 2,000, and 4,000 ppm for 4 hours. Rats were tested on an active (light) signaled shock
avoidance operant response. Rats exposed to 250 ppm TCE showed a significant decrease both
in the total number of lever presses and in avoidance responses at 140 minutes of exposure
compared with controls. The rats did not recover their pre-exposure performance until
140 minutes after the exhaustion of TCE vapor. Exposures in the range 250-2,000 ppm TCE for
4 hours produced concentration related decreases in the avoidance response rate. No apparent
acceleration of the RT was seen during exposure to 1,000 or 2,000 ppm TCE. The latency to a
light signal was somewhat prolonged during the exposure to 2,000-4,000 ppm TCE. It is
estimated that there was depression of the CNS with slight performance decrements and the
corresponding blood concentration was 40 ug/mL during exposure. Depression of the CNS with
anesthetic performance decrements was produced by a blood TCE concentration of about 100
ug/mL. In general, the authors observed dose related reductions in total number of lever presses,
but these changes may be more indicative of impaired motor performance than of cognitive
impairment. In any event, recovery occurred rapidly once TCE exposure ceased.
Isaacson et al. (1990) studied the effects of oral TCE exposure in weanling rats at
exposure doses of 5.5 mg/day for 4 weeks, followed by an additional 2 weeks of exposure at
8.5 mg/day. No significant changes were observed in locomotor activity in comparison to the
control animals. This group actually reported improved performance on a Morris swim test of
spatial learning as reflected in a decrease in latency to find the platform from 14 seconds in
control subjects to 12 seconds in the lower dose TCE group to a latency of 9 seconds in the
higher TCE group. The high-dose group differed significantly from the control and low-dose
groups while these latter two groups did not differ significantly from each other. This
improvement relative to the control subjects occurred despite a loss in hippocampal myelination,
which approached 8% and was shown to be significant using Duncan's multiple range test.
Likewise, Umezu et al. (1997) exposed ICR strain male mice acutely to doses of TCE
ranging from 62.5 to 1,000 mg/kg depending upon the task. They reported a depressed rate of
operant responding in a conditioned avoidance task that reached significance with i.p. injections
of 1,000 mg/kg. Increased responding during the signaled avoidance period at lower doses
(250 and 500 mg/kg) suggests an impairment in ability to inhibit responding or failure to attend
to the signal. However, all testing was performed under TCE intoxication.
D.2.6. Psychomotor Effects
Changes in psychomotor activity such as loss of righting reflex, FOB changes, and
locomotor activity have been demonstrated in animals following exposure to TCE. Summaries
for some of these studies can be found below and are presented in detail in Table D-8.
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D.2.6.1. Loss of Righting Reflex
Kishi et al. (1993) evaluated the activity and performance of male Wistar rats in a series
of tasks following an acute 4-hour exposure to 250, 500, 1,000, 2,000, and 4,000 ppm. They
reported disruption in performance at the highest test levels with CNS depression and anesthetic
performance decrements. Blood TCE concentrations were about 100 ug/mL in Wistar rats (such
blood TCE concentrations were obtained at inhalation exposure levels of 2,000 ppm).
Umezu et al. (1997) studied disruption of the righting reflex following acute injection of
250, 500, 1,000, 2,000, 4000, and 5,000 mg/kg TCE in male ICR mice. At 2,000 mg/kg, loss of
righting reflex (LORR) was observed in only 2/10 animals injected. At 4,000 mg/kg,
9/10 animals experienced LORR, and 100% of the animals experienced LORR at 5,000 mg/kg.
Shih et al. (2001) reported impaired righting reflexes at exposure doses of 5,000 mg/kg in male
Mfl mic although lower exposure doses were not included. They showed, in addition, that
pretreatment prior to TCE with DMSO or disulfiram (which is a CYP2E1 inhibitor) in DMSO
could delay loss of the righting reflex in a dose related manner. By contrast, the alcohol
dehydrogenase inhibitor, 4-metylpyradine did not delay loss of the righting reflex that resulted
from 5,000 mg/kg TCE. These data suggest that the anesthetic properties of TCE involve its
oxidation via CYP2E1 to an active metabolite, a finding that is consistent with the anesthetic
properties of CH.
D.2.6.2. FOB and Locomotor Activity Studies
D.2.6.2.1. FOB and locomotor activity studies with TCE.
A number of papers have measured locomotor activity and used FOBs in order to obtain
a more fine grained analysis of the motor behaviors that are impaired by TCE exposure. While
exposure to TCE has been shown repeatedly to yield impairments in neuromuscular function
acutely, there is very little evidence that the effects persist beyond termination of exposure.
One of the most extensive evaluations of TCE on innate neurobehavior was conducted by
Moser et al. (2003; 1999) using FOB testing procedures. Moser et al. (1995) evaluated the
effects of acute and subacute (14-day) gavage administration of TCE in adult female F344 rats.
Testing was performed both 4 hours post TCE administration and 24 hours after TCE exposure,
and a comparison of these two time points along with comparison between the first day and the
last day of exposure provides insight into the persistence of effects observed. Various outcome
measures were grouped into five domains: autonomic, activity, excitability, neuromuscular, and
sensorimotor. Examples of tests included in each of these groupings are as follows:
autonomic—lacrimation, salivation, palpebral closure, pupil response, urination, and defecation;
activity—rearing, motor activity counts home cage position; excitability—ease of removal,
handling reactivity, arousal, clonic, and tonic movements; and neuromuscular—gait score,
righting reflex, fore- and hindlimb grip strength, and landing foot splay; sensorimotor-tail-pinch
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response, click response, touch response, and approach response. Scoring was performed on a 4-
point scale ranging from "1" (normal) to "4" (rare occurrence for control subjects). In the acute
exposure, the exposure doses utilized were 150, 500, 1,500, and 5,000 mg/kg TCE in corn oil.
These doses represent 3, 10, 30, and 56% of the limit dose. For the 14-day subacute exposure,
the doses used were 50, 150, 500, and 1,500 mg/kg. Such doses represent 1, 3, 10, and 30% of
the limit dose for TCE.
The main finding for acute TCE administration is that a significant reduction in activity
level occurred after the highest dose of TCE (5,000 mg/kg) only. This effect showed substantial
recovery 24 hours after exposure though residual decrements in activity were noted.
Neuromuscular function as reflected in the gait score was also severely affected only at 5,000-
mg/kg dose and only at the 4-hour test period. Sensorimotor function reflected in response to a
sudden click, was abnormal at both 1,500 and 5,000 mg/kg with a slight difference observed at
1,500 mg/kg and a robust difference apparent at 5,000 mg/kg. Additional effects noted, but not
shown quantitatively were abnormal home-cage posture, increased landing foot splay, impaired
righting and decreased fore and hind limb grip strength. It is uncertain at which doses such
effects were observed.
With the exception of sensorimotor function, these same categories were also disrupted in
the subacute TCE administration portion of the study. The lack of effect of TCE on
sensorimotor function with repeated TCE dosing might reflect either habituation, tolerance, or an
unreliable measurement at one of the time points. Given the absence of effect at a range of
exposure doses, a true dose-response relationship cannot be developed from these data.
In the subacute study, there are no clearly reliable dose-related differences observed
between treated and control subjects. Rearing, a contributor to the activity domain, was elevated
in the 500-mg/kg dose group, but was normal in the 1,500-mg/kg group. The neuromuscular
domain was noted as significantly affected at 15 days, but it is not clear which subtest was
abnormal. It appears that the limited group differences may be random among subjects unrelated
to exposure condition.
In a follow-up study, Moser et al. (2003) treated female F344 rats with TCE by gavage
for periods of 10 days at doses of 0, 40, 200, 800, and 1,200 mg/kg-day, and testing was
undertaken either 4 hours following the first or 10th dose as well as 24 hours after these two time
points. The authors identified several significant effects produced by TCE administration
including a decrease in motor activity, tail pinch responsiveness, reactivity to handling, hind limb
grip strength, and body weight. Rats administered TCE also showed significantly more
piloerection, higher gait scores, lethality, body weight loss, and lacrimation compared to
controls. Only effects observed 4 hours after the 10th exposure dose were presented by the
authors, and no quantitative information of these measurements is provided.
Albee et al. (2006) exposed male and female F344 rats to 250, 800, and 2,500 ppm TCE
for 6 hours/day, 5 days/week for 13 weeks. FOB was performed 4 days prior to exposure and
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then monthly. Auditory impairments found by others (e.g., Boyes et al., 2000; Muijser et al.,
2000: Fechter et al.. 1998: Crofton and Zhao. 1997: Rebertetal.. 1995: Crofton et al.. 1994)
were replicated at the highest exposure dose, but treatment related differences in grip strength or
landing foot splay were not demonstrated. The authors report slight increases in handling
reactivity among female rats and slightly more activity than in controls at an intermediate time
point, but apparently did not conduct systematic statistical analyses of these observations. In any
event, there were no statistically significant effects on activity or reactivity by the end of
exposure.
Kulig (1987) also failed to show significant effects of TCE inhalation exposure on
markers of motor behavior. Wistar rats exposed to 500, 1,000, and 1,500 ppm for 16 hours/day,
5 days/week for 18 weeks failed to show changes in spontaneous activity, grip strength, or
coordinated hind limb movement. Measurements were made every three weeks during the
exposure period and occurred between 45 and 180 minutes following the previous TCE
inhalation exposure. This study establishes a NOAEL of 1,500 ppm TCE with an exposure
duration of 16 hours/day.
D.2.6.2.2. Acute and subacute oral exposure to DCA on functional observational
batteries (FOB).
Moser et al. (1999) conducted a series of experiments on DCA ranging from acute to
chronic exposures. The exposure doses used in the acute experiment were 100, 300, 1,000, and
2,000 mg/kg. In the repeated exposure studies (8 weeks-24 months), doses varied between
16 and 1,000 mg/kg-day. The authors showed pronounced neuromuscular changes in Long-
Evans and F344 rats dosed orally with the TCE metabolite, DCA, over a period ranging from
9 weeks to 24 months at different exposure doses. Using a multitude of exposure protocols,
which most commonly entailed daily exposures to DCA either by gavage or drinking water, the
authors identify effects that were "mostly limited" to the neuromuscular domain. These included
disorders of gait, grip strength, foot splay, and righting reflex that are dose and duration
dependent. Data on gait abnormality and grip strength are presented in greatest detail. In adults
exposed to DCA by gavage, gait scores were "somewhat abnormal" at the 7-week test in both the
adult Long-Evans rats receiving 300 mg/kg-day and those receiving 1,000 mg/kg-day. There
was no adverse effect in the rats receiving 100 mg/kg-day. In the chronic study, which entailed
intake of DCA via drinking water yielding an estimated daily dose of 137 and 235 mg/kg-day,
"moderately to severely abnormal" gait was observed within 2 months of exposure and dosing
was either reduced or discontinued because of the severity of toxicity. For the higher DCA dose,
gait scores remained "severely abnormal" at the 24-month test time even though the DCA had
been discontinued at the 6-month test time. Hindlimb grip strength was reduced to about half the
control value in both exposure doses and remained reduced throughout the 24 months of testing
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even though DC A administration ceased at 6 months for the 235 mg/kg-day group. Forelimb
grip strength showed a smaller and apparently reversible effect among DCA-treated rats.
D.2.6.3. Locomotor Activity
Wolff and Siegmund (1978) administered 182 mg/kg TCE (i.p.) in AB mice and
observed a decrease in spontaneous locomotor activity. In this study, AB mice were injected
with TCE 30 minutes prior to testing for spontaneous activity at one of four time points during a
24 hours/day (0600, 1200, 1800, and 2400 hours). Marked decreases (estimated 60-80% lower
than control mice) in locomotor activity were reported in 15-minute test periods. The reduction
in locomotion was particularly profound at all time intervals save for the onset of light (0600).
Nevertheless, even at this early morning time point, activity was markedly reduced from control
levels (60% lower than controls as approximated from a graph).
Moser et al. (2003; 1995) included locomotor activity as one of their measures of
neurobehavioral effects of TCE given by gavage over a 10-14-day period. In the 1995 paper,
female F344 rats were dosed either acutely with 150, 500, 1,500 or 5,000 mg/kg TCE or for 14
days with 50, 150, 500 or 1,500 mg/kg. In terms of the locomotor effects, they report that acute
exposure produced impaired locomotor scores only at 5,000 mg/kg while in the subacute study,
locomotion was impaired at the 500 mg/kg dose, but not at the 1,500 mg/kg dose. In the Moser
(2003) study, it appears that 200 mg/kg TCE may actually have increased locomotor activity,
while the higher test doses (800 and 1,200 mg/kg) decreased activity in a dose related manner.
What is common to both studies, however, is a depression in motor activity that occurs acutely
following TCE administration and which may speak to the anesthetic, if not CNS depressive,
effects of this solvent.
There are also a number of reports (Waseem et al., 2001; Fredriksson et al., 1993; Kulig,
1987) that failed to demonstrate impairment of motor activity or ability following TCE exposure.
Waseem et al. (2001) failed to show effects of TCE given in the drinking water of Wistar rats
over the course of a 90-day trial. While nominal solvent levels were 350, 700, and 1,400 ppm in
the water, no estimate is provided of daily TCE intake or of the stability of the TCE solution over
time. However, assuming a daily water intake of 25 mL/day and body weight of 330 g, these
exposures would be estimated to be approximately 26, 52, and 105 mg/kg. These doses are far
lower than those studied by Moser and colleagues.
Fredriksson et al. (1993) studied the effects of TCE given by gavage to male NMRI mice
at doses of 50 and 290 mg/kg-day from PNDs 10 to 16 on locomotion assessed either on the day
following exposure or at age 60 days. They found no significant effect of TCE on locomotor
activity and no consistent effects on other motor behaviors (e.g., rearing).
Waseem et al. (2001) studied locomotor activity in Wistar rats exposed for up to 180 days
to 376-ppm TCE by inhalation for 4 hours/day, 5 days/week and acutely intoxicated with TCE.
Here, the authors report seemingly inconsistent effects of TCE on locomotion. After 30 days of
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exposure, the treated rats show an increase in locomotor activity relative to control subjects.
However, after 60 days of exposure, they note a significant increase in distance traveled found
among experimental subjects, but a decrease in horizontal activity in this experimental group.
Moreover, the control subjects vary substantially in horizontal counts among the different time
periods. No differences between the treatment groups are found after 180 days of exposure. It is
difficult to understand the apparent discrepancy in results reported at 60 days of exposure.
D.2.7. Sleep and Mood Disorders
D.2.7.1. Effects on Mood: Laboratory Animal Findings
It is difficult to obtain comparable data of emotionality in laboratory studies. However,
Moser et al. (2003) and Albee et al. (2006) both report increases in handling reactivity among
rats exposed to TCE. In the Moser study, female F344 rats received TCE by gavage for periods
of 10 days at doses of 0, 40, 200, 800, and 1,200 mg/kg-day, while Albee et al. (2006) exposed
F344 rats to TCE by inhalation at exposure doses of 250, 800, and 2,500 ppm for 6 hours/day, 5
days/week for 13 weeks.
D.2.7.2. Sleep Disturbances
Arito et al. (1994) exposed male Wistar rats to 50, 100, and 300 ppm TCE for
8 hours/day, 5 days/week for 6 weeks and measured EEG responses. EEG responses were used
as a measure to determine the number of awake (wakefulness hours) and sleep hours. Exposure
to all of the TCE levels significantly decreased amount of time spent in wakefulness during the
exposure period. Some carry over was observed in the 22-hour postexposure period with
significant decreases in wakefulness seen at 100-ppm TCE. Significant changes in wakefulness-
sleep elicited by the long-term exposure appeared at lower exposure levels. These data seem to
identify a low dose of TCE that has anesthetic properties and established a LOAEL of 50 ppm
for sleep changes.
D.2.8. Mechanistic Studies
D.2.8.1. Dopaminergic Neurons
In two separate animal studies, subchronic administration of TCE has resulted in a decrease
of dopaminergic cells in both rats and mice. Although the mechanism for dopaminergic neurons
resulting from TCE exposure is not elucidated, disruption of dopaminergic-containing neurons has
been extensively studied with respect to Parkinson's disease and parkinsonism. In addition to
Parkinson's disease, significant study of MPTP and of high-dose manganese toxicity provides
strong evidence for extrapyramidal motor dysfunction accompanying loss of dopamine neurons in
the substantia nigra. These databases may provide useful comparisons to the highly limited
database with regard to TCE and dopamine neuron effects. The studies are presented in Table D-9.
Gash et al. (2008) assessed the effects of subchronic TCE administration on
dopaminergic neurons in the CNS. F344 male rats were orally administered by gavage
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1,000 mg/kg TCE in olive oil, 5 days/week for 6 weeks. Degenerative changes in dopaminergic-
containing neurons in the substantia nigra were reported as indexed by a 45% decrease in the
number of tyrosine hydroxylase positive cells. Additionally, there was a decrease in the ratio of
3,4-dihydroxyphenylacetic acid, a metabolite of dopaminergic, to dopaminergic levels in the
striatum. This shift in ratio, on the order of 35%, was significant by Student's t-test, suggesting a
decrease in release and utilization of this neurotransmitter. While it is possible that long-term
adaptation might occur with regard to release rates for dopaminergic, the loss of dopaminergic
cells in the substantia nigra is viewed as a permanent toxic effect. The exposure level used in
this study was limited to one high dose and more confidence in the outcome will depend upon
replication and development of a dose-response relationship. If the results are replicated, they
might be important in understanding mechanisms by which TCE produces neurotoxicity in the
CNS. The functional significance of such cellular loss has not yet been determined through
behavioral testing.
Guehl (1999) also reported persistent effects of TCE exposure on dopaminergic neurons.
In this study, OF1 male mice (n = 10) were injected i.p. daily for 5 days/week for 4 weeks with
TCE (400 mg/kg-day). Following a 7-day period when the subjects did not receive TCE, the
mice were euthanized and tyrosine hydroxylase immunoreactivity was used to measure neuronal
death in the substantia nigra pars compacta. Treated mice presented significant dopaminergic
neuronal death (50%) in comparison with control mice based upon total cell counts conducted by
an examiner blinded as to treatment group in six samples per subject. The statistical comparison
appears to be by Student's t-test (only means, SDs, and a probability ofp < 0.001 are reported).
While this study appears to be consistent with that of Gash et al. (2008), there are some
limitations of this study. Specifically, no photomicrographs are provided to assess adequacy of
the histopathological material. Additionally, no dose-response data are available to characterize
dose-response relationships or identify either a BMD or NOAEL. Behavioral assessment aimed
at determining functional significance was not determined.
The importance of these two studies suggesting death of dopaminergic neurons following
TCE exposure may be addressable by human health studies because they suggest the potential
for TCE to produce a parkinsonian syndrome.
D.2.8.2. GABA and Glutamatergic Neurons
Disruption of GABAergic and glutamatergic neurons by toxicants can represent serious
impairment as GABA serves as a key inhibitory neurotransmitter while glutamate is equally
important as an excitatory neurotoxicant. Moreover, elevations in glutamatergic release have
been identified as an important process by which more general neurotoxicity can occur through a
process identified as excitotoxicity. The data, with regard to TCE exposure and alteration in
GABA and glutamate function, are limited. The studies are presented in Table D-10.
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Briving et al. (1986) conducted a chronic inhalation exposure in Mongolian gerbils to 50-
and 150-ppm TCE continuously for 12 months and reported the changes in amino acids levels in
the hippocampus and cerebellar vermis and on high affinity uptake of GAB A and glutamate in
those same structures. A dose-related elevation of glutamine in the hippocampus of
approximately 20% at 150 ppm was reported, but no other reliable changes in amino acids in
either of these two structures. With regard to high affinity uptake of glutamate and GAB A, there
were no differences in the hippocampal uptake between control and treated gerbils although in
the cerebellar vermis there was a dose related elevation in the high affinity uptake for both of
these neurotransmitter. Glutamate uptake was increased about 50% at 50 ppm and 100% at
150 ppm. The corresponding increases for GAB A were 69 and 74%. Since control tissue uptake
is identified as being 100% rather than as an absolute rate, the ability to assess quality of the
control data are limited. It is unclear if this finding in cerebellar vermis is also present in other
brain tissues and should be studied further. If these findings are reliable, then the changes in
high affinity uptake in cerebellum for GAB A and glutamate might represent alterations that
could have functional outcomes. For example, alteration in GABA release and reuptake from the
cerebellum might be consistent with acute alteration in vestibular function described below.
However, there are presently no compelling data to support such a relationship.
The change in hippocampal glutamine levels is not readily interpretable. What is not
clear from this paper is whether the alterations observed were acute effects observable only while
subjects were intoxicated with TCE or whether they would persist once TCE had been removed
from the neural tissue. This study used inhalation doses that were at least 1 order of magnitude
lower than those required to produce auditory impairment.
A study by Shih et al. (2001) provides indirect evidence in male Mfl mice that TCE
exposure by injection might alter GABAergic function. The mice were injected i.p. with 250,
500, 1,000 and 2,000 mg/kg TCE in corn oil and the effect of these treatments on susceptibility
to seizure induced by a variety of drugs was observed. Shih et al. (2001) reported that doses of
TCE as low as 250 mg/kg could reduce signs of seizure induced by picrotoxin, bicuculline, and
pentylenetetrazol. These drugs are all GABAergic antagonists. TCE treatment had a more
limited effect on seizure threshold induced by non-GABAergic convulsant drugs such as
strychnine (glycine receptor antagonist), 4-aminopyridine (alcohol dehydrogenase inhibitor), and
N-methyl-d-aspartate (glutamatergic agonist) than was observed with the GABAergic
antagonists. While these data suggest the possibility that TCE could act at least acutely on
GABAergic neurons, there are no direct measurements of such an effect. Moreover, there is no
obvious relationship between these findings and those of Briving et al. (1986) with regard to
increased high affinity uptake of glutamate and GABA in cerebellum. Beyond that fact, this
study does not provide information regarding persistent effects of TCE on either seizure
susceptibility or GABAergic function as all measurements were made acutely shortly following a
single injection of TCE.
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D.2.8.3. Demyelination Following TCE Exposure
Because of its anesthetic properties and lipophilicity, it is hypothesized that TCE may
disrupt the lipid-rich sheaths that cover many central and peripheral nerves. This issue has also
been studied both in specific cranial nerves known to be targets of TCE neurotoxicity (namely
the trigeminal nerve) and in the CNS including the cerebral cortex, hippocampus, and cerebellum
in particular. For peripheral and cranial nerves, there are limited nerve conduction velocity
studies that are relevant as a functional measure. For central pathways, the most common
outcomes studied include histological endpoints and lipid profiles.
A significant difficulty in assessing these studies concerns the permanence or persistence
of effect. There is a very large literature unrelated to TCE, which demonstrates the potential for
repair of the myelin sheath and at least partial if not full recovery of function. In the studies
where nerve myelin markers are assessed, it is not possible to determine if the effects are
transient or persistent.
There are two published manuscripts (Isaacson et al., 1990; Isaacson and Taylor, 1989)
that document selective hippocampal histopathology when Sprague-Dawley rats are exposed to
TCE within a developmental model. Both of these studies employed oral TCE administration
via the drinking water. In Isaacson and Taylor (1989), a combined prenatal and neonatal
exposure was used while Isaacson's et al. (1990) report focused on a neonatal exposure. In
addition, Ohta et al. (2001) presented evidence of altered hippocampal function in an in vitro
preparation following acute in vivo TCE intoxication. The latter most manuscript details a shift
in long term potentiation elicited by tetanic shocks to hippocampal slices in vitro. In the two
developmental studies, the exposure doses are expressed in terms of the concentration of TCE
placed in the drinking water and the total daily dose is then estimated based upon average water
intake by the subjects. However, since the subjects' body weight is not provided, it is not
possible to estimate dosage on a mg/kg body weight basis.
Isaacson and Taylor (1989) examined the development of the hippocampus in neonatal
rats that were exposed in utero and in the preweaning period to TCE via their dam. TCE was
added to the drinking water of the dam and daily maternal doses are estimated based upon water
intake of the dam as being 4 and 8.1 mg/day. Based upon body weight norms for 70-day-old
female Sprague-Dawley rats, which would predict body weights of about 250 g at that age, such
a dose might approach 16-32 mg/kg-day initially during pregnancy. Even if these assumptions
hold true, it is not possible to determine how much TCE was received by the pups, although the
authors do provide an estimate of fetal exposure expressed as ug/mL of TCE, TCOH, and TCA.
The authors reported a 40% decline in myelinated fibers in the CA1 region of the hippocampus
of the weanling rats. There was no effect of TCE treatment on myelination in several other brain
regions including the internal capsule, optic tract, or fornix and this effect appears to be restricted
to the CA1 region of the hippocampus at the tested exposures.
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In a second manuscript by that group (Isaacson et al., 1990), weanling rats were exposed
to TCE via their drinking water at doses of 5.5 mg/day for 4 weeks or 5.5 mg/day for 4 weeks, a
2-week period with no TCE and then a final 2 weeks of exposure to 8.5 mg/day TCE. Spatial
learning was studied using the Morris water maze and hippocampal myelination was examined
histologically starting 1 day postexposure. The authors report that the subjects receiving a total
of 6 weeks exposure to TCE showed better performance in the Morris swim test (p < 0.05) than
did controls, while the 4-week-exposed subjects performed at the same level as did controls.
Despite this apparent improvement in performance, histological examination of the hippocampus
demonstrated a dose-dependent relationship with hippocampal myelin being significantly
reduced in the TCE exposed groups, while normal myelin patterns were found in the internal
capsule, optic tract, and fornix. The authors did not evaluate the signs of gross toxicity in treated
animals such as growth rate, which might have influenced hippocampal development.
Ohta et al. (2001) administered 300 or 1,000 mg/kg TCE, i.p., to male ddY mice.
Twenty-four hours after TCE administration, the mice were sacrificed and hippocampal sections
were prepared from the excised brains and long-term potentiation was measured in the slices. A
dose-related reduction in the population spike was observed following a tetanic stimulation
relative to the size of the population spike elicited in the TCE mice prior to tetany. The spike
amplitude was reduced 14% in the 300 mg/kg TCE group and 26% in the 1,000 mg/kg group.
Precisely how such a shift in excitability of hippocampal CA1 neurons relates to altered
hippocampal function is not certain, but it does demonstrate that injection with 300 mg/kg TCE
can have lingering consequences on the hippocampus at least 24 hours following i.p.
administration.
A critical area for future study is the potential that TCE might have to produce
demyelination in the CNS. While it is realistic to imagine that an anesthetic and lipophilic agent
such as TCE might interact with lipid membranes and produce alterations, for example, in
membrane fluidity at least at anesthetic levels, the data collected by Kyrklund and colleagues
suggest that low doses of TCE (50 and 150 ppm chronically for 12 months, 320 ppm for 90 days,
510 ppm 8 hours/day for 5 months) might alter fatty acid metabolism in Sprague-Dawley rats
and Mongolian gerbils. Because they have not included high doses in their studies and because
the low doses produce only sporadic significant effects and these tend to be of very small
magnitude (5-10%), it is not certain that they are truly observing events with biological
significance or whether they are observing random effects. A key problem in determining
whether the effects under study are spurious or are due to ongoing exposure is that the magnitude
and direction of the effect does not grow larger as exposure continues. It could be hypothesized
that the alterations in fatty acid metabolism could be an underlying mechanism for
demyelination. However, there is not enough evidence to determine if the changes in the lipid
profiles lead to demyelination or if the observed effects are purely due to chance. Similarly, the
size of statistically significant effects (5-12%) is generally modest. A broad dose-response
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analysis or the addition of a positive control group that is treated with an agent well-known to
produce central demyelination would be important in order to characterize the potency of TCE as
an agent that disrupts CNS lipid profiles.
Kyrklund and colleagues (e.g., 1986) have generally evaluated the hippocampus, cerebral
cortex, cerebellum, and in some instances, brainstem in adult gerbil. It is not apparent that one
brain region is more vulnerable to the effects of TCE than is another region. While this group
does not report significant changes in levels of cholesterol, neutral and acidic phospholipids, or
total lipid phospholipids, they do suggest a shift in lipid profiles between treated and untreated
subjects. Similarly, inhalation exposure to trichloroethane at 1,200 ppm for 30 days (Kyrklund
andHaglid, 1991) leads to sporadic changes in fatty acid profiles in Sprague-Dawley rats.
However, these changes are small and are not always in the same direction as the changes
observed following TCE exposure. In the case of trichloroethane, a NOAEL of 320 ppm for 30
days 24 hours/day was observed and no other doses were evaluated (Kyrklund et al., 1988).
D.2.9. Summary Tables
Tables D-4 through D-8 summarize the animal studies by neurological domains
(Table D-4—trigeminal nerve; Table D-5—ototoxicity; Table D-6—vestibular and visual
systems; Table D-7—cognition; and Table D-8—psychomotor function and locomotor activity).
For each table, the reference, exposure route, species, dose level, effects, and NOAEL/LOAEL
values are provided. Tables D-9 through D-l 1 summarize mechanistic (Tables D-9 and D-l 1)
and neurochemical studies (Table D-10). Brief summaries of developmental neurotoxicity
studies are provided in Table D-12.
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Table D-4. Summary of mammalian in vivo trigeminal nerve studies
Reference
Barret et al.
(1991)
Barret et al.
(1992)
Albee et al.
(2006)
Exposure route
Direct gastric
administration
Direct gastric
administration
Inhalation
Species, strain,
sex, number
Rat, Sprague-
Dawley, female, 21
Rat, Sprague-
Dawley, female, 18
Rat, F344, male
and female,
10/sex/group
Dose level/
exposure
duration
0, 2.5 g/kg, acute
administration
0, 2.5 g/kg;
1 dose/d, 5 d/wk,
lOwks
0, 250, 800,
2,500 ppm
NOAEL:
LOAEL
LOAEL:
2.5 g/kg
LOAEL: 2.5
g/kg
NOAEL:
2,500 ppm
Effects
Morphometric analysis was
used for analyzing the
trigeminal nerve. Increase in
external and internal fiber
diameter as well as myelin
thickness was observed in the
trigeminal nerve after TCE
treatment.
Trigeminal nerve analyzed
using morphometric analysis.
Increased internode length and
fiber diameter in class A fibers
of the trigeminal nerve observed
with TCE treatment. Changes
in fatty acid composition also
noted.
No effect on trigeminal nerve
function was noted at any
exposure level.
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Table D-5. Summary of mammalian in vivo ototoxicity studies
Reference
Rebert et al.
(1991)
Rebert et al.
(1993)
Rebert et al.
(1995)
Crofton et
al. (1994)
Crofton and
Zhao
(1997);
Boyes et al.
(2000)
Exposure
route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Species, strain,
sex, number
Rat, Long-Evans,
male, 10/group
Rat, F344, male, 4-
5/group
Rat, Long-Evans,
male, 9/group
Rat, Long-Evans,
male, 9/group
Rat, Long-Evans,
male, 7-8/group
Rat, Long-Evans,
male, 9-12/group
Rat, Long-Evans,
male, 8-10/group
Rat, Long-Evans,
male, 8-10/group
Rat, Long-Evans,
male, 8-10/group
Dose level/
exposure
duration
Long-Evans: 0,
1,600, 3,200 ppm;
12 hrs/d, 12 wks
F344: 0, 2000,
3200 ppm;
12 hrs/d, 3 wks
0, 2,500, 3,000,
3,500 ppm;
8 hrs/d, 5 d
0, 2,800 ppm;
8 hrs/d, 5 d
0, 3,500 ppm
TCE; 8 hrs/d, 5 d
0, 4,000, 6,000,
8,000 ppm; 6 hrs
0, 1,600, 2,400,
3,200 ppm;
6 hrs/d, 5 d
0, 800, 1,600,
2,400, 3,200 ppm;
6 hrs/d, 5 d/wk,
4 wks
0, 800, 1,600,
2,400, 3,200 ppm;
6 hrs/d, 5 d/wk,
13 wks
NOAEL;
LOAEL
Long-Evans:
NOAEL:
1,600 ppm;
LOAEL:
3,200 ppm
F344:
LOAEL:
2,000 ppm
NOAEL:
2,500 ppm
LOAEL:
3,000 ppm
LOAEL:
2,800 ppm
LOAEL:
3,500 ppm
NOAEL:
6,000 ppm
LOAEL:
8,000 ppm
NOAEL:
2,400 ppm
LOAEL:
3,200 ppm
NOAEL:
2,400 ppm
LOAEL:
3,200 ppm
NOAEL:
1,600 ppm
LOAEL:
2,400 ppm
Effects
BAERs were measured.
Significant decreases in BAER
amplitude and an increase in
latency of appearance of the
initial peak (PI).
BAERs were measured 1-2 wks
postexposure to assess auditory
function. Significant decreases
in BAERs were noted with TCE
exposure.
BAER measured 2-14 d
postexposure at a 16-kHz tone.
Hearing loss ranged from 55 to
85 dB.
BAER measured and auditory
thresholds determined 5-8 wks
postexposure. Selective
impairment of auditory function
for mid-frequency tones (8 and
16 kHz).
Auditory thresholds as measured
by BAERs for the 16-kHz tone
increased with TCE exposure.
D-104
-------
Table D-5. Summary of mammalian in vivo ototoxicity studies (continued)
Reference
Fechter et
al. (1998)
Jaspers et
al. (1993)
Muijseret
al. (20001
Albee et al.
(2006)
Yamamura
et al. (1983)
Exposure
route
Inhalation
Inhalation
Inhalation
Inhalation
Inhalation
Species, strain,
sex, number
Rat, Long-Evans,
male, 12/group
Rat, Wistar derived
WAG-Rii/MBL,
male, 12/group
Rat, Wistar derived
WAG-Rii/MBL,
male, 8
Rat, F344, male and
female, 10/sex/group
Guinea Pig, albino
Hartley, male, 7-
10/group
Dose level/
exposure
duration
0, 4,000 ppm;
6 hrs/d, 5 d
0, 1,500,
3,000 ppm;
18 hrs/d, 5 d/wk,
3 wks
0, 3,000 ppm
0, 250, 800,
2,500 ppm
0, 6,000, 12,000,
17,000 ppm;
4 hrs/d, 5 d
NOAEL;
LOAEL
LOAEL:
4,000 ppm
LOAEL:
1,500 ppm
LOAEL:
3,000 ppm
NOAEL:800
ppm
LOAEL:
2,500 ppm
NOAEL:
17,000 ppm
Effects
Cochlear function measured 5-7
wks after exposure. Loss of
spiral ganglion cells noted.
Auditory function was
significantly decreased as
measured by compound action
potentials.
Auditory function assessed
repeatedly 1-5 wks postexposure
for 5-, 20-, and 35-kHz tones. No
effect at 5 or 3 5 kHz. Decreased
auditory sensitivity at 20 kHz.
Auditory sensitivity decreased
with TCE exposure at 4-, 8-, 16-,
and 20-kHz tones.
Mild frequency specific hearing
deficits. Focal loss of hair cells
and cochlear lesions.
No change in auditory sensitivity
at any exposure level as
measured by cochlear action
potentials and microphonics.
D-105
-------
Table D-6. Summary of mammalian sensory studies—vestibular and visual
systems
Reference
Exposure route
Species, strain,
sex, number
Dose level/
exposure duration
NOAEL;
LOAEL
Effects
Vestibular system studies
Tham et al.
(1979)
Tham et al.
(1984)
Niklasson
et al. (1993)
Umezu et
al. (1997)
i.v.
i.v.
Inhalation
i.p.
Rabbit, strain
unknown, sex
unspecified, 19
Rat, Sprague-
Dawley, female,
11
Rat, strain
unknown, male
and female, 28
Mouse, ICR,
male, 116
1-5 mg/kg-min
80 ug/kg-min
0, 2,700, 4,200,
6,000, 7,200 ppm;
Ihr
0, 250, 500,
1,000 mg/kg, single
dose and evaluated
30 min
postadministration
LOAEL:
2,700 ppm
NOAEL:
250 mg/kg
LOAEL:
500 mg/kg
Positional nystagmus
developed once blood levels
reached 30 ppm.
Excitatory effects on the
vestibule-oculomotor
reflex. Threshold effect at
blood (TCE) of 120 ppm or
0.9mM/L.
Increased ability to produce
nystagmus.
Decreased equilibrium and
coordination as measured by
the Bridge test (staying time
on an elevated balance
beam).
Visual system studies
Rebert et al.
(1991)
Boyes et al.
(2003)
Boyes et al.
(2QQ5a)
Blain et al.
(1994)
Inhalation
Inhalation
Inhalation
Inhalation
Rat, Long-Evans,
male, 10/group
Rat, F344, male,
4-5/group
Rat, Long-Evans,
male, 9- 10/group
Rat, Long-Evans,
male, 8- 10/group
Rabbit, New
Zealand albino,
male, 6-8/group
0, 1,600, 3,200 ppm;
12 hrs/d, 12 wks
0, 2,000, 3,200 ppm;
12 hrs/d, 3 wks
0 ppm, 4 hrs;
1,000 ppm, 4 hrs;
2,000 ppm, 2 hrs;
3,000 ppm, 1.3 hrs;
4,000 ppm, 1 hr
0 ppm, 4 hrs;
500 ppm, 4 hrs;
1,000 ppm, 4 hrs;
2,000 ppm, 2 hrs;
3,000 ppm, 1.3 hrs;
4,000 ppm, 1 hr;
5,000 ppm, 0.8 hr
0, 350, 700 ppm;
4 hrs/d, 4 d/wk,
12 wks
NOAEL:
3,200 ppm
NOAEL:
3,200 ppm
LOAEL:
1,000 ppm,
4 hrs
LOAEL:
500 ppm,
4 hrs
LOAEL:
350 ppm
No effect on visual function
as measured by VEP
changes.
Visual function significantly
affected as measured by
decreased amplitude (F2) in
Fourier-transformed VEPs.
Visual function significantly
affected as measured by
decreased amplitude (F2) in
Fourier-transformed VEPs.
Significant effects noted in
visual function as measured
by ERG and OPs
immediately after exposure.
No differences in ERG or
OP measurements were
noted at 6 wks post-TCE
exposure.
D-106
-------
Table D-7. Summary of mammalian cognition studies
Reference
Kjellstrand
et al. (1980)
Kulig et al.
(1987)
Isaacson et
al. (1990)
Kishi et al.
(1993)
Umezu et
al. (1997)
Ohta et al.
(2001)
Oshiro et al.
(2004)
Exposure route
Inhalation
Inhalation
Oral, drinking
water
Inhalation
i.p.
i.p.
Inhalation
Species, strain,
sex, number
Gerbil,
Mongolian,
males and
females,
12/sex/dose
Rat, Wistar,
male, 8/dose
Rat, Sprague-
Dawley, male,
12/dose
Rats, Wistar,
male, number
not specified
Mouse, ICR,
male, 6 exposed
to all treatments
Mouse, ddY,
male, 5/group
Rat, Long-
Evans, male, 24
Dose level/
exposure duration
0, 320 ppm; 9 months,
continuous (24 hrs/d)
except 1-2 hrs/wk for
cage cleaning
0, 500, 1,000, 1,500 ppm;
16 hrs/d, 5 d/wk, 18 wks
(1) 0 mg/kg-d, 8 wks;
(2) 5.5 mg/d (47 mg/kg-
da), 4 wks + 0 mg/kg/d, 4
wks;
(3) 5.5 mg/d, 4 wks
(47 mg/kg-da) + 0 mg/kg-
d, 2 wks + 8.5 mg/d
(24 mg/kg-d),a 2 wks
0, 250,500, 1,000, 2,000,
4,000 ppm, 4 hrs
0, 125, 250, 500,
1,000 mg/kg, single dose
and evaluated 30 min
postadministration
0, 300, 1,000 mg/kg,
sacrificed 24 hrs after
injection
0, 1,600, 2,400 ppm;
6 hrs/d, 5 d/wk, 4 wks
NOAEL;
LOAEL
NOAEL:
320 ppm
NOAEL:
500 ppm
LOAEL:
1,000 ppm
NOAEL:
5.5 mg/d, 4 wks
spatial learning
LOAEL:
5.5 mg/d
hippocampal
demyelination
LOAEL:
250 ppm
NOAEL:
500 mg/kg
LOAEL:
1,000 mg/kg
LOAEL:
300 mg/kg
NOAEL:
2,400 ppm
Effects
No significant effect
on spatial memory
(radial arm maze).
Increased latency
time in the two-
choice visual
discrimination task
(cognitive disruption
and/or motor activity
related effect).
Decreased latency to
find platform in the
Morris water maze
(Group #3).
Hippocampal
demyelination
observed in all TCE-
treated groups.
Decreased lever
presses and
avoidance responses
in a shock avoidance
task.
Decreased response
rate in an operant
response-cognitive
task.
Decreased response
(LTP response) to
tetanic stimulation in
the hippocampus.
No change in RT in
signal detection task
and when challenged
with amphetamine,
no change in
response from
control.
amg/kg-day conversion estimated from average male Sprague-Dawley rat body weight from ages 21-49 days
(118 g) for the 5.5 mg dosing period and ages 63-78 days (354 g) for the 8.5 mg dosing period.
D-107
-------
Table D-8. Summary of mammalian psychomotor function, locomotor
activity, and RT studies
Reference
Savolainen et
al. (1977)
Wolff and
Siegmund
(1978)
Kulig et al.
(1987)
Motohashi and
Miyazaki
(1990)
Fredriksson et
al. (1993)
Moser et al.
(1995)
Bushnell
(1997)
Exposure
route
Inhalation
i.p.
Inhalation
i.p.
Oral
Oral
Inhalation
Species/strain/
sex/number
Rat, Sprague-
Dawley, male,
10
Mouse, AB,
male, 144
Rat, Wistar,
male, 8/dose
Rat, Wistar,
male, 44
Mouse, NMRI,
male, 12 (3-
4 litters)
Rat, F344,
female, 8/dose
Rat, Long-
Evans, male, 12
Dose level/
exposure
duration
0, 200 ppm;
6 hrs/d, 4 d
0, 182 mg/kg,
tested 30 min
after injection
0, 500, 1,000,
1,500 ppm;
16 hrs/d, 5 d/wk,
18wks
0, 1. 2 g/kg, tested
30 min after
injection
0, 1.2 g/kg-d, 3 d
0, 50, 290 mg/kg-
d, at d 10-16
0, 150, 500,
1,500,
5,000 mg/kg,
1 dose
0, 50, 150, 500,
1,500 mg/kg-d,
14 d
0, 400, 800,
1,200, 1,600,
2,000, 2,400 ppm,
1-hr/testd,
4 consecutive test
d, 2 wks
NOAEL;
LOAEL
LOAEL:
200 ppm
LOAEL:
182 mg/kg
NOAEL:
1,500 ppm
LOAEL: 1.2 g/kg
LOAEL: 1.2 g/kg
"
NOAEL:
500 mg/kg
LOAEL:
1,500 mg/kg
NOAEL:
150 mg/kg-d
LOAEL:
500 mg/kg-d
NOAEL:
800 ppm
LOAEL:
1,200 ppm
Effects
Increased frequency of
preening, rearing, and
ambulation. Increased
preening time.
Decreased spontaneous
motor activity.
No change in spontaneous
activity, grip strength or
hindlimb movement.
Increased incidence of rats
slipping in the inclined
plane test.
Decreased spontaneous
motor activity.
Decreased rearing. No
evidence of dose response.
Decreased motor activity.
Neuro-muscular and
sensorimotor impairment.
Increased rearing activity.
Decreased sensitivity and
increased response time in
the signal detection task.
D-108
-------
Table D-8. Summary of mammalian psychomotor function, locomotor
activity, and RT studies (continued)
Reference
Umezu et al.
(1997)
Bushnell and
Oshiro
(2000)
Nunes et al.
(2001)
Waseem et
al. (2001)
Moser et al.
(2003)
Albee et al.
(2006)
Exposure route
i.p.
Inhalation
Oral
Oral
Inhalation
Oral
Inhalation
Species/strain/
sex/number
Mouse, ICR,
male, 6
exposed to all
treatments
Rat, Long-
Evans, male,
32
Rat, Sprague-
Dawley, male,
10/group
Rat, Wistar,
male, 8/group
Rat, Wistar,
male, 6/group
Rat, F344,
female,
10/group
Rat, F344,
male and
female,
10/sex/group
Dose level/
exposure duration
0, 2,000, 4,000,
5,000 mg/kg— loss
of righting reflex
measure
0,62.5, 125,250,
500, 1,000 mg/kg,
single dose and
evaluated 30 min
postadministration
0, 2,000,
2,400 ppm;
70 min/d, 9 d
0, 2,000 mg/kg-d,
7d
0, 350, 700,
1,400 ppm in
drinking water for
90 d
0, 376 ppm for up
to 180 d
0, 40, 200, 800,
1,200 mg/kg-d,
10 d
0, 250, 800,
2,500 ppm
NOAEL;
LOAEL
LOAEL:
2,000 mg/kg—
loss of righting
reflex
NOAEL:
500 mg/kg
LOAEL:
1,000 mg/kg—
operant behavior
NOAEL:
125 mg/kg
LOAEL:
250 mg/kg—
punished
responding
LOAEL:
2,000 ppm
LOAEL:
2,000 mg/kg-d
NOAEL:
1,400 ppm
LOAEL:
376 ppm
—
NOAEL:
2,500 ppm
Effects
Loss of righting reflex,
decreased operant
responses, increased
punished responding.
Decreased performance on
the signal detection task.
Increased response time
and decreased response
rate.
Increased foot splay. No
change in any other FOB
parameter (e.g.,
piloerection, activity,
reactivity to handling).
No significant effect on
spontaneous locomotor
activity.
Changes in locomotor
activity but not consistent
when measured over the
180-d period.
Decreased motor activity;
Decreased sensitivity;
Increased abnormality in
gait; Adverse changes in
several FOB parameters.
No change in any FOB
measured parameter.
D-109
-------
Table D-9. Summary of mammalian in vivo dopamine neuronal studies
Reference
Guehl et al.
(1999)
Gash et al.
(2008)
Exposure route
i.p.
administration
Oral
Species/strain/
sex/number
Mouse, OF1, male,
10
Rat, F344, male,
17/group
Dose level/
exposure
duration
0, 400 mg/kg
0, 1,000 mg/kg
NOAEL;
LOAEL
LOAEL:
400 mg/kg
LOAEL:
1,000 mg/kg
Effects
Significant dopaminergic
neuronal death in substantia
nigra.
Degeneration of dopamine-
containing neurons in substantia
nigra.
D-110
-------
Table D-10. Summary of neurochemical effects with TCE exposure
Reference
Exposure
route
Species/strain/
sex/number
Dose level/
exposure duration
NOAEL;
LOAEL
Effects
In vivo studies
Shih et al.
(2001)
Briving et al.
(1986)
Subramoniam
et al. (1989)
Kjellstrand et
al. (1987)
i.p.
Inhalation
Oral
Inhalation
Mouse, Mfl,
male, 6/group
Gerbils,
Mongolian,
male and
female, 6/group
Rat, Wistar,
female,
Mouse, NMRI,
male
Rat, Sprague-
Dawley, female
0, 250 500, 1,000,
2,000 mg/kg,
15 min; followed by
tail infusion of PTZ
(5 mg/mL),
picrotoxin
(0.8 mg/mL),
bicuculline
(0.06 mg/mL),
strychnine
(0.05 mg/mL), 4-AP
(2 mg/mL), or
NMDA (8 mg/mL)
0, 50, 150 ppm,
continuous, 24 hrs/d,
12 months
0, 1,000 mg/kg, 2 or
20hrs
0, 1,000 mg/kg-d,
5 d/wk, 1 yr
0, 150, 300 ppm,
24 hrs/d, 4 or 24 d
0, 300 ppm,
24 hrs/d, 4 or 24 d
-
NOAEL: 50 ppm;
LOAEL: 150 ppm
for glutamate
levels in
hippocampus
NOAEL:
150 ppm for
glutamate and
GABA uptake in
hippocampus
LOAEL: 50 ppm
for glutamate and
GABA uptake in
cerebellar vermis
-
LOAEL:
150 ppm, 4 and
24 d
NOAEL:
300 ppm, 4 d
LOAEL:
300 ppm, 24 d
Increased threshold for
seizure appearance with
TCE pretreatment for
all convulsants. Effects
strongest on the
GABAA antagonists,
PTZ, picrotoxin, and
bicuculline suggesting
GABAA receptor
involvement. NMDA
and glycine Re
involvement also
suggested.
Increased glutamate
levels in the
hippocampus.
Increased glutamate
and GABA uptake in
the cerebellar vermis.
PI and PIP2 decreased
by 24 and 17% at 2 hrs.
PI and PIP2 increased
by 22 and 3 8% at
20 hrs. PI, PIP, and
PIP2 reduced by 52, 23,
and 45% in 1-yr study.
Sciatic nerve
regeneration was
inhibited in both mice
and rats.
D-lll
-------
Table D-10. Summary of neurochemical effects with TCE exposure (continued)
Reference
Haglid et al.
(1981)
Exposure
route
Inhalation
Species/strain/
sex/number
Gerbil,
Mogolian, male
and female, 6-
7/group
Dose level/
exposure duration
0, 60, 320 ppm,
24 hrs/d, 7 d/wk,
3 months
NOAEL;
LOAEL
LOAEL: 60 ppm,
brain protein
changes
NOAEL: 60 ppm;
LOAEL:
320 ppm, brain
DNA changes
Effects
(1) Decreases in total
brain soluble protein
whereas increase in
SI 00 protein.
(2) Elevated DNA in
cerebellar vermis and
sensory motor cortex.
D-112
-------
Table D-ll. Summary of in vitro ion channel effects with TCE exposure
Reference
Cellular
system
Neuronal channel/
receptor
Concentrations
Effects
In vitro studies
Shafer et al.
(2005)
Beckstead et al.
(2000)
Lopreato et al.
(2003)
Krasowski and
Harrison
(2000)
PC12 cells
Xenopus
oocytes
X. oocytes
Human
embryonic
kidney 293
cells
Voltage sensitive
calcium channels
(VSCC)
Human recombinant
Glycine receptor al,
GABAA receptors,
alpl, alp2y2L
Human recombinant
serotonin 3 A receptor
Human recombinant
Glycine receptor al,
GABAA receptors
a2pl
0, 500, 1,000,
1,500, 2,000 uM
0, 390 uM
Not provided
Not provided
Shift of VSCC activation to a more
hyperpolarizing potential. Inhibition of
VSCCs at a holding potential of -70 mV.
50% potentiation of the GABAA
receptors; 100% potentiation of the
glycine receptor.
Potentiation of serotonin receptor
function.
Potentiation of glycine receptor function
with an EC50 of 0.65 ± 0.05 mM.
Potentiation of GABAA receptor function
with an EC50 of 0.85 ±0.2.
EC50 = median effective concentration
D-113
-------
Table D-12. Summary of mammalian in vivo developmental neurotoxicity
studies—oral exposures
Reference
Fredriksson
et al. (19931
George et al.
(1986)
Isaacson and
Taylor
(1989)
Noland-
Gerbec et al.
(1986)
Taylor et al.
(1985)
Species/strain/
sex/number
Mouse, NMRI,
male pups,
12 pups from
3 to 4 different
litters/group
Rat, F334, male
and female,
20 pairs/
treatment group,
40 controls/sex
Rat, Sprague-
Dawley,
females,
6 dams/group
Rat, Sprague-
Dawley,
females, 9-
11 dams/group
Rat, Sprague-
Dawley,
females, number
dams/group not
reported
Dose level/
exposure duration
0, 50, 290 mg/kg-d
PNDs 10-16
0,0.15,0.30,0.60%
microencapsulated
TCE.
Breeders exposed 1 wk
premating, then for
13 wks; pregnant $s
throughout pregnancy
(i.e., 18-wk total).
0,312, 625 mg/L.
(0,4.0, 8.1 mg/d)b
Dams (and pups)
exposed from 14 d
prior to mating until
end of lactation.
0,3 12 mg/L
(Avg. total intake of
dams: 825 mg TCE
over61d.)b
Dams (and pups)
exposed from 14 d
prior to mating until
end of lactation.
0,312,625,
1,250 mg/L
Dams (and pups)
exposed from 14 d
prior to mating until
end of lactation.
Route/vehicle
Gavage in a
20% fat
emulsion
prepared from
egg lecithin and
peanut oil
Dietary
Drinking water
Drinking water
Drinking water
NOAEL;
LOAEL3
Developmental
LOAEL:
50 mg/kg-d
LOAEL: 0.15%
Developmental
LOAEL:
3 12 mg/L
Developmental
LOEL:
3 12 mg/L
Developmental
LOAEL:
3 12 mg/L
Effects
Rearing activity
statistically significant
I at both dose levels on
PND60.
Open field testing in
pups: a statistically
significant dose-related
trend toward t time
required for male and
female pups to cross the
first grid in the test
device.
Statistically significant
J, myelinated fibers in the
stratum lacunosum-
moleculare of pups.
Reduction in myelin in
the hippocampus.
Statistically significant
| uptake of [3H]-2-DG
in whole brains and
cerebella (no effect in
hippocampus) of
exposed pups at 7, 11,
and 16 d, but returned to
control levels by 2 1 d.
Exploratory behavior
statistically significant
t in 60- and 90-d old
male rats at all treatment
levels. Locomotor
activity was higher in rats
from dams exposed to
l,250ppmTCE.
aNOAEL, LOAEL, and LOEL are based upon reported study findings.
bDose conversions provided by study author(s).
D-114
-------
E. ANALYSIS OF LIVER AND CO-EXPOSURE ISSUES FOR THE
TCE TOXICOLOGICAL REVIEW
The purpose of this Appendix is to provide scientific support and rationale for the hazard
and dose-response sections of the Toxicological Review of Trichloroethylene (TCE) regarding
liver effects and those of co-exposures. It is not intended to be a comprehensive treatise on the
chemical or toxicological nature of TCE. Please refer to the Toxicological Review of
Trichloroethylene (TCE) for characterization of EPA's overall confidence in the quantitative and
qualitative aspects of hazard and dose-response for TCE-induced liver effects. Matters
considered in this appendix include knowledge gaps, uncertainties, quality of data, and scientific
controversies. This characterization is presented in an effort to make apparent the scientific
issues regarding the data and mode-of-action considerations for experimental animal data for
liver effects in the TCE assessment.
E.I. BASIC PHYSIOLOGY AND FUNCTION OF THE LIVER—A STORY OF
HETEROGENEITY
The liver is a complex organ whose normal function and heterogeneity are key to
understanding and putting into context perturbations by TCE, cancer biology, and variations in
response observed, and anticipated for susceptible lifestages and background conditions.
E.I.I. Heterogeneity of Hepatocytes and Zonal Differences in Function and Ploidy
Malarkey et al. (2005) state that: (1) the liver transcriptome (i.e., genes expressed as
measured by mRNA) is believed only second to the brain in its complexity and includes about
25-40% of the approximately 50,000 mammalian genes; (2) during disease states, the
transcriptome can double or triple and its increased complexity is due not only to differential
gene expression (up- and downregulation of genes) but also to the mRNA contributions from the
heterogeneous cell populations in the liver; and (3) when one considers that over a dozen cell
types comprise the liver in varying proportions, particularly in disease states, knowledge about
the cell types and cell-specific gene expression profiles help unravel the complex genomic and
proteomic data sets. Gradients of gene and protein activity varying from the periportal region to
the centrilobular region also exist for sinusoidal endothelial cells, Kupffer cells, hepatic stellate
cells, and the matrix in the space of Disse. Malarkey et al. (2005) also estimate that hepatocytes
constitute 60%, sinusoidal endothelial cells 20%, Kupffer cells 15%, and stellate cells 5% of
liver cells. Therefore, in experimental paradigms where liver homogenates are used for the
determination of "changes in liver," gene expression, or other parameters, the individual changes
from cells residing in differing zones and by differing cell type is lost. Malarkey et al. (2005)
define the need to better characterize the histological cellular components of the tissues from
E-l
-------
which mRNA and protein is extracted and referred to "phenotypic anchoring" and cite
acetaminophen as a "model hepatotoxicant under study to assess the strengths and weaknesses of
genomics and proteomics technologies" as well as "a good example for understanding and
utilizing phenotypic anchoring to better understand genomics data." After acetaminophen
exposure "there is an unexplained and striking inter and intralobular variability in acute hepatic
necrosis with some regions having massive necrosis and adjacent areas within the same lobe or
other lobes showing no injury at all." Malarkey et al. (2005) go on to cite similar lobular
variability in response for "copper distribution, iron and phosphorous, chemical and spontaneous
carcinogenesis, cirrhosis and regeneration" and suggest that although uncertain "factors such as
portal streamlining of blood to the liver, redistribution of blood to core of the liver secondary to
nerve stimulation, and exposures during fetal development and possibly lobular gradients are
important." Hepatic interlobe differences exist for initiating agents in terms of DNA alkylation
and cell replication. In the rat, diethylnitrosamine (DEN) alkylation has been reported to occur
preferentially in the left and right median lobes, while cell replication was higher in the right
median and right anterior lobes (Richardson et al., 1986). Richardson et al. (1986) reported that
exposure to DEN induced a 100% incidence of HCC in the left, caudate, left median, and right
median lobes of the liver by 20 weeks vs. only 30% in the right anterior and right posterior
hepatic lobes. There was a reported interlobe difference in adduct formation, cell proliferation,
liver lobe weight gain, number and size of y-glutamyltranspeptidase (GGT)+ foci, and carbon
14 labeling from a single dose of DEN. Richardson et al. (1986) suggest that many growth-
selection studies utilizing the liver to evaluate the carcinogenic potential of a chemical often
focus on only one or two of the hepatic lobes, which is especially true for partial hepatectomy,
and that for DEN and possibly other chemicals, this procedure removes the lobes most likely to
get tumors. Thus, the "distribution of toxic insult may not be correctly assessed with random
sampling of the liver tissue for microarray gene expression analysis" (Malarkey et al., 2005) and
certainly any such distributional differences are lost in studies of whole-liver homogenates.
The liver is normally quiescent with few hepatocytes undergoing mitosis and, as
described below, normally occurring in the periportal areas of the liver. Mitosis is observed only
in approximately 1 in every 20,000 hepatocytes in adult liver (Columbano and Ledda-
Columbamx 2003). The studies of Schwartz-Arad et al. (1989). Zajicek et al. (1991). Zajicek
and Schwartz-Arad (1990), and Zajicek et al. (1989) have specifically examined the birth, death,
and relationship to zone of hepatocytes as the "hepatic streaming theory." They report that
hepatocytes and littoral cells continuously stream from the portal tract toward the terminal
hepatic vein and that the hepatocyte differentiates as it goes with biological age closely related to
cell differentiation. In other words, the acinus may be represented by a tube with two orifices,
one for cell inflow situated at the portal tract rim and the other for cell outflow, at the terminal
hepatic vein with hepatocytes streaming through the tube in an orderly fashion. In normal liver,
cell proliferation is suggested as the only driving force of this flow with each mitosis associated
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with displacement of the cells by one cell location and the greater the cell production, the faster
the flow and vice versa (Zajicek et al., 1991). Thus, the microscopic section of the liver
"displays an instantaneous image of a tissue in flux" (Schwartz-Arad et al., 1989). Schwartz-
Arad et al. (1989) further suggest that:
throughout its life the hepatocyte traverses three acinus zones; in each it is
engaged in different metabolic activity. When young it performs among other
functions gluconeogenesis, which is found in zone 1 hepatocytes (i.e. periportal),
and when old it turns into a zone 3 cell (i.e., pericentral), with a pronounced
glycolitic make up. The three zones thus represent differentiation stages of the
hepatocyte, and since they differ by their distance from the origin, e.g. zone 2
(i.e., midzonal) is more distant than zone 1, again, hepatocyte differentiation is
proportional to its distance.
Chen et al. (1995) report that:
Hepatocytes are a heterogeneous population that are composed of cells expressing
different patterns of genes. For example, gamma-glutamyl transpeptidase and
genes related to gluconeogenesis are expressed preferential in periportal
hepatocytes, whereas enzymes related to glycolysis are more abundant in the
centrilobular area. Glutamine synthetase is expressed in a small number of
hepatocytes surrounding the central veins. Most cytochrome p450 enzymes are
expressed or induced preferentially in centrilobular hepatocytes relative to
periportal hepatocytes.
Along with changes in metabolic function, Vielhauer et al. (2001) reported that there is
evidence of zonal differences in carcinogen DNA effects and, also, chemical-specific differences
for DNA repair enzyme and that enhanced DNA repair is a general feature of many carcinogenic
states including the enzymes that repair alkylating agents but also oxidative repair. As part of
this process of differentiation and as livers age, the hepatocyte changes and increases its ploidy
with polyploid cells predominant in zone 2 of the acinus (Schwartz-Arad et al., 1989). The
reported decrease in DNA absorbance in zone 3 may be due to: (1) a decline in chromatin
affinity to the dye; (2) cell death; and (3) DNA exit from intact cells and Zajicek and Schwartz-
Arad (1990) suggest that the fewer metabolic demands in Zone 3, under normal conditions,
causes the cell to "deamplify" its genes and for DNA excess to leak out cells adjacent to the
terminal hepatic vein or to be eliminated by apoptosis reflecting cell death. Thus, the three
acinus zones represent differentiation states of one and the same hepatocyte, which increase
ploidy as functional demands change. Zajicek and Schwartz-Arad (1990) also report that nuclear
size is generally proportional to DNA content and that as DNA accumulates, the nucleus
enlarges. This has import for histopathological descriptions of hepatocellular hypertrophy and
attendant nuclear changes after toxic insult as well.
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The gene amplification associated with polyploidy is manifested by DNA accumulation
that involves the entire genome (Zajicek and Schwartz-Arad, 1990). Polyploidization is always
attended by the intensification of the transcription and translation and in rat liver the amino acid
label and activity of many enzymes increases proportionately to their ploidy. "Individual
chromosomes of a tetraploid genome of a hepatocyte reduplicate in the same sequence as in a
diploid one. In this case the properties of the chromosomes evidently remain unchanged and
polyploidy only means doubling the indexes of the diploid genome" (Brodsky and Uryvaeva,
1977). Polyploidy will be manifested in the liver by either increases in the number of
chromosomes per nucleus in an individual cell or by the appearance of two nuclei in a single cell.
Most cell polyploidization occurs in youth with mitotic polyploidization occurring
predominantly from 2 to 3 weeks postnatally and increases with age in mice (Brodsky and
Uryvaeva, 1977). Hepatocytes progress through a modified or polyploidizing cell cycle, which
contains gaps and S-phases, but proceeds without cytokinesis. The result is the formation of the
first polyploidy cell, which is binucleated with diploid nuclei and has increased cell ploidy but
not cell number. The subsequent proliferation of binucleated hepatocytes occurs with a fusion of
mitotic nuclei during metaphase that gives rise to mononucleated cells with higher levels of
ploidy. Thus, during normal liver ontogenesis, a polyploidizing cell cycle without cytokinesis
alternates with a mitotic cycle of binucleated cells and results in progressive and irreversible
increases in either cell or nuclear ploidy (Brodsky and Uryvaeva, 1977).
Polyploidization of the liver occurs during maturation in rodents, and therefore,
experimental paradigms that treat or examine rodent liver during that period should take into
consideration the normally changing baseline of polyploidy in the liver. The development of
polyploidy has been correlated in rodents to correspond with maturation. Brodsky and Uryvaeva
(Brodsky and Uryvaeva, 1977) report that it is cells with diploid nuclei that proliferate in young
mice, but that among the newly formed cells, the percentage of those with tetraploid nuclei is
high. By 1 month, most mice (CBA/C57BL mice) already have a polyploid parenchyma, but
binucleated cells with diploid nuclei predominate. In adult mice, the ploidy class with the
highest percentage of hepatocytes was the 4n X 2 class. The intensive proliferation of diploid
hepatocytes occurs only in baby mice during the first 2 weeks of life and then toward 1 month,
the diploid cells cease to maintain themselves and transform into polyploid cells. In aged
animals, the parenchyma retains only 0.02% of the diploid cells of the newborn animal. While
the weight of the liver increases almost 30 times within 2 years, the number of cells increase
much less than the weight or mean ploidy. Hence, the postnatal growth of the liver parenchyma
is due to cell polyploidization (Brodsky and Uryvaeva, 1977). In male Wistar rats, fetal
hepatocytes (22 days gestation) were reported to be 85.3% diploid (2n) and 7.4% polyploid (4n +
8n) cells with 7.3% of cells in S-phase (SI and S2). By 1 month of age (25-day-old suckling
rats) there were 92.9% diploid and 2.5% polyploid; at 2 months, 47.5% diploid and 50.9%
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polyploid; at 6 months, 29.1% diploid and 69.6% polyploid; and by 8 months, 11.1% diploid and
87.3% polyploidy (Sanz et al., 1996). However, mouse and rat differ in their polyploidization.
In the mouse, which has a higher degree of polyploidy than the rats, the scheme of
polyploidization differs in that each cell class, including mononucleate cells,
forms from the preceding one without being supplemented by self-maintenance.
Each cell class is regarded as the cell clone and it is implied that the cells of each
class have the same mitotic history and originate from diploid initiator cells with
similar properties. In this model 1 reproduction would give a 2n x 2 cell, the
second reproduction a 4n cell, and third reproduction a 4n X 2 cell all coming
from an originator diploid cell (Brodsky and Uryvaeva, 1977).
The cell polyploidy is most extensive in mouse liver, but also common for rat and
humans livers. The livers of young and aged mice differ considerably in the ploidy of the
parenchymal cells, but still perform fundamentally the same functions. In some mammals, such
as the mouse, rats, dog and human, the liver is formed of polyploid hepatocytes. In others, for
example, guinea pig and cats, the same functions are performed by diploid cells (Brodsky and
Uryvaeva, 1977). One obvious consequence of polyploidization is enlargement of the cells. The
volume of the nucleus and cytoplasm usually increases proportionately to the increase in the
number of chromosome sets with polyploidy reducing the surface/volume ratio. The labeling of
tritium doubles with the doubling of the number of chromosomes in the hepatocyte nucleus
(Brodsky and Uryvaeva, 1977). Kudryavtsev et al. (1993) have reported that the average levels
of cell and nuclear ploidy are relatively lower in humans than in rodent, but the pattern of
hepatocyte polyploidization is similar, and at maturity and especially during aging, the rate of
hepatocyte polyploidization increases with elderly individuals having binucleated and polyploid
hepatocytes constituting about one-half of liver parenchyma. Gramantieri et al. (1996) report
that in adult human liver, a certain degree of polyploidization is physiological; the polyploidy
compartment (average 33% of the total hepatocytes) includes both mononucleated (28%) and
binucleated (72%) cells and the average percentage of binucleated cells in the total hepatocyte
population is 24% (Melchiorri et al., 1994).
Historically, aging in human liver has been characterized by fewer and larger
hepatocytes, increased nuclear polyploidy, and a higher index of binucleate hepatocytes (Popper,
1986), but Schmucker (2005) notes that data concerning the effect of aging on hepatocyte
volume in rodent and humans are in conflict with some showing increases in volume to be
unchanged and to increase by 25% by age 60 by others in humans. The irreversibility of
hepatocyte polyploidy has been used in efforts to identify the origin of tumor progenitor cells
(diploid vs. polyploidy) (see Section E.3.1.8, below). The associations with polyploidy and
disease have been an active area of study in cancer mode-of-action studies (see Sections E.3.1.4
and E.3.3.1, below).
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Not only are polyploid cells most abundant in zone 2 of the liver acinus and increase in
number with age, but polyploid cells have been reported to be more abundant following a
number of toxic insults and exposure to chemical carcinogens. Wanson et al. (1980) reported
that one of the earliest lesions obtained in the liver after 7V-nitrosomorpholine treatment
development of hypertrophic parenchymal cells presenting a high degree of ploidy. Gupta
(2000) reports hepatic polyploidy is often encountered in the presence of liver disease and that
for animals and people, polyploidy is observed during advancement of liver injury due to
cirrhosis or other chronic liver disease (often described as large-cell dysplasia referring to
nuclear and cytoplasmic enlargement, nuclear pleomorphisms, and multinucleation and probably
representing increased prevalence of polyploidy cells) and in old animals with toxic liver injury
and impaired recovery. Gorla et al. (2001) report that weaning and commencement of feeding,
compensatory liver hypertrophy following partial hepatectomy, toxin and drug-induced liver
disease, and administration of specific growth factors and hormones may induce hepatic
polyploidy. They go on to state that "although liver growth control has long been studied,
whether the replication potential of polyploidy hepatocytes is altered remains unresolved, in part,
owing to difficulties in distinguishing between cellular DNA synthesis and generation of
daughter cells." Following carbon tetrachloride intoxication, the liver ploidy rises and more cells
become binucleate (Zajicek et al., 1989). Minamishima et al. (2002) report that in 8-12-week-
old female mice before partial hepatectomy, there were 78.6% 2C, 19.1% 4C, and 2.3% 8C cells
but 7 days after, there were 42.0% 2C, 49.1% 4C, and 9.0% 8C. Zajicek et al. (1991) describe
how hepatocyte streaming is affected after the rapid hepatocyte DNA synthesis that occurs after
the mitogenic stimulus of a partial hepatectomy. These data are of relevance to findings of
increased DNA synthesis and liver weight gain following toxic insults and disease states.
Zajicek et al. (1991) suggest that following a mitogenic stimulus, not all DNA synthesizing cells
do divide but accumulate newly formed DNA and turn polyploid (i.e., during the first 3 days
after partial hepatectomy in rats 50% of synthesized DNA was accumulated) and that since the
acinus increased 15% and cell density declined 10%, overall cell mass increased 5%. However,
cell influx rose 1,300%. "In order to accommodate all these cells, the 'acinus-tube' ought to
swell 13-fold, while in reality it increased only 5%" and that on day 3 "the liver remnant did not
even double in its size." Zajicek et al. (1991) conclude that apparently "cells were eliminated
very rapidly, and may have even been sloughed off, since the number of apoptotic bodies was
very low" and therefore, "partial hepatectomy triggers two processes: an acute process lasting
about a week marked by massive and rapid cell turnover during which most newly formed
hepatocytes are eliminated, probably sloughed off into the sinusoids; and a second more
protracted process which served for liver mass restoration mainly by forming new acini." Thus,
a mitogenic stimulus may induce increased ploidy and increased cell number as a result of
increased DNA synthesis, and many of the rapidly expanding number of cells resulting from
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such stimulation are purged, and therefore, do not participate in subsequent disease states of the
liver.
Zajicek et al. (1989) note that the accumulation of DNA rather than proliferation of
hepatocytes "should be considered when evaluating the labeling index of hepatocytes labeled
with tritiated thymidine" as the labeling index, defined as the proportion of labeled cells, can
serve as a proliferation estimate only if it is assumed that a synthesizing cell will ultimately
divide. In tissues, such as the liver, "where cells also accumulate DNA, proliferation estimates
based on this index may fail" (Zajicek et al., 1989). The tendency to accumulate DNA is also
accompanied by a decreasing probability of a cell to proliferate, since young hepatocytes
generally divide after synthesizing DNA, while older cells prefer instead to accumulate DNA.
However, polyploidy per se does not preclude cells from dividing (Zajicek et al., 1989). The
ploidy level achieved by the cell, no matter how high, does not, in itself, prevent it from going
through the next mitotic cycle and the reproduction of hepatocytes in the ploidy classes of 8n and
8n X 2 is common phenomenon (Brodsky and Uryvaeva, 1977). However, along with a reduced
capacity to proliferate, Sigal et al. (1999) report that the onset of polyploidy increases the
probability of cell death. The proliferative potentials of hepatocytes depend not only on their
ploidy, but also on the age of the animals, with liver restoration occurring more slowly in aged
animals after partial hepatectomy (Brodsky and Uryvaeva, 1977). Species differences in the
ability of hepatocytes to proliferate and respond to a mitogenic stimulus have also been
documented (see Section E.3.2, below). The importance of the issues of cellular proliferation vs.
DNA accumulation and the differences in ability to respond to a mitogenic stimulus becomes
apparent as identification of the cellular targets of toxicity (i.e., diploid vs. polyploidy) and the
role of proliferation in proposed modes of action are brought forth. Polyploidization, as
discussed above, has been associated with a number of types of toxic injury, disease states, and
carcinogenesis by a variety of agents.
E.1.2. Effects of Environment and Age: Variability of Response
The extent of polyploidization of the liver not only changes with age, but structural and
functional changes, as well as environmental factors (e.g., polypharmacy), also affect the
vulnerability of the liver to toxic insult. In a recent review by Schmucker (2005), several of
these factors are discussed. Schmucker (2005) reports that approximately 13% of the population
of the United States is over the age of 65 years, that the number will increase substantially over
the next 50 years, and that increased age is associated with an overall decline in health and
vitality contributing to the consumption of nearly 40% of all drugs by the elderly. Schmucker
(2005) estimates that 65% of this population is medicated and many are on polypharmacy
regimes with a major consequence of a marked increase in the incidence of adverse drug
reactions (ADRs) (i.e., males and females exhibit three- and fourfold increases in ADRs,
respectively, when 20- and 60-year-old groups are compared). The percentage of deaths
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attributed to liver diseases dramatically increases in humans beyond the age of 45 years with data
from California demonstrating a fourfold increase in liver disease-related mortality in both men
and women between the ages of 45 and 85 years (Siegel andKasmin, 1997). Furthermore,
Schmucker (2005) cites statistics from the U.S. Department of Health and Human Services to
illustrate a loss in potential lifespan prior to 75 years of age due to liver disease (i.e., liver disease
reduced lifespan to a greater extent than colorectal and prostatic cancers, to a similar extent as
chronic obstructive pulmonary disease, and nearly as much as HIV). Thus, the elderly are
predisposed to liver disease.
As stated above, the presence of high polyploidy cell in normal adults, nuclear
polyploidization with age, and increase in the mean nuclear volume have been reported in
people. Watanabe et al. (1978) reported the results from a cytophotometrical analysis of
35 cases of sudden death including 22 persons over 60 years of age that revealed that although
the nuclear size of most hepatocytes in a senile liver remains unchanged, there was an increase in
cells with larger nuclei. Variations in both cellular area and nucleocytoplasmic ratio were also
analyzed in the study, but the binuclearity of hepatocytes was not considered. No cases with a
clinical history of liver disease were included. Common changes in senile liver were reported to
include atrophy, fatty metamorphosis of hepatocytes, and occasional collapse of cellular cords in
the centrilobular area, slight cellular infiltration and proliferation of Kupffer cells in sinusoids,
and elongation of Glisson's triads with slight to moderate fibrosis in association with round cell
infiltration. Furthermore, cells with giant nuclei, with each containing two or more prominent
nucleoli, and binuclear cells were increased. There was a decrease in diploid populations with
age and an increase in tetraploid population and a tendency of polyploidy cells with higher
values than hexaploids with age. Cells with greater nuclear size and cellular sizes were observed
in livers with greater degrees of atrophy.
Schmucker notes that one of the most documented age-related changes in the liver is a
decline in organ volume but also cites a decrease in functional hepatocytes and that other studies
have suggested that the size or volume of the liver lobule increases as a function of increasing
age. Data are cited for rats suggesting sinusoidal perfusion rate in the rat liver remains stable
throughout the lifespan (Vollmar et al., 2002) but evidence in humans shows age-related shifts in
the hepatic microcirculation attributable to changes in the sinusoidal endothelium (McLean et al.,
2003) (i.e., a 60% thickening of the endothelial cell lining and an 80% decline in the number of
endothelial cell fenestrations, or pores, with increasing age in humans) that are similar in baboon
liver (Cogger et al., 2003). Such changes could impair sinusoidal blood flow and hepatic
perfusion, and the uptake of macromolecules such as lipoproteins from the blood. Schmucker
reports that there is a consensus that hepatic volume and blood flow decline with increasing age
in humans but that the effects of aging on hepatocyte structure are less clear. In rats, the volume
of individual hepatocytes was reported to increase by 60% during development and maturation,
-------
but subsequently decline during senescence yielding hepatocytes of equivalent volumes in
senescent and very young animals (Schmucker, 2005).
The smooth surfaced endoplasmic reticulum (SER), which is the site of a variety of
enzymes involved in steroid, xenobiotic, lipid, and carbohydrate metabolism, also demonstrated
a marked age-related decline rat hepatocytes (Schmucker et al., 1978; Schumucker et al., 1977).
Schmucker also notes that several studies have reported that the older rodents have less effective
protection against oxidative injury in comparison to the young animals, age-related decline in
DNA base excision repair, and increases in the level of oxidatively damaged DNA in the livers
of senescent animals in comparison to young animals. Age-related increases in the expression an
activity of stress-induced transcription factors (i.e., increased NF-KB binding activity but not
expression) were also noted, but that the importance of changes in gene expression to the role of
oxidative stress in the aging process remains unsolved. An age-related decline in the
proliferative response of rat hepatocytes to growth factors following partial hepatectomy was
noted, but despite a slower rate of hepatic regeneration, older livers eventually achieved their
original volume with the mechanism responsible for the age-related decline in the
posthepatectomy hepatocyte proliferative response unidentified.
As with other tissues, telomere length has been identified as a critical factor in cellular
aging with the sequential shortening of telomeres to be a normal process that occurs during cell
replication (see Sections E.3.1.1 and E.3.1.5, below). An association in telomere length and
strain susceptibility for carcinogenesis in mice has been raised. Herrera et al. (1999) examined
susceptibility to disease with telomere shortening in mice. However, this study only cites shorter
telomeres for C57BL6 mice in comparison to mixed C57BL6/129sv mice. The actual data are
not in this paper and no other strains are cited. Of the differing cell types examined, Takubo and
Kaminishi (2001) report that hepatocytes exhibited the next fastest rate of telomere shortening
despite being relatively long-lived cells raising the question of whether or not there are
correlations between age, hepatocyte telomere length, and the incidence of liver disease
(Schmucker, 2005). Aikata et al. (2000) and Takubo et al. (2001) report that the mean telomere
length in healthy livers is approximately 10 kilobase (kb) pairs at 80 years of age and these
hepatocytes retain their proliferative capacity but that in diseased livers of elderly subjects was
approximately 5 kb pairs. Thus, short telomere length may compromise hepatic regeneration and
contribute to a poor prognosis in liver disease or as a donor liver (Schmucker, 2005).
Schmucker (2005) reports that interindividual variability in Phase I drug metabolism was
so large in human liver microsomes, particularly among older subjects, that the determination of
any statistically significant age or gender-related differences were precluded. In fact, Schmucker
(2001) notes that "the most remarkable characteristic of liver function in the elderly is the
increase in interindividual variability, a feature that may obscure age-related differences."
Schumer notes that The National Institute on Aging estimates that only 15% of individuals aged
over 65 years exhibit no disease or disability with this percentage diminishing to 11 and 5% for
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men and women respectively over 80 years. Thus, the large variability in response and the
presence of age-related increases in pharmacological exposures and disease processes are
important considerations in predicting potential risk from environmental exposures.
E.2. CHARACTERIZATION OF HAZARD FROM TCE STUDIES
The 2001 Draft assessment of the health risk assessment of TCE (U.S. EPA. 2001)
extensively cited the review article by Bull (2000) to describe the liver toxicity associated with
TCE exposure in rodent models. Most of the attention has been paid to the study of TCE
metabolites, rather than the parent compound, and the review of the TCE studies by Bull (2000)
was cursory. In addition, gavage exposure to TCE has been associated with a significant
occurrence of gavage-related accidental deaths and vehicle effects, and TCE exposure through
drinking water has been reported to decrease palatability and drinking water consumption, and to
have significant loss of TCE through volatilization, thus further limiting the TCE database.
In its review of the draft assessment, U.S. EPA's Science Advisory regarding this topic
suggested that in its revision, the studies of TCE should be more fully described and
characterized, especially those studies considered to be key for the hazard assessment of TCE.
Although the database for studies of the parent compound is somewhat limited, a careful review
of the rodent studies involving TCE can bring to light the consistency of observations across
these studies, and help inform many of the questions regarding potential modes of action of TCE
toxicity in the liver. Such information can inform current mode-of-action hypothesis (e.g., such
as PPARa activation) as well. Accordingly, the primary acute, subchronic, and chronic studies
of TCE will be described and examined in detail below with comments on consistency, major
conclusions, and the limitations and uncertainties in their design and conduct. Since all chronic
studies were conducted primarily with the goal of ascertaining carcinogenicity, their descriptions
focus on that endpoint, however, any noncancer endpoints described by the studies are described
as well. For details regarding evidence of hepatotoxicity in humans and associations with
increased risk of HCC, please refer to Sections 4.5.1 and 4.5.2. Some of the earlier studies with
TCE were contaminated with epichlorhydrin and are discussed in Sections 4.6 and 4.7 of the
TCE assessment document.
E.2.1. Acute Toxicity Studies
A number of acute studies have been undertaken to describe the early changes in the liver
after TCE administration with the majority using the gavage route of administration. Some have
been detailed examinations, while others have reported primarily liver weight changes as a
marker of TCE-response. The matching and recording of age, but especially initial and final
body weight for control and treatment groups, is of particular importance for studies using liver
weight gain as a measure of TCE-response as difference in these parameters affect TCE-induced
liver weight gain. Most data are for exposures of at least 10 days.
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E.2.1.1. Soni et al. (1998)
Soni et al. (1998) administered TCE in corn oil to male Sprague-Dawley rats (200-250 g,
8-10 weeks old) i.p. at exposure levels of 250, 500, 1,250, and 2,500 mg/kg. Groups (4-
6 animals per group) were sacrificed at 0, 6, 12, 24, 36, 48, 72, and 96 hours after administration
of TCE or corn oil. Using this paradigm only 50% of rats survived the 2,400 mg/kg i.p. TCE
administration with all deaths occurring between days 1 and 3 after TCE administration.
Tritiated thymidine was also administered i.p. to rats 2 hours prior to euthanasia. Light
microscopic sections of the central lobe in 3-4 sections were examined for each animal. The
grading scheme reported by the authors was: 0, no necrosis; +1 minimal, defined as only
occasional necrotic cells in any lobule; +2, mild, defined as less than one-third of the lobular
structure affected; +3, moderate, defined as between one-third and two-thirds of the lobular
structure affected; and +4 severe, defined as greater than two-thirds of the lobular structure
affected. At the 2,500 mg/kg dose, histopathology data were obtained for the surviving rats
(50%). Lethality studies were done separately in groups of 10 rats. The survival in the groups of
rats administered TCE and sacrificed from 0 to 96 hours was given as 30% mortality at 48 hours
and 50% mortality by 72 hours.
The authors report that controls and 0-hour groups did not show signs of tissue injury or
abnormality. The authors only report a single number with one significant figure for each group
of animals with no means or SDs provided. In terms of the extent of necrosis there was no
difference between the 250 and 500 mg/kg/treated dose groups though 96 hours with a single
+1 given as the maximal amount of hepatocellular necrosis (minimal as defined by occasional
necrotic cells in any lobule). At the 1,250 mg/kg dose, the maximal score was achieved 24 hours
after TCE administration and was reported as simply +2 (mild, defined as less than one-third of
lobular structure affected). The level of necrosis was reported to diminish to a score of 0 by
72 hours after 250 mg/kg TCE with no decrease at 500 mg/kg. At 1,250 mg/kg, the extent of
necrosis was reported to diminish from +2 to +1 by 72 hours after administration. At the
2,500 mg/kg dose (LDso for this route) by 48 hours, the surviving rats were reported to have a
score of+4 (severe as defined by greater than two thirds of the lobular structure affected). The
authors report that:
The necrosed cells were concentrated mostly in the midzonal areas and the cells
around central vein area were unaffected. Extensive necrosis was observed
between 24 and 48 hours for both 1250 and 2500 mg/kg groups. Injury was
maximal in the group receiving 2500 mg/kg between 36 and 48 hours as
evidenced by severe midzonal necrosis, vacuolization, and congestion.
Infiltration of polymorphonuclear cell was evident at this time as a mechanism for
cleaning dead cells and tissue debris from the lobules. At the highest dose, the
injury also started to spread toward the centrilobular areas. At the highest dose,
30 and 50% lethality was observed at 48 and 72 h, respectively. After 48 h, the
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number of necrotic cells decreased and the number of mitotic cells increased. The
groups receiving 500 and 1250 mg/kg TCE showed relatively higher mitotic
activity as evidenced by cells in metaphase compared to other groups.
The authors do not give a quantitative estimate or indication as to the magnitude of the
number of cells going through mitosis. Although there was variability in the number of animals
dying at 1,250 mg/kg through this route of exposure, no indication of variability in response
within these treatment groups was given by the authors in regard to extent of histopathological
changes. The authors do not comment on the manner of death using this paradigm or of the
effects of i.p. administration regarding potential peritonitis and inflammation.
TCE hepatotoxicity was "assessed by measuring plasma" SDH and ALT after TCE
administration with vehicle treated control groups reported to induce no increases in these
enzymes. Plasma SDH levels were reported to increase in a linear fashion after 250, 500, and
1,250 mg TCE/kg i.p. administration by 6 hours (i.e., ~3-, 10.5-, 22-, and 24.5-fold in
comparison to controls from 250, 500, 1,250, and 2,500 mg/kg TCE, respectively) with little
difference between the 1,250 and 250 mg/kg dose. By 12 hours the 250, 500, and 1,250 mg/kg
levels had diminished to levels similar to that of the 250 mg/kg dose at 6 hours. The
2,500 mg/kg levels was somewhat diminished from its 6-hour level. By 24 hours after TCE
exposure by the i.p. route of administration, all doses were similar to that of the 250-mg/kg-TCE
6-hour level. This pattern was reported to be similar for 5-, 36-, 48-, 72-, and 96-hour time
points as well. The results presented were the means and SE for four rats per group. The authors
did not indicate which rats were selected for these results from the 4-6 that were exposed in each
group. Thus, only SDH levels showed dose-dependence in results at the 6-hour time point, and
such increases did not parallel the patterns reported for hepatocellular necrosis from
histopathological examination of liver tissues.
For ALT, the pattern of plasma concentrations after i.p. TCE administration differed both
from that of SDH and from liver histopathology. Plasma ALT levels were reported to increase in
a nonlinear fashion and to a much smaller extent than SDH (i.e., -2.7-, 1.9-, 2.1-, and 4.0-fold of
controls from 250, 500, 1,250, and 2,500 mg/kg TCE, respectively). The patterns for 12, 24, 36,
48, 72, and 96 hours were similar to that of the 6-hour exposure and did not show a dose-
response. The authors injected carbon tetrachloride (2.5.mL/kg) into a separate group of rats and
then incubated the resulting plasma with unbuffered TCA (TCA; 0, 200, 600, or 600 nmol) with
decreases in enzyme activity in vitro at the two higher concentrations. It is not clear whether in
vitro unbuffered TCA concentrations of this magnitude, which could precipitate proteins and
render the enzymes inactive, are relevant to the patterns observed in the in vivo data. The extent
of extinguishing of SDH and ALT activity at the two highest TCA levels in vitro were the same,
suggestive of the generalized in vitro pH effect. However, the enzyme activity levels after TCE
exposure had different patterns, suggesting that in vitro TCA results are not representative of the
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in vivo TCE results. Neither ALT nor SDH levels corresponded to time course or dose-response
reported for the histopathology of the liver presented in this study.
Tritiated thymidine results from isolated nuclei in the liver did not show a pattern
consistent with either the histopathology or enzyme results. These results were for whole-liver
homogenates and were not separated by nuclear size or cell origin. Tritiated thymidine
incorporation was assumed by the authors to represent liver regeneration. There was no
difference between treated and control animals at 6 hours after i.p. TCE exposure and only a
decrease (-50% decrease) in thymidine incorporation after 12 hours of the 2,500 mg/kg TCE
exposure level. By 24 hours, there was 5.6- and 2.8-fold tritiated thymidine incorporation at the
500 and 1,250 mg/kg TCE levels, with the 250 and 2,500 mg/kg levels similar to controls. For
36, 48, and 72 hours after i.p. TCE exposure, there continued to be no dose-response and no
consistent pattern with enzyme or histopathological lesion patterns. The authors presented "area
under the curve" data for tritiated thymidine incorporation for 0-95 hours, which did not include
control values. There was a slight elevation at 500 mg/kg TCE and a slight decrease at
2,500 mg/kg from the 250 mg/kg TCE levels. Again, these data did not fit either histopathology
or enzyme patterns and also can include the contribution of nonparenchymal cell nuclei as well
as changes in ploidy.
The use of an i.p. route of administration is difficult to compare to oral and inhalation
routes of exposure given that peritonitis and direct contact with TCE and corn oil with liver
surfaces may alter results. Whereas Soni et al. (1998) report the LDso to be 2,500 mg/kg TCE
via i.p. administration, both Elcombe et al. (1985) and Melnick et al. (1987) do not report
lethality from TCE administered for 10 days at 1,500 mg/kg in corn oil, or up to 4,800 mg/kg-
day for 10 days in encapsulated feed. Also, TCE administered via gavage or oral administration
through feed will enter the liver through the circulation with periportal areas of the liver the first
areas exposed with the entire liver exposed in a fashion dependent on blood concentration levels.
However, with i.p. administration, the absorption and distribution pattern of TCE will differ.
The lack of concordance with measures of liver toxicity from this study and the lack
concordance of patterns and dose-response relationships of toxicity reported from other more
environmentally and physiologically relevant routes of exposure make the relevance of these
results questionable.
E.2.1.2. Soni et al. (1999)
A similar paradigm and the same results were reported for Soni et al. (1999), in which
hepatocellular necrosis, tritiated thymidine incorporation, and in vitro inhibition of SDH and
ALT data were presented along with dose-response studies with ally alcohol and a mixture of
TCE, thioacetamide, allyl alcohol, and chloroform. The same issues with interpretation present
for Soni et al. (1998) also apply to this study as well.
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E.2.1.3. Okino et al. (1991)
This study treated adult Wistar male rats (8 weeks of age) with TCE after being on a
liquid diet for 3 weeks and either untreated or pretreated with phenobarbital or ethanol. TCE
exposure was at 8,000 ppm for 2 hours, 2,000 or 8,000 ppm for 2 hours, and 500 or 2,000 ppm
for 8 hours. Each group contained five rats. Livers from rats, that were not pretreated with
either ethanol or phenobarbital, were reported to show only a few necrotic hepatocytes around
the central vein at 6 and 22 hours after 2 hours of 8,000 ppm TCE exposure. At increased
lengths and/or concentrations of TCE exposure, the frequencies of necrotic hepatocytes in the
centrilobular area were reported to be increased, but the number of necrotic hepatocytes was still
relatively low (out of-150 hepatocytes the percentages of necrotic pericentral hepatocytes were
0.2 ± 0.4, 0.3 ± 0.4, 2.7 ± 1.0, 0.2 ± 0.4, and 3.5 ± 0.4% for control, 2,000 ppm TCE for 2 hours,
8,000 ppm TCE for 2 hours, 500 ppm TCE for 8 hours, and 2,000 ppm TCE for 8 hours,
respectively).
"Ballooned" hepatocytes were reported to be zero for controls and all TCE treatments
with the exception of 0.3 ± 0.6% ballooned midzonal hepatocytes after 8,000 ppm TCE for
2 hours of exposure. Microsomal protein (mg/g/liver) was increased with TCE exposure
concentration and duration, but not reported to be statistically significant (i.e., mg/g/liver
microsomal protein was 21.2 ± 4.3, 22.0 ± 1.5, 25.9 ± 1.3, 23.3 ± 0.8, and 24.1 ± 1.0 for control,
2,000 ppm TCE for 2 hours, 8,000 ppm TCE for 2 hours, 500 ppm TCE for 8 hours, and
2,000 ppm TCE for 8 hours, respectively).
The metabolic rate of TCE was reported to be increased after exposures over 2,000 ppm
TCE (i.e., metabolic rate of TCE in nmol/g/liver/minute was 29.5 ± 5.7, 51.3 ± 6.0, 63.1 ± 16.0,
37.3 ± 3.3, and 69.5 ± 4.3 for control, 2,000 ppm TCE for 2 hours, 8,000 ppm TCE for 2 hours,
500 ppm TCE for 8 hours, and 2,000 ppm TCE for 8 hours, respectively). However, the CYP
content of the liver was not reported to increase with TCE exposure concentration or duration.
The liver/body weight ratios were reported to increase with all TCE exposures except
500 ppm for 8 hours (i.e., the liver/body weight ratio was 3.18 ± 0.15, 3.35 ± 0.10, 3.39 ± 0.20,
3.15 ± 0.10, and 3.57 ± 0.14% for control, 2,000 ppm TCE for 2 hours, 8,000 ppm TCE for
2 hours, 500 ppm TCE for 8 hours, and 2,000 ppm TCE for 8 hours, respectively). These values
represent 1.05-, 0.99-, 1.06-, and 1.12-fold of control in the 2,000 ppm TCE for 2 hours,
8,000 ppm TCE for 2 hours, 500 ppm TCE for 8 hours, and 2,000 ppm TCE for 8 hours
treatment groups, respectively. A statistically significant difference observed after 8 hours of
2,000 ppm TCE exposure. Initial body weights and those 22 hours after cessation of exposure
were not reported, which may have affected liver weight gain. However, these data suggest that
TCE-related increases in metabolism and liver weight occurred as early as 22 hours after
exposures of this magnitude from 2 to 8 hours of TCE with little concurrent hepatic necrosis.
Ethanol and phenobarbital pretreatment were reported to enhance TCE toxicity. In
ethanol-treated rats, a few necrotic hepatocytes were reported to be around the central vein along
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with hepatocellular swelling without pyknotic nuclei at 6 hours after TCE exposure with no
pathological findings in the midzonal or periportal areas. At 22 hours, centrilobular hepatocytes
were reported to have a few necrotic hepatocytes and cell infiltrations around the central vein,
but midzonal areas were reported to have ballooned hepatocytes with pyknotic nuclei frequently
accompanied by cell infiltrations. In phenobarbital-treated rats 6 hours after TCE exposure,
centrilobular hepatocytes showed prenecrotic changes with no pathological changes reported to
be observed in the periportal areas. By 22 hours, zonal necrosis was reported in centrilobular
areas or in the transition zone between centrilobular and periportal areas. Treatment with
phenobarbital or ethanol induced hepatocellular necrosis primarily in centrilobular areas with
phenobarbital having a greater effect (89.1 ± 8.5% centrilobular necrosis) at the higher dose and
shorter exposure duration (8,000 ppm x 2 hours) with ethanol having a greater effect
(16.8 ± 5.3% centrilobular necrosis) at the lower concentration and longer duration of exposure
(2,000 ppm x 8 hours).
E.2.1.4. Nunes et al. (2001)
This study was focused on the effects of TCE and lead co-exposure but treated male
75-day-old Sprague-Dawley rats with 2,000 mg/kg TCE for 7 days via corn-oil gavage (n = 10).
The rats ranged in weight from 293 to 330 g (-12%) at the beginning of treatment and were
pretreated with corn oil for 9 days prior to TCE exposure. TCE was reported to be 99.9% pure.
Although the methods section states that rats were exposed to TCE for 7 days, Table 1 of the
study reports that TCE exposure was for 9 days. The beginning body weights were not reported
specifically for control and treatment groups, but the body weights at the end of exposure were
reported to be 342 ± 18 g for control rats and 323 ± 3 g for TCE-exposed rats, and that difference
(-6%) to be statistically significant. Because beginning body weights were not reported, it is
difficult to distinguish whether differences in body weight after TCE treatment were treatment-
related or reflected differences in initial body weights. The liver weights were reported to be
12.7 ± 1.0 g in control rats and 14.0 ± 0.8 g for TCE treated rats with the percent liver/body
weight ratios of 3.7 and 4.3%, respectively. The increase in percent liver/body weight ratio
represents 1.16-fold of control and was reported to be statistically significant. However,
difference in initial body weight could have affected the magnitude of difference in liver weight
between control and treatment groups. The authors report no gross pathological changes in rats
gavaged with corn oil or with corn oil plus TCE, but observed that one animal in each group had
slightly discolored brown kidneys. Histological examinations of "selected tissues" were reported
to show an increased incidence of chronic inflammation in the arterial wall of lungs from
TCE-dosed animals. There were no descriptions of liver histology given in this report for
TCE-exposed animals or corn-oil controls.
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E.2.1.5. Tao et al. (2000)
The focus of this study was to assess the effects of methionine on methylation and
expression of c-Jun and c-Myc in mouse liver after 5 days of exposure to TCE (1,000 mg/kg in
corn oil) and its metabolites. Female 8-week-old B6C3Fi mice (n = 4-6) were administered
TCE ("molecular biology or HPLC grade") for 5 days with and without methionine (300 mg/kg
i.p.). Data regarding percent liver/body weight was presented as a figure. Of note is the
decrease in liver/body weight ratio by methionine treatment alone (-4.6% liver/body weight for
control and -4.0% liver/body weight for control mice with methionine or -13% difference in
liver/body weight ratios between these groups). Neither initial body weights nor body weights
after exposure were reported by the authors, so the reported effects of treatment could have
reflected differences in initial body weights of the mice. TCE exposure was reported to increase
the percent liver/body weight ratio to -5.8% without methionine and to increase percent
liver/body weight ratio to -5.7% with methionine treatment. These values represent 1.26-fold of
control levels from TCE exposure without methionine and 1.43-fold of control from TCE
exposure with methionine. The number of animals examined was reported to be 4-6 per group.
The authors reported the differences between TCE treated animals and their respective controls
to be statistically significant, but did not examine the differences between controls with and
without methionine. There were no descriptions of liver histology given in this report for
TCE-exposed animals or corn-oil controls.
E.2.1.6. Tucker et al. (1982)
This study describes acute LD50, and 5- and 14-day studies of TCE in a 10% emulphor
solution administered by gavage. Screening-level subchronic drinking water experiments with
TCE dissolved in 1% emulphor in mice were also conducted but with little detail reported. The
authors did describe the strains used (CD-I and ICR outbred albino) and that they were
"weanling mice," but the ages of the mice and their weights were not given. The TCE was
described as containing 0.004% diisopropylamine as the preservative and that the stabilizer had
not been found carcinogenic or overtly toxic. The authors report that "the highest concentration
a mouse would receive during these studies is 0.03 mg/kg/day." The main results are basically
an LD50 study and a short-term study with limited reporting for 4- and 6-month studies of TCE
exposure. Importantly, the authors documented the loss of TCE from drinking water solutions
(<20% of the TCE was lost during the 3 or 4 days in the water bottles at 1.0, 2.5, and 5.0 mg/mL
concentrations, but in the case of 0.1 mg/mL, up to 45% was lost over a 4-day period). The
authors also report that high doses of TCE in drinking water reduced palatability to such an
extent that water consumption by the mice was significantly decreased.
The LD50 with 95% confidence were reported to be 2,443 mg/kg (1,839-3,779 mg/kg) for
female mice and 2,402 mg/kg (2,065-2,771 mg/kg) for male mice. However, the number of
mice used in each dosing group was not given by the authors. The deaths occurred within
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24 hours of TCE administration with no animals recovering from the initial anesthetic effect of
TCE dying during the 14-day observation period. The authors reported that the only gross
pathology observed was hyperemia of the stomach of mice dying from lethal doses of TCE, and
that mice killed at 14 days showed no gross pathology.
In a separate experiment, male CD-I mice were exposed to TCE by daily gavage for
14 days at 240 and 24 mg/kg. These two doses did not cause treatment-related deaths and body
weight and "most" organ weights were reported by the authors to not be significantly affected
but the data were not shown. The only effect noted was increased liver weight, which appeared
to be dose dependent but was reported to be significant only at the higher dose. The only
significant difference found in hematology was a 5% lower hematocrit in the higher dose group.
The number of animals tested in this experiment was not given by the authors.
Male CD-I mice (n = 11) were given TCE via gavage for 5 days (0.73 g/kg TCE twice on
day 0, 1.46 g/kg twice on day 1, 2.91 g/kg twice on day 3, and 1.46 g/kg TCE on days 4 and 5)
with only 4 of 11 mice treated with TCE surviving.
In a subchronic study, male and female CD-I mice received TCE in drinking water at
concentrations of 0, 0.1, 1.0, 2.5, and 5 mg/mL in 1% emulphor, and a naive group received
deionized water. There were 140 animals of each sex in the naive group and in each treatment
group, except for 260 mice in the vehicle groups. Thirty mice of each sex and treatment were
selected for recording body weights for 6 months. The method of "selection" was not given by
the authors. These mice were weighed twice weekly and fluid consumption was measured by
weighing the six corresponding water bottles. The authors reported that male mice at the two
highest doses of TCE consumed 41 and 66 mL/kg-day less fluid over the 6 months of the study
than mice consuming vehicle only and that this same decreased consumption was also seen in the
high dose (5 mg/mL) females. They report that weight gain was not affected except at the high
dose (5mg/mL) and even though the weight gain for both sexes was lower than the vehicle
control group, it was not statistically significant. However, these data were not shown. The
authors report that gross pathological examinations performed on mice killed at 4 and 6 months
were unremarkable and that a number of mice from all of the dosing regimens had liver
abnormalities, such as pale, spotty, or granular livers. They report that 2 of 58 males at
4 months, and 11 of 59 mice at 6 months had granular livers and obvious fatty infiltration, and
that mice of both sexes were affected. Animals in the naive and vehicle groups were reported to
infrequently have pale or spotty livers, but exhibit no other observable abnormalities. No
quantitation or more detailed descriptions of the incidence of or severity of effects were given in
this report.
The average body weight of male mice receiving the highest dose of TCE was reported to
be 10% lower at 4 months and 11% lower at 6 months with body weights of female mice at the
highest dose also significantly lower. Enlarged livers (as percentage of body weight) were
observed after both durations of exposure in males at the three highest doses and in females at
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the highest dose. In the 4-month study, brain weights of treated females were significantly
increased when compared to vehicle control. However, the authors state:
This increase is apparently because the values for the vehicle group were low,
because the naive group was also significantly increased when compared to
vehicle control. A significant increase in kidney weight occurred at the highest
dose in males at 6 months and in females, after both 4 and 6 months of TCE
exposure. Urinalysis indicated elevated protein and ketone levels in high-dose
females and the two highest dose males after 6 months of exposure (data not
shown).
The authors describe differences in hematology to include:
a decreased erythrocyte count in the high dose males at 4 and 6 months (13% and
16%, respectively); decreased leukocyte counts, particularly in the females at
4 months and altered coagulation values consisting of increased fibrinogen in
males at both times and shortened prothrombin time in females at 6 months (data
not shown). No treatment-related effects were detected on the types of white cells
in peripheral blood.
It must be noted that effects reported from this study may have also been related to
decreased water consumption, this study did not include any light microscopic evaluation, and
that most of the results described are for data not shown. However, this study does illustrate the
difficulties involved in trying to conduct studies of TCE in drinking water, that the LD50 values
for TCE are relatively high, and that liver weight increases were observed with TCE exposure as
early as a few weeks and increased liver weight were sustained through the 6-month study
period.
E.2.1.7. Goldsworthy and Popp (1987)
The focus of this study was peroxisomal proliferation activity after exposure to a number
of chlorinated solvents. In this study 1,000 mg/kg TCE (99+% epoxide stabilizer free) was
administered to male F-344 rats (170-200 g or -10% difference) and B6C3Fi (20-25 g or -20%
difference) mice for 10 days in corn oil via gavage. The ages of the animals were not given. The
TCE-exposed animals were studied in two experiments (experiments #1 and #3). In
experiment #2, corn oil and methyl cellulose vehicles were compared. Animals were killed
24 hours after the last exposure. The authors did not show data on body weight, but stated that
the administration of test agents (except WY-14,643 to rats which demonstrated no body weight
gain) to rats and mice for 10 days "had little or no effect on body weight gain." Thus,
differences in initial body weight between treatment and control groups, which could have
affected the magnitude of TCE-induced liver weight gain, were not reported. The liver/body
weight ratios in corn oil gavaged rats were reported to be 3.68 ± 0.06 and 4.52 ± 0.08% after
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TCE treatment, which represented 1.22-fold of control (n = 5). Cyanide-(CN-)insensitive
palmitoyl CoA12 oxidation (PCO) was reported to be 1.8-fold increased after TCE treatment in
this same group. In B6C3Fi mice the liver/body weight ratio in corn oil gavaged mice was
reported to be 4.55 ± 0.13 and 6.83 ± 0.13% after TCE treatment which represented 1.50-fold of
control (n = 7). CN-insensitive PCO activity was reported to be 6.25-fold of control after TCE
treatment in this same group. The authors report no effect of vehicle on PCO activity, but do not
show the data nor discuss any effects of vehicle on liver weight gain. Similarly, the results for
experiment #3 were not shown nor liver weight discussed with the exception of PCO activity
reported to be 2.39-fold of control in rat liver and 6.25-fold of control for mouse liver after TCE
exposure. The number of animals examined in Experiment #3 was not given by the authors or
the variation between enzyme activities. However, there appeared to be a difference in PCO
activity in experiments #1 and #3 in rats. There were no descriptions of liver histology given in
this report for TCE-exposed animals or corn-oil controls.
E.2.1.8. Elcombe et al. (1985)
In this study, preservative-free TCE was given via gavage to rats and mice for
10 consecutive days with a focus on changes in liver weight, structure, and hepatocellular
proliferation induced by TCE. Male Alderley Park rats (Wistar derived) (180-230 g), male
Osborne-Mendel rats (240-280 g), and male B6C3Fi or male Alderley Park Mice (Swiss)
weighing 30-35 g were administered 99.9% pure TCE dissolved in corn oil via gavage. The
ages of the animals were not given by the authors. The animals were exposed to 0, 500, 1,000,
or 1,500 mg/kg body weight TCE for 10 consecutive days. The number of mice and rats varied
widely between experiments and treatment groups and between various analyses. In some
experiments, animals were injected with tritiated thymidine approximately 24 hours following
the final dose of TCE and killed 1 hour later. The number of hepatocytes undergoing mitosis
was identified in 25 random high-power fields (X40) for each animal with 5,000 hepatocyte per
animal examined. There was no indication by the authors that zonal differences in mitotic index
were analyzed. Sections of the liver were examined by light and electron microscopy by
conventional staining techniques. Tissues selected for electron microscopy included central vein
and portal tract so that zonal differences could be elucidated. Morphometric analysis of
peroxisomes was performed "according to general principles of Weibel et al. (1964) on
electronphotomicrographs from pericentral hepatocytes." DNA content of samples and
peroxisomal enzyme activities were determined in homogenized liver (catalase and PCO
activity).
The authors reported that TCE treatment had no significant effect on body-weight gain in
either strain of rat or mouse during the 10-day exposure period. However, marked increases (up
12CoA = coenzyme A.
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to 175% of control value) in the percent liver/body weight ratio were observed in TCE-treated
mice. Smaller increases (up to 130% of control) in relative liver weight were observed in
TCE-treated rats. No significant effects of TCE on hepatic water content were seen, so the liver
weight did not represent increased water retention.
An interesting feature of this study was that it was conducted in treatment blocks at
separate times with separate control groups of mice for each experimental block. Therefore,
there were three control groups of B6C3Fi mice (n = 10 for each control group) and three control
groups for Alderley Park (n = 9-10 for each control group) mice that were studied concurrently
with each TCE treatment group. However, the percent liver/body weight ratios were not the
same between the respective control groups. There was no indication from the authors as to how
controls were selected or matched with their respective experimental groups. The authors did
not give liver weights for the animals, so the actual changes in liver weights were not given. The
body weights of the control and treated animals were also not given by the authors. Therefore, if
there were differences in body weight between the control groups or treatment groups, the
liver/body weight ratios could also have been affected by such differences. The percentage
increase over control could also have been affected by what control group each treatment group
was compared to. There was a difference in the mean percent liver/body weight ratio in the
control groups, which ranged from 4.32 to 4.59% in the B6C3Fi mice (-6% difference) and from
5.12 to 5.44% in the Alderley Park mice (-6% difference). The difference in average percent
liver/body weight ratio for untreated mice between the two strains was -16%. Because the ages
of the mice were not given, the apparent differences between strains may have been due to both
age or to strain.
After TCE exposure, the mean percent liver/body weight ratios were reported to be
5.53% for 500 mg/kg, 6.50% for 1,000 mg/kg, and 6.74% for 1,500 mg/kg TCE-exposed
B6C3Fi mice. This resulted in 1.20-, 1.50-, and 1.47-fold values of control in percent liver
weight/body weight for B6C3Fi mice. For Alderley Park mice, the percent liver/body weight
ratios were reported to be 7.31, 8.50, and 9.54% for 500, 1,000, and 1,500 mg/kg TCE treatment,
respectively. This resulted in 1.43-, 1.56-, and 1.75-fold of control values. Thus, there appeared
to be more of a consistent dose-related increase in liver/body weight ratios in the Alderley Park
mice than the B6C3Fi mice after TCE treatment. However, the variability in control values may
have distorted the dose-response relationship in the B6C3Fi mice. The SDs for liver/body
weight ratio were as much as 0.52% for the treated B6C3Fi mice and 0.91% for the Alderley
Park treated mice. In regard to the correspondence of the magnitude of the TCE-induced
increases in percent liver/body weight with the magnitude of difference in TCE exposure
concentrations, in the B6C3Fi, mice the increases were similar (approximately twofold) between
the 500 and 1,000 mg/kg TCE exposure groups. For the Alderley Park mice, the increases in
TCE exposure concentrations were slightly less than the magnitude of increases in percent
liver/body ratios between all of the concentrations (i.e., ~1.3-fold of control vs. 2-fold for 500
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and 1,000 mg/kg TCE dose and 1.3-fold of control vs. 1.5-fold for the 1,000 and 1,500 mg/kg
TCE dose).
The DNA content of the liver varied greatly between control animal groups. For B6C3Fi
mice it ranged from 2.71 to 2.91 mg/g liver. For Alderley Park mice, it ranged from 1.57 to
2.76 mg/g liver. The authors do not discuss this large variability in baseline levels of DNA
content. The DNA content in B6C3Fi mice was mildly depressed by TCE treatment in a
nondose-dependent manner. DNA concentration decrease from control ranged from 20 to 25%
between all three TCE exposure levels in B6C3Fi mice. For Alderley Park mice there was also
nondose related decrease in DNA content from controls that ranged from 18 to 34%. Thus, the
extent of decrease in DNA content of the liver from TCE treatment in B6C3Fi mice was similar
to the variability between control groups. The lack of dose-response for apparent treatment-
related effects in B6C3Fi mice and especially in the Alderley Park mice was confounded by the
large variability in the control animals. The changes in liver weight after TCE exposure for the
AP mice did not correlate with changes in DNA content further, raising doubt about the validity
of the DNA content measures. However, a small difference in DNA content due to TCE
treatment in all groups was reported for both strains and this is consistent with hepatocellular
hypertrophy.
The reported results for incorporation of tritiated thymidine in liver DNA showed large
variation in control groups and SDs that were especially evident in the Alderley Park mice. For
B6C3Fi mice, mean control levels were reported to range from 5,559 to 7,767 dpm/mg DNA
with SDs ranging from 1,268 to 1,645 dpm/mg DNA. In Alderley Park mice, mean control
levels were reported to range from 6,680 to 10,460 dpm/mg DNA with SDs ranging from 308 to
5,235 dpm/mg DNA. For B6C3Fi mice, TCE treatment was reported to induce an increase in
tritiated thymidine incorporation with a very large SD, indicating large variation between
animals. For the 500 mg/kg TCE treatment group, the values were reported as 12,334 ± 4,038,
for the 1,000 mg/kg TCE treatment group, 21,909 ± 13,386, and for the 1,500 mg/kg treatment
TCE group, 26,583 ± 10,797 dpm/mg DNA. In Alderley Park mice, TCE treatment was reported
to give an increase in tritiated thymidine incorporation also with a very large SD. For 500 mg/kg
TCE, the values were reported as 19,315 ± 12,280; for 1,000 mg/kg, TCE 21,197 ± 8,126; and
for 1,500 mg/kg TCE, 38,370 ± 13,961. As a percentage of concurrent control, the increase in
tritiated thymidine was reported to be 2.11-, 2.82-, and 4.78-fold of control in B6C3Fi mice, and
2.09-, 2.03-, and 5.74-fold of control in Alderley Park mice. Accordingly, the change in tritiated
thymidine incorporation did show a treatment related increase but not a dose-response.
Similar to the DNA content of the liver, the large variability in measurements between
control groups and variability between animals limit quantitative interpretation of these data.
The increase in tritiated thymidine, seen most consistently only at the highest exposure level in
both strains of mice, could have resulted from either a change in ploidy of the hepatocytes or cell
number. However, the large change in volume in the liver (75%) in the Alderley Park mice,
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could not have resulted from only a fourfold of control in cell proliferation even if all tritiated
thymidine incorporation had resulted from changes in hepatocellular proliferation. As mentioned
in Section E.I.I above, the baseline level of hepatocellular proliferation in mature control mice is
very low and represents a very small percentage of hepatocytes.
In the experiments with male rats, the same issues discussed above, associated with the
experimental design, applied to the rat experiments with the additional concern that the numbers
of animals examined varied greatly (i.e., 6-10) between the treatment groups. In Osborne-
Mendel rats, the control liver/body weight ratio was reported to vary from 4.26 to 4.36% with the
SDs varying between 0.22 and 0.27%. For the Alderley Park rats, the liver/body weight ratios
were reported to vary between 4.76 and 4.96% (in control groups) with SDs varying between
0.24 and 0.47%. TCE treatment was reported to induce a dose-related increase in liver/body
weight ratio in Osborne-Mendel rats with mean values of 5.16, 5.35, and 5.53% in 500, 1,000,
and 1,500 mg/kg TCE treated groups, respectively. This resulted in 1.18-, 1.26-, and 1.30-fold
values of control. In Alderley Park rats, TCE treatment was reported to result in increased liver
weights of 5.45, 5.83, and 5.65% for 500, 1,000, and 1,500 mg/kg TCE respectively. This
resulted in 1.14-, 1.17-, and 1.17-fold values of control. Again, the variability in control values
may have distorted the nature of the dose-response relationships in Alderley Park rats. TCE
treatment was reported to result in SDs that ranged from 0.31 to 0.48% for Osborne-Mendel rats
and from 0.24 to 0.38% for Alderley Park rats. What is clear from these experiments is that TCE
exposure was associated with increased liver/body weight in rats.
The reported mean hepatic DNA concentrations and SDs varied greatly in control rat
liver as it did in mice. The variation in DNA concentration in the liver varied more between
control groups than the changes induced by TCE treatment. For Osborne-Mendel rats, the mean
control levels of mg DNA/g liver were reported to range from 1.99 to 2.63 mg DNA/liver with
SDs varying from 0.17 to 0.33 mg DNA/g. For Alderley Park rats, the mean control levels of mg
DNA/g liver were reported to be 2.12-3.16 mg DNA/g with SD ranging from 0.06 to 1.04 mg
DNA/g. TCE treatment decreased the liver DNA concentration in all treatment groups. For
Osborne-Mendel rats, the decrease ranged from 8 to 13% from concurrent control values and for
Alderley Park rats the decrease ranged from 8 to 17%. There was no apparent dose response in
the decreases in DNA content, with all TCE treatment levels giving a similar decrease from
controls and the same limitations discussed above for the mouse data apply here. The magnitude
of increases in liver/body ratios shown by TCE treatment were not correlated with the changes in
DNA content. However, as with the mouse data, the small differences in DNA content due to
TCE treatment in all groups and in both strains were consistent with hepatocellular hypertrophy.
Incorporation of tritiated thymidine was reported to be even more variable between
control groups of rats than it was for mice and was reported to be especially variable between
control groups (i.e., 2.7-fold difference between control groups within strain) and differed
between the strains (average of 2.5-fold between strains). For Osborne-Mendel rats, the mean
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control levels were reported to range from 13,315 to 33,125 dpm/mg DNA, while for Alderley
Park rats, tritiated thymidine incorporation ranged from 26,613 to 69,331 dpm/mg DNA for
controls. The SDs were also very large (i.e., for control groups of Osborne-Mendel rats, they
were reported to range from 8,159 to 13,581 dpm/mg DNA, while for Alderley Park rats, they
ranged from 9,992 to 45,789 dpm/mg DNA). TCE treatment was reported to induce increases
over controls of 110, 118, and 106% for 500, 1,000, and 1,500 mg/kg TCE-exposed groups,
respectively, in Osborne-Mendel rats with large SDs for these treatment groups as well. In
Alderley Park rats, the increases over controls were reported to be 206, 140, and 105% for 500,
1,000, and 1,500 mg/kg TCE, respectively. In general, these data do indicate that TCE treatment
appeared to give a mild increase in tritiated thymidine incorporation but the lack of dose-
response can be attributable to the highly variable measurements of tritiated thymidine
incorporation in control animal groups. The variation in the number of animals examined
between groups and small numbers of animals examined additionally decrease the likelihood of
being able to discern the magnitude of difference between species- or strain-related effects for
this parameter. Again, given the very low level of hepatocyte turnover in control rats, this does
not represent a large population of cells in the liver that may be undergoing proliferation and
cannot be separated from changes in ploidy.
The authors report that the reversibility of these phenomena was examined after the
administration of TCE to Alderley Park mice for 10 consecutive days. Effects upon liver weight,
DNA concentration, and tritiated thymidine incorporation 24 and 48 hours after the last dose of
TCE were reported to be still apparent. However, 6 days following the last dose of TCE, all of
these parameters were reported to return to control values with the authors not showing the data
to support this assertion. Thus, cessation of TCE exposure would have resulted in a 75%
reduction in liver weight by one week in mice exposed to the highest TCE concentration.
Analyses of hepatic peroxisomal enzyme activities were reported for catalase and
p-oxidation (PCO activity) following administration of TCE to B6C3Fi mice and Alderley Park
rats exposed to 1,000 mg/kg TCE for 10 days. The authors only used five control and five
exposed animals for these tests. An 8-fold of control value for PCO activity and a 1.5-fold of
control value for catalase activity were reported for B6C3Fi mice exposed to 1,000 mg/kg TCE.
In the Alderley Park rats, no significant changed occurred. It is unclear which mice or rats were
selected from the previous experiments for these analyses and what role selection bias may have
played in these results. The reduced number of animals chosen for this analysis also reduces the
power of the analysis to detect a change. In rats, there was a reported 13% increase in PCO;
however, the variation between the TCE-treated rats was more than double that of the control
animals in this group and the other limitations described above limit the ability to detect a
response. There was no discussion given by the authors as to why only one dose was tested in
half of the animals exposed to TCE or why the strain with the lowest liver weight change due to
TCE exposure was chosen as the strain to test for peroxisomal proliferative activity.
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The authors provided a description of the histopathology at the light microscopy level in
B6C3Fi mice, Alderley Park mice, Osborne-Mendel rats, and Alderley Park rats, but did not
provide a quantitative analysis or specific information regarding the variability of response
between animals within groups. There appeared to be 20 animals examined in the 1,000 mg/kg
TCE exposed group of B6C3Fi mice but no explanation as to why there were only 10 animals
examined in analyses for liver weight changes, DNA concentration, and tritiated thymidine
incorporation. There was no indication by the authors regarding how many rats were examined
by light microscopy.
Apart from a few inflammatory foci in occasional animals, hematoxylin and eosin (H&E)
section from B6C3Fi control mice were reported to show no abnormalities. The authors suggest
that this is a normal finding in the livers of mice kept under "non-SPF conditions." A stain for
neutral lipid was reported to not be included routinely in these studies, but subsequent electron
microscopic examination of lipid was reported to show increases in the livers of corn-oil treated
control animals. The individual fat droplets were described as "generally extremely fine and are
not therefore detectable in conventionally process H&E stained sections, since both glycogen
and lipid are removed during this procedure." Thus, this study documents effects of using corn
oil gavage in background levels of lipid accumulation in the liver.
The finding of little evidence of gross hepatotoxicity in TCE-treated mice was reported,
even at a dose of 1,500 mg/kg. Specifically,
Of 19 animals examined receiving 1500 mg/kg body weight TCE, only 6 showed
any evidence of hepatocyte necrosis, and this pathology was restricted to single
small foci or isolated single cells, frequently occurring in a subcapsular location.
Examination of 20 animals receiving 1000 mg/kg body wt TCE demonstrated no
hepatocyte necrosis. Of 20 animals examined receiving 500 mg/kg body wt TCE,
1 showed necrosis of single isolated hepatocytes; however, this change was not a
treatment-related finding.
TCE-treated mice were reported to show:
a change in staining characteristic of the hepatocytes immediately adjacent to the
central vein of the hepatocyte lobules, giving rise to a marked 'patchiness' of the
liver sections. Often this change consisted of increased eosinophilia of the central
cells. There was some evidence of cell hypertrophy in the centrilobular regions.
These changes were evident in most of the TCE treated animals, but there was a
dose-related trend, relatively few of the 500 mg/kg animals being affected, while
the majority of the 1,500 mg/kg animals showed central change. No other
significant abnormalities were seen in the liver of TCE treated mice compared to
controls apart from occasional mitotic figures and the appearance of isolated
nuclei with an unusual chromatin pattern. This pattern generally consisted of a
course granular appearance with a prominent rim of chromatin around the
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periphery of the nucleus. These nuclei may have been in the very early stages of
mitosis. Similar changes were not seen in control mice.
The authors briefly commented on the findings in the Alderley Park mice stating that:
H& E sections from Alderley Park mice gave similar results as for B6C3Fi mice.
No evidence of hepatotoxicity was seen at a dose of 500 mg/kg body wt TCE.
However, a few animals at the higher doses showed some necrosis and other
degenerative changes. This change was very mild in nature, being restricted to
isolated necrotic cells or small foci, frequently in subcapsular position.
Hypertrophy and increased eosinophilia were also noticed in the centrilobular
regions at higher doses.
Thus, from the brief description given by the authors, the centrilobular region is
identified as the location of hepatocellular hypertrophy due to TCE exposure in mice, and for it
to be dose-related with little evidence of accompanying hepatotoxicity.
The description of histopathology for rats was even more abbreviated than for the mouse.
H& E sections from Osborne-Mendel rats showed that:
livers from control rats contained large quantities of glycogen and isolated
inflammatory foci, but were otherwise normal. The majority of rats receiving
1,500 mg/kg body weight TCE showed slight changes in centrilobular
hepatocytes. The hepatocytes were more eosinophilic and contained little
glycogen. At lower doses, these effects were less marked and were restricted to
fewer animals. No evidence of treatment-related hepatotoxicity (as exemplified
by single cell or focal necrosis) was seen in any rat receiving TCE. H& E
sections from Alderley Park Rats showed no signs of treatment-related
hepatotoxicity after administration of TCE. However, some signs of dose-related
increase in centrilobular eosinophilia were noted.
Thus, both mice and rats exhibited pericentral hypertrophy and eosinophilia as noted
from the histopathological examination.
The study did report a quantitative analysis of the effects of TCE on the number of
mitotic figures in livers of mice. Few if any control mice exhibited mitotic figures. But, the
authors report:
a considerable increase in both the numbers of figures per section was noted after
administration of TCE." The numbers of animals examined for mitotic figures
ranged from 75 (all control groups were pooled for mice) to 9 in mice, and ranged
from 15 animals in control rat groups to as low as 5 animals in the TCE treatment
groups. The range of mitotic figures found in 25 high-power fields was reported
and is equivalent to the number of mitotic figures per 5,000 hepatocytes examined
in random fields.
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Thus, the predominance of mitotic figures in any zone of the liver cannot be ascertained.
For B6C3Fi mice, the number of animals with mitotic figures was reported to be 0/75,
3/20, 7/20, and 5/20 for control, 500, 1,000, and 1,500 mg/kg TCE exposed mice, respectively.
The range of the number of mitotic figures seen in 5,000 hepatocytes was reported to be 0, 0-1,
0-5, and 0-5 for those same groups with group means of 0, 0.15 ± 0.36, 0.6 ± 1.1, and 0.5 ± 1.2.
These results demonstrate a very small and highly variable response due to TCE treatment in
B6C3Fi mice in regard to mitosis. Thus, the highest percentage of cells undergoing mitosis
within the window of observation would be on average 0.012% with a SD twice that value. The
data presented for mitotic figures also indicated no differences in results between 1,000 and
1,500 mg/kg treated B6C3Fi mice in regard to mitotic figure detection. However, the tritiated
thymidine incorporation data indicated that thymidine incorporation was approximately twofold
greater at 1,500 than 1,000 mg/kg TCE in B6C3Fi mice. For Alderley Park mice, the number of
animals with mitotic figures was reported to be 1/15, 0/9, 4/9, and 2/9 for control, 500, 1,000,
and 1,500 mg/kg TCE exposed mice. The range of the number of mitotic figures seen in
5,000 hepatocytes was 0-1, 0, 0-2, and 0-1 for those same groups with group means of 0.06 ±
0.25, 0.7 ± 0.9, and 0.2 ± 0.4. These results reveal the detection of, at the most, two mitotic
figures in 5,000 hepatocytes for any mouse an any treatment group and no dose-related increased
after TCE treatment in Alderl ey Park mice. Thus, the highest percentage of cells with a mitotic
figure would be on average 0.014% with a SD twice that value. The small number of animals
examined reduces the power of the experiment to draw any conclusions as to a dose-response.
Similar to the B6C3Fi mice, there did not appear to be concordance between mitotic
figure detection and thymidine incorporation for Alderley Park mice. Thymidine incorporation
showed a 2-fold increase over control for 500 and 1,000 mg/kg TCE and a 5.7-fold increase for
1,500 mg/kg TCE treated animals. However, in regard to mitotic figure detection, there were
fewer mitotic figures in 500 mg/kg TCE treated mice than controls, and fewer animals with
mitotic figures and fewer numbers of figures in the 1,500 mg/kg dose than the 1,000 mg/kg
exposed group. The inconsistencies between mitotic index data and thymidine incorporation
data in both strains of mice suggest that either thymidine incorporation is representative of only
DNA synthesis and not mitosis, an indication of changes in ploidy rather than proliferation, or
that this experimental design is incapable of discerning the magnitude of these changes
accurately. Data from both mouse strains show very little, if any, hepatocyte proliferation due to
TCE exposure with the mitotic figure index data having that advantage of being specific for
hepatocytes and to not to also include nonparenchymal cells or inflammatory cells in the liver.
The results for rats were similar to those for mice and even more limited by the varying
and low number of animals examined. For Osborne-Mendel rats, the numbers of animals with
mitotic figures were reported to be 8/15, 2/9, 0/7, and 0/6 for control, 500, 1,000, and
1,500 mg/kg TCE exposed rats groups, respectively, with the respective ranges of the number of
mitotic figures seen in 5,000 hepatocytes to be 0-8, 0-3, 0, and 0. The group means were 1.5 ±
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2.0, 0.4 ± 1.0, 0, and 0 for these groups. It would appear from these results that there are fewer
mitotic figures after TCE treatment with the highest percentage of cells undergoing mitosis to be
on average 0.03% in control rats. However, thymidine incorporation studies show a modest
increase at all treatment levels over controls in Osborne-Mendel rats rather than a decrease from
controls. For Alderley Park rats, the numbers of animals with mitotic figures were reported to be
13/15, 5/9, 9/9, and 4/9 for control, 500, 1,000, and 1,500 mg/kg TCE exposed rat groups with
the ranges of the number of mitotic figures seen in 5,000 hepatocytes to be 0-26, 0-5, 1-7, and
0-9. The group means were 7.2 ± 4.7, 1.6 ± 4.3, 3.8 ± 3.4, and 1.8 ± 2.9 for these groups.
It would appear that there are fewer mitotic figures after TCE treatment with the highest
percentage of cells to an average of 0.14% in control rats. However, thymidine incorporation
studies show twofold greater level at 500 mg/kg TCE than for control animals and a 40 and 5%
increase at 1,000 and 1,500 mg/kg TCE exposure groups, respectively. Similar to the results
reported in mice, results in both rat strains show an inconsistency in mitotic index and thymidine
incorporation. The control rats appear to have a much greater mitotic index than any of the
mouse groups (treated or untreated) or the TCE-treatment groups. However, it is the mice that
were exhibiting the largest increased in liver weight after TCE exposure. By either thymidine
incorporation or mitosis, these data do provide a consistent result that at 10 days of exposure,
very little sustained hepatocellular proliferation is occurring in either mouse or rat and neither is
correlated well with the concurrent changes in liver weight observed from TCE exposure.
This study provided a qualitative discussion and quantitative analysis of structural
changes using electron microscopy. The qualitative discussion was limited and included
statements about increased observances without quantitative data shown other than the
morphometric analysis. The authors reported that:
the ultrastructure of control mouse liver was essentially normal, although mild
dilatation of RER and SER was a frequent finding. Lipid droplets were also
usually present in the cell cytoplasm. The ultrastructural changes seen in mouse
liver following administration of up to 1,500 mg/kg body wt TCE for 10 days
were essentially similar in the B6C3Fi mouse and the Alderley Park mouse. The
most notable change in both strains of mouse was a dramatic increase in the
number of peroxisomes. This change was only apparent in the cells immediately
surrounding the central veins. Peroxisome proliferation was not noticeable in
periportal cells. The induced peroxisomes were generally small and very electron
dense and frequently lacked the characteristic nucleoid core found in peroxisomes
of control livers.
The authors conclude that:
morphometric analysis showed evidence of a dose-related response, peroxisomal
induction appearing to reach a maximum at 1,000 mg/kg in B6C3Fi mice.. .Lipid
was increased in the livers of treated mice at all doses and was present both as
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free droplets in the cytoplasm and as liposomes (small lipid droplets in ER
cisternae). The centrilobular cell, which showed the greatest increase in numbers
of peroxisomes, showed no evidence of this lipid accumulation: fatty change was
more prominent in those cells away from the central vein (i.e., zone 2 of the liver
acinus). Accumulation of lipid, particularly in liposomes, was less marked in
Alderley Park mouse than in B6C3Fi mouse. Mild proliferation of smooth
endoplasmic reticulum was seen in both strains and both rough and smooth
endoplasmic reticulum was generally more dilated than in control mice.
Electron microscopic results for rat liver were reported
to show similar changes in Osborne-Mendel and Alderley Park rat treated with
TCE.. .Rats receiving either 1,000 or 1,500 mg/kg TCE for 10 days generally
showed mild proliferation of SER in centrilobular hepatocytes. The cisternae of
RER were frequently dilated, giving rise to a rather disorganized appearance in
contrast to the parallel stacks seen in control livers, although no detachment of
ribosomes was evident. The SER was also dilated. In contrast to mice,
peroxisomes were only very slightly and not significantly, increased in the liver of
TCE -treated rats. Morphometric analysis confirmed this observation, with the
volume density of peroxisomes in the cytoplasm of centrilobular hepatocytes
being only slightly increased in rats of both strains receiving 1,000 or 1,500
mg/kg body wt TCE.. .Lipid droplets were occasionally increased in some livers
obtained from rats receiving TCE, but the degree of fatty change generally
appeared similar to that found in control rats receiving corn oil. There were no
changes in membrane -bound liposomes, other organelles, or Golgi condensing
vesicles. Centrilobular glycogen was somewhat depleted in male rats receiving
1,500 mg/kg TCE. Periportal cells were ultrastructurally normal in all rats.
For the morphometric analysis, the number of mice examined ranged from seven in the
control group to eight in the 1,500 mg/kg TCE exposed group. The authors did not indicate
which control animals were used for the morphometric analysis from the 75 animals examined
for mitotic index, the 20 examined by light microscopy, or the 30 mice used as concurrent
controls in the liver weight, DNA concentration, and tritiated thymidine incorporation studies.
The authors stated that morphometry was performed on three randomly selected
photomicrographs from each of three randomly selected pericentral hepatocytes for each animal
(i.e., nine photomicrographs per animal). A mean value representing the exposure group was
reported with the variability between photomicrographs per animal or the variation between
animals unclear. The morphometric analysis did not examine all treatment groups (e.g., only the
control and 500 mg/kg TCE group were examined in Alderley Park mice).
The percent cytoplasmic volume of the peroxisomal compartment (mean ± SD) was
reported to be 0.6 ± 0.6% for controls, 4.8 ± 3.3% for 500 mg/kg TCE, 6.7 ± 1.9% for
1,000 mg/kg TCE, and 6.4 ± 2.5% for 1,500 mg/kg TCE in B6C3Fi mice. In Alderley Park
mice, only 12 control and 12,500 mg/kg TCE exposed mice were examined and, similarly, their
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selection criteria was not given. The percent cytoplasmic volume of the peroxisomal
compartment was 1.2 ± 0.4% for control and 4.7 ± 2.8% for 500 mg/kg TCE exposed mice.
For Osborne-Mendel rats, control rats (n = 9) were reported to have a percent
cytoplasmic volume of the peroxisomal compartment of 1.8 ± 0.4%; 1,000 mg/kg TCE (n = 5),
2.3 ± 1.6%, and 1,500 mg/kg exposed rats (n = 7), 2.3 ± 2.0%. For Alderley Park rats, only two
groups were examined (control and 1,000 mg/kg TCE exposure). The percent cytoplasmic
volume of the peroxisomal compartment for control rats (n = 15) was reported to be 1.8 ± 0.8%
and for 1,000 mg/kg TCE (n = 16), 2.4 ± 1.2%. The varying numbers of animals examined, the
varying and inconsistent number of treatment groups examined, the limited number of
photomicrographs per animal, and the potential selection bias for animals examined make
quantitative conclusions regarding this analysis difficult. Although control levels differed by a
factor of 2 between the two strains of mice examined, as well as the number of control animals
examined (7 vs. 12), it appears that the 500-mg/kg TCE-exposed B6C3Fi and Alderley Park
mice had similar percentages of peroxisomal compartment in the pericentral cells examined
(-4.8%). There also appeared to be little difference between 1,000 mg/kg TCE treated Osborne-
Mendel and Alderley Park rats for this parameter (-2.4%). Although few animals were
examined, there was little difference reported between 500, 1,000, and 1,500 mg/kg TCE
exposure groups in regard to percentages of peroxisomal compartment in B6C3Fi mice (4.8-
6.7%). For the few rats of the Osborne-Mendel strain examined, there also did not appear to be a
difference between 1,000 and 1,500 mg/kg TCE exposure for this parameter (2.3%).
Based on peroxisome compartment volume data, one would expect there to be little
difference between TCE exposure groups in mice or rats in regard to enzyme activity or other
"associated events." However, such comparisons are difficult due to limited power to detect
differences and the possibility of bias in selection of animals in differing assays. For the B6C3Fi
mice, only 5 animals per group were examined for enzyme analysis, 7-8 animals for
morphometric analysis, 75 animals in control, and 20 animals in 1,000 mg/kg TCE-exposed
groups for mitotic figure identification, and 10 animals per group for thymidine incorporation.
Since only a few animals were tested for enzyme activity, the comparison between peroxisomal
compartment volume and that parameter is very limited. There was a reported 47% increase in
catalase activity between control (n = 5) and 1,000 mg/kg TCE exposed B6C3Fi mice (n = 5)
and a 7.8-fold increase in PCO activity. The percent peroxisome compartment was reported to
be 10.6-fold greater (0.6 vs. 6.4%). However, the B6C3Fi control percent volume of
peroxisomal compartment was reported to be half that of the Alderley Park mouse control. An
accurate determination of the quantitative differences in peroxisomal proliferation would be
dependent on an accurate and stable control value. For Alderley Park rats, there was an 8%
decrease in catalase activity between control (n = 5) and 1,000 mg/kg TCE exposed rats (n = 5),
and a 13% increase in PCO activity. The percent peroxisome compartment was reported to be
33% greater in the TCE-exposed than control group. Thus, for the very limited data that were
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available to compare peroxisomal compartment volume with enzyme activity, there was
consistency in result.
However, were such increases in peroxisomes associated with other events reported in
this study? Mouse peroxisome proliferation associated enzyme activities in B6C3Fi mice at
1,000 mg/kg TCE were reported to be 8-fold over control values in mice after 10 days of
treatment. However, this increase in activity was not accompanied by a similar increase in
thymidine incorporation (2.8-fold of control) or concordant with increases in mitotic figures
(7/20 mice having any mitotic figures at all with a range of 0-5 and a mean of 0.014% of cells
undergoing mitosis for 1,000 mg/kg TCE vs. 0 for control).
Although results reported in the rat showed discordance between thymidine incorporation
and detection of mitotic figures, there was also discordance with these indices and those for
peroxisomal proliferation. In comparison to controls, there was a reported 13% increase in PCO
activity in Alderley Park rats exposed to 1,000 mg/kg TCE, a group mean of mitotic figures half
that in the TCE treated animals vs. controls, and increase in thymidine incorporation of 40%.
Thus, these results are not consistent with TCE induction of peroxisome enzyme activity to be
correlated with hepatocellular proliferation by either mitotic index or thymidine incorporation.
Thymidine incorporation in liver DNA seen with TCE exposure also did not correlate with
mitotic index activity in hepatocytes and suggests that this parameter may be a reflection of
polyploidization rather than hepatocyte proliferation. More importantly, these data show that
hepatocyte proliferation, indicated by either measure, is confined to a very small population of
cells in the liver after 10 days of TCE exposure. Hepatocellular hypertrophy in the centrilobular
region appears to be responsible for the liver weight gains seen in both rats and mice rather than
increases in cell number. These results at 10 days do not preclude the possibility that a greater
level of hepatocyte proliferation did not occur earlier and then had subsided by 10 days, as is
characteristic of many mitogens. Thymidine incorporation represents the status of the liver at
one time point rather than over a period of whole week, and thus, would not capture the earlier
bouts of proliferation. However, there is no evidence of a sustained proliferative response, as
measured at the 10-day time period, in hepatocytes in response to TCE indicated from these data.
In regards to weight gain, although the volume of the peroxisomal compartment was
reported to be similar at 500 mg/kg TCE in B6C3Fi and Alderley Park mice (4.3%), the liver
weight/body weight gain in comparison to control was 20% higher in B6C3Fi mice vs. 43%
higher in Alderley Park mice after 10 days of exposure. The liver/body weight ratio was 5.53%
in the B6C3Fi mice and 7.31% in the Alderley Park mice at 500 mg/kg TCE for 10 days.
Similarly, although the peroxisomal compartment was similar at 1,000 mg/kg TCE in
Osborne-Mendel (2.3%) and Alderley Park rats (2.4%), the liver weight/body weight gain was
26% in Osborne-Mendel rats but 17% in Alderley Park rats at this level of TCE exposure. The
liver/body weight ratio was 5.35% in the Osborne-Mendel rats and 5.83% in the Alderley Park
mice at 1,000 mg/kg TCE for 10 days. Although there are several limitations regarding the
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quantitative interpretation of the data, as discussed above, the data suggest that liver weight and
weight gain after TCE treatment was not just a function of peroxisome proliferation. This study
does clearly demonstrate TCE-induced changes at the lowest level tested in several parameters
without toxicity and without evidence of regenerative hyperplasia or sustained hepatocellular
proliferation. In regards to susceptibility to liver cancer induction in more susceptible (B6C3Fi)
vs. less susceptible (Alderley Park/Swiss) strains of mice (Maltoni etal., 1988), there was a
greater baseline level of liver weight/body weight ratio change, a greater baseline level of
thymidine incorporation as well as greater responses for those endpoints due to TCE exposure in
the "less susceptible" strain. However, both strains showed a hepatocarcinogenic response to
TCE induction and the limitations of being able to make quantitative conclusions regarding
species and strain susceptibility TCE toxicity from this study have been described in detail
above.
E.2.1.9. Dees and Travis (1993)
The focus of this study was to evaluate the nature of DNA synthesis induced by TCE
exposure in mice. The mitotic rate of liver cells was extrapolated using tritiated thymidine
uptake into DNA of male and female mice treated with HPLC grade (99 + pure) TCE. Male and
female hybrid B6C3Fi mice 8 weeks of age (male mice weighed 24-27 g [-12% difference] and
females weighing 18-21 g [-4% difference]) were dosed orally by gavage for 10 days with 100,
250, 500, and 1,000 mg/kg body weight TCE in corn oil (n = 4 per treatment group). Sixteen
hours after the last daily dose of TCE, mice received tritiated thymidine and were sacrificed
6 hours later. Hepatic DNA was extracted from whole liver and standard histopathology was
also performed. Hepatic DNA content and cellular distributions were also determined for
thymidine uptake using autoradiography of tissue sections. Tritiated thymidine incorporation
into DNA was determined by microscopic observations of autoradiography slides and reported as
positive cells per 100 (200x power) fields.
Changes in the treatment groups were reported to:
include an increase in eosinophilic cytoplasmic staining of hepatocytes located
near central veins, accompanied by loss of cytoplasmic vacuolization.
Intermediate zones appeared normal and no changes were noted in portal triad
areas. Male and female mice given 1,000 mg/kg body weight TCE exhibited
apoptosis located near central veins. No evidence of cellular proliferation was
seen in the portal areas. No evidence of increased lipofuscin was seen in liver
sections from male and female mice treated with TCE. Evaluation of cell death in
male and female mice receiving TCE was performed by enumerating apoptosis.
The apoptosis "did not appear to be in proportion to the applied TCE dose given to male
or female mice." The mean number of apoptosis per 100 (400x) fields in each group of
4 animals (male mice) was 0, 0, 0, 1, and 8 for control, 100, 250, 500, and 1,000 mg/kg TCE
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treated groups, respectively. Variations in number of apoptosis between mice were not given by
the authors. Feulgen stain was <1 for all doses except for 9 at 1,000 mg/kg.
Mitotic figure were reported to be:
frequently seen in liver sections from both male and female mice treated with
TCE. Dividing cells were most often found in the intermediate zone and
resembled mature hepatocytes. Incorporation of the radiolabel into cells located
near the portal triad areas was rare. In general, mitotic figures were very rare, but
when found they were usually located in the intermediate zone. Little or no
incorporation of label was seen in areas near the bile duct epithelia or in areas
close to the portal triad.
No quantitative description of mitotic index was reported by the authors, but this
description is consistent with there being replication of mature hepatocytes induced by TCE.
The distribution of tritiated thymidine was given for specific cell types in the livers of
five animals per treatment group and radiolabel was reported to be predominantly associated
with perisinusoidal cell in control mice. The authors state that the label was more often found in
cells resembling mature hepatocytes. The mean number of labeled cells in autoradiographs per
100 (200x power) fields was reported to be -125 and -150 labeled perisinusoidal cells in
controls male and female mice, respectively. The authors do not give any SDs for the female
perisinusoidal data except for the 1,000-mg/kg exposure group. For mature hepatocytes, the
mean baseline level of cell labeling for control male and female mice were reported to be
-65 and -90 labeled cells, respectively. Although the baseline levels of hepatocyte labeling
were reported to differ between male and female mice, the mean peak level of labeling was
similar at -250 labeled cells for male and female mice treated with TCE. In male mouse liver,
the number of labeled cells increased approximately twofold of control levels after 500 and
1,000 mg/kg TCE and in female mouse liver increased approximately fourfold of control levels
after 250, 500, and 1,000 mg/kg TCE over their respective control levels.
Incorporation of tritiated thymidine into DNA extracted from whole liver in male and
female mice was reported to be significantly elevated after TCE treatment but, unlike the
autoradiographic data, there was no difference between genders and the mean peak level of
tritiated thymidine incorporation occurred at 250 mg/kg TCE treatment and remained constant
for the 500 and 1,000 mg/kg treated groups. Increased thymidine incorporation into DNA
extracted from liver of male and female mice were reported to show a very large SD with TCE
treatment (e.g., at 100 mg/kg TCE exposure, male mice had a mean of-130 dpm tritiated
thymidine/ug DNA with the upper bound of the SD to be 225 dpm). The increased thymidine
incorporation peaked at a level that was a little <2-fold of control level. Thus, for both male and
female mice both autoradiographs and total hepatic DNA were reported to show that male and
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female mice had similar peaks of increased thymidine incorporation after TCE exposure that
reached a plateau at the 250 mg/kg TCE exposure level and did not increase with increasing
exposure concentration. These data also indicate a very small population undergoing mitosis due
to TCE exposure after 10 days of exposure. If higher levels of hepatocyte replication had
occurred earlier, such levels were not sustained by 10 days of TCE exposure. More importantly,
these data suggest that tritiated thymidine levels were targeted to mature hepatocytes and in areas
of the liver where greater levels of polyploidization occur. The ages and weights of the mice
were described by these authors, unlike Elcombe et al. (1985), and a different strain was used.
However, these results are consistent with those of Elcombe in regard to the magnitude of
thymidine incorporation induced by TCE treatment and the lack of a dose response once a
relative low level of exposure has been exceeded.
The total liver DNA content of male and female mice treated with TCE were also
determined with the total micrograms DNA/g liver reported to be ~4 ug/g for female control
mice and ~2 ug/g for male control mice. Although not statistically significant, the total DNA
concentration dropped from ~4 to ~3 at 100 mg/kg through 1,000 mg/kg exposure to TCE in
female mice. For male mice, the total DNA rose slightly in the 250- and 500-mg/kg groups to
~3 ug/g and was similar to control levels at the 100 and 1,000 mg/kg TCE treatment groups. The
SD in male mice was very large and the number of animals small making quantitative judgments
regarding this parameter difficult. The slight decrease reported for female mice would be
consistent with the results of Elcombe et al. (1985) who describe a slight decrease in hepatic
DNA in male mice. However, the reported slight increase in hepatic DNA in male mice in this
study is not consistent. Given the small number of animals and the large deviations for female
and male mice in the TCE treated groups, this study may not have had the sensitivity to detect
slight decreases reported by Elcombe et al. (1985).
In regard to clinical evaluation and weight analyses, both male and female mice given
TCE were reported "to appear clinically ill. These mice showed reduced activity and failed to
groom. Control mice showed no adverse effects. Female mice were markedly more affected by
TCE than their male counterparts. Several deaths of female mice occurred during the course of
the TCE treatment regimen." The authors do not give cause of deaths but state that two female
mice died in the group receiving 250 mg/kg TCE and one in the group receiving 1,000 mg/kg
during the gavage regimen of the female mice. This appears to be similar gavage error or
"accidental death" reported in NTP studies chronic studies of TCE (see below).
The authors report:
no significant difference in the absolute body weight of male and female mice
were noted in control groups. Body weight gain in female and males mice treated
with TCE was not significantly different from that of control mice. Liver weights
in male mice given 500 or 1,000 mg/kg and corrected for total body weight were
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significantly elevated. The corrected liver weights of female mice increase
proportionally with the applied dose of TCE.
For male mice, liver weights were reported to be 1.40 ±0.16, 1.38± 1.23, 1.48 ±0.09,
1.61 ± 0.07, and 1.63 ± 0.11 g for control, 100, 250, 500, and 1,000 mg/kg TCE in male mice
(n = 5), respectively. Body weights were smaller for the 100 mg/kg TCE treatment group
although not statistically significant. The liver weights after treatment had a much larger
reported SD (1.23 g for 100 mg/kg group vs. <0.16 for all other groups). The percent liver/body
weight ratios were reported to be 5.40, 5.41, 5.42, 5.71, and 6.34% for the same groups in male
mice. This represents 1.06- and 1.17-fold of control at the 500 and 1,000 mg/kg dose. The
authors report a statistically significant increase in percent liver/body weight ratio only for the
500 mg/kg (i.e., 1.06-fold of control) and 1,000 mg/kg (i.e., 1.17-fold of control) TCE exposure
groups.
The results for female mice liver weights were reported in Table III of the paper, which
was mistakenly labeled as for male mice. The reported values for liver weight were 1.03 ± 0.07,
1.05 ±0.10, 1.15 ±0.98, 1.21 ±0.18, and 1.34 ± 0.08 g for control, 100,250, 500, and
1,000 mg/kg TCE in female mice (n = 5, except for 250 and 1,000 mg/kg groups), respectively.
The percent liver/body weight ratios were 5.26, 5.44, 5.68, 6.24, and 6.57% for the same groups.
These values represent 1.03-, 1.08-, 1.19-, and 1.25-fold of controls in percent liver/body weight.
The magnitude of increase in TCE-induced percent liver/body weight ratio in female mice is
reflective of the magnitude of the difference in dose up to 1,000 mg/kg where it is slightly lower.
The female mice were reported to have statistically significant increases in percent liver/body
ratios at the lowest dose tested (100 mg/kg TCE) after 10 days of TCE exposure that also
increased proportionately with dose. Male mice were not reported to have a significant increase
in percent liver/body weight until 500 mg/kg TCE but a statistically significant increase in liver
weight at 250 mg/kg TCE. Male mice had a much larger variation in initial body weight than did
female mice (range of means of 24.86-27.84 g between groups for males or ~11% difference and
range of means of 19.48-20.27 g for females or -4%), which may contribute to an apparent lack
of effect for a parameter that is dependent on body weight. Only five mice were used in each
group so the power to detect a change was relatively small.
The results from this experiment are consistent with those of Elcombe et al. (1985) in
showing a slight increase in thymidine incorporation (approximately twofold of control) and
mitotic figures that are rare after TCE exposure. This study also records a lack of apoptosis with
TCE treatment except at the highest exposure level (i.e., 1,000 mg/kg). The increases in liver
weight induced by TCE were reported to be dose-related, especially in female mice where
baseline body weights were more consistent. However, the incorporation of tritiated thymidine
reached a plateau at 250 mg/kg TCE in the DNA of both genders of mice. This study
specifically identified where thymidine incorporation and mitotic figures were occurring in
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TCE-treated livers and noted that the mature hepatocyte that appeared to be primarily affected,
as well as in the portion of the liver where mature hepatocytes with higher ploidy are found. The
authors note that the "lack of thymidine incorporation in the periportal area, where the liver stem
cells are reside," suggesting that the mature hepatocyte is the target of TCE effects on DNA
synthesis. This finding is consistent with a change in ploidy accompanying hepatocellular
hypertrophy and not just cell proliferation after 10 days of TCE exposure. Like Elcombe et al.
(1985), these data represent "a snapshot in time," which does not show whether increased cell
proliferation may have happened at an earlier time point and then subsided by 10 days.
However, like Elcombe et al. (1985), it suggests that sustained proliferation is not a feature of
TCE exposure and that the level of DNA synthesis (which is very low in quiescent control liver)
is increased in a small population of hepatocytes due to TCE exposure that is not dose-dependent
(only twofold increase over control in animals exposed from 250 to 1,000 mg/kg TCE). In
regards to toxicity, no evidence of increased lipid peroxidation in TCE-treated animals was
reported using histopathologic sections stained to enhance observation of lipofuscin. No
necrosis is noted by these authors and the deaths in female mice are likely due to gavage error.
E.2.1.10. Nakajima et al. (2000)
This study focused on the effect of TCE treatment on PPARa-null mice in terms of
peroxisome proliferation but also included information on differences in liver weight between
null and wild-type mice, as well as gender-related effects. SV129 wild-type and PPARa-null
mice (10 weeks of age) were treated with corn oil or 750 mg/kg TCE in corn oil daily for
2 weeks via gavage (n = 6 per group). A small portion of the liver was removed for
histopathological examination but the lobe used was not specified by the authors. Liver
peroxisome proliferation was reported to be evaluated morphologically using 3,3'-diamino-
benzidine (DAB) staining of sections and electron photomicroscopy to detect the volume density
of peroxisomes (percent of cytoplasm) in 15 micrographs of the pericentral area per liver. A
number of p-oxidation enzymes and P450s were analyzed by immunoblot of liver homogenates.
The final body weights, liver weights, and percent liver/body weight ratios were reported
for all treatment groups. For male mice, vehicle treated PPARa-null mice had slightly lower
mean body weights (24.5 ± 1.8 vs. 25.4 ± 1.9 g [SD]), slightly larger liver weights (1.14 ± 0.13
vs. 1.05 ± 0.15 g or -9%), and slightly higher percent liver/body weight ratios (4.12 ± 0.32 vs.
4.10 ± 0.37%) than wild-type mice. The mean values for final body weights of the groups of
mice in this study were reported and were similar which, as demonstrated by the inhalation
studies by Kjellstrand et al. (1983b) (see Section E.2.2.5), is particularly important for
determining the effects of TCE treatment on percent liver/body weight ratios. For both groups of
male mice, 2 weeks of TCE treatment significantly increased both liver weight and percent
liver/body weight ratios. For male wild-type mice, the increase in percent liver/body weight was
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1.50-fold of vehicle control and for male PPARa-null mice the increase was 1.26-fold of control
after 2 weeks of TCE treatment.
For female mice, vehicle-treated PPARa-null mice had slightly higher mean body
weights (22.7 ± 2.1 vs. 22.4 ± 2.0 g), slightly larger liver weights (0.98 ±0.15 vs. 0.95 ± 0.14 g
or -3%), and slightly higher percent liver/body weight ratios (4.32 ± 0.35 vs. 4.24 ± 0.41%) than
wild-type mice. For both groups of female mice, 2 weeks of TCE treatment significantly
increased percent liver/body weight ratios. For liver weights, there was a reporting error for
PPARa-null female treated with TCE so that liver weight changes due to TCE treatment cannot
be determined for this group. For female wild-type mice, the increase in percent liver/body
weight was 1.24-fold of vehicle control and for female PPARa-null mice, the increase was
1.26-fold of control after 2 weeks of TCE treatment.
Thus, for both wild-type and PPARa-null mice, TCE exposure resulted in increased
percent liver/body weight over controls that was statistically significant after 2 weeks of gavage
exposure using corn oil as the vehicle. For male mice, there was a greater TCE-induced increase
in percent liver/body weight in wild-type than PPARa-null mice (1.50- vs. 1.26-fold of control)
that was statistically significant, but for female mice, the induction of increased liver weight was
statistically increased but the same in wild-type and PPARa-null mice (i.e., both were ~1.25-fold
of control). These date indicate that TCE-induced increases in mouse liver weight were not
dependent on a functional PPARa receptor in female mice and suggest that some portion may be
in male mice.
In regard to light and electron microscopic results, the numbers of peroxisomes in
hepatocytes of wild-type mice were reported to be increased, especially in the pericentral area of
the hepatic lobule, to a similar extent in both males and females (15 micrographs, n = 4 mice).
TCE exposure was reported to increase the volume density of peroxisomes twofold of control in
the pericentral area with no evident change in peroxisomes in the periportal areas, but data were
not shown for that area of the liver lobule. In contrast, no increase in peroxisomes was reported
to be observed in PPARa-null mice. Therefore, increases in liver weight observed in
PPARa-null mice after TCE treatment did not result from peroxisome proliferation. Similarly,
the small twofold increase in peroxisome volume from 2 to 4% of cytoplasmic volume in the
pericentral area of the liver lobule in wild-type mice could not have been responsible for the 50%
increase liver weight observed in male wild-type mice.
Although no difference was reported between male and female wild-type mice in regard
to TCE-induced peroxisome proliferation in wild-type mice, the levels of hepatic enzymes
associated with peroxisomes (acyl-CoA [AOX], peroxisomal bifunctional protein [PH],
peroxisomal thiolase [PT], very long chain acyl-CoA synthetase, and D-type peroxisomal
bifunctional protein [DBF], cytosolic enzyme [cytosolic thioesterase II (CTEII)], mitochondrial
enzymes [mitochondrial trifunctional protein a subunits a and P(TPa and TPP)], and microsomal
enzymes [CYP 4A1 (CYP4A1)]) as measured by immunoblot analysis were significantly
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elevated in male wild-type mice (n = 4) by a factor of-2-3, but except for a slight elevation in
PH and PT, were reported to not be elevated in female wild-type mice (n = 4). The magnitude of
increase in peroxisomal enzymes was similar to that of peroxisomal volume in male mice. No
TCE-induced increases in any of these enzymes were reported in male or female PPARa-null
mice by the authors. For CYP4A1, an enzyme reported to be induced by peroxisomal
proliferators, TCE exposure resulted in a much lower amount in female than male wild-type
mice (i.e., 2% of the level induced by TCE in males). However, the expression of catalase was
reported to be "nearly constant in all samples" (at most -30% change), which the authors
suggested resulted from induction by TCE that was independent of PPARa. The basis for
selection of four mice for this comparison out of the six studied per group was not given by the
authors. A comparison of control wild-type and PPARa-null mice showed that in males
background levels of the enzymes examined were generally similar except for DBF in which the
null mice had values -50% of the wild-type controls. A similar decrease was reported for female
PPARa-null mice. With regard to gender differences in wild-type mice, females had similar
values as males with the exceptions of TPa, TPp, and CYP2E1, which were in untreated female
wild-type mice at a 3.06-, 2.38-, and 1.63-fold for 1 TPa, TPp, and CYP2E1 levels over males,
respectively. Female PPARa-null mice had increases of 2.50-, 1.54-, and 2.07-fold over male
wild-type mice.
With regard to the induction of TCE metabolizing enzymes (CYP1A2, CYP2E1, and
ALDH), CYP1A2 was reported to be decreased by TCE treatment of both male and female wild-
type mice but liver CYP2E1 reported to be increased in male mice and constant in female mice
which resulted in similar expression level in both genders after TCE treatment. There was no
gender difference in ALDH activity reported after TCE exposure and activity was reported to be
independent of PPARa. The authors concluded that TCE metabolizing abilities of the liver of
male and female mice were similar, and therefore, poor induction of peroxisomal related
enzymes was not due to gender-related differences in TCE metabolism.
To investigate whether the a gender-related difference peroxisomal enzymes after TCE
exposure was due to a lower levels of PPARa and RXRa receptors, western blotting was
employed (n = 3). The level of PPARa protein was reported to be increased in both male wild-
type mice with less induction in females (control vs. TCE, 1.00 ± 0.20 vs. 2.17 ± 0.24 in males
and 0.95 ± 0.25 vs. 1.44 ± 0.09 in females) after TCE treatment. The hepatic level of RXRa was
also reported to be increased in the same manner as PPARa (control vs. TCE, 1.00 ± 0.33 vs.
1.92 ± 0.04 in males 0.81 ± 0.16 vs. 1.14 ± 0.10 in females). Northern blot analysis of hepatic
PPARa mRNA was reported to show greater TCE induction in male (2.6-fold of control) than in
female (1.5-fold of control) wild-type mice. Thus, males appeared to have higher induction of
the two receptor proteins as well as a greater response in peroxisomal enzymes and CYP4A1,
even though TCE-induced increases in peroxisomal volume was similar between male and
female mice. The increased response in males for induction of the two receptor proteins is
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consistent with liver weight data that shows some portion of the induction of increased liver
weight response in male mice using this paradigm may be due to gender-specific differences in
PPARa response. However, as noted below (see Section E.2.2), corn oil vehicle has liver effects
alone, especially in the male liver, that have also been associated with PPARa responses.
E.2.1.11. Berman et al. (1995)
This study included TCE in a suite of compounds used to compare endpoints for
toxicological screening methods. Female F344 rats of 77 days of age (n = 8 per group) were
administered TCE in corn oil for 1 day (0, 150, 500, 1,500, or 5,000 mg/kg-day) or for 14 days
(0, 50, 150, 500, or 1,500 mg/kg-day). Blood samples were taken 24 hours after the last dose
and livers were weighed and H&E sections were examined for evidence of parenchymal cell
degeneration, necrosis, or hypertrophy. No details were provided by the authors for the extent or
severity of the liver affects by histopathological examination. The serum chemistry analysis
included LDH, ALP, ALT, AST, total bilirubin, creatine, and BUN. The starting and ending
body weights of the animals or the absolute liver weights were not reported by the authors.
The results of a multivariate analysis were reported to show a lowest effective dose of
1,500 mg/kg after 1 day of TCE exposure and 150 mg/kg after 14 days of TCE exposure that was
statistically significant. Liver weight and liver weight changes were not reported by the authors
but the percent liver to body weight ratios were. For the two control groups, there was a
difference in percent liver/body weight of-8% (3.43 ± 0.74% for the 1-day control group and
3.16 ± 0.41% for the 14-day control group, mean ± SEM). For the 1-day groups, only the
5,000 mg/kg group was reported to show a statistically significant difference in percent
liver/body weight between control and TCE treatment (i.e., ~1.08-fold increase). Hepatocellular
necrosis was noted to occur in the 1,500 and 5,000 mg/kg groups in 6/7 and 6/8 female rats,
respectively, but not to occur in lower doses. The extent of necrosis was not noted by the authors
for the two groups exhibiting a response after 1 day of exposure. Serum enzymes indicative of
liver necrosis were not presented and because only positive results were presented in the paper,
were presumed to be negative. Therefore, the extent of necrosis was not of a magnitude to affect
serum enzyme markers of cellular leakage.
After 14 days of TCE exposure, there was a dose-related increase reported for percent
liver/body weight ratios that was statistically significant at all TCE dose levels although the
multivariate analysis indicated the lowest effective dose to be 150 mg/kg. The percent
liver/body weight ratio was 3.16 ± 0.41, 3.38 ± 0.56, 3.49 ± 0.69, 3.82 ± 0.76, and 4.47 ± 0.66%
for control, 50, 150, 500, and 1,500 mg/kg TCE exposure levels, respectively, after 14 days of
exposure. No hepatocellular necrosis was reported at any dose and hepatocellular hypertrophy
was reported only at the 1,500 mg/kg dose and in all rats. These rat liver weights were 1.07-,
1.10-, 1.21-, and 1.41-fold of controls for the 50, 150, 500, and 1,500 mg/kg TCE dose groups,
respectively. The 7% increase in liver weight at the 50 mg/kg dose was approximately the same
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difference between the two control groups for days 1 and 14 treatments. Without the data for
starting and final body weights and an examination of whether the control animals had similar
body weight, it is impossible to discern whether the reported effects at the low dose of TCE were
also reflected differences between the control groups. No serum enzyme levels changes were
reported after 14 days of exposure to TCE for any group.
The authors note that their study provided evidence of liver effects at lower levels than
other studies citing Elcombe et al. (1985) and Goldsworthy and Popp (1987). They suggest that
the differences in sensitivity to TCE between their results and those of these two studies may
reflect differences in strain or gender of the rats examined. However, they did not study male
rats of this strain concurrently so that differences in gender may have reflected differences
between experiments. The increase in liver weight without reporting increases in hepatocellular
hypertrophy as well as the lack of necrosis as low doses is consistent with the results of Melnick
et al. (1987) in male Fischer rats given TCE orally (see Section E.2.1.12).
E.2.1.12. Melnick et al. (1987)
The focus of this study was to assess microencapsulation as a way to expose rodents to
substances such as TCE that have issues related to volatilization in drinking water or apparent
gavage-related deaths. In this study, liver weight changes, extent of focalized necrosis, and
indicators of peroxisome proliferation were reported as metrics of TCE toxicity. TCE (99+ %)
was encapsulated in gelatin-sorbitol microcapsules and was 44.1% TCE w/w. The TCE
microcapsules were administered to male F344 rats (6-week-old and weighing between 89 and
92 g or -3% difference) in the diet (0, 0.55, 1.10, 2.21, and 4.42% TCE in the diet) for 14 days.
The number of animals in each group was 10. A parallel group of animals was administered
TCE in corn oil gavage for 14 consecutive days (corn oil control, 0.6, 1.2, and 2.8 g/kg-day
TCE). The dosage levels of TCE in the gavage study were reported to be "adjusted 5 times
during the 14-day" treatment period to be similar to the dosage levels of TCE in the feed study.
The TWA dosage levels of TCE in the feed study were reported to be 0.6, 1.3, 2.2, and 4.8 g/kg-
day.
There was less food consumption reported in the 2.2 and 4.8 g/kg-day dose feed groups,
which the authors attribute to either palatability or toxicity. There were no deaths in any of the
groups treated with microencapsulated TCE while, similar to many other gavage studies of TCE
reported in the literature, there were four deaths in the high-dose gavage group. Mean body
weight gains of the two highest dose groups of the feed study and of the highest dose group of
the gavage study were reported to be significantly lower than the mean body weight gains of the
respective control groups (i.e., -22 and -35% reduction at 2.2 and 4.8 g/kg-day in the feed study,
respectively, and -33% reduction at 2.8 g/kg-day TCE in the gavage study).
After 14 days of treatment, liver weights were reported to be 8.1 ± 0.8, 8.4 ± 0.8, 9.5 ±
0.5, 10.1 ± 1.2, 8.9 ± 1.3, and 7.4 ± 0.5 g for untreated control, placebo control, 0.6, 1.3, 2.2, and
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4.8 g/kg TCE exposed feed groups, respectively. The corresponding percent liver/body weight
ratios were reported to be 5.2 ±0.3, 5.3 ±0.2, 6.0 ±0.3, 6.5 ± 0.5, 7.0 ±0.9, and 7.1 ±0.5% for
untreated control, placebo control, 0.6, 1.3, 2.2, and 4.8 g/kg TCE exposed groups, respectively.
The increased percent liver/body weight ratio represents 1.13-, 1.23-, 1.32-, and 1.34-fold of
placebo controls, respectively.
For the gavage experiment, after 14 days of treatment, liver weights were reported to be
7.1 ± 1.3, 9.3 ± 1.2, 9.1 ± 0.9, and 7.7 ± 0.4 g for corn oil control, 0.6, 1.2, and 2.8 g/kg TCE
exposed groups, respectively. The corresponding percent liver/body weight ratios were reported
to be 5.0 ± 0.4, 6.0 ± 0.4, 6.1 ± 0.3, and 7.3 ± 0.5% for corn oil control, 0.6, 1.2, and 2.8 g/kg
TCE exposed groups, respectively. The percent liver/body weight ratios represent 1.20-, 1.22-,
and 1.46-fold of corn oil controls, respectively. The 2.8 g/kg TCE gavage results are reflective
of the 6 surviving animals in the group rather than 10 animals in the rest of the groups. There
was no explanation given by the authors for the lower liver weights in the control gavage group
than the placebo control in the feed group (i.e., 20% difference), although the initial and final
body weights appeared to be similar. The decreased body weights in the feed and gavage study
are reflective if TCE systemic toxicity and appeared to affect the TCE-induced liver weight
increases in those groups.
The authors reported that the only treatment-related lesion observed microscopically in
rats from either dosed-feed or gavage groups was individual cell necrosis of the liver with the
frequency and severity of this lesion similar at each dosage levels of TCE administered
microencapsulated in the feed or in corn oil. Using a scale of minimal = 1-3 necrotic
hepatocytes/10 microscopic 200x fields, mild = 4-7 necrotic hepatocytes/10 microscopic 200x
fields, and moderate = 8-12 necrotic hepatocytes/10 microscopic 200x fields, the frequency of
lesion was 0-1/10 for controls, 2/10 for 0.6 and 1.3 g/kg, and 9/10 for 2.2 and 4.8 g/kg feed
groups. The mean severity was reported to be 0.0-0.1 for controls, 0.3-0.4 for 0.6 and 1.3 g/kg,
and 2.0-2.5 for 2.2 and 4.8 g/kg feed groups. For the corn oil gavage study, the corn oil control
and 0.6 g/kg groups were reported to have a frequency of 0 lesions/10 animals; the 1.2 g/kg
group had a frequency of 1/10 animals, while the 2.8 g/kg group had a frequency of 5/6 animals.
The mean severity score was reported to be 0 for the control and 0.6 g/kg groups, 0.1 for the
1.2 g/kg groups, and 1.8 for the remaining six animals in the 2.8 g/kg group. The individual cell
necrosis was reported to be randomly distributed throughout the liver lobule with the change to
not be accompanied by an inflammatory response. The authors also report that there was no
histologic evidence of cellular hypertrophy or edema in hepatic parenchymal cells. Thus,
although there appeared to be TCE-treatment-related increases in focal necrosis after 14 days of
exposure, the extent was, even at the highest doses, mild and involved few hepatocytes.
Microsomal NADPH cytochrome c-reductase was reported to be elevated in the 2.2 and
4.8 g/kg feed groups and in the 1.2 and 2.8 g/kg gavage groups. CYP levels were reported to be
elevated only in the two highest dose groups of the feed study. The authors reported a dose-
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related increase in peroxisome PCO and catalase activities in liver homogenates from rats treated
with TCE microcapsules or by gavage, and that treatment with corn oil alone, but not placebo
capsules, caused a slight increase in PCO activity.
After 14 days of treatment, PCO activities were reported to be 270 ± 12, 242 ± 17, 298 ±
64, 424 ± 55, 651 ± 148, and 999 ± 266 nmol hydrogen peroxide (H2O2) produced/minute/g liver
for untreated control, placebo control, 0.6, 1.3, 2.2, and 4.8 g/kg TCE exposed feed groups,
respectively. This represents 1.23-, 1.75-, 2.69-, and 4.13-fold of placebo controls, respectively.
After 14 days of treatment, catalase activities were reported to be 8.49 ± 0.81, 7.98 ± 1.62, 8.49 ±
1.92, 8.59± 1.31, 13.03 ±2.01, and 15.76± 1.11 nmol H2O2 produced/minute/g liver for
untreated control, placebo control, 0.6, 1.3, 2.2, and 4.8 g/kg TCE exposed groups, respectively.
This represents 1.06-, 1.07-, 1.63-, and 1.97-fold of placebo controls, respectively. Thus,
although reported to be dose related, only the two highest exposure levels of TCE increased
catalase activity and to a smaller extent than PCO activity in microencapsulated TCE fed rats.
For the gavage experiment, after 14 days of treatment, PCO activities were reported to be
318 ± 27, 369 ± 26, 413 ± 40, and 1,002 ± 271 nmol H2O2 produced/minute/g liver for corn oil
control, 0.6, 1.2, and 2.8 g/kg TCE exposed groups, respectively. This represents 1.16-, 1.29-,
and 3.15-fold of corn oil controls. After 14 days of treatment, catalase activities were reported to
be 8.59 ± 0.91, 10.10 ± 1.82, 12.83 ± 3.43, and 13.54 ± 2.32 nmol H2O2 produced/minute/g liver
for corn oil control, 0.6, 1.2, and 2.8 g/kg TCE exposed groups, respectively. This represents
1.18-, 1.49-, and 1.58-fold of corn oil controls. As stated by the authors, the corn oil vehicle
appeared to elevate catalase activities and PCO activities.
In regard to dose-response, liver and body weight were affected by decreased body
weight gain in the higher dosed animals in this experiment (i.e., 2.2 g/kg-day TCE exposure and
above) and by gavage related deaths in the highest-dosed group. The lower liver weight in the
gavage control group also may have affected the determination of the magnitude of TCE-related
liver weight gain at that dose. At the two doses, below which body weight gain was affected,
there appeared to be an approximately 20% increase in percent liver/body weight ratio in the
gavage study and a 13 and 23% weight increase in the feed study.
The extent of PCO activity appeared to increase more steeply with dose in the feed study
than did liver weight gain (i.e., a 1.23-fold of liver/body weight ratio at 1.3 g/kg-day
corresponded with a 1.75-fold PCO activity over control). At the two highest doses in the feed
study, the increase in PCO activity was 2.69- and 4.13-fold of control, but the increase in liver
weight was not more than 34%. For the gavage study, there was also a steeper increase in PCO
activity than liver weight gain. For catalase activity, the increase was slightly less than that of
liver/body weight ratio percent for the two doses that did not decrease body weight gain in the
feed study. In the gavage study, they were about the same. In regard to what the cause of liver
weight gain was, the authors report that there was no histologic evidence of cellular hypertrophy
or edema in hepatic parenchymal cells and do not describe indicators of hepatocellular
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proliferation or increased polyploidy. Accordingly, the cause of liver weight gain after TCE
exposure in this paradigm is not readily apparent.
E.2.1.13. Laughter et al. (2004)
Although the focus of the study was an exploration of potential modes of action for TCE
effects through macroarray transcript profiling (see Section E.3.1.2 for discussions of limitations
of this approach and especially the need for phenotypic anchoring, Section E.3.4.1.3 for use of
PPARa knockout mice, and Section E.3.4.2.2 for discussion of genetic profiling data for TCE),
information was reported regarding changes in the liver weight of PPARa-null mouse and their
background strains. SV129 wild-type and PPARa-null male mice (9 ± 1.5 weeks of age) were
treated with three daily doses of TCE in 0.1% methyl cellulose for either 3 days or 3 weeks
(n = 4-5/group). Thus, this paradigm does not use corn oil, which has been noted to affect
toxicity (see Section E.2.2 below), but is not comparable to other paradigms that administer the
total dose in one daily gavage administration rather than to give the same cumulative dose but in
three daily doses of lower concentration. The initial or final body weights of the mice were not
reported. Thus, the effects of systemic toxicity from TCE exposure on body weight and the
influence of differences in initial body weight on percent liver/body weight determinations
cannot be made.
For the 3-day study, mice were administered 1,500 mg/kg TCE or vehicle control. For
the 3-week study, mice were administered 0, 10, 50, 125, 500, 1,000, or 1,500 mg/kg TCE
5 days/week except for 4 days/week on the last week of the experiment. In a separate study,
mice were given TCA or DCA at 0.25, 0.5, 1, or 2 g/L (pH -7) in the drinking water for 7 days.
For each animal, a block of the left, anterior right, and median liver lobes was reported to be
fixed in formalin with five sections stained for H&E and examined by light microscopy. The
remaining liver samples were combined and used as homogenates for transcript arrays. In the
3-week study, bromodeoxyuridine (BrdU) was administered via miniosmotic pump on day 1 of
week 3 and sections of the liver assessed for BrdU incorporation in at least 1,000 cells per animal
in 10-15 fields.
Although initial body weights, final body weights, and the liver weights were not
reported, the percent liver/body ratios were. In the 3-day study, control wild-type and
PPARa-null mice were reported to have similar percent liver/body weight ratios of-4.5%.
These animals were -10 weeks of age upon sacrifice. However, at the end of the 3-week
experiment, the percent liver/body weight ratios were increased in the PPARa-null male mice
and were 5.1%. There was also a slight difference in the percent liver/body weight ratios in the
1-week study (4.3 ± 0.4 vs. 4.6 ± 0.2% for wild-type and PPARa-null mice, respectively). These
results are consistent with an increasing baseline of hepatic steatosis with age in the PPARa-null
mice and increase in liver weight.
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In the 3-day study, the mean report for the percent liver/body ratio was 1.4-fold of the
wild type animals tested with TCE in comparison to the control level. In the PPARa-null mice,
there was a 1.07-fold of control level reported by the authors to not be statistically significant.
However, given the low number of animals tested (the authors give only that four to five animals
were tested per group without identification as to which groups had four animals and which had
five), the ability of this study to discern a statistically significant difference is limited.
In the 3-week study, wild-type mice exposed to various concentrations of TCE had
percent liver/body weights that were within -2% of control values except for the 1,000 mg/kg
and 1,500 mg/kg groups that were -1.18- and 1.30-fold of control levels, respectively. For the
PPARa-null mice exposed to TCE for 3 weeks, the variability in percent liver/body weight was
greater than that of the wild-type mice in most of the groups. The baseline level percent
liver/body weight was 1.16-fold in the PPARa-null mice in comparison to wild-type mice. At
the 1,500 mg/kg TCE exposure level, percent liver/body weights were not recorded because of
the death of the null mice at this level. The authors reported that at the 1,500 mg/kg level, all
PPARa-null mice were moribund and had to be removed from the study. However, at the
1,000 mg/kg TCE exposure level, there was a 1.10-fold of control percent liver/body weight
value that was reported to not be statistically significant. As noted above, the power of the study
was limited due to low numbers of animals and increased variability in the null mice groups.
The percent liver/body weight reported in this study was actually greater in the null mice than the
wild-type male mice at the 1,000 mg/kg TCE exposure level (5.6 ± 0.4 vs. 5.2 ± 0.5%, for null
and wild-type mice, respectively).
Thus, at 1 and 3 weeks, TCE appeared to induce increases in liver weight in PPARa-null
mice, although not reaching statistical significance in this study, with concurrent background of
increased liver weight reported in the knockout mice. At 1,000 mg/kg TCE exposure for
3 weeks, percent liver/body weight was reported to be 1.18-fold in wild-type and 1.10-fold in
null mice of control values. As discussed above, Nakajima et al. (2000) reported statistically
significant increased liver weight in both wild-type and PPARa-null mice after 2 weeks of
exposure with less TCE-induced liver weight increases in the knockout mice (see
Section E.2.1.10). They also used more mice, carefully matched to weights of their mice, and
used a single dose of TCE each day with corn oil gavage.
The authors noted that inspection of the livers and kidneys of the moribund null mice,
who were removed from the 3-week study, "did not reveal any overt signs of toxicity in this dose
group that would lead to morbidity" but did not show the data and did not indicate when the
animals were affected and removed. For the wild-type mice exposed to the same concentration
(1,500 mg/kg) but whose survival was not affected by TCE exposure, the authors reported that
these mice exhibited mild granuloma formation with calcification or mild hepatocyte
degeneration, but gave no other details or quantitative information as to the extent of the lesions
or what parts of the liver lobule were affected. The authors noted that "wild-type mice
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administered 1000 and 1500 mg/kg exhibited centrilobular hypertrophy" and that "the mice in
the other groups did not exhibit any gross pathological changes after TCE exposure." Thus, the
hepatocellular hypertrophy reported in this study for TCE appeared to be correlated with
increases in percent liver/body weight in wild-type mice. In regard to the PPARa-null mice, the
authors stated that "differences in the liver to body weights in the control PPARa-null mice
[between Study 1 and 2 the 3-day and 3-week studies] were noted and may be due to differences
in the degree of steatosis that commonly occurs in this strain." Further mention of the
background pathology due to knockout of the PPARa was not discussed. The increased percent
liver/body weight reported between control and 1,000 mg/kg TCE exposed mice (5.1 vs. 5.6%)
was not accompanied by any discussion of pathological changes that could have accounted for
the change.
Direct comparisons of the effects of TCE, DCA, and TCA cannot be made from this
study as they were not studied for similar durations of exposure. However, while TCE induced
increased in percent liver/body weight ratios after 3 days and 3 weeks of exposure in wild-type
mice at the highest dose levels, for TCA exposure, percent liver/body weight after 1 week
exposure in drinking water was slightly elevated at all dose levels with no dose-response (-10%
increase), and for DCA exposure in drinking water, a similar elevation in percent liver/body
weight was also reported for the 0.25, 0.5, and 1.0 g/L dose levels (~11%) and that was increased
at the 2.0 g/L level by -25% reaching statistical significance. The authors interpret these data to
show no TCA-related changes in wild-type mice but the limited power of the study makes
quantitative conclusions difficult.
For PPARa-null mice, there was a slight decrease in percent liver/body weight between
control and TCA treated mice at the doses tested (-2%). For DCA-treated mice, all treatment
levels of DCA were reported to induce a higher percent liver/body weight ratio of at least -5%
with a 13% increase at the 2.0 g/L level. Again, the limited power of the study and the lack of
data for TCE at similar durations of exposure as those studied for TCA and DCA makes
quantitative conclusions difficult and comparisons between the chemicals difficult. However,
the pattern of increased percent liver/body weight appears to be more similar between TCE and
DCA than TCA in both wild-type and PPARa-null mice.
In terms of histological description of effects, the authors note that "livers from the 2 g/L
DCA-treated wild-type and PPARa-null mice had hepatocyte cytoplasmic rarefication probably
due to an increase in glycogen accumulation." However, no special procedures of staining were
performed to validate the assumption in this experiment. No other pathological descriptions of
the DCA treatment groups were provided. In regard to TCA, the authors noted that "the livers
from wild-type but not PPARa-null mice exposed to 2.0g/L TCA exhibited centrilobular
hepatocyte hypertrophy." No quantitative estimate of this effect was given and although the
extent of increase of percent liver/body weight was similar for all dose levels of TCA, there is no
indication from the study that lower concentrations of TCA also increased hepatocellular
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hypertrophy or why there was no concurrent increase in liver weight at the highest dose of TCA
in which hepatocellular hypertrophy was reported. Thus, reports of hepatocellular hypertrophy
for DC A and TCA in the 1-week study were not correlated with changes in percent liver/body
weight.
For control animals, BrdU incorporation in the last week of the 3-week study was
reported to be at a higher baseline level in PPARa-null mice than wild-type mice (-2.5-fold).
For wild-type mice the authors reported a statistically significant increase at 500 and
1,000 mg/kg TCE at levels of ~1 and -4.5% hepatocytes incorporating the label after 5 days of
BrdU incorporation. Whether this measure of DNA synthesis is representative of cellular
proliferation or of polyploidization was not examined by the authors. Even at 1,000 mg/kg TCE,
the percent of cells that had incorporated BrdU was <5% of hepatocytes in wild-type mice. The
magnitude percent liver/body weight ratio change at this exposure level was fourfold greater than
that of hepatocytes undergoing DNA synthesis (16% increase in percent liver/body weight ratio
vs. 4% increase in DNA synthesis). The -1% of hepatocytes undergoing DNA synthesis at the
500 mg/kg TCE level, reported to be statistically significant by the authors, was not correlated
with a concurrent increase in percent liver/body weight ratio. Thus, TCE-induced changes in
liver weight were not correlated with increases in DNA synthesis in wild-type mice after 3 weeks
of TCE exposure.
For PPARa-null mice, there was an approximately threefold of control value for the
percent of hepatocytes undergoing DNA synthesis at the 1,000 mg/kg TCE exposure level. The
higher baseline level in the null mouse, large variability in response at this exposure level, and
low power of this experimental design limited the ability to detect statistical significance of this
effect, although the level was greater than that reported for the 500 mg/kg TCE exposure in wild-
type mice that was statistically significant. Thus, TCE appeared to induce an increase in DNA
synthesis in PPARa-null mice, albeit at a lower level than wild-type mice. However, the -2%
increase in percent of hepatocytes undergoing DNA synthesis during the 3rd week of a 3-week
exposure to 1,000 mg/kg TCE in PPARa-null mice was insufficient to account for the -10%
observed increase in liver weight. For wild-type and PPARa-null mice, the magnitude of
TCE-induced increases in liver weight were four- to fivefold higher than that of increases in
DNA-synthesis under this paradigm and in both types of mice, a relatively small portion of
hepatocytes were undergoing DNA synthesis during the last week of a 3-week exposure
duration. Whether the increases in liver weight could have resulted from an early burst of DNA
synthesis as well as whether the DNA synthesis results reported here represents either
proliferation or polyploidization, cannot be determined from this experiment. Because of the
differences in exposure protocol (i.e., use of three daily doses in methylcellulose rather than one
dose in corn oil), the time course of the transient increase in DNA synthesis reported cannot be
assumed to be the same for this experiment and others.
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Not only were PPARa-null mice different than wild-type mice in terms of background
levels of liver weights, and hepatic steatosis, but this study also reported that background levels
of PCO activity to be highly variable and, in some instances, different between wild-type and
null mice. There was reported to be approximately sixfold PCO activity in PPARa-null control
mice in comparison to wild-type control mice in the 1-week DCA/TCA experiment (~0.15 vs.
0.85 units of activity/g protein). However, in the same figure, a second set of data are reported
for control mice for comparison to WY-14,643 treatment in which PCO activity was slightly
decreased in PPARa-null control mice vs. wild-type controls (-0.40 vs. 0.65 units of activity/g
protein). In the experimental design description of the paper, WY-14,643 treatment and a
separate control were not described as part of the 1-week DCA/TCA experiment. For the only
experiment in which PCO activity was compared between wild-type and PPARa-null mice
exposed to TCE (i.e., 3-day exposure study), there was a reported increased over the control
value of -2.5-fold that was reported to be statistically significant at 1,500 mg/kg TCE (1.5 vs.
0.60 units of activity/g protein). For control mice in the 3-day TCE experiment, there was an
increase in this activity in PPARa-null mice in comparison to wild-type mice (-0.60 vs.
0.35 units of activity/g protein). While not statistically significant, there appeared to be a slight
increase in PCO activity after 1,500 mg/kg TCE exposure for 3 days in PPARa-null mice of
-30%. However, as noted above, the background levels of this enzyme activity varied widely
between the experiments with not only values for control animals varying as much as sixfold
(i.e., for PPARa-null mice), but also for WY-14,643 administration. There was a 6.6-fold
difference in PCO results for WY-14,643 in PPARa-null mice at the same concentration of
WY-14,643 in the 3-day and 1-week experiment, and a 1.44-fold difference in results in wild-
type mice in these two data sets.
E.2.1.14. Ramdhan et al. (2008)
Ramdhan et al. (2008) examined the role of CYP2E1 in TCE-induced hepatotoxicity,
using CYP2E1 +/+ (wild-type) and CYP2E1 -/- (null) Sv/129 male mice (6/group) that were
exposed for 7 days to 0, 1,000, or 2,000 ppm TCE by inhalation for 8 hours/day. The exposure
concentrations are noted by the authors to be much higher than occupational exposures and to
have increased liver toxicity after 8 hours of exposure as measured by plasma AST levels. To
put this exposure concentration into perspective, the Kjellstrand et al. (1983a: 1983b) inhalation
studies for 30 days showed that these levels were well above the 150-ppm exposure levels in
male mice that induced systemic toxicity. Nunes also reported hepatic necrosis up to 4% in rats
at 2,000 ppm for just 8 hours not 7 days. AST and ALT were measured at sacrifice. Histological
changes were scored using a qualitative scale of 0 = no necrosis, 1 = minimal as defined as only
occasional necrotic cells in any lobule, 2 = mild as defined as less than one-third of the lobule
structure affected, 3 = moderate as defined as between one-third and two-thirds of the lobule
structure affected, and 4 = severe defined as greater than two-thirds of the lobule structure
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affected. Real-time polymerase chain reaction (PCR) was reported for mRNA encoding a
number of receptors and proteins. Total RNA and Western Blot analysis was obtained from
whole-liver homogenates. The changes in mRNA expression were reported as means for six
mice per group after normalization to a level of p-actin mRNA expression and were shown
relative to the control level in the CYP2E1 wild-type mice.
The deletion of the CYP2E1 gene in the null mouse had profound effects on liver weight.
The body were was significantly increased in control CYP2E1 -/- mice in comparison to wild-
type controls (24.48 ± 1.44 g for null mice vs. 23.66 ± 2.44 g, m ± SD). This represents a 3.5%
increase over wild-type mice. However, the liver weight was reported in the CYP2E1 -/- mice to
be 1.32-fold of that of CYP2E1 +/+ mice (1.45 ± 0.10 g vs. 1.10±0.14g). The percent
liver/body weight ratio was 5.47 vs. 4.63% or 1.18-fold of wild-type control for the null mice.
The authors report that 1,000 and 2,000 ppm TCE treatment did induce a statistically
significant change body weight for null or wild-type mice. However, there was an increase in
body weight in the wild-type mice (i.e., 23.66 ± 2.44, 24.52 ±1.17, and 24.99 ± 1.78 for control,
1,000, and 2,000 ppm groups, respectively) and an increase in the variability in response in the
null mice (i.e., 24.48 ± 1.44, 24.55 ± 2.26, and 24.99 ± 4.05, for control, 1,000, and 2,000 ppm
exposure groups, respectively). The percent liver/body weight was 5.47 ± 0.23, 5.51 ± 0.27, and
5.58 ± 0.70% for control, 1,000, and 2,000 ppm the CYP2E1 -/- mice, respectively. The percent
liver/body weight was 4.63 ± 0.13, 6.62 ± 0.40, and 7.24 ± 0.84% for control, 1,000, and
2,000 ppm wild-type mice, respectively. Therefore, while there appeared to be little difference
in the TCE and control exposures for percent liver/body weights in the CYP2E1 -/- mice (2%),
there was a 1.56-fold of control level after 2,000 ppm in the wild-type mice after 7 days of
inhalation exposure.
The authors reported that "in general, the urinary TCE level in CYP2E1 -/- mice was less
than half that in CYP2E1 +/+ mice: urinary TCA levels in the former were about one-fourth
those in the latter." Of note is the large variability in urinary TCE detected in the 2,000-ppm
TCE exposed wild-type mice, especially after day 4, and that, in general, the amount of TCE in
the urine appeared to be greatest after the 1st day of exposure and steadily declined between
1 and 7 days (i.e., -45% decline at 2,000 ppm and a -70% decline at 1,000 ppm) in the wild-type
mice. The amount of TCE in the urine was proportional to the difference in dose at days 1 and 5
(i.e., a twofold difference in dose resulted in a twofold difference in TCE detected in the urine).
As the detection of TCE in the urine declined with time, the amount of TCA was reported to
steadily increase between days 1 and 7 (e.g., from -3 mg TCA after the 1st day to -5.5 mg after
7 days after 2,000 ppm exposure in wild-type mice). However, unlike TCE, there was a much
smaller differences in response between the two TCE exposure levels (i.e., a 12-44% or 1.12-
1.44-fold difference in TCA levels in the urine at days 1-7 for exposure concentrations that
differ by a factor of 2). This could be indicative of saturation in metabolism and TCA clearance
into urine at these high concentrations levels. The authors note that their results suggest that the
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metabolism of TCE in both null and wild-type mice may have reached saturation at 1,000 ppm
TCE.
For ALT and AST activities in CYP2E1 -/- or CYP2E1 +/+ mice, both liver enzymes
were significantly elevated only at the 2,000 ppm level in CYP2E1 +/+ mice. Although the
increases in excreted TCA in the urine differed by only -33% between the 1,000 and 2,000 ppm
levels, liver enzyme levels in plasma differed by a much greater extent after 7 days exposure
between the 1,000 and 2,000 ppm groups of CYP2E1 +/+ mice (i.e., 1.26- and 1.83-fold of
control [ALT] and 1.40- and 2.20-fold of control [AST] for 1,000 and 2,000 ppm TCE exposure
levels, respectively). The authors reported a correlation between plasma ALT and both TCE (r =
0.7331) and TCA (r = 0.8169) levels but do not report details of what data were included in the
correlation (i.e., were data from CYP2E1 +/+ mice combined with those of the CYP2E1 -/- mice
and were control values included with treated values?).
The authors show photomicrograph of a section of liver from control CYP2E1 +/+ and
CYP2E1 -/- mice and describe the histological structure of the liver to appear normal. This
raises the question as to the cause of the hepatomegaly for the CYP2E1 mice in which the liver
weight was increased by a third.
The qualitative scoring for each of the six animals per group showed that none of the
CYP2E1 -/- control or treated mice showed evidence of necrosis. For the CYP2E1 +/+ mice,
there was no necrosis reported in the control mice and in three of six mice treated with
1,000 ppm TCE. Of the three mice that were reported to have necrosis, the score was reported as
1-2 for two mice and 1 for the third. It is not clear what a score of 1-2 represented given the
criteria for each score given by the authors, which defined a score of 1 as minimal and 2 as mild.
For the 2,000 ppm TCE-exposed mice, all mice were reported to have at least minimal necrosis
(i.e., four mice were reported to have scores of 1-2, one mouse a score of 3, and one mouse a
score of 1).
What is clear from the histopathology data are that there appeared to be great
heterogeneity of response between the six animals in each TCE-exposure group in CYP2E1 +/+
mice and that there was a greater necrotic response in the 2,000 ppm exposed mice than the
1,000 ppm mice. These results are consistent with the liver enzyme data but not consistent with
the small difference between the 1,000 and 2,000 ppm exposure groups for TCA content in urine
and, by analogy, metabolism of TCE to TCA. A strength of this study is that it reports the
histological data for each animal so that the heterogeneity of liver response can be observed (e.g.,
the extent of liver necrosis was reported to range from only occasional necrotic cells in any
lobule to between one-third and two-thirds of the lobular structure affected after 2,000 ppm TCE
exposure for 7 days). Immunohistochemical analysis was reported to show that CYP2E1 was
expressed mainly around the centrilobular area in CYP2E1 +/+ mice where necrotic changes
were observed after TCE treatment.
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Given the large variability in response within the liver after TCE exposure in CYP2E1
mice, phenotypic anchoring becomes especially important for the interpretation of mRNA
expression studies (see Sections E.I.I and E.3.1.2 for macroarray transcript profiling limitations
and the need for phenotypic anchoring). However, the data for mRNA expression of PPARa,
peroxisomal bifunctional protein (hydratase+3-hydroxyacyl-CoA dehydrogenase),very long
chain acyl-CoA dehydrogenase (VLCAD), CYP4A10, NFKB (p65, P50, P52), and iKBa was
reported at the means ± SD for six mice per group and represented total liver homogenates. A
strength of the study was that they did not pool their RNA and can show means and SDs between
treatment groups. The low numbers of animals tested, however, limits the ability to detect
statistically significance of the response. By reporting the means, differences in the responses
within dose groups was limited and reflected differential response and involvement for different
portions of the liver lobule and for the responses of the heterogeneous group of liver cells
populating the liver.
The authors reported that they normalized values to the level of p-actin mRNA in the
same preparation with a value of 1 assigned as the mean from each control group. The values for
mRNA and protein expression reported in the figures appeared to have all been normalized to the
control values for the CYP2E1 -/- mice. Although all of the CYP2E1 -/- control values were
reported as a value of 1, the control values for the CYP2E1+/+ mice differed with the greatest
difference being presented for the CYP4A10-mRNA (i.e., the control level of CYP4A10 mRNA
was approximately threefold higher in the CYP2E1+/+ mice than the CYP2E1 -/- mice). Further
characterization of the CYP2E1 mouse model was not provided by the authors.
The mean expression of PPARa mRNA was reported slightly reduced after TCE
treatment in CYP2E1 -/- mice (i.e., 0.72- and 0.78-fold of control after 1,000 and 2,000 ppm
TCE exposure, respectively). The CYP2E1 -/- mice had a higher baseline of PPARa mRNA
expression than the CYP2E1+/+ mice (i.e., the control level of the CYP2E1 -/- mice was 1.5-fold
of the CYP2E1+/+ mice). After TCE exposure, the CYP2E1 +/+ had a similar increase in
PPARa mRNA (~2.3-fold) at both 1,000 and 2,000 ppm TCE. Thus, without the presence of
CYP2E1, there did not appear to be increased PPARa mRNA expression. For PPARa protein
expression, there was a similar pattern with ~1.6-fold of control levels of protein in the
CYP2E1 -/- mice after both 1,000 and 2,000 ppm TCE exposures.
In the CYP2E1 +/+ mice, the control level of PPARa protein was reported to be ~1.5-fold
of the CYP2E1 -/- control level. Thus, while the mRNA expression was less, the protein level
was greater. After TCE treatment, there was a 2.9-fold of control level of protein at 1,000 ppm
TCE and a 3.1-fold of control level of protein at 2,000 ppm. Thus, the magnitude of mRNA
increase was similar to that of protein expression for PPARa in CYP2E1 +/+ mice. The
magnitude of both was threefold or less over control after TCE exposure. This pattern was
similar to that of TCA concentration formed in the liver where there was very little difference
between the 1,000 and 2,000 ppm exposure groups in CYP2E1 +/+ mice. However, this pattern
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was not consistent with the liver enzyme and histopathology of the liver that showed a much
greater response after 2,000 ppm exposure than 1,000 ppm TCE. In addition, where the mean
enzyme markers of liver injury and individual animals displayed marked heterogeneity in
response to TCE exposure, there was a much smaller degree of variability in the mean mRNA
expression and protein levels of PPARa.
For peroxisomal bifunctional protein, there was a greater increase after 1,000 ppm
TCE-treated exposure than after 2,000 ppm TCE-treatment for both the CYP2E1 -/- and
CYP2E1 +/+ mice (i.e., there was a 2:1 ratio of mRNA expression in the 1,000 vs. 2,000 ppm
exposed groups). The CYP2E1 +/+ mice had a much greater response than the CYP2E1 -/- mice
(i.e., the CYP2E1 -/- mice had a 2-fold of control and the CYP2E1 +/+ mice had a 7.8-fold of
control level after 1,000 ppm TCE treatment). For peroxisomal bifunctional protein expression,
the magnitude of protein induction after TCE exposure was much greater than the magnitude of
increase in mRNA expression. In the CYP2E1 -/- mice, 1,000 ppm TCE exposure resulted in a
6.9-fold of control level of protein, while the 2,000 ppm TCE group had a 2.3-fold level.
CYP2E1 +/+ mice had a -50% higher control level than CYP2E1 mice and after TCE exposure,
the level of peroxisomal bifunctional protein expression was 44-fold of control at 1,000 ppm
TCE and 40-fold of control at 2,000 ppm. Thus, CYP2E1 -/- mice were reported to have less
mRNA expression and peroxisomal bifunctional protein formed than CYP2E1 +/+ mice after
TCE exposure. However, there appeared to be more mRNA expression after 1,000 than
2,000 ppm TCE in both groups and protein expression in the CYP2E1 -/- mice. After 2,000 ppm
TCE, there was similar peroxisomal bifunctional protein expression between the 1,000 and
2,000 ppm TCE treated CYP2E1 +/+ mice. Again, this pattern was more similar to that of TCA
detection in the urine—not that of liver injury.
For VLC AD, the expression of mRNA was similar between control and treated
CYP2E1 -/- mice. For CYP2E1 +/+ mice, the control level of VLCAD mRNA expression was
half that of the CYP2E1 -/- mice. After 1,000 ppm TCE, the mRNA level was 3.7-fold of
control and after 2,000 ppm TCE the mRNA level was 3.1-fold of control. For VLCAD, protein
expression was 1.8-fold of control after 1,000 ppm and 1.6-fold of control after 2,000 ppm in
CYP2E1 -/- mice. The control level of VLCAD protein in CYP2E1 +/+ mice appeared to be
1.2-fold control CYP2E1 -/- mice. After 1,000-ppm TCE treatment, the CYP2E1 -/- mice were
reported to have 3.8-fold of control VLCAD protein levels and after 2,000-ppm TCE treatment,
3.9-fold of control protein levels. Thus, although showing no increase in mRNA, there was an
increase in VLCAD protein levels that was similar between the two TCE exposure groups in
CYP2E1 -/- mice. Both VLCAD mRNA and protein levels were greater in CYP2E1 +/+ mice
than CYP2E1 -/- mice after TCE exposure. This was not the case for peroxisomal bifunctional
protein. The magnitudes of TCE-induced increases in mRNA and protein increases were similar
between the 1,000 and 2,000 ppm TCE exposure concentrations, a pattern more similar to TCA
detection in the urine but not that of liver injury.
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Finally, for CYP4A10 mRNA expression, there was an increase in expression after TCE
treatment of threefold for 1,000 ppm and fivefold after 2,000 ppm in CYP2E1 -/- mice. Thus,
although the enzyme assumed to be primarily responsible for TCE metabolism to TCA was
missing, there was still a response for the mRNA of this enzyme commonly associated with
PPARa activation. Of note is that urinary concentrations of TCA were not zero after TCE
exposure in CYP2E1 -/- mice. Both 1,000 and 2,000 ppm TCE exposure resulted in -0.44 mg
TCA after 1 day or about 15-22% of that observed in CYP2E1 +/+ mice. Thus, some
metabolism of TCE to TCA is taking place in the null mice, albeit at a reduced rate. For
CYP2E1 +/+ mice, 1,000 ppm TCE resulted in an 8.3-fold of control level of CYP4A10 mRNA
and 2,000 ppm TCE resulted in a 9.3-fold of control level.
The authors did not perform an analysis of CYP4A10 protein. The authors state that "in
particular, the mRNA levels of microsomal enzyme CYP4A10 significantly increased in
CYP2E1+/+ mice after TCE exposure in a dose-dependent manner." However, the twofold
difference in TCE exposure concentrations did not result in a similar difference in response as
shown above. Both resulted in approximately ninefold of control response in CYP2E1 +/+ mice.
As with PPARa, peroxisomal bifunctional protein, and VLCAD, the response was more similar
to that of TCA detection in the urine and not measured of hepatic toxicity. These data show that
CYP2E1 metabolism of TCE is important in the manifestation of TCE liver toxicity; however,
data suggest that effects other than TCA concentration and indicators of PPARa are responsible
for acute hepatotoxicity resulting from very high concentrations of TCE.
The NFKB family and IicBa were also examined for mRNA and protein expression.
These cell signaling molecules are involved in inflammation and carcinogenesis and are
discussed in Sections E.3.3.3.3 and E.3.4.1.4. Given that presence of hepatocellular necrosis in
some of the CYP2E1 +/+ mice to varying degrees, inflammatory cytokines and cell signaling
pathways would be expected to be activated. The authors reported that:
overall, TCE exposure did not significantly increase the expression of p65 and
p50 mRNAs in either CYP2E1+/+ or CYP2E1 -/- mice... However, p52 mRNA
expression significantly increased in the 2,000 ppm group of CYP2E1+/+ mice,
and correlation analysis showed that a significant positive relationship existed
between the expression of NFKB p52 mRNA and plasma ALT activity.., while no
correlation was seen between NFKB p64 or p50 and ALT activity (data not
shown).
The authors also note that TCE treatments "did not increase the expression of TNFR1 and
TNFR2 mRNA in CYP2E1+/+ and CYP2E1 -/- mice (data not shown)."
A more detailed examination of the data reveals that there was a similar increases in p65,
p50, and p52 mRNA expression increases with TCE treatment in CYP2E1 +/+ mice at both TCE
exposure levels. However, only p52 levels for the 2,000 ppm exposed mice were reported to be
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statistically significant (see comment above about the statistical power of the experimental
design and variability between animals). For 1,000 ppm TCE exposure, the levels of p65, p50,
and p52 mRNA expression were 1.5-, 1.8-, and 2.0-fold of control. For 2,000 ppm TCE, the
levels of p65, p50, and p52 mRNA expression were 1.8-, 1.8-, and 2.1-fold of control. Thus,
there was generally a similar response in all of these indicators of NFKB mRNA expression in
CYP2E1 +/+ mice that was mild with little to no difference between the 1,000 and 2,000 ppm
TCE exposure levels. For IicBa mRNA expression, there was no difference between control and
treatment groups for either type of mice. For CYP2E1 -/- mice, there appeared to be a -50%
decrease in P52 mRNA expression in mice treated with both exposure concentrations of TCE.
The authors plotted the relationship between p52 mRNA and plasma ALT concentration for both
CYP2E1 -/- and CYP2E1 +/+ mice together and claimed that the correlation coefficient
(r = 0.5075) was significant. However, of note is that none of the CYP2E1 -/- mice were
reported to have either hepatic necrosis or significant increases in ALT detection.
For protein expression, the authors showed results for p50 and p42 proteins. The control
CYP2E1 -/- mice appeared to have a slightly lower level of p50 protein expression (-30%) with
a much larger increase in p52 protein expression (i.e., 2.1-fold) than CYP2E1 +/+ mice. There
appeared to be a 2-fold increase in p50 protein expression after both 1,000 and 2,000 ppm TCE
exposures in the CYP2E1 +/+ mice and a similar increase in p52 protein levels (i.e., 1.9- and
2.5-fold of control for 1,000- and 2,000-ppm TCE exposures, respectively). Thus, the magnitude
of mRNA and protein levels were similar for p50 and p52 in CYP2E1 +/+ mice and there was no
difference between the 1,000 and 2,000 ppm treatments. For the CYP2E1 -/- mice, there was a
modest increase in p50 protein after TCE exposure (1.1- and 1.3-fold of control for 1,000 and
2,000 ppm respectively) and a slight decrease in p52 protein (0.76- and 0.79-fold of control).
There was little evidence that the patterns of either expression or protein production of NFicB
family and IicBa corresponded to the markers of hepatic toxicity or that they exhibited a dose-
response. The authors note that although he expression of p50 protein increased in CYP2E1 +/+
mice, "the relationship between p50 protein and ALT levels was not significant (data not
shown)." For TNFR1, there appeared to be less protein expression in the CYP2E1 +/+ mice than
the CYP2E1 -/- mice (i.e., the null mice levels were 1.8-fold of the wild-type mice levels).
Treatment with TCE resulted in mild decrease of protein levels in the CYP2E1 -/- mice and a
1.4- and 1.7-fold of control level in the CYP2E1 +/+ mice for 1,000 and 2,000 ppm levels,
respectively. For p65, although TCE treatment-related effects were reported, of note is that the
levels of protein were 2.4 higher in the CYP2E1 +/+ mice than the CYP2E1 -/- mice. Thus,
protein levels of the NFicB family appeared to have been altered in the knockout mice. Also, as
noted in Section E.3.4.1.4, the origin of the NF-KB is crucial as to its effect in the liver and the
results of this report are for whole-liver homogenates that contain parenchymal as well as
nonparenchymal cell and have been drawn from liver that are heterogeneous in the magnitude of
hepatic necrosis. The authors suggest that "TCA may act as a defense against hepatotoxicity
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cause by TCE-delivered reactive metabolite(s) via PPARa in CYP2E1+/+ mice." However, the
data from this do not support such an assertion.
E.2.1.15. Ramdhan et al. (2010)
Ramdhan et al. (2010) examined the role of mouse and human PPARa in TCE-induced
hepatic steatosis and toxicity using male wild type, PPARa-null and PPARa-null mice with
human PPARa inserted (hPPARa) (Cheung et al.. 2004) on Sv/129 male mice (6/group), which
were exposed for 7 days to 0, 1,000, or 2,000 ppm TCE by inhalation for 8 hours/day. This was
a similar paradigm as that used in Ramdhan et al. with results between wild type mice directly
comparable. The expression of human PPARa cDNA in the humanized mice was limited to
hepatocytes under the control of tetracycline regulatory system.
Plasma aminotransferase activities (AST and ALT) were measured in plasma as well as
triglycerides. Hepatic triglyceride levels were measured as well. Urinary metabolites were
measured similarly to Ramdhan et al. (2008). Hepatic steatosis was identified based on the
presence of vacuoles consistent with lipid accumulation and classified as microvesicular steatosis
if the nucleus remained in the center of the hepatocyte. Hepatocyte proliferation was classified
based on the presence of large hepatocytes with prominent eosinophilic cytoplasm.
Histopathology findings were scored in 20 randomly selected 200x microscopic fields per
section with steatotic scores of 0-3: none, mild 5-44% of parenchymal involvement of steatosis),
moderate (33-66%), or severe (>66%). Necrotic cells were scored as 0-4: no necrosis, minimal
(only occasional necrotic cells in any lobule), mild (two-thirds of the
lobular structure affected). Hepatocyte proliferation was scored as 0 (absent) or 1 (present).
Real-time PCR analysis was performed on total RNA from whole liver. Western Blot
analysis was also performed on whole liver (derived from both hepatocytes and non-
parenchymal cells) for NFicB, p65, p50, p52, and PPARa.
Significant differences were observed among control mice for each genotype. The mean
body weight of hPPARa mice was 14 and 8.5% less than wild type mouse and PPARa-null mice,
respectively. The mean liver weight of hPPARa mice was 11% less than PPARa-null mice and
the liver/body weight ratio of PPARa-null mice was 11% higher than wild type mice. TCE, at
both 1,000 and 2,000 ppm, significantly increased liver weight in the three mouse lines to a
similar extent (i.e., 38 and 49% in wild type mice, 20 and 37% in PPAR-null mice, and 28 and
32% in hPPARa mice). The increases were not statistically significant between doses within
each strain. Liver/body weight ratios were also significantly increased with TCE exposure at
1,000 and 2,000 ppm relative to controls (i.e., 38 and 43% in wild type mice, 24 and 36% in
PPARa-null mice, and 27 and 39% in hPPARa mice, respectively). The difference between
2.000 and 1.000 ppm TCE exposure was statistically significant in PPARa-null mice.
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The authors reported no differences in urinary volume by genotype or exposure but did
not show the data. TCA and TCOH were detected in all exposed mice with no significant
differences between the 1,000 and 2,000 ppm TCE levels. TCA concentrations were reported to
be significantly lower and TCOH levels significantly higher in PPARa-null mice relative to wild
type mice with no differences in genotype between the sum of total TCA and TCOH
concentrations between genotypes.
AST and ALT liver injury biomarkers were reported to vary <10% among control mice
of each strain and to be significantly increased in all exposed mice relative to controls (41-74%
and 36-79% higher, respectively) with mean levels within each group higher, though not
statistically significantly different, with exposure to 2,000 vs. 1,000 ppm TCE.
Higher levels of plasma triglycerides were reported in untreated hPPARa mice than wild-
type mice (52%). Significantly higher liver triglyceride levels were reported in untreated
hPPARa mice than wild type mice or PPARa-null mice (77 and 30%, respectively) and between
untreated PPARa-null mice and wild-type mice (36%). Exposure to 2,000 ppm TCE was
reported to induce an even greater difference between the wild type and PPARa-null mice
(113%). Exposure to 1,000 ppm TCE was reported to induce greater liver triglyceride level in
hPPARa mice (50%) compared to wild type mice as well as 2,000 ppm TCE (87%). There were
no significant difference in mean plasma or liver triglyceride levels between the 2,000 and
1,000 ppm TCE treatment groups within each genotype. Hepatic triglyceride levels were
reported to be significantly correlated with liver/body weight ratios of all mice used in the study
(r = 0.54).
Neither necrosis nor inflammatory cells were reported in liver sections from unexposed
mice. The authors reported small cytoplasmic vacuoles in sections from unexposed PPARa-null
mice and hPPARa mice that resulted in steatosis scores >0. Steatosis was reported to be absent
in unexposed wild type mice and significantly increased in exposed vs. unexposed PPARa-null
and hPPARa mice. Steatosis scores were reported to be significantly higher in the 2,000 vs.
1,000 ppm TCE exposures to PPARa-null mice. The authors reported steatosis scored to be
significantly correlated with liver triglyceride levels of all mice examined in the study (r = 0.75).
Macrovesicular steatosis was reported to occur more frequently in hPPARa than PPARa-null
mice. Necrosis scores were reported to be significantly higher in TCE exposed mice relative to
controls in all three genotype mice and to be significantly higher with 2,000 vs. 1,000 ppm TCE
exposure in wild type mice and hPPARa mice. Inflammation scores were reported to be
significantly higher with exposed group than control with 2,000 ppm TCE exposure than controls
for each genotype group with a difference between the 2,000 and 1,000 ppm exposure groups in
wild type mice. Hepatocyte proliferation was reported to be significantly increased with
2,000 ppm TCE exposure in wild-type mice, but not in the other genotypes or exposure
concentrations. Of note, the criteria for "proliferation" did not employ quantitative methods of
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DNA synthesis but phenotypic descriptions of enlarged hepatocytes that may be indicative of
polyploidy.
Background expression levels of several genes were reported to differ significantly
between strains in control mice. VLCAD, medium chain acyl-CoA dehydrogenase (MCAD),
peroxisomal bifunctional protein (hydratase+3-hydroxyacyl-CoA dehydrogenase) (PH),
peroxisomal thiolase (PT), diacylflicerol acyltransferase 1 (DGAT1), and p52 mRNA levels
were reported to be higher in untreated hPPARa mice than wild type mice and PPARa-null mice.
PPARa, proliferation cell nuclear antigen (PCNA), p50, and tumor necrosis factor alpha (TNFa)
mRNA levels were reported to be higher in untreated hPPARa mice than PPARa-null mice.
VLCAD, PH, and PT mRNA levels were reported to be significantly lower in untreated
PPARa-null mice than wild type mice and p50, p52, PPARy, and TNFa were higher in untreated
PPARa-null mice than wild type mice.
Exposure to TCE was reported to not increase the expression of human PPARa mRNA in
hPPARa mice but 2,000 ppm TCE exposure did significantly increase mouse PPARa mRNA in
wild type mice. PCNA mRNA expression and mRNA expression of VLCAD, MCAD, PH, and
PT was increased in TCE exposed vs. control wild type mice and hPPARa mice. More
pronounced induction of PH and PT mRNA was reported for exposed wild type mice.
Significant differences were not reported in gene expression between 1,000 and 2,000 ppm TCE
exposures.
DGAT1 and DGAT2 mRNA was reported to be significantly increased in hPPARa mice
exposed to 2,000 ppm TCE and PPARa-null mice exposed to 1,000 and 2,000 ppm TCE in
comparison to respective control mice. Exposure to 1,000 and 2,000 ppm TCE was reported to
significantly increase PPARy mRNA in PPARa-null and hPPARa mice. DGAT1 and DGAT2,
PPARy mRNA levels were not changed with TCE exposure in wild type mice.
NFicB p65 mRNA was reported to be significantly increase after TCE exposure in
PPARa-null and hPPARa mice but not wild type mice. NFKB p50 mRNA expression was
reported to be significantly increased with exposure to TCE in PPARa-null mice only but NFicB
p52 and TNFa mRNA expression was increased significantly with exposure in all strains. The
authors reported that NFicB p52 mRNA levels were significantly correlated with plasma ALT
levels in all mice used in the study (r = 0.54).
Protein expression levels were reported to differ between the genotypes of untreated
mice. PPARa levels were 10.4 times higher in untreated hPPARa mice than wild type mice.
VLCAD, PT, acyl-CoA(ACOX) A, and ACOX B proteins were reported to be significantly
higher in untreated hPPARa mice than wild type and PPARa-null mice and NFicB p65 to be
lower in hPPARa mice than PPARa-null mice. VLCAD, MCAD, PH, PT, ACOX A, and
ACOX B expression was reported to be slightly lower and p65 and p52 expression slightly
higher in untreated PPARa-null mice vs. wild type mice.
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TCE exposure was reported to increase VLCAD, PH, PT, ACOX A, and ACOX B in
wild type and hPPARa mice but not to induce PPARa protein expression. MCAD protein was
significantly increased after TCE exposure in hPPARa mice only. PCNA protein was increased
in TCE exposed mice in comparison to controls in all strains. NFicB p52 and TNFa proteins
were also increased from TCE exposure in all strains but NFicB p50 and p65 proteins were
increased in TCE-exposed PPARa-null mice only. 4-Hydroxy-2- nonenal protein (a marker of
oxidative stress) was increased by 1,000 ppm TCE exposure in PPARa-null mice and by
2,000 ppm TCE exposure in wild type and hPPARa mice.
The authors reported that they measured hepatic protein expression of CYP2E1 and
ALDH2 enzymes and did not observe a significant difference among controls (data not shown)
and that TCE exposure did not alter hepatic CYP2E1 expression but did decrease ALDH2
expression to a comparable extent in all mouse lines (data not shown). Thus, changes in urinary
TCA levels in the differing strains were not related to changes in expression of these metabolic
enzymes.
While the authors of the paper suggested that the increased susceptibility of PPARa-null
mice and hPPATa mice to TCE toxicity is indicative of "protection" by having intact and normal
PPARa expression in mice, the disturbances they reported in these genotypes without treatment
shows that an already compromised animal is more susceptible to additional insult by high levels
of TCE exposure. This study provides an extensive set of parameters altered in the PPARa-null
and hPPARa mice by such genetic manipulation alone. In particular, insertion of human PPAR
in the null mice did not return the mice to a normal state. The authors noted that hepatic
triglyceride levels were the highest in untreated hPPARa among the three strains suggesting that
human PPARa insertion did not restore proper lipid regulation in the liver. The humanized mice
in particular exhibited a > 10-fold expression of PPAR in an untreated state. Functional
differences between the human and rodent versions of PPAR are difficult to ascertain from this
study given the large differences in PPAR protein expression between wild type and humanized
mice and the presence of human PPAR only in the hepatocytes in this model. The authors noted
that the replacement of human PPARa in the humanized mouse may not have been sufficient to
prevent steatosis and that the differences in responses between wild type and humanized mice
may reflect functional consequences related to the use of an artificial construct of the reinserted
gene without normal control elements in addition to or instead of any differences between human
or mouse PPARa. They stated that because they used genetically modified mice with underlying
dysregulation, and evaluated very high TCE exposures, their findings may not directly reveal the
differences in human PPARa function between mice and humans. The increased toxicity from
overexpression of human PPARa in this model is also acknowledged as leading to greater
background toxicity in unexposed humanized mice.
Responses reported for gene expression are for liver homogenates so that NFicB and
TNFa mRNA expression changes could not be distinguished between Kupffer cell or
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hepatocytes origin. The authors noted the similarity of TCE induced hepatomegaly in PPARa
null mice in this study and that of Nakajima et al. (2000). They noted that TCE induction of
PCNA protein (cell proliferation marker) was increased in all three group but using their
phenotypic marker of increased cell size of evidence of increased hepatocyte proliferation in
wild type mice.
The authors noted differences in this study and their study of similar design (Ramdhan et
al., 2008) for gene expression induced by TCE exposure in wild type mice. Differences in
TCE-induced effects between the two studies include less pronounced induction of PPARa, more
pronounced increases in PH protein and VLCAD mRNA expression, and ALT and AST levels
for this study than the previous one for wild type mice. They stated that urinary TCA levels in
wild type mice were incorrectly reported by Ramdhan et al. (2008) but have been corrected in
this study. They also noted discrepancies in mRNA and protein expression for some genes in
this study. Finally, the authors acknowledged that the small number of mice examined in each
group limits the power to identify statistically significant biological effects.
E.2.2. Subchronic and Chronic Studies of TCE
For the purposes of this discussion, studies of duration of >4 weeks are considered
subchronic. Like those of shorter duration, there is variation in the depth of study of liver
changes induced by TCE with many of the longer duration studies focused on the induction of
liver cancer. Many subchronic studies were conducted a high doses of TCE that caused toxicity
with limited reporting of effects. Similar to acute studies, some of the subchronic and chronic
studies have detailed examinations of the TCE-induced liver effects while others have reported
primarily liver weight changes as a marker of TCE-response. Similar issues also arise with the
impact of differences in initial and final body weights between control and treatment groups on
the interpretation of liver weight gain as a measure of TCE-response.
For many of the subchronic inhalation studies, issues associated with whole-body
exposures make determination of dose levels difficult. For gavage experiments, death from
gavage dosing, especially at higher TCE exposures, is a recurring problem and, unlike inhalation
exposures, the effects of vehicle can also be at issue for background liver effects. Concerns
regarding effects of oil vehicles, especially corn oil, have been raised with Kim et al. (1990a)
noting that a large oil bolus will not only produce physiological effects, but alter the absorption,
target organ dose, and toxicity of VOCs. Charbonneau et al. (1991) reported that corn oil
potentiates liver toxicity from acetone administration that is not related to differences in acetone
concentration. Several oral studies, in particular, document that the use of corn oil gavage
induces a different pattern of toxicity, especially in male rodents (see Merrick et al., 1989,
Section E.2.2.1 below). Several studies listed below report the effects of hepatocellular DNA
synthesis and indices of lipid peroxidation (i.e.. Channel et al., 1998) are especially subject to
background vehicle effects. Rusyn et al. (1999) report that a single dose of dietary corn oil
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increases hepatocyte DNA synthesis 24 hours after treatment by ~3.5-fold, activation of NF-KB
to a similar extent ~2 hours after treatment almost exclusively in Kupffer cells, a ~3-4-fold
increase in hepatocytes after 8 hours, and increased in TNFa mRNA between 8 and 24 hours
after a single dose in female rats. In regard to studies that have used the i.p. route of
administration, as noted by Kawamoto et al. (1988b) (see Section E.2.2.11), injection of TCE
may result in paralytic ileus and peritonitis and that subcutaneous treatment paradigm will result
in TCE not immediately being metabolized but retained in the fatty tissue. Wang and Stacey
(1990) state that "intraperitoneal injection is not particularly relevant to humans" and that
intestinal interactions require consideration in responses such as increase serum bile acid (see
Section E.2.6).
E.2.2.1. Merrick et al. (1989)
The focus of this study was the examination of potential differences in toxicity or orally
gavaged TCE administered in corn oil an aqueous vehicle in B6C3Fi mice. As reported by
Melnick et al. (1987) above, corn oil administration appeared to have an effect on peroxisomal
enzyme induction. TCE (99.5% purity) was administered in corn oil or an aqueous solution of
20% Emulphor to 14-17-week-old mice (n = 12/group) at 0, 600, 1,200, and 2,400 mg/kg-day
(males) and 0, 450, 900, and 1,800 mg/kg-day (females) 5 times/week for 4 weeks. The authors
stated that due to "varying lethality in the study, 10 animals per dose group were randomly
selected (where possible) among survivors for histological analysis." Hepatocellular lesions
were characterized:
as a collection of approximately 3-5 necrotic hepatocytes surrounded by
macrophages and polymorphonuclear cells and histopathological grading was
reported as based on the number of necrotic lesions observed in the tissue
sections: 0 = normal; 1 = isolated lesions scattered throughout the section; 2 = one
to five scattered clusters of necrotic lesions; 3 = more than five scattered clusters
of necrotic lesions; and 4 = clusters of necrotic lesions observed throughout the
entire section."
The authors described lipid scoring of each histological section as "0 = no Oil-
Red O staining present; 1 = <10% staining; 2 = 10-25% staining; 3 = 25-30% staining;
and 4 = >50% staining.
The authors reported dose-related increases in lethality in both males and females
exposed to TCE in Emulphor with all male animals dying at 2,400 mg/kg-day with
8/12 females dying at 1,800 mg/kg-day. In both males and females, 2/12 animals also
died at the next highest dose as well with no unscheduled deaths in control or lowest dose
animals. For corn oil gavaged mice, there were 1-2 animals in each TCE treatment
groups of male mice that died while there were no unscheduled deaths in female mice.
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The authors stated that lethality occurred within the first week after chemical exposure.
The authors presented data for final body weight and liver/body weight values for
4 weeks of exposure and listed the number of animals per group to be 10-12 for corn oil
gavaged animals. The reduced number of animals in the Emulphor gavaged animals are
reflective of lethality and limit the usefulness of this measure at the highest doses (i.e.,
1,800 mg/kg-day for female mice). In mice treated with TCE in Emulphor gavage, the
final body weight of control male animals appeared to be lower than those that were
treated with TCE while for female mice the final body weights were similar between
treated and control groups. For male mice treated with Emulphor, body weights were
22.8 ± 0.8, 25.3 ± 0.5, and 24.3 ± 0.4 g for control, 600, and 1,200 mg/kg-day and for
female mice body weights were 20.7 ± 0.4, 21.4 ± 0.3, and 20.5 ± 0.3 g for control, 450,
and 900 mg/kg-day of TCE.
For percent liver/body weight ratios, male mice were reported to have 5.6 ± 0.2,
6.6 ± 0.1, and 7.2 ± 0.2% for control, 600, and 1,200 mg/kg-day and for female mice were 5.1 ±
0.1, 5.8 ± 0.1, and 6.5 ± 0.2% for control, 450 and 900 mg/kg-day of TCE. These values
represent 1.11- and 1.07-fold of control for final body weight in males exposed to 600 and
1,200 mg/kg-day and 1.18- and 1.29-fold of control for percent liver/body weight, respectively.
For females, they represent 1.04- and 0.99-fold of control for final body weights in female
exposed to 450mg/kg-day and 900 mg/kg-day and 1.14- and 1.27-fold of control for percent
liver/body weight, respectively.
In mice treated with corn oil gavage, the final body weight of control male mice was
similar to the TCE treatment groups and higher than the control value for male mice given
Emulphor vehicle (i.e., 22.8 ± 0.8 g for Emulphor control vs. 24.3 ± 0.6 g for corn oil gavage
controls or a difference of-7%). The final body weights of female mice were reported to be
similar between the vehicles and TCE treatment groups. The baseline percent liver/body weight
was also lower for the corn oil gavage control male mice (i.e., 5.6% for Emulphor vs. 4.7% for
corn oil gavage or a difference of-19% that was statistically significant). Although the final
body weights were similar in the female control groups, the percent liver/body weight was
greater in the Emulphor vehicle group (5.1 ± 0.1% in Emulphor vehicle group vs. 4.7 ± 0.1% for
corn oil gavage or a difference of-9%, which was statistically significant). For male mice
treated with corn oil, final body weights were 24.3 ± 0.6, 24.3 ± 0.4, 25.2 ± 0.6, and 25.4 ± 0.5 g
for control, 600, 1,200, and 2,400 mg/kg-day, and for female mice, body weights were 20.2 ±
0.3, 20.8 ± 0.5, 21.8 ± 0.3, and 22.6 ± 0.3 g for control, 450, 900, and 1,800 mg/kg-day of TCE.
For percent liver/body weight ratios, male mice were reported to have 4.7 ± 0.1, 6.4 ±
0.1, 7.7 ± 0.1, and 8.5 ± 0.2% for control, 600, 1,200, and 2,400 mg/kg-day and for female mice
were reported to have 4.7 ± 0.1, 5.5 ± 0.1, 6.0 ± 0.2, and 7.2 ± 0.1% for control, 450, 900, and
1,800 mg/kg-day of TCE. These values represent 1.0-, 1.04-, and 1.04-fold of control for final
body weight in males exposed to 600, 1,200, and 2,400 mg/kg-day TCE and 1.36-, 1.64-, and
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1.81-fold of control for percent liver/body weight, respectively. For females, they represent
1.03-, 1.08-, and 1.12-fold of control for body weight for 450, 900, and 1,800 mg/kg-day and
1.17-, 1.28-, and 1.53-fold of control for percent liver/body weight, respectively.
Because of premature mortality, the difference in TCE treatment between the highest
doses that are vehicle-related cannot be determined. The decreased final body weight and
increased percent liver/body weight ratios in the Emulphor control animals make comparisons of
the exact magnitude of change in these parameters due to TCE exposure difficult to determine as
well as differences between the vehicles. The authors did not present data for age-matched
controls, which did not receive vehicle so that the effects of the vehicles cannot be determined
(i.e., which vehicle control values were most similar to untreated controls given that there was a
difference between the vehicle controls).
A comparison of the percent liver/body weight ratios at comparable doses between the
two vehicles shows little difference in TCE-induced liver weight increases in female mice.
However, the corn oil vehicle group was reported to have a greater increase in comparison to
controls for male mice treated with TCE at the two lower dosage groups. Given that the control
values were approximately 19% higher for the Emulphor group, the apparent differences in
TCE-dose response may have reflected the differences in the control values rather than TCE
exposure. Because controls without vehicle were not examined, it cannot be determined whether
the difference in control values was due to vehicle administration or whether a smaller or
younger group of animals was studied on one of the control groups. The body weight of the
animals was also not reported by the authors at the beginning of the study, so that the impact of
initial differences between groups vs. treatment cannot be accurately determined.
Serum enzyme activities for ALT, AST, and LDH (markers of liver toxicity) showed that
there was no difference between vehicle groups at comparable TCE exposure levels for male or
female mice. Enzyme levels appeared to be elevated in male mice at the higher doses (i.e.,
1,200 and 2,400 mg/kg-day for ALT and 2,400 mg/kg-day for AST), with corn oil gavage
inducing similar increases in LDH levels at 600, 1,200, and 2,400 mg/kg-day TCE. For ALT
and AST, there appeared to be a dose-related increase in male mice with the 2,400 mg/kg-day
treatment group having much greater levels than the 1,200 mg/kg-day group. In Emulphor
treatment groups there was a similar increase in ALT levels in males treated with 1,200 mg/kg
TCE as with those treated with corn oil and those increases were significantly elevated over
control levels. For LDH levels, there were similar increase at 1,200 mg/kg-day TCE for male
mice treated using either Emulphor or corn oil.
The authors report that visible necrosis was observed in 30-40% of male mice
administered TCE in corn oil, but not that there did not appear to be a dose-response (i.e., the
score for severity of necrosis was reported to be 0, 4, 3, and 4 for corn oil control, 600, 1,200,
and 2,400 mg/kg-day treatment groups from 10 male mice in each group). No information in
regard to variation between animals was given by the authors. For male mice given Emulphor
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gavage the extent of necrosis was reported to be 0, 0, and 1 for 0, 600, and 1,200 mg/kg-day TCE
exposure, respectively. For female mice, the extent of necrosis was reported to be 0 for all
control and TCE treatment groups using either vehicle.
Thus, except for LDH levels in male mice exposed to TCE in corn oil, there was not a
correlation with the extent of necrosis and the increases in ALT and AST enzyme levels.
Similarly, there was an increase in ALT levels in male mice treated with 1,200 mg/kg-day
exposure to TCE in Emulphor that did not correspond to increased necrosis.
For Oil-Red O staining, there was a score of 2 in the Emulphor-treated control male and
female mice, while 600 mg/kg-day TCE exposure in Emulphor gavaged male mice and
900 mg/kg-day TCE in corn oil gavaged female mice had a score of 0, along with the corn oil
gavage controls in male mice. For female control mice treated with corn oil gavage, the staining
was reported to have a score of 3. Thus, there did not appear to be a dose-response in Oil-Red
oil staining, although the authors claimed that there appeared to be a dose-related increase with
TCE exposure.
The authors described lesions produced by TCE exposure as:
focal and were surrounded by normal parenchymal tissue. Necrotic areas were
not localized in any particular regions of the lobule. Lesions consisted of central
necrotic cells encompassed by hepatocytes with dark eosinophilic staining
cytoplasm, which progressed to normal-appearing cells. Areas of necrosis were
accompanied by localized inflammation consisting of macrophages and
polymorphonuclear cells.
No specific descriptions of histopathology of mice given Emulphor were provided in
terms of effects of the vehicle or TCE treatment. The scores for necrosis were reported to be
only a 1 for the 1,200 mg/kg-day concentration of TCE in male mice gavaged with Emulphor,
but 3 for male mice given the same concentration of TCE in corn oil. However, enzyme levels
of ALT, AST, and LDH were similarly elevated in both treatment groups.
These results do indicate that administration of TCE for 4 weeks via gavage using
Emulphor resulted in mortality of all of the male mice and most of the female mice at a dose in
corn oil that resulted in few deaths. Not only was there a difference in mortality, but vehicle also
affected the extent of necrosis and enzyme release in the liver (i.e., Emulphor vehicle caused
mortality as the highest dose of TCE in male and female mice that was not apparent from corn
oil gavage, but Emulphor and TCE exposure induced little, if any, focal necrosis in males at
concentrations of TCE in corn oil gavage that caused significant focal necrosis). In regard to
liver weight and body weight changes, TCE exposure in both vehicles at nonlethal doses induced
increased percent liver/body weight changes male and female mice that increased with TCE
exposure level. The difference in baseline control levels between the two vehicle groups
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(especially in males) make a determination of the quantitative difference that the vehicle had on
liver weight gain problematic, although the extent of liver weight increase appeared to be similar
between male and female mice given TCE via Emulphor and female mice given TCE via corn
oil. In general, enzymatic markers of liver toxicity and results for focal hepatocellular necrosis
were not consistent and did not reflect dose-responses in liver weight increases. The extent of
necrosis did not correlate with liver weight increases and was not elevated by TCE treatment in
female mice treated with TCE in either vehicle, or in male mice treated with Emulphor. There
was a reported difference in the extent of necrosis in male mice given TCE via corn oil and
female mice given TCE via corn oil, but the necrosis did not appear to have a dose-response in
male mice. Female mice given corn oil and male and female mice given TCE in Emulphor had
no to negligible necrosis, although they had increased liver weight from TCE exposure.
E.2.2.2. Goel et al. (1992)
The focus of this study was the description of TCE exposure-related changes in mice
after 28 days of exposure with regard to TCE-induced pathological and liver weight change.
Male Swiss mice (20-22 g body weight or 9% difference) were exposed to 0, 500, 1,000, or
2,000 mg/kg-day TCE (BDH analytical grade) by gavage in groundnut oil (n = 6 per group)
5 days/week for 28 days. The ages of the mice were not given by the authors. Livers were
examined for "free -SH contents," total proteins, catalase activity, acid phosphatase activity, and
"protein specific for peroxisomal origin of approx, 80 kd."
The authors report no statistically significant change in body weight with TCE treatment
but a significant increase in liver weight. Body weight (mean ± SE) was reported to be 32.67 ±
1.54, 31.67 ±0.61, 33.00 ± 1.48, and 27.80 ± 1.65 g from exposure to oil control, 500, 1,000,
and 2,000 mg/kg-day TCE, respectively. There was a 15% decrease in body weight at the
highest exposure concentration of TCE that was not statistically significant, but the low number
of animals examined limits the power to detect a significant change. The percent relative
liver/body weight was reported to be 5.29 ± 0.48, 7.00 ± 0.36, 7.40 ± 0.39, and 7.30 ± 0.48%
from exposure to oil control, 500, 1,000, and 2,000 mg/kg-day TCE, respectively. This
represents 1.32-, 1.41-, and 1.38-fold of control in percent liver/body weight for 500, 1,000, and
2,000 mg/kg-day TCE, respectively.
The "free -SH content" in umol -SH/g tissue was reported to be 5.47 ±0.17, 7.46 ±0.21,
7.84 ± 0.34, and 7.10 ± 0.34 from exposure to oil control, 500, 1,000, and 2,000 mg/kg-day TCE,
respectively. This represents 1.37-, 1.44-, and 1.30-fold of control in -SH/g tissue weight for
500, 1,000, and 2,000 mg/kg-day TCE, respectively. Total protein content in the liver in mg/g
tissue was reported to be 170 ±3, 183 ± 5, 192 ± 7, and 188 ± 3 from exposure to oil control,
500, 1,000, and 2,000 mg/kg-day TCE, respectively. This represents 1.08-, 1.13-, and 1.11-fold
of control in total protein content for 500, 1,000, and 2,000 mg/kg-day TCE, respectively. Thus,
the increases in liver weight, "free -SH content," and protein content were generally parallel and
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all suggest that liver weight increases had reached a plateau at the 1,000 mg/kg-day exposure
concentration, perhaps reflecting toxicity at the highest dose as demonstrated by decreased body
weight in this study.
The enzyme activities of 5-ALA dehydrogenase ("a key enzyme in heme biosynthesis"),
catalase, and acid phosphatase were assayed in liver homogenates. Treatment with TCE
decreased 5-ALA dehydrogenase activity to a similar extent at all exposure levels (32-35%
reduction). For catalase the activity as units of catalase/mg, protein was reported to be
25.01 ± 1.81, 32.46 ±2.59, 41.11 ±5.37, and 33.96 ± 3.00 from exposure to oil control, 500,
1,000, and 2,000 mg/kg-day TCE, respectively. This represents 1.30-, 1.64-, and 1.36-fold in
catalase activity for 500, 1,000, and 2,000 mg/kg-day TCE, respectively. The increasing
variability in response with TCE exposure concentration is readily apparent from these data as is
the decrease at the highest dose, perhaps reflective of toxicity. For acid phosphatase activity in
the liver, there was a slight increase (5-11%) with TCE exposure that did not appear to be dose-
related.
The authors report that histologically, "the liver exhibits swelling, vacuolization,
widespread degeneration/necrosis of hepatocytes as well as marked proliferation of endothelial
cells of hepatic sinusoids at 1,000 and 2,000 mg/kg TCE doses." Only one figure is given at the
light microscopic level in which it is impossible to distinguish endothelial cells from Kupffer
cells and no quantitative measures or proliferation were examined or reported to support the
conclusion that endothelial cells are proliferating in response to TCE treatment. Similarly, no
quantitation regarding the extent or location of hepatocellular necrosis is given. The presence or
absence of inflammatory cells was not noted by the authors. In terms of white blood cell count,
the authors noted that it was slightly increased at 500 mg/kg-day but decreased at 1,000 and
2,000 mg/kg-day TCE, perhaps indicating macrophage recruitment from blood to liver and
kidney, which was also noted to have pathology at these concentrations of TCE.
E.2.2.3. Kjellstrand et al. (1981b)
This study was conducted in mice, rats, and gerbils and focused on the effects of 150 ppm
TCE exposure via inhalation on body and organ weight. No other endpoints other than organ
weights were examined in this study and the design of the study is such that quantitative
determinations of the magnitude of TCE response are very limited. NMRI mice (weighing -30 g
with age not given), Sprague-Dawley rats (weighing -200 g with age not given), and Mongolian
gerbils (weighing -60 g with age not given) were exposed to 150-ppm TCE continuously. Mice
were exposed for 2, 5, 9, 16, and 30 days with the number of exposed animals and controls in the
2, 5, 9, and 16 days groups being 10. For 30-day treatments, there were two groups of mice
containing 20 mice per group and one group containing 12 mice per group. In addition, there
was a group of mice (n = 15) exposed to TCE for 30 days and then examined 5 days after
cessation of exposure and another group (n = 20) exposed to TCE for 30 days and then examined
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30 days after cessation of exposure. For rats, there were three groups exposed to TCE for
30 days, which contained 24, 12, and 10 animals per group. For gerbils, there were three groups
exposed to TCE for 30 days, which contained 24, 8, and 8 animals per group. The groups were
reported to consist of equal numbers of males and females but for the mice exposed to TCE for
30 days and then examined 5 days later, the number was 10 males and 5 females. Body weights
were reported to be recorded before and after the exposure period. However, the authors state
"for technical reasons the animals within a group were not individually identified, i.e., we did not
know which initial weight in the group corresponded to which final one." They authors stated
that this design presented problems in assessing the precision of the estimate. They go on to
state that rats and gerbils were partially identifiable as the animals were housed three to a cage
and cage averages could be estimated. Not only were mice in one group housed together, but:
even worse: at the start of the experiment, the mice in M2 [group exposed for
2 days] and M9 [group exposed for 9 days] were housed together, and similarly
M5 [group exposed for 5 days] and Ml6 [group exposed for 16 days]. Thus, we
had, e.g., 10 initial weights for exposed female mice in M2 and M9 where we
could not identify those 5 that were M2 weights. Owing to this bad design
(forced upon us by the lack of exposure units), we could not study weight gains
for mice and so we had to make do with an analysis of final weights.
The problems with the design of this study are obvious from the description given by the
authors themselves. The authors stated that they assumed that the larger the animal, the larger
the weight of its organs so that all organ weights were converted into relative weights as
percentage of body weight. The fallacy of this assumption is obvious, especially if there was
toxicity that decreased body weight and body fat but at the same time caused increased liver
weight, as has been observed in many studies at higher doses of TCE. In fact, Kjellstrand et al.
(1983b) reported that a 150 ppm TCE exposure for 30 days does significantly decreases body
weight while elevating liver weight in a group of 10 male NMRI mice. Thus, the body weight
estimates from this study are inappropriate for comparison to those in studies where body
weights were actually measured. The liver/body weight ratios that would be derived from such
estimates of body weights would be meaningless.
The group averages for body weight reported for female mice at the beginning of the
30-day exposure varied significantly and ranged from 23.2 to 30.2 g (-24%). For males, the
group averages ranged from 27.3 to 31.4 g (-14%). For male mice, there was no weight estimate
for the animals that were exposed for 30 days and then examined 30 days after cessation of
exposure.
The authors only report relative organ weight at the end of the experiment rather than the
liver weights for individual animals. Thus, these values represent extrapolations based on what
body weight may have been. For mice that were exposed to TCE for 30 days and examined after
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30 days of exposure, male mice were reported to have "relative organ weight" for liver of 4.70 ±
0.10 vs. 4.27 ± 0.13% for controls. However, there were no initial body weights reported for
these male mice, and the body weights are extrapolated values. Female mice exposed for
30 days and examined 30 days after cessation of exposure were reported to have "relative organ
weights" for liver of 4.42 ± 0.11 vs. 3.62 ± 0.09% for controls. The group average of initial
body weights for this group was reported by the authors.
Although the initial body weight for female control mice as a group average was reported
to be similar between the female group exposed to 30 days of TCE and sacrificed 30 days later
and those exposed for 30 days and sacrificed 5 days later (30.0 vs. 30.8 g), the liver/body weight
ratio varied significantly in these controls (4.25 ± 0.19 vs. 3.62 ± 0.09) as did the number of
animals studied (5 female mice in the animals sacrificed after 5 days exposure vs. 10 female
mice in the group sacrificed after 30 days exposure). In addition, although there were
differences between the three groups of mice exposed to TCE for 30 days and then sacrificed
immediately, the authors present the data for extrapolated liver/body weight as pooled results
between the three groups. In comparison to control values, the authors report 1.14-, 1.35-, 1.58-,
1.47-, and 1.75-fold of control for percent liver/body weight using body weight extrapolated
values in male mice at 2, 5, 9, 16, and 30 days of TCE exposure, respectively. For females, they
report 1.27-, 1.28-, 1.49-, 1.41-, and 1.74-fold of control at 2, 5, 9, 16, and 30 days of TCE,
respectively.
Although the authors combine female and male relative increases in liver weight in a
figure, assign error bars around these data point, and attempt to draw assign a time-response
curve to it, it is clear that these data, especially for female mice, do not display time-dependent
increase in liver/body weight from 5 to 16 days of exposure and that a comparison of results
between 5 and 26 animals is very limited in interpretation. Of note is the wide variation in the
control values for relative liver/body weight.
For male mice, there did not seem to be a consistent pattern with increasing duration of
the experiment, with values of 4.61, 5.15, 5.05, 4.93, and 4.04% for 2-, 5-, 9-, 16-, and 30-day
exposure groups. This represented a difference of-27%. For female mice, the relative
liver/body weight was 4.14, 4.58, 4.61, 4.70, and 3.99% for 2-, 5-, 9-, 16-, and 30-day exposure
groups. Thus, it appears that the average relative liver/body weight percent was higher in the 5-,
9-, and 16-day treatment group for both genders than that in the 30-day group and was consistent
between these days. There is no apparent reason for there to be such large difference between
the 16- and 30-day treatment groups due to increasing age of the animals. Of note is that for the
control groups paired with animals treated for 30 days and then examined 30 days later, the male
mice had increased relative liver/body weights (4.27 vs. 4.04%), but that the females had
decreases (3.62 vs. 3.99%). Such variation between controls does not appear to be age or size
related, but rather due to variations in measure or extrapolations, which can affect comparisons
between treated and untreated groups and add more uncertainty to the estimates. In addition, the
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number of mice in the groups exposed to 2-16 days were only 5 animals for each gender in each
group, while the number of animals reported in the 30-day exposure group numbered 26 for each
gender.
For animals exposed to 30 days and then examined after 5 or 30 days, male mice were
reported to have percent liver/body weight 1.26- and 1.10-fold of control after 5 and 30 days
cessation of exposure, while female mice were reported to have values of 1.14- and 1.22-fold of
control after 5 and 30 days cessation of exposure, respectively. Again, the male mice exposed
for 30 days and then examined after 30 days of cessation of exposure did not have reported
initial body weights, giving this value a great deal of uncertainty. Thus, while liver weights
appeared to increase during 30 days of exposure to TCE and decrease after cessation of exposure
in both genders of mice, the magnitudes of the increases and decreases cannot be determined
from this experimental design. Of note is that liver weights appeared to still be elevated after
30 days of cessation exposure.
In regard to initial weights, the authors reported that the initial weights of the rats were
different in the three experiments they conducted with them and state that "in those 2 where
differences were found in females, their initial weights were about 200 g and 220 g, respectively,
while the corresponding weights were only about 160 g in that experiment where no differences
were found." The differences in initial body weight of the rat groups were significant. In
females, group averages were 198, 158, and 224 g, for groups 1, 2, and 3, respectively, and for
males, group averages were 222, 166, and 248 g for groups 1, 2, and 3 respectively. This
represents as much as a 50% difference in initial body weights between these TCE treatment
groups. Control values varied as well with group averages for controls ranging from 167 g for
group 2 to 246 g for group 3 at the start of exposure. For female rats, control groups ranged from
158 to 219 g at the start of the experiment.
The number of animals in each group varied greatly as well, making quantitative
comparison even more difficult with the numbers varying between 5 and 12 for each gender in
rats exposed for 30 days to TCE. The authors pooled the results for these very disparate groups
of rats in their reporting of relative organ weights. They reported 1.26- and 1.21-fold of control
in male and female rat percent relative liver/body weight after 30 days of TCE exposure.
However, as stated above, these estimates are limited in their ability to provide a quantitative
estimate of liver weight increase due to TCE.
There were evidently differences between the groups of gerbils in response to TCE with
one group reported to have larger weight gain than control and the other two groups reported to
not show a difference by the authors. Of the three groups of gerbils, group 1 contained
12 animals per gender but groups 2 and 3 only 4 animals per gender. As with the rat
experiments, the initial average weights for the groups varied significantly (30% in females and
males). The authors pooled the results for these very disparate groups of gerbils in their
reporting of relative organ weights as well. They reported a nearly identical increase in relative
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liver/body weight increase for gerbils (1.22-fold of control value in males and 1.25-fold in
females) as for the rats after 30 days of TCE exposure. However, similar caveats should be
applied in the confidence in this experimental design to determine the magnitudes of response to
TCE exposure.
E.2.2.4. Woolhiser et al. (2006)
An unpublished report by Woolhiser et al. (2006) was received by the U.S. EPA to fill
the "priority data needed" for the immunotoxicity of TCE as identified by the ATSDR and
designed to satisfy U.S. EPA OPPTS 870.7800 Immunotoxicity Test Guidelines. The study was
conducted on behalf of the Halogenated Solvents Industry Alliance and has been submitted to the
U.S. EPA but not published. Although conducted as an immunotoxicity study, it does contain
information regarding liver weight increases in female Sprague-Dawley female rats exposed to 0,
100, 300, and 1,000 ppm TCE for 6 hours/day, 5 days/week for 4 weeks. The rats were 7 weeks
of age at the start of the study. The report gives data for body weight and food weight for
16 animals per exposure group and the mean body weights ranged between 181.8 and 185.5 g on
the first day of the experiment. Animals were weighed pre-exposure, twice during the first week,
and then "at least weekly throughout the study." All rats were immunized with a single i.v.
injection of SRBCs via the tail vein at day 25. Liver weights were taken and samples of liver
retained "should histopathological examination have been deemed necessary." But,
histopathological analysis was not conducted on the liver.
The effect on body weight gain by TCE inhalation exposure was shown by 5 days and
continued for 10 days of exposure in the 300 and 1,000 ppm groups. By day 28, the mean body
weight for the control group was reported to be 245.7 g, but 234.4, 232.4, and 232.4 g for the
100, 300, and 1,000 ppm groups, respectively. Food consumption was reported to be decreased
in the day 1-5 measurement period for the 300 and 1,000 ppm exposure groups and in the 5-
10-day measurement period for the 100 ppm group.
Although body weight and food consumption data are available for 16 animals per
exposure group, for organ and organ/body weight summary data, the report gives information for
only eight rats per group. The report gives individual animal data in its appendix so that the data
for the eight animals in each group examined for organ weight changes could be examined
separately. The final body weights were reported to be 217.2, 212.4, 203.9, and 206.9 g for the
control, 100, 300, and 1,000 ppm exposure groups containing only eight animals. For the
8-animal exposure groups, the mean initial body weights were 186.6, 183.7, 181.6, and 181.9 g
for the control, 100, 300, and 1,000 ppm groups. Thus, there was a difference from the initial
and final body weight values given for the groups containing 16 rats and those containing 8 rats.
The ranges of initial body weights for the eight animals were 169.8-204.3, 162.0-191.2, 169.0-
201.5, and 168.2-193.7 g for the control, 100-, 300 -, and 1,000-ppm groups. Thus, the control
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group began with a larger mean value and large range of values (20% difference between highest
and lowest weight rat) than the other groups.
In terms of the percent liver/body weight ratios, an increase due to TCE exposure is
reported in female rats, although body weights were larger in the control group and the two
higher exposure groups did not gain body weight to the same extent as controls. The mean
percent liver/body weight ratios were 3.23, 3.39, 3.44, and 3.65%, respectively, for the control,
100, 300, and 1,000 ppm exposure groups. This represented 1.05-, 1.07-, and 1.13-fold of
control percent liver/body weight changes in the 100, 300, and 1,000 ppm groups. However, the
small number of animals and the variation in initial animal weight limit the ability of this study
to determine statistically significant increases and the authors report that only the 1,000 ppm
group had statistically significant liver weight increases.
E.2.2.5. Kjellstrand et al. (1983b)
This study examined seven strains of mice (wild, C57BL, DBA, B6CBA, A/sn, NZB, and
NMRI) after continuous inhalation exposure to 150 ppm TCE for 30 days. "Wild" mice were
reported to be composed of "three different strains: 1. Hairless (HR) from the original strain,
2. Swiss (outbred), and 3. Furtype Black Pelage (of unknown strain)." The authors did not state
the age of the animals prior to TCE exposure, but stated that weight-matched controls were
exposed to air only chambers. The authors stated that "the exposure methods" have been
described earlier (Kjellstrand et al., 1980) but the only reference provided was (Kjellstrand et al.,
1981b). In both this study (Kjellstrand et al., 1983b) and the 1981 study, animals were
continuously exposed with only a few hours of cessation of exposure noted each week, for a
change of food and bedding. Under this paradigm, there is the possibility of additional oral
exposure to TCE due to grooming and consumption of TCE on food in the chamber.
The study was reported to be composed of two independent experiments with the
exception of strain NMRI, which had been studied in Kjellstrand et al. (1983a: 1981b). The
number of animals examined in this study ranged from three to six in each treatment group. The
authors reported "significant difference between the animals intended for TCE exposure and the
matched controls intended for air-exposure were seen in four cases (Table 1)," and stated that the
grouping effects developed during the 7-day adaptation period. Premature mortality was
attributed to an accident for one TCE-exposed DBA male and fighting to the deaths of two
TCE-exposed NZB females and one B6CB A male in each air exposed chamber. Given the small
number of animals examined in this study in each group, such losses significantly decrease the
power of the study to detect TCE-induced changes. The range of initial body weights between
the groups of male mice for all strains was between 18 g (as mean value for the A/sn strain) and
32 g (as mean value for the B6CBA strain) or -44%. For females, the range of initial body
weights between groups for all strains was 15 g (as mean value for the A/sn strain) and 24 g (as
mean value for the DBA strain) or -38%.
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Rather than reporting percent liver/body weight ratios or an extrapolated value, as was
done in Kj ell strand et al. (1981b), this study only reported actual liver weights for treated and
exposed groups at the end of 30 days of exposure. The authors reported final body weight
changes in comparison to matched control groups at the end of the exposure periods but not the
changes in body weight for individual animals. They reported the results from statistical
analyses of the difference in values between TCE and air-exposed groups.
A statistically significant decrease in body weight was reported between TCE-exposed
and control mice in experiment 1 of the C57BL male mice (-20% reduction in body weight due
to TCE exposure). This group also had a slight but statistically significant difference in body
weight at the beginning of exposure, with the control group having a -5% difference in starting
weight. There was also a statistically significant decrease in body weight of 20% reported after
TCE exposure in one group of male B6CBA mice that did not have a difference in body weight
at the beginning of the experiment between treatment and control groups. One group of female
and both groups of male A/sn mice had statistically significant decreases in body weight after
TCE exposure (10% for the females, and 22 and 26% decreases in the two male groups) in
comparison to untreated mice of the same strain. The magnitude of body weight decrease in this
strain after TCE treatment also reflects differences in initial body weight as there were also
differences in initial body weight between the two groups of both treated and untreated A/sn
males that were statistically significant, 17 and 10% respectively. One group of male NZB mice
had a significant increase in body weight after TCE exposure of 14% compared to untreated
animals. A female group from the same strain treated with TCE was reported to have a
nonsignificant 7% increase in final body weight in comparison to its untreated group. The one
group of male NMRI mice (n = 10) in this study was reported to have a statistically significant
12% decrease in body weight compared to controls.
For the groups of animals with reported TCE exposure-related changes in final body
weight compared to untreated animals, such body weight changes may also have affected the
liver weights changes reported. The authors did not explicitly state that they did not record liver
and body weights specifically for each animal, and thus, would be unable to determine liver/body
weight ratios for each. However, they did state that the animals were housed 4-6 in each cage
and placed in exposure chambers together. The authors only present data for body and liver
weights as the means for a cage group in the reporting of their results. While this approach lends
more certainty in their measurements than the approach taken by Kj ell strand et al. (1981b) as
described above, the relative liver/body weights cannot be determined for individual animals.
It appears that the authors tried to carefully match the body weights of the control and
exposed mice at the beginning of the experiment to minimize the effects of initial body weight
differences and distinguish the effects of treatment on body weight and liver weight. However,
there was no ability to determine liver/body weight ratios and adjust for difference in initial body
weight from changes due to TCE exposure. For the groups in which there was no change in
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body weight after TCE treatment and in which there was no difference in initial body weight
between controls and TCE-exposed groups, the reporting of liver weight changes due to TCE
exposure is a clearer reflection of TCE-induced effects and the magnitude of such effects.
Nevertheless, the small number of animals examined in each group is still a limitation on the
ability to determine the magnitude of such responses and there statistical significance.
In wild-type mice, there were no reported significant differences in the initial and final
body weight of male or female mice before or after 30 days of TCE exposure. For these groups
there was 1.76- and 1.80-fold of control values for liver weight in groups 1 and 2 for female
mice, and for males 1.84- and 1.62-fold of control values for groups 1 and 2, respectively. For
DBA mice, there were no reported significant differences in the initial and final body weight of
male or female mice before or after 30 days of TCE exposure. For DBA mice, there was
1.87- and 1.88-fold of control for liver weight in groups 1 and 2 for female mice, and 1.45- and
2.00-fold of control for group 1 and 2 males, respectively. These groups represent the most
accurate data for TCE-induced changes in liver weight not affected by initial differences in body
weight or systemic effects of TCE, which resulted in decreased body weight gain. These results
suggest that there is more variability in TCE-induced liver weight gain between groups of male
than female mice.
The C57BL, B6CBA, NZB, and NMRI groups all had at least one group of male mice
with changes in body weight due to TCE exposure. The A/sn group had not only decreased body
weight in both male groups after TCE exposure (along with differences between exposed and
control groups at the initiation of exposure), but also decreased body weight in one of the female
groups. Thus, the results for TCE-induced liver weight change in these male groups also
reflected changes in body weight. These results suggest a strain-related increased sensitivity to
TCE toxicity as reflected by decreased body weight.
For C57BL mice, there was 1.65- and 1.60-fold of control for liver weight after TCE
exposure was reported in groups 1 and 2 for female mice, and for males, 1.28-fold (the group
with decreased body weight) and 1.82-fold of control values for groups 1 and 2, respectively.
For B6CBA mice there was 1.70- and 1.69-fold of controls values for liver weight after TCE
exposure in groups 1 and 2 for female mice, and for males, 1.21-fold (the group with decreased
body weight) and 1.47-fold of control values reported for groups 1 and 2, respectively. For the
NZB mice, there was 2.09-fold (n = 3) and 2.08-fold of control values for liver weight after TCE
exposure in groups 1 and 2 for female mice, and for males, 2.34- and 3.57-fold (the group with
increased body weight) of control values reported for groups 1 and 2, respectively. For the
NMRI mice, whose results were reported for one group with 10 mice, there was 1.66-fold of
control value for liver weight after TCE exposure for female mice, and for males, 1.68-fold of
control value reported (a group with decreased body weight). Finally, for the A/sn strain that had
decreased body weight in all groups but one after TCE exposure and significantly smaller body
weights in the control groups before TCE exposure in both male groups, the results still show
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TCE-related liver weight increases. For the As/n mice, there was 1.56- and 1.72-fold (a group
with decreased body weight) of control value for liver weight in groups 1 and 2 for female mice,
and for males, 1.62-fold (a group with decreased body weight) and 1.58-fold (a group with
decreased body weight) of control values reported for groups 1 and 2, respectively.
The consistency between groups of female mice of the same strain for TCE-induced liver
weight gain, regardless of strain examined, is striking. The largest difference within female
strain groups occurred in the only strain in which there was a decrease in TCE-induced body
weight. For males, even in strains that did not show TCE-related changes in body weight, there
was greater variation between groups than in females. For strains in which one group had
TCE-related changes in body weight and another did not, the group with the body weight
decrease always had a lower liver weight as well. Groups that had increased body weight after
TCE exposure also had an increased liver weight in comparison to the groups without a body
weight change. These results demonstrate the importance of carefully matching control animals
to treated animals and the importance of the effect of systemic toxicity, as measured by body
weight decreases, on the determination of the magnitude of liver weight gain induced by TCE
exposure. These results also show the increased variation in TCE-induced liver weight gain
between groups of male mice and an increase incidence of body weight changes due to TCE
exposure in comparison to females, regardless of strain.
In terms of strain sensitivity, it is important not only to take into account differing effects
on body weight changes due to TCE exposure but also to compare animals of the same age or
beginning weight as these, parameters may also affect liver weight gain or toxicity induced by
TCE exposure. The authors do not state the age of the animals at the beginning of exposure and
report, as stated above, a range of initial body weights between the groups as much as 44% for
males and 38% for females. These differences can be due to strain and age. The differences in
final body weight between the groups of controls, when all animals would have been 30 days
older and more mature, was still as much as 48% for males and 44% for females.
The data for female mice, in which body weight was decreased by TCE exposure only in
one group in one strain, suggest that the magnitude of TCE-induced liver weight increase was
correlated with body weight of the animals at the beginning of the experiment. For the C57BL
and As/n strains, female mice starting weights were averaged 17.5 and 15.5 g, respectively,
while the average liver weights were 1.63- and 1.64-fold of control after TCE exposure,
respectively. For the B6CBA, wild-type, DBA, and NZB female groups, the starting body
weights averaged 22.5, 21.0, 23.0, and 21.0 g, respectively, while the average liver weight
increases were 1.70-, 1.78-, 1.88-, and 2.09-fold of control after TCE exposure. Thus, groups of
female mice with higher body weights, regardless of strain, generally had higher increases in
TCE-induced liver weight increases.
The NMRI group of female mice, did not follow this general pattern and had the highest
initial body weight for the single group of 10 mice reported (i.e., 27 g) associated with a
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1.66-fold of control value for liver weight. It is probable that the data for these mice had been
collected from another study. In fact, the starting weights reported for these groups of 10 mice
are identical to the starting weights reported for 26 mice examined in Kjellstrand et al. (1981b).
However, while this study reports a 1.66-fold of control value for liver weight after 30 days of
TCE exposure, the extrapolated percent liver/body weight given in the 1981 study for 30 days of
TCE exposure was 1.74-fold of control in female NMRI mice. In the Kjellstrand et al. (1983a)
study, discussed below, 10 female mice were reported to have a 1.66-fold of control value for
liver weight after 30 days exposure to 150 ppm TCE with an initial starting weight of 26.7 g.
Thus, these data appear to be from that study. Thus, differences in study design, variation
between experiments, and strain differences may account for the differences results reported in
Kjellstrand et al. (1983b) for NMRI mice and the other strains in regard to the relationship to
initial body weight and TCE response of liver weight gain.
These data suggest that initial body weight is a factor in the magnitude of TCE-induced
liver weight induction rather than just strain. For male mice, there appeared to be a difference
between strains in TCE-induced body weight reduction, which in turn affects liver weight. The
DBA and wild-type mice appeared to be the most resistant to this effect (with no groups
affected), while the C57BL, B6CBA, and NZB strains appearing to have at least one group
affected, and the A/sn strain having both groups of males affected. Only one group of NMRI
mice were reported in this study and that group had TCE-induced decreases in body weight.
As stated above, there appeared to be much greater differences between groups of males
within the same strain in regard to liver weight increases than for females and that the increases
appeared to be affected by concurrent body weight changes. In general, the strains and groups
within strains, that had TCE-induced body weight decreases had the smallest increases in liver
weight, while those with no TCE-induced changes in body weight in comparison to untreated
animals (i.e., wild-type and DBA) or had an actual increase in body weight (one group of NZB
mice) had the greatest TCE-induced increase in liver weight. Therefore, only examining liver
weight in males rather than percent liver/body weight ratios would not be an accurate predictor
of strain sensitivity at this dose due to differences in initial body weight and TCE-induced body
weight changes.
E.2.2.6. Kjellstrand et al. (1983a)
This study was conducted in male and female NMRI mice with a similar design as
Kjellstrand et al. (1983b). The ages of the mice were not given by the authors. Animals were
housed 10 animals per cage and exposed from 30 to 120 days at concentrations ranging from
37 to 3,600 ppm TCE. TCE was stabilized with 0.01% thymol and 0.03% diisopropylene.
Animals were exposed continuously with exposure chambers being opened twice a week for
change of bedding food and water resulting in a drop in TCE concentration of ~1 hour. A group
of mice was exposed intermittently with TCE at night for 16 hours. This paradigm results not
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only in inhalation exposure, but also oral exposure from TCE adsorption to food and grooming
behavior. The authors state that "the different methodological aspects linked to statistical
treatment of body and organ weights have been discussed earlier (Kjellstrand et al., 1981b). The
same air-exposed control was used in three cases." The design of the experiment, in terms of
measurement of individual organ and body weights and the inability to assign a percent
liver/body weight for each animal, and limitations are similar to that of Kjellstrand et al. (1983a).
The exposure design was for groups of male and female mice to be exposed to 37, 75,
150, and 300 ppm TCE continuously for 30 days (n = 10 per gender and group except for the
37 ppm exposure groups) and then for liver weight and body weight to be determined.
Additional groups of animals were exposed for 150 ppm continuously for 120 days (n = 10).
Intermittent exposure of 4 hours/day for 7 days/week were conducted for 120 days at 900 ppm
and examined immediately or 30 days after cessation of exposure (n = 10). Intermittent
exposures of 16 hours/day at 255-ppm group (n = 10), 8 hours/day at 450 ppm, 4 hours/day at
900 ppm, 2 hours/day at 1,800 ppm, and 1 hour/day at 3,600 ppm 7 days/week for 30 days were
also conducted (n = 10 per group).
As in Kjellstrand et al. (1983b), body weights for individual animals were not recorded in
a way that the initial and final body weights could be compared. The approach taken by the
authors was to match the control group at the initiation of exposure and compare control and
treated average values. At the beginning of the experiment, only one group began the
experiment with a statistically significant change in body weight between treated and control
animals (female mice exposed 16 hours a day for 30 days). In regard to final body weight, which
would indicate systemic TCE toxicity, five groups had significantly decreased body weight (i.e.,
males exposed to 150 ppm continuously for 30 or 120 days, males and females exposed
continuously to 300 ppm for 30 days) and two groups significantly increased body weight (i.e.,
males exposed to 1,800 ppm for 2 hours/day and 3,600 ppm for 1 hour/day for 30 days) after
TCE exposure.
Thus, the accuracy of determining the effect of TCE on liver weight changes, reported by
the authors in this study for groups in which body weight were also affected by TCE exposure,
would be affected by similar issues as for data presented by Kjellstand et al. (1983b). In
addition, comparison in results between the 37 ppm exposure groups and those of the other
groups would be affected by difference in number of animals examined (10 vs. 20). As with
Kjellstrand et al. (1983b), the ages of the animals in this study are not given by the author.
Difference in initial body weight (which can be affected by age and strain) reported by
Kjellstrand et al. (1983b) appeared to be correlated with the degree of TCE-induced change in
liver weight. Although each exposed group was matched to a control group with a similar
average weight, the average initial body weights in this study varied between groups (i.e., as
much as 14% in female control, 16% in TCE-exposed female mice, 12% in male control, and
16% in male exposed mice).
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For female mice exposed to 37-300 ppm TCE continuously for 30 days, only the 300 pm
group experienced a 16% decrease in body weight between control and exposed animals. Thus,
liver weight increased reported by this study after TCE exposure were not affected by changes in
body weight for exposures <300 ppm in female mice. Initial body weights in the TCE-exposed
female mice were similar in each of these groups (i.e., range of 29.2-31.6 g, or 8%), with the
exception of the females exposed to 150 ppm TCE for 30 days (i.e., initial body weight of
27.3 g), reducing the effects of differences in initial body weight on TCE-induced liver weight
induction. Exposure to TCE continuously for 30 days resulted in a dose-dependent change in
liver weight in female mice with 1.06-, 1.27-, 1.66-, and 2.14-fold of control values reported for
liver weight at 37, 75, 150, and 300 ppm TCE, respectively. In females, the increase at 300 ppm
was accompanied by statistically significant decreased body weight in the TCE exposed groups
compared to control (-16%). Thus, the response in liver weight gain at that exposure is in the
presence of toxicity. However, the TCE-induced increases in liver weight consistently increased
with dose of TCE in a linear fashion.
For male mice exposed to 37-300 ppm TCE continuously for 30 days, both the 150 and
300 ppm groups experienced a 10 and 18% decrease in body weight after TCE exposure,
respectively. The 37 and 75 ppm groups did not have decreased body weight due to TCE
exposure, but varied by 12% in initial body weight. Thus, there are more factors affecting
reported liver weight increases from TCE exposure in the male than female mice, most
importantly toxicity. Exposure to TCE continuously for 30 days resulted in liver weights of
1.15-, 1.50-, 1.69-, and 1.90-fold of control for 37, 75, 150, and 300 ppm, respectively. The
flattening of the dose-response curve for liver weight in the male mice is consistent with the
effects of toxicity at the two highest doses, and thus, the magnitude of response at these doses
should be viewed with caution. Consistent with Kjellstrand et al. (1983b) results, male mice in
this study appeared to have a higher incidence of TCE-induced body weight changes than female
mice.
The effects of extended exposure, lower durations of exposure but at higher
concentrations, and of cessation of exposure were examined for >150 ppm TCE. Mice exposed
to TCE at 150 ppm continuously for 120 days were reported to have increased liver weight (i.e.,
1.57-fold of control for females and 1.49-fold of control for males), but in the case of male mice,
also to have a significant decrease in body weight of 17% in comparison to control groups.
Increasing the exposure concentration to 900-ppm TCE and reducing exposure time to
4 hours/day for 120 days also resulted in increased liver weight (i.e., 1.35-fold of control for
females and 1.49-fold of controls for males) but with a significant decrease in body weight in
females of 7% in comparison to control groups. For mice that were exposed to 150 ppm TCE for
30 days and then examined 120 days after the cessation of exposure, liver weights were 1.09-fold
of control for female mice and the same as controls for male mice.
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With the exception of 1,800 and 3,600 ppm TCE groups exposed at 2 and 1 hour,
respectively, exposure from 225, 450, and 900 ppm at 16, 8, and 4 hours, respectively, for
30 days did not result in decreased body weight in males or female mice. These exposures did
result in increased liver weights in relation to control groups and for female mice the magnitude
of increase was similar (i.e., 1.50-, 1.54-, and 1.51-fold of control for liver weight after exposure
to 225 ppm TCE 16 hours/day, 450 ppm TCE 8 hours/day, and 900 ppm TCE 4 hours/day,
respectively). For these groups, initial body weights varied by 13% in females and 14% in
males. Thus, under circumstances without body weight changes due to TCE toxicity, liver
weight appeared to have a consistent relationship with the product of duration and concentration
of exposure in female mice.
For male mice, the increases in TCE-induced liver weight were more variable (i.e., 1.94-,
1.74-, and 1.61-fold of control for liver weight after exposure to 225 ppm TCE 16 hours/day,
450 ppm TCE 8 hours/day, and 900 ppm TCE 4 hours/day, respectively) with the product of
exposure duration and concentration did not result in a consistent response in males (e.g., a lower
dose for a longer duration of exposure resulted in a greater response than a larger dose at a
shorter duration of exposure).
Kjellstrand et al. (1983a) reported light microscopic findings from this study and report
that:
after 150 ppm exposure for 30 days, the normal trabecular arrangement of the
liver cells remained. However, the liver cells were generally larger and often
displayed a fine vacuolization of the cytoplasm. The nucleoli varied slightly to
moderately in size and shape and had a finer, granular chromatin with a varying
basophilic staining intensity. The Kupffer cells of the sinusoid were increased in
cellular and nuclear size. The intralobular connective tissue was infiltrated by
inflammatory cells. There was no sign of bile stasis. Exposure to TCE in higher
or lower concentrations during the 30 days produced a similar morphologic
picture. After intermittent exposure for 30 days to a time weighted average
concentration of 150 ppm or continuous exposure for 120 days, the trabecular
cellular arrangement was less well preserved. The cells had increased in size and
the variations in size and shape of the cells were much greater. The nuclei also
displayed a greater variation in basophilic staining intensity, and often had one or
two enlarged nucleoli. Mitosis was also more frequent in the groups exposed for
longer intervals. The vacuolization of the cytoplasm was also much more
pronounced. Inflammatory cell infiltration in the interlobular connective tissue
was more prominent. After exposure to 150 ppm for 30 days, followed by
120 days of rehabilitation, the morphological picture was similar to that of the air-
exposure controls except for changes in cellular and nuclear sizes.
Although not reporting comparisons between changes in male and female mice in the
results section of the paper, the authors stated in the discussion section that "However, liver mass
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increase and the changes in liver cell morphology were similar in TCE-exposed male and female
mice."
The authors do not present any quantitative data on the lesions they describe, especially
in terms of dose-response. Most of the qualitative description is for the 150 ppm exposure level,
in which there are consistent reports of TCE induced body weight decreases in male mice. The
authors suggest that lower concentrations of TCE give a similar pathology as those at the
150 ppm, but did not present data to support that conclusion. Although stating that Kupffer cells
were increased in cellular and nuclear size, no differential staining was applied light microscopy
sections distinguish Kupffer from endothelial cells lining the hepatic sinusoid in this study.
Without differential staining, such a determination is difficult at the light microscopic level.
Indeed, Goel et al. (1992) describe proliferation of sinusoidal endothelial cells after 1,000 and
2,000 mg/kg-day TCE exposure for 28 days in male Swiss mice. However, the described
inflammatory cell infiltrates in the Kjellstrand et al. (1983a) study are consistent with invasion of
macrophages and well as polymorphonuclear cells into the liver, which could activate resident
Kupffer cells.
Although not specifically describing the changes as consistent with increased
polyploidization of hepatocytes, the changes in cell size and especially the continued change in
cell size and nuclear staining characteristics after 120 days of cessation of exposure are
consistent with changes in polyploidization induced by TCE. Of note is that in the histological
description provided by the authors, although vacuolization is reported and consistent with
hepatotoxicity or lipid accumulation, which is lost during routine histological slide preparation,
there is no mention of focal necrosis or apoptosis resulting from these exposures to TCE.
E.2.2.7. Buben and O'Flaherty (1985)
This study was conducted with older mice than those generally used in chronic exposure
assays (male Swiss-Cox outbred mice between 3 and 5 months of age) with a weight range
reported between 34 and 45 g. The mice were administered distilled TCE in corn oil by gavage
5 times/week for 6 weeks at exposure concentrations of either 0, 100, 200, 400, 800, 1,600,
2,400, or 3,200 mg TCE/kg-day. While 12-15 mice were used in most exposure groups, the
100 and 3,200 mg/kg groups contained 4-6 mice and the two control groups consisted of 24 and
26 mice. Liver toxicity was determined by "liver weight increases, decreases in liver glucose-
6-phosphate (G6P) activity, increases in liver triglycerides, and increases in serum glutamate-
pyruvate transaminase (SGPT) activity." Livers were perfused with cold saline prior to testing
for weight and enzyme activity and hepatic DNA was measured.
The authors reported the mice to tolerate the 6-week exposed with TCE with few deaths
occurring except at the highest dose and that such deaths were related to CNS depression. Mice
in all dose groups were reported to continue to gain weight throughout the 6-week dosing period.
However, TCE exposure caused "dose-related increases in liver weight to body weight ratio and
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since body weight of mice were generally unaffected by treatment, the increases represent true
liver weight increases." Exposure concentrations, as low as 100 mg/kg-day, were reported to be
"sufficient to cause statistically significant increase in the liver weight/body weight ratio," and
the increases in liver size to be "attributable to hypertrophy of the liver cells, as revealed by
histological examination and by a decrease in the DNA concentration in the livers."
Mice in the highest dose group were reported to display liver weight/body weight ratios
that were about -75% greater than those of controls and even at the lowest dose there was a
statistically significant increase (i.e., control liver/body weight percent was reported to be 5.22 ±
0.09 vs. 5.85 ± 0.20% in 100 mg/kg-day exposed mice). The percent liver/body ratios were
5.22 ± 0.09, 5.84 ± 0.20, 5.99 ± 0.13, 6.51 ± 0.12, 7.12 ± 0.12, 8.51 ± 0.20, 8.82 ± 0.15, and
9.12 ± 0.15% for control (n = 24), 100 (n = 5), 200 (n = 12), 400 (n = 12), 800 (n = 12),
1,600 (n = 12), 2,400 (n = 12), and 3,200 (n = 4) mg/kg-day TCE. This represents 1.12-, 1.15-,
1.25-, 1.36-, 1.63-, 1.69-, and 1.75-fold of control for these doses. All dose groups of TCE
induced a statistically significant increase in liver/body weight ratios. For the 200-1,600 mg/kg-
day exposure levels, the magnitudes of the increases in TCE exposure concentrations were
similar to the magnitudes of TCE-induced increases in percent liver/body weight ratios (i.e., an
approximately twofold increase in TCE dose resulted in ~1.7-fold increase change in percent
liver/body weight).
TCE exposure was reported to induce a dose-related trend towards increased triglycerides
(i.e., control values of 3.08 ± 0.29 vs. 6.89 ± 1.40 at 2,400 mg/kg TCE) with variation of
response increased with TCE exposure. For liver triglycerides, the reported values in mg/g liver
were 3.08 ± 0.29 (n = 24), 3.12 ± 0.49 (n = 5), 4.41 ± 0.76 (n = 12), 4.53 ± 1.05 (n = 12),
5.76 ± 0.85 (n = 12), 5.82 ± 0.93 (n = 12), 6.89 ± 1.40 (n = 12), and 7.02 ± 0.69 (n = 4) for
control, 100, 200, 400, 800, 1,600, 2,400, and 3,200 mg/kg-day dose groups, respectively.
For G6P, the values in ug phosphate/mg protein/20 minutes were 125.5 ± 3.2 (n = 12),
117.8±6.0(n = 5), 116.4 ± 2.8 (n = 9), 117.3 ± 4.6 (n = 9), 111.7 ± 3.3 (n = 9), 89.9 ± 1.7
(n = 9), 83.8 ± 2.1 (n = 8), and 83.0 ± 7.0 (n = 3) for the same dose groups. Only the
2,400 mg/kg-day group was reported to be statistically significantly increased for triglycerides
after TCE exposure although there appeared to be a dose-response. For decreases in G6P, doses
>800 mg/kg-day were statistically significant.
The numbers of animals varied between groups in this study but, in particular, only a
subset of the animals were tested for G6P with the authors providing no rationale for the
selection of animals for this assay. The differences in the number of animals per group and small
number of animals per group affected the ability to determine a statistically significant change in
these parameters but the changes in liver weights were robust enough and the variation was small
enough between groups that all TCE-induced changes were described as statistically significant.
The livers of TCE treated mice, although enlarged, were reported to appear normal.
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A dose-related decrease in G6P activity was reported with similar small decreases
(-10%) observed in the TCE exposed groups that did not reach statistical significance until the
dose reached 800 mg/kg TCE exposure. SGPT activity was not observed to be increased in
TCE-treated mice except at the two highest doses and even at the 2,400 mg/kg-day dose half of
the mice had normal values. The large variability in SGPT activity was indicative of
heterogeneity of this response between mice at the higher exposure levels for this indicator of
liver toxicity. However, the results of this study also demonstrate that hepatomegaly was a
robust response that was observed at the lowest dose tested, was dose-related, and was not
accompanied by toxicity.
Liver histopathology and DNA content were determined only in control, 400, and
1,600 mg/kg-day TCE exposure groups. DNA content was reported to be significantly decreased
from 2.83 ± 0.17 mg/g liver in controls to 2.57 ± 0.14 in 400 mg/kg-day TCE treated group, and
to 2.15 ± 0.08 mg/kg-day liver in the 1,600 mg/kg-day exposed group. This result was consistent
with a decreased number of nuclei/g of liver and hepatocellular hypertrophy.
Liver degeneration was reported as swollen hepatocytes and to be common with
treatment. "Cells had indistinct borders; their cytoplasm was clumped and a vesicular pattern
was apparent. The swelling was not simply due to edema, as wet weight/dry weight ratios did
not increase." Karyorrhexis (the disintegration of the nucleus) was reported to be present in
nearly all specimens and suggestive of impending cell death. A qualitative scale of negative, 1,
2, 3, or 4 was given by the authors to rate their findings without further definition or criterion
given for the ratings. "No karyorrhexis, necrosis, or polyploidy was reported in controls, but a
score of 1 for karyorrhexis was given for 400 mg/kg TCE and 2 for 1,600 mg/kg TCE." Central
lobular necrosis reported to be present only at the 1,600 mg/kg-day TCE exposure level and as a
score of 1. "Polyploidy was also characteristic in the central lobular region" with a score of 1 for
both 400 and 1,600 mg/kg TCE. The authors reported that "hepatic cells had two or more nuclei
or had enlarged nuclei containing increased amounts of chromatin, suggesting that a regenerative
process was ongoing" and that there were no fine lipid droplets in TCE-exposed animals.
The finding of "no polyploidy" in control mouse liver is unexpected given that binucleate
and polyploid hepatocytes are a common finding in the mature mouse liver. It is possible that
the authors were referring to unusually high instances of "polyploidy" in comparison to what
would be expected for the mature mouse. The score given by the authors for polyploidy did not
indicate a difference between the two TCE exposure treatments and that it was of the lowest
level of severity or occurrence.
No score was given for centrolobular hypertrophy although the DNA content and liver
weight changes suggested a dose response. The "karyorrhexis" described in this study could
have been a sign of cell death associated with increased liver cell number or dying of maturing
hepatocytes associated with the increased ploidy, and suggests that TCE treatment was inducing
polyploidization. Consistent with enzyme analyses, centrilobular necrosis was only seen at the
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highest dose and with the lowest qualitative score, indicating that even at the highest dose there
was little toxicity.
Thus, the results of this study of TCE exposure for 6 weeks are consistent with acute
studies and show that the region of the liver affected by TCE is the centrilobular region, that
hepatocellular hypertrophy is observed in that region, and that increased liver weight is induced
at the lowest exposure level tested and much lower than those inducing overt toxicity. These
authors suggest that polyploidization is occurring as a result of TCE exposure, although a
quantitative dose-response cannot be determined from these data.
E.2.2.8. Channel et al. (1998)
This study was performed in male hybrid B6C3Fi/CrlBR mice (13 weeks old, 25-30 g)
and focused on indicators of oxidative stress. TCE was administered by gavage 5 days/week in
corn oil for up to 55 days for some groups. Although the study design indicated that water
controls, corn oil controls, and exposure levels of 400, 800, and 1,200 mg/kg-day TCE in corn
oil, results were not presented for water controls for some parameters measured. Initial body
weights and those recorded during the course of the study were not reported for individual
treatment groups. Liver samples were collected on study days 2, 3, 6, 10, 14, 21, 28, 35, 42, 49,
and 56. Histopathology was studied from a single section taken from the median lobe.
Thiobarbituric acid-reactive substances (TEARS) were determined from whole-liver
homogenates. Nuclei were isolated from whole-liver homogenates and DNA assayed for
8-hydroxy-2' deoxyguanosine (8-OHdG). There was no indication that parenchymal cell and
nonparenchymal cells were distinguished in the assay. Free radical electron paramagnetic
resonance (EPR) for total radicals was analyzed in whole-liver homogenates. For peroxisome
detection and analysis, livers from three mice from the 1,200 mg/kg-day TCE and control (oil
and water) groups were analyzed via electron microscopy. Only centrilobular regions, the area
stated by the authors to be the primary site of peroxisome proliferation, were examined. For
each animal, 7 micrographs of randomly chosen hepatocytes immediately adjacent to the central
vein were examined with peroxisomal area to cytoplasmic area, the number of peroxisomes per
unit area of cytoplasm, and average peroxisomal size quantified. Proliferation cell nuclear
antigen (PCNA), described as a marker of cell cycle except GO, was examined in histological
sections for a minimum of 18 fields per liver section. The authors did not indicate what areas of
the liver lobule were examined for PCNA. Apoptosis was detected on liver sections using a
apoptosis kit using a single liver section from the median lobe and based on the number of
positively labeled cells per 10 mm2 in combination with the morphological criteria for apoptosis
of Columbano et al. (1985). However, the authors did not indicate what areas of the liver lobule
were specifically examined.
The authors reported that body weight gain was not adversely affected by TCE dosing of
the time course of the study but did not show the data. No gross lesions were reported to be
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observed in any group. For TEARS, no water control data were reported by the authors. Data
were presented for six animals per group for the corn oil control group and the 1,200 mg/kg-day
group (error bars representing the SE). No data were presented without corn oil so that the
effects of corn oil on the first day of the study (day 2 of dosing) could not be determined.
After 2 and 3 days of dosing, the corn oil and 1,200 mg/kg-day TCE groups appeared to
have similar levels of TEAR detected in whole liver as nmol TBARS/mg protein. However, by
day 6, the corn oil treated control had a decrease in TEAR that continued until day 15 where the
level was -50% of that reported on days 2 and 3. The variation between animals as measured by
SE was reported to be large on day 10. By day 20, there was a slight increase in variation that
declined by day 35 and stayed the same through day 55. For the TCE-exposed group, the
TEARS remained relatively consistent and began to decline by about day 20 to a level that
similar to the corn oil declines by day 35. Therefore, corn oil alone had a significant effect on
TEAR detection inducing a decline by 6 days of administration that persisted thought 55 days.
TCE administration at the 1,200 mg/kg-day dose in corn oil appeared to have a delayed decline
in TEARS. The authors interpreted this pattern to show that lipid peroxidation was elevated in
the 1,200 mg/kg-day TCE group at day 6 over corn oil. However, corn oil alone induced a
decrease in TBARs. At no time was TEARS in the TCE treatment groups reported to be greater
than the initial levels at days 2 and 3, a time in which TCE and corn oil treatment groups had
similar levels. Rather than inducing increasing TBARS over the time course of the study, TCE,
at the 1,200 mg/kg-day dose, appeared to delay the corn oil induced suppression of TBARS
detection. Because the authors did not present data for aqueous control animals, the time course
of TBARS detection in the absence of corn oil cannot be established.
For the 800 and 400 mg/kg-day TCE data, the authors presented a figure, without SE
information, for up to 35 days that shows little difference between 400 mg/kg TCE treatment and
corn oil suppression of TEAR induction. There was little difference between the patterns of
TEAR detection for 800 and 400 mg/kg-day TCE, indicating that both delayed TBARS
suppression by corn oil to a similar extent and did not induce greater TBARS than corn oil alone.
For 8-OHdG levels, the authors reported that elevations were modest with the greatest
increase noted in the 1,200 mg/kg-day TCE treatment group of 196% of oil controls on day 56.
Levels fluctuated throughout the study with most of the time points that were elevated showing
129% of control for the 1,200 mg/kg-day group. Statistically significant elevations were noted
on days 2, 10, 28, 49, and 56 with depression on day 3. On all other days (i.e., days 6, 14, 21,
35, and 42), the 8-OHdG values were similar to those of corn oil controls. No statistically
significant effects were reported to be observed at lower doses.
The figure presented by the authors shows the percent of controls by TCE treatment at
1,200 mg/kg-day but not the control values themselves. The pattern by corn oil is not shown and
neither is the SE of the data. As a percent of control values, the variations were very large for
many of the data points and largest for the data given at day 55 in which the authors report the
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largest difference between control and TCE treatment. There was no apparent pattern of
elevation in 8-OHdG when the data were presented in this manner. Because the data for the corn
oil control was not given, as well as no data given for aqueous controls, the effects of corn oil
alone cannot be discerned.
Given that for TEARS corn oil had a significant effect and showed a pattern of decline
after 6 days, with TCE showing a delayed decline, it is especially important to discern the effects
of corn oil and to see the pattern of the data. At time points when TEARS levels were reported
to be the same between corn oil and TCE (days 42, 49 and 56), the pattern of 8-OHdG was quite
different with a lower level at day 42, a slightly increased level at day 49, and the highest
difference reported at day 56 between corn oil control and TCE treated animals. The authors
reported that the pattern of "lipid peroxidation" was similar between the 1,200 and 800 mg/kg-
day doses of TCE, but that there was no significant difference between 800 mg/kg-day TCE and
corn oil controls. Thus, the pattern of TEARS as a measure of lipid peroxidation and 8-OHdG
level in nuclear DNA did not match.
In regard to total free radical levels as measured by EPR, results were reported for the
1,200 mg/kg TCE as a signal that was subtracted from control values with the authors stating that
only this dose level induced an elevation significantly different from controls. Again, aqueous
control values were not presented to discern the effects of corn oil or the pattern that may have
arisen with time of corn oil administration.
The pattern of total free radical level appeared to differ from that of lipid peroxidation
and for that of 8-OHdG DNA levels, with no changes at days 2, 3, a peak level at day 6, a rapid
drop at day 10, mild elevation at day 20, and a significant decrease at day 49. The percentage
differences between control and treated values reported at days 6 and 20 by the authors was not
proportional to the fold-difference in signal indicating that there was not a consistent level for
control values over the time course of the experiment. While differences in lipid peroxidation
detection between 1,200 mg/kg-day TCE and corn oil control were greatest at day 14, total free
radicals showed their biggest change between corn oil controls and TCE exposure on day 6, time
points in which 8-OHdG levels were similar between TCE treatment and corn oil controls.
Again, there was no reported difference between corn oil control and the 800 mg/kg-day TCE
exposed group in total free radical formation, but for lipid peroxidation, the 800 mg/kg-day TCE
exposed group had a similar pattern as that of 1,200 mg/kg-day TCE.
Only the 1,200 mg/kg-day group was evaluated for peroxisomal proliferation at days 6,
10, and 14. Thus, correlations with peroxisome proliferation and other parameters in the report
at differing times and TCE exposure concentrations could not be made. The authors reported
that there was a treatment and time effect for percent peroxisomal area, a "treatment only" effect
for number of peroxisome and no effect for peroxisomal size. They also reported that
hepatocytes examined from corn oil control rats were no different than those from water control
rats for all peroxisomal parameter, thus discounting a vehicle effect.
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However, there was an effect on peroxisomal size between corn oil control and water
with corn oil decreasing the peroxisomal size in comparison to water on all days tested. The
highest TCE-induced percent peroxisomal area and number occurred on day 10 of the three time
points measured for this dose and the fold increase was -4.5- and ~3.1-fold increase,
respectively. The day-10 peak in peroxisomal area and number did not correlate with the
reported pattern of free radical or 8-OHdG generation.
For cell proliferation and apoptosis, data were given for days 2, 6, 10, 14, and 21 in a
figure. PCNA cells, a measure of cells that have undergone DNA synthesis, was elevated only
on day 10 and only in the 1,200 mg/kg-day TCE exposed group with a mean of-60 positive
nuclei per 1,000 nuclei for six mice (-6%). Given that there was little difference in PCNA
positive cells at the other TCE doses or time points studied, the small number of affected cells in
the liver could not account for the increase in liver size reported in other experimental paradigms
at these doses.
The PCNA positive cells as well as "mitotic figures" were reported to be present in
centrilobular, midzonal, and periportal regions with no observed predilection for a particular
lobular distribution. No data were shown regarding any quantitative estimates of mitotic figures
and whether they correlated with PCNA results. Thus, whether the DNA synthesis phases of the
cell cycle indicated by PCNA staining were indentifying polyploidization or increased cell
number cannot be determined. The authors reported that there was no cytotoxicity manifested as
hepatocellular necrosis in any dose group and that there was no significant difference in
apoptosis between treatment and control groups with data not shown. The extent of apoptosis in
any of the treatment groups, or which groups and timepoints were studied for this effect cannot
be determined. No liver weight or body weight data were provided in this study.
These results confirm that as a vehicle corn oil is not neutral in its affects in the liver.
The TEARS results indicate a reduction in detection of TEARS in the liver with increasing time
of exposure to corn oil alone. Although control animals "treated with water" gavage were
studied, only the results for peroxisome proliferation were presented by the study, so that the
effects of corn oil gavage were not easy to discern. In addition, the data were presented in such a
way for 8-OHdG and total free radical changes that the pattern of corn oil administration was
obscured. It is not apparent from this study that TCE exposure induces oxidative damage.
E.2.2.9. Dorfmueller et al. (1979)
The focus of this study was the evaluation of "teratogenicity and behavioral toxicity with
inhalation exposure of maternal rats" to TCE. Female Long-Evans hooded rats (n = 12) of
-210 g weight were treated with 1,800 ± 200 ppm TCE for 6 hours/day, 5 days/week, for
22 ± 6 days (until pregnancy confirmation) continuing through GD 20. Control animals were
exposed 22 ± 3 days before pregnancy confirmation. The TCE used in this study contained 0.2%
epichlorohydrin. Body weights were monitored as well as maternal liver weight at the end of
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exposure. Other than organ weight, no other observations regarding the liver were reported in
this study. The initial weights of the dams were 212 ± 39 g (mean ± SD) and 204 ± 35 g for
treated and control groups, respectively. The final weights were 362 ± 32 g and 337 ± 48 g for
treated and control groups, respectively. There was no indication of maternal toxicity by body
weight determinations as a result of TCE exposure in this experiment and there was also no
significant difference in absolute or relative percent liver/body weight between control and
treated female rats in this study.
E.2.2.10. Kumar et al. (200la)
In this study, adult male Wistar rats (130 ± 10 g body weight) were exposed to
376 ± 1.76 ppm TCE ("AnalaR grade") for 8, 12, and 24 weeks for 4 hours/day 5 days/week.
The ages of the rats were not given by the authors. Each group contained six rats. The animals
were exposed in whole-body chambers and thus, additional oral exposure was probable. Along
with histopathology of light microscopic sections, enzymatic activities of ALP and acid
phosphatase, glutamic oxoacetate transaminase, glutamic pyruvate transaminase, reduced GSH,
and "total sulphydryl" were assayed in whole-liver homogenates as well as total protein. The
authors stated that "the size and weight of the liver were significantly increased after 8, 12, and
24 weeks of TCE exposure." However, the authors did not report the final body weight of the
rats after treatment nor did they give quantitative data of liver weight changes. In regard to
histopathology, the authors stated:
After 8 weeks of exposure enlarged hepatocytes, with uniform presence of fat
vacuoles were found in all of the hepatocytes affecting the periportal, midzonal,
and centrilobular areas, and fat vacuoles pushing the pyknosed nuclei to one side
of hepatocytes. Moreover congestion was not significant. After exposure of 12
and 24 weeks, the fatty changes became more progressive with marked necrosis,
uniformly distributed in the entire organ.
No other description of pathology was provided in this report. In regard to the
description of fatty change, the authors only did conventional H&E staining of sections with no
precautions to preserve or stain lipids in their sections. The authors provided a table with
histological scoring of simply + or - for minimal, mild, or moderate effects and do not define the
criteria for that scoring. There was also no quantitative information given as to the extent,
nature, or location of hepatocellular necrosis. The authors reported "no change was observed in
GOT and GPT levels of liver in all the three groups. The GSH level was significantly decreased
while TSH level was significantly increased during 8, 12, and 24 weeks of TCE exposure. The
acid and ALPs were significantly increased during 8, 12, and 24 weeks of TCE exposure." The
authors presented a series of figures that are poor in quality to demonstrate histopathological
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TCE-induced changes. No mortality was observed from TCE exposure in any group despite the
presence of liver necrosis.
E.2.2.11. Kawamoto et al. (1988b)
The focus of this study was the long-term effects of TCE treatment on induction of
metabolic enzymes in male adult Wistar rats. The authors reported that eight rats weighing
200 g were treated with 2 g/kg TCE in olive oil administered subcutaneously twice a week for
15 weeks with seven rats serving as olive oil controls. In a separate experiment, five rats were
injected with 1 g/kg TCE in olive oil i.p. once a day for 5 continuous days. For comparative
purposes, groups of five rats each were administered 3-methylcholanthrene (20 mg/kg in olive
oil i.p.), phenobarbital (80 mg/kg in saline i.p.) for 4 days as well as ethanol administered in
drinking water containing 10% ethanol for 14 days. Microsomes were prepared 1 week after the
last exposure from rats administered TCE for 15 weeks and 24 hours after the last exposure for
the other treatments.
Body weights were reported to be slightly less for the TCE treated group than for controls
with the initial weights, shown in a figure, to be similar for the first weeks of exposure. At
15 weeks, there appeared to be -7.5% difference in mean body weights between control and
TCE treated rats, which the authors reported to not be significantly different. Organ weights at
the termination of the experiment were reported to only be different for the liver with a 1.21-fold
of control value reported as a percentage of body weight with TCE treatment. The authors
reported their increase in liver weights in male rats from subcutaneous exposure to TCE in olive
oil (2.0 g/kg) to be consistent with the range of liver weight gain in rats reported by Kjellstrand
et al. (1981b) for 150 ppm TCE inhalation exposure (see comments on that study above). The
5-day i.p. treatment with TCE was also reported to only produce increased liver weight but the
data were not shown and the magnitude of the percentage increase was not given by the authors.
No liver pathology results were studied or reported.
Along with an increase in liver weight, 15-week treatment with TCE was reported to
cause a significant increase of microsomal protein/g liver of-20% (10.64 ± 0.88 vs.
12.58 ± 0.71 mg/g liver for olive oil controls and TCE treatment, respectively). Microsomal
CYP content was reported to show a mild increase that was not statistically significant of
1.08-fold (1.342 ± 0.205 vs. 1.456 ±0.159 nmol/mg protein for olive oil controls and TCE
treatment, respectively) of control. However, CYP content showed 1.28-fold of control value
(14.28 ± 2.41 vs. 18.34 ± 2.31 nmol/g liver for olive oil controls and TCE treatment,
respectively) in terms of g/liver. Chronic treatment of TCE was also reported to cause a
significant increase in cytochrome b-5 level (-1.35-fold of control) and NADPH-cytochrome c
reductase activity (-1.50-fold of control) in g/liver.
The 5-day TCE treatment via the i.p. route of administration was reported to cause a
significant increase in microsomal protein (-20%) and induce CYP (-50% increase g/liver and
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22% increase in microsomal protein), but to also increase cytochrome b-5 and NADPH-
cytochrome c reductase activity by 50 and 70% in g/liver, respectively. Although weaker, 5-day
i.p. treatment with TCE induced an enzyme pattern more similar to that of phenobarbital and
ethanol rather methylcholanthrene (i.e., increased CYP but not microsomal protein and NADPH-
cytochrome c reductase). Direct quantitative comparisons of vehicle effects and potential impact
on response to TCE treatments for 15 weeks subcutaneous exposure and 5-day i.p. exposure
could not be made as baseline levels of all enzyme and protein levels changed as a function of
age.
Of note is that, in the discussion section of the paper, the authors disclosed that injection
of TCE 2 or 3 g/kg i.p. for 5 days resulted in paralytic ileus from TCE exposure as unpublished
observations. They noted that the rationale for injecting TCE subcutaneously was that it not only
did not require an inhalation chamber, but also guarded against peritonitis that sometimes occurs
following repeated i.p. injection. In terms of comparison with inhalation or oral results, the
authors noted that the subcutaneous treatment paradigm will result in TCE not immediately
being metabolized but retained in the fatty tissue and that after cessation of exposure, TCE
metabolites continued to be excreted into the urine for >2 weeks.
E.2.2.12. NTP (1990)
E.2.2.12.1. 13-Week studies
The NTP conducted a 13-week study of 7-week-old F344/N rats (10 rats per group) that
received doses of 125-2,000 mg/kg (males [0, 125, 250, 500, 1,000, or 2,000 mg/kg]) and
62.5 to 1,000 mg/kg (females [0, 62.5, 125, 250, 500, or 1,000 mg/kg] TCE via corn oil gavage
5 days/week (see Table E-l). For 7-week-old B6C3Fimice (n = 10 per group), the dose levels
were reported to be 375-6,000 mg/kg TCE (0, 375, 750, 1,500, 3,000, or 6,000 mg/kg). Animals
were exposed via corn oil gavage to TCE that was epichlorhydrin-free.
Table E-l. Mice data for 13 weeks: mean body and liver weights
Dose (mg/kg
TCE)
Survival
Body weight
(mean in g)
Initial
Final
Liver weight
(mean final in g)
% liver weight/body
weight
(fold change vs. control)
Male
0
375
750
1,500
3,000
6,000
10/10
10/10
10/10
8/10
3/10
0/10
21
20
21
19
20
22
36
35
32
29
30
-
2.1
1.74
2.14
2.27
2.78
-
5.8
5.0 (0.86)
6.8(1.17)
7.6(1.31)
8.5 (1.46)
-
Female
0
375
750
10/10
10/10
9/10
18
17
17
26
26
26
1.4
1.31
1.55
5.5
5.0 (0.91)
5.8(1.05)
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1,500
3,000
6,000
9/10
9/10
1/10
17
15
15
26
26
27
1.8
2.06
2.67
6.5(1.18)
7.8 (1.42)
9.5 (1.73)
All rats were reported to survive the 13-week study, but males receiving 2,000 mg/kg
exhibited a 24% difference in final body weight. However, there was great variation in initial
weights between the dose groups with mean initial weights at the beginning of the study reported
to be 87, 88, 92, 95, 101, and 83 g for the control, 125, 250, 500, 1,000, and 2,000 mg/kg dose
groups in male rats, respectively. This represents a 22% difference between the highest and
lowest initial weights between groups. Thus, changes in final body weight after TCE treatment
also reflect differences in starting weights between the groups that, in the case of the 500 and
1,000 mg/kg groups, would result in a lower-than-expected change in weight due to TCE
exposure.
For female rats, the mean initial starting weights were reported to be 81, 72, 74, 75, 73,
and 76 g, respectively for the control, 62.5, 125, 250, 500, and 1,000 mg/kg dose groups. This
represents a -13% difference between initial weights. In the case of female rats, the larger mean
initial weight in the control group would tend to exaggerate the effects of TCE exposure on final
body weight. The authors did not report the variation in initial or final body weights within the
dose groups. At the lowest doses for male and female rats, body mean weights were reported to
be decreased by 6 and 7% in male and female rats, respectively. Organ weight changes were not
reported for rats.
For male mice, mean initial body weights ranged from 19 to 22 g (-16% difference) and
for female mice ranged between 18 and 15 g (20% difference), and thus, similar to rats, the final
body weights in the groups dose with TCE reflect not only the effects of the compound but also
differences in initial weights. For male mice, the mean final body weights were reported to be
3-17% less than controls for the 375-3,000 mg/kg doses. For female mice, the percent
difference in final body weight was reported to be the same except for the 6,000 mg/kg dose
group, but this lack of difference between controls and treated female mice reflected no change
in mice that started at differing weights.
Male mice started to exhibit mortality at 1,500 mg/kg with 8/10 surviving the
1,500 mg/kg dose, 3/10 surviving the 3,000 mg/kg dose, and none surviving the 6,000 mg/kg
dose of TCE until the end of the study. For females, 1 animal out of 10 died in the 750, 1,500,
and 3,000 mg/kg dose groups and 1/10 survived the 6,000 mg/kg group.
In general, the magnitude of increase in TCE exposure concentration was similar to the
magnitude of increase in percent liver/body weight for the 750 and 1,500 mg/kg TCE exposure
groups in male B6C3Fi mice and for the 750-3,000 mg/kg TCE exposure groups in female mice
(i.e., a twofold increase in TCE exposure resulted in an approximate twofold increase in percent
liver/body weight).
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The descriptions of pathology in rats and mice given by this study were not very detailed.
For rats, only control and high-dose rats were examined histologically. For mice, only controls
and the two highest dose groups were examined histologically. Only mean liver weights were
reported with no statistical analyses provided to ascertain quantitative differences between study
groups.
Pathological results were reported to reveal that 6/10 males and 6/10 female rats had
pulmonary vasculitis at the highest concentration of TCE. This change was also reported to have
occurred in 1/10 control male and female rats. Most of those animals were also reported to have
had mild interstitial pneumonitis. The authors report that viral liters were positive during this
study for Sendai virus.
In mice, liver weights (both absolute and as a percent of body weight) were reported to
increase with TCE-exposure level. Liver weights were reported to have increased by >10%
relative to controls for males receiving >750 mg/kg and for females receiving >1,500 mg/kg.
The most prominent hepatic lesions detected in the mice were reported to be centrilobular
necrosis, observed in 6/10 males and 1/10 females administered 6,000 mg/kg.
Although centrilobular necrosis was not seen in either males or females
administered 3000 mg/kg, 2/10 males had multifocal areas of calcifications
scattered throughout their livers. These areas of calcification were considered to
be evidence of earlier hepatocellular necrosis. Multifocal calcification was also
seen in the liver of a single female mouse that survived the 6000 mg/kg dosage
regime. One female mouse administered 3000 mg/kg also had a hepatocellular
adenoma, an extremely rare lesion in female mice of this age (20 weeks).
There appeared to be consistent decrease in liver weight at the lowest dose in both female and
male mice after 13 weeks of TCE exposure. Liver weight was increased at exposure
concentrations in which there was not increased mortality due to TCE exposure at 13 weeks of
TCE exposure.
E.2.2.12.2. 2-Year Studies
In the 2-year phase of the NTP study, TCE was administered by corn oil gavage to
groups of 50 male and 50 female F344/N rats, and B6C3Fi mice. Dosage levels were 500 and
1,000 mg/kg for rats and 1,000 mg/kg for mice. TCE was administered 5 times/week for
103 weeks and surviving animals were killed between weeks 103 and 107. The same number of
animals receiving corn oil gavage served as controls. The animals were 8 weeks old at the
beginning of exposure. The focus of this study was to determine if there was a carcinogenic
response due to TCE exposure so there was little reporting of non-neoplastic pathology or
toxicity. There was no report of liver weight at termination of the study, only body weight.
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The authors reported that there was no increase in necrosis in the liver from TCE
exposure in comparison to control mice. In control male mice, the incidence of HCC (tumors
with markedly abnormal cytology and architecture) was reported to be 8/48 in controls, and
3 1/50 in TCE-exposed male mice. For female control mice, HCCs were reported in 2/48 of
controls and 13/49 of TCE-exposed female mice. Specifically, the authors described liver
pathology in mice as follows:
Microscopically the hepatocellular adenomas were circumscribed areas of
distinctive hepatic parenchymal cells with a perimeter of normal appearing
parenchyma in which there were areas that appeared to be undergoing
compression from expansion of the tumor. Mitotic figures were sparse or absent
but the tumors lacked typical lobular organization. The hepatocellular carcinomas
had markedly abnormal cytology and architecture. Abnormalities in cytology
included increased cell size, decreased cell size, cytoplasmic eosinophilia,
cytoplasmic basophilia, cytoplasmic vacuolization, cytoplasmic hyaline bodies,
and variations in nuclear appearance. In many instance, several or all of the
abnormalities were present in different areas of the tumor. There were also
variations in architecture with some of the hepatocellular carcinomas having areas
of trabecular organization. Mitosis was variable in amount and location.
The authors reported that the non-neoplastic lesion in male mice differing from controls
was focal necrosis in four vs. one animal in the dosed group (8 vs. 2%). There was no fatty
metamorphosis in treated male mice vs. two animals in control. In female mice, there was focal
inflammation in 29 vs. 19% of animals (dosed vs. control) and no other changes. Therefore, the
reported pathological results of this study did not show that the liver was showing signs of
toxicity after 2 years of TCE exposure except for neoplasia.
For hepatocellular adenomas, the incidence was reported to be "7/48 control vs.
14/50 dosed in males and 4/48 in control vs. 16/49 dosed female mice." The administration of
TCE to mice was reported to cause increased incidences of HCCs in males (control, 8/48; dosed,
3 1/50: p = 0.001) and in females (control 2/48; dosed 13/49; p< 0.005). HCCs were reported to
metastasize to the lungs in five dosed male mice and one control male mouse, while none were
observed in females. The incidences of hepatocellular adenomas were reported to be increased
in male mice (control 7/48; dosed 14/50) and in female mice (control 4/48; dosed 16/49;
The survival of both low- and high-dose male rats and dosed male mice was reported to
be less than that of vehicle controls with body weight decreases dose dependent. Female mice
body weights were comparable to controls. The authors report adjusted rates of 20.6% for
control vs. 53.1% for dosed males for adenoma, 22.1% control, and 92.9% for carcinoma in
males, and liver carcinoma or adenoma adjusted rates of 100%. For female mice, the adjusted
rates were reported to be 12.5% adenoma for control vs. 55.6% for dosed, and 6.2% control
carcinoma vs. 43.9% dosed, with liver carcinoma or adenoma adjusted rates of 18.7% for control
E-88
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vs. 69.7% for dosed. All of the liver results for male and female mice were reported to be
statistically significant. The administration of TCE was reported to cause earlier expression of
tumors as the first animals with carcinomas were 57 weeks for TCE-exposed animals and
75 weeks for control male mice.
In male rats, there was no reported treatment-related non-neoplastic liver lesions. In
female rats, a decrease in basophilic cytological change was reported to be of note in TCE
treated rats (-50% in controls but -5% in TCE treatment groups). However, the authors reported
that "the results in male F344/N rats were considered equivocal for detecting a carcinogenic
response because both groups receiving TCE showed significantly reduced survival compared to
vehicle controls (35/70, 70%; 20/50, 40%; 16/50, 32%) and because 20% of the animals in the
high-dose group were killed accidently by gavage error." Specifically 1 male control, 3 low-
dose males, 10 high-dose males, 2 female controls, 5 low-dose females, and 5 high-dose female
rats were killed by gavage error.
E.2.2.13. NTP (1988)
The studies described in the NTP (1988) TCE report were conducted "to compare the
sensitivities of four strains of rats to diisopropylamine-stabilized TCE." However, the authors
concluded:
that because of chemically induced toxicity, reduced survival, and incomplete
documentation of experimental data, the studies are considered inadequate for
either comparing or assessing TCE-induced carcinogenesis in these strains of rats.
TCE (more than 99% pure, stabilized with 8ppm diisopropylamine) was
administered via corn oil gavage at exposure concentrations of 0, 500 or 1000
mg/kg per day, 5 days per week, for 103 weeks to 50 male and female rats of each
strain. The survival of "high-dose male Marshal rats was reduced by a large
number of accidental deaths (25 animals were accidentally killed).
However, the report stated that survival was decreased at both exposure levels of TCE
because of mortality that occurred during the administration of the chemical. The number of
animals accidently killed were reported to be: 11 male ACI rats at 500 mg/kg, 18 male ACI rats
at 1,000 mg/kg, 2 vehicle control female ACI rats, 14 female ACI rats at 500 mg/kg, 12 male
ACI rats at 1,000 mg/kg, 6 vehicle control male August rats, 12 male August rats at 500 mg/kg,
11 male August rats at 1,000 mg/kg, 1 vehicle control female August rats, 6 female August rats
at 500 mg/kg, 13 male August rats at 1,000 mg/kg, 2 vehicle control male Marshal rats, 12 male
Marshal rats at 500 mg/kg, 25 male Marshal rats at 1,000 mg/kg, 3 vehicle control female
Marshal rats, 14 female Marshal rats at 500 mg/kg, 18 female Marshal rats at 1,000 mg/kg,
1 vehicle control male Osborne-Mendel rat, 6 male Osborne-Mendel rats at 500 mg/kg, 7 male
Osborne-Mendel rats at 1,000 mg/kg, 8 vehicle control female Osborne-Mendel rats, 6 female
Osborne-Mendel rats at 500 mg/kg, and 6 female Osborne-Mendel rats at 1,000 mg/kg. The ages
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of the rats "when placed on the study" were reported to differ and were for ACT rats (6.5 weeks),
August rats (8 weeks), Marshal rats (7 weeks), and Osborne-Mendel rats (8 weeks). The ages of
sacrifice also varied and were 17-18 weeks for the ACT and August rats and 110-111 weeks for
the Marshal rats.
Results from a 13-week study were briefly mentioned in the report. For the 13-week
duration of exposure, groups of 10 male ACT and August rats were administered 0,125, 250, 500,
1,000, or 2,000 mg/kg TCE in corn oil gavage. Groups of 10 female ACT and August rats were
administered 0, 62.5, 125, 250, 500, or 1,000 mg/kg TCE. Groups of 10 male Marshal rats
received 0, 268, 308, 495, 932, or 1,834 mg/kg and groups of female Marshal rats were given 0,
134, 153, 248, 466, or 918 mg/kg TCE. With the exception of three male August rats receiving
2,000 mg/kg TCE, all animals survived to the end of the 13-week experimental period. "The
administration of the chemical for 13 weeks was not associated with histopathological changes."
In the 2-year study the report noted that there:
was no evidence of liver toxicity described as non-neoplastic changes in male
ACT rats due to TCE exposure with 4% or less incidence of any lesion in control
or treated animals. For female ACT rats, the incidence of fatty metamorphosis
was 6% in control vehicle, 9% in low dose TCE, and 13% in high dose TCE
groups. There was also a 2%, 11%, and 8% incidence of clear cell change,
respectively. A 6% incidence of hepatocytomegaly was reported in vehicle
control and 15% incidence in the high dose group.
All other descriptors had reported incidences of <4%.
For August rats, there was also little evidence of liver toxicity. In male August rats, there
was a reported incidence of 8, 4, and 10% focal necrosis in vehicle control, low dose, and high
dose, respectively. Fatty metamorphosis was reported to be 8% in control, and 2 and 4% in low
and high dose. All other descriptors were reported to be <4%. In female August rats, all
descriptors of pathology were reported to have a <4% incidence except for hepatomegaly, which
was 10% for vehicle control, 6% for the low dose, and 2% for high dose TCE.
For male Marshal rats, there was a reported 63% incidence of inflammation, NOS in
vehicle control, 12% in low dose, and values not recorded at the high dose. There was a reported
6 and 14% incidence of fatty metamorphosis in control and low-dose male rats. Clear cell
change was 8% in vehicle with all other values <4%. For female Marshal rats, all values were
<4% except for fatty metamorphosis in 6% of vehicle controls.
For male Osborne-Mendel rats, there was a reported 4, 10, and 4% incidence of focal
necrosis in vehicle control, low, and high dose, respectively. For "cytoplasmic change/NOS,"
there were reported incidences of 26, 32, and 27% in vehicle, low-dose, and high-dose animals,
respectively. All other descriptors were reported to be <4%. In female Osborne-Mendel rats,
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there was a reported incidence of 10% of focal necrosis at the low dose with all other descriptors
reported at <4%.
Obviously, the negative results in this bioassay are confounded by the killing of a large
portion of the animals accidently by experimental error. Still, these large exposure
concentrations of TCE did not seem to be causing overt liver toxicity in the rat. Organ weights
were not reported in this study, which would have been hard to interpret if they had been
reported because of the mortality.
E.2.2.14. Fukuda et al. (1983)
In this 104-week bioassay designed primarily to determine a carcinogenic response,
female noninbred Crj:CD-l (ICR) mice and female Crj:CD (Sprague-Dawley) rats 7 weeks of
age were exposed to "reagent grade" TCE at 0, 50, 150, and 450 ppm for 7 hours/day,
5 days/week. During the 2-year duration of the experiment, inhalation concentrations were
reported to be within 2% of target values. The numbers of animals per group were reported to be
49-50 mice and 49-51 rats at the beginning of the experiment. The impurities in the TCE were
reported to be 0.128% carbon tetrachloride benzene, 0.019% epichlorohydrin, and 0.019%
1,1,2-trichloroethane. After 107 weeks from commencement of the exposure, surviving animals
were reported to be killed and completely necropsied. "Tumors and abnormal organs as well as
other major organs were excised and prepared for examination in H&E sections." No other
details of the methodologies used for pathological examination of tissues were given including
what areas of the liver and number of sections examined by light microscopy.
Body weights were not given, but the authors reported that "body weight changes of the
mice and rats were normal with a normal range of standard deviation." It was also reported that
there were no significant differences in average body weight of animals at specified times during
the experiments and no significant difference in mortality between the groups of mice. The
report included a figure showing, that for the first 60 weeks of the experiment, there was a
difference in cumulative mortality at the 450 ppm dose in ICR mice and the other groups. The
authors reported that significantly increased mortalities in the control group of rats compared to
the other dosed groups were observed at 85 weeks and after 100 weeks, reflecting many deaths
during the 81-85-week and 96-100-week periods for control rats. No significant comparable
clinical observations were reported to be noted in each group but that major symptoms such as
bloody nasal discharge (in rats), local alopecia (in mice and rats), hunching appearance (in mice),
and respiratory disorders (in mice and rats) were observed in some animals mostly after 1 year.
The authors reported that "the numbers of different types of tumors were counted and
only malignant tumors were counted when both malignant and benign tumors were observed
within one organ." They also reported that "all animals were included in the effective numbers
except for a few that were killed accidently, severely autolyzed or cannibalized, and died before
the first appearance of tumors among the groups."
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In mice, the first tumors were observed at 286 days as thymic lymphoma and most of the
malignant tumors appearing later were described as lymphomas or lymphatic leukemias. The
incidences of mice with tumors were 37, 36, 54, and 52% in the control, 50, 150 and 450 ppm
groups, respectively, by the end of the experiment. "Tumors of the ovary, uterus, subcutaneous
tissue, stomach, and liver were observed in the dose groups at low incidences (2-7%) but not in
the controls." For the liver, the control, 50 and 150 ppm groups were all reported to have no
liver tumors with one animal (2%) having an adenoma at the 450 ppm dose.
For rats, the first tumor was reported to be observed at 410 days and the incidences of
animals with tumors were 64, 78, 66, and 63% for control, 50, 150, and 450 ppm TCE,
respectively, by the end of the experiment. Most tumors were distributed in the pituitary gland
and mammary gland with other tumors reported at a low incidence of 2-4% with none in the
controls. For the liver, there were no liver tumors in the control or 150 ppm groups, but one
animal (2%) had a cystic cholangioma in 50 ppm group and one animal (2%) had a HCC in the
450 ppm group of rats. No details concerning the pathology of the liver or organ weight changes
were given by the authors, including any incidences of hepatomegaly or preneoplastic foci. Of
note is that there were no background liver tumors in either strain, indicative of the relative
insensitivity of these strains to hepatocarcinogenicity. However, the carcinogenic potential of
TCE was reflected by a number of other tumor sites in this paradigm.
E.2.2.15. Henschler et al. (1980)
This report focused on the potential carcinogenic response of TCE in mice (NMRI
random bred), rats (WIST random bred), and hamsters (Syrian random bred) exposed to 0, 100,
and 500 ppm TCE for 6 hours/day 5 days/week for 18 months. The TCE used in the experiment
was reported to be pure with the exception of trace amounts of chlorinated hydrocarbons,
epoxides, and triethanolamines (<0.000025% w/w) and stabilized with 0.0015% triethanolamine.
The number of animals in each group was 30 and the ages and initial and final body weights of
the animals were not provided in the report. For the period of exposure (8 am-2 pm), animals
were deprived of food and water. The exposure period was for 18 months with mice and
hamsters sacrificed after 30 months and rats after 36 months. "Deceased animals" were reported
to be autopsied; spleen, liver, kidneys, lungs, and heart were weighed; and these organs, as well
as stomach, CNS, and tumorous tissues, were examined in H&E sections.
Body weight gain was reported to be normal in all species with no noticeable differences
between control and exposed groups but data were not shown. However, a "clearly dose-
dependent decrease in the survival rate for both male and female mice" was reported to be
statistically significant in both sexes and concentrations of TCE with no other significant
differences reported in other species. The increase in mortality was more pronounced in male
mice, especially after 50 weeks of exposure. Hence, the opportunity for tumor development was
diminished due to decreased survival in TCE treated groups.
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No organ weights were provided for the study due to the design, in which a considerable
period of time occurred between the cessation of exposure and the sacrifice of the animals. Liver
weights changes due to TCE may have been diminished with time.
For the 30 autopsied male mice in the control group, one hepatocellular adenoma and one
HCC was reported. Whether they occurred in the same animal cannot be determined from the
data presentation. In the 29 animals in the 100 ppm TCE exposure group, two hepatocellular
adenomas and one mesenchymal liver tumor were reported but no HCCs also without a
determination as whether they occurred in the same animal or not. In the 30 animals autopsied in
the 500 ppm exposure group, no liver tumors were reported. In female mice, of the 29 animals
autopsied in the control group, 30 animals autopsied in the 100 ppm group, and the 28 animals
autopsied in the 500 ppm group, there were also no liver tumors reported.
In both the 100 and 500 ppm exposure groups, of male mice especially, low numbers of
animals studied, abbreviated TCE exposure duration, and lower numbers of animals surviving to
the end of the experiment limit the power of this study to determine a treatment-related
difference in liver carcinogenicity. As discussed in Section E.2.3.2 below, the use of an
abbreviated exposure regime or study duration and low numbers of animals examined limits the
power of a study to detect a treatment-related response. The lack of any observed background
liver tumors in the female mice and a very low background level of two tumors in the male mice
are indicative of a low sensitivity to detect liver tumors in this paradigm, which may have
occurred either through its design, or a low sensitivity of mouse strain used for this endpoint.
However, the carcinogenic potential of TCE in mice was reflected by a number of other tumor
sites in this paradigm.
For rats and hamsters the authors reported "no dose-related accumulation of any kind of
tumor in either sex of these species." For male rats, there was only one hepatocellular
adenoma reported at 100 ppm in the 30 animals autopsied and no carcinomas. For female rats,
there were no liver tumors reported in control animals but, more significantly, at 100 ppm, there
was one adenoma and one cholangiocarcinoma reported at 100 ppm, and at 500 ppm, there were
two cholangioadenomas. Although not statistically significant, the occurrence of this relatively
rare biliary tumor was observed in both TCE dose groups in female rats. The difference in
survival, as reported in mice, did not affect the power to detect a response in rats, but the low
numbers of animals studied, abbreviated exposure duration, and apparent low sensitivity to
detect a hepatocarcinogenic response suggest a study of low power. Nevertheless, the
occurrence of cholangioadenomas and one cholangiocarcinoma in female rats after TCE
treatments is of concern, especially given the relationship in origin and proximity of the bile and
liver cells and the low incidence of this tumor. For hamsters, the low background rate of tumors
of any kind suggests that in this paradigm, the sensitivity for detection of this tumor is relatively
low.
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E.2.2.16. Maltoni et al. (1986)
The report by Maltoni et al. (1986) included a series of "systematic and integrated
experiments (BT 301, 302, 303, 304, 304bis, 305, 306 bis) started in sequence, testing TCE by
inhalation and by ingestion." The first experiment (BT 301) was begun in 1976 and the last in
1983, with this report representing the completed summary of the findings and results of project.
The focus of the study was detection of a neoplastic response with only a generalized description
of tumor pathology phenotype given and no reporting of liver weight changes induced by TCE
exposure.
In experiment BT 301, TCE was administered in male and female Sprague-Dawley rats
(13 weeks at start of experiment) via olive oil gavage at control, 50, or 250 mg/kg exposure
levels for 52 weeks (4-5 days weekly). The animals (30 male, 30 female for each dose group)
were examined during their lifetime. In experiment BT 302, male and female Sprague-Dawley
rats (13 weeks old at start of the experiment) were exposed to TCE via inhalation at 0, 100, and
600 ppm, 7 hours/day, 5 days/week, for 8 weeks. The animals (90 animals in each control group,
60 animals in each 100 ppm group, and 72 animals in each 600 ppm group) were examined
during their lifetime. In experiment BT 304, male and female Sprague-Dawley rats (12 weeks
old at start of the experiment) were exposed TCE via inhalation at 0, 100, 300, and 600 ppm
7 hours/day, 5 days/week, for 104 weeks. The animals (95 male, 100 female rats control groups,
90 animals in each 100 ppm group, 90 animals in each 300 ppm group, and 90 animals in each
600 ppm group) were examined during their lifetime. In experiment BT304bis, male and female
Sprague-Dawley rats (12 weeks old at start of the experiment) were exposed to TCE via
inhalation at 0, 100, 300, and 600 ppm for 7 hours/day, 5 days/week, for 104 weeks. The
animals (40 male, 40 female rats control groups, 40 animals in each 100 ppm group, 40 animals
in each 300 ppm group, and 40 animals in each 600 ppm group) were examined during their
lifetime.
In experiment BT 303, Swiss mice (11 weeks old at the start of the experiment) were
exposed to TCE via inhalation in for 8 weeks using the same exposure concentrations as for
experiment BT 302. The animals (100 animals in each control group, 60 animals in the 100 ppm
exposed group, and 72 animals in each 600 ppm group) were examined during their lifetime. In
experiment BT 305, Swiss mice (11 weeks old at the start of the experiment) were exposed to
TCE via inhalation in for 78 weeks, 7 hours/day, 5 days/week. The animals (90 animals in each
control group, 90 animals in the 100 ppm exposed group, 90 animals in the 300 ppm group, and
90 animals in each 600 ppm group) were examined during their lifetime. In experiment BT 306,
B6C3Fi mice (from NCI source) (12 weeks old at the start of the experiment) were exposed to
TCE via inhalation in for 78 weeks, 7 hours a day, 5 days a week. The animals (90 animals in
each control group, 90 animals in the 100-ppm-exposed group, 90 animals in the 300-ppm group,
and 90 animals in each 600-ppm group) were examined during their lifetime. In experiment
BT 306bis, B6C3Fi mice (from Charles River Laboratory as source) (12 weeks old at the start of
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the experiment) were exposed to TCE via inhalation for 78 weeks, 7 hours/day, 5 days/week.
The animals (90 animals in each control group, 90 animals in the 100 ppm exposed group,
90 animals in the 300 ppm group, and 90 animals in each 600 ppm group) were examined during
their lifetime.
In all experiments, TCE was supplied, tested, and reported by the authors of the study to
be was highly purified and epoxide free with butyl-hydroxy-toluene at 20 ppm used as a
stabilizer. Extra virgin olive oil was used as the carrier for ingestion experiments and was
reported to be free of pesticides. The authors described the treatment of the animals and running
of the facility in detail and reported that:
Animal rooms were cleaned every day and room temperature varied from
19 degrees to 22 degrees and was checked 3 times daily. Bedding was changed
every two days and cages changes and washed once weekly. The animals were
handled very gently and, therefore, were neither aggressive nor nervous.
Concentrations of TCE were checked by continuous gas-chromatographic
monitoring. Treatment was performed by the same team. In particular, the same
person carried out the gavage of the same animals. This is important, since
animals become accustomed to the same operators. The inhalation chambers
were maintained at 23 ± 2 degrees C and 50 ± 10% relative humidity. Ingestion
from Monday to Friday was usually performed early in the morning. The status
and behavior of the animals were examined at least three times daily and
recorded. Every two weeks the animals were submitted to an examination for the
detection of the gross changes, which were registered in the experimental records.
The animals which were found moribund at the periodical daily inspection were
isolated in order to avoid cannibalism. The animals were weight every two weeks
during treatment and then every eight weeks. Animals were kept under
observation until spontaneous death. A complete necropsy was performed.
Histological specimens were fixed in 70% ethyl alcohol. A higher number of
samples was taken when particular pathological lesions were seen. All slides
were screened by a junior pathologist and then reviewed by a senior pathologist.
The senior pathologist was the same throughout the entire project. Analysis of
variance was used for statistical evaluation of body weights. Results are
expressed as means and standard deviations. Survival time is evaluated using the
Kruskal-Wallis test. For different survival rates between groups, the incidence of
lesions is evaluated by using the Log rank test. Non-neoplastic, preneoplastic,
and neoplastic lesions were evaluated using the Chi-square of Fisher's exact test.
The effect of different doses was evaluated using the Cochran-Armitage test for
linear trends in proportions and frequencies.
The authors stated that: "Although the BT project on TCE was started in 1976 and most
of the experiments were performed from the beginning of 1979, the methodological protocol
adopted substantially met the requirements of the Good Laboratory Practices Act." Finally, it
was reported that "the experiments ran smoothly with no accidents in relation to the conduct of
the experiment and the health of the animals, apart from an excess in mortality in the male
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B6C3Fi mice of the experiment BT 306, due to aggressiveness and fighting among the animals."
This is in contrast to the description of the gavage studies conducted by NTP (1990, 1988) in
which gavage error resulted in significant loss of experimental animals.
Questions have been raised about the findings, experimental conditions, and experimental
paradigm of the European Ramazzini Foundation (ERF) from which the Maltoni et al. (1986)
experiments were conducted (EFSA, 2006). However, these concerns were addressed by
Caldwell et al. (2008a), who concluded that the ERF bioassay program produced credible results
that were generally consistent with those of NTP
In regards to effects of TCE exposure on survival:
a nonsignificant excess in mortality correlated to TCE treatment was observed
only in female rats (treated by ingestion with the compound) and in male B6C3Fi
mice. In B6C3Fi mice of the experiment BT 306 bis, the excess in mortality in
treated animals was higher (p < 0.05 after 40 weeks) but was not dose correlated.
No excess in mortality was observed in the other experiments.
The authors reported that "no definite effect of TCE on body weight was observed in any
of the experiments, apart from experiment BT 306 bis, in which a slight nondose correlated
decrease was found in exposed animals."
In mice, "hepatoma" was the term used by the authors of these studies to describe all
malignant tumors of hepatic cells, of different subhistotypes, and of various degrees of
malignancy. The authors reported that the hepatomas induced by exposure to TCE:
may be unique or multiple, and have different sizes (usually detected grossly at
necropsy). Under microscopic examination these tumors proved to be of the
usual type observed in Swiss and B6C3Fi mice, as well as in other mouse strains,
either untreated or treated with hepatocarcinogens. They frequently have
medullary (solid), trabecular, and pleomorphic (usually anaplastic) patterns. The
hepatomas may produce distant metastases, more frequently in the lungs.
In regard to the induction of "hepatomas" by TCE exposure, the authors report that in
Swiss mice exposed to TCE by inhalation for 8 weeks (BT303), the percentage of animals with
hepatomas was 1.0% in male mice and 1.0% in female mice in the control group (n = 100 for
each gender). For animals exposed to 100 ppm TCE, the percentage in female mice was 1.7%
and male mice 5.0% (n = 60 for each gender). For animals exposed to 600 ppm TCE, the
percentage in female mice was 0% and in male mice 5.5% (n = 72 for each gender).
The relatively larger number of animals used in this bioassay, in comparison to NTP
standard assays, allows for a greater power to detect a response. It is also apparent from these
results that Swiss mice in this experimental paradigm are a "less sensitive" strain in regard to
spontaneous liver cancer induction over the lifetime of the animals. These results suggest that
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8 weeks of TCE exposure via inhalation at 100 or 600 ppm may have been associated with a
small increase in liver tumors in male mice in comparison to concurrent controls.
In Swiss mice exposed to TCE via inhalation for 78 weeks (BT 305), the percentage of
animals with hepatomas was reported to be 4.4% in male mice and 0% in female mice in the
control group (n = 90 for each gender). For animals exposed to 100 ppm TCE, the percentage in
female mice was reported to be 0% and male mice 2.2% (n = 90 for each gender). For animals
exposed to 300 ppm TCE, the percentage in female mice was reported to be 0% and in male
mice 8.9% (n = 90 for each gender). For animals exposed to 600 ppm TCE, the percentage in
female mice was reported to be 1.1% and in male mice 14.4%. As with experiment BT303, there
is a consistency in the relatively low background level of hepatomas reported for Swiss mice in
this paradigm. After 78 weeks of exposure, there appears to be a dose-related increase in
hepatomas in male but not female Swiss mice via inhalation exposure.
In B6C3Fi mice exposed to TCE by inhalation for 78 weeks (BT306), the percentage of
animals with hepatomas was reported to be 1.1% in male mice and 3.3% in female mice in the
control group (n = 90 for each gender). For animals exposed to 100 ppm TCE, the percentage in
female mice was reported to be 4.4% and in male mice 1.1% (n = 90 for each gender). For
animals exposed to 300 ppm TCE, the percentage in female mice was reported to be 3.3% and in
male mice 4.4% (n = 90 for each gender). For animals exposed to 600 ppm TCE, the percentage
in female mice was reported to be 10.0% and in male mice 6.7%. This was the experimental
group with excess mortality in the male group due to fighting. The excess mortality could have
affected the results. The authors reported that there was a difference in the percentage of males
bearing benign and malignant tumors that was due to early mortality among males in experiment
BT306. It is unexpected for the liver cancer incidence to be less in male mice than female mice
and not consistent with the results reported for the Swiss mice.
In B6C3Fi male mice exposed to TCE via inhalation (BT 306 bis), the percentage of
animals with hepatomas was reported to be 18.9% in male mice in the control group (n = 90).
For animals exposed to 100 ppm TCE, the percentage in male mice was reported to be 21.1%
(n = 90). For animals exposed to 300 ppm TCE, the percentage in male mice was reported to be
30.0% (n = 90). For animals exposed to 600 ppm TCE, the percentage in male mice was
reported to be 23.3%. This experiment did not examine female mice. The authors reported a
decrease in survival in mice from this experiment that could have affected results. It is apparent
from the BT 306 and BT 306 bis experiments that the background level of liver cancer was
significantly different in male mice, although they were supposed to be of the same strain. The
finding of differences in response in animals of the same strain but from differing sources has
also been reported in other studies for other endpoints (see Section E.3.1.2).
The authors reported four liver angiosarcomas: one in an untreated male rat (BT 304);
one in a male and one in a female rat exposed to 600 ppm TCE for 8 weeks (experiment BT302);
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and one in a female rat exposed to 600 ppm TCE for 104 weeks (BT 304). The authors
concluded that:
the tumors observed in the treated animals cannot be considered to be correlated
to TCE treatment, but are spontaneously arising. These findings are underlined
because of the extreme rarity of this tumor in control Sprague-Dawley rats,
untreated or treated with vehicle materials. The morphology of these tumors is of
the liver angiosarcoma type produced by vinyl chloride in this strain of rats.
In rats treated for 104 weeks, TCE was reported to not affect the percentages of animals
bearing benign and malignant tumor and of animals bearing malignant tumors. Moreover, it did
not affect the number of total malignant tumors per 100 animals. This study did not report a
treatment-related increase in liver cancer in rats. The report only explicitly described positive
findings so it is assumed that there were no increases in "hepatomas" in rat liver associated with
TCE treatment. The authors concluded that "under the tested experimental conditions, the
evidence of TCE (without epoxide stabilizer) carcinogenicity, gives the result of TCE treatment-
related hepatomas in male Swiss and B6C3Fi mice. A borderline increased frequency of
hepatomas was also seen after 8 weeks of exposure in male Swiss mice." Thus, the increase in
liver tumors in both strains of mice exposed to TCE via inhalation reported in this study is
consistent with the gavage results from the NTP (1990) study in B6C3Fi mice, where male mice
had a higher background level and greater response from TCE exposure than females.
E.2.2.17. Maltoni et al. (1988)
This report was an abbreviated description of an earlier study (Maltoni et al., 1986)
focusing on the identification of a carcinogenic response in rats and mice by chronic TCE
exposure.
E.2.2.18. Van Duuren et al. (1979)
This study exposed male and female noninbred HA:ICR Swiss mice at 6-8 weeks of age
to distilled TCE with no further descriptions of purity. Gavage feeding of TCE was once weekly
in 0.1 mL trioctanoin. Neither initial nor final body weights were reported by the authors. The
authors reported that, at the termination of the experiments or at death, animals were completely
autopsied with specimens of all abnormal-appearing tissues and organs excised for
histopathologic diagnosis. Tissues from the stomachs, livers, and kidneys were reported to be
taken routinely for the intragastric feeding experiments. Tissues were reported to be stained for
H&E for pathologic examination, but no further description of the lobe(s) of the liver examined
or the sections examined was provided by the authors.
Results were only reported for the no of mice with forestomach tumors exposed to
0.5 mg/mouse of TCE treatment given once a week in 0.1 mL trioctanoin. Mouse body weights
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were not given, so the dose in mg/kg for the mice cannot be ascertained. The protocol used in
this experiment kept the mg/mouse constant with a 1-week dosing schedule so that as the mice
increased weight with age, the dose as a function of body weight was decreased. The days on
test were reported to be 622 for 30 male and female mice.
Two male and one female mice were reported as having forestomach tumors. For
30 mice treated with trioctanoin alone, the number of forestomach tumors was reported to be
zero. For mice with no TCE treatment, 5 of 100 male mice were reported to have forestomach
tumors and of 8 of 60 female mice were reported to have forestomach tumors for 636 and
649 days on test. No results for liver were presented by the authors by the intragastric route of
administration including background rates of the incidences of liver tumors or treatment results.
The authors noted that except for repeated skin applications of certain chemicals, no significant
difference between the incidence of distant tumors in treated animals compared with no
treatment and vehicle control groups was noted. Given the uncertainties in regard to dose, the
once-a-week dosing regime, the low number of animals tested with resulting low power, and the
lack of reporting of experimental results, the ability to use the results from this experiment in
regard to TCE carcinogenicity is very limited.
E.2.2.19. NCI (1976)
This bioassay was "initiated in 1972 according to the methods used and widely accepted
at that time" with the design of carcinogenesis bioassays having "evolved since then in some
respects and several improvements" having been developed. The most notable changes reported
in the foreword of the report are changes "pertaining to preliminary toxicity studies, numbers of
controls used, and extent of pathological examination." Industrial-grade TCE was tested (99%
TCE, 0.19% 1,2,-epoxybutane, 0.04% ethyl acetate, 0.09% epichlorhydrin, 0.02% TV-methyl
pyrrole, and 0.03% diisobutylene) with rats and mice exposed via gavage in corn oil
5 times/week for 78 weeks using 50 animals per group at two doses with both sexes of Osborne-
Mendel rats and B6C3Fi mice. However, for control groups, only 20 of each sex and species
were used. Rats were killed after 110 weeks and mice after 90 weeks. Rats and mice were
initially 48 and 35 days of age, respectively, at the start of the experiment with control and
treated animals born within 6 days of each other. Initial weight ranges were reported for treated
and control animals to be 168-229 g for male rats, 130-170 g for female rats, 11-22 g for male
mice, and 11-18 g for female mice. Animals were reported to be "randomly assigned to
treatment groups so that initially the average weight in each group was approximately the same."
Mice treated with TCE were reported to be:
maintained in a room housing other mice being treated with one of the following
17 compounds: 1,1,2-2-tetrachloroethane, chloroform, 3-chloropropene,
chloropicrin, 1,2-dibromochloropropane, 1,2, dibromoethane, ethylene dichloride,
1,1-diochloroethane, 3-sulfolene, idoform, methyl chloroform, 1,1,2-trichloro-
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ethane, tetrachloroethylene, hexachloroethane, carbon disulfide,
trichlorofluoromethane, and carbon tetrachloride. Nine groups of vehicle controls
and 9 groups of untreated controls were also housed in this same room.
The authors noted that:
TCE-treated rats and their controls were maintained in a room housing other rats
being treated with one of the following compounds: dibromochloropropane,
ethylene dichloride, 1,1-dichloroethane, and carbon disulfide. Four groups of
vehicle-treated controls were in the same room." Thus, there was the potential of
co-exposure to a number of other chemicals, especially for the mice, resulting
from exhalation in treated animals housed in the same room, including the control
groups, as noted by the authors. The authors also noted that "samples of ambient
air were not tested for presence of volatile materials" but state that "although the
room arrangement is not desirable as is stated in the Guidelines for Carcinogen
Bioassay in Small Rodents, there is no evidence the results would have been
different with a single compound in a room.
The initial doses of TCE for rats were reported to be 1,300 and 650 mg/kg. However,
these levels were changed based on survival and body weight data "so that the TWA doses were
549 and 1,097 mg/kg for both male and female rats." For mice, the initial doses were reported to
be 1,000 and 2,000 mg/kg for males and 700 and 1,400 mg/kg for females. The "doses were
increased so that the time weighted average doses were 1,169 and 2,339 mg/kg for male mice
and 869 and 1,739 mg/kg for female mice."
The authors reported that signs of toxicity, including reduction in weight, were evident in
treated rats, which, along with increased mortality, "necessitated a reduction in doses during the
test." In contrast "very little evidence of toxicity was seen in mice, so doses were increased
slightly during the study." Doses were "changed for the rats after 7 and 16 weeks of treatment,
and for the mice after 12 weeks." At 7 weeks of age, male and female rats were dosed with
650 mg/kg TCE, at 14 weeks they were dosed with 750 mg/kg TCE, and at 23 weeks of age
500 mg/kg TCE. For the high exposure level, the exposure concentrations were 1,300, 1,500,
and 1,000 mg/kg TCE, respectively, for the same changes in dosing concentration. For rats the
percentage of TCE in corn oil remained constant at 60%. For female mice, the TCE exposure at
the beginning of dosing was 700 mg/kg TCE (10% in corn oil) at 5 weeks of age for the "lower
dose" level. The dose was increased to 900 mg/kg-day (18% in corn oil) at 17 weeks of age and
maintained until 83 weeks of age. For male mice, the TCE exposure at the beginning of dosing
was 1,000 mg/kg TCE (15% in corn oil) at 5 weeks of age for the "lower dose" level. At
11 weeks, the level of TCE remained the same but the percentage of TCE in corn oil was
reduced to 10%. The dose was increased to 1,200 mg/kg-day at 17 weeks of age (24% in corn
oil) and maintained until 83 weeks of age. For the "higher dose," the TCE exposure at the
beginning of dosing was 1,400 mg/kg TCE (10% in corn oil) at 5 weeks of age in female mice.
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At 11 weeks of age, the exposure level of TCE was kept the same but the percentage of TCE in
corn oil increased to 20%. By 17 weeks of age, the exposure concentration of TCE in corn oil
was increased to 1,800 mg/kg (18% in corn oil) in female mice. For the "higher dose" in male
mice, the TCE exposure at the beginning of dosing was 2,000 mg/kg (15% in corn oil) which
was maintained at 11 weeks in regard to TCE administered but the percent of TCE corn oil was
increased to 20%. For male mice, the exposure concentration was increased to 2,400 mg/kg
(24% in corn oil). For all of the mice, treatment continued on a 5 days/week schedule of gavage
dosing throughout the timecourse of treatment (78 weeks of treatment). Thus, not only did the
total dose administered to the animals change, but the volumes of vehicle in which TCE was
administered changed throughout the experiment.
The authors stated that at 37 weeks of age, "To help assure survival until planned
termination the dosing schedule was changed for rats to a cycle of 1 week of no treatment
followed by 4 weeks of treatment." for male and female rats. Thus, the duration of exposure in
rats was also changed. All lobes of the liver were reported to be taken including the free margin
of each lobe with any nodule or mass represented in a block 10*5x3 mm cut from the liver
and fixed in a marked capsule.
Body weights (mean ± SD) were reported to be 193 ± 15.0 g (n = 20), 193 ± 15.8 g
(n = 50), and 195 ± 16.7 g (n = 50) for control, low-, and high-dose male rats at initiation of the
experiment. By 1 year of exposure (50 weeks), 20/20 control male rats were still alive to be
weighed, 42/50 of the low dose rats were alive and 34/50 of high dose rats were still alive. The
body weights of those remaining were decreased by 6.2 and 17% in the low- and high-dose
animals in comparison with the controls. For female rats, the mean body weights were reported
to be 146 ± 11.4 g (n = 20), 144 ± 11.0 g (n = 50), and 144 ± 9.5 g (n = 50) for control, low-, and
high-dose female rats at initiation of the experiment. By 1 year of exposure (50 weeks),
17/20 control female rats were still alive; 28/50 low-dose and 39/50 high-dose rats were alive.
The body weights of those remaining were decreased by 25 and 30% in the low- and high-dose
animals in comparison with the controls.
For male mice, the initial body weights were 17 ± 0.5 g (n = 20), 17 ± 2.0 g (n = 50), and
17 ± 1.1 g (n = 50) for control, low, and high doses. By 1 year of exposure (50 weeks), 18/20
control male mice were still alive; 47/50 or the low-dose and 34/50 high-dose mice were still
alive. The body weights of those remaining were unchanged in comparison to controls. For
female mice, the initial body weights were 14 ± 0.0 g (n = 20), 14 ± 0.6 g (n = 50), and 14 ±
0.7 g (n = 50) for control, low, and high doses. By 1 year of exposure (50 weeks), 18/20 control
male mice were still alive; 45/50 of the low dose and 41/50 of the high-dose groups were still
alive. The body weights of those remaining were unchanged in comparison to controls.
A high proportion of rats were reported to die during the experiment with 17/20 control,
42/50 low-dose, and 47/50 high-dose animals dying prior to scheduled termination. For female
rats, 12/20 control, 35/48 low-dose, and 37/50 high-dose animals were reported to die before
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scheduled termination with two low-dose females reported to be missing and not counted in the
denominator for that group. The authors reported that earlier death was associated with higher
TCE dose. A decrease in the percentage of tumor-bearing animals was reported to be lower in
treated animals and attributed by the authors to be likely related to the decrease in their survival.
A high percentage of respiratory disease was reported to be observed among the rats
without any apparent difference in the type, severity, or morbidity as to sex or group. The
authors reported that "no significant toxic hepatic changes were observed" but no other details
regarding results in the liver of rats were provided.
Carbon tetrachloride was administered to rats as a positive control. A low incidence of
both HCC and neoplastic nodule was reported to be found in both colony controls (1/99 HCC
and 0/99 neoplastic nodule in male rats and 0/98 HCC and 2/98 neoplastic nodules in female
rats) and carbon-tetrachloride-treated rats. Hepatic adenomas were included in the description of
neoplastic nodules in this study with the diagnosis of HCC to be "based on the presence of less
organized architecture and more variability in the cells comprising the neoplasms."
The authors reported that "increased mortality in treated male mice appears to be related
to the presence of liver tumors." For both male and female mice, the incidences of HCC were
reported to be high from TCE treatment with 1/20 in age matched controls, 26/50 in low-dose,
and 31/48 in high-dose males. Colony controls for male mice were reported to be 5/77 for
vehicle and 5/70 for untreated mice. For female mice HCCs were reported to be observed in
0/20 age-matched controls, 4/50 low-dose, and 11/47 high-dose mice. Colony controls for
female mice were reported to be 1/80 for vehicle and 2/75 for untreated mice. In male mice,
HCCs were reported to be observed early in the study with the first seen at 27 weeks. HCCs
were not observed so early in low-dose male or female mice.
The diagnosis of HCC was reported to be based on histologic appearance and the
presence of metastasis especially to the lung with no other lesions significantly elevated in
treated mice. The tumors were reported to be:
varied from those composed of well differentiated hepatocytes in a relatively
uniform trabecular arrangement to rather anaplastic lesions in which mitotic
figures occurred in cells which varied greatly in size and tinctorial characteristics.
Many of the tumors were characterized by the formation of relatively discrete
areas of highly anaplastic cells within the tumor proper which were, in turn,
surrounded by relatively well differentiated neoplastic cells. In general, various
arrangements of the hepatocellular carcinoma occurred, as described in the
literature, including those with an orderly cord-like arrangement of neoplastic
cells, those with a pseudoglandular pattern resembling adenocarcinoma, and those
composed of sheets of highly anaplastic cells with minimal cord or gland-like
arrangement. Multiple metaplastic lesions were observed in the lung, including
several neoplasms which were differentiated and relative benign in appearance."
The authors noted that almost all mice treated with carbon tetrachloride exhibited
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liver tumors and that the "neoplasms occurring in treated [sic carbon tetrachloride
treated] mice were similar in appearance to those noted in the TCE-treated mice.
Thus, phenotypically this study reported that the liver tumors induced in mice by TCE
were heterogeneous and typical of those arising after carbon tetrachloride administration. The
descriptions of liver tumors in this study and the tendency of metastasis to the lung are similar to
the descriptions provided by Maltoni et al. (1986) for TCE-induced liver tumors in mice via
inhalation.
In terms of noncancer pathology of the liver, one control male rat was reported to display
fatty metamorphosis of the liver at 102 weeks. However, for the low dose, three male rats were
reported to display fatty metamorphosis (90, 110, and 110 weeks), two rats to display cystic
inflammation (76, 110 weeks), and one rat to display general inflammation (110 weeks). At the
high dose, six rats were reported to display fatty metamorphosis (12, 35, 49, 52, 52, and
58 weeks), one rat to display cytomegaly (42 weeks), two rats to display centrilobular
degeneration (53 and 58 weeks), one rat to display diffuse inflammation (62 weeks), 1 rat to
display congestion (Week 12), and five rats to display angiectasis or abnormally enlarged blood
vessels, which can be manifested by hyperproliferation of endothelial cells and dilatation of
sinusoidal spaces (35, 42, 52, 54, and 65 weeks). One control female rat was reported o display
fatty metamorphosis of the liver at 110 weeks, and one control female rats to display
"inflammation" of the liver at 110 weeks. Of the TCE dosed female rats, only one high-dose
female rat displayed fatty metamorphosis at week 96.
Thus, for male rats, there was liver pathology present in some rats due to TCE exposure
examined from 12 weeks to a year at their time of their premature death. For mice, the liver
pathology was dominated by the presence of HCC with additional hyperplasia noted in two mice
of the high-dose male and female groups and one or less mouse exhibiting hyperplasia in the
control or low-dose groups.
The authors noted that "while the absence of a similar effect in rats appears most likely
attributable to a difference in sensitivity between the Osborne-Mendel rat and B6C3Fi mouse,
the early mortality of rats due to toxicity must also be considered." They concluded that "the test
in rats is inconclusive: large numbers of rats died prior to planned termination; in addition, the
response of this rat strain to the hepatocarcinogenicity of the positive control compound, carbon
tetrachloride, appeared relatively low." Finally, the authors noted that "while the results
obtained in the present bioassay could possibly have been influenced by an impurity in the TCE
used, the extremely low amounts of impurities found make this improbable."
E.2.2.20. Herren-Freund et al. (1987)
This study gave results primarily in initiated male B6C3Fi mice that were also exposed to
TCE metabolites in drinking water for 61 weeks. However, in Table 1 of the report, results were
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given for mice that received no initiator but were given 40 mg/L TCE or 2 g/L sodium chloride
as control. The mice were reported to be 28 days of age when placed on drinking water
containing TCE. The authors reported that concentrations of TCE fell by about half at the
40 mg/L dose of TCE during the twice a week change in drinking water solution. For control
animals (n = 22), body weight at termination was reported to be 32.93 ± 0.54 g, liver weight
1.80 ± 0.05 g, and percent liver/body weight 5.47% ± 0.16%. For TCE treated animals (n = 32),
body weight at termination was reported to be 35.23 ± 0.66 g, liver weight was 1.97 ± 0.10 g,
and percent liver/body weight was 5.57% ± 0.24%. Thus, hepatomegaly was not reported for
this paradigm at this time of exposure. The study reported that for 22 control animals the
prevalence of adenomas was 2/22 animals (or 9%), with the mean number of adenomas per
animal to be 0.09 ± 0.06 (SEM). The prevalence of carcinomas in the control group was
reported to be 0/22. For 32 animals exposed to 40 mg/L TCE, the prevalence of adenomas was
3/32 animals (or 9%), with the mean number of adenomas per animal to be 0.19 ± 0.12 (SEM).
The prevalence of animals with HCCs was 3/32 animals (or 9%) with the mean number of HCCs
to be 0.10 ±0.05 (SEM).
Thus, similar to the acute study of Tucker et al. (1982), significant loss of TCE is a
limitation for trying to evaluate TCE hazard in drinking water. However, despite difficulties in
establishing accurately the dose received, an increase in adenomas per animal and an increase in
the number of animals with HCCs were reported to be associated with TCE exposure after
61 weeks of exposure. Also of note is that the increase in tumors was reported without
significant increases in hepatomegaly at the end of exposure. The authors did not report these
increases in tumors as being significant but did not do a statistical test between TCE exposed
animals without initiation and control animals without initiation. The low numbers of animal
tested limits the statistical power to make such a determination. However, for carcinomas, there
was none reported in controls but 9% of TCE-treated mice had HCCs.
E.2.2.21. Anna et al. (1994)
This report focused on presenting incidence of cancer induction after exposure to TCE or
its metabolites and included a description of results for male B6C3Fi mice (8 weeks old at the
beginning of treatment) receiving 800 mg/kg-day TCE via gavage in corn oil, 5 days/week for
76 weeks. There was very limited reporting of results other than tumor incidence. There was no
reporting of liver weights at termination of the experiment. Although the methods section of the
report gives 800 mg/kg-day as the exposure level, Table 1 in the results section reports that TCE
was administered at 1,700 mg/kg-day. This could be a typographical error in the table as a
transposition with the dose of "perc" administered to other animals in the same study. The
methods section of the report states that the authors based their dose in mice that used in the
1990 (NTP) study. The NTP study only used a 1,000 mg/kg-day in mice, suggesting that the
table is mislabeled and that the actual dose is 800 mg/kg-day in the Anna et al. (1994) study.
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All treated mice were reported to be alive after 76 weeks of treatment. For control
animals, 10 animals exposed to corn oil and 10 untreated controls were killed in a 9-day period.
The remaining controls were killed at 96, 103, and 134 weeks of treatment. Therefore, the
control group (all) contains a heterogeneous group of animals that were sacrificed from 76 to
134 weeks and were not comparable to the animals sacrificed at 76 weeks.
At 76 weeks, 3 of 10 the untreated and two of the 10 corn oil treated controls were
reported to have one small hepatocellular adenoma. None of the controls examined at 76 weeks
were reported to have any observed HCCs. The authors reported no cytotoxicity for TCE, corn
oil, and untreated control group. At 76 weeks, 75 mice treated with 800 mg/kg-day TCE were
reported to have a prevalence of 50/75 animals having adenomas with the mean number of
adenomas per animal to be 1.27 ± 0.14 (SEM). The prevalence of carcinomas in these same
animals was reported to be 30/70 with the mean number of HCCs per animal to be 0.57 ± 0.10
(SEM).
Although not comparable in terms of time until tumor observation, corn oil control
animals examined at much later time points did not have as great a tumor response as did those
exposed to TCE. At 76-134 weeks, 32 mice treated with corn oil were reported to have a
prevalence of 4/32 animals having adenomas with the mean number of adenomas per animal to
be 0.13 ± 0.06 (SEM). The prevalence of carcinomas in these same animals was reported to be
4/32 with the mean number of HCCs per animal to be 0.12 ± 0.06 (SEM). Despite only
examining one exposure level of TCE and the limited reporting of findings other than incidence
data, this study also reported that TCE exposure in male B6C3Fi mice to be associated with
increased induction of adenomas and HCC, without concurrent cytotoxicity.
In terms of liver tumor phenotype, Anna et al. (1994) reported the percent of H-ras codon
61 mutations in tumors from concurrent control animals (water and corn oil treatment groups
combined) examined in their study, historical controls in B6C3Fi mice, and in tumors from TCE
or DCA (0.5% in drinking water) treated animals. From their concurrent controls, they reported
H-ras codon 61 mutations in 17% (n = 6) of adenomas and 100% (n = 5) of carcinomas. For
historical controls (published and unpublished), they reported mutations in 73% (n = 33) of
adenomas and in 70% (n = 30) of carcinomas. For tumors from TCE-treated animals, they
reported mutations in 35% (n = 40) of adenomas and 69% (n = 36) of carcinomas, while for
DCA-treated animals, they reported mutations in 54% (n = 24) of adenomas and in 68% (n = 40)
of carcinomas. The authors reported that "in this study, the H-ras codon 61 mutation frequency
was not statistically different in liver tumors from DCA and TCE-treated mice and combined
controls (62, 51 and 69%, respectively)." In regard to mutation spectra in H-ras oncogenes
detected B6C3Fi mouse liver "tumors," the authors reported combined results for concurrent and
historical controls of 58% AAA, 27% CGA, and 14% CTA substitutions for CAA at codon 61
out of 58 mutations. For TCE "tumors" the substitution pattern was reported to be 29% AAA,
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24% CGA, and 40% CTA substitutions for CAA at codon 61 out of 39 mutations and for DCA
28% AAA, 35% CGA, and 38% CTA substitutions for CAA at codon 61 out of 40 mutations.
E.2.2.22. Bull et al. (2002)
This study primarily presented results from exposures to TCE, DCA, TCA, and
combinations of DCA and TCA after 52 weeks of exposure with some animals examined at
87 weeks. It only examined and described results for liver. In a third experiment, 1,000 mg/kg
TCE was administered once daily 7 days/week for 79 weeks in 5% alkamuls in distilled water to
40 B6C3Fi male mice (6 weeks old at the beginning of the experiment). At the time of
euthanasia, the livers were removed, tumors were identified, and the tissues section was
examined by a pathologist and immunostaining. Liver weights were not reported. For the TCE
gavage experiment, there were 6 gavage-associated deaths during the course of this experiment
among a total of 10 animals that died with TCE treatment. No animals were lost in the control
group.
The limitations of this experiment were discussed in Caldwell et al. (2008b).
Specifically, for the DCA- and TCA-exposed animals, the experiment was limited by low
statistical power, a relatively short duration of exposure, and uncertainty in reports of lesion
prevalence and multiplicity due to inappropriate lesions grouping (i.e., grouping of hyperplastic
nodules, adenomas, and carcinomas together as "tumors"), and incomplete histopathology
determinations (i.e., random selection of gross lesions for histopathology examination).
For the TCE results, Bull et al. (2002) reported a high prevalence (23/36 B6C3Fi male
mice) of adenomas and HCC (7/36) and gave results of an examination of approximately half of
the lesions induced by TCE exposure. Tumor incidence data were provided for only 15 control
mice and reported as 2/15 (13%) having adenomas and 1/15 (7%) carcinomas. Thus, this study
presents results that are consistent with other studies of chronic exposure that show TCE
induction of HCC in male B6C3Fi mice.
For determinations of immunoreactivity to c-Jun as a marker of differences in "tumor"
phenotype, Bull et al. (2002) did include all lesions in most of their treatment groups, decreasing
the uncertainty of his findings. The exceptions were the absence of control lesions and inclusion
of only 16/27 and 38/72 lesions for 0.5 g/L DCA + 0.05 g/L TCA and 1 g/kg-day TCE exposure
groups, respectively. Immunoreactivity results were reported for the group of hyperplastic
nodules, adenomas, and carcinomas. Thus, changes in c-Jun expression between the differing
types of lesions were not determined.
Bull et al. (2002) reported lesion reactivity to c-Jun antibody to be dependent on the
proportion of the DCA and TCA administered after 52 weeks of exposure. Given alone, DCA
produced lesions in mouse liver for which approximately half displayed a diffuse
immunoreactivity to a c-Jun antibody, half did not, and none exhibited a mixture of the two.
After TCA exposure alone, no lesions were reported to be stained with this antibody. When
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given in various combinations, DCA and TCA co-exposure induced a few lesions that were only
c-Jun+, many that were only c-Jun-, and a number with a mixed phenotype whose frequency
increased with the dose of DCA. For TCE exposure of 79 weeks, TCE-induced lesions also had
a mixture of phenotypes (42% c-Jun+, 34% c-Jun-, and 24% mixed) and were most consistent
with those resulting from DCA and TCA co-exposure but not either metabolite alone.
Mutation frequency spectra for the H-ras codon 61 in mouse liver "tumors" induced by
TCE (n = 37 tumors examined) were reported to be significantly different than that for TCA
(n = 41 tumors examined), with DCA-treated mice tumors giving an intermediate result
(n = 64 tumors examined). In this experiment, TCA-induced "tumors" were reported to have
more mutations in codon 61(44%) than those from TCE (21%) and DCA (33%). This frequency
of mutation in the H-ras codon 61 for TCA is the opposite pattern as that observed for a number
of peroxisome proliferators in which the mutation spectra in tumors has been reported to be
much lower than spontaneously arising tumors (see Section E.3.4.1.5).
Bull et al. (2002) noted that the mutation frequency for all TCE-, TCA-, or DCA-induced
tumors was lower in this experiment than for spontaneous tumors reported in other studies (they
had too few spontaneous tumors to analyze in this study), but that this study utilized lower doses
and was of shorter duration than that of Ferreira-Gonzalez et al. (1995). These are additional
concerns along with the effects of inappropriate lesion grouping, in which a lower stage of
progression is grouped with more advanced stages. In a limited subset of tumor that were both
sequenced and characterized histologically, only 8 of 34 (24%) TCE-induced adenomas but
9/15 (60%) of TCE-induced carcinomas had mutated H-ras at codon 61, which the authors
suggest is evidence that this mutation is a late event.
The issues involving identification of mode of action through tumor phenotype analysis
are discussed in detail below for the more general case of liver cancer as well as for specific
hypothesized modes of action (see Sections E.3.1.4, E.3.1.8, E.3.2.1, and E.3.4.1.5). In an earlier
paper, Bull (2000) suggested that "the report by Anna et al. (1994) indicated that TCE-induced
tumors possessed a different mutation spectra in codon 61 of the H-ras oncogene than those
observed in spontaneous tumors of control mice." Bull (2000) stated that "results of this type
have been interpreted as suggesting that a chemical is acting by a mutagenic mechanism" but
went on to suggest that it is not possible to a priori rule out a role for selection in this process and
that differences in mutation frequency and spectra in this gene provide some insight into the
relative contribution of different metabolites to TCE-induced liver tumors. Bull (2000) noted
that data from Anna et al. (1994), Ferreira-Gonzalez et al. (1995), and Maronpot et al. (1995a)
indicated that mutation frequency in DCA-induced tumors did not differ significantly from that
observed in spontaneous tumors, that the mutation spectra found in DCA-induced tumors has a
striking similarity to that observed in TCE-induced tumors, and that DCA-induced tumors were
significantly different than that of TCA-induced liver tumors.
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What is clear from these observations is that the phenotype of TCE-induced tumors
appears to be more like DCA-induced tumors (which are consistent with spontaneous tumors), or
those resulting from a co-exposure to both DCA and TCA, than from those induced by TCA.
More importantly, these data suggest that using measures other than dysplasticity and tincture
indicate that mouse liver tumors induced by TCE are heterogeneous in phenotype. The
descriptions of tumors in mice reported by the NTP (1990) and Maltoni et al. (1986) studies are
also consistent with phenotypic heterogeneity as well as consistency with spontaneous tumor
morphology.
E.2.3. Mode of Action: Relative Contribution of TCE Metabolites
Several metabolites of TCE have also been shown to induce liver cancer in rodents with
DCA and TCA having been the focus of study as potential active agent(s) of TCE liver toxicity
and/or carcinogenesis and both able to induce peroxisome proliferation (Caldwell and Keshava,
2006). A variety of DCA effects from exposure have been noted that are consistent with
conditions that increase risk of liver cancer (e.g., effects on the cytosolic enzyme GST-zeta,
diabetes, and glycogen storage disease), with the pathological changes induced by DCA on
whole liver consistent with changes observed in preneoplastic foci from a variety of agents
(Caldwell and Keshava, 2006). CH is one of the first metabolites from oxidative metabolism of
TCE with a large fraction of TCE metabolism appearing to go through CH and then subsequent
metabolism to TCA and TCOH (Chiu et al., 2006b). Similarities in toxicity may indicate that
common downstream metabolites may be lexicologically important, and differences may
indicate the importance of other metabolic pathways.
Although both induce liver tumors, DCA and TCA have distinctly different actions
(Caldwell and Keshava, 2006) and apparently differ in induced tumor phenotype (see discussions
above in Section E.2.2. and many studies have been conducted to try to elucidate the nature of
those differences (Caldwell et al., 2008b). Limitations of all of the available chronic studies of
TCA and most of the studies of DCA include less-than-lifetime exposures, varying and small
numbers of animals examined, and few exposure concentrations that were relatively high.
E.2.3.1. Acute studies of DCA/TCA
The studies in this section focus on studies of DCA and TCA that examine, to the extent
possible, similar endpoints using similar experimental designs as those of TCE examined above
and that give insight into proposed modes of action for all three. Of note for any experiment
involving TCA is whether exposure solutions were neutralized. Unbuffered TCA is commonly
used as a reagent to precipitate proteins so that any result from studies using unbuffered TCA
could potentially be confounded by the effects on pH.
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E.2.3.1.1. Sanchez and Bull (1990)
In this report TCA and DCA were administered to male B6C3Fi mice (9 weeks of age)
and male and female Swiss-Webster mice (9 weeks of age) for up to 14 days. At 2, 4, or
14 days, mice were injected with tritiated thymidine. Experiments were replicated at least once
but results were pooled so that variation between experiments could not be determined. B6C3Fi
male mice were given DCA or TCA at 0, 0.3, 1.0, or 2.0 g/L in drinking water (n = 4 for each
group for 2 and 5 days, but n = 15 for control and n = 12 for treatment groups at day 14). Swiss-
Webster mice (n = 4) at were exposed to DCA only on day 14 at 0, 1.0, or 2.0 g/L. Mice were
injected with tritiated thymidine 2 hours prior to sacrifice. The pH of the drinking water was
adjusted to 6.8-7.2 with sodium hydroxide. Concentrations of TCA and DCA were reported to
be stable for a minimum of 3 weeks.
Hepatocyte diameters were reported to be determined by randomly selecting five
different high power fields (400x) in five different sections per animals (total of 25 fields/animal
with "cells in and around areas of necrosis, close to the edges of the section, or displaying
mitotic figures were not included in the cell diameter measurements." PAS staining was
reported to be done for glycogen and lipofuscin determined by autofluorescence. Tritiated
thymidine was reported to be given to the animals 2 hours prior to sacrifice. In two of three
replications of the 14-day experiment, a portion of the liver was reported to be set aside for DNA
extraction with the remaining group examined autoradiographically for tritiated thymidine
incorporation into individual hepatocytes. Autoradiographs were also reported to be examined in
the highest dose of either DCA or TCA for the 2- and 5-day treatment groups. Autoradiographs
were reported to be analyzed in randomly selected fields (5 sections per animal in 10 different
fields) for a total of 50 fields/animal and reported as percentage of cells in the fields that were
labeled. There was no indication by the authors that they characterized differing zones of the
liver for preferential labeling. DNA thymidine incorporation results were not examined in the
same animals as those for individual hepatocyte incorporation and also not examined at 2- or
5-day time periods. The only analyses reported for the Swiss-Webster mice were of hepatic
weight change and histopathology. Variations in results were reported as SE of the mean.
Liver weights were reported but not body weights, so the relationship of liver/body
weight ratio could not be determined for the B6C3Fi mice. For liver weight, the numbers of
animals examined varied greatly between and within treatment groups. The number of control
animals examined were reported to be n = 4 on day 2, n = 8 on day 5, and n = 15 on day 14.
There was also a large variation between control groups in regard to liver weight. Control liver
weights for day 2 were reported to be 1.3 ± 0.1, day 5 to be 1.5 ± 0.05, and for day 14 to be 1.3 ±
0.04 g. Liver weights in Day 5 control animals were much greater than those for day 2 and
day 14 animals and thus, the means varied by as much as 15%.
For DCA, there was no reported change in liver weights compared to controls values at
any exposure level of DCA after 2 days of exposure. After 5 days of exposure, there was no
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difference in liver weight between controls and 0.3 g/L exposed animals. However, the animals
exposed at 1.0 or 2.0 g/L DCA had identical increases in liver weight of 1.7 ± 0.13 and 1.7 ±
0.8 g, respectively. Due to the low power of the experiment, only the 2.0 g/L DCA result was
identified by the authors as significantly different from the control value. For TCA, there was a
slight decrease reported between control values and the 0.3 g/L treatment group (1.2 ± 0.1 g vs.
1.3 ± 0.1 g), but the 1.0 and 2.0 g/L treatment groups had similar slight increases over control
(for 1.0 g/L liver weight was 1.5 ± 0.1 and for 2.0 g/L liver weight was 1.4 ± 0.1 g). The same
pattern was apparent for the 5-day treatment groups for TCA as for the 2-day treatment groups.
For 14-day exposure periods, the number of animals studied was increased to!2 for the
TCA and DCA treatment groups. After 14 days of DCA treatment, there was a reported dose-
related increase in liver weight that was statistically significant at the two highest doses (i.e., at
0.3 g/L DCA liver weight was 1.4 ± 0.04, at 1.0 g/L DCA liver weight was 1.7 ± 0.07 g, and at
2.0 g/L DC A liver weight was 2.1 ± 0.08 g). This was 1.08-, 1.31-, and 1.62-fold of controls,
respectively. After 14 days of TCA exposure, there was a dose-related increase in liver weight
that the authors reported to be statistically significant at all exposure levels (i.e., at 0.3 g/L liver
weight was 1.5 ± 0.06, at 1.0 g/L liver weight was 1.6 ± 0.07 g, and at 2.0 g/L liver weight was
1.8 ± 0.10 g). This represents 1.15-, 1.23-, and 1.38-fold of control.
The authors note that at 14 days, that DCA-associated increases in hepatic liver weight
were greater than that of TCA. What is apparent from these data are that while the magnitude of
difference between the exposures was ~6.7-fold between the lowest and highest dose, the
differences between TCA exposure groups for change in liver weight was -2.5. For DCA, the
slope of the dose-response curve for liver weight increases appeared to be closer to the
magnitude of difference in exposure concentrations between the groups (i.e., a difference of
7.7-fold between the highest and lowest dose for liver weight induction). Given that the control
animal weights varied as much as 15%, the small number of animals examined, and that body
weights were also not reported, there are limitations for making quantitative comparisons
between TCA and DCA treatments. However, after 14 days of treatment, it is apparent that there
was a dose-related increase in liver weight after either DCA or TCA exposure at these exposure
levels. For male and female Swiss-Webster mice, 1 and 2 g/L DCA treatment (n = 4) was
reported to also induce an increase in percent liver/body weight that was similar to the magnitude
of exposure difference (see below).
Grossly, livers of B6C3Fi mice treated with DCA for 1 or 2 g/L were reported to have
"pale streaks running on the surface" and occasionally, discrete, white, round areas were also
observed on the surface of these livers. Such areas were not observed in TCA-treated or control
B6C3Fi mice. Pale streaks on the surface of the liver were not observed in Swiss-Webster mice.
Again there was no significant effect on total body or renal weights (data not shown).
Swiss-Webster mice were reported to have dose-related increases in hepatic weight and
hepatic/body weight ratios were observed. DCA-associated increases in relative hepatic weights
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in both sexes were comparable to those in B6C3Fi mice. The authors report liver weights for the
Swiss-Webster male mice (n = 4 for each group) to be 2.1 ± 0.1 g for controls, 2.1 ± 0.1 g for
1.0 g/L DC A, and 2.4 ± 0.2 g for 2.0 g/L DCA 14-day treatment groups. The percent liver/body
weights for these same groups were reported to be 6.4 ± 0.4, 6.9 ± 0.2, and 8.1 ± 0.3%,
respectively. For female Swiss-Webster mice (n = 4 for each group), the liver weights were
reported to be 1.1 ± 0.1 g for controls, 1.5 ± 0.1 g for 1.0 g/L DCA, and 1.7 ± 0.2 g for 2.0 g/L
DCA 14-day treatment groups. The percent liver/body weights for these same groups of Swiss
mice were reported to be 4.8 ± 0.2, 6.0 ± 0.2, and 6.8 ± 0.4%, respectively.
Thus, while there was no significant difference in "liver weight" between the control and
the 1.0 g/L DCA treatment group for male or female Swiss-Webster mice, there was a
statistically significant difference in liver/body weight ratio reported by the authors. These data
illustrate the importance of reporting both measures and the limitations of using small numbers
of animals (n = 4 for the Swiss Webster vs. n = 12-14 for B6C3Fi 14-day experiments).
Relative liver weights were reported by the authors for male B6C3Fi mice only for the
14-day groups, as a function of calculated mean water consumption, as pooled data from the
three experiments, and as a figure that was not comparable to the data reported for Swiss-
Webster mice. The liver weight data indicate that male mice of the same age appeared to differ
in liver weight between the two strains without treatment (i.e., male B6C3Fi mice had control
liver weights at 14 days of 1.3 ± 0.04 g for 15 mice, while Swiss-Webster mice had control
values of 2.1 ±0.1 for 4 mice). While the authors report that results were "comparable" between
the B6C3Fi mice in regard to DCA-induced changes in liver weight, the increase in percent
liver/body weight ratios were 1.27-fold of control for Swiss-Webster male mice (n = 4) and
1.42-fold of control for females while the increase in liver weight for B6C3Fi male mice
(n = 12-14) was 1.62-fold of controls after 14 days of exposure to 2 g/L DCA.
The concentration of DNA in the liver was reported as mg hepatic DNA/g of liver. This
measurement can be associated with hepatocellular hypertrophy when decreased, or increased
cellularity (of any cell type), increased DNA synthesis, and/or increased hepatocellular ploidy in
the liver when increased. The number of animals examined for this parameter varied. For
control animals, there were four animals reported to be examined at 2 days, eight animals
examined at 5 days, and at 14 days eight animals were examined.
The mean DNA content in control livers were not reported to vary greatly, however, and
the variation between animals was relatively low in the 5- and 14-day control groups (i.e., 1.67 ±
0.27, 1.70 ± 0.05, and 1.69 mg DNA/g, for 2-, 5-, or 14-day control animals, respectively). For
treatment groups, the number of animals reported to be examined appeared to be the same as the
control animals.
For DCA treatment, there did not appear to be a dose-response in hepatic DNA content
with the 1 g/L exposure level having the same reported value as control but the 0.3 and 2.0 g/L
values reported to be lower (mean values of 1.49 and 1.32 mg DNA/g, respectively). After
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5 days of exposure, all treatment groups were reported to have a lower DNA content that the
control value (i.e., 1.44 ± 0.06, 1.47 ± , and 1.30 ± 0.14 mg DNA/g, for 0.3, 1.0, and 2.0 g/L
exposure levels of DC A, respectively). After 14 days of exposure, there was a reported increase
in hepatic DNA at the 0.3 g/L exposure level, but significant decreases at the 1.0 and 2.0 g/L
exposure levels (i.e., 1.94 ±0.20, 1.44 ± 0.14, and 1.19 ± 0.16 mg DNA/g for the 0.3, 1.0, and
2.0 g/L exposure levels of DC A, respectively).
Changes in DNA concentration in the liver were not correlated with the pattern of liver
weight increases after DCA treatment. For example, while there was a clear dose-related
increase in liver weight after 14 days of DCA treatment, the 0.3 g/L DCA exposed group was
reported to have a higher rather than lower level of hepatic DNA than controls. After 2 or 5 days
of DCA treatment, liver weights were reported to be the same between the 1.0 and 2.0 g/L
treatment groups but hepatic DNA was reported to be decreased.
For TCA, there appeared to be a dose-related decrease in reported hepatic DNA after
2 days of treatment (i.e., 1.63 ± 0.07, 1.53 ± 0.08, and 1.43 ± 0.04 mg DNA/g for the 0.3, 1.0,
and 2.0 g/L exposure levels of TCA, respectively). After 5 days of TCA exposure, there was a
reported decrease in hepatic DNA for all treatment groups that was similar at the 1.0 and 2.0 g/L
exposure groups (i.e., 1.45 ±0.17, 1.29 ± 0.18, and 1.26 ± 0.22 mg DNA/g for the 0.3, 1.0, and
2.0 g/L exposure levels of TCA, respectively). After 14 days of TCA treatment, there was a
reported decrease in all treatment groups in hepatic DNA content that did not appear to be dose-
related (i.e., 1.31 ± 0.17, 1.21 ± 0.17, and 1.33 ± 0.18 mg DNA/g for the 0.3, 1.0, and 2.0 g/L
exposure levels of TCA, respectively).
Thus, similar to the results reported for DCA, the patterns of liver weight gain did not
match those of hepatic DNA decrease for TCA-treated animals. For example, although there
appeared to be a dose-related increase in liver weight gain after 14 days of TCA exposure, there
was a treatment- but not dose-related decrease in hepatic DNA content.
In regard to the ability to detect changes, the low number of animals examined after
2 days of exposure (n = 4) limited the ability to detect a significant change in liver weight and
hepatic DNA concentration. For hepatic DNA determinations, the larger number of animals
examined at 5- and 14-day time points and the similarity of values with relatively smaller SE of
the mean reported in the control animals made quantitative differences in this parameter easier to
determine. However, animals varied in their response to treatment and this variability exceeded
that of the control groups. For DCA, results reported at 14 days and those for TCA reported at
5 and 14 days, the SEs for treated animals showed a much greater variability than those of the
control animals (range of 0.04-0.05 mg DNA/g for control groups, but ranges of 0.17-0.22 mg
DNA/g for TCA at 5 days and 0.14-0.20 mg DNA/g for DCA or TCA at 14 days). The authors
stated that:
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the increases in hepatic weights were generally accompanied by decreases in the
concentration of DNA. However, the only clear changes were in animals treated
with DCA for 5 or 14 days where the ANOVAs were clearly significant (P<0.020
and 0.005, respectively). While changes of similar magnitude were observed in
other groups, the much greater variation observed in the treated groups resulted in
not significant differences by ANOVA ( p = 0.41, 0.66. 0.26, 0.15 for DCA - 2
days, and TCA for 2,5, and 14 days, respectively).
The size of hepatocytes is heterogeneous and correlated with its ploidy, zone, and age of
the animal (see Section E. 1.1). The authors did not indicate if there was predominance in zone
or ploidy for hepatocytes included in their analysis of average hepatocyte diameter in the random
selection of 25 fields per animal (n = 3-7 animals). There appeared to be a dose-related increase
in cell diameter associated with DCA exposure and a treatment but not dose-related increase with
TCA treatment after 14 days of treatment. For control B6C3Fi male mice (n = 7), the hepatocyte
diameter was reported to be 20.6 ± 0.4 microns. For mice exposed to DCA, hepatocyte diameter
was reported to be 22.2 ± 0.2, 25.2 ± 0.6, and 26.0 ± 1.0 microns for 0.3, 1.0, and 2.0 g/L treated
mice (n = 4 for each group), respectively. For mice exposed to TCA hepatocyte diameter was
reported to be 22.2 ± 0.2, 22.4 ± 0.6, and 23.2 ± 0.4 microns for 0.3, 1.0, and 2.0 g/L treated
mice (n = 4 for the 0.3 and 1.0 g/L groups and n = 3 for the 2.0 g/L group), respectively.
The small number of animals examined limited the power of the experiment to determine
statistically significant differences with the authors reporting that only the 1.0 g/L DCA and
2.0 g/L DCA- and TCA-treated groups statistically significant from control values. The dose-
related increases in reported cell diameter were consistent with the dose-related increases in liver
weight reported for DCA after 14 days of exposure. However, the pattern for hepatic DNA
content did not. For TCA, the dose-related increases in cell diameter were also consistent with
the dose-related increases in liver weight after 14 days of exposure. Similar to DCA results, the
changes in hepatic DNA content did not correlate with changes in cell size. In regard to the
magnitude of increases over control values, the 68 vs. 38% increase in liver weight for DCA vs.
TCA at 2.0 g/L, was less than the 26 and 13% increases in cell diameter for the same groups,
respectively. Therefore, for both DCA and TCA exposure, there appeared to be dose-related
hepatomegaly and increased cell size after 14-days of exposure.
The authors reported PAS staining for glycogen content as an attempt to examine the
nature of increased cell size by DCA and TCA. However, they did not present any quantitative
data and only provided a brief discussion. The authors reported that:
hepatic sections of DCA-treated B6C3Fi mice (1 and 2 g/L) contained very large
amounts of perilobular PAS-positive material within hepatocytes. PAS stained
hepatic sections from animals receiving the highest concentration of TCA
displayed a much less intense staining that was confined to periportal areas.
Amylase digesting confirmed the majority of the PAS-positive material to by
glycogen. Thus, increased hepatocellular size in groups receiving DCA appears
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to be related to increased glycogen deposition. Similar increases in glycogen
deposition were observed in Swiss-Webster mice.
There is no way to discern whether DCA-induced glycogen deposition was dose-related
and therefore correlated with increased liver weight and cell diameter. While the authors suggest
that Swiss-Webster mice displayed "similar increased in glycogen deposition," the authors did
not report a similar increase in liver weight gain after DCA exposure at 14 days (1.27-fold of
control percent liver/body weight ratio in Swiss male mice and 1.42-fold in female Swiss-
Webster mice vs. 1.62-fold of control in B6C3Fi mice after 14 days of exposure to 2 g/L DCA).
Thus, the contribution of glycogen deposition to DCA-induced hepatomegaly and the nature of
increased cell size induced by acute TCA exposure cannot be determined by this study.
However, this study does show that DCA and TCA differ in respect to their effects on glycogen
deposition after short-term exposure.
The authors report that:
localized areas of coagulative necrosis were observed histologically in both
B6C3Fi and Swiss-Webster mice treated with DCA at concentrations of 1 and
2 g/L for 14 days. The necrotic areas corresponded to the pale streaked areas seen
grossly. These areas varied in size, shape and location within sections and
occupied up to several mm2. An acute inflammatory response characterized by
thin rims of neutrophils was associated with the necrosis, along with multiple
mitotic figures. No such areas of necrosis were observed in animals treated at
lower concentrations of DCA, or in animals receiving the chemical for 2 or
5 days. Mice treated with 2 g/L TCA for 14 days have some necrotic areas, but at
such low frequency that it was not possible to determine if it was treatment-
related (2 lesions in a total of 20 sections examined). No necrosis was observed
in animals treated at the lower concentrations of TCA or at earlier time points.
Again there were no quantitative estimates given of the size of necrotic areas, variation
between animals, variation between strain, or dose-response of necrosis reported for DCA
exposure by the authors. The lack of necrosis after 2 and 5 days of exposure at all treatment
levels and at the lower exposure level at 14 days of exposure is not correlated with the increases
in liver weight reported for these treatment groups.
Autoradiographs of randomly chosen high powered fields (400x) (50 fields/animal) were
reported as the percentage of cells in the fields that were labeled. There was significant variation
in the number of animals examined and in the reported mean percent of labeled cells between
control groups. The number of control animals was not given for the 2-day group but for the
5- and 14-day groups were reported to be n = 4 and n = 11, respectively. The mean percent of
labeling in control animals was reported at 0.11 ± 0.03, 0.12 ± 0.04, and 0.46 ± 0.07% of
hepatocytes for 2-, 5-, and 14-day control groups, respectively. Only the 2.0 g/L exposures of
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DCA and TCA were examined at all three times of exposure, while all groups were examined at
14 days. However, the number of animals examined in all treatment groups appeared to be only
four animals in each group.
There was not an increase over controls reported in the 2.0 g/L DCA or TCA 2- and
5-day exposure groups in hepatocyte labeling with tritiated thymidine. After 14 days of
exposure, there was a statistically significant but very small dose-related increase over the
control value after DCA exposure (i.e., 0.46 ± 0.07, 0.64 ±0.15, 0.75 ± 0.22, and 0.94 ± 0.05%
labeling of hepatocytes in control, 0.3, 1.0, and 2.0 g/L DCA treatment groups, respectively).
For TCA, there was no change in hepatocyte labeling except for a 50% decrease from control
values at after 14 days of exposure to 2.0 g/L TCA (i.e., 0.46 ± 0.07, 0.50 ± 0.14, 0.52 ± 0.26,
and 0.26 ± 0.14% labeling of hepatocytes in control, 0.3, 1.0, and 2.0 g/L TCA treatment groups,
respectively). The authors report that:
labeled cells were localized around necrotic areas in these [sic DCA treated]
groups. Since counts were made randomly, the local increased in DCA-treated
animals at concentrations of 1 and 2 g/L are in fact much higher than indicated by
the data. Labeling indices in these areas of proliferation were as high as 30%.
Labeled hepatocytes in TCA-treated and the control animals were distributed
uniformly throughout the sections. There was an apparent decrease in the
percentage of labeled cells in the group of animals treated with the highest dose of
TCA. This is because no labeled cells were found in any of the fields examined
for one animal.
The data for control mice in this experiment are consistent with others showing that the
liver is quiescent in regard to hepatocellular proliferation with few cells undergoing mitosis (see
Section E. 1.1). For up to 14 days of exposure with either DCA or TCA, there was little increase
in hepatocellular proliferation except in instances and in close proximity to areas of proliferation.
The increases in liver weight reported for this study were not correlated with and cannot be a
result of hepatocellular proliferation as only a very small population of hepatocytes is
undergoing DNA synthesis. For TCA, there was no increase in DNA synthesis in hepatocytes,
even at the highest dose, as shown by autoradiographic data of tritiated thymidine incorporation
in random fields.
Whole-liver sections were examined for tritiated thymidine incorporation from DNA
extracts. The number of animals examined varied (i.e., n = 4 for the 2-day exposure groups and
n = 8 for 5- and 14-day exposure groups), but the number of control animals examined was the
same as the treated groups for this analysis. The levels of tritiated thymidine incorporation in
hepatic DNA (dpm/mg DNA expressed as mean x 103 ± SE of the number of animals) were
reported to be similar across control groups (i.e., 56 ± 11, 56 ± 6, and 56 ± 7 dpm/mg DNA, for
2-, 5-, and 14-day treatment groups, respectively).
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After 2 days of DC A exposure, there appeared to be a slight treatment-related, but not
dose-related, increase in reported tritiated thymidine incorporation into hepatic DNA (i.e., 72 ±
23, 80 ± 6, and 68 ± 7 dpm/mg DNA for 0.3, 1.0, or 2.0 g/L DCA, respectively). After 5 days of
DCA exposure, there appeared to be a dose-related increase in reported tritiated thymidine
incorporation into hepatic DNA (i.e., 68 ± 18, 110 ± 20, and 130 ± 7 dpm/mg DNA for 0.3, 1.0,
or 2.0 g/L DCA, respectively). However, after 14 days of DCA exposure, levels of tritiated
thymidine incorporation were less than those reported at 5 days and the level for the 0.3 g/L
exposure group was less than the control value (i.e., 33 ± 11, 77 ± 9, and 81 ± 12 dpm/mg DNA
for 0.3, 1.0, or 2.0 g/L DCA, respectively).
After 2 days of TCA exposure, there did not appear to be a treatment-related increase in
tritiated thymidine incorporation into hepatic DNA (i.e., 82 ± 16, 52 ± 7, and 54 ± 7 dpm/mg
DNA for 0.3, 1.0, or 2.0 g/L TCA, respectively). Similar to the reported results for DCA, after
5 days of TCA exposure, there appeared to be a dose-related increase in reported tritiated
thymidine incorporation into hepatic DNA (i.e., 79 ± 23, 86 ± 17, and 158 ± 33 dpm/mg DNA
for 0.3, 1.0, or 2.0 g/L TCA, respectively). After 14 days of TCA exposure, there were
treatment-related increases, but not a dose-related increase, in reported tritiated thymidine
incorporation into hepatic DNA (i.e., 71 ± 10, 73 ± 14, and 103 ± 14 dpm/mg DNA for 0.3, 1.0,
or 2.0 g/L TCA, respectively).
It would appear that for both TCA and DCA, the increase in tritiated thymidine
incorporation into hepatic DNA was dose related and peaked after 5 days of exposure. The
authors report that the decrease in incorporation into hepatic DNA observed after 14 days of
DCA treatment at 0.3 g/L to be statistically significant as well as the increases after 5 and
14 days of TCA exposure at the 2.0 g/L level. The small numbers of animals examined, the
varying number of animals examined, and the degree of variation in treatment-related effects
limit the statistical power of this experiment to detect quantitative changes.
Given the limitations of this experiment, determination of an accurate measure of the
quantitative differences in tritiated thymidine incorporation into whole-liver DNA or that
observed in hepatocytes are hard to determine. In general, the results for tritiated thymidine
incorporation into hepatic DNA were consistent with those for tritiated thymidine incorporation
into hepatocytes in that they show that there were, at most, a small population of hepatocytes
undergoing DNA synthesis after up to 14 days of exposure at relative high levels of exposure to
DCA and TCA (i.e., the largest percentage of hepatocytes undergoing DNA synthesis for any
treatment group was <1% of hepatocytes). The highest increases over control levels for hepatic
DNA incorporation for the whole liver were reported at the highest exposure level of TCA
treatment after 5 days of treatment (threefold of control) and after 14 days of TCA treatment
(twofold of control).
Although the authors report small areas of focal necrosis with concurrent localized
increases in hepatocyte proliferation in DCA-treated animals exposed tol.O g/L and 2.0 g/L
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DCA, the levels of whole-liver tritiated thymidine incorporation were only slightly elevated over
controls at these concentrations, and were decreased at the 0.3 g/L exposure concentration for
which no focal necrosis was reported. The whole-liver DNA incorporation of tritiated thymidine
was not consistent with the pattern of tritiated thymidine incorporation observed in individual
hepatocytes. The authors state that "at present, the mechanisms for increased tritiated thymidine
uptake in the absence of increased rates of cell replication with increasing doses of TCA cannot
be determined." The authors do not discuss the possibility that the difference in hepatocyte
labeling and whole-liver DNA tritiated thymidine incorporation could have been due to the
labeling representing increased polyploidization rather than cell proliferation, as well as
increased numbers of proliferating nonparenchymal and inflammatory cells. The increased cell
size due from TCA exposure without concurrent increased glycogen deposition could have been
indicative of increased polyploidization. Finally, although both TCA- and DCA-induced
increases in liver weight were generally consistent with cell size increases, they were not
correlated with patterns of change in hepatic DNA content, incorporation of tritiated thymidine
in DNA extracts from whole liver, or incorporation of tritiated thymidine in hepatocytes. In
regard to cell size, although increased glycogen deposition with DCA exposure was noted by the
authors of this study, lack of quantitative analyses of that accumulation precludes comparison
with DCA-induced liver weight gain.
E .2.3.1.2. Nelson et al. (1989) and Nelson and Bull (1988)
Nelson and Bull (1988) administered TCE (0, 3.9, 11.4, 22.9, and 30.4 mmol/kg) in
Tween 80® via gavage to male Sprague-Dawley rats and male B6C3Fi mice, sacrificed them
4 hours after treatment (n = 4-7), and measured the rate of DNA unwinding under alkaline
conditions. They assumed that this assay represented increases in SSBs. For rats, there was little
change from controls up to 11.4 mmol/kg (1.5 g/kg TCE) but a significantly increased rate of
unwinding at 22.9 and 30.4 mmol/kg TCE (approximately twofold greater at 30.4 mmol). For
mice, there was a significantly increased level of DNA unwinding at 11.4 and 22.9 mmol.
Concentrations >22.9 mmol/kg were reported to be lethal to the mice. In this same study, TCE
metabolites were administered in unbuffered solution using the same assay. DCA was reported
to be most potent in this assay with TCA being the lowest, while CH closely approximated the
dose-response curve of TCE in the rat. In the mouse, the most potent metabolite in the assay was
reported to be TCA followed by DCA with CH considerably less potent.
The focus of the Nelson et al. (1989) study was to examine whether reported SSBs in
hepatic DNA induced by DCA and TCA (Nelson and Bull, 1988) were secondary to peroxisome
proliferation also reported to be induced by both. Male B6C3Fi mice (25-30 g but no age
reported) were given DCA (10 mg/kg or 500 mg/kg) or TCA (500 mg/kg) via gavage in 1%
aqueous Tween 80® with no pH adjustment. The animals were reported to be sacrificed 1, 2, 4,
or 8 hours after administration, and livers were examined for SSBs as a whole-liver homogenate.
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In a separate experiment (experiment #2), treatment was parallel to the first (500 mg/kg
treatment of DCA or TCA), but levels of PCO activity were measured as an indication of
peroxisome proliferation and expressed as umol/minute/g liver. In a separate experiment
(experiment #3), mice were administered 500 mg/kg DCA or TCA for 10 days with Clofibrate
administered at a dose of 250 mg/kg as a positive control. Twenty-four hours after the last dose,
animals were killed, and liver was examined by light microscopy and PCO activity. Finally, in
an experiment parallel in design to experiment #3, SSBs were measured in total hepatic DNA
after 500 mg/kg exposure to TCA (experiment #4). Electron microscopy was performed on two
animals/group for vehicle, DCA, or TCA treatment, with six randomly chosen micrographic
fields utilized for peroxisome profiles. These micrographs were analyzed without identification
as to what area of the liver lobules they were being taken from. Hence, there is a question as to
whether the areas that are known to be peroxisome rich were assayed or not.
The data from all control groups were reported as pooled data in figures, but statistical
comparisons were made between concurrent control and treated groups. The results for DNA
SSBs were reported for "13 control animals" and each experimental time point "as at least 6
animals."
DNA strand breaks were reported to be significantly increased over concurrent control by
a single exposure to 10 or 500 mg/kg DCA or 500 mg/kg TCA for 1,2, or 4 hours after
administration but not at 8 or 24 hours. There did not appear to be a difference in the magnitude
of response between the three treatments (the fraction of unwound DNA was -2.5 times that of
control). PCO activity was reported to be not increased over control within 24 hours of either
DCA or TCA treatment (n = 6 animals per group). The fraction of alkaline unwinding rates as
an indicator of SSBs were reported to not be significantly different from controls and TCA-
treated animals after 10 days of exposure (n = 5).
Relative to controls, body weights were reported to not be affected by exposures to DCA
or TCA for 10 days at 500 mg/kg (data were not shown.) (n = 6 per group). However, both DCA
and TCA were reported to significantly increase liver weight and liver/body weight ratios (i.e.,
liver weights were 1.3 ± 0.05, 2.1 ± 0.10, and 1.7 ± 0.09 g for control, 500 mg/kg DCA, and
500 mg/kg TCA treatment groups, respectively while percent liver/body weights were
4.9 ± 0.14, 7.5 ± 0.18, and 5.7 ± 0.14% for control, 500 mg/kg DCA, and 500 mg/kg TCA
treatment groups, respectively).
PCO activity (umol/minute/g liver) was reported to be significantly increased by DCA
(500 mg/kg), TCA (500 mg/kg), and Clofibrate (250 mg/kg) treatment (i.e., levels of oxidation
were 0.63 ± 0.07, 1.03 ± 0.09, 1.70 ± 0.08, and 3.26 ± 0.05 for control, 500 mg/kg DCA,
500 mg/kg TCA, and 250 mg/kg Clofibrate treatment groups, respectively). Thus, the increases
were -1.63-, 2.7-, and 5-fold of control for DCA, TCA, and Clofibrate treatments.
Results from randomly selected electron photomicrographs from two animals (six per
animal) were reported for DCA and TCA treatment and to show an increase in peroxi somes per
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unit area that was reported to be statistically significant (i.e., 9.8 ± 1.2, 25.4 ± 2.9, and 23.6 ±1.8
for control, 500 mg/kg DCA, and 500 mg/kg TCA, respectively). The 2.5- and 2.4-fold of
control values for DCA and TCA gave a different pattern than that of PCO activity. The small
number of animals examined limited the power of the experiment to quantitatively determine the
magnitude of peroxisome proliferation via electron microscopy. The enzyme analyses suggested
that both DCA and TCA were weaker inducers of peroxisome proliferation than Clofibrate.
The authors reported that there was no evidence of gross hepatotoxicity in vehicle or
TCA-treated mice. Light microscopic sections from mice exposed to TCA or DCA for 10 days
were stained with H&E and PAS for glycogen. For TCA treatment, PAS staining "produced
approximately the same intensity of staining and amylase digesting revealed that the vast
majority of PAS-positive staining was glycogen." Hepatocytes were reported to be "slightly
larger in TCA-treated mice than hepatocytes from control animals throughout the liver section
with the architecture and tissue pattern of the liver intact." The histopathology after DCA
treatment was reported to be "markedly different than that observed with either vehicle or TCA
treatments" with the "most pronounced change in the size of hepatocytes." DCA was reported
to:
produce marked cellular hypertrophy uniformly throughout the liver. The
hepatocytes were approximately 1.4 times larger in diameter than control liver
cells. This hypertrophy was accompanied by an increase in PAS staining;
indicating greater glycogen deposition than in TCA-treated and control liver
tissue. Multiple white streaks were grossly visible on the surface of the liver of
DCA-treated mice. The white areas corresponded with subcapsular foci of
coagulative necrosis. These localized necrotic areas were not encapsulated and
varied in size. The largest necrotic foci occupied the area of a single lobule.
These necrotic areas showed a change in staining characteristics. Often this
change consisted of increased eosinophilia. A slight inflammatory response,
characterized by neutrophil infiltration, was present. These changed were evident
in all DCA-treated mice.
The results from this experiment cannot inform as to dose-response relationships for the
parameters tested with the exception of DNA SSBs where two concentrations of DCA were
examined (10 and 500 mg/kg). For this parameter, the 10 mg/kg exposure of DCA was as
effective as the 500 mg/kg dose where toxicity was observed. This effect on DNA was observed
before evidence of induction of peroxisome proliferation. The authors did not examine
Clofibrate for effects on DNA so whether it too, would have produced this effect is unclear. The
results from this study are consistent with those of Sanchez and Bull (1990) for induction of
hepatomegaly by DCA and TCA, the lack of hepatotoxicity at this dose by TCA, and the
difference in glycogen deposition between DCA and TCA.
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E.2.3.1.3. Styles et al. (1991)
In this report, a similar paradigm is used as Nelson et al. (1989) for the intention of
repeating that work on SSBs and to study DNA synthesis and peroxisome proliferation. In
regard to the findings of SSBs, Styles et al. (1991) reported for a similar paradigm of 500 mg/kg
neutralized TCA administered to male B6C3Fi mice (7-8 weeks of age) and examined at 1, 4, 8,
and 24 hours after dosing. They reported no increased unwinding of DNA 1 or 24 hours after
TCA administration. In a separate experiment, tritiated thymidine was administered to mice
1 hour before sacrifice at 24, 36, 48, 72, and 96 hours after the first dose of 500 mg/kg TCA for
3 days via gavage (n = 5 animals per group).
The hepatic DNA uptake of tritiated thymidine was reported to be similar to control
levels up to 36 hours after the first dose and then to increase to a level approximately sixfold
greater than controls by 72 hours after the first dose of TCA. By 96 hours, the level of tritiated
thymidine incorporation had fallen to approximately fourfold greater than controls. The
variation, reported by SD, was very large in treated animals (e.g., SD was equal to approximately
±1.3-fold of control for 48 hour time point). Individual hepatocytes were examined with the
number of labeled hepatocytes/1,000 cells reported for each animal.
The control level was reported to be ~1 with a SD of similar magnitude. The number of
labeled hepatocytes was reported to decrease between 24 and 36 hours and then to rise slowly
back to control levels at 48 hours and then to be significantly increased 72 hours after the first
dose of TCA (~9 cells/1,000 with a SD of 3.5) and then to decrease to a level of ~5 cells/1,000.
Thus, it appears that increases in hepatic DNA tritiated thymidine uptake preceded those of
increased labeled hepatocytes and did not capture the decrease in hepatocyte labeling at
36 hours. By either measure, the population of cells undergoing DNA synthesis was small, with
the peak level being <1% of the hepatocyte population.
The authors go on to report the zonal distribution of mean number of hepatocytes
incorporating tritiated thymidine but no variations between animals were reported. The decrease
in hepatocyte labeling at 36 hours was apparent at all zones. By 48 hours, there appeared to be
slightly more periportal than midzonal cells undergoing DNA synthesis with centrilobular cells
still below control levels. By 72 hours, all zones of the liver were reported to have a similar
number of labeled cells. By 96 hours, the midzonal and centrilobular regions have returned
almost to control levels while the periportal areas were still elevated. These results are consistent
with all hepatocytes showing a decrease in DNA synthesis by 36 hours and then a wave of DNA
synthesis occurring starting at the periportal zone and progressing through to the pericentral zone
until 72 hours and then the midzonal and pericentral hepatocytes completing their DNA
synthesis activity.
Peroxisome proliferation was assessed via electron photomicrographs taken in mice (four
controls and four treated animals) given 10 daily doses of 500 mg/kg TCA and killed 14 hours
after the last dose. No details were given by the authors as to methodology for peroxisome
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volume estimate (e.g., how many photos per animals were examined and whether they were
randomly chosen). The mean percent cell volume occupied by peroxisome was reported to be
2.1 ± 0.386 and 3.9 ± 0.551% for control and 500 mg/kg TCA, respectively. Given that there
were no time points examined before 10 days for peroxisome proliferation, correlations with
DNA synthesis activity induced by TCA cannot be made from this experiment. However, it is
clear from this study that a wave of DNA synthesis occurs throughout the liver after treatment of
TCA at this exposure concentration and that it has peaked by 72 hours even with continuous
exposure to 96 hours. Whether the DNA synthesis represents polyploidization or cell
proliferation cannot be determined from these data; neither can a dose-response be determined.
E .2.3.1.4. Carter et al. (1995)
The aim of this study was to "use correlative biochemical, pathologic and morphometric
techniques to characterize and quantify the acute, short-term responses of hepatocytes in the
male B6C3Fi mouse to drinking water containing DCA." This report used tritiated thymidine
incorporation, DNA concentration, hepatocyte number per field (cellularity), nuclear size, and
binuclearity (polyploidy) parameters to study 0, 0.5, and 5 g/L neutralized DCA exposures up to
30 days. Male B6C3Fi mice were started on treatment at 28 days of age. Tritiated thymidine
was administered by miniosmotic pump 5 days prior to sacrifice.
The experiment was conducted in two phases, which consisted of 5-15 days of treatment
(Phase I) and 20-30 days of treatment (Phase II) with five animals per group in groups sacrificed
at 5-day intervals. Liver sections were stained for H&E, PAS (for glycogen) or methyl green
pyronin stain (for RNA). DNA was extracted from liver homogenates and the amount of tritiated
thymidine determined as dpm/ug DNA. Autoradiography was performed with the number of
hepatocyte nuclei scored in 1,000 hepatocytes selected randomly to provide a labeling index of
"number of labeled cells/1000 X 100%." Changes in cellularity, nuclear size and number of
multinucleate cells were quantified in H&E sections at 40 x power. Hepatocyte cellularity was
determined by counting the number of nuclei in 50 microscopic fields with multinucleate cells
being counted as one cell and nonparenchymal cells not counted. Nuclear size was also
measured in 200 nuclei with the mean area plus 2 SD was considered to be the largest possible
single nucleus. Therefore, polyploid diploid cells were identified by the authors but not cells that
had undergone polyploidy with increased DNA content in a single nucleus.
Mean body weights at the beginning of the experiment varied between 18.7 and 19.6 g in
the first three exposure groups of Phase I of the study. Through 15 days of exposure, there did
not appear to be a change in body weight in the 0.5 g/L exposure groups but in the 5 g/L
exposure group body weight was reduced at 5, 10, and 15 days with that reduction statistically
significant at 5 and 15 days. Liver weights did not appear to be increased at day 5 but were
increased at days 10 and 15 in both treatment groups (i.e., means ± SEM. for day 10, 1.36 ± 0.03,
1.46 ± 0.03, and 1.59 ± 0.08 g for control, 0.5, and 5 g/L DCA, respectively; and for day 15,
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1.51 ± 0.06, 1.72 ± 0.05, and 2.08 ± 0.11 g for control, 0.5, and 5 g/L DCA, respectively). The
percent liver/body weight followed a similar pattern with the exception that at day 5, the 5 g/L
exposure group had a statistically significant increase over control (i.e., for day 10, 6.00 ± 0.10,
6.72 ± 0.17, and 8.21 ± 0.10% for control, 0.5, and 5 g/L DCA, respectively; and for day 15,
6.22 ± 0.08, 6.99 ± 0.15, and 10.37 ± 0.27% g for control, 0.5, and 5 g/L DCA, respectively).
In Phase II of the study, control body weights were smaller than Phase I and varied
between 16.6 and 16.9 g in the first three exposure groups. Liver weights of controls were also
smaller making it difficult to quantitatively compare the two groups in terms of absolute liver
weights. However, the pattern of DCA-induced increases in liver weight and percent liver/body
weight remained. The patterns of body weight reduction only in the 5 g/L treatment groups and
increased liver weight with DCA treatment at both concentrations continued from 20 to 30 days
of exposure.
For liver weight, there was a slight but statistically significant increase in liver weight for
the 0.5 g/L treatment groups over controls (i.e., for day 20, 1.02 ± 0.02, 1.18 ± 0.05, and 1.98 ±
0.05 g for control, 0.5, and 5 g/L DCA, respectively; for day 25, 1.15 ± 0.03, 1.34 ± 0.04, and
2.06 ± 0.12 g for control, 0.5, and 5 g/L DCA, respectively, for day 30, 1.15 ± 0.03, 1.39 ± 0.08,
and 1.90 ± 0.12 g for control, 0.5, and 5 g/L DCA, respectively). For percent liver/body weight,
there was a small increase at 0.5 g/L that was not statistically significant but all other treatments
induced increases in percent liver/body weight that were statistically significant (i.e., for day 20,
4.82 ± 0.07, 5.05 ± 0.09, and 9.71 ± 0.11% for control, 0.5, and 5 g/L DCA, respectively; for
day 25, 5.08 ± 0.04, 5.91% ± 0.09, and 10.38 ± 0.58% for control, 0.5, and 5 g/L DCA,
respectively; for day 30, 5.17 ± 0.09, 6.01 ± 0.08, and 10.28 ± 0.28% for control, 0.5, and 5 g/L
DCA, respectively).
Of note is the dramatic decrease in water consumption in the 5 g/L treatment groups that
were consistently reduced by 64% in Phase I and 46% in Phase II. The 0.5 g/L treatment groups
had no difference from controls in water consumption at any time in the study. The effects of
such water consumption decreases would affect body weight as well as dose received. Given the
differences in the size of the animals at the beginning of the study and the concurrent differences
in liver weights and percent liver/body weight in control animals between the two phases, the
changes in these parameters through time from DCA treatments cannot be accurately determined
(e.g., control liver/body weights averaged 6.32% in Phase I but 5.02% in Phase II). However,
percent liver/body weight increase were reported to be consistently increased within and between
both phases of the study for the 0.5 g/L DCA treatment from 5 to 30 days of treatment (i.e., for
Phase I, the average increase was 9.5% and for Phase II, the average increased was 12.5% for
0.5 g/L DCA treated groups). Although increased at 5 days, the nonsignificance of the change
may be resultant from the small number of animals examined. The difference in magnitude of
dose and percent liver/body weight increase is difficult to determine given that the 5 g/L dose of
DCA reduced body weight and significantly reduced water consumption by -50% in both phases
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of the study. Of note is that the differences in DCA-induced percent liver/body weight were
~6-fold for the 15, 25, and 30-day data between the 0.5 and 5 g/L DCA exposures rather than the
10-fold difference in exposure concentration in the drinking water.
The incorporation of tritiated thymidine into total hepatic DNA control treatment groups
was reported to be 73.34 ± 11.74 dpm/ug DNA at 5 days, 34 ± 4.12 dpm/ug DNA at 15 days,
and 28.48 ± 3.24 dpm/ug DNA at 20 days but was not reported for other treatments. The results
for 0.5 g/L treatments were not reported quantitatively but the authors stated that the results
"showed similar trends of initial inhibition followed by enhancement of labeling, the changes
relative to controls were not statistically significant." For 5 g/L treatment groups, the 5-day
treated groups DNA tritiated thymidine incorporation was reported to be 42.8% of controls and
followed by a transient increase at 15 and 20 days (i.e., 2.65- and 2.45-fold of controls,
respectively) but after 25 and 30 days, was not significantly different from controls (data not
shown).
Labeling indices of hepatocytes were reported as means, but variations as either SEM or
SD were not reported. Control means were reported as 5.5, 4, 2, 2, 3.2, and 3.5% of randomly
selected hepatocytes for 5, 10, 15, 20, 25, and 30 days, respectively, for four to five animals per
group. In contrast to the DNA incorporation results, no increase in labeling of hepatocytes was
reported to be observed in comparison to controls for any DCA treatment group from 5 to
30 days of DCA exposure. The 5 g/L treatment group showed an immediate decrease in
hepatocyte labeling from day 5 onwards that gradually increased approximately half of control
levels by day 30 of exposure (i.e., <0.5% labeling index at day 5, -1% labeling index at day 10,
-0.6% labeling index at day 20, 1% labeling index at day 25, and 2% labeling index at day 30).
For the 0.5 g/L treatment, the labeling index was reported to not differ from controls from days 5
though 15, but to be significantly decreased between days 20 and 30 to levels similar to those
observed for the 5 g/L exposures. The relatively higher number of hepatocytes incorporating
label reported in this study than others can be a reflection of the longer times of exposure to
tritiated thymidine. Here, incorporation was shown for 1 weeks worth of exposure and reflects
the percent of cell undergoing synthesis during that time period. Also, the higher labeling index
in control animals at the 5- and 10-day exposure periods is probably a reflection of the age of the
animals at the time of study.
From the data reported by the authors, there was a correlation between the patterns of
total DNA incorporation of label and hepatocyte labeling indices in control groups (i.e., higher
level of labeling at 5 days than at 15 and 20 days). However, the patterns of decreased thymidine
labeling reported for hepatocytes were not correlated with a transient increase in total DNA
thymidine incorporation reported with DCA treatment, especially at the 5 g/L exposure level
with a large decrease reported for the number of labeled hepatocytes at the same time an increase
in total DNA thymidine incorporation was reported.
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Although reported to be transiently increased, the total hepatic DNA labeling still
represented at most a 2.5-fold increase over control liver, which represents a small population of
cells. Given that the study examined hepatocyte labeling in random fields and did not report
quantitative zonal differences in proliferation, a more accurate determination of what hepatocytes
were undergoing proliferation cannot be made from the labeling index results. Also, although
the authors report signs of inflammatory cells for 5-day treatment there is no reference to any
inflammatory changes that may have been observed at later time periods when cellular
degeneration and loss of nuclei were apparent. Such an increase inflammatory infiltrates can
increase the DNA synthesis measurements in the liver. The difference in labeling index and total
DNA synthesis could reflect differences in nonparenchymal cell proliferation or ploidy changes
vs. mitoses in hepatocytes. Clearly, the increases in liver weight that were reported as early as
5 days of exposure could not have resulted from increased hepatocyte proliferation.
The H&E sections were reported to have been fixed in an aqueous solution that reduced
glycogen content. However, residual PAS positive material (assumed to be glycogen) was
reported to be present indicating that not all of the glycogen had been dissolved. The authors
report changes in pathology between 5 and 30 days in control animals that included straightening
of hepatocyte cording, decreased mitoses, less clarity and more fine granularity of pericentral
hepatocellular cytoplasm, increased numbers of larger nuclei that were not labeled, and reported
differences between animals in the amount of glycogen present (i.e., two or three animals out of
the five had less glycogen than other members of the group with less glycogen in the central and
midzonal areas). These changes are consistent with increased polyploidization expected for
maturing mice (see Sections E. 1.1 and E. 1.2).
After 5 days of treatment, 0.5 g/L exposed animals were reported to have livers with
fewer mitoses and tritiated thymidine hepatocyte labeling, but by 10 days, there was an increase
in nuclear size. Labeling was reported to be predominantly in small nuclei. Animals given
0.5 g/L DC A for 15, 20, and 25 days were reported to have "focal cells in the middle zone with
less detectable or no cell membranes and loss of the coarse granularity of the cytoplasm" with
some cells not having nuclei or cells having a loss of nuclear membrane and apparent karyolysis.
"Cells without nuclei because the plane of the section did not pass through the nuclei had the
same type of nuclei. Cells without nuclei not related to plane of section had a condensed
cytoplasm." Livers from 20-day and later sacrifice groups treated with 0.5 g/L DCA were
reported to have normal architecture. After 25 days of treatment, apoptotic bodies were reported
to be observed with fewer nuclei around the central veins nuclei that were larger in central and
midzonal areas.
In animals treated with 5 g/L DCA, the authors report similar features as for 0.5 g/L but
in a zonal pattern. Inflammatory cells were reported to not be observed, and after 5 and 10 days,
a marked decrease in labeled nuclei. After 5 days of 5 g/L DCA, nuclear depletion in the central
and mid-zonal areas was reported. In methyl green pyronin-stained slides a marked loss of
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cellular membranes was reported at 5 days with a loss of nuclei and formation of "lakes of liver
cell debris." After 15 days of treatment, there was a reported increase in labeling in comparison
to animals sacrificed after 5 or 10 days. The cells nearest to the triads were reported to have
clearing of their cytoplasms and an increase in PAS positivity. Hepatocytes of both 0.5 and 5
g/L DC A treatment groups were reported to have "enlarged, presumably polyploidy nuclei."
Some of the nuclei were reported to be "labeled, usually in hepatocytes in the mid-zonal area."
The morphometric analyses of liver sections were reported to reveal statistically
significant changes in cellularity, nuclear size (as measured by either nuclear area or mean
diameter of the nuclear area equivalent circle), and multinucleated cells during 30 days of
exposure to DC A. The authors reported that the concentration of total DNA in the liver, reported
as total ug nuclear DNA/g liver, ranged between 278.17 ±16.88 and 707.00 ± 25.03 in the
control groups (i.e., two- to fivefold range). No 0.5 g/L DCA treatment groups differed from
their control group in terms of liver DNA concentration. However, for 10-30 days of exposure,
hepatic DNA concentrations were reported to be decreased in the 5 g/L treatment groups (at
5 days, there appeared to be -30% increase over control). The number of cells per field was
reported to range between 24.28 ± 1.94 and 43.81 ± 1.93 in control livers (i.e., 1.8-fold range).
From 5 to 15 days, the number of cells/field decreased with 0.5 g/L DCA treatment, although
only at day 15 was the change statistically significant. From 20 to 30 days of treatment, only the
30-day treatment showed a slight decrease in cells/field and that change was statistically
significant. After 5 days of treatment, the number of cells/field was 1.6-fold of control, by
15 days, it was reduced by -20%, and for 20-30 days, it continued to be reduced by as much as
40%.
Although the authors reported that the changes in cellularity and DNA concentration to
be closely correlated, the patterns in the number of cells/field varied in their consistency with
those of DNA concentration (i.e., for days 5, 20, and 25 the direction of change with dose was
similar between the two parameters but not for days 10, 15, and 30). If changes in liver weight
were due to hepatocellular hypertrophy, the increased liver size would be matched by a decrease
in liver DNA concentration and by the number of cells/field. The large increases in liver/body
weight induced by 5 g/L DCA were matched by decreases in liver DNA concentration except for
the 5-day exposure group. In general, the small increases in liver/body weight consistently
induced by 0.5 g/L treatment from days 5 through 30 were not correlated with DNA
concentrations or cells/field.
The small number of animal examined for these parameters (i.e., n = 4-5) and the highly
variable control values limit the power to accurately detect changes. The apparent dehydration
in the animals treated at 5 g/L DCA was cited by the authors for the transient increase in
cellularity and DNA concentration in the 5-day exposure group. However, drinking water
consumption was reported to be similarly reduced at all treatment periods for 5 g/L DCA-treated
animals so that all groups would experience the same degree of dehydration.
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The percentage of mononucleated cells was reported as percent of mononucleated
hepatocytes with results given as means, but with no reports of variation within groups. The
mean control values were reported to range between 60 and 75% for Phase I and between 58 and
71% for Phase II of the experiment (n = 4-5 animals per group). The percent of mononucleated
hepatocytes was reported to be similar between control and DCA treatment groups at 5- and
10-day exposures. At 15 days, both DCA treatments were reported to give a similar increase in
mononucleated hepatocytes (-80 vs. 60% in control) with only the 5 g/L DCA group statistically
significant. The increase in mononucleated cells reported for DCA treatment is similar in size to
the variation between control values. For Phase II of the study, DCA treatment was reported to
increase the number of mononucleated cells in at all concentrations and exposure time periods in
comparison to control values. However, only the increases for the 5 g/L treatments at days 20
and 25, and the 0.5 g/L treatment at day 30 were reported to be statistically significant. Again,
small numbers of animals limit the ability to accurately determine a change. However, the
consistent reporting of an increasing number of mononucleated cells between 15 and 30 days
could be associated with clearance of mature hepatocytes as suggested by the report of
DCA-induced loss of cell nuclei.
Mean nuclear area was reported to range between 45 and 54 u2 in Phase I and between
41 and 48 u2 in Phase II of the experiment with no variation in measurements given by the
authors. The only statistically significant differences reported between control and treated
groups in Phase I was a decrease from 54 to -42 u2 in the 0.5 g/L DCA 10-day treatment group
and a small increase from 50 to -52 u2 in the 15-day treatment group. Clearly, the changes
reported by the authors as statistically significant did not show a dose-related pattern and were
within the range of variation reported between control groups. For Phase II of the experiment,
both DCA treatment concentrations were reported to induce a statistically significant increase the
nuclear area that was dose-related, with the exception of day 30 in which the nuclear area was
similar between the 0.5 and 5 g/L treatment groups. The largest increase in nuclear area was
reported at 20 days for the 5 g/L treatment group (-72 vs. 41 u2 for control).
The patterns of increases in nuclear area were correlated with those of increased
percentage of mononucleated cells in Phase II of the study (20-30 days of treatment) as well as
the small changes seen in Phase I of the experiment. An increase in nuclear cell area is
consistent with increase polyploidization without mitosis, as cells are induced towards
polyploidization. A decrease in the numbers of binucleated cells in favor of mononucleated cells
is consistent with clearance of mature binucleated hepatocyte as well induction of further
polyploidization of diploid or tetraploid binucleated cell to tetraploid or octoploid
mononucleated cells. The authors suggested that the "large hyperchromatic mononucleated
hepatocytes are tetraploid" and suggest that such increases in tetraploid cells have also been
observed with nongenotoxic carcinogens and with di(2-ethylhexyl) phthalate (DEHP).
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In terms of increased cellular granularity observed by the authors with DCA treatment,
this result is also consistent with a more differentiated phenotype (Sigal etal., 1999). Thus, these
results for DCA are consistent with a DCA-induced change in polyploidization of the cells
without cell proliferation.
The pattern of consistent increase in percent liver/body weight induced by 0.5 g/L DCA
treatment from days 5 though 30 was not consistent with the increased numbers of
mononucleated cells and increase nuclear area reported from day 20 onward. The large
differences in liver weight induction between the 0.5 and 5 g/L treatment groups at all times
studied also did not correlate with changes in nuclear size and percent of mononucleated cells.
Thus, increased liver weight was not a function of cellular proliferation, but probably included
both aspects of hypertrophy associated with polyploidization and increased glycogen deposition
induced by DCA. The similar changes reported after short-term exposure for both the 0.5 and
5 g/L exposure concentration were suggested by the authors to indicate that the carcinogenic
mechanism at both concentrations would be similar. Furthermore, they suggest that although
there is evidence of cytotoxicity (e.g., loss of cell membranes and apparent apoptosis), DeAngelo
et al. (1999) suggested that the present study does not support that the mechanism of
DCA-induced hepatocellular carcinogenesis is one of regenerative hyperplasia following
massive cell death nor peroxisome proliferation as the 0.5 g/L exposure concentration has been
shown to increase hepatocellular lesions after 100 weeks of treatment without concurrent
peroxisome proliferation or cytotoxicity.
E .2.3.1.5. DeAngelo et al. (1989)
Various strains of rats and mice were exposed to TCA (12 and 31 mM) or DCA (16 and
39 mM) for 14 days with Sprague-Dawley rats and B6C3Fi mice exposed to an additional
concentration of 6 mM TCA and 8 mM DCA. Although noting that in a previous study, with
high concentrations of chloracids, there was decreased water consumption, the authors did not
measure drinking water consumption in this study.
This study exposed several strains of male rats and mice to TCA at two concentrations in
drinking water (12 and 31 mM neutralized TCA) for 14 days. The conversion of mmol/L or mM
TCA is 5, 2, and 1 g/L TCA for 31, 12, and 6 mM TCA, respectively. The conversion of
mmol/L of mM DCA is 5, 2, and 1 g/L DCA for 39, 16, and 8 mM DCA, respectively. The
strains of mice tested were Swiss-Webster, B6C3Fi, C57BL/6, and C3H and for rats were
Sprague-Dawley, Osborne-Mendel, and F344. For the F344 rat and B6C3Fi mice, data from two
separate experiments were reported for each. The number of animals in each group was reported
to be six for most experiments with the exception of the Sprague-Dawley rats (n = 3 at the
highest dose of TCA and n = 4 or 5 for the control and the lower TCA dose), one study in
B6C3Fi mice (n = 4 or 5 for all groups), and one study in F344 rats (n = 4 for all groups).
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The body weight of the controls was reported to range from 269 to 341 g in the differing
strains of rats (1.27-fold) and 21-28 g in the differing strains of mice (1.33-fold, age not
reported). For percent liver/body weight ratios, the range was 4.4-5.6% in control rats
(1.27-fold) and 5.1-6.8% in control mice (1.33-fold).
As discussed in other studies, the determination of PCO activity appears to be highly
variable. This enzyme activity is often used as a proxy for peroxisome proliferation. For PCO
activity, the range of activity in controls was much greater than for either body weight or percent
liver/body weight. For rats, there was a 2.8-fold difference in PCO control activity, and in mice,
there was a 4.6-fold difference in PCO activity. Between the two studies performed in the same
strain of rat (F344), there was a 2.83-fold difference in PCO activity between controls, and for
the two studies in the same strain of mouse (B6C3Fi) there was a 3.14-fold difference in PCO
activity between controls. Not only were there differences between strains and experiments in
the same strain, but also differences in control values between species with a wider range of
values in the mice. The lowest level of PCO activity in control rats, expressed as nanomoles
NAD reduced/minute/mg/protein, was 3.34, and for control mice, was 1.40. The highest level
reported in control in rats was 9.46, and for control mice, was 6.40.
These groups of rats and mice were exposed to 2 g/L sodium chloride, or 2 or 5 g/L TCA
in drinking water for 14 days and their PCO activity was assayed. These doses of TCA did not
affect body weight except for the Sprague-Dawley rats, which lost -16% of their body weight.
This was also the same group in which only three rats survived treatment. The Osborne-Mendel
and F344 strains did not exhibit loss of body weight or mortality due to TCA exposure.
There was a large variation in response to TCA exposure between the differing strains of
rats and mice with a much larger difference between the strains of mice. For the three rat strains
tested, there was a range between 0% change and 2.38-fold of control for PCO activity at the
5 g/L TCA exposure. For the 2 g/L TCA exposure, there was a range of 0% change to 1.54-fold
of control for PCO activity. The Osborne-Mendel rats had 1.54-fold of control value for PCO
activity at 2 g/L TCA and 2.38-fold of control value for PCO activity reported at 5 g/L,
exhibiting the most consistent increase in PCO with increased dose of TCA. Two experiments
were reported for F344 rats with one reporting a 1.63-fold of control and the other a 1.79-fold of
control value for 5 g/L TCA. Only one of the F334 experiments also exposed rats to 2 g/L TCA
and reported no change from control values.
For the four strains of mice tested, there was a range of 7.44-22.13-fold of control values
reported at the 5 g/L TCA exposures and 3.76-25.92-fold of control values at the 2 g/L TCA
exposures for PCO activity. For the C57BL/6 strain of mice, there was little difference between
the 5 and 2 g/L TCA exposures and a generally threefold higher induction of PCO activity by
TCA at the 5 g/L TCA exposure level than for the other mouse strains. Although there was a
2.5-fold difference between the 5 and 2 g/L TCA exposure dose, the difference in magnitude of
PCO activity between these doses ranged from 0.85- to 2.23-fold for all strains of mice. For the
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B6C3Fi mice, there was a difference between reported increases of PCO activity in the text (i.e.,
reported as 9.59-fold of control) for one of the experiments and that presented graphically in
Figure 2 (i.e., 8.70-fold of control). Nevertheless in the two studies of B6C3Fi mice, 5 g/L TCA
was reported to induce 7.78-fold of control and 8.70-fold of control for PCO activity, and 2 g/L
TCA was reported to induce 5.56-fold of control and 4.70-fold of control for PCO activity.
For the two F344 rat studies in which -200 mg/kg or 5 g/L TCA was administered for
10 or 14 days, there was 1.63-fold of control and 1.79-fold of control values reported for PCO
activity. Thus, for experiments in which the same strain and dose of TCA were administered,
there was not as large a difference in PCO response than between strains and species.
Whether increases in percent liver/body weight ratios were similar in magnitude to
increased PCO activity can be assessed by examination of the differences in magnitude of
increase over control for the 5 and 2 g/L TCA treatments in the varying rat and mouse strains.
The relationship in exposure concentration was a 2.5:1 ratio for the 5 and 2 g/L doses. For rats
treatment of 5 g/L TCA to Sprague-Dawley rats resulted in a significant decrease in body weight,
and therefore, affected the magnitude of increase in percent liver/body weight ratio for this
group. However, for the rest of the rat and mouse data, this dose was not reported to affect body
weight so that there is more confidence in the dose-response relationship.
For the Sprague-Dawley rat, there was no change in the percent liver/body weight ratio at
2 g/L but a 10% decrease at 5 g/L TCA exposure with no change in PCO activity for either.
However, for the Osborne-Mendel rats, there was no change in percent liver/body weight ratios
for either exposure concentration of TCA, but PCO activity was reported to be 1.54-fold of
control at 2 g/L and 2.38-fold of control at 5 g/L TCA. Thus, there was a ratio of 2.5-fold
increase in PCO activity between the 5 and 2 g/L treatment groups. For the F344 rats, there was
a 2-fold difference in liver weight increases (i.e., 12 vs. 6% increase over control) between the
two exposure concentrations but 1.6-fold of control value for PCO activity at the 5 g/L TCA
exposure concentration and no increase in PCO activity at the 2 g/L level. Thus, for the three
strains of rats, there did not appear to be a consistent correlation between liver weight induction
by TCA and PCO activity.
For differing strains of mice, similar concentrations of TCA were reported to vary in the
induction of liver weight increases. The range of liver weight induction was 1.26-1.66-fold of
control values between the four strains of mice at 5 g/L TCA and 1.16-1.63-fold at 2 g/L TCA.
In general, for mice the magnitudes of the difference in the increase in dose between the 5 g/L
and 2 g/L TCA exposure concentration (2.5-fold) was generally higher than the increase percent
liver/body weight ratios at these doses. The differences in liver weight induction between the
2 and 5 g/L doses were -40% for the Swiss-Webster, C3H, and for one of the B6C3Fi mouse
experiments. For the C57BL/6 mouse, there was no difference in liver weight induction between
the 2 and 5 g/L TCA exposure groups. For the other B6C3Fi mouse experiments, there was a
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2.5-fold greater induction of liver weight increase for the 5 g/L TCA group than for the 2 g/L
exposure group (1.39- vs. 1.16-fold of control for percent liver/body weight, respectively).
For PCO activity, the Swiss-Webster, C3H, and one of the B6C3Fi mouse experiments
were reported to have approximately twofold difference in the increase in PCO activity between
the two doses. For the other B6C3Fi mouse experiment, there was only about a 50% increase
and for the C57BL/6 mouse data, there was 15% less PCO activity induction reported at the 5
g/L TCA dose that at the 2 g/L dose. None of the difference in increases in liver weight or PCO
activity in mice from the 2 or 5 g/L TCA exposures were of the same magnitude as the difference
in TCA exposure concentration (i.e., 2.5-fold) except for liver weight from the one experiment in
B6C3Fi mice. These are also the data used for comparisons with the Sprague-Dawley rat
discussed below.
In regard to strain differences for TCA response in mice, there did not appear to be
correlations of the magnitude of 5 g/L TCA-induced changes in percent liver/body weight ratio
or PCO activity with the body weights reported for control mice for each strain. The control
weights between the four strains of mice varied from 21 to 28 g. The strain with the greatest
response (C57B1/6) for TCA-induced changes in percent liver/body weight ratio (i.e., 1.66-fold
of control) and PCO activity (22.13-fold of control) had a mean body weight reported to be 26 g
for controls. At this dose, the range of percent liver/body weight for the other strains was
reported to be 1.26-1.39-fold of control and the range of PCO activity reported to be of 7.48-
8.71-fold of control.
Of note is that in the literature, this study has been cited as providing evidence of
differences between rats and mice for peroxisomal response to TCA and DC A. Generally, the
PCO data from the Sprague-Dawley rats and B6C3Fi mice at the highest dose of TCA and DCA
have been cited. However, the Sprague-Dawley strain was reported to have greater mortality
from TCA at this exposure than the other strains tested (i.e., only three rats survived and
provided PCO levels) and a lower PCO response (no change in PCO activity over control) that
the other two strains tested in this study (i.e., Osborne-Mendel rats was reported to have had
2.38-fold of control and the F344- had a 1.63-1.79-fold of control for PCO activity after
exposure to 5 g/L TCA with no mortality). The B6C3Fi mouse was reported to have a 7.78- or
8.71-fold of control for PCO activity from 5 g/L TCA exposure. Certainly, the male mouse is
more responsive to TCA induction of PCO activity. However, as discussed above, there are
large variations in control levels of PCO activity and in the magnitude and dose-response of
TCA-induction of PCO activity between rat and mouse strains and between species. It is not
correct to state that the rat is refractory to TCA-induction of peroxisome activity.
Unfortunately, the authors chose the Sprague-Dawley rat (i.e., the most unresponsive
strain for PCO activity and most sensitive to toxicity) for studies for comparative studies
between DCA and TCA effects. The authors also tested for carnitine acetyl CoA transferase
(CAT) activity as a marker of peroxisomal enzyme response and took morphometric analysis of
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peroxisome number and cytoplasmic volume for one liver section for each of two B6C3Fi mice
or Sprague-Dawley rats from the 5 g/L TCA and 5 g/L DCA treatment groups. Only six electron
micrograph fields were analyzed from each section (12 fields total) were analyzed without
identification as to what area of the liver lobules they were being taken from. Hence, there is a
question as to whether the areas that are known to be peroxisome rich were assayed of not. Also
as noted above, previous studies have indicate that such high concentration of DCA and TCA
inhibit drinking water consumption and therefore, raising issues not only about toxicity, but also
the dose that rats and mice received.
The number of peroxisomes per 100 um3 and cytoplasmic volume of peroxisomes was
reported to be 6.60 and 1.94%, respectively, for control rats, and 6.89 and 0.61% for control
mice, respectively. For 5 g/L TCA and 5 g/L DCA, the numbers of peroxisomes were reported
to be increased to 7.14 and 16.75, respectively, in treated Sprague-Dawley rats. Thus, there was
2.5- and 1.08-fold of control reported in peroxisome numbers for 5 g/L DCA and TCA,
respectively. The cytoplasmic volume of peroxisomes was reported to be 2.80 and 0.89% for
5 g/L DCA and 5 g/L TCA, respectively (i.e., a 1.44-fold of control and -60% reduction for
5 g/L DCA and 5 g/L TCA, respectively). Thus, 5 g/L TCA was reported to slightly increase the
number of peroxisomes, but decrease the percent of the cytoplasmic volume occupied by
peroxisome by half. For DCA, the reported pattern was for both to increase. PCO activity was
reported to increase by a similar magnitude as peroxisome numbers but not volume in the 5 g/L
TCA treated Sprague-Dawley rats. However, although peroxisomal volume was reported to be
cut nearly in half and for peroxisome number to be similar, 5 g/L TCA treatment was not
reported to change PCO activity in the Sprague-Dawley rat.
For comparisons between DCA and TCA, B6C3Fi mice were examined at 1, 2, and 5 g/L
concentrations. DCA was reported to induce a higher percent liver/body weight ratio that did
TCA at every concentration (i.e., 1.55-, 1.27-, and 1.21-fold of control for DCA and 1.39-, 1.16-,
and 1.08-fold of control for TCA at 1,2, and 5 g/L concentrations, respectively). As noted
above, for other strains of mice tested and a second experiment with B6C3Fi mice, there was
<40% difference in percent liver/body weight ratio between the 2 and 5 g/L exposures to TCA,
but for this experiment, there was a 2.5-fold difference. Thus, at 5 g/L, there was -40% greater
induction of liver weight for DCA than TCA.
In the B6C3Fi mice, 5 g/L TCA was reported to increase peroxisome number to
30.75 and cytoplasmic volume to 4.92% (i.e., 4.4- and 8.1-fold of control, respectively). For
5 g/L DCA treatment, the peroxisome number was reported to be 30.77 and 3.75% (i.e., 4.5- and
6.1-fold of control, respectively). While there was no difference in peroxisome number and
-40% difference in cytoplasmic volume at the 5 g/L exposures of DCA and TCA, there was a
greater difference in the magnitude of PCO activity increase. The 5 g/L TCA exposure was
reported to induce 4.3-fold of control for PCO activity, while 5 g/L DCA induced as 9.6-fold of
control PCO activity (although a figure in the report shows 8.7-fold of control), which is a
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~2.5-fold difference between DC A and TCA at this exposure concentration. Thus, for one of the
B6C3Fi mouse studies, 5 g/L DCA and TCA treatments were reported to give a similar increase
peroxisome number, TCA to induce a 40% greater increase in peroxisomal cytoplasmic volume
than DCA and a 2.5-fold greater increase in PCO activity, but DCA to induce -40% greater liver
weight induction than TCA.
Not only were PCO activity, peroxisome number, and cytoplasmic volume occupied by
peroxisomes analyzed, but also CAT activity as a measure of peroxisome proliferation. For TCA
and DCA, the results were opposite those reported for PCO activity. In Sprague-Dawley rats,
control levels of CAT were reported to be 1.81 nmoles of carnitine transferred/min/mg/protein.
Exposure to 5 g/L TCA was reported to increase CAT activity by 3.21-fold of control, while
5 g/L DCA was reported to induce CAT activity to 10.33-fold of control levels in Sprague-
Dawley rats. However, while PCO activity was reported to be the same as controls and
peroxisomal volume decreased, 5 g/L TCA increased CAT activity 3.21-fold of control in these
rats. The level of CAT induced by 5 g/L DCA was over 10-fold of control in the rat while
peroxisome number was only 2.5-fold of control and cytoplasmic volume 1.4-fold of control.
Thus, the fold increases for these three measures were not the same for DCA treatment and for
TCA in rats. Nevertheless for CAT, DCA was a stronger inducer in rats than was TCA.
In B6C3Fi mice, 5 g/L TCA and 5 g/L DCA induced CAT activity to a similar extent
(4.50- and 5.61-fold of control, respectively). The magnitude of CAT induction was similar to
that of peroxisome number for both 5 g/L DCA and 5 g/L TCA and lower than PCO activity in
DCA-treated mice and cytoplasmic volume in TCA-treated mice by about half. Thus, using
CAT as the marker of peroxisome proliferation, the rat was more responsive than the mouse to
DCA and nearly as responsive to TCA as the mouse at this high dose in these two specific
strains. These data illustrate the difficulty of using only one measure for peroxisome
proliferation and show that the magnitude of increased PCO activity is not necessarily predictive
of the peroxisome number or cytoplasmic volume or CAT activity. The difficulty of
interpretation of the data from so few animals and sections for the electron microscopy analysis,
and the low number of animals for PCO activity and CAT activity (n = 3-6), the high dose
studied (5 g/L), and the selection of a rat strain that appears to be more resistant to this activity
but more susceptible to toxicity than the others tested, should be taken into account before
conclusions can be made about differences between these chemicals for peroxisome activity
between species.
Of note is that PCO activity was also shown to be increased by corn oil alone in F344 rats
and to potentiate the induction of PCO activity of TCA. After 10 days of exposure to either
water, corn oil, 200 mg/kg-day TCA in corn oil, or 200 mg/kg TCA in water via gavage dosing,
there was 1.40-fold PCO activity from corn oil treatment alone in comparison to water, a
1.79-fold PCO activity from TCA in water treatment in comparison to water, and a 3.14-fold
PCO activity from TCA in corn oil treatment in comparison to water.
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The authors provided data for three concentrations of DC A and TCA for Sprague-Dawley
and for one experiment in the B6C3Fi mouse for examination of changes in body and percent
liver/body weight ratios (1, 2, or 5 g/L DCA or TCA) after 14 days of exposure. As noted above,
not only did the 5 g/L exposure concentration of DCA result in mortality in the Sprague-Dawley
strain of rat, but the 5 and 2 g/L concentrations of DCA were reported to decrease body weight
(-20 and 25%, respectively). The 5 g/L dose of TCA was also reported to induce a statistically
significant decrease in body weight in the Sprague-Dawley rat. There were no differences in
final body weight in any of the mice exposed to TCA or DCA.
As noted above, no TCA or DCA exposure group of Sprague-Dawley rats was reported
to have a statistically significant increase in percent liver/body weight ratio over control. For the
B6C3Fi male mice, the percent liver/body weight ratio was 1.22-, 1.27-, and 1.55-fold of control
after exposure to 1, 2, and 5 g/L DCA, respectively, and 1.08-, 1.16-, and 1.39-fold of control
after exposure to 1,2, and 5 g/L TCA, respectively. Thus, for DCA, there was only a 20%
increase in liver weight corresponding to the twofold increase between the 1 and 2 g/L exposure
levels of DCA. Between the 2 and 5 g/L exposure concentrations of DCA, there was a 2-fold
increase in liver weight corresponding to a 2.5-fold increase in exposure concentration. For
TCA, the magnitude of increase in dose was reported to be proportional to the magnitude of
increase in percent liver/body weight ratio in the B6C3Fi male mouse. As stated above, the
correspondence between magnitude of dose and percent liver weight for TCA exposure in this
experiment differed from the other experiment reported for this strain of mouse and also differed
from the other three strains of mice examined in this study where the magnitude in liver weight
gain was much less than exposure concentration.
E.2.3.2. Subchronic and Chronic Studies of DCA and TCA
Several experiments have been conducted with exposure to DCA and TCA, generally at
very high levels with a limited dose range, for less periods of time than standard carcinogenicity
bioassays, and with very limited information on any endpoints other than the liver tumor
induction. Caldwell and Keshava (2006) and Caldwell et al. (2008b) have examined these
studies for inferences of modes of action for TCE. Key studies are briefly described below for
comparative purposes of results reported in TCE studies.
E.2.3.2.1. Snyder et al. (1995)
Studies of TCE have reported either no change or a slight increase in apoptosis only after
a relatively high exposure level (Channel et al., 1998; Dees and Travis, 1993). Inhibition of
apoptosis, which has been suggested to prevent removal of "initiated" cells from the liver and
lead to increased survival of precancerous cells, has been proposed as part of the mode of action
for peroxisome proliferators (see Section E.3.4). The focus of this study was to examine whether
DCA, which has been shown to inhibit DNA synthesis after an initial transient increase (see
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Section E.2.3.1.1), also alters the frequency of spontaneous apoptosis in mice. This study
exposed 28-day-old male B6C3Fi male mice (n = 5) to 0, 0.5 or 5.0 g/L buffered DCA in
drinking water for up to 30 days (Phase I = 5-15 days exposure and Phase II = 20-30 days
treatment).
Portions of the left lobe of the liver were prepared for histological examination after H&E
staining. Hepatocyte number was determined by counting nuclei in 50 fields with
nonparenchymal cell nuclei excluded on the basis of nuclear size. Multinucleate cells were
counted as one cell. Apoptotic cells were visualized by in situ TDT nick end-labeling assay from
2 to 4 different liver sections from each control or treated animal. The average number of
apoptotic cells was then determined for each animal in each group. The authors reported that in
none of the tissues examined were necrotic foci observed, there was no any indication of
lymphocyte or neutrophil infiltration indicative of an inflammatory response, and suggested that
no necrotic cells contributed to the responses in their analysis.
Control animals were reported to exhibit apoptotic frequencies ranging from -0.04 to
0.085% and that over the 30-day period the frequency rate declined. The authors suggested that
this result is consistent with reports of the livers of these young animals undergoing rapid
changes in cell death and proliferation. They note that animals receiving 0.5 g/L DCA also had a
similar trend of decreasing apoptosis with age, supportive of the decrease being a physiological
phenomenon. The 0.5 g/L exposure level of DCA was reported to decrease the percentage of
apoptotic hepatocytes as the earliest time point studied and to remain statistically significantly
decreased from controls from 5 to 30 days of exposure. The rate of apoptosis ranged from
-0.025 to 0.060% after 0.5 g/L DCA exposure during the 30-day period (i.e., and -30-40%
reduction). Animals receiving the 5.0 g/L DCA dose exhibited a significant reduction at the
earliest time point that was sustained at a similar level and statistically significant throughout the
time-course of the experiment (percent apoptosis ranged from 0.015 to 0.030%).
The results of this study not only provide a baseline of apoptosis in the mouse liver,
which is very low, but also show the importance of taking into account the effects of age on such
determinations. The authors reported that the for rat liver, the estimated frequency of
spontaneous apoptosis to be -0.1%, and therefore, greater than that of the mouse. The
significance of the DCA-induced reduction in apoptosis, of a level that is already inherently low
in the mouse, for the mode of action for induction of cancer is difficult to discern.
E .2.3.2.2. Mather et al. (1990)
This 90-day study in male Sprague-Dawley rats examined the body and organ weight
changes, liver enzyme levels, and PCO activity in livers from rats treated with estimated
concentrations of 3.9, 35.5, 345 mg/kg-day DCA or 4.1, 36.5, or 355 mg/kg-day TCA from
drinking water exposures (i.e., 0, 50, 500, and 5,000 ppm or 0.05, 0.5, or 5.0 g/L DCA or TCA in
the drinking water). All dose levels of DCA and TCA were reported to result in a dose-
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dependent decrease in fluid intake at 2 months of exposure. The rats were 9 (DCA) or 10 (TCA)
weeks old at the beginning of the study (n = 10/group). Animals with body weights that varied
>20% of mean weights were discarded from the study. The DCA and TCA solutions were
neutralized. The mean values for initial weights of the animals in each test group varied <3%.
DCA treatment induced a dose-related decrease in body weight that was statistically
significant at the two highest levels (i.e., a 6, 9.5, and 17% decrease from control). TCA
treatment also resulted in lower body weights that were not statistically significant (i.e., 2.1, 4.4,
and 5.9%). DCA treatments were reported to result in a dose-related increase in absolute liver
weights (1.01-, 1.13-, and 1.36-fold of control that were significantly different at the highest
level) and percent liver/body weight ratios (1.07-, 1.24-, and 1.69-fold of control that were
significant at the two highest dose levels). TCA treatments were reported to not result in
changes in either absolute liver weights or percent liver/body weight ratios with the exception of
statistically significant increase in percent liver/body weight ratios at the highest level of
treatment (1.02-fold of control).
Total serum protein levels were reported to be significantly depressed in all animals
treated with DCA with animals in the two highest dose groups also exhibiting elevations of ALP.
Alanine-aminotransferase levels were reported to be elevated only in the highest treatment
group. No consistent treatment-related effect on serum chemistry was reported to be observed
for the TCA-treated animals with data not shown.
In terms of PCO activity, there was only a mild increase at the highest dose of 15% for
TCA and a 2.5-fold level of control for DCA treatment that were statistically significant. The
difference in PCO activity between control groups for the DCA and TCA experiments was
reported to be 33%. No treatment effect was reported to be apparent for hepatic microsomal
enzymes, or measures of immunotoxicity for either DCA or TCA, but data were not shown.
Focal areas of hepatocellular enlargement in both DCA- and TCA-treated rats were reported to
be present with intracellular swelling more severe with the highest dose of DCA treatment.
Livers from DCA treated rats were reported to stain positively for PAS, indicating significant
amounts of glycogen with TCA treated rats reported to display "less evidence of glycogen
accumulation." Of note is that, in this study of rats, DCA was reported to induce a greater level
of PCO activity than did TCA.
E .2.3.2.3. Parrish et al. (1996)
Parrish et al. (1996) exposed male B6C3Fi mice (8 weeks old and 20-22 g upon
purchase) to TCA or DCA (0, 0.01, 0.5, and 2.0 g/L) for 3 or 10 weeks (n = 6). Livers were
excised and nuclei isolated for examination of 8-OHdG and homogenates examined for cyanide
insensitive acyl-CoA oxidase (AGO) and laurate hydroxylase activity. The authors noted that
control values between experiments varied as much as a factor of twofold for PCO activity and
that data were presented as percent of concurrent controls. Initial body weights for treatment
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groups were not presented and thus, differences in mean values between the groups cannot be
ascertained.
Final body weights were reported to not be statistically significantly changed by DCA or
TCA treatments at 21 or 71 days of treatment (all were within -8% of controls). The mean
percent liver/body ratios were reported to be 5.4, 5.3, 6.1, and 7.2% for control, 0.1, 0.5, and
2.0 g/L TCA, respectively, and 5.4, 5.5, 6.7, and 7.9% for control, 0.1, 0.5, and 2.0 g/L DCA,
respectively, after 21 days of exposure. This represents 0.98-, 1.13-, and 1.33-fold of control
levels with these exposure levels of TCA and 1.02-, 1.24-, and 1.46-fold of control levels with
DCA after 21 days of exposure. For 71 days of exposure, the mean percent liver/body ratios
were reported to be 5.1, 4.6, 5.8, and 6.9% for control, 0.1, 0.5, and 2.0 g/L TCA, respectively
and 5.1, 5.1, 5.9, and 8.5% for control, 0.1, 0.5, and 2.0 g/L DCA, respectively. This represents
0.90-, 1.14-, and 1.35-fold of control with TCA exposure and 1.0-, 1.15-, and 1.67-fold of
control with DCA exposure after 71 days of exposure. The magnitude of difference between the
0.1 and 0.5 g/L TCA doses is 5, and between 0.5 and 2.0 g/L doses is fourfold.
For the 21- and 71-day exposures the magnitudes of the increases in percent liver/body
weight over control values were greater for DCA than TCA exposure at same concentration with
the exception of 0.5 g/L doses at 71 days in which both TCA and DCA induced similar
increases. For TCA, the 0.01 g/L dose produces a similar 10% decrease in percent liver/body
weight. Although there was a fourfold increase in magnitude between the 0.5 and 2.0 g/L TCA
exposure concentrations, the magnitude of increase for percent liver/body weight increase was
2.5-fold between them at both 21 and 71 days of exposure. For DCA, the 0.1 g/L dose was
reported to have a similar value as control for percent liver/body weight ratio. Although there
was a 4-fold difference in dose between the 0.5 and 2.0 g/L DCA exposure concentrations, there
was a ~2-fold increase in percent liver/body weight increase at 21 days and -4.5-fold increase at
71 days.
As a percentage of control values, TCA was reported to induce a dose-related increase in
PCO activity at 21 days (-1.5-, 2.2-, and -4.1-fold of control, for 0.1, 0.5, and 2 g/L TCA
exposures). Only the 2.0 g/L dose of DCA was reported to induce a statistically significant
increase at 21 days of exposure of PCO activity over control (~ 1.8-fold of control) with the
0.1 and 0.5 g/L exposure PCO activity to be slightly less than control values (-20% less). Thus,
although there was no increase in percent liver/body weight at 0.1 g/L TCA, the PCO activity
was reported to be increased by -50% after 21 days. A 13% increase in liver weight at 0.5 g/L
TCA was reported to be associated with 2.2-fold of control level of PCO activity and a 33%
increase in liver weight after 2.0 g/L TCA to be associated with 4.1-fold of control level of PCO
activity.
Thus, increases in PCO activity were not necessarily correlated with concurrent
TCA-induced increases in liver weight and the magnitudes of increase in liver weight between
0.5 and 2.0 g/L TCA (2.5-fold) was greater than the corresponding increase in PCO activity
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(1.8-fold of control). Although there was a 20-fold difference in TCA dose, the magnitude of
increase in PCO activity between 0.1 and 2.0 g/L TCA was -2.7-fold. As stated above, the
4-fold difference in TCA dose at the two highest levels resulted in a 2.5-fold increase in liver
weight. For DCA, the increases in liver weight at 0.1 and 0.5 g/L DCA exposures were not
associated with increased PCO activity after 21 days of exposure. The 2.0 g/L DCA exposure
concentration was reported to induce 1.8-fold of control PCO activity.
After 71 days of treatment, TCA induced a dose-related increase in PCO activity that was
approximately twice the magnitude as that reported at 21 days (i.e., ~9-fold greater at 2.0 g/L).
After 71 days, for DCA the 0.1 and 0.5 g/L doses produced a statistically significant increase in
PCO activity (-1.5- and 2.5-fold of control, respectively). The administration of 1.25 g/L
clofibric acid in drinking water was used as a positive control and reported to induce
approximately six- to sevenfold of control PCO activity at 21 and 71 days of exposure.
Laurate hydroxylase activity was reported to be elevated significantly only by TCA at
21 days (2.0 g/L TCA dose only) and to increased to approximately the same extent (-1.4-
1.6-fold of control values) at all doses tested. For 0.1 g/L DCA, the laurate hydroxylase activity
was reported to be similar to that of 0.1 g/L TCA (-1.4-fold of control) but to be -1.2-fold of
control at both the 0.5 and 2.0 g/L DCA exposures. At 71 days, both the 0.5 and 2.0 g/L TCA
exposures induced a statistically significant increase in laurate hydroxylase activity (i.e., 1.6- and
2.5-fold of control, respectively) with no change after DCA exposure. The actual data rather
than percent of control values were reported for laurate hydroxylase activity. The control values
for laurate hydroxylase activity varied 1.7-fold between 21 and 71 days experiments.
The results for 8-OHdG levels are discussed in Section E.3.4.2.3. Of note is that the
increases in PCO activity noted for DCA and TCA were not associated with 8-OHdG levels
(which were unchanged, see Section E.3.4.2.3) and also not with changes laurate hydrolase
activity or percent liver/body weight ratio increases observed after either DCA or TCA exposure.
A strength of this study is that it examined exposure concentrations that were lower than those
examined in many other short-term studies of DCA and TCA.
E.2.3.2.4. Bull et al. (1990)
The focus of this study was the determination of "dose-response relationships in the
tumorigenic response to these chemicals [sic DCA and TCA] in B6C3Fi mice, determine the
nature of the nontumor pathology that results from the administration of these compounds in
drinking water, and test the reversibility of the response." Male and female B6C3Fi mice (age
37 days) were treated from 15 to 52 weeks with neutralized TCA and TCA. A highly variable
number and generally low number of animals were reported to be examined in the study with
n = 5 for all time periods except for 52 weeks where in males the n = 35 for controls, n =11 for
1 g/L DCA, n = 24 for 2 g/L DCA, n = 11 for 1 g/L TCA, and n = 24 for 2 g/L TCA exposed
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mice. Female mice were only examined after 52 weeks of exposure and the number of animals
examined was n = 10 for control, 2 g/L DCA, and 2 g/L TCA exposed mice.
"Lesions to be examined histologically for pathological examination were selected by a
random process" with lesions reported to be selected from 31 of 65 animals with lesions at
necropsy. 73 of 165 lesions identified in 41 animals were reported to be examined
histologically. All hyperplastic nodules, adenomas, and carcinomas were lumped together and
characterized as hepatoproliferative lesions. Accordingly, there were only exposure
concentrations available for dose-response analyses in males and only "multiplicity of
hepatoproliferative lesions" were reported from random samples. Thus, these data cannot be
compared to other studies and are unsuitable for dose-response with inadequate analysis
performed on random samples for pathological examination.
The authors state that some of the lesions taken at necropsy and assumed to be
proliferative were actually histologically normal, necrotic, or an abscess as well. It is also
limited by a relatively small number of animals examined in regard to adequate statistical power
to determine quantitative differences. Similar concerns were raised by Caldwell et al. (2008b)
with a subsequent study (eg.. Bull et al., 2002). For example, the authors report that
5/11 animals had "lesions" at 1 g/L TCA at 52 weeks and 19/24 animals had lesions at 2 g/L
TCA at 52 weeks. However, while 7 lesions were examined in 5 mice bearing lesions at 1 g/L
TCA, only 16 of 30 lesions from 11 of the 19 animals bearing lesions examined in the 2 g/L
TCA group. Therefore, almost half of the mice with lesions were not examined histologically in
that group along with only half of the "lesions."
The authors reported the effects of DCA and TCA exposure on liver weight and percent
liver/body changes (m ± SEM) and these results gave a pattern of hepatomegaly generally
consistent with short-term exposure studies. The authors report "no treatment produced
significant changes in the body weight or kidney weight of the animals (data not shown)."
In male mice (n = 5) at 37 weeks of exposure, liver weights were reported to be 1.6 ± 0.1,
2.5 ± 0.1, and 1.9 ± 0.1 g for control, 2 g/L DCA, and 2 g/L TCA exposed mice, respectively.
The percent liver/body weights were reported to be 4.1 ± 0.3, 7.3 ± 0.2, and 5.1 ± 0.1% for
control, 2 g/L DCA, and 2 g/L TCA exposed mice, respectively. In male mice at 52 weeks of
exposure, liver weights were reported to be 1.7 ± 0.1, 2.5 ± 0.1, 5.1 ± 0.1, 2.2 ± 0.1, and 2.7 ±0.1
g for control (n = 35), 1 g/L DCA (n = 11), 2 g/L DCA (n = 24), 1 g/L TCA (n = 11), and 2 g/L
TCA (n = 24) exposed mice, respectively. In male mice at 52 weeks of exposure, percent
liver/body weights were reported to be 4.6 ± 0.1, 6.5 ± 0.2, 10.5 ± 0.4, 6.0 ± 0.3, and 7.5 ± 0.5%
for control, 1 g/L DCA, 2 g/L DCA, 1 g/L TCA, and 2 g/L TCA exposed mice, respectively. For
female mice (n = 10) at 52 weeks of exposure, liver weights were reported to be 1.3 ± 0.1, 2.6 ±
0.1, and 1.7 ± 0.1 g for control, 2 g/L DCA, and 2 g/L TCA exposed mice, respectively. The
percent liver/body weights were reported to be 4.8 ± 0.3, 9.0 ± 0.2, and 6.0 ± 0.3% for control,
2 g/L DCA, and 2 g/L TCA exposed mice, respectively.
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Although the number of animals examined varied threefold between treatment groups in
male mice, the authors reported that all DCA and TCA treatments were statistically increased
over control values for liver weight and percent body/liver weight in both genders of mice. In
terms of percent liver/body weight ratio, female mice appeared to be as responsive as males at
the exposure concentration tested. Thus, hepatomegaly reported at these exposure levels after
short-term exposures appeared to be further increased by chronic exposure with equivalent levels
of DCA inducing greater hepatomegaly than TCA.
Interestingly, after 37 weeks of treatment and then a cessation of exposure for 15 weeks,
liver weights were assessed in control male mice, 2 g/L DCA treated mice, and 2 g/L TCA
treated mice (n = 11 for each group but results for controls were pooled and therefore, n = 35).
Liver weights were reported to be 1.7 ± 0.1, 2.2 ±0.1, and 1.9 ± 0.1 g for control, 2 g/L DCA,
and 2 g/L TCA exposed mice, respectively. The percent liver/body weights were reported to be
4.6 ± 0.1, 5.7 ± 0.3, and 5.4 ± 0.2% for control, 2 g/L DCA, and 2 g/L TCA exposed mice,
respectively. After 15 weeks of cessation of exposure, liver weight and percent liver/body
weight were reported to still be statistically significantly elevated after DCA or TCA treatment.
The authors partially attributed the remaining increases in liver weight to the continued
presence of hyperplastic nodules in the liver. The authors stated that because of the low
incidence of lesions in the control group and the two groups that had treatments suspended, all of
the lesions from these groups were included for histological sectioning. However, the authors
presented a table indicating that, of the 23 lesions detected in seven mice exposed to DCA for
37 weeks, 19 were examined histologically. Therefore, groups that were exposed for 52 weeks
had a different procedure for tissue examination as those at 37 weeks.
In terms of liver tumor induction, the authors stated that "statistical analysis of tumor
incidence employed a general linear model ANOVA with contrasts for linearity and deviations
from linearity to determine if results from groups in which treatments were discontinued after
37 weeks were lower than would have been predicted by the total dose consumed." The
multiplicity of tumors observed in male mice exposed to DCA or TCA at 37 weeks and then
sacrificed at 52 weeks were reported by the authors to have a response in animals that received
DCA very close to that which would be predicted from the total dose consumed by these
animals. The response to TCA was reported by the authors to deviate significantly (p = 0.022)
from the linear model predicted by the total dose consumed.
Multiplicity of lesions per mouse and not incidence was used as the measure. Most
importantly, the data used to predict the dose response for "lesions" used a different
methodology at 52 weeks than those at 37 weeks. Not only were not all animal's lesions
examined but foci, adenomas, and carcinomas were combined into one measure. Therefore, foci,
of which a certain percentage have been commonly shown to spontaneously regress with time,
were included in the calculation of total "lesions." Pereira and Phelps (1996) note that in
initiated mice treated with DCA, the yield of altered hepatocytes decreases as the tumor yields
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increase between 31 and 51 weeks of exposure suggesting progression of foci to adenomas.
Initiated and noninitiated control mice also had fewer foci/mouse with time.
Because of differences in methodology and the lack of discernment between foci,
adenomas, and carcinomas for many of the mice exposed for 52 weeks, it is difficult to compare
differences in composition of the "lesions" after cessation of exposure. For TCA treatment, the
number of animals examined for determination of which "lesions" were foci, adenomas, and
carcinomas was 11/19 mice with "lesions" at 52 weeks, while all 4 mice with lesions after
37 weeks of exposure and 15 weeks of cessation were examined.
For DCA treatment, the number of animals examined was only 10/23 mice with "lesions"
at 52 weeks while all 7 mice with lesions after 37 weeks of exposure and 15 weeks of cessation
were examined. Most importantly, when lesions were examined microscopically, they did not all
turn out to be preneoplastic or neoplastic. Two lesions appeared "to be histologically normal"
and one necrotic. Not only were a smaller number of animals examined for the cessation
exposure than continuous exposure, but only the 2 g/L exposure levels of DCA and TCA were
studied for cessation. The number of animals bearing "lesions" at 37 and then 15 week cessation
weeks was 7/11 (64%) while the number of animals bearing lesions at 5 weeks was 23/24 (96%)
after 2 g/L DCA exposure. For TCA, the number of animals bearing lesions at 37 weeks and
then 15 weeks cessation was 4/11 (35%), while the number of animals bearing lesions at
52 weeks was 19/24 (80%). While suggesting that cessation of exposure diminished the number
of "lesions," conclusions regarding the identity and progression of those lesions with continuous
vs. noncontinuous DCA and TCA treatment are tenuous.
Macroscopically, the "livers of many mice receiving DCA in their drinking water
displayed light colored streaks on the surface" at every sacrifice period and "corresponded with
multi-focal areas of necrosis with frequent infiltration of lymphocytes." At the light microscopic
level, the lesions were described to also be present in the interior of the liver as well. For
TCA-treated mice, "similar necrotic lesions were also observed... but at a much lower
frequency, making it difficult to determine if they were treatment-related." Control animals were
reported not to show degenerative changes. "Marked cytomegaly" was reported for mice treated
with either 1 or 2 g/L DCA "throughout the liver." In regard to cell size, the authors did not give
any description in the methods section of the paper as to how sections were selected for
morphometric analysis or what areas of the liver acinus were examined but reported after
52 weeks of treatment the long axis of hepatocytes measured (mean ± S.E.) 24.9 ± 0.3,
38.5 ± 1.0, and 29.3 ±1.4 um in control, DCA-, and TCA-treated mice, respectively.
Mice treated with TCA (2 g/L) for 52 weeks were reported to have livers with
"considerable dose-related accumulations of lipofuscin." However, no quantitative analyses
were presented. A series of figures representative of treatment showed photographs (l,000x) of
lipofuscin fluorescence indicating greater fluorescence in TCA treated liver than control or DCA
treated liver.
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A series of photographs of H&E sections in the report (see Figures 2a, b, and c) were
shown as representative histology of control mice, mice treated with 2 g/L DCA and 2 g/L TCA.
The area of the liver from which the photographs were taken did not include either portal tract or
central veins and the authors did not give the zone of the livers from which they were taken. The
figure representing TCA treatment shows only a mild increase in cell volume in comparison to
controls, while for DCA treatment, the hepatocyte diameter was greatly enlarged, pale stained so
that cytoplasmic contents appear absent, nuclei often pushed to the cell perimeter, and the
sinusoids appearing to be obscured by the swollen hepatocytes. The apparent reduction of
sinusoidal volume by the enlarged hepatocytes raises the possibility of decreased blood flow
through the liver, which may have been linked to focal areas of necrosis reported for this high
exposure level.
In a second set of figures, glycogen accumulation was shown with PAS staining at the
same level of power (400x) for the same animals. In control animals, PAS-positive material was
not uniformly distributed between or within hepatocytes but tended to show a zonal pattern of
moderate intensity. PAS positive staining (which the authors reported to be glycogen) appeared
to be slightly less than controls but with a similar pattern in the photograph representing TCA
exposure. However, for DCA, the photograph showed a uniform and heavy stain within each
hepatocyte and across all hepatocytes.
The authors stated in the results section of the paper that "the livers of TC A-treated
animals displayed less evidence of glycogen accumulation and it was more prominent in
periportal than centrilobular portions of the liver acinus." In their abstract they state "TCA
produced small increases in cell size and a much more modest accumulation of glycogen." Thus,
the statement in the text, which is suggestive that TCA induced an increase in glycogen over
controls that was not as much as that induced by DCA, and the statement in the abstract, which
concludes TCA exposure increased glycogen is not consistent with the photographs. In the
photograph shown for TCA, there is less not more PAS-positive staining associated with TCA
treatment in comparison to controls.
In Sanchez and Bull (1990), the authors report that "TCA exposure induced a much less
intense level of PAS staining that was confined to periportal areas" but do not compare PAS
staining to controls but only to DCA treatment. In the discussion section of the paper, the
authors state "Except for a small increase in liver weight and cell size, the effects produced by
DCA were not observed with TCA." Thus, there seems to be a discrepancy with regard to what
the effects of TCA are in relation to control animals from this report that has caused confusion in
the literature. Kato-Weinstein et al. (2001) reported that in male mice exposed to DCA and TCA
the DCA increased glycogen and TCA decreased glycogen content of the liver using chemical
measurement of glycogen in liver homogenates and using ethanol-fixed sections stained with
PAS, a procedure designed to minimize glycogen loss.
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E .2.3.2.5. Nelson et al. (1990)
Nelson et al. (1990) reported that they used the same exposure paradigm as Herren-
Freund et al. (1987), with little description of methods used in treatment of the animals. Male
B6C3Fi mice were reported to be exposed to DCA (1 or 2 g/L) or TCA (1 or 2 g/L) for
52 weeks. The number of animals examined for nontumor tissue was 12 for controls. The
number of animals varied from two to eight for examination of nontumor tissue, hyperplastic
nodules, and carcinoma tissues for c-Myc expression. There was no description for how
hyperplastic nodules were defined and whether they included adenomas and foci. For the
52-week experiments, the results were pooled for lesions that had been obtained by exposure to
the higher or lower concentrations of DCA or TCA (i.e., the TCA results are for lesions induced
by either 1.0 or 2.0 g/L TCA).
A second group of mice were reported to be given either DCA or TCA for 37 weeks and
then normal drinking water for the remaining time until 52 weeks with no concentrations given
for the exposures to these animals. Therefore, it is impossible to discern what dose was used for
tumors analyzed for c-Myc expression in the 37-week treatment groups and if the same dose was
used for 37 and 52 week results.
Autoradiography was described for three different sections per animal in five different
randomly chosen high power fields per section. The number of hyperplastic nodules or the
number of carcinomas per animal induced by these treatments was not reported nor the criteria
for selection of lesions for c-Myc expression. Apparently, a second experiment was performed
to determine the expression of c-H-ras. Whereas in the first experiment, there were no
hyperplastic nodules, in the second, one control animal was reported to have a hyperplastic
nodule. The number of control animals reported to be examined for nontumor tissue in the
second group was 12. The numbers of animals in the second group was reported to vary from
one to seven for examination of nontumor tissue, hyperplastic nodules, and carcinoma tissues for
c-H-ras expression. The number of animals per group for the investigation of H-ras did not
match the numbers reported for that of c-Myc. The number of animals treated to obtain the
"lesion" results was not presented (i.e., how many animals were tested to get a specific number
of animals with tumors that were then examined). The number of lesions assessed per animal
was not reported.
At 52 weeks of exposure, hyperplastic nodules (n = 8 animals) and carcinomas
(n = 6 animals) were reported to have approximately twofold expression of c-Myc relative to
nontumor tissue (n = 6 animals) after DCA treatment. After 37 weeks of DCA treatment and
cessation of exposure, there was a -30% increase in c-Myc in hyperplastic nodules (n = 4
animals) that was not statistically significant. There were no carcinomas reported at this time.
After 52 weeks of TCA exposure, there was approximately twofold of nontumor tissue
reported for c-Myc in hyperplastic nodules (n = 6 animals) and approximately threefold reported
for carcinomas (n = 6 animals). After 37 weeks of TCA exposure, there was ~2-fold c-Myc in
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hyperplastic nodules (n = 2 animals) that was not statistically significant and -2.6-fold increase
in carcinomas (n = 3 animals) that was reported to be statistically significant over nontumor
tissue. There was no difference in c-Myc expression between untreated animals and nontumor
tissue in the treated animals.
The authors reported that c-Myc expression in TCA-induced carcinomas was "almost
6 times that in control tissue (corrected by subtracting nonspecific binding)," and concluded that
c-Myc in TCA-induced carcinomas was significantly greater than in hyperplastic nodules or
carcinomas and hyperplastic nodules induced by DC A. However, the c-Myc expression reported
as the number of grains per cells was -2.6-fold in TCA-induced carcinomas and ~2-fold in
DCA-induced carcinomas than control or nontumor tissue at 52 weeks. The hyperplastic nodules
from DCA and TCA treatments at 52 weeks gave identical ratios of approximately twofold. In
three animals per treatment, c-Myc expression was reported to be similar in "selected areas of
high expression" for either DCA or TCA treatments of 52 weeks.
There did not appear to be a difference in c-H-ras expression between control and
nontumor tissue from DCA- or TCA-treated mice. The levels of c-H-ras transcripts were
reported to be "slightly elevated" in hyperplastic nodules induced by DCA (-67%) or TCA
(-43%) but these elevations were not statistically significant in comparison to controls.
However, carcinomas "derived from either DCA- or TCA-treated animals were reported to have
significantly increased c-H-ras levels relative to controls." The fold increase of nontumor tissue
at 52 weeks for DCA-induced carcinomas was -2.5-fold, and for TCA induced carcinomas,
-2.0-fold. Again, the authors stated that "if corrected for nonspecific hybridization, carcinomas
expressed approximately 4 times as much c-H-ras than observed in surrounding tissues" Given
that control and nontumor tissue results were given as the controls for the expression increases
observed in "lesions," it is unclear what the usefulness of this "correction" is. The authors
reported that "focal areas of increased expression of c-H-ras were not observed within
carcinomas."
The limitations of this experiment include uncertainty as to what doses were used and
how many animals were exposed to produce animals with tumors. In addition, results of
differing doses were pooled and the term hyperplastic nodule was undefined. The authors state
that c-Myc expression in itself is not sufficient for transformation and that its overexpression
commonly occurs in malignancy. They also state that "Unfortunately, the limited amount of
tissue available prevented a more serious pursuit of this question in the present study." In regard
to the effects of cessation of exposure, the authors do not present data on how many animals
were tested with the cessation protocol, what doses were used, and how many lesions comprised
their results and thus, comparisons between these results and those from 52 weeks of continuous
exposure are hard to make. Quantitatively, the small number of animals, whose lesions were
tested, was n = 2-4 for the cessation groups. Bull et al. (1990) is given as the source of data for
the cessation experiment (see Section E.2.3.2.4).
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E .2.3.2.6. DeAngelo et al. (1999)
The focus of this study was to "determine a dose response for the hepatocarcinogenicity
of DC A in male mice over a lifetime exposure and to examined several modes of action that
might underlie the carcinogenic process." As DeAngelo et al. (1999) pointed out, many studies
of DC A had been conducted at high concentrations and were less-than-lifetime studies, and
therefore, were of suspect relevance to environmental concentrations. This study is one of the
few that examined DCA at a range of exposure concentrations to determine a dose-response in
mice. The authors concluded that DCA-induced carcinogenesis was not dependent on
peroxisome proliferation or chemically sustained proliferation. The number of HCCs/animals
was reported to be significantly increased over controls at all DCA treatments including 0.05 g/L
and a NOEL was not observed. Peroxisome proliferation was reported to be significantly
increased at 3.5 g/L DCA only at 26 weeks and did not correlate with tumor response. No
significant treatment effects on labeling of hepatocytes (as a measure of proliferation) outside
proliferative lesions were reported, and thus, the DCA-induced liver cancer was not dependent
on peroxisome proliferation or chemically sustained cell proliferation.
Male B6C3Fi mice were 28-30 days of age at the start of study and weighed 18-21 g (or
-14% range). They were exposed to 0, 0.05, 0.5, 1.0, 2.0, and 3.5 g/L DCA via drinking water
as a neutralized solution. The time-weighted mean daily water consumption calculated over the
100-week treatment period was reported to be 147, 153, 158, 151, 147, and 124 (84% of
controls) mL/kg/day for 0, 0.05, 0.5, 1, 2, and 3.5 g/L DCA, respectively. The number of
animals used for interim sacrifices was 35, 30, 30, 30, and 30 for controls, 0.5, 1.0, 2.0, and
3.5 g/L DCA-treated groups respectively (i.e., 10 mice per treatment group at interim sacrifices
of 26, 52, and 78 weeks). The number of animals at final sacrifice was reported to be 50, 33, 24,
32, 14 and 8 for controls, 0.05, 0.5, 1.0, 2.0, and 3.5 g/L DCA-treated groups respectively. The
number of animals with unscheduled deaths before final sacrifice was reported to be 3, 2, 1, 9,
11, and 8 for controls, 0.05, 0.5, 1.0, 2.0, and 3.5 g/L DCA-treated groups respectively. The
Authors reported that early mortality tended to occur from liver cancer.
The number of animals examined for pathology were reported to be 85, 33, 55, 65, 51,
and 41 for controls, 0.05, 0.5, 1.0, 2.0, and 3.5 g/L DCA treated groups, respectively. The
experiment was conducted in two parts with control, 0.5, 1.0, 2.0, and 3.5 g/L groups treated and
then 1 months later, a second group consisting of 30 control group mice and 35 mice in a
0.05 g/L DCA exposure group were studied.
The authors reported no difference in prevalence and multiplicity of hepatocellular
neoplasms in the two groups so that data were summed and reported together. The number of
animals reported as examined for tumors were n = 10 animals, with controls reported to be
35 animals split among three interim sacrifice times—exact number per sacrifice time is
unknown. The number of animals reported "with pathology" and assumed to be included in the
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tumor analyses from Table 1, and the sum of the number of animals "scheduled for sacrifice that
survived until 100 weeks" and "interim sacrifices" do not equal each other. For the 1 g/L DC A
exposure group, 30 animals were sacrificed at interim periods 32 animals were sacrificed at
100 weeks and 9 animals were reported to have unscheduled deaths, but of those 71 animals,
only 65 animals were reported to have pathology for the group. Therefore, some portion of
animals with unscheduled deaths must have been included in the tumor analyses. The exact
number of animals that may have died prematurely but included in analyses of pathology for the
100-week group is unknown.
In Figure 3 of the study, the authors reported prevalence and multiplicity of HCCs
following 79-100 weeks of DCA exposure in their drinking water. The number of animals in
each dose group used in the tumor analysis for 100 weeks was not given by the authors. Given
that the authors included animals that survived past the 78-week interim sacrifice period but died
unscheduled deaths in their 100-week results, the number must have been greater than those
reported as present at final sacrifice. A comparison of the data for the 100-week data presented
in Table 3a and Figure 3 shows that the data reported for 100 weeks is actually for animals that
survived from 79 to 100 weeks.
The authors report a dose-response that is statistically significant from 0.5 to 3.5 g/L
DCA for HCC incidence and a dose-response in HCC multiplicity that is significantly increased
over controls from 0.05 to 0.5 g/L DCA that survived 79-100 weeks of exposure (i.e., 0, 8-, 84-,
168-, 315-, and 429 mg/kg-day dose groups with prevalences of 26, 33, 48, 71, 95, and 100%,
respectively, and multiplicities of 0.28, 0.58, 0.68, 1.29, 2.47, and 2.90, respectively).
Hepatocellular adenoma incidence or multiplicity was not reported for the 0.05 g/L DCA
exposure group.
In Table 3 of the report, the time course of HCCs and adenoma development are given
and summarized in Table E-2.
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Table E-2. Prevalence and multiplicity data from DeAngelo et al. (1999)
Prevalence
52 wks control = 0% carcinomas, 0% adenoma
0.5 g/L DCA = 0/10 carcinoma, 1/10 adenomas
1.0 g/L DCA = 0/10 carcinomas, 1/10 adenomas
2.0 g/L DCA = 2/10 carcinomas, 0/10 adenomas
3.5 g/L DCA = 5/10 carcinomas, 5/10 adenomas
78 wks control = 10% carcinomas, 10% adenomas
0.5 g/L DCA = 0/lOcarcinoma, 1/10 adenomas
1.0 g/L DCA = 2/10 carcinomas, 2/10 adenomas
2.0 g/L DCA = 5/10 carcinomas, 5/10 adenomas
3.5 g/L DCA = 7/10 carcinomas, 5/10 adenomas
100 wks control = 26% carcinoma, 10% adenoma
0.5 g/L DCA = 48% carcinoma, 20% adenomas
1.0 g/L DCA = 71% carcinomas, 51.4% adenomas
2.0 g/L DCA = 95% carcinomas, 42.9% adenomas
3.5 g/L DCA = 100% carcinomas, 45% adenomas
Multiplicity
(lesions/animal m ± SEM)
Carcinomas
0
0
0
0.20 ±0.13
0.70 ±0.25
0.10 ±0.10
0
0.20 ±0.13
1.0 ±0.47
1.20 ±0.37
0.28 ±0.07
0.68 ±0.17
1.29 ±0.17
2.47 ±0.29
2.90 ±0.40
Adenomas
0
0.10 ±0.09
0.10 ±0.09
0
0.80 ±0.31
0.10 ±0.09
0.10 ±0.09
0.20 ±0.13
1.00 ±-0.42
1.00 ±0.42
0.12 ±0.05
0.32 ±0.14
0.80 ±0.17
0.57 ±0.16
0.64 ±0.23
The authors reported HCCs and number of lesions/animal in mice that survived 79-
100 weeks of exposure. They combined exposure groups to be animals after the week 78
sacrifice time that did and did not make it to 100 weeks. These are the same data reported above
for the 100-week exposure with the inclusion of the 0.05 g/L DCA data. The difference between
number of animals at interim and final sacrifices and those "with pathology" and used in the
tumor analysis but most likely coming from unscheduled deaths is reported in Table E-3 as
"extra" and varied across treatment groups.
Table E-3. Difference in pathology by inclusion of unscheduled deaths from
DeAngelo et al. (1999)
Dose = prevalence of
hepatocellular carcinoma
Control = 26%
0.05 g/L = 33%
0.5 g/L = 48%
1 g/L = 71%
2 g/L,, = 95%
3. 5 g/L =100%
Number hepatocellular
carcinoma/animal
0.28
0.58
0.68
1.29
2.47
2.9
n = at 100 wks
50
33
24
32
14
8
Extra added in
0
0
1
3
7
3
These data show a dose-related increase in tumor formation and decrease in time-to-
tumor associated with DCA exposure at the lowest levels examined. These findings are limited
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by the small number of animals examined at 100 weeks but especially those examined at
"interim sacrifice" periods (n = 10). The data illustrate the importance of examining multiple
exposure levels at lower concentrations at longer durations of exposure and with an adequate
number of animals to determine the nature of a carcinogenic response.
Preneoplastic and non-neoplastic hepatic changes were reported to have been described
previously and summarized as large preneoplastic foci observed at 52 weeks with multiplicities
of 0.1, 0.1, 0.2 and 0.16 for 0.5, 1, 2, and 3.5 g/L DCA exposure, respectively. At 100 weeks, all
values were reported to be significant (0.03, 0.06, 0.14, 0.27 for 0.5, 1, 2, and 3.5 g/L DCA
exposure respectively). Control values were not reported by the authors.
The authors reported that the prevalence and severity of hepatocellular cytomegaly and of
cytoplasmic vacuolization with glycogen deposition to be dose-related and considered significant
in all dose groups examined when compared to control liver. However, no quantitative data
were shown.
The authors reported a severity index of 0 = none, 1 = <25%, 2 = 50-75%, and 4 = 75%
of liver section for hepatocellular necrosis and report, at 26 weeks, scores (n = 10 animals) of
0.10 ±0.10, 0.20 ±0.13, 1.20 ±0.38, 1.20 ± 0.39, and 1.10 ± 0.28 for control, 0.5, 1, 2, and
3.5 g/L DCA treatment groups, respectively. Thus, there appeared to be a treatment-related, but
not dose-related, increase in hepatocellular necrosis that does not involve most of the liver from
1 to 3.5 g/L DCA at this time point. At 52 weeks of exposure, the score for hepatocellular
necrosis was reported to be 0, 0, 0.20 ± 0.13, 0.40 ± 0.22, and 1.10 ± 0.43 for control, 0.5, 1, 2,
and 3.5 g/L DCA treatment groups, respectively. At 78 weeks of exposure, the score for
hepatocellular necrosis was reported to be 0, 0, 0, 0.30 ± 0.21, and 0.20 ± 0.13 for control, 0.5, 1,
2, and 3.5 g/L DCA treatment groups, respectively. Finally, at the final sacrifice time when
more animals were examined, the extent of hepatocellular necrosis was reported to be 0.20 ±
0.16, 0.20 ± 0.08, 0.42 ± 0.15, 0.38 ± 0.20, and 1.38 ± 0.42 for control, 0.5, 1, 2, and 3.5 g/L
DCA treatment groups, respectively.
Thus, there was no reported increase in hepatocellular necrosis at any exposure period for
0.5 g/L DCA treatment, and the mild hepatocellular necrosis seen at the three highest exposure
concentrations at 26 weeks had diminished with further treatment except for the highest dose at
up tolOO weeks of treatment. Clearly, the pattern of hepatocellular necrosis did not correlate
with the dose-related increases in HCCs reported by the authors and was not increased over
control at the 0.5 g/L DCA level where there was a DCA-related tumor increase.
The authors cited previously published data and state that CN-insensitive palmitoyl CoA
oxidase activity (a marker of peroxisome proliferation) data for the 26-week time point plotted
against 100-week HCC prevalence of animals bearing tumors was significantly enhanced at
concentrations of DCA that failed to induce "hepatic PCO" activity. The authors reported that
neither 0.05 nor 0.5 g/L DCA had any marked effect on PCO activity and that it was "only
significantly increased after 26 weeks of exposure to 3.5 g/L DCA and returned to control level
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at 52 weeks (data not shown)." In regards to hepatocyte labeling index after treatment for 5 days
with tritiated thymidine, the authors reported that animals examined in the dose-response
segment of the experiment at 26 and 52 weeks were examined but no details of the analysis were
reported. The authors commented on the results from this study and a previous one that included
earlier time points of study and stated that there were "no significant alterations in the labeling
indexes for hepatocytes outside of proliferative lesions at any of the DC A concentrations when
compared to the control values with the exception of 0.05 g/L DCA at 4 weeks (4.8 ± 0.6 vs.
2.7 ± 0.4 control value; data not shown)."
The effects of DCA on body weight, absolute liver weight, and percent liver/body weight
were given in Table 2 of the paper for 26, 52, 78, and 100 weeks of exposure. For 52- and
78-week studies, 10 animals per treatment group were examined. Liver weights were not
determined for the lowest exposure concentration (0.05 g/L DCA) except for the 100-week
exposure period. At 26 weeks of exposure, there was not a statistically significant change in
body weight among the exposure groups (i.e., 35.4 ± 0.7, 37.0 ± 0.8, 36.8 ± 0.8, 37.9 ± 0.6, and
34.6 ± 0.8 g for control, 0.5, 1, 2, and 3.5 g/L DCA, respectively). Absolute liver weight was
reported to have a dose-related significant increase in comparison to controls at all exposure
concentrations examined, with liver weight reaching a plateau at the 2 g/L concentration (i.e.,
1.86 ± 0.07, 2.27 ± 0.10, 2.74 ± 0.08, 3.53 ± 0.07, and 3.55 ± 0.1 g for control, 0.5, 1, 2, and
3.5 g/L DCA, respectively). The percent liver/body weight ratio increases due to DCA exposure
were reported to have a similar pattern of increase (i.e., 5.25 ± 0.11, 6.12 ± 0.16, 7.44 ±0.12,
9.29 ± 0.08, and 10.24 ± 0.12% for control, 0.5, 1, 2, and 3.5 g/L DCA, respectively). This
represented a 1.17-, 1.41-, 1.77-, and 1.95-fold of control percent liver/body weight at these
exposures at 26 weeks.
At 52 weeks of exposure, there was not a statistically significant change in body weight
among the exposure groups except for the 3.5 g/L exposed group in which there was a significant
decrease in body weight (i.e., 39.9 ± 0.8, 41.7 ± 0.8, 41.7 ± 0.9, 40.8 ± 1.0, and 35.0 ± 1.1 g for
control, 0.5, 1, 2, and 3.5 g/L DCA, respectively). Absolute liver weight was reported to have a
dose-related significant increase in comparison to controls at all exposure concentrations
examined with liver weight reaching a plateau at the 2 g/L concentration (i.e., 1.87 ± 0.13,
2.39 ± 0.04, 2.92 ± 0.12, 3.47 ± 0.13, and 3.25 ± 0.24 g for control, 0.5, 1, 2, and 3.5 g/L DCA,
respectively). The percent liver/body weight ratio increases due to DCA exposure were reported
to have a similar pattern of increase (i.e.,4.68 ±0.30, 5.76 ±0.12, 7.00 ±0.15, 8.50 ±0.26, and
9.28 ± 0.64% for control, 0.5, 1, 2, and 3.5 g/L DCA, respectively).
For liver weight and percent liver/body weight, there was much larger variability between
animals within the treatment groups compared to controls and other treatment groups. There
were no differences reported for patterns of change in body weight, absolute liver weight, or
percent liver/body weight between animals examined at 26 weeks and those examined at
52 weeks.
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At 78 weeks of exposure, there was not a statistically significant change in body weight
among the exposure groups except for the 3.5 g/L exposed group in which there was a significant
decrease in body weight (i.e., 46.7 ± 1.2, 43.8 ± 1.5, 43.4 ± 0.9, 42.3 ± 0.8, and 40.2 ± 2.2 g for
control, 0.5, 1, 2, and 3.5 g/L DCA, respectively). Absolute liver weight was reported to have a
dose-related increase in comparison to controls at all exposure concentrations examined, but
none were reported to be statistically significant (i.e., 2.55 ± 0.14, 2.16 ± 0.09, 2.54 ± 0.36,
3.31 ± 0.63, and 3.93 ± 0.59 g for control, 0.5, 1, 2, and 3.5 g/L DCA, respectively). The percent
liver/body weight ratio increases due to DCA exposure were reported to have a similar pattern of
increase over control values but only the 3.5 g/L exposure level was reported to be statistically
significant (i.e., 5.50 ± 0.35, 4.93 ± 0.09, 5.93 ± 0.97, 7.90 ± 1.55, and 10.14 ± 1.73% for
control, 0.5, 1, 2, and 3.5 g/L DCA, respectively).
Finally, for the animals reported to be sacrificed between 90 and 100 weeks, there was
not a statistically significant change in body weight among the exposure groups except for the
2.0 and 3.5 g/L exposed groups in which there was a significant decrease in body weight (i.e.,
43.9 ± 0.8, 43.3 ± 0.9, 42.1 ± 0.9, 43.6 ± 0.7, 36.1 ± 1.2, and 36.0 ± 1.3 g for control, 0.05, 0.5, 1,
2, and 3.5 g/L DCA, respectively). Absolute liver weight did not show a dose-response pattern
at the two lowest exposure levels but was elevated with the three highest doses with the two
highest being statistically significant (i.e., 2.59 ± 0.26, 2.74 ± 0.20, 2.51 ± 0.24, 3.29 ± 0.21,
4.75 ± 0.59, and 5.52 ± 0.68 g for control, 0.05, 0.5, 1, 2, and 3.5 g/L DCA, respectively). The
percent liver/body weight ratio increases due to DCA exposure were reported to have a similar
pattern of increase over control values but only the 2.0 and 3.5 g/L exposure levels were reported
to be statistically significant (i.e., 6.03 ± 0.73, 6.52 ± 0.55, 6.07 ± 0.66, 7.65 ± 0.55, 13.30 ±
1.62, and 15.70 ± 2.16% for control, 0.05, 0.5, 1, 2, and 3.5 g/L DCA, respectively).
It must be recognized that liver weight increases, especially in older mice, will reflect
increased weight due to tumor burden and thus, DCA-induced hepatomegaly will be somewhat
obscured at the longer treatment durations. However, by 100 weeks of exposure, there did not
appear to be an increase in liver weight at the 0.05 and 0.5 g/L exposures, while there was an
increase in tumor burden reported. Examination of the 0.5 g/L exposure group from 26 to
100 weeks shows that slight hepatomegaly, reported as either absolute liver weight increase over
control or change in percent liver/body ratio, was present by 26 weeks (i.e., 22% increase in liver
weight and 17% increase in percent liver/body weight), decreased with time, and while similar at
52 weeks, was not significantly different from control values at 78- or 100-week durations of
exposure. However, tumor burden was increased at this low concentration of DCA.
The authors present a figure comparing the number of HCCs per animal at 100 weeks
compared with the percent liver/body weight at 26 weeks and show a linear correlation
(r2 = 0.9977). Peroxisome proliferation and DNA synthesis, as measured by tritiated thymidine,
were reported to not correlate with tumor induction profiles and were also not correlated with
early liver weight changes induced by DCA exposure. Most importantly, in a paradigm that
E-149
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examined tumor formation after up to 100 weeks of exposure, DCA-induced tumor formation
was reported to occur at concentrations that did not also cause cytotoxicity and at levels 20-
40 times lower than those used in "less than lifetime" studies reporting concurrent cytotoxicity.
E .2.3.2.7. Carter et al. (2003)
The focus of this study was to present histopathological analyses that included
classification, quantification, and statistical analyses of hepatic lesions in male B6C3Fi mice
receiving DCA at doses as low as 0.05 g/L for 100 weeks and at 0.5, 1.0, 2.0, and 3.5 g/L for
between 26 and 100 weeks. This analysis used tissues from the DeAngelo et al. (1999) (two
blocks from each lobe and all lesions found at autopsy).
This study used the following diagnostic criteria for hepatocellular changes. Altered
hepatic Foci (AHF) were defined as histologically identifiable clones that were groups of cells
smaller than a liver lobule that did not compress the adjacent liver. Large foci of cellular
alteration (LFCA) were defined as lesions larger than the liver lobule that did not compress the
adjacent architecture [previously referred to as hyperplastic nodules by Bull et al. (1990)1 but
had different staining. These are not non-neoplastic proliferative lesions termed "hepatocellular
hyperplasia" that occur secondary to hepatic degeneration or necrosis. Adenomas showed
growth by expansion resulting in displacement of portal triad and had alterations in both liver
architecture and staining characteristics. Carcinomas were composed of cells with a high
nuclear-to-cytoplasmic ration and with nuclear pleomorphism and atypia that showed evidence
of invasion into the adjacent tissue. They frequently showed a trabecular pattern characteristic of
mouse hepatocellular carcinomas.
The report grouped lesions as eosinophilic, basophilic and/or clear cell, and dysplastic.
"Eosinophilic lesions included lesions that were eosinophilic but could also have clear cell,
spindle cell or hyaline cells. Basophilic lesions were grouped with clear cell and mixed cell (i.e.,
mixed basophilic, eosinophilic, hyaline, and/or clear cell) lesions." The authors reported that:
this grouping was necessary because many lesions had both a basophilic and clear
cell component and a few <10 % had an eosinophilic or hyaline
component.. .Lesions with foci of cells displaying nuclear pleomorphism,
hyperchromasia, prominent nucleoli, irregular nuclear borders and/or altered
nuclear to cytoplasmic ratios were considered dysplastic irrespective of their
tinctorial characteristics.
Therefore, Carter et al. (2003) lumped mixed phenotype lesions into the basophilic
grouping so that comparisons with the results of Bull et al. (2002) or Pereira (1996), which
segregate mixed phenotype from those without mixed phenotype, cannot be done.
This report examined type and phenotype of preneoplastic and neoplastic lesions pooled
across all time points. Therefore, conclusions regarding what lesions were evolving into other
E-150
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lesions have left out the factor of time. Bannasch (1996) reported that examining the evolution
of foci through time is critical for discerning neoplastic progression and described foci evolution
from eosinophilic or basophilic lesions to more basophilic lesions. Carter et al. (2003) suggested
that size and evolution into a more malignant state are associated with increasing basophilia, a
conclusion consistent with those of Bannasch (1996). The analysis presented by Carter et al.
(2003) also suggested that there was more involvement of lesions in the portal triad, which may
give an indication where the lesions arose. Consistent with the results of DeAngelo et al. (1999),
Carter et al. (2003) reported that "DCA (0.05 - 3.5 g/L) increased the number of lesions per
animal relative to animals receiving distilled water and shortened the time to development of all
classes of hepatic lesions." They also concluded that:
although this analysis could not distinguish between spontaneously arising lesions
and additional lesions of the same type induced by DCA, only lesions of the kind
that were found spontaneously in control liver were found in increased numbers in
animals receiving DCA.. .Development of eosinophilic, basophilic and/or clear
cell and dysplastic AHF was significantly related to DCA dose at 100 weeks and
overall adjusted for time.
The authors concluded that the presence of isolated, highly dysplastic hepatocytes in
male B6C3Fi mice chronically exposed to DCA suggested another direct neoplastic conversion
pathway other than through eosinophilic or basophilic foci.
It appears that the lesions being characterized as carcinomas and adenomas in
DeAngelo et al. (1999) were not the same as those by Carter et al. (2003) at 100 weeks even
though they were from the same tissues (see Table E-4). Carter et al. (2003) identified all
carcinomas as dysplastic despite tincture of lesion and subdivided adenomas by tincture. If the
differing adenoma multiplicities are summed for Carter et al. (2003), they do not add up to the
same total multiplicity of adenoma given by DeAngelo et al. (1999).
Table E-4. Comparison of data from Carter et al. (2003) and DeAngelo et al.
(1999)
Exposure
level of DCA
at 79-
100 wks
(g/L)
0
0.05
0.5
1.0
2.0
3.5
Total adenoma
multiplicity
(Carter)
0.22
0.48
0.44
0.52
0.60
1.48
Total adenoma
multiplicity
(DeAngelo)
0.12
-
0.32
0.80
0.57
0.64
Total
carcinoma
multiplicity
(Carter)
0.05
O.025
0.20
0.30
1.55
1.30
Total carcinoma
multiplicity
(DeAngelo)
0.28
0.58
0.68
1.29
2.47
2.90
Sum of
adenomas
and
carcinoma
multiplicity
(Carter)
0.27
-0.50
0.64
0.82
2.15
2.78
Sum of
adenomas
and
carcinoma
multiplicity
(DeAngelo)
0.40
-
1.0
2.09
3.27
3.54
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It is unclear how many animals were included in the differing groups in both studies for
pathology. The control and high-dose groups differ in respect to "animals with pathology"
between DeAngelo et al. (1999) and the "number of animals in groups" examined for lesions in
Carter et al. (2003). Neither report gave how many animals with unscheduled deaths were
treated in regards to how the pathology data were included in presentation of results. Given that
DeAngelo et al. (1999) represents animals at 100 weeks as also animals from 79 to 100 weeks of
exposure, it is probable that the animals that died after 79 weeks were included in the group of
animals sacrificed at 100 weeks. However, the number of animals affecting that result (which
would be a mix of exposure times) for either DeAngelo et al. (1999) or Carter et al. (2003) is
unknown from published reports.
In general, it appears that Carter et al. (2003) reported more adenomas/animal for their
100 week animals than DeAngelo et al. (1999) did, while DeAngelo et al. (1999) reported more
carcinomas/animal.
In order to compare these data with others (eg., Pereira and Phelps, 1996) for estimates of
multiplicity by phenotype or tincture it would be necessary to add foci and LFCA together as
foci, and adenomas and carcinomas together as tumors. It would also be necessary to lump
mixed foci together as "basophilic" from other data sets as was done for Carter et al. (2003) in
describing "basophilic lesions." If multiplicity of carcinomas and adenomas are summed from
each study to control for differences in identification between adenoma and carcinoma, there are
still differences in the two studies in multiplicity of combined lesions/animal with DeAngelo et
al. (1999) giving consistently higher estimates. However, both studies show a dose response of
tumor multiplicity with DCA and a difference between control values and the 0.05 DCA
exposure level. Error is introduced by having to transform the data presented as a graph in
Carter et al. (2003). Also no SEM is given for the Carter data.
In regard to other histopathological changes, the authors report that:
necrosis was found in 11.3% of animals in the study and the least prevalent toxic
or adaptive response. No focal necrosis was found at 0.5 g/L. The incidence of
focal necrosis did not differ from controls at 52 or 78 weeks and only was greater
than controls at the highest dose of 3.5 g/L at 100 weeks. Overall necrosis was
negatively related to the length of exposure and positively related to the DCA
dose. Necrosis was an early and transitory response. There was no difference in
necrosis 0 and 0.05 g/L or 0.5 g/L. There was an increase in glycogen at 0.5 g/L
at the perioportal area. There was no increase in steatosis but a dose-related
decrease in steatosis. Dysplastic LFCA were not related to necrosis indicating
that these lesions do not represent, regenerative or reparative hyperplasia.
Nuclear atypia and glycogen accumulation were associated with dysplastic
adenomas. Necrosis was not related to occurrence of dysplastic adenomas.
Necrosis was of borderline significance in relation to presence of hepatocellular
carcinomas. Necrosis was not associated with dysplastic LFCAs or Adenomas.
E-152
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They concluded that "the degree to which hepatocellular necrosis underlies the
carcinogenic response is not fully understood but could be significant at higher DCA
concentrations (>1 g/L)."
E .2.3.2.8. Stauber and Bull (1997)
This study was designed to examine the differences in phenotype between altered hepatic
foci and tumors induced by DCA and TCA. Male B6C3Fi mice (7 weeks old at the start of
treatment) were treated with 2.0 g/L neutralized DCA or TCA in drinking water for 38 or
50 weeks, respectively. They were then treated with additional exposures (n = 12) of 0, 0.02,
0.1, 0.5, 1.0, or 2.0 g/L DCA or TCA for an additional 2 weeks. Three days prior to sacrifice in
DCA-treated mice or 5 days for TCA-treated mice, animals had miniosmotic pumps implanted
and administered BrdU.
Immunohistochemical staining of hepatocytes from randomly selected fields (minimum
of 2,000 nuclei counter per animal) from five animals per group were reported for 14- and
28-day treatments. It was unclear how many animals were examined for 280- and 350-day
treatments from the reports. The percentage of labeled cells in control livers was reported to
vary between 0.1 and 0.4% (i.e., fourfold).
There was a reported ~3.5-fold of control level for TCA labeling at a 14-day time period
and a ~5.5-fold for DCA. At 28 days, there was -2.5-fold of control for TCA, but a -2.3-fold
decrease of control for DCA. At 280 days, there was no data reported for TCA, but for DCA,
there was a ~2-fold decrease in labeling over control. At 350 days, there were no data for DCA,
but a reported ~2.3-fold decrease in labeling of control with TCA. The authors reported that the
increases at day 14 for TCA and DCA exposure and the decrease at day 28 for DCA exposure
were statistically significant, although a small number of animals were examined. Thus,
although there may be some uncertainty in the exact magnitude of change, there was, at most,
~5-fold of control labeling for DCA within after 14 days of exposure that was followed by a
decrease in DNA synthesis by day 28 of treatment. These data show that hepatocytes
undergoing DNA synthesis represented a small population of hepatocytes with the highest level
with either treatment <1% of hepatocytes. Rates of cell division were reported to be less than
control for both DCA and TCA by 40 and 52 weeks of treatment.
In this study, the authors reported that there was no necrosis with the 2.0 g/L DCA dose
for 52 weeks and concluded that necrosis is a recurring but inconsistent result with chronic DCA
treatment. Histological examination of the livers involved in the present study found little or no
evidence of such damage or overt cytotoxicity. It was assumed that this effect has little bearing
on data on replication rates.
Foci and tumors were combined in reported results, and therefore, cannot be compared
the results Bull et al. (2002) or to DeAngelo et al. (1999). Prevalence rates were not reported.
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Data were reported in terms of "lesions" with DCA-induced "lesions" containing a number of
smaller lesions that were heterogeneous and more eosinophilic with larger "lesions" tending to
less numerous and more basophilic. For TCA results using this paradigm, the "lesions" were
reported to be less numerous, more basophilic, and larger than those induced by DCA. The
DCA-induced larger "lesions" were reported to be more "uniformly reactive to c-Jun and c-Fos
but many nuclei within the lesions displaying little reactivity to c-Jun." The authors stated that
while most DCA-induced "lesions" were homogeneously immunoreactive to c-Jen and C-Fos
(28/41 lesions), the rest were stained heterogeneously. For TCA-induced lesions, the authors
reported not difference in staining between "lesions" and normal hepatocytes in TCA-treated
animals. Again, of note is that not only were "lesions" comprised of foci and tumors at different
stages of progression reported in these results, but that also DCA and TCA results were reported
for different durations of exposure.
E.2.3.2.9. Pereira (1996)
The focus of this study was to report the dose-response relationship for the carcinogenic
activity of DCA and TCA in female B6C3Fi mice and the characteristics of the lesions. Female
B6C3Fi mice (7-8 weeks of age) were given drinking water with either DCA or TCA at 2.0,
6.67, or 20 mmol/L and neutralized with sodium hydroxide to a pH or 6.5-7.5. The control
received 20 mmol/L sodium chloride. Conversion of mmol/L to g/L was as follows:
20.0 mmol/L DCA = 2.58 g/L, 6.67 mmol/L DCA = 0.86 g/L, and 2.0 mmol/L = 0.26 g/L;
20.0 mmol/L TCA = 3.27 g/L, 6.67 mmol/L TCA =1.10 g/L, and 2.0 mmol/L TCA = 0.33 g/L.
The concentrations were reported to be chosen so that the high concentration was comparable to
those previously used by us to demonstrate carcinogenic activity. The mice were exposed until
sacrifice at 360 (51 weeks), or 576 days (82 weeks) of exposure.
Whole liver was reported to be cut into ~3 mm blocks and along with representative
sections of the visible lesions, fixed and embedded in paraffin and stained with H&E for
histopathological evaluation of foci of altered hepatocytes, hepatocellular adenomas, and HCCs.
The slides were reported to be evaluated blind. Foci of altered hepatocytes in this study were
defined as containing six or more cells and hepatocellular adenomas were distinguished from
foci by the occurrence of compression at >80% of the border of the lesion.
Body weights were reported to be decreased only the highest dose of DCA from
40 weeks of treatment onward. For TCA, there were only two examination periods (weeks 51
and 82) that had significantly different body weights from control and only at the highest dose.
Liver/body weight percentage was reported in comparison to concentration graphically and
shows a dose-response for DCA with steeper slope than that of TCA at 360 and 576 days of
exposure. The authors reported that all three concentrations of DCA resulted in increased
vacuolation of hepatocytes. Such vacuolization was probably due to glycogen removal from
tissue processing. Using a score of 1-3 (with 0 indicating the absence of vacuolization,
E-154
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+1 indicating vacuolated hepatocytes in the periportal zone, + 2 indicating distribution of
vacuolated hepatocytes in the midzone, and +3 indicating maximum vacuolization of hepatocytes
throughout the liver), the authors also reported that "the extent of vacuolization of the
hepatocytes in the mice administered 0, 2.0, 6.67 or 20.0 mmol/1 DCA was scored as 0.0, 0.80 ±
0.08, 2.32 ± 0.11, or 2.95 ± 0.05, respectively."
Cell proliferation was reported to be determined in treatment groups containing 10 mice
each and exposed to either DCA or TCA for 5, 12, or 33 days with animals implanted with
miniosmotic pumps 5 days prior to sacrifice and administered BrdU. Tissues were
immunohistochemically stained for BrdU incorporation. At least 2,000 hepatocytes/mouse were
reported to be evaluated for BrdU-labeled and unlabeled nuclei and the BrDU-labeling index was
calculated as the percentage of hepatocytes with labeled nuclei.
Pereira (1996) reported a dose-related increase in BrDU labeling in 2,000 hepatocytes
that was statistically significant at 6.67 and 20.mmol/L DCA at 5 days of treatment but that
labeling at all exposure concentrations decreased to control levels by days 12 and 33 of
treatment. The largest increase in BrdU labeling was reported to be twofold of controls at the
highest concentration of DCA after 5 days of exposure. For TCA, all doses (2.0, 6.67, and
20 mmol/L) gave a similar and statistically significant increase in BrDU labeling by 5 days of
treatment (~3-fold of controls) but by days 12 and 33, there were no increases above control
values at any exposure level. Given the low level of hepatocyte DNA synthesis in quiescent
control liver, these results indicate a small number of hepatocytes underwent increased DNA
synthesis after DCA or TCA treatment and that by 12 days of treatment, these levels were similar
to control levels in female B6C3Fi mice.
Incidence of foci and tumors in mice administered DCA or TCA (prevalence or number
of animals with tumors of those examined at sacrifice) in this report are given in Tables E-5 and
E-6.
E-155
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Table E-5. Prevalence of foci and tumors in mice administered NaCl, DCA,
or TCA from Pereira (1996)
Treatment
N
Foci
Number
%
Adenomas
Number
%
Carcinomas
Number
%
82wks
20.0 mmol NaCl
20.0 mmol DCA
6.67 mmol DCA
2.0 mmol DCA
20.0 mmol TCA
6.67 mmol TCA
2.0 mmol TCA
90
19
28
50
18
27
53
10
17
11
7
11
9
10
11.1
89.5a
39.3a
14.0
61. la
33.3a
18.9
2
16
7
3
7
3
4
2.2
84.2a
25.0a
6.0
38.9a
11.1
7.6
2
5
1
0
5
5
0
2.2
26.3a
3.6
0
27.8a
18.5a
0
51 wks
20.0 mmol NaCl
20.0 mmol DCA
6.67 mmol DCA
2.0 mmol DCA
20.0 mmol TCA
6.67 mmol TCA
2.0 mmol TCA
40
20
20
40
20
19
40
0
8
1
0
0
0
3
0
40.0a
5
0
0
0
7.5
1
7
3
0
2
3
3
2.5
35a
15
0
15.8
7.5
2.5
0
1
0
0
5
0
0
0
5
0
0
25a
0
0
NaCl = sodium chloride control
E-156
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Table E-6. Multiplicity of foci and tumors in mice administered NaCl, DCA,
or TCA from Pereira (1996)
Treatment
N
Foci/mouse
Adenomas/mouse
Carcinomas/mouse
82wks
20.0 mmol NaCl
20.0 mmol DCA
6.67 mmol DCA
2.0 mmol DCA
20.0 mmol TCA
6.67 mmol TCA
2.0 mmol TCA
90
19
28
50
18
27
53
0.11 ±0.03
7.95 ± 2.00a
0.39±0.11b
0.14 ±0.05
1.33±0.31a
0.41±0.13b
0.26 ±0.08
0.02 ± 0.02
5.58±1.14a
0.32±0.13b
0.06 ±0.03
0.61±0.22b
0.11 ±0.06
0.08 ±0.04
0.02 ± 0.02
0.37±0.17b
0.04 ± 0.04
0
0.39±0.16b
0.22±0.10b
0
51wks
20.0 mmol NaCl
20.0 mmol DCA
6.67 mmol DCA
2.0 mmol DCA
20.0 mmol TCA
6.67 mmol TCA
2.0 mmol TCA
40
20
20
40
20
19
40
0
0.60 ± 0.22a
0.05 ± 0.05
0
0
0
0.08 ±0.04
0.03 ±0.03
0.45±0.17a
0.20 ±0.12
0
0.15±0.11
0.21 ±0.12
0.08 ±0.04
0
0.10 ±0.10
0
0
0.50±0.18b
0
0
V < 0.05.
These data show the decreased power of using fewer than 50 mice, especially at shorter
durations of exposure. By 82 weeks of exposure, increased adenomas and carcinomas induced
by TCA or DCA treatment are readily apparent.
The foci of altered hepatocytes and the tumors obtained from this study were reported to
be basophilic, eosinophilic, or mixed containing both characteristics and are shown in Tables E-7
and E-8. DCA was reported to induce a predominance of eosinophilic foci and tumors, with over
80% of the foci and 90% of the tumors in the 6.67 and 20.0 mmol/L concentration groups being
eosinophilic. Only approximately half of the lesions were characterized as eosinophilic with the
rest being basophilic in the group administered 2.0 mmol/L DCA. The eosinophilic foci and
tumors were reported to consistently stained immunohistochemically for the presence of GST-u,
while basophilic lesions did not stain for GST-u, except for a few scattered cells or small areas
comprising <10% of foci.
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Table E-7. Phenotype of foci reported in mice exposed to NaCl, DCA, or
TCA by Pereira (1996)
Treatment
at 51 and 82 wks
20.0 mmol NaCl
20.0 mmol DCA
6.67 mmol DCA
2.0 mmol DCA
20.0 mmol TCA
6.67 mmol TCA
2.0 mmol TCA
N
10
150
11
7
22
11
13
% Foci
Basophilic
70
3.3
18.2
42.8
36.4
45.5
38.5
Eosinophilic
30
96.7
81.8
57.2
54.6
54.5
61.5
Mixed
0
0
0
0
9.1
0
0
Table E-8. Phenotype of tumors reported in mice exposed NaCl, DCA, or
TCA by Pereira (1996)
Treatment
at 51 and 82 wks
20.0 mmol NaCl
20.0 mmol DCA
6.67 mmol DCA
2.0 mmol DCA
20.0 mmol TCA
6.67 mmol TCA
2.0 mmol TCA
N
4
105
10
3
18
6
4
Tumors
Basophilic
50
2.9
10
0
61.1
100
100
Eosinophilic
25
96.1
90
100
22.2
0
0
Mixed
25.5
1
0
0
16.7
0
0
The foci of altered hepatocytes in the TCA treatment groups were approximately equally
distributed between basophilic and eosinophilic in tincture. However, the tumors were
predominantly basophilic, lacking GST-pi (21 of 28 or 75%) including all 11 HCCs. The limited
numbers of lesions (i.e., 14) in the sodium chloride (vehicle control) group were characterized as
64.3, 28.6, and 7.1% basophilic, eosinophilic, and mixed, respectively.
These data for female B6C3Fi mice show that DCA and TCA treatment induced a
mixture of basophilic or eosinophilic foci. The pooling of the data between time and adenoma
vs. carcinoma decreases the ability to ascertain the phenotype of tumor due to treatment or the
progression of phenotype with time as well as the small number of tumor examined at lower
exposure concentrations. Foci that occurred at 51 and 82 weeks were presented as one result.
Adenoma and carcinoma data were pooled as one endpoint (n = number of total foci or tumors
examined). Therefore, evolution of phenotype between less to more malignant stages of tumor
were lost.
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E .2.3.2.10. Pereira and Phelps (1996)
The focus of this study was to determine tumor response and phenotype in methyl
nitrosourea (MNU)-treated mice after DCA or TCA exposure. The concentrations of DCA or
TCA were the same as Pereira (1996). For Pereira (1996), the animals were reported to be 7-
8 weeks of age when started on treatment and sacrificed after 360 or 576 days of exposure (51 or
82 weeks). For this study and Tao et al. (2004b), animals were reported to be 6 weeks of age
when exposed to DCA or TCA via drinking water and to be 31 or 52 weeks of age at sacrifice.
Thus, exposure time would be -24 or 45 weeks. A control group of non-MNU treated animals
was presented for female B6C3Fi mice treated for 31 or 52 weeks and are discussed in
Table E-9.
Table E-9. Multiplicity and incidence data (31 week treatment) from Pereira
and Phelps (1996)
Treatment
20.0 mmol NaCl
20.0 mmol DCA
6.67 mmol DCA
2.0 mmol DCA
20.0 mmol TCA
6.67 mmol TCA
2.0 mmol TCA
Number
15
10
10
15
10
10
15
Foci/mouse
0.13±0.13
0.40 ±0.16
0.10 ±0.10
0
0
0
0
incidence %
6.7
40
10
0
0
0
0
Adenomas/mou se
0.13 ±0.13
0
0
0
0
0
0
incidence %
not reported
0
0
0
0
0
0
Although this paradigm appears to be the same paradigm as those reported in Pereira
(1996), fewer animals were studied. The number of animals in each group varied between
8 controls and 14 animals in the 2.0 mmol/L treatment groups. In mice that were not treated with
MNU but were treated with either DCA or TCA at 31 weeks, there were no reported statistically
significant treatment-related effects upon the yield of foci or altered hepatocytes and liver tumors
but the number of animals examined was small and therefore, of limited power to detect a
response. The results below indicate a DCA-related increase in foci and percentage of mice with
foci.
See Section E.4.2.3 for further discussion of the results of co-exposures to MNU and
DCA or TCA from this study.
E .2.3.2.11. Ferreira-Gonzalez et al. (1995) HCCs induced by TCA or DCA in male
B6C3Fi mice. Mice (28-day
The focus of this study was the investigation of differences in H-ras mutation spectra in
old) were exposed for 104 weeks to 0. 1.0, or 3.5 g/L DCA or 4.5 g/L TCA that was pH adjusted.
Tumors observed from this treatment were diagnosed as either hepatocellular adenomas or
carcinomas. DNA was extracted from either spontaneous, DCA- or TCA-induced HCCs.
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Samples for analysis were chosen randomly in the treatment groups, of which 19% of untreated
mice had spontaneous liver HCCs (0.26 carcinomas/animal).
DCA treatment induced 100% prevalence at 3.5 g/L (5.06 carcinomas/animal) and 70.6%
carcinomas at 1.0 g/L (1.29 carcinomas/animal). TCA treatment was reported to induce 73.3%
prevalence at 4.5 g/L (1.5 carcinomas/animal). The number of samples analyzed was 32 for
spontaneous carcinomas, 33 for mice treated with 3.5 g/L DCA, 13 from mice treated with
1.0 g/DCA, and 11 from mice treated with 4.5 g/L TCA.
This study has the advantage of comparison of tumor phenotype at the same stage of
progression (HCC), for allowance of the full expression of a tumor response (i.e., 104 weeks),
and an adequate number of spontaneous control lesions for comparison with DCA or TCA
treatments. However, tumor phenotype at an endstage of tumor progression reflects of tumor
progression and not earlier stages of the disease process.
There were no ras mutations detected except at H-61 in DNA from spontaneously arising
tumors of control mice. Only 4/57 samples from carcinogen-treated mice were reported to
demonstrate mutation other than in the second exon of H-ras. In spontaneous liver carcinomas,
58% were reported to show mutations in H-61 as compared with 50% of tumor from 3.5 g/L
DCA-treated mice and 45% of tumors from 4.5.g/L TCA-treated mice. Thus, there was a
heterogeneous response for this phenotypic marker for the spontaneous, DCA-, and TCA-
treatment induced HCCs.
All samples positive for mutation in the exon 2 of H-ras were sequenced for the
identification of the base change responsible for the mutation. The authors noted that H-ras
mutations occurring in spontaneously developing HCCs from B6C3Fi male mice are largely
confined to codon 61 and involve a change from CAA to either AAA or CGA or CTA in a ratio
of 4:2:1. They noted that in this study, all of the H-ras second codon mutations involved a single
base substitution in H-61 changing the wild-type sequence from CAA to AAA (80%), CGA
(20%), or CTA for the 18 HCCs examined.
In the 16 HCCs from 3.5 g/L DCA treatment with mutations, 21% were AAA
transversions, 50% were CGA transversions, and 29% were CTA transversions. For the six
HCCs from 1.0 g/L DCA with mutations, 16% were an AAA transversion, 50% were a CGA
transversion, and 34% were a CTA transversion. For the five HCCs from 4.5 g/L TCA with
mutations, 80% were AAA transversions, 20% CGA tranversions, and 0% were CTA
transversions. The authors note that the differences in frequency between DCA and TCA base
substitutions did not achieve statistical significance due to the relatively small number of tumors
from TCA-treated mice. They note that the finding of essentially equal incidence of H-ras
mutations in spontaneous tumors and in tumors of carcinogen-treated mice did not help in
determining whether DCA and TCA acted as "genotoxic" or "nongenotoxic" compounds.
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E.2.3.2.12. Pereira et al. (2004a)
Pereira et al. (2004a) exposed 7-8-week-old female B6C3Fi mice treated with "AIN-76A
diet" to neutralized 0 or 3.2 g/L DCA in the drinking water and 4.0 or 8.0 g/kg L methionine
added to their diet. The final concentration of methionine in the diet was estimated to be
11.3 and 15.3 g/kg. Mice were sacrifice 8 and 44 weeks after exposure to DCA, with body and
liver weights evaluated for foci, adenomas, and HCCs. No histological descriptions were given
by the authors other than tinctorial phenotype of foci and adenomas for a subset of the data. The
number of mice examined was 36 for the DCA + 8.0 g/kg methionine or 4.0 g/kg methionine
group sacrificed at 44 weeks. However, for the DCA-only treatment group, the number of
animals examined was 32 at 44 weeks and for those groups that did not receive DCA but either
methionine at 8.0 or 4.0 g/kg, there were only 16 animals examined. All groups examined at
8 weeks had eight animals per group.
Liver glycogen was reported to be isolated from 30 to 50 mg of whole liver. Peroxisomal
acyl-CoA oxidase activity was reported to be determined using lauroyl-CoA as the substrate and
was considered a marker of peroxisomal proliferation. Whole-liver DNA methylation status was
analyzed using a 5-MeC antibody.
Methionine (8.0 g/kg) and DCA co-exposure was reported to result in the death of three
mice, while treatment with methionine (4.0 g/kg) and DCA or methionine (8.0 g/kg) alone was
reported to kill one mouse in each group. The authors reported that "There was an increased in
body weight during weeks 12 to 36 in the mice that received 8.0 g/kg methionine without DCA.
There was no other treatment-related alteration in body weight." However, the authors do not
present the data and initial or final body weights were not presented for the differing treatment
groups.
DCA treatment was reported to increase percent liver/body weight ratios at 8 and
44 weeks to about the same extent (i.e., ~2.4-fold of control at 8 weeks and 2.2-fold of control at
44 weeks). Methionine co-exposure was reported to not affect that increase (-2.4-, 2.2-, and
2.1-fold of control after DCA treatment alone, DCA/4 g/kg methionine, and DC A/8 mg/kg
methionine treatment for 8 weeks, respectively). There was a slight increase in percent
liver/body weight ratio associated with 8.0 g/kg methionine treatment alone in comparison to
controls (-7%) at 8 weeks with no difference between the two groups at 44 weeks.
After 8 weeks of only DCA exposure, the amount of glycogen in the liver was reported to
be ~2.09-fold of the value for untreated mice (115 vs. 52.5 mg/g glycogen in treated vs. control,
respectively, at 8 weeks). Both 4 and 8 g/kg methionine co-exposure reduced the amount of
DCA-induced glycogen increase in the liver (~1.64-fold of control for DC A/4.0 g/kg methionine
and ~1.54-fold of control for DCA/8.0 mg/kg methionine). Thus, for treatment with DCA alone
or with the two co-exposure levels of methionine, the magnitude of the increase in liver weight
was greater than that of the increase in liver glycogen (i.e., 2.42- vs. 2.09-fold of control percent
liver/body weight vs. glycogen content for DCA alone, 2.20- vs. 1.64-fold of control percent
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liver/body weight vs. glycogen content for DCA/4.0 g/kg methionine, 2.10- vs. 1.54-fold of
control percent liver/body weight vs. glycogen content for DCA/8.0 g/kg methionine). Thus, the
magnitudes of treatment-related increases were higher for percent liver/body weight than for
glycogen content in these groups.
In regard to percentage of liver mass that glycogen represented, the control value for this
study is similar to that presented by Kato-Weinstein et al. (2001) in male mice (-60 mg
glycogen/g liver) and represents -6% of liver mass. Therefore, a doubling of the amount of
glycogen is much less than the twofold increases in liver weight observed for DCA exposure in
this paradigm. These data suggest that DCA-related increases in liver weight gain are not only
the result of increased glycogen accumulation, and that methionine co-exposure is affecting
glycogen accumulation to a much greater extent than the other underlying processes that are
contributing to DCA-induced hepatomegaly after 8 weeks of exposure. The authors reported that
8-weeks of DCA exposure alone did not result in a significant increase in cell proliferation as
measured by PCN index (neither data nor methods were shown). This is consistent with other
data showing that DCA effects on DNA synthesis were transient and had subsided by 8 weeks of
exposure.
The levels of lauroyl-CoA oxidase activity were reported to be increased (~1.33-fold of
control) by DCA treatment alone at 8 weeks and to be slightly reduced by 8 g/kg methionine
treatment alone (~0.83-fold of control). Methionine co-exposure was reported to have little
effect on DCA-induced increases in lauroyl-CoA oxidase activity. The levels of DNA
methylation were reported to be increased by 8.0 g/kg methionine only treatment at 8 weeks
~1.32-fold of control, and reduced by DCA only treatment to ~0.44-fold of control. DCA and
4.0 g/kg methionine co-exposure gave similar results as controls (within 2%). Co-exposures of
DCA and 8.0 g/kg methionine treatments were reported to increase DNA methylation 1.22-fold
of controls after 8 weeks of co-exposure.
In the 44-week study, the authors reported that foci and hepatocellular adenomas were
found. However, the authors do not report the incidences of these lesions in their study groups
(how many of the treated animals developed lesions). As noted above, the numbers of animals in
these groups varied widely between treatments (e.g., n = 36 for DCA and co-exposure to 8.0 g/kg
methionine but only n = 16 for 8 g/kg methionine treatment alone). Although reporting
unscheduled deaths in the 8.0 g/kg methionine and DCA co-exposure groups, the authors did not
indicate whether these mortalities occurred in the 44- or 8-week study groups.
Multiplicities of foci and adenoma data were presented. DCA was reported to induce
2.42 ± 0.38 foci/mouse and 1.28 ± 0.31 adenomas/mouse (mean ± SE) after 44 weeks of
treatment. The DCA-induced foci and adenomas were reported to stain as eosinophilic with
"relatively large hepatocytes and nuclei." The authors did not present data on the percent of foci
and adenomas that were eosinophilic using this paradigm. The addition of 4.0 or 8.0 g/kg
methionine to the AIN-76A diet was reported to reduce the number of DCA-induced
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adenomas/mouse to 0.167 ± 0.093 and 0.028 ± 0.028, respectively. However, the addition of
4.0 g/kg methionine to the DCA treatment was reported to increase the number of foci/mouse
(3.4 ± 0.46 foci/mouse). The addition of 8.0 g/kg methionine to the DCA treatment was reported
to yield 0.94 ± 0.24 foci/mouse. There were no foci or tumors in the 16 mice that received either
the control diet or the 8.0 g/kg methionine treatment without DCA. The authors did not report
whether methionine treatment had an effect on the tincture of the foci or adenomas induced by
DCA.
Therefore, a very high level of methionine supplementation to an AIN-760A diet, was
shown to affect the number of foci and adenomas (i.e., decrease them) after 44 weeks of co-
exposure to very high exposure concentration of DCA. However, a lower level of methionine
co-exposure increased the incidence of foci at the same concentration of DCA. Methionine
treatment alone at the 8 g/kg level was reported to increase liver weight, decrease lauroyl-CoA
activity, and increase DNA methylation.
No histopathology was given by the authors to describe the effects of methionine alone.
Co-exposure of methionine with 3.2 g/L DCA was reported to decrease by -25% DCA-induced
glycogen accumulation and increase mortality, but not to have much of an effect on peroxisome
enzyme activity (which was not elevated by >33% over control for DCA exposure alone). The
authors suggested that their data indicate that methionine treatment slowed the progression of
foci to tumors. Whether these results would be similar for lower concentrations of DCA and
lower concentrations of methionine that were administered to mice for longer durations of
exposure cannot be ascertained from these data. It is possible that in a longer-term study, the
number of tumors would be similar. Whether methionine treatment co-exposure had an effect on
the phenotype of foci and tumors was not presented by the authors in this study. Such data
would have been valuable to discern if methionine co-exposure at the 4.0 mg/kg level that
resulted in an increase in DCA-induced foci, resulted in foci of a differing phenotype or resulted
in a more heterogeneous composition than DCA treatment alone.
E .2.3.2.13. DeAngelo et al. (2008)
In this study, neutralized TCA was administered in drinking water to male B6C3Fi mice
(28-30 days old) in three studies. In the first study, control animals received 2 g/L sodium
chloride while those in the second study were given 1.5 g/L neutralized acetic acid (HAC) to
account for any taste aversion to TCA dosing solutions. In a third study, deionized water served
as the control.
No differences in water uptake were reported. Mean initial weights were reported to not
differ between the treatment groups (19.5 ± 2.5 g - 21.4 ± 1.6 g or -10% difference). The first
study was reported to be conducted at the U.S. EPA laboratory in Cincinnati, Ohio in which mice
were exposed to 2 g/L sodium chloride, or 0.05, 0.5, or 5 g/L TCA in drinking water for
60 weeks. There were five animals at each concentration that were sacrificed at 4, 15, 31, and
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45 weeks with 30 animals sacrificed at 60 weeks of exposure. There were 3 unscheduled deaths
in the 0.05 g/L TCA group leaving 27 mice at final necropsy. For the other exposure groups,
there were 29 or 30 animals at final necropsy.
In the second study, also conducted in the same laboratory, mice were reported to be
exposed to 1.5 g/L neutralized acetic acid or 4.5 g/L TCA for 104 weeks. Serial necropsies were
conducted (5 animals per group) at 15, 30, and 45 weeks of exposure and 10 animals in the
control group at 60 weeks. For this study, a total of 25 animals were sacrificed in interim
necropsies in the 1.5 g/L HAC group and 15 in the 4.5 g/L TCA group. There were
7 unscheduled deaths in the HAC group and 12 in the 4.5 g/L TCA group, leaving 25 and
30 animals in the final necropsy groups, respectively.
Study 3 was conducted at the U.S. EPA laboratory in Research Triangle Park, North
Carolina. Mice were exposed to deionized water or 0.05 or 0.5 g/L TCA in the drinking water
for 104 weeks with serial necropsies (n = 8 per group) conducted at 26, 52, and 78 weeks. There
were 19-21 animals reported at interim sacrifices and 17 unscheduled deaths in the deionized
water group, 24 unscheduled deaths in the 0.05 g/L TCA group, and 24 unscheduled deaths in
the 0.5 g/L TCA group. This left 34 mice at final necropsy in the control group, 29 mice in the
0.05 g/L TCA group, and 27 mice in the 0.5 g/L group.
At necropsy, liver, kidneys, spleen, and testes weights were reported to be taken and
organs examined for gross lesions. Tissues were prepared for light microscopy and stained with
H& E. At termination of the exposure periods, a complete rodent necropsy was reported to be
performed. Representative blocks of tissue were examined only in five mice from the high-dose
and control groups with the exception of gross lesions, liver, kidney, spleen, and testis at interim
and terminal sacrifices. If the number of any histopathologic lesions in a tissue was
"significantly increased above that in control animals," then that tissue was reported to be
examined in all TCA dose groups.
For Study #3, a second contract pathologist reviewed 10% of the described hepatic
lesions. No "major differences" were reported between the two pathologic diagnoses.
The prevalence and multiplicity of hepatic tumors were reported to be derived by
performing a histopathologic examination of surface lesions and four sections cut from each of
four tissue blocks excised from each liver lobe. Tumor prevalence was reported to be calculated
as the percentage of the animals with a neoplastic lesion compared to the number of animals
examined. Tumor multiplicity was reported to be calculated by dividing the number of each
lesion or combined adenomas and carcinomas by the number of animals examined.
Preneoplastic large foci of cellular alteration were also observed over the course of the study.
The prevalence and severity of hepatocellular cytoplasmic alterations, inflammation, and
necrosis were reported to be determined using a scale based on the amount of liver involved of
1 = minimal (occupying 25%), 2 = mild (occupying 25-50%), 3 = moderate (occupying 50-
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75%), and 4 = marked (occupying >75%). The only "significant change outside of the liver"
was reported to be testicular degeneration.
LDH was determined in arterial blood collected at 30 and 60 weeks (Study 1) and 4, 30,
and 104 weeks (Study 2). Cyanide insensitive PCO was also reported to be measured. Five days
prior to sacrifice, tritiated thymidine (Studies 1 and 2) or BrdU (Study 3) was administered via
miniosmotic pumps and the number of hepatocyte nuclei with grain counts >6 were scored in
1,000 cells or chromogen pigment over nuclei (BrdU). The labeling index was calculated by
dividing the number of labeled hepatocyte nuclei by the total number of hepatocytes scored.
Total neoplastic and preneoplastic lesions (multiplicity) were counted individually or
combined (adenomas and carcinomas) for each animal. The analysis of tumor prevalence data
was reported to include only those animals examined at the scheduled necropsies or animals
surviving to week 60 (Study 1) or longer than 78 weeks (Studies 2 and 3). The data from all of
the scheduled necropsies were combined for an overall test of treatment-related effect.
For Study #1 (60-week exposure), all TCA-treated groups experienced a decrease in
drinking water consumption, with the decreases in drinking water for the 0.5 and 5 g/L TCA
exposure groups reported as statistically significant by the authors. The water consumption in
mL/kg-day was reported to be reduced by 11, 17, and 30% in the 0.05, 0.5, and 5 g/L TCA
treated groups compared to 2 g/L sodium chloride control animals as measured by time-weighted
mean daily water consumption measured over the study. The control value was reported to be
171 mL/kg/day. Although the 0.05 g/L exposure concentrations were not measured, the 0.5 and
5 g/L solutions were within 4% of target concentrations. The authors estimated that the mean
daily doses were 0, 8, 68, and 602 mg/kg-day.
For the 102-week studies, the mean water consumption with deionized water was
reported to be 112 and 132 mL/kg-day for control animals given 1.5 g/L HAC. Therefore, there
appeared to be a 35% decrease in water consumption between the controls in Study #1 given
2 g/L sodium chloride and controls in Study #3 given deionized water but conducted at a
different laboratory. There appeared to be a 23% reduction in water consumption between
animals given 2 g/L sodium chloride and those given 1.5 g/L HAC at the same laboratory
(Study #2).
As the concentrations of TCA were increased, there would be a corresponding increase in the
amount of sodium hydroxide needed to neutralize the solutions and a corresponding increase in
salts in the solution as well as TCA. The authors did not address nor discuss the differences in
drinking water consumption between the differing control solutions between the studies.
DeAngelo et al. (1999) reported mean drinking water consumption of 147 mL/kg/day in
control mice of over 100 weeks and that the highest dose of DC A (3.5 g/L) reduced drinking
water consumption by 26%. Carter et al. (1995) reported that DCA at 5 g/L decreased drinking
water consumption by 64 and 46%, but 0.5 g/L DCA did not affect drinking water consumption.
In this study, while reporting that Study #1 showed that increasing TCA concentration decreased
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drinking water consumption, the drinking water consumption in Studies #2 and #3 were similar
between controls and TCA exposure groups with both being less than the control and low TCA
concentration values reported in Study #1 (i.e., in Study #2, the 1.5 g/L HAC and 4.5 g/L TCA
drinking water consumption was -130 mL/kg/day and in Study #3, the drinking water
consumption was ~112 mL/kg/day for the deionized water control and 0.05 and 0.5 g/L TCA
exposure groups). Thus, the drinking water concentrations for Study #3 was -35% less than for
the control values for Study #1 and was also -25% less than for DeAngelo et al. (1999). The
reasons for the apparently lower drinking water averages for Study #3 and the lack of effect of
the addition of 0.5 g/L TCA that was reported in Study #1 and in other studies, was not discussed
by the authors.
In Study #1, there was little difference between exposure groups (n = 5) noted for the
final body weights (mean range of 27.6-28.1 g) in mice sacrificed after 4 weeks of exposure.
However, absolute liver weight and percent liver/body weight ratios increased with TCA dose.
The percent liver/body weight ratios were 5.7 ± 0.4, 6.2 ± 0.3, 6.6 ± 0.4, and 7.7 ± 0.6% for the
2 g/L sodium chloride control, 0.05, 0.5, and 5 g/L TCA exposure groups, respectively. These
represent 1.09-, 1.16-, and 1.35-fold of control levels that were statistically significant.
At 15 weeks of exposure the fold increases in percent liver/body weight ratios were 1.14-,
1.16-, and 1.47-fold of controls for 0.05, 0.5, and 5 g/L TCA. At 31 weeks of exposure, the fold
increases in percent liver/body weight ratios were 0.98-, 1.09-, and 1.59-fold of controls for 0.05,
0.5, and 5 g/L TCA. At 45 weeks of exposure, the fold increases in percent liver/body weight
ratios were 1.13-, 1.45-, and 1.98-fold of controls for 0.05, 0.5, and 5 g/L TCA. At 60 weeks of
exposure, the percent liver/body weight ratios were 0.94-, 1.25-, 1.60-fold of controls for 0.05,
0.5, and 5 g/L TCA.
Thus, the range of increase at the lowest level of TCA exposure (i.e., 0.05 g/L) was 0.94-
1.14-fold of controls. These data consistently show TCA-induced increases in liver weight from
4 to 60 weeks of the study that were dose-related. For the 0.5 g/L exposure group, the magnitude
of the increase compared to control was reported to be about the same between weeks 4 and 30,
with the highest increase reported to be at week 45 (1.45-fold of control). In regard to the
correspondence with magnitude of difference in dose of TCA and liver weight increase, there
was -2-fold increase in liver weight gain corresponding to 10-fold increases in TCA
concentration at 4 weeks of exposure. For the 4- and 15-week exposures, there were -3.3- and
3.9-fold difference in liver weight that corresponded to a 100-fold difference in exposure
concentration of TCA (i.e., 0.05 vs. 5.0 g/L TCA).
The small number of animals examined, n = 5, limit the power of the study to determine
the change in percent liver/body weight up to 45 weeks, especially at the lowest dose. However,
the 0.05 g/L TCA exposure groups at 4 and 15 weeks were reported to significantly increased
percent liver/body weight ratios.
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The percent liver/body weight ratios for all of the treatment groups and the ability to
detect significant changes were affected by changes in final body weight and changing numbers
of animals. After 4-30 weeks of exposure, the final body weights of mice increased in control
animals but were within 11% of each other between weeks 31 and 60. The percent liver/body
weight ratios in controls decreased from 4 to 31 weeks and were slightly elevated by 60 weeks
compared to the 31-week level. Although control values were changing, there appeared to be no
difference between control values and treated values in final body weight for any duration of
exposure with the exception of the 5 g/L TCA exposure group after 60 weeks of exposure, which
was decreased by -15%. At the 31- and 60-week exposure durations, the 0.05 g/L TCA groups
did not have increased percent liver/body weight ratios over controls.
In Study #2, conducted in the same laboratory but with a 1.5 g/L HAC solution used for
control groups, there was <5% difference in final body weights between control mice give HAC
and those treated with 4.5 g/L TCA up to 45 weeks. However, final body weight was reduced by
TCA treatment by 104 weeks by -15%. Between the interim sacrifices of 15, 30, and 45 weeks,
the percent liver/body weight ratios in control mice were similar at 15 and 45 weeks (-4.8%) but
greater in the 30-week control group (5.3 or -10% greater than other interim control groups).
The TCA-induced increases in body weight were 1.60-, 1.40-, and 1.79-fold of control for the
15-, 30-, and 45-week groups exposed to 4.5 g/L TCA in Study #2. The smaller magnitude of
TCA-induced liver weight increase at 30 weeks than that for 15 and 45 weeks, was a reflection
of the increased percent liver/body weight ratio reported for the HAC control at that time point.
Comparisons can be made between Studies #1 and #2 for 4.5 or 5.0 g/L TCA exposure
levels and controls for 15, 30/31, and 45 weeks of exposure to ascertain the consistency of
response from the same laboratory. Although the two studies had differing control solutions and
reported different drinking water consumption overall, they were exposing the TCA groups to
almost the same concentration of TCA in the same buffered solutions for the same periods of
time with the same number of mice per group.
Between Studies #1 and #2, there were consistent percent liver/body weight ratios
induced by either 5.0 or 4.5 g/L TCA at weeks 15 and 30/31 (i.e., within 3% of each other). The
percent liver/body ratios for these exposure groups ranged from 7.3 to 7.7% between weeks 15
and 30/31 for the -5.0 g/L TCA exposure in both studies. Final body weights were within 10%.
While the percent liver/body weight ratios induced by -5.0 g/L TCA were similar, the magnitude
of increase in comparison to the controls was 1.47- and 1.59-fold of control for Study #1, and
1.60- and 1.40-fold of control for Study #2 after 15 and 30/31 weeks of exposure, respectively.
At 45 weeks, the percent liver/body weight ratios were within 11% of each other (9.4 vs. 8.4%)
and final body weights were within 2% of each for this exposure concentration between the two
studies giving a 1.98- and 1.79-fold of control percent liver/body weight, respectively. Thus, the
apparent magnitude of TCA-induced increase in percent liver/body weight was affected by
control values used as the basis for comparison. The percent liver/body weights reported for
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either 4.5 or 5.0 g/L TCA exposure groups for weeks 15 and 30/31 was similar between the two
studies conducted in the same laboratory.
Study #3 was conducted in a separate laboratory, interim sacrifice times were not the
same as for Study #1, the number of animals examined differed (n = 5 for Study #1 and n = 8 for
Study #3), and control animals studied for comparative purposes were given different drinking
water solutions (deionized water vs. 2 g/L sodium chloride). Most importantly, the body weights
reported at 52 weeks were much greater than that reported at 45 weeks for Studies #1 and #2.
However, a comparison of TC A-induced liver weight gain and the effects of final body
weight can be made between the 0.05 and 0.5 g/L TCA exposure groups at 30 weeks (Study #1)
and 26 weeks (Study #3), at 45 weeks and 60 weeks (Study #1), and 52 weeks (Study #3). At
31 weeks, there was <2% difference in mean final body weights between control and the two
TCA-treatment groups in Study #1. There was also little difference between the TCA-treated
groups at week in Study #3 at week 26 and the TCA treatment groups in at week 31 in Study #1
(i.e., range of 42.6-43.5 g for 0.05 and 0.5 g/L TCA treatments in Studies #1 and #3). However,
in Study #3, the control value was 12% lower than that of Study #1 for mean final body weight.
Based on final body weights, there would be an expectation of similar results between the two
studies at the 26- and 30-week time points.
At the 45-week (Study #1), 52-week (Study #3), and 60-week (Study #1) durations of
exposure, the mean final body weights varied little between their corresponding control groups at
each sacrifice time (<4% variation between control and TCA-treated groups). However, there
was variation in mean final body weights between the differing sacrifice times. Control and
TCA-treated groups were reported to have lower mean final body weights at 45 weeks of
exposure in Study #1 than at either 30 or 60 weeks. The 45-week mean final body weights in
Study #1 were also reported to be lower than those at 52 weeks in Study #3. Control mean body
weight values were 28% higher at 52 weeks in Study #3 than 45 weeks in Study #1 and 15%
higher for 60 weeks in Study #1. In essence, for Study #1, mean final body weights went down
between 31 and 45 weeks of exposure and then went back up at 60 weeks of exposure for control
mice (-43, -40, and -44 g for 31, 45, and 60 weeks, respectively) as well as for both TCA
concentrations. However, for Study #3, final mean body weights went up between 26 and
52 weeks of exposure for control mice (-39 vs. -51 g) and for both TCA concentrations.
While for Study #1, the percent liver/body weight ratios were 0.98- and 1.09-fold of
control at 31 weeks of exposure, at week 45, the ratios were 1.13- and 1.45-fold of control, and
at week 60, they were 0.94- and 1.25-fold of controls for the 0.05 and 0.5 g/L TCA exposure
levels, respectively. For Study #3, the pattern differed than that of Study #1. There was a
1.07- and 1.18-fold of control percent liver/body weight for 26 weeks but a 0.92- and 1.04-fold
of control percent liver/body weight change at 52 weeks of exposure at 0.05 and 0.5 g/L TCA
exposure, respectively.
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Thus, there appeared to be differences in control and the treatment groups at the 26-week
sacrifice groups in Study #3 that was not apparent at the 52-week sacrifice time. Overall, the
final body weights appeared to be similar between controls and TCA treatment groups at the
52-week sacrifice time in Study #3 and at the 31-, 45-, and 60-week sacrifice times in Study #1.
However, although consistent within sacrifice times, the final body weights differed between the
various sacrifice times in Studies #1 and #3. The patterns of percent liver/body weight at
differing and similar sacrifice times appeared to differ between the Studies #1 and #3 at the same
concentrations of TCA. The largest difference appeared to be between week 45 group in
Study #1 and week 52 group in Study #3 where both concentrations of TCA were reported to
induce increases in percent liver/body weight in one study but to have little difference in the
other. The differences in mean final body weights between these two sacrifice times were also
the largest although control and TCA-treatment groups had little difference on this parameter.
Similar to the work of Kjellstrand and colleagues with TCE (Kjellstrand et al., 1983a), the
groups with the lower body weight appeared to have the greatest response in liver weight
increase.
These data illustrate the variability in findings of percent liver weight induction between
laboratories, studies, choice of controls solutions, and the effects of final body weights on this
parameter. They also illustrate the limitations for determining either the magnitude or pattern of
liver weight increases using a small number of test animals. As animals age, the size of their
liver changes, but also during the latter parts of the lifespan, foci and spontaneously occurring
liver tumors can affect liver weight. The results of Study #1 show a consistent dose-response in
TCA liver weight increases at 4- and 15-week time periods over a range of concentration from
0.05 to 5 g/L TCA.
In regard to non-neoplastic pathological changes, the authors reported that:
Increased incidences and severity of centrilobular cytoplasmic alterations,
inflammation, and necrosis were the only nonproliferative changes seen in livers
of animals exposed to TCA for 60 weeks (Tables 7-9; Study 1. Incidences were
between 21 and 93%; severity ranged from minimal to mild; and some lesions
were transient. Centrilobular cytoplasmic alterations (Table 7) were the most
prominent nonproliferative lesion. The incidence and severity were dose related
and significantly increased at all TCA concentrations. Centrilobular alterations
are a low-grade degeneration of the hepatocytes characterized by an intense
eosinophilic cytoplasm with deep basophilic granularity (microsomes) and slight
hepatomegaly. The distribution ranged from centrilobular to diffuse. The
incidence of inflammation was increased significantly in the 5 g/L TCA treatment
group (Table 8), but was significantly lower in the 0.05- and 0.5 g/L groups
between 31 and 45 weeks, but abated by 60 weeks. There was a significant dose-
related trend, but a significant increase in severity was only found at 5 g/L. No
alteration in the severity of this lesion was observed. The occurrence and severity
of nonproliferative lesions in animals exposed to 0.5 and 4.5 g/L TCA for
104 weeks were similar to those observed at 60 weeks (data not shown). No
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pathology outside the liver was observed except for a significant dose-related
trend and incidence of testicular tubular degeneration at 0.5 and 5 g/L TCA.
The results shown in Table 7 by the authors for the 60-week TCA-exposed mice did not
show a dose-response for either incidence or severity of centrilobular cytoplasmic alterations.
They reported a 7, 48, 21, and 93% incidence and a 0.10 ± 0.40, 0.70 ± 0.82, 0.34 ± 0.72, and
1.60 ± 0.62 mean severity score for control, 0.05, 0.5, and 5.0 g/L TCA exposure groups,
respectively. Thus, for control, 0.05, and 0.5 g/L TCA exposure, there was less than minimal
(i.e., score of 1 or occupying <25% of the microscopic field) severity of this finding for the 27-
30 mice examined in each group. Only slight hepatomegaly is noted by the authors to be
included in their description of the centrilobular cytoplasmic alteration. Interestingly, the
elevation of this parameter for both incidence and severity in the 0.05 g/L TCA exposed group
compared to 0.5 g/L exposure group did not correspond to an increase in percent liver/body
weight for this same exposure group. While the percent liver/body weight ratio was 32% higher,
the incidence and severity of this lesion were reported to be half that in the 0.5 vs. 0.05 g/L
exposure groups after 60 days of TCA exposure. Thus, TCA-induced hepatomegaly did not
appear to be associated with this centrilobular cytoplasmic change.
Similarly the incidence of hepatic inflammation was reported to be 10, 0, 7, and 24% and
severity, 0.11 ±0.40, 0.09 ± 0.30, 0.12 ±0.33, and 0.29 ± 0.48 for control, 0.05, 0.5, and 5.0 g/L
TCA exposure groups, respectively. Thus, at no TCA exposure concentration was the incidence
>24%, and the severity was considerably less than minimal. The reported results for hepatic
necrosis were pooled from data from the five mice exposed for either 30 or 45 weeks (n = 10
total). No incidences of necrosis were reported for either control or 0.05 g/L TCA exposed mice.
At 0.5 g/L, TCA 3/10 mice were reported to have necrosis but at a severity level of 0.50 ± 0.97.
At 5.0 g/L, TCA 5/10 mice were reported to have necrosis but at a severity level of 1.30 ± 1.49.
The limitations of the small number of animals pooled in these data are obvious. However, there
does not appear to be much more than minimal necrosis at the highest dose of TCA between
30 and 45 weeks and this response is reported by the authors to be transient.
Serum LDH activity was reported by the authors for 31- and 60-week TCA exposures in
Study #1. They state that:
There was a dose-related trend at 31 weeks; serum LDH was significantly
increased at 0.5 and 5 g/L TCA (161 ± 39 and 190 ± 44, respectively vs. 100 ± 28
IU for the control). LDH activity returned to control levels at 60 weeks.
Similarly, elevated LDH levels were observed at early time periods for 0.5 and
4.5 g/L TCA during the 104 week exposure (data not shown: Studies 2 and 3).
The data presented by the author for Study #1 are from 5 animals/group for the 30-week
results and 30 animals/group for the 60-week results. Of interest is for the 60-week data, there
appears to be 50% decreased in LDH activity at 0.05 and -25% decrease in LDH activity at
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0.5 g/L TCA treatment with the LDH level reported to be the same as control for the 5 g/L TCA
exposure group. For the 31-week data, in which only five animals were tested in each treatment
group, there appeared to be a slight increase at the 0.5 g/L (60% increase over control) and 5 g/L
(90% increase over control) treatment groups. The data for necrosis detected by light
microscopy and by LDH level is consistent with no changes from control detected at the 0.05 g/L
TCA treatment group and less than minimal necrosis of on a 60% increase in LDH level over
control reported for 0.5 g/L TCA treatment. Even at the highest dose of 5.0 g/L TCA, there is
still little necrosis or LDH release reported over control.
Data for testicular tubular degeneration was reported for Study #1 after 60 weeks of TCA
exposure. The incidence of testicular tubular degeneration was reported to be 7, 0, 14, and 21%
for mice exposed to 2.0 g/L sodium chloride, 0.05, 0.5, and 5.0 g/L TCA. The severity of the
lesions was reported to be 0.10± 0.40, 0, 0.17 ± 0.47, and 0.21 ± 0.41 with a significant trend
with dose reported by the authors for severity and for the 0.5 and 5 g/L treatment groups to be
significantly increased over control incidence levels. Of note, similar to the percent liver/body
weight ratios and hepatic inflammation values for this data set, the values for testicular tubular
degeneration were slightly higher in control mice than 0.05 g/L TCA exposed mice. In regard to
mean severity levels for testicular degeneration, although still minimal, there was little difference
between the results for reported for the 0.5 and 5.0 g/L TCA exposed mice.
In regard to peroxisome proliferation, liver PCO activity was presented for up to
60 weeks (Study #1) and 104 weeks (Study #2). Similar to the data for LDH activity,
-30 animals were examined at the 60-week time point but only 5 animals per exposure group
were examined for 4-, 15-, 31-, and 45-week results. The data are presented in a figure, and in
some instances, it is hard to determine the magnitude of change.
Similar to other reports, the baseline level of PCO activity was variable between control
groups and ranged 2.7-fold (-1.49-4.06 nmol NAD reduced/minute/mg protein given by the
authors). There appeared to be little change in PCO activity between the 0.05 g/L TCA exposure
and control levels for up to 45 weeks of exposure (i.e., the groups with n = 5) in Study #1. For
the 60-week group, the 0.05 g/L TCA group PCO activity was -1.7-fold of control but was not
statistically significant. For the 0.5 g/L TCA treatment groups, the increase ranged from -1.3- to
2.7-fold of control after 4, 15, 31, and 45 weeks of exposure with the largest differences reported
at 4 and 60 weeks (i.e., 2.2- and 2.7-fold of control, respectively). For the 5.0 g/L TCA exposure
groups, the increase ranged from -3.2- to -5.7-fold of control after 4, 15, 31, and 45 weeks of
exposure.
While the data at 60 weeks had the most animals examined (-30 vs. 5) with -1.7-, 2.7-,
and 4.5-fold of control PCO activity, at this time period, the authors report the occurrence of
tumors had already occurred. At the earlier time points of 4 and 15 weeks, there was a difference
in the magnitude of TCA-induced increases in PCO activity. As displayed graphically, at
4 weeks, the PCO increases were -1.3-, 2.4-, and 5.3-fold of control for 0.05, 0.5, and 5.0 g/L
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TCA, respectively, while at 15 weeks, the PCO levels were decreased by 5%, increased to
1.3-fold, and increased to 3.2-fold of control with only the 5.0 g/L treatment group difference to
be statistically significant.
For Study #2, the authors present a figure (Figure #4) that states that PCO values were
given for mice given HAC or 4.5 g/L TCA for 4-60 weeks. However, the data presented in #4
appears to be for 15-, 30-, 45-, and 104-week exposures. The number of mice is not given in the
figure but the methods section states that serial sections were conducted on 5 mice/group for
these interim sacrifice periods. The number of mice examined for PCO activity at 104 weeks
was not given by the authors but the number of mice at final sacrifice was given as 25. The
levels of PCO in the control tissues varied by -33% for weeks 15-45 but there was a ~5-fold
difference between the level reported at 104 weeks and that for the earlier time periods in control
mice shown in the figures (-2.23 vs. 0.41 nmol NAD reduced/min/mg protein as given by the
authors). The increase over control induced by 4.5 g/L TCA in Study #2 was shown to be -6.9-,
4.8-, 3.6-, and 19-fold of controls for 15, 30, 45 and 104 weeks, respectively.
Therefore, at a comparable level of TCA exposure (-5.0 g/L), number of mice examined
(n = 5), and durations of exposure (15, 30, and 45 weeks), the increase in PCO activity induced
by -5.0 g/L TCA varied between 3.2- and 5.7-fold of control in Study #1 and between 3.6- and
6.9-fold of control in Study #2. There was not a consistent pattern between the two studies in
regard to level of PCO induction from -5 g/L TCA and duration of exposure. The lowest
TCA-induced PCO activity increase was recorded at 15 weeks in Study #1 (i.e., 3.2-fold of
control) and highest PCO activity increase was recorded at 15 weeks in Study #2 (i.e., 6.9-fold of
control). No PCO data were reported for data in Study #3 with the exception of the authors
stating that "PCO activity was significantly elevated for the 0.5 g/L TCA exposure over the
104 weeks (study 3). The extent of the increases was similar to those measured for 0.5 g/L TCA
(200-375%: data not shown) in Study 1." No other details are given for PCO activity in
Study #3.
Hepatocyte proliferation was reported by the authors to be assessed by either
incorporation of tritiated thymidine (Studies #1 and #2) or BrdU (Study #3) into hepatocyte
nuclei. As noted previously, these techniques measure DNA synthesis and not necessarily
hepatocyte proliferation. The authors did not report whether specific areas of the liver were
analyzed by autoradiographs or how many autoradiographs were examined in the analyses they
conducted. For later time points of examination (60-104 weeks), the authors did not indicate
whether hepatocytes in foci or adenomas were excluded from DNA synthesis reports. The
authors present data for what are clearly, 31-, 45-, and 60-week exposures for Study #1 as the
percent tritiated thymidine labeled nuclei. An early time point that appears to be 8 weeks is also
given.
However, for Study #1, only 4- and 15-week durations were tested, so it cannot be
established what time period the earlier time point represents. What is very apparent from the
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data presented for Study #1 is that the baseline level of tritiated thymidine incorporation was
relatively high and was highly variable for the five animals examined (-8% of hepatocytes were
labeled). There did not appear to be an apparent pattern of TCA treatment groups at this
timepoint, with the 0.05 and 5.0 g/L TCA groups having a similar percentage of labeled
hepatocytes and for 0.5 g/L TCA reported to have a 60% reduction in labeled hepatocytes.
After 31 weeks of exposure, the control values were reported to be 2% of hepatocytes
labeled. The authors report that only the 5.0 g/L TCA group had a statistically significant
increase of control and was elevated to -6% of hepatocytes. The two lower exposure
concentrations of TCA had similar reported incidences of labeled hepatocytes of 4.5% that were
not reported to be statistically significant.
For the 45-week exposure period in Study #1, the control value was reported to be 1.2%,
with only the 5.0 g/L TCA value reported to be statistically significantly increased at 3.2% and
the other two TCA groups to be similar to control. Finally, for the 60-week group from Study
#1, the control value was reported to be 0.6% of hepatocytes labeled and only the 0.5 g/L TCA
dose reported to be statistically significantly increased over control at 3.2%.
What is clear from this study is that the control value for the unidentified early time point
is much higher than the other values. There should not be such a large difference in mature mice
nor such a high level. The difference in control values between the earlier time point and the
31-week time point was fourfold. The difference between the earlier time point and the 45-week
time point was approximately sevenfold. There did not appear to be an increase in hepatocyte
tritiated thymidine labeling due to any concentration of TCA at the early unidentified time point
(approximately week 10 from the figure) from Study #1. There was no dose-response apparent
for the other study periods and the percent of hepatocytes labeled were <3%. These results
indicated that DNA synthesis was not increased by 10-60-week exposures to TCA exposure that
induced increased liver tumor response.
For Study #2, results were reported for tritiated thymidine incorporation into hepatocytes
in a figure that was labeled as 4.5 g/L TCA and control tissue for 104 weeks but showed data for
15, 30, and 45 weeks of exposure. Of note is that the control values for this study were much
lower than that reported for Study #1. The percent of hepatocytes labeled with tritiated
thymidine was reported to be -2% for the 15-week exposure period and <1% for the 30- and
45-week exposure periods. For the 4.5 g/L TCA exposures, the percent hepatocytes labeled with
tritiated thymidine were -2-4% at all time points with only the 45-week period identified by the
authors as statistically significant.
For Study #3, rather than tritiated thymidine, BrdU was used as a measure of DNA
synthesis. The results are presented in Figure #8 of the report in which the 0.5 g/L TCA
concentration is mislabeled as 0 g/L and the figure is mislabeled as having a duration of
104 weeks, but the data are presented for 26, 52, and 78 weeks of exposure. The percent of
hepatocytes at 26 weeks was reported to be -1-2% for the control, 0.05, and 0.5 g/L TCA
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groups. At 52 weeks, the control value was -1%, the 0.05 g/L TCA value was <0.1% and the
0.5 g/L TCA value was -3.5% but was not statistically significant. At 78 weeks of exposure, the
control value was reported to be -0.2% with only the 0.05 g/L TCA group having a statistically
significant increase over control.
From these data, the estimated control values for DNA synthesis at similar time points of
exposure ranged from 0.4 to 2% at 26-31 weeks and -0.1-1.2% at 45-52 weeks. The results for
Studies #1 and #2 were inconsistent in regard to the magnitude of tritiated thymidine
incorporation, but were consistent in that there was a lot of variability in these measurements, not
a consistent pattern with time that was TCA-dose related, and, even at the highest dose of TCA,
did not indicate much of an increase in cell proliferation at 15^15 weeks of exposure. Similarly,
the results for Studies #1 and #3 indicate that at the two lower doses of TCA, there were not
generally statistically significant increases in DNA synthesis from 15 to 45 weeks of exposure,
although there was an increase in liver tumor response at later time points.
The authors reported that "all gross and microscopic histopathological alterations were
consistent across the three studies." However, the histological descriptions that follow were
focused on the liver for both neoplastic and non-neoplastic parameters. As stated above, only a
few animals (n = 5) from the control and high TCA dose level were examined for lesions other
than liver, kidneys, spleen, and testes. Thus, whether other neoplastic lesions were induced by
TCA exposure cannot be determined from this set of studies.
Study #1 was conducted for 60 weeks. Although of short duration and using
<30 animals, the authors reported in the text that:
a significant trend with dose was found for liver cancer. The prevalence and
multiplicity of adenomas (38%; 0.55 ± 0.15) or carcinoma (38%; 0.42 ± 0.11)
were statistically significant at 602 mg/kg-day TCA compared to control (7%;
0.07 ± 0.05) [sic for both adenoma and carcinoma the same value was given,
mean ± SD]. When either an adenoma or a carcinoma was present, statistical
significant was seen at both 5 g/L (55%; 1.00 ± 0.19) and 0.5 g/L (38%: 0.52±
0.14 TCA exposure groups compared to control (13%; 0.13 ± 0.06).
No significant changes in liver neoplasia were reported to be observed by the
authors at 0.05 g/L TCA. Preneoplastic large foci of cellular alteration (24%) were seen
in the 5 g/L TCA group compared to control.
Although not statically significant, there was an incidence of 15% adenoma in the
0.05 g/L TCA treatment group (n = 27) and a multiplicity of 0.15 ± 0.07 adenomas/mouse
reported, with both values being twice that of the values given for the controls (n = 30). The
incidence and multiplicity for carcinomas was approximately the same for the 0.05 g/L TCA
treatment group and the control group. Given the small number of animals examined, the study
was limited in its ability to determine statistical significance for the lower TCA exposure level.
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The fold increases of incidence and multiplicity of adenomas at 60 weeks was 2.1-, 3.0-,
and 5.4-fold of control incidence and 2.1-, 3.4-, and 7.9-fold of control multiplicity for 0.05, 0.5,
and 5 g/L exposure to TCA. For multiplicity of adenomas and carcinomas combined, there was
a 1.46-, 4.0-, and 7.68-fold of control values. Analysis of tumor prevalence data for this study
included only animals examined at scheduled necropsy. Since most animals survived until
60 weeks, most were included and a consistent time point for tumor incidence was reported.
There are significant discrepancies for reporting of data for tumor incidences in this
report for the 104-week data. While the methods section and table describing the dose
calculation and animal survival indicate that Study #3 control animals were administered
deionized water and those from Study#2 were given HAC, Table 6 of the report gives 2 g/L
sodium chloride as the control solution given for Study #2 and 1.5 g/L HAC for Study #3. A
comparison of the descriptions of animal survival and tumor incidence and multiplicity between
the results given in DeAngelo et al. (2008) and George et al. (2000) (see Table E-10) shows not
only that the control data presented in DeAngelo et al. (2008) for Study #3 are the same data as
that presented by George et al. (2000) previously, but also indicates that rather than 1.5 g/L
HAC, the tumor data presented in DeAngelo et al. (2008) is for mice exposed to deionized water.
DeAngelo et al. (2008) did not report that these data were from a previous publication.
Table E-10. Comparison of descriptions of control data between George et
al. (2000) and DeAngelo et al. (2008)
Descriptor
Species
Strain
Gender
Age
Source
Mean initial body weight
Water consumption
Laboratory
Number of animals at start
Number of animals at interim sacrifice
Number of unscheduled deaths
Number of animals at final sacrifice
Number of animals for pathology
Adenoma incidence
Adenoma multiplicity
Carcinoma incidence
Carcinoma multiplicity
George et al. (2000)
Mouse
B6C3FJ
Male
28-30 d
Charles River, Portage
19.5 ± 2.5 g
111.7mL/kg-d
Research Triangle Park, North
Carolina
72
22
16
34
65
21.40%
0.21 ±0.06
54.80%
0.74 ±0.12
DeAngelo et al. (2008)
Mouse
B6C3FJ
Male
28-30 d
Charles River, Portage
19.5 ± 2.5 g
112mL/kg-d
Research Triangle Park, North
Carolina
72
21
17
34
63
21%
0.21 ±0.06
55%
0.74 ±0.12
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For Studies #2 and #3, tumor prevalence data were reported in the methods section of the
report to include necropsies of animals that survived >78 weeks and thus, included animals that
were scheduled for necropsy but also those that were moribund and sacrificed at differing times.
Thus, for the longer times of study, there was a mixture of exposure durations that
included animals that were ill and sacrificed early and those that survived to the end of the study.
Animals that were allowed to live for longer periods or did not die before scheduled sacrifice
times had a greater opportunity to develop tumors. However, animals that died early may have
died from tumor-related causes.
The mislabeling of the tumor data in DeAngelo et al. (2008) has effects on the
interpretation of results; if the tumor results table was not mislabeled, it would indicate that
17 animals were included in the liver tumor analysis that were not included in the final necropsy
and that the seven unscheduled deaths could not account for the total number of "extra" mice
included in the tumor analysis, so some of the animals had to have come from interim sacrifice
times (<78 weeks) and that for Study #3, the data from 9 animals at terminal sacrifice were not
used in the tumor analysis. Not only does it appear that the control data was mislabeled for
Study #3, but the control data were also apparently mislabeled for Study #2 as being 2.0 g/L
sodium chloride rather than 1.5 g/L HAC. Of the 42 animals used for the tumor analysis in
Study #3, only 34 were reported to have survived to interim sacrifice so that 8 animals were
included from unscheduled deaths. However, the authors report that there were 17 unscheduled
deaths in the study, but not all were included in the tumor analysis. The basis for the selection of
the eight animals for tumor analysis was not given by the authors.
Not only are the numbers of control animals used in the tumor analysis different between
two studies (25 mice in Study #2 and 42 mice in Study #3), but the liver tumor results reported
for Study #2 and Study #3 were very different. Of the 42 "control" mice examined from
Study #3, the incidence and multiplicity of adenomas were reported to be 21% and 0.21 ± 0.06,
respectively. For carcinomas, the incidence and multiplicity were reported to be 55% and
0.74 ± 0.12, respectively, and the incidence and multiplicity of adenomas and carcinomas
combined were reported to be 64% and 0.93 ± 0.12, respectively. For the 25 mice reported by
the authors for Study #2 to have been treated with "2.0g/L Nad" but were probably exposed to
1.5 g/L HAC, the incidence and multiplicity of adenomas was 0%. For carcinomas, the
incidence and multiplicity were reported to be 12% and 0.20 ± 0.12, respectively, and the
incidence and multiplicity of adenomas and carcinomas combined were reported to be 12% and
0.20 ± 0.12, respectively. Therefore, while -64% the 42 control mice in Study #3 were reported
to have adenomas and carcinomas, only 12% of the 25 mice were reported to have adenomas and
carcinomas in Study #2 for 104 weeks.
While the effect of using fewer mice in one study vs. the other will be to reduce the
power of the study to detect a response, there are additional factors that raise questions regarding
the tumor results. Not only were the tumor incidences reported to be higher in control mice from
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Study #3 than Study #2, but the number of unscheduled deaths was reported to also be twofold
higher. The age, gender, and strain of mouse were reported to be the same between Studies #2
and #3 with only the vehicles differing and weight of the mice to be reported to be different.
Although the study by George et al. (2000) described the same control data set as for Study #3 as
being for animals given deionized water, there is uncertainty as to the identity of the vehicle used
for the tumor results reported for Study #3 and there are some discrepancies in reporting between
the two studies. As discussed below in Section E.2.5, the differences in the weight of the mice
between Studies #1, #2, and #3 is critical to the issue of differences in background tumor rate
and hence interpretability of the study.
As noted by Leakey et al. (2003a), the greatest correlation with liver tumor incidence and
body weight appears between the ages of 20 and 60 weeks in male mice. As reported in
Section E.2.5, the mean 45-week body weight reported for control male B6C3Fi mice in the
George et al. (2000) study, which is the same control data as DeAngelo et al. (2008) was -50 g.
This is a much greater body weight than reported for Study #1 at 45 weeks (i.e., 39.6 g) and for
Study #2 at 45 weeks (i.e., 39.4 g). Using probability curves presented by Leakey et al. (2003a),
the large background rate of 64% of combined adenomas and carcinomas for Study #3 is in the
range predicted for such a large body weight (i.e., -65%). Such a high background incidence
compromises a 2-year bioassay, as it prevents demonstration of a positive dose-response
relationship. Thus, Study #3 of DeAngelo et al. (2008) is not comparable to the results in
Studies #1 and #2 for the determination of the dose-response for TCA.
The accurate determination of the background liver tumor rate is very important in
determining a treatment-related effect. The very large background level of tumor incidence
reported for Study #3 makes the detection of a TC A-related change in tumor incidence at low
exposure levels very difficult to determine. Issues also arise as to what the source of the tumor
data were in the TCA-treatment and control groups in Study #3. While 29 mice exposed to
0.05 g/L TCA were reported to have been examined at terminal sacrifice, 35 mice were used for
liver tumor analysis. Similarly, while 27 mice exposed to 0.5 g/L TCA were reported to have
been examined at terminal sacrifice, 37 mice were used for tumor analysis. Finally, for the
42 control animals examined for tumor pathology in the control group, 34 were examined at
terminal sacrifice. Clearly, more animals were included in the analyses of tumor incidence and
multiplicity than were sacrificed at the end of the experiment. What effect differential addition
of the results from mice not sacrificed at 104 weeks and the selection bias that may have resulted
from their inclusion on these results cannot be determined. Not only were the background levels
of tumors reported to be increased in the control animals in Study #3 compared to Study #2 at
104 weeks, but the rate of unscheduled deaths was doubled. This is also an expected
consequence of using much larger mice (Leakey et al., 2003a).
For the 35 mice examined after 0.05 g/L TCA in Study #3, the incidence and multiplicity
of adenomas were reported to be 23% and 0.34 ±0.12, respectively. For carcinomas, the
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incidence and multiplicity were reported to be 40% and 0.71 ± 0.19, respectively, and the
incidence and multiplicity of adenomas and carcinomas combined were reported to be 57% and
1.11 ± 0.21, respectively. For the 37 mice examined after 0.5 g/L TCA in Study #3, the
incidence and multiplicity of adenomas were reported to be 51% and 0.78 ±0.15, respectively.
For carcinomas, the incidence and multiplicity were reported to be 78% and 1.46 ± 0.21,
respectively, and the incidence and multiplicity of adenomas and carcinomas combined were
reported to be 87% and 2.14 ± 0.26, respectively.
Thus at 0.5 g/L TCA, the results presented for this study for the "104 week" liver tumor
data were significantly increased over the reported control values. However, these results are
identical to those reported in Study #3 for a 10-fold higher concentration of TCA (4.5 g/L TCA)
for the same 104 weeks of exposure but in the much larger mice. Of the 36 animals exposed to
4.5 g/L TCA in Study #2 and included in the tumor analysis, 30 animals were reported to be
examined at 104 weeks. The incidence and multiplicity of adenomas were reported to be 59%
and 0.61 ± 0.16, respectively. For carcinomas, the incidence and multiplicity were reported to be
78% and 1.50 ± 0.22, respectively, and the incidence and multiplicity of adenomas and
carcinomas combined were reported to be 89% and 2.11 ± 0.25, respectively.
The importance of selection and determination of the control values for comparative
purposes of tumor induction are obvious from these data. The very large difference in control
values between Study #2 and Study #3 is the determinant of the magnitude of the dose response
for TCA after 104 weeks of exposure. The tumor response for 0.5 and 4.5 g/L TCA exposure
between the two experiments was identical. Therefore, only the background tumor rate
determined the magnitude of the response to treatment. If similar control values (i.e., a historical
control value) were used in these experiments, there would appear to be no difference in
TCA-tumor response between 0.5 and 4.5 g/L TCA at 104 weeks of exposure. DeAngelo et al.
(1999) report for male B6C3Fi mice exposed only water for 79-100 weeks the incidence of
carcinomas to be 26% and multiplicity to be 0.28 lesions/mouse. For 100-week data, the
incidence and prevalence of adenomas were reported to be 10% and 0.12 ± 0.05 and to be 26%
and 0.28 ± 0.07 for carcinomas.
Issues with reporting for that study have already been discussed in Section E.2.3.2.6.
However, the data for DeAngelo et al. (1999) are more consistent with the control data for
"1.5 g/L HAC" for Study #2 in which there were 0% adenomas and 12% carcinomas with a
multiplicity of 0.20 ± 0.12, than for the control data for Study #3 in which 64% of the control
mice were reported to have adenomas and carcinomas and the multiplicity was 0.93 ± 0.12. If
either the control data from DeAngelo et al. (1999) or Study #2 were used for comparative
purposes for the TCA-treatment results of Study #2 or #3, there would be a dose-response
between 0.05 and 0.5 g/L TCA but no difference between 0.5 and 4.5 g/L TCA after 100 weeks
of exposure. The tumor incidence would have peaked at -90% in the 0.5 and 4.5 g/L TCA
exposure groups. These results would be more consistent with the 60-week results in Study #1
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in which 0.5 and 5 g/L TCA exposure groups already had incidences of 38 and 55% of adenomas
and carcinomas combined, respectively, compared to the 13% control level. With increased time
of exposure, the differences between the two highest TCA exposure concentrations may diminish
as tumor progression is allowed to proceed further. However, the use of the larger and more
tumor prone mice in Study #3 also increases the tumor incidence at the longer period of study.
The authors also presented data for multiplicity of combined adenomas or carcinomas for
mice sacrificed at weeks 26, 52, and 78 for Study #3 (n = 8 per group). No indication of
variability of response, incidence data, statistical significance, or data for adenomas vs.
carcinomas, or the incidence of adenomas was reported. The authors reported that "neoplastic
lesions were first found in the control and 0.05 g/L TCA groups at 52 weeks. At 78 weeks,
adenomas or carcinomas were found in all groups (0.29, 0.20, and 0.57 tumors/animals for
control, 0.05 g/L TCA, and 0.5 g/L TCA, respectively)." Because no other data were presented
at the 52 and 78 week time points in this study, these results cannot be compared to those
presented for Study #1, which was conducted for 60 weeks. Of note, the results presented from
Study #1 for 60 weeks of exposure to control, 0.05, or 0.5 g/L TCA exposure in 27-30 mice
show a 13, 15, and 38% incidence of hepatocellular adenomas and carcinomas and a multiplicity
of 0.13 ± 0.06, 0.19 ± 0.09, and 0.52 ± 0.14, respectively. Both the incidence and multiplicity of
adenomas were twofold higher in the 0.05 g/L TCA treatment group than for the control.
However, the interim data presented by the authors from Study #3 for 52 weeks of exposure in
only eight mice per group gives a higher multiplicity of adenomas and carcinomas for control
animals (-0.25) than for either 0.05 or 0.5 g/L TCA treatments. Again, comparisons between
Studies #2 and #3 are difficult due to difference in mouse weight.
Of note, there are no descriptions given in this report in regard to the phenotype of the
tumors induced by TCA or for the liver tumors reported to occur spontaneously in control mice.
Such information would have been of value, as this study reports results for a range of TCA
concentration and for 60 and 100 weeks of exposure. Insight could have been gained as to the
effects of differing concentrations of TCA exposure and whether TCA-induced liver tumors had
a similar phenotype as those occurring spontaneously, as well as information in regard to effects
on tumor progression and heterogeneity.
Although only examining tissues from five mice from the control and high-dose groups
only at 104 weeks at organ sites other than the liver, the authors report that:
neoplastic lesions at 104 weeks (Studies #2 and #3) at organ sites other than the
liver were found in the lung, spleen, lymph nodes, duodenum (lymphosarcoma),
seminal vesicles, skin, and thoracic cavity of control and treated animals. All
were considered spontaneous for the male B6C3Fi mouse and did not exceed the
tumor incidences when compared to a historical control database (NIEHS, 1998;
Haseman et al.. 1984).
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No data were shown. The limitations involved in examining only five animals in the
control and high-dose groups, and the need to examine the concurrent control data in each
experiment, especially given the large variation in liver tumor response between long-term
studies carried out in the two different laboratories used for Study #2 and Study #3 using the
same strain and gender of mouse, make assertions regarding extrahepatic carcinogenicity of TCA
from this study impossible to support.
A key issue raised from this study is whether changes in any of the parameters measured
in interim sacrifice periods before the appearance of liver tumors (i.e., 4-15 weeks) corresponded
to the induction of liver tumors. The first obstacle for determining such a relationship is the
experimental design of these studies in which only a full range of TCA concentrations is treated
for 60 weeks of exposure with a small number of animals available for determination of a
carcinogenic response (i.e., <30 animals in Study #1) and a very small number of animals
(n = 5 group) examined for other parameters. Also as stated above, PCO activity was highly
variable between controls and between treatment groups (e.g., the PCO activity for Studies #1
and #2 at ~5 g/L exposure for 15 weeks).
On the other hand, most of the animals that were examined at terminal sacrifice were also
utilized for the tumor results without the differential deletion or addition of "extra" animals for
the tumor analysis. For the 60-week data in Study #1, there appeared to be a consistent dose-
related increase in the incidence and multiplicity of tumors after TCA exposure (Table E-l 1).
The TCA-induced increases in liver tumor responses can be compared with both increased liver
weight and PCO activity that were also reported to be increased with TCA dose as earlier events.
Although the limitations of determining the exact magnitude of responses has already been
discussed, as shown below, the incidence and multiplicity of adenomas show a dose-related
increase at 60 weeks. However, the magnitude of differences in TCA concentrations was not
similar to the magnitude of increased liver tumor induction by TCA after 60 weeks of exposure.
First of all, the greater occurrence of TCA-induced increases in adenomas than
carcinomas reported after 60 weeks of exposure would be expected for this abbreviated duration
of exposure as they would be expected to occur earlier than carcinomas. For adenoma induction,
there was an approximately twofold increase between the 0.05 g/L dose of TCA and the control
group for incidence (7 vs. 15%) and multiplicity (0.07 vs. 0.15 tumors/animals). However, an
additional 10-fold increase in TCA dose (0.5 g/L) only resulted in a reported 1.8-fold greater
incidence (15 vs. 21%) and 2.2-fold increase in multiplicity (0.15 vs. 0.24 tumors/animal) of
control adenoma levels. An additional 10-fold increase in dose (5.0 vs. 0.5 g/L TCA) resulted in
a 2.2-fold increase in incidence (21 vs. 38%) and 2.9-fold increase in multiplicity (0.24 vs.
0.55 tumors/animal) of control adenoma levels.
Thus, a 100-fold difference in TCA exposure concentration resulted in differences of
fourfold of control incidence and sixfold of control multiplicity for adenomas. For adenomas or
carcinomas combined (a parameter that included carcinomas for which only the two highest
E-l 80
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exposure levels of TCA were reported to increase incidence and multiplicity), the incidences
were reported to be 13, 15, 38, and 55%, and the multiplicity was reported to be 0.13, 0.19, 0.52,
and 1.00 for control, 0.05, 0.5, and 5.0 g/L TCA at 60 weeks. For multiplicity of adenomas or
carcinomas, the 0.05 g/L TCA exposure induced a 1.5-fold increase over control. An additional
10-fold increase in TCA (0.5 g/L) induced a 6-fold increase in tumors/animal. An additional
10-fold increase in TCA (5.0 vs. 0.5 g/L) induced an additional 2.2-fold increase in
tumors/animal. Therefore, using combinations of adenomas or carcinomas, there was a 13-fold
increase in multiplicity that corresponded with a 100-fold increase in dose.
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Table E-ll. TCA-induced increases in liver tumor occurrence and other parameter over control after 60 weeks
(Study #1)
Dose TCA g/L
Sodium chloride
0.05
0.5
5.0
Adenomas
Incidence 7%
15% (2. 1-fold)
21% (3. 0-fold)
38% (5.4-fold)
Multiplicity 0.07
0.15 (2. 1-fold)
0.24 (3.4-fold)
0.55 (7.9-fold)
Adenomas or carcinomas
Incidence 13%
15% (1.2-fold)
38% (2.9-fold)
55% (4.2-fold)
Multiplicity 0.13
0.19 (1.5-fold)
0.52 (4.0-fold)
1.00 (7.7-fold)
% liver/body weight
4-wk
1.09-fold
1.16-fold
1.35-fold
15-wk
1.14-fold
1.16-fold
1.47-fold
PCO activity
4-wk
1.3-fold
2.4-fold
5.3-fold
15-wk
1.0 -fold
1.3-fold
3.2-fold
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The results for adenoma induction at 60 weeks of TCA exposure (i.e., ~2-fold increased
incidences and 2-3-fold increases in multiplicity with 10-fold increases in TCA dose) are similar
to the ~2-fold increase in liver weight gain resulting from 10-fold differences in dose reported at
4 weeks of exposure. For PCO activity, there was a -30% increase in PCO activity from control
at 0.05 g/L TCA. A 10-fold increase in TCA exposure concentration (0.5 g/L) resulted in an
additional ~5-fold increase in PCO activity. However, another 10-fold increase in TCA
concentration (0.5 vs. 5 g/L) resulted in a 3-fold increase in PCO activity. The 100-fold increase
in TCA dose (0.05 vs. 5 g/L TCA) was correlated with a 14-fold increase in PCO activity. For
15 weeks of TCA exposure, there was no difference in 0.05 g/L and control PCO activity and
only a 30% difference between the 0.05 and 0.5 g/L TCA exposures. There was a sevenfold
difference in PCO activity between the 0.5 and 5.0 g/L TCA exposure concentrations. The
increases in PCO activity and liver weight data at 15 weeks did not fit the magnitude of increases
in tumor multiplicity or incidence data at 60 weeks as well as did the 4-week data. However, the
TCA-induced increase in tumors at 60 weeks (especially adenomas) seemed to correlate more
closely with the magnitude of liver weight increase than for PCO activity at both 4 and 15 weeks.
In regard to Studies #1 and #2, there were consistent periods of study for percent
liver/body weight with the consistency of the control values being a large factor in the magnitude
of TCA-induced liver weight increases. As discussed above, there were differences in the
magnitude of percent liver/body weight increase at the same concentration between the two
studies (e.g., a 1.47-fold of control percent liver/body weight in the 5 g/L TCA exposed group in
Study #1 and 1.60-fold of control in Study #2 at 15 weeks). For the two studies that had
extended durations of exposure (Studies #2 and #3), the earliest time period for comparison of
percent liver/body weight is 26 weeks (Study #3) and 30 weeks (Study #2). If those data sets
(26 weeks for Study #3 and 30 weeks for Study #2) are combined, then 0.05, 05, and 4.5 g/L
TCA gives a percent liver body/weight increase of 1.07-, 1.18-, and 1.40-fold over concurrent
control levels. Using this parameter, there appears to be a generally consistent pattern as that
reported for Study #1 at weeks 4 and 15. Generally, a 10-fold increase in TCA exposure
concentration resulted in ~2.5-fold increased in additional liver weight observed at -30 weeks of
exposure, which correlated more closely with adenoma induction at 60 weeks than did changes
in PCO activity. A similar comparison between Studies of longer duration (Studies #2 and #3)
could not be made for PCO activity as data were not reported for Study #3.
For 104-week studies of TCA-tumor induction (Studies #2 and #3), the lower TCA
exposure levels (0.05 and 0.5 g/L TCA) were assayed in a separate experiment and by a separate
laboratory than the high dose (5.0 g/L TCA) and most importantly in larger, more tumor prone
mice. The total lack of similarity in background levels of tumors in Studies #2 and #3, the
differences in the number of animals included in the tumor analyses, and the low number of
animals examined in the tumor analysis at 104 weeks (<30 for the TCA treatment groups) makes
the determination of a dose-response TCA-induced liver tumor formation after 104-weeks of
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exposure problematic. The correlation of percent liver/body weight increases with incidence and
multiplicity of liver tumors in Study #1 and the similarity of dose-response for early induction of
percent liver/body weight gain between Study #1 suggest that there should be a similarity in
tumor response. However, as noted above, the 104-week studies had very difference background
rates of spontaneous tumors reported in the control mice between Studies #2 and #3.
Table E-12 shows the incidence and multiplicity data for Studies #2 and #3 along with
the control data for DeAngelo et al. (1999) for the same paradigm. It also provides an estimate
of the magnitude of increase in liver tumor induction by TCA treatments if the control values
from the DeAngelo et al. (1999) data set were used as the background tumor rate. As shown
below, the background rates for Study #2 are more consistent with those of DeAngelo et al.
(1999). Whereas there was a 2:1 ratio of multiplicity for adenomas and adenomas and
carcinomas between 0.5 and 5.0 g/L TCA after 60 weeks of exposure, there was no difference in
any of the data (i.e., adenoma, carcinoma, and combinations of adenoma and carcinoma
incidence and multiplicity) for these exposure levels in Studies #2 and #3 for 104 weeks. The
difference in the incidences and multiplicities for all tumors was twofold between the 0.05 and
0.5 g/L TCA exposure groups in Study #2. These results are consistent with the two highest
exposure levels, reaching a plateau of response with a long enough duration of exposure (-90%
of animals having liver tumors) and with the 2-fold difference in liver tumor induction between
concentrations of TCA that differed by 10-fold, reported in Study #1.
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Table E-12. TCA-induced increases in liver tumor occurrence after 104 weeks (Studies #2 and #3)
Dose TCA
Adenomas
Incidence
Multiplicity
Carcinomas
Incidence
Multiplicity
Adenomas or carcinomas
Incidence
Multiplicity
Study #3
1.5g/LHAC(H2O?)
0.05 g/L TCA
0.5 g/L TCA
21%
23%
(1.1 -fold)
51%
(2.4-fold)
0.21
0.34
(1.6-fold)
0.78
(3.7-fold)
55%
40%
(0.7-fold)
78%
(1.4-fold)
0.74
0.71
(1.0-fold)
1.46
(2.0-fold)
64%
57%
(0.9-fold)
87%
(1.4-fold)
0.93
1.11
(1.2-fold)
2.14
(2.3-fold)
Study #2
2.0 g/L NaCl (HAC?)
4.5 g/L TCA
0%
59%
(?)
0
0.61
(?)
12%
78%
(6.5-fold)
0.20
1.50
(7.5-fold)
12%
89%
(7.4-fold)
0.20
2.14
(11 -fold)
DeAngelo et al. (1999)
H20
0.05 g/TCA (S #3)
0.5 g/L TCA (S #3)
5.0 g/L TCA (S #2)
10%
(2.3-fold)
(5.1 -fold)
(5.9-fold)
0.12
(2.8-fold)
(6.5-fold)
(6.5-fold)
26%
(1.5-fold)
(3.0-fold)
(3.0-fold)
0.28
(2.5-fold)
(5.2-fold)
(5.4-fold)
H2O = water
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If either the control values for Study #2 or the control values from DeAngelo et al. (1999)
were used for as the background rate of spontaneous liver tumor formation, the magnitude of
liver tumor induction by the 0.05 g/L TCA over control levels differs dramatically from that
reported as control tumor rates in Study #3. To put the 64% incidence data for carcinomas and
adenomas reported in DeAngelo et al. (2008) for the control group of Study #3 in context, other
studies cited in this review for B6C3Fi mice show a much lower incidence in liver tumors in
that: (1) the NCI (1976) study of TCE reports a colony control level of 6.5% for vehicle and
7.1% incidence of HCCs for untreated male B6C3Fi mice (n = 70-77) at 78 weeks; (2) Herren-
Freund et al. (1987) report a 9% incidence of adenomas in control male B6C3Fi mice with a
multiplicity of 0.09 ± 0.06 and no carcinomas (n = 22) at 61 weeks; (3) NTP (1990) reports an
incidence of 14.6% adenomas and 16.6% carcinomas in male B6C3Fi mice after 103 weeks (n =
48); and (4) Maltoni et al. (1986) report that B6C3Fi male mice from the "NCI source" had a
1.1% incidence of "hepatoma" (carcinomas and adenomas) and those from "Charles River Co."
had a 18.9% incidence of "hepatoma" during the entire lifetime of the mice (n = 90 per group).
The importance of examining an adequate number of control or treated animals before
confidence can be placed in those results in illustrated by Anna et al. (1994) in which at 76
weeks 3/10 control male B6C3Fi mice that were untreated and 2/10 control animals given corn
oil were reported to have adenomas but from 76 to 134 weeks, 4/32 mice were reported to have
adenomas (multiplicity of 0.13 ± 0.06) and 4/32 mice were reported to have carcinomas
(multiplicity of 0.12 ± 0.06).
Using concurrent control values reported in Study #3, there is no increase in incidence of
multiplicity of adenomas and carcinomas for the 0.05 g/L exposure group. However, compared
to either the control data from DeAngelo et al. (1999) or the control data from Study #3, there is
a -2-3- or ~5-fold increased in incidence or multiplicity of liver tumors, respectively. Thus,
trying to determine a correspondence with either liver weight increases or increases in PCO
activity at earlier time points will depend on the confidence placed in the concurrent control data
reported in Study #3 in the 104 week studies. As noted previously, the use of larger, tumor
prone mice in Study #3 limits its usefulness to determine the dose-response for TCA.
The authors provided a regression analysis for "tumors/animal" or multiplicity as a
percent of control values and PCO activity for the 60- and 104-week data. Whether adenomas
and carcinomas combined or individual tumor type were used was not stated. In addition,
comparing PCO activity at the end of the experiments, when there was already a significant
tumor response rather than at earlier time points, may not be useful as an indicator of PCO
activity as a key event in tumorigenesis. A regression analysis of these data are difficult to
interpret because of the dose spacing of these experiments as the control and 5 g/L exposure
levels will basically determine the shape of the dose-response curve. The 0.05 and 0.5 g/L
exposure groups in the regression were so close to the control value in comparison to the 5 g/L
exposure, that the dose response will appear linear between control and the 5.0 g/L value with
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the two lowest doses not affecting the slope of the line (i.e., "leveraging" the regression). The
value of this analysis is limited by: (1) the use of tumor prone larger mice in Study #3 that had
large background rates of tumors, which make inappropriate the apparent combination of results
from Studies #2 and #3 for the multiplicity as percentages of control values; (2) the low and
varying number of animals analyzed for PCO values and the variability in PCO control values;
(3) the appropriateness of using PCO values from later time points; and (4) the dose-spacing of
the experiment.
Similarly, the authors reported a regression analysis that compares "percent of
hepatocellular neoplasia," which again, is indicated by tumor multiplicity with TCA dose as
represented by mg/kg-day. This regression analysis also is of limited value for the same reasons
as that for PCO with added uncertainty, as the exposure concentrations in drinking water have
been converted to an internal dose and each study gave different levels of drinking water with
one study showing a reduction of drinking water at the 5 g/L level. The authors attempted to
identify a NOEL for tumorigenicity using tumor multiplicity and TCA dose. However, it is not
an appropriate descriptor for these data, especially given that "statistical significance" of the
tumor response is the determinant of the conclusions regarding a dose in which there is no
TCA-induced effect. Only the 60-week experiment (i.e., Study #1) is useful for the
determination of tumor dose-response due to the issues related to appropriateness of control in
Study #3. A power calculation of the 60-week study shows that the type II error, which should
be >50% and thus greater than the chances of "flipping a coin," was 41 and 71% for incidence
and 7 and 15% for multiplicity of adenomas for the 0.05 and 0.5 g/L TCA exposure groups. For
the combination of adenomas and carcinomas, the power was 8 and 92% for incidence and 6 and
56% for multiplicity at 0.05 and 0.5 g/L TCA exposure. Therefore, the designed experiment
could accept a false null hypothesis, especially in terms of tumor multiplicity, at the lower
exposure doses and erroneously conclude that there is no response due to TCA treatment.
E .2.3.2.14. DeAngelo et al. (1997)
The design of this study appears to be similar to that of DeAngelo et al. (2008) but to
have been conducted in F344 rats. Rats (28-30 day old rats), reported to be of similar weights,
were exposed to 2.0 g/L sodium chloride, 0.05, 0.5, or 5.0 g/L TCA in drinking water for
104 weeks. There were groups of animals sacrificed at 15, 30, 45, and 60 weeks (n = 6) for PCO
analysis. There were 23, 24, 19, and 22, animals reported to be examined at terminal sacrifice at
104 weeks and 23, 24, 20, and 22 animals reported to be used in the liver tumor analysis reported
by the authors for the control, 0.05, 0.5, and 5.0 g/L treatment groups, respectively. Complete
pathological exams were reported to be performed for all tissues from animals in the high-dose
TCA group at 104 weeks. No indication was given as to whether a complete necropsy and
pathological exam was performed for controls at terminal sacrifice. Tritiated thymidine was
reported to be administered at interim sacrifices 5 days prior to sacrifice and to be examined with
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autoradiography. The 5 g/L TCA treatment group was reported to have a reduction in growth to
89.3% of controls.
For water consumption, TCA was reported to slightly decrease water consumption at all
doses with a 7, 8, and 4% decrease in water consumption reported for 0.05, 0.5, and 5.0 g/L TCA,
respectively. Body weight was decreased by 5.0 g/L TCA dose only through 78 weeks of
exposure to 89.3% of the control value. All of the percent liver/body weight ratios were reported
to be slightly decreased (1-4%) by all of the exposure concentrations of TCA but the data shown
do not indicate if the liver weight data were taken at interim sacrifice times and appear to be only
for animals at terminal sacrifice of 104 weeks.
No data were shown for hepatocyte proliferation but the authors reported no TCA
treatment effects. For PCO, there was a 2.3-fold difference between control values between the
15- and 104-week data. For the 0.05 and 0.5 g/L TCA treatment groups, there was not a
statistically significant difference reported between control and treated group PCO levels. At
15 weeks, the PCO activity was reduced by 55%, increased to 1.02-fold, and increased 2.12-fold
of control for 0.05, 0.5, and 5.0 g/L TCA exposures, respectively. For the 30-week exposure
groups, the 0.05 and 0.5 g/L TCA groups were reported to have PCO levels within 5% of the
control level. However, for the 5.0 g/L TCA treatment groups, there was approximately twofold
of control PCO activity at the 15, 30, 45, and 60 weeks and at 104 weeks, there was a fourfold of
control PCO activity. Of note is that the control PCO value was lowest at 104 weeks, while the
TCA treatment group was similar to interim values.
For analysis of liver tumors, there were 20-24 animals examined in each group. Unlike
the study of DeAngelo et al. (2008), it appeared that most of the animals that were sacrificed at
104 weeks were used in the tumor analysis without addition of "extra" animals or deletion of
animal data. The incidence of adenomas was reported to be 4.4, 4.2, 15, and 4.6% and the
incidence of HCCs was reported to be 0, 0, 0, and 4.6% for the control, 0.05, 0.5, and 5.0 g/L
TCA exposure groups. The multiplicity or tumors/animal was reported to be 0.04, 0.08, 0.15, and
0.05 for adenomas and 0, 0, 0, and 0.05 for carcinomas for the control, 0.05, 0.5, and 5.0 g/L TCA
exposure groups.
Although there was an increase in the incidence of adenomas at 0.5 g/L and an increase in
carcinomas at 5.0 g/L TCA, they were not reported to be statistically significant by the authors;
neither were the increases in adenoma multiplicity at the 0.05 and 0.5 g/L exposures. However,
using such a low number of animals per treatment group (n = 20-24) limits the ability of this
study to determine a statistically significant increase in tumor response and to be able to
determine that there was no treatment-related effect. A power calculation of the study shows that
the type II error, which should be >50% and thus, greater than the chances of "flipping a coin,"
was <6% for incidence and multiplicity of tumors at all exposure DC A concentrations with the
exception of the incidence of adenomas for 0.5 g/L treatment group (58.7%). Therefore, the
designed experiment could accept a false null hypothesis, especially in terms of tumor
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multiplicity, at the lower exposure doses and erroneously conclude that there is no response due
to TCA treatment. Thus, while suggesting a lower response than for mice for TCA-induced liver
tumors, the study is inconclusive for determination of whether TCA induces a carcinogenic
response in the liver of rats. The experimental design is such that extrahepatic carcinogenicity of
TCA in the male rat cannot be determined.
E .2.3.2.15. DeAngelo et al. (1996)
In this study, 28-day-old male F344 rats were given drinking water containing DCA at
concentrations of 0, 0.05, 0.5, or 5.0 g/L with another group was provided water containing
2.0 g/L sodium chloride for 100 weeks. This experiment modified its exposure protocol due to
toxicity (peripheral neuropathy) such that the 5.0 g/L group was lowered to 2.5 g/L at 9 weeks,
then 2.0 g/L at 23 weeks, and finally to 1.0 g/L at 52 weeks. When the neuropathy did not
reverse or diminish, the animals were sacrificed at 60 weeks and excluded from the results.
Based on measured water intake in the 0, 0.05, and 0.5 g/L groups, the TWA doses were reported
to be 0, 3.6, and 40.2 mg/kg-day respectively. This experiment was conducted at a U.S. EPA
laboratory in Cincinnati and the controls for this group were given 2.0 g/L sodium chloride
(Study #1). In a second study, rats were given either deionized water or 2.5 g/L DCA, which
was also lowered to 1.5 g/L at 8 weeks and to 1.0 g/L at 26 weeks of exposure (Study #2).
Although 23 animals were reported to be sacrificed at terminal sacrifice that had been
given 2 g/L sodium chloride, the number of animals reported to be examined in this group for
hepatocellular lesions was 3. The incidence data for this group for adenomas was 4.4%, so this is
obviously a typographical error. The number of rats included in the water controls for tumor
analysis was reported to be 33, which was the same number as those at final sacrifice. The
number of animals at final sacrifice was reported to be 23 for 2 g/L sodium chloride, 21 for
0.05 g/L DCA, 23 for 0.5 g/L DCA in experiment #1, and 33 for deionized water and 28 for the
initial dose of 2.5 g/L DCA in experiment #2.
Although these were of the same strain, the initial body weight was 59.1 vs. 76 g for the
2.0 g/L control group vs. deionized water group. The treatment groups in both studies were
similar to the deionized water group. The percent liver/body weights were greater (4.4 vs. 3.7%
in the sodium chloride vs. deionized water control groups [-20%]). The number of unscheduled
deaths was greater in Study #2 (22%) than in Study #1 (12%). Interim sacrifice periods were
conducted.
As with the DeAngelo et al. (DeAngelo et al., 2008) study in mice, the number of animals
reported at final sacrifice was not the same as the number examined for liver tumors in Study #1
(five more animals examined than sacrificed at the 0.05 g/L DCA and six more animals examined
than sacrificed at the 0.5 g/L DCA exposure groups) with n = 23, n = 26, and n = 29 for the 2 g/L
sodium chloride, 0.05 g/L DCA, and 0.5 g/L DCA groups utilized in the tumor analysis. For
Study #2, the same number of rats was reported to be sacrificed as examined. The source of the
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extra animals for tumor analysis in Study #1, whether from interim sacrifice or unscheduled
deaths, was not given by the authors and is unknown. Carcinoma prevalence data were not
reported for the control group or 0.05 g/L DCA group in Study #1 and multiplicity data were not
reported for the control group or 0.05 g/L DCA group. Multiplicity was not reported for
adenomas in the 0.05 g/L DCA group in Study #1.
There was a lack of hepatocyte DNA synthesis and necrosis reported at any dose group
carried out to final sacrifice at 100 weeks. The authors reported the incidence of adenomas to be
4.4% in 2 g/L sodium chloride control, 0 in 0.05 g/L DCA, and 17.2% in the 0.5 g/L DCA
exposure groups. For carcinomas, no data were reported for the control or 0.05 g/L DCA group
but an incidence of 10.3% was reported for the 0.5 g/L DCA group. The authors reported
increased hepatocellular adenomas and carcinomas in male F344 rats, although no data were
reported for carcinomas in the control and 0.05 g/L exposure groups. They reported that for
0.5 g/L DCA, 24.1 vs. 4.4% adenomas and carcinomas combined (Study #1) and 28.6 vs. 3.0%
(Study #2) at what was initially 2.5 g/L DCA but continuously reduced. Tumor multiplicity was
reported to be significantly increased in the 0.5 g/L DCA group (0.04 adenomas and carcinomas/
animal in control vs. 0.31 in 0.5 g/L DCA in Study #1 and 0.03 in control vs. 0.36 in what was
initially 2.5 g/L DCA in Study #2). The issues of use of a small number of animals, additional
animals for tumor analysis in Study #1, and most of all, the lack of a consistent dose for the
2.5 g/L animals in Study #2, are obvious limitations for establishment of a dose-response for
DCA in rats.
E .2.3.2.16. Richmond et al. (1995)
This study was conducted by the same authors as DeAngelo et al. (1996) and appears to
report results for the same data set for the 2 g/L sodium chloride control, 0.05 g/L DCA and
0.5 g/L DCA exposed groups. Of note is that while DeAngelo et al. (1996) refer to the 28-day-
old rats as "weanlings," the same aged rats are referred to as "adults" in this study. Male
F344 rats were administered TWA concentrations of 0, 0.05, 0.5, or 2.4 g/L DCA in drinking
water. Concentrations were kept constant but due to hind-limb paralysis, all 2.4 g/L DCA rats
had been sacrificed by 60 weeks of exposure. In the 104-week sacrifice time, there were 23 rats
reported to be analyzed for incidence of hepatocellular adenomas and carcinomas in the control
group, 26 rats in the 0.05 g/L DCA group, and 29 rats in the 0.5 g/L DCA exposed group. This
is the same number of animals included in the tumor analysis reported in DeAngelo et al. (1996).
Tumor multiplicity was not given.
Richmond et al. (1995) reported that there was a 4% incidence of adenomas reported in
the 2.0 g/L sodium chloride control animals, 0% at 0.05 g/L DCA, and 21% in the 0.5 DCA group
at 104 weeks. These figures are similar to those reported by DeAngelo et al. (1996) for the same
data set, with the exception of a 17.2% incidence of adenomas reported for the 0.5 g/L DCA
group.
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There were no HCCs reported in the control or 0.05 g/L exposure groups, but a 10%
incidence reported in the 0.5 g/L DCA exposure group at 104 weeks of exposure. While
carcinomas were not reported by DeAngelo et al. (1996) for the control and 0.05 g/L groups, they
are assumed to be zero in the summary data for carcinomas and adenomas combined. The 10%
incidence at 0.5 g/L DCA is similar to the 10.4% incidence reported for this group by DeAngelo
et al. (1996).
At 60 weeks at 2.4 g/L DCA, the incidences of hepatocellular adenomas were reported to
be 26% and HCCs to be 4%. This is not similar to the values reported by DeAngelo for 2.5 g/L
DCA that was continuously decreased so that the estimated final concentration was 1.6 g/L DCA
for 100 weeks. For those animals, the incidence of adenomas was reported by DeAngelo et al.
(1996) to be 10.7% and carcinomas 21.4%, probably more a reflection of longer exposure time
allowing for adenoma to carcinoma progression. The authors did not report any of the results of
DCA-induced increases of adenomas and carcinomas to be statistically significant. As it appears
the same data set was used for the 2.0 g/L sodium chloride control, 0.05 g/L DCA, and 0.5 g/L
DCA exposure groups as was reported in DeAngelo et al. (1996), the same issues arise as
regarding the differences in numbers of animals that were included in tumor analysis than were
reported to have been present at final sacrifice. As stated previously for the DeAngelo et al.
(1997) study of TCA in rats, the use of small numbers of rats limits the detection of and ability to
determine whether there was no treatment-related effects, especially at the low concentrations of
DCA exposure.
E.2.4. Summaries and Comparisons Between TCE, DCA, and TCA Studies
There are a number of studies of TCE that have reported effects on the liver. However,
the study of this compound is difficult as its concentration does not remain stable in drinking
water, some studies have been carried out using TCE with small quantities of a carcinogenic
stabilizing agent, some studies have been carried out in whole-body inhalation chambers that
resulted in additional oral administration and for which individual animal data were not recorded
throughout the experiment, and the results of gavage studies have been limited by gavage-related
deaths and vehicle effects. In addition, some studies have been conducted using the i.p. route of
administration, which results in route-related toxicity and inflammation. For many studies, liver
effects consisted of measured increases in liver weight with little or no description of attendant
histological changes induced by TCE treatment. A number of studies were conducted at a few
relatively high doses with attendant effects on body weight, indicative of systemic toxicity and
affecting TCE-induced liver weight gain. Although many studies have been performed in male
mice, the inhalation studies of Kjellstrand et al. (1981b, 1983a, 1983b) indicate that male mice,
regardless of strain appear to have a greater variability in response, as measured by TCE-induced
liver weight gain, and susceptibility to TCE-induced decreases in body weight than female mice.
However, the body of the TCE literature is consistent in identifying the liver as a target of TCE-
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induced effects, with the most commonly reported change to be a dose-related TCE-induced
increase in liver weight in multiple species, strains, and genders from both inhalation and oral
routes of exposure.
The following sections will not only summarize results for studies of TCE reported in
Sections E.2. l-E.2.2, but provide comparison of studies of either TCA or DCA that have used
similar paradigms or investigated similar parameters described in Sections E.2.3.1 and E.2.3.2. A
synopsis of the results from studies of CH and in comparison with TCE results is presented in
Section E.2.5. While the study of Bull et al. (2002). described in Section E.2.2.2.2, presents data
for combinations of DCA or TCA exposure for comparisons of tumor phenotype with those
induced by TCE, the examination of co-exposure studies of TCE metabolites in rodents that are
also exposed to a number of other carcinogens, and descriptions of the toxicity data for
brominated haloacetates that also occur with TCE in the environment, are presented in
SectionE.4.3.3.
E.2.4.1. Summary of Results For Short-term Effects of TCE
In regard to early changes in DNA synthesis, the data for TCE are very limited. The study
by Mirsalis et al. (1989) used an in vivo-in vitro hepatocyte DNA repair and S-phase DNA
synthesis in primary hepatocytes from male F344 rats (180-300 g) and male and female B6C3Fi
mice (20-29 g for male mice and 18-25 g female mice) administered TCE by gavage in corn oil.
They reported negative results 2-12 hours after treatment from 50 to 1,000 mg/kg TCE in rats and
mice (male and female) for UDS and repair using three animals per group. After 24 and 48 hours
of 200 or 1,000 mg/kg TCE in male mice (n = 3) and after 48 hours of 200 (n = 3) or
1,000 (n = 4) mg/kg TCE in female mice, similar values of 0.30-0.69% of hepatocytes were
reported as undergoing DNA synthesis in those hepatocytes in primary culture with only the
1,000 mg/kg TCE dose in male mice at 48 hours giving a result considered to be positive (-2.2%).
No statistical analyses were performed on these measurements, which were obviously limited by
both the number of animals examined and the relevance of the paradigm.
TCE-induced increases in liver weight have been reported to occur quickly. The
inhalation study of Okino et al. (1991) in male rats demonstrates that liver weight and
metabolism were increased with as little as 8 hours of TCE exposure (500 and 2,000 ppm) and as
early as 22 hours after cessation of such exposures with little concurrent hepatic necrosis.
Laughter et al. (2004) reported increase liver weight in SV129 mice in their 3-day study (see
below). Tao et al. (2000) reported a 1.26-fold of control percent liver/body weight in female
B6C3Fi mice fed 1,000 mg/kg TCE in corn oil for 5 days. Elcombe et al. (1985) and Dees and
Travis (1993) reported gavage results in mice and rats after 10 days exposure to TCE, which
showed TCE-induced increases in liver weight (see below for more detail on dose-response).
Tucker et al. (1982) reported that 14 days of exposure to 24 and 240 mg/kg TCE via gavage
induced a dose-related increase in liver weight in male CD-I mice but did not show the data.
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TCE-induced increases in percent liver/body weight ratios have been studied most
extensively in B6C3Fi and Swiss mice. Both strains have been shown to have a TCE-induced
increase in liver tumors from long-term exposure as well (see Section E.2.4.3). A number of
studies have provided dose-response information for TCE-induced increases in liver weight from
10 days to 13 weeks of exposure in mice. Most studies have reported that the magnitude of
increase in TCE exposure concentration is similar to the magnitude increase of percent liver/body
weight increase. For example a twofold increase in TCE exposure has often resulted in a twofold
increase in the percent change in liver/body weight over control (i.e., 500 mg/kg TCE induces a
20% increase in liver weight and 1,000 mg/kg TCE induces a 50% increase in liver weight as
reported by Elcombe et al. (1985). The range in which this relationship is valid has been reported
to vary from 100 mg/kg TCE at 10 days (Dees and Travis, 1993) to 1,600 mg/kg (Buben and
O'Flahertv. 1985) at 6 weeks and up to 1,500 mg/kg TCE for 13 weeks (NTP. 1990). The
consistency in the relationship between magnitude of liver weight increase and TCE exposure
concentration has been reported for both genders of mice, across oral and inhalation routes of
exposure, and across differing strains of mice tested. For rats, there are fewer studies with fewer
exposure levels tested, but both Berman et al. (1995) and Melnick et al. (1987) report that short-
term TCE exposures from 150 to -2,000 mg/kg induced percent liver/body weight that increased
proportionally with the magnitude of TCE exposure concentration.
Dependence of PPARa activation for TCE-liver weight gain has been investigated in
PPARa null mice by both Nakajima et al. (2000) and Laughter et al. (2004). After 2 weeks of
750 mg/kg TCE exposure to carefully matched SV129 wild-type or PPARa-null male and female
mice (n = 6 group), there was a reported 1.50-fold of control in wild-type and 1.26-fold of control
percent liver/body weight in PPARa-null male mice by Nakajima et al. (2000). For female mice,
there was ~1.25-fold of control percent liver/body weight ratios for both wild-type and
PPARa-null mice. Ramdhan et al. (2010) also reported increased liver weight in male
PPARa-null mice after high levels of inhalation exposure that were comparable to that in wild
type mice after 7 days of exposure (up to 40-50% increases at the highest dose). Thus,
TCE-induced liver weight gain was not dependent on a functional PPARa receptor in female
mice, and data indicate that a significant portion of it may have also not have been PPARa
receptor-dependent in male mice.
Nakajima et al. (2000) report that both wild-type male and female mice have similar
increases in the number of peroxisome in the pericentral area of the liver after TCE exposure and,
although increased twofold, were still only -4% of cytoplasmic volume. Female wild-type mice
were reported to have less TCE-induced elevation of very long chain acyl-CoA synthetase,
D-type peroxisomal bifunctional protein, mitochondrial trifunctional protein a subunits a and P,
and CYP 4A1 than males mice, even though peroxisomal volume was similarly elevated in male
and female mice. The induction of PPARa protein by TCE treatment was also reported to be
slightly less in female than male wild-type mice (2.17- vs. 1.44-fold of control, respectively).
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Ramdhan et al. (2010) examined TCE-induced hepatice steatosis and toxicity using male
wild type, PPARa-null, and human PPARa-inserted mice (humanized) exposed to high inhalation
concentrations of TCE for 7 days. Significant differences were observed among control mice for
each genotype with reduced body weight in untreated humanized mice. Liver/body weight ratios
were 11% higher in untreated PPARa- null mice than wild type mice. Higher levels of liver
triglycerides and hepatic steatosis were reported in the untreated humanized mice and
PPARa-null mice than wild type mice. Background expression of a number of genes and protein
expression levels were significantly different between the untreated strains. In particular, human
PPARa protein levels were >10-fold greater in the humanized mice than mouse PPARa in
untreated wild type mice. Insertion of human PPARa in the null mice did not return the mice to a
normal state. Both PPARa-null and humanized mice were more susceptible to TCE toxicity as
evidenced by serum AST and ALT (liver injury biomarkers), hepatic triglyceride levels, and
hepatic steatosis. Hepatomegally was induced in all strains to a similar extent after TCE
exposure. However, urinary TCA concentrations were reported to be significantly lower and
TCOH levels significantly higher in TCE-treated PPARa-null mice in comparison to treated wild
type mice. This difference was not related to changes in expression of metabolic enzymes. Thus,
TCE-induced liver toxicity was not dependent on PPARa with dysregulation of the receptor in
null or humanized mice, rendering them more susceptible to TCE-induced toxicity.
Laughter et al. (2004) also studied SV129 wild-type and PPARa-null male mice treated
with 3 daily doses of TCE in 0.1% methyl cellulose for either 3 days (1,500 mg/kg TCE) or
3 weeks (0, 10, 50, 125, 500, 1,000, or 1,500 mg/kg TCE 5 days/week). However, not only is the
paradigm not comparable to other gavage paradigms, but no initial or final body weights of the
mice were reported and thus, the influence of differences in initial body weight on percent
liver/body weight determinations could not be ascertained. In the 3-day study, while control
wild-type and PPARa-null mice were reported to have similar percent liver/body weight ratios
(-4.5%), at the end of the 3-week experiment, the percent liver/body weight ratios were reported
to be increased in the PPARa-null male mice (5.1%).
TCE treatment for 3 days was reported to increase the percent liver/body weight ratio
1.4-fold of control in the wild-type mice and 1.07-fold of control in the null mice. In the 3-week
study, wild-type mice exposed to various concentrations of TCE had percent liver/body weights
that were reported to be within -2% of control values except for the 1,000 and 1,500 mg/kg
groups (-1.18- and 1.30-fold of control levels, respectively). For the PPARa-null mice, the
variability in percent liver/body weight was reported to be greater than that of the wild-type mice
in most of the groups, and the baseline level of percent liver/body weight ratio also 1.16-fold
greater. TCE exposure was apparently more toxic in the null mice with death at the 1,500 mg/kg
TCE exposure level, resulting in the prevention of recording of percent liver/body weights. At the
1,000 mg/kg TCE exposure level, there was a reported 1.10-fold of control percent liver/body
weight in the PPARa-null mice.
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None of the increases in percent liver/body weight in the null mice were reported to be
statistically significant by Laughter et al. (2004). However, the statistical power of the study was
limited due to low numbers of animals and increased variability in the null mice groups. The
percent liver/body weight after TCE treatment that was reported in this study was actually greater
in the null mice than the wild-type male mice at the 1,000 mg/kg TCE exposure level (5.6 ±
0.4 vs. 5.2 ± 0.5%, for null and wild-type mice, respectively). At 1-week and at 3-weeks, TCE
appeared to induce increases in liver weight in PPARa-null mice, although not reaching statistical
significance in this study. At a 1,000 mg/kg TCE exposure for 3 weeks, percent liver/body
weights were reported to be 1.18-fold of control in wild-type and 1.10-fold of control in null
mice. Although the experiments in Laughter et al. (2004) for DC A and TCA were not conducted
using the same paradigm, the TCE-induced increase in percent liver/body weight more closely
resembled the dose-response pattern for DCA than for DCA wild-type SV129 and PPARa-null
mice.
Many studies have used cyanide-insensitive PCO as a surrogate for peroxisome
proliferation. Of note is that several studies have shown that this activity is not correlated with
the volume or number of peroxisomes that are increased as a result of exposure to TCE or its
metabolites (Nakajima et al.. 2000: Nelson etal.. 1989: Elcombe et al.. 1985). This activity
appears to be highly variable both as a baseline measure and in response to chemical exposures.
Laughter et al. (2004) presented data showing that WY-14,643 induced increases in PCO activity
varied up to sixfold between experiments in wild-type mice. They also showed that PCO activity,
in some instances, was up to sixfold of wild-type mice values in untreated PPARa-null mice.
Parrish et al. (1996) noted that control values between experiments varied as much as a factor of
2-fold for PCO activity and thus, their data were presented as percent of concurrent controls.
Goldsworthy and Popp (1987) reported that 1,000 mg/kg TCE induced a 6.25-fold of control PCO
activity in B6C3Fi mice in two 10-day experiments. However, for F344 rats, the increases over
control between two experiments conducted at the same dose were reported to vary by >30%.
Finally, Melnick et al. (1987) have reported that corn oil administration alone can elevate PCO
activity as well as catalase activity.
For TCE there are two key 10-days studies (Dees and Travis, 1993: Elcombe et al., 1985)
that examine the effects of short-term exposure in mice and rats via gavage exposure and attempt
to determine the nature of the dose- response in a range of exposure concentrations that include
levels below which there is concurrent decreased body weights. Although they have limitations,
they reported generally consistent results. In regard to liver weight in mice, gavage exposure to
TCE at concentrations ranging from 100 to 1,500 mg/kg TCE produced increases in liver/body
weight that was dose-related (Dees and Travis, 1993: Elcombe et al., 1985).
Elcombe et al. (1985) reported a small decrease in DNA content with TCE treatment
(consistent with hepatocellular hypertrophy) that was not dose-related, increased tritiated
thymidine incorporation in whole mouse liver DNA that was that was treatment-related but not
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dose-related (i.e., two-, two-, and fivefold of control values in mice treated with 500, 1,000, and
1,500 mg/kg TCE), and slightly increased numbers of mitotic figures that were treatment-related,
but not dose-related and not correlated with DNA synthesis as measured by thymidine
incorporation. Elcombe et al. (1985) reported an increase in peroxisome volume after TCE
exposure that was correlated with the magnitude of increase in peroxisomal-associated enzyme
activity at the only dose in which both were tested. Peroxisome increases after TCE treatment in
mice livers were identified as being pericentral in location. After TCE treatment, increased
peroxisomal volumes in B6C3Fi mice were reported to be not dose-related (i.e., there was little
difference between 500 and 1,500 mg/kg TCE exposures). The TCE-induced increases in
peroxisomal volumes were also not correlated with the reported increases in thymidine
incorporation or mitotic activity in mice.
Neither TCE-induction of peroxisomes nor hepatocellular proliferation, as measured by
either mitotic index or thymidine incorporation, was correlated with TCE-induced liver weight
increases. Elcombe et al. (1985) only measured PCO activity in a subset of B6C3Fi mice at the
1,000 mg/kg TCE exposure level for 10 days of exposure and reported an 8-fold of control PCO
activity and a 1.5-fold of control catalase activity. This result was similar to that of Goldsworthy
and Popp (1987), who reported 6.25-fold of control PCO activity in male B6C3Fi mice exposed
to 1,000 mg/kg-day TCE for 10 days in two separate experiments.
Similar to Elcombe et al. (1985), who reported no difference in response between 500 and
1,000 mg/kg TCE treatments, (Dees and Travis, 1993) reported that incorporation of tritiated
thymidine in DNA from mouse liver was elevated after TCE treatment and the mean peak level of
tritiated thymidine incorporation occurred at 250 mg/kg TCE treatment level remaining constant
for the 500 and 1,000 mg/kg treated groups. (Dees and Travis, 1993) specifically report that
mitotic figures, although very rare, were more frequently observed after TCE treatment, most
often in the intermediate zone, and in cells resembling mature hepatocytes. They reported that
there was little tritiated thymidine incorporation in areas near the bile duct epithelia or close to the
portal triad in liver sections from both male and female mice. They also reported no evidence of
increased lipofuscin and that increased apoptosis from TCE exposure "did not appear to be in
proportion to the applied TCE dose given to male or female mice" (i.e., the mean number of
apoptosis 0, 0, 0, 1, and 8 for control, 100, 250, 500, and 1,000 mg/kg TCE treated groups,
respectively). Both Elcombe et al. (1985) and (Dees and Travis, 1993) reported no changes in
apoptosis other than increased apoptosis only at a treatment level of 1,000 mg/kg TCE.
Elcombe et al. (1985) reported increases in percent liver/body weight after TCE treatment
in both the Osborne-Mendel and Alderley Park rat strain, although to a smaller extent than in
mice. For both strains, Elcombe et al. (1985) reported no TCE-induced changes in body weight at
doses ranging from 500 to 1,500 mg/kg. For male Osborne-Mendel rats, administration of TCE
in corn oil gavage resulted in a 1.18-, 1.26-, and 1.30-fold of control percent liver/body weight at
500, 1,000, and 1,500 mg/kg-day exposures, respectively. For Alderley Park rats, those increases
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were 1.14-, 1.17-, and 1.17-fold of control at the same respective exposure levels for 10 days of
exposure.
In regard to liver weight increases, Melnick et al. (1987) reported a 1.13- and 1.23-fold of
control percent liver/body weight in male F344 rats fed 600 and 1,300 mg/kg-day TCE in
capsules, respectively. There was no difference in the extent of TCE-induced liver increase
between the two lowest dosed groups administered TCE in corn oil gavage (-20% increase in
percent liver/body weight at 600 and 1,300 mg/kg-day TCE) for 14 days. However, the
magnitude of increases in percent liver/body weight in these groups was affected by difference
between control groups in liver weight although initial and final body weights appeared to be
similar. By either type of vehicle, Melnick et al. (1987) reported decreases in body weights in
rats treated with concentrations of TCE >2,200 mg/kg-day for 14 days. Similarly, Nunes et al.
(2001) reported decreased body weight in Sprague-Dawley rats administered 2,000 mg/kg-day for
7 days in corn oil. Melnick et al. (1987) reported that both exposures to either 600 or
1,300 mg/kg-day TCE in capsules did not result in decreased body weight and caused less than
minimal focal necrosis randomly distributed in the liver. At 2,200 and 4,800 mg/kg TCE fed via
capsule, Melnick et al. (1987) reported that although there was decreased body weight in rats
treated at these exposures, there was little TCE-induced necrosis, and no evidence of
inflammation, cellular hypertrophy or edema with TCE exposure. Similarly, Berman et al. (1995)
reported increases in liver weight gain at doses as low as 50 mg/kg TCE, no necrosis up to doses
of 1,500 mg/kg, and hepatocellular hyper trophy only at the 1,500 mg/kg level in female
F344 rats.
For rats, Elcombe et al. (1985) reported an increase over untreated rats of 1.13-fold of
control PCO activity in Alderley Park rats after 1,000 mg/kg-day TCE exposure for 10 days,
while Goldsworthy and Popp (1987) reported a 1.8- and 2.39-fold of control in male F344 rats at
the same exposure in two separate experiments. Melnick et al. (1987) reported PCO activity of
1.23- and 1.75-fold of control in male F344 rats fed 600 and 1,300 mg/kg-day TCE for 14 days in
capsules. For rats treated by gavage with 600 or 1,200 mg/kg-day TCE corn oil, they reported
1.16- and 1.29-fold of control values. However, control levels of PCO were 16% higher in corn
oil controls than in untreated controls. In addition, Melnick et al. (1987) reported little catalase
increases in rats fed TCE via capsules in food (<6% increase) but a 1.18- and 1.49-fold of control
catalase activity in rats fed 600 or 1,200 mg/kg/TCE via corn oil gavage, indicative of a vehicle
effect.
The data from Elcombe et al. (1985) included reports of TCE-induced pericentral
hypertrophy and eosinophilia for both rats and mice but with "fewer animals affected at lower
doses." In terms of glycogen deposition, Elcombe et al. (1985) report "somewhat" less glycogen
pericentrally in the livers of rats treated with TCE at 1,500 mg/kg than controls with less marked
changes at lower doses restricted to fewer animals. They do not comment on changes in glycogen
in mice. Dees and Travis (1993) reported TCE-induced changes to "include an increase in
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eosinophilic cytoplasmic staining of hepatocytes located near central veins, accompanied by loss
of cytoplasmic vacuolization." Since glycogen is removed using conventional tissue processing
and staining techniques, an increase in glycogen deposition would be expected to increase
vacuolization and thus, the report from Dees and Travis (1993) is consistent with less, not more,
glycogen deposition. Neither study produced a quantitative analysis of glycogen deposition
changes from TCE exposure. Although not explicitly discussing liver glycogen content or
examining it quantitatively in mice, these studies suggest that TCE-induced liver weight increases
did not appear to be due to glycogen deposition after 10 days of exposure, and any decreases in
glycogen were not necessarily correlated with the magnitude of liver weight gain either.
For both rats and mice, the data from Elcombe et al. (1985) showed that tritiated
thymidine incorporation in total liver DNA observed after TCE exposure did not correlate with
mitotic index activity in hepatocytes with both Elcombe et al. (1985) and Dees and Travis (1993)
reporting a small mitotic indexes and evidence of periportal hepatocellular hypertrophy from TCE
exposure. Neither mitotic index or tritiated thymidine incorporation data support a correlation
with TCE-induced liver weight increase in the mouse. If higher levels of hepatocyte replication
had occurred earlier, such levels were not sustained by 10 days of TCE exposure. Both Elcombe
et al. (1985) and Dees and Travis (1993) present data that represent "a snapshot in time," which
do not show whether increased cell proliferation may have happened at an earlier time point and
then subsided by 10 days. These data suggest that increased tritiated thymidine levels were
targeted to mature hepatocytes and in areas of the liver where greater levels of polyploidization
occur. Both Elcombe et al. (1985) and Dees and Travis (1993) show that tritiated thymidine
incorporation in the liver was approximately twofold of controls between 250 and 1,000 mg/kg
TCE, a result consistent with a doubling of DNA. Thus, given the normally quiescent state of the
liver, the magnitude of this increase over control levels, even if a result of proliferation rather than
polyploidization, would be confined to a very small population of cells in the liver after 10 days
of TCE exposure.
Laughter et al. (2004) reported that there was an increase in DNA synthesis after aqueous
gavage exposure to 500 and 1,000 mg/kg TCE given as three boluses a day for 3 weeks with
BrdU given for the last week of treatment in mice. An examination of DNA synthesis in
individual hepatocytes was reported to show that 1 and 4.5% of hepatocytes had undergone DNA
synthesis in the last week of treatment for the 500 and 1,000 mg/kg doses, respectively. Both
Elcombe et al. (1985) and Dees and Travis (1993) show TCE-induced changes for several
parameters at the lowest level tested without toxicity and without evidence of regenerative
hyperplasia or sustained hepatocellular proliferation.
In regards to susceptibility to liver cancer induction, the more susceptible (B6C3Fi) vs.
less susceptible (Alderley Park/Swiss) strains of mice to TCE-induced liver tumors (Maltoni et
al., 1988), the "less susceptible" strain was reported by Elcombe et al. (1985) to have a greater
baseline level of liver weight/body weight ratio, a greater baseline level of thymidine
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incorporation, and greater responses for those endpoints due to TCE exposure. However, both
strains showed a hepatocarcinogenic response after TCE exposure, although there are limitations
regarding determination of the exact magnitude of response for these experiments as previously
discussed.
E.2.4.2. Summary of Results For Short-Term Effects of DCA and TCA: Comparisons
With TCE
Short-term exposures from DCA and TCA have been studied either through gavage or in
drinking water. Palatability became an issue at the highest level of DCA tested in drinking water
experiments (5 g/L), which caused a significant reduction of drinking water intake in mice of 46-
64% (Carter et al., 1995). Decreases in drinking water consumption have also been reported for a
range of concentrations of DCA and TCA from 0.05 to 5.0 g/L, in both mice and rats, and with
generally the higher concentrations producing the highest decrease in drinking water (DeAngelo
etal.. 1999: DeAngelo et al.. 1997: Carter etal.. 1995: Mather et al.. 1990): (DeAngelo et al..
2008). However, results within studies (e.g., DeAngelo et al., 2008) and between studies have
been reported to vary as to the extent of the reduction in drinking water from the presence of TCA
or DCA. Some drinking water studies of DCA or TCA have not reported drinking water
consumption. Therefore, although in general, DCA and TCA studies have do not include vehicle
effects, such as those posed by corn oil, they have been affected by differences in drinking water
consumption not only changing the dose received by the rodents and therefore, potentially the
shape of the dose-response curve, but also the effects of dehydration are potentially added to any
chemically-related reported effects.
Studies have attempted to determine short-term effects on DNA by TCE and its
metabolites. Nelson and Bull (1988) administered TCE male to Sprague-Dawley rats and male
B6C3Fi mice and measured the rate of DNA unwinding under alkaline conditions 4 hours later.
For rats, there was a significantly increased rate of unwinding at the two highest dose and for
mice, there was a significantly increased level of DNA unwinding at a lower dose. In this same
study, DCA was reported to be most potent in this assay with TCA being the lowest, while CH
closely approximated the dose-response curve of TCE in the rat. In the mouse, the most potent
metabolite in the assay was reported to be TCA, followed by DCA with CH considerably less
potent. Nelson and Bull (1988) and Nelson et al. (1989) have reported increases in SSBs after
DCA and TCA exposure. However, Styles et al. (1991) (for mice) and Chang et al. (1992) (for
mice and rats) did not. Austin et al. (1996) report that the alkaline unwinding assay, a variant of
the alkaline elution procedure, is noted for its variability and inconsistency depending on the
techniques used while performing the procedure. In regard to oxidative damage as measured by
TEARS for lipid peroxidation and 8-OHdG levels in DNA, increases appear to be small (<50%
greater than control levels) and transient after DCA and TCA treatment in mice (see
Section E.3.4.2.3) with TCE results confounded by vehicle or route of administration effects.
E-199
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Although there is no comparative data for TCE, the study of Styles et al. (1991) is
particularly useful for determining effects of TCA from 1 to 4 days of exposure in mice. Styles et
al. (1991) reported no change in "hepatic" DNA uptake of tritiated thymidine up to 36 hours, a
peak at 72 hours (~6-fold of control), and falling levels by 96 hours (~4-fold of controls) after
500 mg/kg TCA gavage exposure. Incorporation of tritiated thymidine observed for individual
hepatocytes decreased between 24 and 36 hours, rose slowly back to control levels at 48 hours,
significantly increased by 72 hours, and then decreased by 96 hours. Thus, increases in "hepatic"
DNA tritiated thymidine uptake did not capture the decrease observed in individual hepatocytes at
36 hours. By either measure, the population of cells undergoing DNA synthesis was small, with
the peak level being <1% of the hepatocyte population. Zonal distribution of labeled hepatocytes
were decreased at 36 hours in all zones, appeared to be slightly greater in periportal than midzonal
cells with centrilobular cells still below control levels by 48 hours, similarly elevated over
controls in all zones by 72 hours, and to have returned to near control levels in the midzonal and
centrilobular regions but with periportal areas still elevated by 96 hours. These results are
consistent with all hepatocytes showing a decrease in DNA synthesis by 36 hours and then a wave
of DNA synthesis to occur, starting at the periportal zone and progressing through the liver acinus
that is decreased by 4 days after exposure.
Along with changes in liver weight, DNA synthesis, and glycogen accumulation, several
studies of DCA and TCA have focused on the extent of peroxisome proliferation as measured by
changes in peroxisome number, cytoplasmic volume and enzyme activity induction as potential
"key events" occurring from shorter-term exposures that may be linked to chronic effects such as
liver tumorigenicity. As noted above in Section E.2.4.1, TCE-induced liver weight gain has been
reported to not be dependent on a functional PPARa receptor in female mice while as well as a
significant portion of it not dependent on functional PPARa receptor in male mice. Also as noted,
cyanide-insensitive PCO has also been reported to not be correlated with the volume or number of
peroxisomes that are increased as a result of exposure to TCE or it metabolites (Nakajima et al.,
2000; Nelson etal., 1989; Elcombe et al., 1985) and to be highly variable both as a baseline
measure and in response to chemical exposures (e.g., variation of up to 6-fold between after
WY-14,643 exposure in mice). Also as noted above, the vehicle used in many TCE gavage
experiments, com oil, has been reported to elevate PCO activity as well as catalase activity.
A number of short-term studies have examined the effects of TCA and DCA on liver
weight increases and evidence of peroxisome proliferation and changes in DNA synthesis. In
particular, two studies of DCA and TCA used a similar paradigm presented by Elcombe et al.
(1985) and Dees and Travis (1993) for TCE effects in mice. Nelson et al. (1989) report findings
from gavage doses of unbuffered TCA (500 mg/kg) and DCA (500 mg/kg) in male B6C3Fi mice;
Styles et al. (1991) also provide data on peroxisome proliferation using the same paradigm.
Nelson et al. (1989) reported levels of PCO activity in mice administered 500 mg/kg DCA or
TCA for 10 days with 250 mg/kg Clofibrate administration serving as a positive control. DCA
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and TCA exposure were reported to not affect body weight, but both to significantly increase liver
weight (1.63-fold of control for DCA and 1.30-fold of control for TCA treatments), and percent
liver/body weight ratios (1.53-fold of control for DCA and 1.16-fold of control for DCA
treatments). PCO activity was reported to be significantly increased by -1.63-, 2.7-, and 5-fold of
control for DCA, TCA, and Clofibrate treatments, respectively, and indicated that both DCA and
TCA were weaker inducers of this activity than Clofibrate.
Results from randomly selected electron photomicrographs showed an increase in
peroxisomes per unit area but gave a different pattern than PCO enzyme activity (i.e., 2.5- and
2.4-fold of control peroxisome volume for DCA and TCA, respectively). Evidence of gross
hepatotoxicity was reported to not occur in vehicle or TCA-treated mice. Light microscopic
sections were reported to show TCA and control hepatocytes to have the same intensity of PAS
staining, but with slightly larger hepatocytes occurring in TCA-treated mice throughout the liver
section with architecture and tissue pattern of the liver intact. For DCA, the histopathology was
reported to be markedly different than control mice or TCA treated mice. DCA was reported to
induce a marked increase in the size of hepatocytes throughout the liver with an approximately
1.4-fold of control diameter that was accompanied by increased PAS staining (indicative of
glycogen deposition). All DCA-treated mice were reported to have multiple white streaks grossly
visible on the surface of the liver corresponding with subcapsular foci of coagulative necrosis that
were not encapsulated, varied in size, and accompanied by a slight inflammatory response
characterized by neutrophil infiltration.
A quantitative comparison of effects from equivalent exposures of TCE, TCA, and DCA
(500 mg/kg for 10 days in mice via corn oil gavage for TCE) shown in Table E-13 can be drawn
between the Elcombe et al. (1985), Dees and Travis (1993), Styles et al. (1991), and Nelson et al.
(1989) data for relationship to control values for percent liver/body weight, PCO, and
qualitatively for glycogen deposition.
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Table E-13. Comparison of liver effects from TCE, TCA, and DCA (10-day
exposures in mice)
Model
Exposure
% Liver/body
weight
Peroxisome
volume
Peroxisome
enzyme activity
Glycogen
deposition
Nelson et al. (1989)"
B6C3FJ male
TCA
DCA
1.16-fold
1.53-fold
2.4-fold
2.5-fold
2.7-fold
1.63 -fold
No change
Increased
Styles et al. (1991)
B6C3FJ male
TCA
NR
1.9-fold
NR
NR
Elcombe et al. (1985)
B6C3FJ male
Alderley Park male (Swiss)
TCE
TCE
1.20-fold
1.43 -fold
8-fold
4-fold
NR
NR
NR
NR
Dees and Travis (1993)
B6C3FJ male
B6C3FJ female
TCE
TCE
1.05-foldb
1.18-fold
NR
NR
NR
NR
NR
NR
""Unbuffered. NR = not reported as no analysis was performed for this dose or the authors did not report this finding
(i.e., did not note a change in glycogen in description of exposure-related changes).
bStatistically significant although small increase.
Although using a similar species, route of exposure, and dose, the comparison of
responses for TCE and its metabolites shown above are in male mice and also are reflective of
variability in strain, and variability and uncertainty of initial body weights. As described in more
detail in Section E.2.2, initial age and body weight have an impact on TCE-related increases in
liver weight. Male mice have been reported to have greater variability in response than female
mice within and between studies and most of the comparative data for the 10-day 500 mg/kg
doses of TCE or its metabolites were from studies in male mice. Corn oil, used as the vehicle for
TCE gavage studies but not those of its metabolites, has been noted to specifically affect
peroxisomal enzyme induction, body weight gain, and hepatic necrosis, specifically, in male mice
(Merrick et al., 1989). Corn oil alone has also been reported to increase PCO activity in F344 rats
and to potentiate the induction of PCO activity of TCA (DeAngelo et al., 1989). Thus,
quantitative inferences regarding the magnitude of response in these studies are limited by a
number of factors.
The variability in the magnitude of TCE-induced increases in percent liver/body weight
across studies is readily apparent, but for TCE, TCA, and DCA, there is an increase in liver
weight in mice at this dose after 10 days of exposure. The volume of the peroxisomal
compartment in hepatocytes was reported to be more greatly increased from TCE-treatment by
Elcombe et al. (1985) than for either TCA or DCA by Nelson et al. (1989) or Styles et al. (1991).
However, the control values for the B6C3Fi mice were half that of the other strain reported by
Elcombe et al. (1985) and this parameter in general did not match the pattern of PCO activity
values reported for TCA and DCA (Nelson et al., 1989). There is no PCO activity data at this
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dose for TCE, but Elcombe et al. (1985) reported that the magnitude of TCE-induced increase in
peroxisome volume was similar to that of PCO activity at the only dose where both were tested
(1,000 mg/kg TCE).
However, Elcombe et al. (1985) reported that increased peroxisomal volumes in B6C3Fi
mice after 10 days of TCE treatment were not dose-related (i.e., there was little difference
between 500, 1,000, and 1,500 mg/kg TCE exposures in the magnitude of TCE-induced increases
in peroxisomal volume). The lack of dose-response for TCE-induced peroxisomal volume
increases was not consistent with increases in percent liver/body weight that increased with
increasing TCE exposure concentration. Also as noted above, PCO activity appears to be highly
variable in untreated and treated rodents and to vary between experiments and between studies.
From the above comparison, it is clear that TCE, DCA, and TCA exposures were
associated with increased liver weight in mice but a question arises as to what changes account
for the liver weight increases. For TCE and TCA 500 mg/kg treatments, changes in glycogen
were not reported in the general descriptions of histopathological changes (Dees and Travis,
1993; Styles et al., 1991; Elcombe et al., 1985) or were specifically described by the authors as
being similar to controls (Nelson etal., 1989). However, for DCA, glycogen deposition was
specifically noted to be increased with treatment, although no quantitative analyses were
presented that could give information as to the nature of the dose-response (Nelson et al., 1989).
Issues in regard to not only whether TCE and its metabolites each gives a similar response for a
number of parameters, but also potential changes may be associated with carcinogenicity from
long-term exposures can be examined by a comparison of the dose-response curves for these
parameters from a range of exposure concentrations and durations of exposure. In addition, if
glycogen accumulation results from DCA exposure, what proportion of DCA-induced liver
weight increases result from such accumulation or other events that may be similar to those
occurring with TCE exposure (see Section E.4.2.4)?
As noted in Section E.2.4.1, TCE-induced changes in liver weight appear to be
proportional to the exposure concentration across route of administration, gender, and rodent
species. As an indication of the potential contribution of TCE metabolites to this effect, a
comparison of the shape of the dose-response curves for liver weight induction for TCE and its
metabolites is informative. A number of studies of TCA and DCA in drinking water, conducted
from 10 days to 4 weeks, have attempted to measure changes in liver weight induction,
peroxisomal enzyme activity, and DNA synthesis predominantly in mice to provide insight into
the mode(s) of action for liver cancer induction (DeAngelo et al., 2008; Parrish et al., 1996;
Carter etal.. 1995: Sanchez and Bull 1990: DeAngelo et al.. 1989).
Direct comparisons are harder to make between the drinking water studies of DCA and
TCA and the gavage studies of TCE (Tables E-14, E-15, and E-16). Similar to 10-day gavage
exposures to TCE, 14-day exposures to TCA or DCA via drinking water were reported to induce
dose-related increases in liver weight in male B6C3Fi mice (0.3, 1.0, and 2.0 g/L TCA or DCA)
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with a greater increase in liver weight from DC A than TC A at 2 g/L and a difference in the shape
of the dose-response curve (Sanchez and Bull, 1990). They reported a 1.08-, 1.31-, and 1.62-fold
of control liver weight for DCA and a 1.15-, 1.22-, and 1.38-fold of control values for TCA at 0.3,
1.0, and 2.0 g/L concentrations, respectively (n = 12-14 mice). While the magnitude of
difference between the exposures was -6.7-fold between the lowest and highest dose, the
differences between TCA exposure groups for change in percent of liver weight was -2.5, but for
DCA, the slope of the dose-response curve for liver weight increases appeared to be closer to the
magnitude of difference in exposure concentrations between the groups (i.e., a difference of
7.7-fold between the highest and lowest dose for liver weight induction).
DeAngelo et al. (1989) reported that after 14 days of exposure to 5 or 2 g/L TCA in male
mice, the magnitudes of the difference in the increase in exposure concentration (2.5-fold) was
generally higher than the increase percent liver/body weight ratios at these doses (i.e., -40% for
the Swiss-Webster, C3H, and for one of the B6C3Fi mouse experiments, and for the C57BL/6
mouse, there was no difference in liver weight induction between the 2 and 5 g/L TCA exposure
groups). There was a range in the magnitude of percent liver/body weight ratio increases between
the strains of mice with liver weight induction reported to range between 1.26- and 1.66-fold of
control values for the four strains of mice at 5 g/L TCA and to range between 1.16- and 1.63-fold
of control values at 2 g/L TCA. One strain, B6C3Fi, was chosen to compare responses between
DCA and TCA. At 1, 2, and 5 g/L TCA or DCA, DCA was reported to induce a greater increase
in liver weight that TCA (i.e., 1.55- vs. 1.39-fold of control percent liver/body weight ratio for
5.0 g/L DCA vs. TCA, respectively). At the 5 g/L exposures, DCA induced -40% greater percent
liver/body weight than TCA. Although as noted above, the majority of the data from this study in
mice did not indicate that the magnitude of difference in exposure concentration was the same as
that of liver weight induction for TCA, in the particular experiment that examined both DCA and
TCA, the increase in percent liver/body weight ratios were similar to the magnitude of difference
in dose between the 2 and 5 g/L exposure concentrations for both DCA and TCA (i.e., 2-2.5-fold
increase in liver weight change corresponding to a 2.5-fold difference in exposure concentration).
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Table E-14. Liver weight induction as percent liver/body weight fold-of-control in male B6C3Fi mice from DCA
or TCA drinking water studies
Concentration
(g/L)
Duration of exposure
14 or 15 d
20 or 21 d
25 d
28 or 30 d
Mean for average of
d 14-30
DCA
0.1
0.3
0.5
1.0
2.0
5.0
1.08-fold
1.12-fold
1.31-fold
1.62-fold
1.67-fold
1.02-fold
1.24-fold, 1.05-fold
1.46-fold, 2.01-fold
1.16-fold
2.04-fold
1.16-fold
1.99-fold, 1.42-fold
1.02-fold
1.08-fold
1.15-fold
1.31-fold
1.83-fold
1.67-fold
TCA
0.05
0.1
0.3
0.5
1.0
2.0
3.0
5.0
1.15-fold
1.23 -fold, 1.08-fold
1.38-fold, 1.16-fold, 1.26-fold
1.39-fold, 1.35-fold
0.98-fold
1.1 3 -fold
1.33-fold
1.09-fold
1.16-fold
1.33-fold
1.09-fold
0.98-fold
1.15-fold
1.15-fold
1.16-fold
1.30-fold
1.33-fold
1.37-fold
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Table E-15. Liver weight induction as percent liver/body weight fold-of-control in male B6C3Fi or Swiss mice
from TCE gavage studies
Concentration (mg/kg-
d)
10 d
28 d
42 d
Mean for average of d 10^12
B6C3F!
100
250
500
600
1,000
1,200
1,500
2,400
1.00-fold
1.00-fold
1.20-fold, 1.06-fold
1.50-fold, 1.17-fold, 1.50-fold
1.47-fold
1.36-fold
1.64-fold
1.81-fold
1.00-fold
1.00-fold
1.1 3 -fold
1.36-fold
1.39-fold
1.64-fold
1.47-fold
1.81-fold
Swiss
100
200
400
500
800
1,000
1,500
1,600
2,000
2,400
1.43 -fold
1.56-fold
1.75-fold
1.32-fold
1.41 -fold
1.38-fold
1.69-fold
1.12-fold
1.15-fold
1.25-fold
1.36-fold
1.63 -fold
1.12-fold
1.15-fold
1.25-fold
1.38-fold
1.36-fold
1.49-fold
1.75-fold
1.63 -fold
1.38-fold
1.69-fold
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Carter et al. (1995) examined 0.5 and 5.0 g/L exposures to DCA in B6C3Fi male mice and
reported that percent liver/body weights were increased consistently from 0.5 g/L DCA treatment
from 5 to 30 days of treatment (i.e., a range of 1.05-1.16-fold of control). For 5.0 g/L DCA
exposure, the range of increase in percent liver/body weight was reported to be 1.37-2.04-fold of
control for the same time period. At the 15 days of exposures, the percent liver/body weight
ratios were 1.67- and 1.12-fold of control for 5.0 and 0.5 g/L DCA and at 30 days were 1.99- and
1.16-fold, respectively. The difference in magnitude of dose and percent liver/body weight
increase is difficult to determine given that the 5 g/L dose of DCA reduced body weight and
significantly reduced water consumption by -50%. The differences in DCA-induced percent
liver/body weights were ~6-fold for the 15-, 25-, and 30-day data between the 0.5 and 5 g/L DCA
exposures rather than the 10-fold difference in exposure concentration in the drinking water.
Table E-16. B6C3F! and Swiss (data sets combined)
Concentration (mg/kg-d)
100
200
250
400
500
600
800
1,000
1,200
1,500
1,600
2,000
2,400
Mean for average of d 10-42
.06-fold
.15-fold
.00-fold
.25-fold
.26-fold
.36-fold
.36-fold
.49-fold
.64-fold
.61 -fold
.63 -fold
.38-fold
.75-fold
Parrish et al. (1996) reported that for male B6C3Fi mice exposed to TCA or DCA (0,
0.01, 0.5, and 2.0 g/L) for 3 or 10 weeks, the 4-5-fold magnitude of difference in doses resulted
in increases in percent liver/body weight for the 21- and 71-day exposures that were greater for
DCA than TCA. The percent liver/body weight ratio were 0.98-, 1.13-, and 1.33-fold of control
levels at 0.1, 0.5, and 2.0 g/L TCA and for DCA were 1.02-, 1.24-, and 1.46-fold of control levels,
respectively, after 21 days of exposure. Both TCA and DCA exposures at 0.1 g/L resulted in
difference in percent liver/body weight change of <2%. For TCA, although there was a fourfold
increase in magnitude between the 0.5 and 2.0 g/L TCA exposure concentrations, the magnitude
of increase for percent liver/body weight increase was 2.5-fold between them at both 21 and
71 days of exposure. For DCA, the fourfold difference in dose between the 0.5 and 2.0 g/L DCA
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exposure concentrations were reported to result in a ~2-fold increase in percent liver/body weight
increase at 21 days and ~4.5-fold increase at 71 days.
DeAngelo et al. (2008) studied three exposure concentrations of TCA in male B6C3Fi
mice, which were an order of magnitude apart, for 4 weeks of exposure. The percent liver/body
weight ratios were 1.09-, 1.16-, and 1.35-fold of control levels, for 0.05, 0.5, and 5.0 g/L TCA
exposures, respectively. The 10-fold differences in exposure concentration of TCA resulted in
~2-fold differences in percent liver/body weight increases. No dose-response inferences can be
drawn from the 4-week study of DCA and TCA in B6C3Fi male mice by Kato-Weinstein et al.
(2001), but 2 g/L DCA and 3 g/L TCA in drinking water were reported to induce percent
liver/body weights of 1.42- and 1.33-fold of control, respectively (n = 5).
The majority of short-term studies of DCA and TCA in mice have been conducted in the
B6C3Fi strain and in males. Studies conducted from 14 to 30 days show a consistent increase in
percent liver/body weight induction by TCA or DCA. Analyses of this information regarding
inferences for attribution and comparisons of dose-response have been published by Evans et al.
(2009). Chiu et al., (2004). and Chiu (2011). and is discussed in Chapter 4 of the TCE assessment
document and in Appendix A. A broader discussion of primarily issues and data related to Evans
(2009) is contained below.
An examination of all of the data from Parrish et al. (1996), Sanchez and Bull (1990),
Carter et al. (1995), Kato-Weinstein et al. (2001), and DeAngelo et al. (2008: 1989) from 14 to 30
days of exposure in male B6C3Fi mice can give an approximation of the dose-response
differences between DCA and TCA for liver weight induction as shown in Table E-14 and Figure
E-l. Although the data for B6C3Fi mice from Sanchez and Bull (1990) are reported as the fold of
liver weight rather that percent liver/body weight increase, they are included in the comparison as
both reflect increase in liver weight. Similar data can be assessed for TCE for comparative
purposes. Short-duration studies (10-42 days) were selected because: (1) in chronic studies, liver
weight increases are confounded by tumor burden; (2) multiple studies are available; (3) in this
duration range, Kjellstrand et al. (198la) reported that TCE-induced increases in liver weight
plateau; and (4) TCA studies do not show significant duration-dependent differences in this
duration range. These comparisons are presented in Table E-14.
DeAngelo et al. (1989) and Carter et al. (1995) used up to 5 g/L DCA and TCA in their
experiments with Carter et al. (1995) noting a dramatic decrease in water consumption in the
5 g/L DCA treatment groups (46-64% reduction), which can affect body weight as well as dose
received. DeAngelo et al. (1989) did not report drinking water consumption. The drinking water
consumption was reported by DeAngelo et al. (2008) to be reduced by 11, 17, and 30% in the
0.05, 0.5, and 5 g/L TCA treated groups compared to 2 g/L sodium chloride control animals over
60 weeks. DeAngelo et al. (1999) reported mean drinking water consumption to be reduced by
26% in mice exposed to 3.5 g/L DCA over 100 weeks. Carter et al. (1995) reported that DCA at
5 g/L to decrease drinking water consumption by 64 and 46% but 0.5 g/L DCA to not affect
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drinking water consumption. Thus, it appears that the 5 g/L concentrations of either DCA or
TCA can significantly affect drinking water consumption as well as inducing reductions in body
weight. Accordingly, an estimation of the shape of the dose-response curve for comparative
purposes between DCA or TCA drinking water studies is best examined at concentrations at
<2 g/L, especially for DCA.
Male B6C3F1 mice liver weight for TCA and DCA in drinking water - days 14-30
1.0
0.5 1.0 1.5 2.0 2.5
Concentration of DCA or TCA (g/l)
Reproduced from Section 4.5.
Sources: (2008: Kato-Weinstein et al.. 2001: Parrish et al.. 1996: Carter et al..
1995: Sanchez and Bull 1990: DeAngelo et al.. 1989)).
Figure E-l. Comparison of average fold-changes in relative liver weight to
control and exposure concentrations of <2 g/L in drinking water for TCA
and DCA in male B6C3Fi mice for 14-30 days
The dose-response curves for similar concentrations of DCA and TCA are presented in
Figure E-l for durations of exposure from 14 to 28 days in the male B6C3Fi mouse, which was
the most common sex and strain used. For this comparative analysis, an average is provided
between two values for a given concentration and duration of exposure for comparison with other
doses and time points. As noted in the discussion of individual experiments, there appears to be a
linear correlation between dose in drinking water and liver weight induction up to 2 g/L of DCA.
However, the shape of the dose-response curve for TCA appears to be quite different (i.e., lower
concentrations of TCA inducing larger increase that does DCA but then the response reaching an
E-209
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apparent plateau for TCA at higher doses while that of DCA continues to increase). As shown by
DeAngelo et al. (2008), 10-fold differences in the magnitude of exposure concentration to TCA
corresponded to approximately twofold differences in liver weight induction increases. In
addition, TCA studies did not show significant duration-dependent difference in liver weight
induction in this duration range as shown in Table E-14.
Of interest is the issue of how the dose-response curves for TCA and DCA compare to
that of TCE in a similar model and dose range. Since TCA and DCA have strikingly different
dose-response curves, which one, if either, best fits that of TCE and thus, can give insight as to
which is causative agent for TCE's effects in the liver? In the case of the TCE database in the
mouse two strains have been predominantly studied, Swiss and B6C3Fi, and both have been
reported to get liver tumors in response to chronic TCE exposure.
Rather than administered in drinking water, oral TCE studies have been conducted via
gavage and generally in corn oil for 5 days of exposure per week. The study by Goel et al. (1992)
was conducted in ground-nut oil. Vehicle effects, the difference between daily and weekly
exposures, the dependence of TCE effects in the liver on its metabolism to a variety of agents
capable inducing effects in the liver, differences in response between strains, and the inherent
increased variability in use of the male mouse model all add to increased difficulty in establishing
the dose-response relationship for TCE across studies and for comparisons to the DCA and TCA
database. Despite difference in exposure route, etc., a consistent pattern of dose-response
emerges from combining the available TCE data. The effects of oral exposure to TCE from 10 to
42 days on liver weight induction is shown in Figure E-2 using the data of Elcombe et al. (1985),
Dees and Travis (1993), Goel et al. (1992), Merrick et al. (1989), Goldsworthy and Popp (1987),
and Buben and O'Flaherty (1985). More detailed discussion of the 4-6-week studies is presented
in Section E.2.4.3 (e.g., for (Goeletal., 1992: Merrick et al., 1989: Buben and O'Flahertv, 1985)).
For this comparative analysis, an average is provided between two values per concentration and
duration of exposure for comparison with other doses and time points. As shown by the 10-day
data in B6C3Fi mice, there are significant differences in response between studies of male
B6C3Fi mice at the same dose of TCE. This variability is similar to findings from inhalation
studies of TCE in male mice (Kjellstrand et al., 1983b).
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Male mice liver weight for TCE oral gavage - days 10-42
2.0
1.8 -
.2> 1.6-1
^ 1.4 -
1.2 -
1.0 +-e
0 500 1000 1500 2000 2500 3000
Concentration of TCE (mg/kg/day)
Male mice liver weight for TCE oral gavage - days 10-42
2.0
1.4 -
1.2 -
1.0
B6C3F1 and Swiss
Plot 2 Rear
500 1000 1500 2000 2500
Concentration of TCE (mg/kg/day)
3000
Reproduced from Section 4.5.
Sources: (Dees and Travis, 1993; Merrick et al., 1989; Goldsworthy and Popp,
1987: Elcombe et al.. 1985)).
Figure E-2. Comparisons of fold-changes in average relative liver weight and
gavage dose of (top panel) male B6C3Fi mice for 10-28 days of exposure and
(bottom panel) in male B6C3Fi and Swiss mice.
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As shown in Figure E-2, oral TCE administration in male B6C3Fi and Swiss mice
appeared to induce a dose-related increase in percent liver/body weight that was generally
proportional to the increase in magnitude of dose, though as expected, with more variability than
observed for a similar exercise for DCA or TCA in drinking water. Common exposure
concentrations between B6C3Fi and Swiss mice were 100, 500, 1,000, 1,500 and 2,400 mg/kg-
day TCE, which corresponded to a 5-, 2-, 1.5-, and 1.6-fold difference in the magnitude of dose.
For the data from studies in B6C3Fi mice, there was no increase reported at 100 mg/kg-day TCE
but between 500 and 1,000, 1,000 and 1,500, and 1,500 and 2,400 mg/kg-day TCE, the magnitude
of difference in doses matched that of the magnitude of increase in percent liver/body weight (i.e.,
a 2.6-, 1.4-, and 1.7-fold increase in liver weight was matched by a 2-, 1.5-, and 1.6-fold increase
in TCE exposure concentration at these exposure intervals).
However, only a 10-day interval was available for doses between 100 and 500 mg/kg in
B6C3Fi mice and at the lower doses, a 10-day interval may have been too short for the increase in
liver weight to have been fully expressed. The database for the Swiss mice, which has more data
from 28 and 42 days of exposure, support this conclusion. At 28-42 days of exposure, there was
a much greater increase in liver weight from TCE exposure in Swiss mice than the 10-day data in
B6C3Fi mice.
In Figure E-2, the 10-day data are included for comparative purpose for the B6C3Fi data
set and the Swiss and B6C3Fi data sets combined. Both the combined TCE data and that for only
B6C3Fi mice shows a correlation with the magnitude of dose and magnitude of percent
liver/body weight increase. The slope of the dose-response curves are both closer to that of DCA
than TCA. The correlation coefficients for the linear regressions presented for the B6C3Fi data
are R2 = 0.861 and for the combined data sets is R2 = 0.712. Comparisons of the slopes of the
dose-response curves indicate that TCA is not responsible for TCE-induced liver effects. In this
regression, all data points were treated equally, although some came from several sets of data and
others did not. Of note is that the 2,000 mg/kg TCE data point in the combined data set, which is
much lower in liver weight response than the other data, is from one experiment (Goel et al.,
1992), from six mice, at one time point (28 days), and one strain (Swiss). Deletion of these data
point from the rest of the 23 used in the study results in a better fit to the data of the regression
analysis.
A more direct comparison would be on the basis of dose rather than drinking water
concentration. The estimations of internal dose of DCA or TCA from drinking water studies have
been reported to vary with DeAngelo et al. (1989) calculated DCA drinking water concentrations
of 1.0, 2.0, and 5.0 g/L to result in 90, 166, and 346 mg/kg-day, respectively, based on previous
analyses in their laboratory. For TCA, 0.05, 0.5, 1.0, 2.0, and 5 g/L drinking water exposures
were reported to result in 5.8 (range 3.6-8.0), 50 (range of 32.5-68), 131, 261, and 469 (range
364-602) mg/kg-day doses. The estimations of internal dose of DCA or TCA from drinking
water studies, while varying considerably (DeAngelo et al., 2008; 1989), nonetheless suggest that
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the doses of TCE used in the gavage experiments were much higher than those of DC A or TCA.
However, only a fraction of ingested TCE is metabolized to DCA or TCA, as, in addition to
oxidative metabolism, TCE is also cleared by GSH conjugation and by exhalation.
While DCA dosimetry is highly uncertain (see Sections 3.3 and 3.5), the mouse PBPK
model, described in Section 3.5 was calibrated using extensive in vivo data on TCA blood,
plasma, liver, and urinary excretion data from inhalation and gavage TCE exposures, and makes
robust predictions of the rate of TCA production. If TCA were predominantly responsible for
TCE-induced liver weight increases, then replacing administered TCE dose (e.g., mg
TCE/kg/day) by the rate of TCA produced from TCE (mg TCA/kg/day) should lead to dose-
response curves for increased liver weight consistent with those from directly administered TCA.
Figure E-3 shows this comparison using the PBPK model-based estimates of TCA
production for four TCE studies from 28 to 42 days in the male NMRI, Swiss, and B6C3Fi mice
(Goeletal.. 1992: Merrick et al.. 1989: Buben and O'Flaherty. 1985: Kj ell strand etal.. 1983a) and
four oral TCA studies in B6C3Fi male mice at <2 g/L drinking water exposures (DeAngelo et al.,
2008: Kato-Weinstein et al.. 2001: Parrish et al.. 1996: DeAngelo et al.. 1989) from 14 to 28 days
of exposure. The selection of the 28-42 day data for TCE was intended to address the decreased
opportunity for full expression of response at 10 days. PBPK modeling predictions of daily
internal doses of TCA in terms of mg/kg-day via produced via TCE metabolism would be indeed
lower than the TCE concentrations in terms of mg/kg-day given orally by gavage. The predicted
internal dose of TCA from TCE exposure studies are of a comparable range to those predicted
from TCA drinking water studies at exposure concentrations in which palatability has not been an
issue for estimation of internal dose. Thus, although the TCE data are for higher exposure
concentrations, they are predicted to produce comparable levels of TCA internal dose estimated
from direct TCA administration in drinking water.
E-213
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2.5
ra
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T3
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2
1.5
» TCE Studies [28-42 d]
o TCA Studies [14-28 d]
• — - Linear (TCA Studies [14-28 d])
Linear (TCE Studies [28-42 d])
100
400
200 300
mg TCA/kg-d
(produced [TCE studies] or administered [TCA studies])
500
(Reproduced from Section 4.5.)
Abscissa for TCE studies consists of the median estimates of the internal dose of
TCA predicted from metabolism of TCE using the PBPK model described in
Section 3.5 of the TCE risk assessment. Lines show linear regression with
intercept fixed at unity. All data were reported fold-change in mean liver
weight/body weight ratios, except for Kjellstrand et al. (1983a), with were the
fold-change in the ratio of mean liver weight to mean body weight. In addition, in
Kjellstrand et al. (1983a), some systemic toxicity as evidence by decreased total
body weight was reported in the highest-dose group.
Sources: Kjellstrand et al. (1983a): (Goeletal.. 1992: Merrick et al.. 1989:
Buben and O'Flahertv. 1985): (DeAngelo et al.. 2008: Kato-Weinstein et al..
2001: Parrish et al.. 1996: DeAngelo et al.. 1989).
Figure E-3. Comparison of fold-changes in relative liver weight for data sets
in male B6C3Fi, Swiss, and NRMI mice between TCE studies [duration 28-
42 days]) and studies of direct oral TCA administration to B6C3Fi mice
[duration 14-28 days]).
Figure E-3 clearly shows that for a given amount of TCA produced from TCE, but going
through intermediate metabolic pathways, the liver weight increases are substantially greater than,
and highly inconsistent with, that expected based on direct TCA administration. In particular, the
response from direct TCA administration appears to "saturate" with increasing TCA dose at a
level of about 1.4-fold, while the response from TCE administration continues to increase with
E-214
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dose to 1.75-fold at the highest dose administered orally in Buben and O'Flaherty (1985) and over
2-fold in the inhalation study of Kjellstrand et al. (1983a). For this analysis, it is unlikely that
strain differences can account for this inconsistency in the dose-response curves.
TCE-induced increases in liver weight appear to be generally similar between B6C3Fi and
Swiss male mice (see Table E-14) via oral exposure and between NMRI male and female mice
after inhalation, although the NMRI strain appeared to be more prone to TCE-induced toxicity in
male mice, and females appeared to have a smaller TCE-induced liver weight increase than other
strains (Kjellstrand et al., 1983a). As noted previously, the difference in response between strains
and between studies in the same strain for TCE liver weight increases can be highly variable.
Little data exist to examine this issue for TCA studies, although DeAngelo et al. (1989) report a
range of 1.16-1.63-fold of control percent liver/body weight increase after 14 days exposure at
2 g/L TCA in the Swiss-Webster, C3H, C57BL/6, and B6C3Fi strains, with differences also noted
between two studies of the B6C3Fi mouse.
Furthermore, while as noted previously, oral studies appear to report a linear relationship
between TCE exposure concentration and liver weight induction, the inclusion of inhalation
studies on the basis of internal dose led to a highly consistent dose-response curve among TCE
studies. Therefore, it is unlikely that differing routes of exposure can explain the inconsistencies
in dose-response. The PBPK model predicted that matching average TCA production by TCE
with the equivalent average dose from drinking water-administered TCA also led to an equivalent
AUC of TCA in the liver.
Moreover, Dees and Travis (1993) administered 100-1,000 mg/kg-day TCA by gavage to
male and female B6C3Fi mice for 11 days, and did not observe increases in liver/body weight
ratios >1.28-fold, no higher than those observed with drinking water exposures. Finally, the dose-
response consistency between TCE inhalation and gavage studies argues against route of
exposure significantly impacting liver weight increases. Thus, no level of TCA administration
appears able account for the continuing increase in liver weights observed with TCE,
quantitatively inconsistent with TCA being the predominant metabolite responsible for
TCE-induced liver weight changes. Involvement of other metabolites, besides TCA, is implicated
as the causes of TCE-induced liver effects.
Additional analyses do, however, support a role for oxidative metabolism in TCE-induced
liver weight increases, and that the parent compound TCE is not the likely active moiety (as
suggested previously by Buben and O'Flaherty, 1985). In particular, the same studies are shown
in Figure E-4 using PBPK-model based predictions of the AUC of TCE in blood and total
oxidative metabolism, which produces chloral, TCOH, DCA, and other metabolites in addition to
TCA. The dose-response relationship between TCE blood levels and liver weight increase, while
still having a significant trend, shows substantial scatter and a low R2 of 0.43. On the other hand,
using total oxidative metabolism as the dose-metric leads to substantially more consistency dose-
response across studies, and a much tighter linear trend with an R2 of 0.90 (see Figure E-4). A
E-215
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similar consistency is observed using liver-only oxidative metabolism as the dose-metric, with
R2 of 0.86 (not shown). Thus, while the slope is similar between liver weight increase and TCE
concentration in the blood and liver weight increase and rate of total oxidative metabolism, the
data are a much better fit for total oxidative metabolism.
CD
CO
CO
CD
00
i
p
CN
R2=0.426e
o o.
\ \ \ \ \ \
0 100 200 300 400 50!
Daily AUC TCE in Bid
CD
CO lO
CO _:
CD ^
O
00
cp
CNl
R =0.8955
500 1000 1500
Daily TCE Oxidized (n
(Reproduced from Section 4.5).
Lines show linear regression. Use of liver oxidative metabolism as a dose-metric
gives results qualitatively similar to (B), with R2 = 0.86.
Sources: Kjellstrand et al. (1983a); (Goeletal.. 1992: Merrick et al.. 1989: Buben and
O'Flaherty. 1985).
Figure E-4. Fold-changes in relative liver weight for data sets in male
B6C3Fi, Swiss, and NRMI mice reported by TCE studies of duration 28-
42 days using internal dose-metrics predicted by the PBPK model described
in Section 3.5: (A) dose-metric is the median estimate of the daily AUC of
TCE in blood, (B) dose-metric is the median estimate of the total daily rate of
TCE oxidation.
As stated in many of the discussions of individual studies, there is a limited ability to
detect a statistically significant change in liver weight change in experiments that use a relatively
small number of animals. Many experiments have been conducted with 4-6 mice per dose group.
The experiments of Buben and O'Flaherty used 12-14 mice per group, giving it a greater ability
to detect a TCE-induced dose response. In some experiments, greater care was taken to document
and age and weight match the control and treatment groups before the start of treatment. The
approach taken above for the analyses of TCE, TCA, and DCA uses data across several data sets
E-216
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and gives a more robust description of these dose-response curves, especially at lower exposure
levels. For example, the data from DeAngelo et al. (2008) for TCA-induced percent liver/body
weight ratio increases in male B6C3Fi mice were only derived from five animals per treatment
group after 4 weeks of exposure. The 0.05 and 0.5 g/L exposure concentrations were reported to
give a 1.09- and 1.16-fold of control percent liver/body weight ratios, which were consistent with
the increases noted in the cross-study database above. However, a power calculation shows that
the type II error, which should be >50% and thus, greater than the chances of "flipping a coin,"
was only a 6 and 7% and therefore, the designed experiment could accept a false null hypothesis.
Although the qualitative similarity to the linear dose-response relationship between DCA
and liver weight increases is suggestive of DCA being the predominant metabolite responsible for
TCE liver weight increases, due to the highly uncertain dosimetry of DCA derived from TCE, this
hypothesis cannot be tested on the basis of internal dose. Similarly, another TCE metabolite, CH,
has also been reported to induce liver tumors in mice; however, there are no adequate comparative
data to assess the nature of liver weight increases induced by this TCE metabolite (see
Section E.2.5). Whether its formation in the liver after TCE exposure correlates with
TCE-induced liver weight changes cannot be determined. Of note is the high variability in total
oxidative metabolism reported in mice and humans of Section 3.3, which suggests that the
correlation of total TCE oxidative metabolism with TCE-induced liver effects should not only
lead to a high degree of variability in response in rodent bioassays, which is the case (see
Section E.2.4.4), but also make detection of liver effects more difficult in human epidemiological
studies.
The bioavailability of TCA has been assumed to be 100% in the analyses in Figure E-3.
Further analyses are presented in Appendix A and in Chiu (2011) regarding the assertions by
Sweeney, et al. (Sweeney et al., 2009) that previously unpublished kinetic data for mice exposed
to TCA in drinking water indicates much lower absorption. The conclusions of Sweeney et al.
(2009) were based on the TCE PBPK model of Hack et al. (2006) and not that of Evans et al.
(2009) and Chiu et al. (2009). The analyses by Chiu (Chiu, 2011) show that while there is some
decreased absorption of TCA at higher doses, it was not as low as that estimated by Sweeney et
al. (2009) and as discussed in Appendix A, it may be more accurate to characterize the fractional
absorption as an empirical parameter reflecting unaccounted-for biological processes as well as
experimental variation. The Chiu (2011) re-analyses the data on TCE- and TCA-induced
hepatomegaly, using the central estimates of the fractional absorption of TCA, showed that while
reduced fractional absorption inferred from drinking water data reported by Sweeney et al. (2009)
accounts for part of the difference in dose-responses between TCE- and TCA-induced
hepatomegaly reported by Evans et al. (2009), it does not appear to be able to account for the
entire difference. The inability of TCA to account for TCE-induced hepatomegaly was
confirmed statistically by ANOVA and even with an assumption of reduced TCA bioavailability,
E-217
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the available data are inconsistent with the toxicological hypothesis that TCA can fully account
for TCE-induced hepatomegaly.
What mechanisms or events are leading to liver weight increases for DCA, TCA, and TCE
can be examined by correlations between changes in glycogen content, hepatocyte volume, and
evidence of polyploidization noted in short-term assays. Data have been reported regarding the
nature of changes the TCE and its metabolites induce in the liver and are responsible for the
reported increases in liver weight. Increased liver weight may result from increased size or
hypertrophy of hepatocytes through changes in glycogen deposition, but also through increased
polyploidization. Increased cell number may also contribute to increased liver weight. As noted
in Section E.2.4.1, hepatocellular hypertrophy appeared to be related to TCE-induced liver weight
changes after short-term exposures. However, neither glycogen deposition, DNA synthesis, nor
increases in mitosis appear to be correlated with liver weight increases. In particular, DNA
synthesis increases were similar from 250 to 1,000 mg/kg and peroxisomal volume was similar
between 500 and 1,500 mg/kg TCE exposures after 10 days. Autoradiographs identified
hepatocytes undergoing DNA synthesis in "mature" hepatocytes that were in areas where
polyploidization typically takes place in the liver.
By 14 days of exposure, Sanchez and Bull (1990) reported that both dose-related
TCA- and DCA-induced increases in liver weight were generally consistent with changing cell
size increases, but were not correlated with patterns of change in hepatic DNA content,
incorporation of tritiated thymidine in DNA extracts from whole liver, or incorporation of
tritiated thymidine in hepatocytes. There are conflicting reports of DNA synthesis induction in
individual hepatocytes for up to 14 days of DCA or TCA exposure and a lack of correlation with
patterns observed for this endpoint and those of whole-liver thymidine incorporation. The
inconsistency of whole-liver DNA tritiated thymidine incorporation with that reported for
hepatocytes was noted by the Sanchez and Bull (1990) to be unexplained. Carter et al. (1995)
also report a lack of correlation between hepatic DNA tritiated thymidine incorporation and
labeling in individual hepatocytes in male mice. Carter et al. (1995) reported no increase in
labeling of hepatocytes in comparison to controls for any DCA treatment group from 5 to 30 days
of DCA exposure. Rather than increase hepatocyte labeling, DCA induced a decrease with no
change reported from days 5 though 15 but significantly decreased levels between days 20 and 30
for 0.5 g/L that were similar to those observed for the 5 g/L exposures.
The most comparable time periods between TCE, TCA, and DCA results for whole-liver
thymidine incorporation are the 10- and 14-day durations of exposure when peak tritiated
thymidine incorporation into individual hepatocytes and whole liver for TCA and DCA have been
reported to have already passed (Pereira, 1996; Carter et al., 1995; Styles et al., 1991; Sanchez
and Bull, 1990). Whole-liver DNA synthesis was elevated over control levels by approximately
twofold after from 250 to 1,000 mg/kg TCE exposure after 10 days of exposure but did not
correlate with mitosis (Dees and Travis, 1993; Elcombe et al., 1985). After 3 weeks of exposure
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to TCE, Laughter et al. (2004) reported that 1 and 4.5% of individual hepatocytes had undergone
DNA synthesis in the last week of treatment for the 500 and 1,000 mg/kg TCE levels,
respectively. More importantly, these data show that hepatocyte proliferation in TCE-exposed
mice at 10 days of exposure or for DCA- or TCA-exposed mice for up to 14 days of exposure is
confined to a very small population of cells in the liver.
In regard to cell size, although increased glycogen deposition with DCA exposure was
noted by Sanchez and Bull (1990), lack of quantitative analyses of that accumulation in this study
precludes comparison with DCA-induced liver weight gain. Although not presenting a
quantitative analysis, Sanchez and Bull (1990) reported DCA-treated B6C3Fi mice to have large
amounts of PAS staining material and Swiss-Webster mice to have similar increase despite
reporting differences of DCA-induced liver weight gain between the two strains. The lack of
concordance of the DCA-induced magnitude of increase in liver weight with that of glycogen
deposition is consistent with the findings for longer-term exposures to DCA reported by Kato-
Weinstein et al. (2001) and Pereira et al. (2004a) in mice (see Section E.2.4.4). Carter et al.
(1995) reported that in control mice, there was a large variation in apparent glycogen content and
also did not perform a quantitative analysis of glycogen deposition. The variability of this
parameter in untreated animals and the extraction of glycogen during normal tissue processing
for light microscopy makes quantitative analyses for dose-response difficult unless specific
methodologies are employed to quantitatively assess liver glycogen levels as was done by Kato-
Weinstein et al. (2001) and Pereira et al. (2004a).
Although suggested by their data, polyploidization was not examined for DCA or TCA
exposure in the study of Sanchez and Bull (1990). Carter et al. (1995) reported that hepatocytes
from both 0.5 and 5 g/L DCA treatment groups were reported to have enlarged, presumably
polyploidy nuclei with some hepatocyte nuclei labeled in the mid-zonal area. There were
statistically significant changes in cellularity, nuclear size, and multinucleated cells during
30 days exposure to DCA. The percentage of mononucleated cells hepatocytes was reported to
be similar between control and DCA treatment groups at 5- and 10-day exposures.
However, at 15 days and beyond, DCA treatments were reported to induce increases in
mononucleated hepatocytes. At later time periods, there were also reports of DCA-induced
increases nuclear area, consistent with increased polyploidization without mitosis. The consistent
reporting of an increasing number of mononucleated cells between 15 and 30 days could be
associated with clearance of mature hepatocytes as suggested by the report of DCA-induced loss
of cell nuclei. The reported decrease in the numbers of binucleate cells in favor of
mononucleated cells is not typical of any stage of normal liver growth (Brodsky and Uryvaeva,
1977). The linear dose-response in DCA-induced liver weight increase was not consistent with
the increased numbers of mononucleate cells and increased nuclear area reported from day 20
onward by Carter et al. (1995). Specifically, the large differences in liver weight induction
between the 0.5 g/L treatment group and the 5 g/L treatment groups at all times studied also did
E-219
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not correlate with changes in nuclear size and percent of mononucleated cells. Thus,
DCA-induced increases in liver weight were not a function of cellular proliferation, but probably
included hypertrophy associated with polyploidization, increased glycogen deposition, and other
factors.
In regard to necrosis, Elcombe et al. (1985) reported only small incidence of focal
necrosis in 1,500 mg/kg TCE-exposed mice and no necrosis at exposures up to 1,000 mg/kg for
10 days as did Dees and Travis (1993). Sanchez and Bull (1990) report DCA-induced localized
areas of coagulative necrosis both for B6C3Fi and Swiss-Webster mice at higher exposure
levels (1 or 2 g/L) by 14 days but not at the 0.3 g/L level or earlier time points. For TCA
treatment, necrosis was reported to not be associated with TCA treatment for up to 2 g/L and up
to 14 days of exposure. Carter et al. (1995) reported that mice given 0.5 g/L DCA for 15, 20,
and 25 days had midzonal focal cells with less detectable or no cell membranes and loss of the
coarse granularity of the cytoplasm, with some cells having apparent karyolysis, but for liver
architecture to be normal.
As for apoptosis, both Elcombe et al. (1985) and Dees and Travis (1993) reported no
changes in apoptosis other than increased apoptosis only at a treatment level of 1,000 mg/kg
TCE. Rather than increases in apoptosis, peroxisome proliferators have been suggested to
inhibit apoptosis as part of their carcinogenic mode of action (see Section E.3.4.1). However,
the age and species studied appear to greatly affect background rates of apoptosis. Snyder et al.
(1995) report that control mice were reported to exhibit apoptotic frequencies ranging from
-0.04 to 0.085%, that over the 30-day period of their study, the frequency rate of apoptosis
declined, and suggest that this pattern is consistent with reports of the livers of young animals
undergoing rapid changes in cell death and proliferation. They reported rat liver to have a
greater the estimated frequency of spontaneous apoptosis (-0.1%) and therefore, greater than
that of the mouse.
Carter et al. (1995) reported that after 25 days of 0.5 g/L DCA treatment apoptotic
bodies were reported as well as fewer nuclei in the pericentral zone and larger nuclei in central
and midzonal areas. This would indicate an increase in the apoptosis associated potential
increases in polyploidization and cell maturation. However, Snyder et al. (1995) report that
mice treated with 0.5 g/L DCA over a 30-day period had a similar trend as control mice of
decreasing apoptosis with age. The percentage of apoptotic hepatocytes decreased in
DCA-treated mice at the earliest time point studied and remained statistically significantly
decreased from controls from 5 to 30 days of exposure. Although the rate of apoptosis was very
low in controls, treatment with 0.5 g/L DCA reduced it further (-30-40% reduction) during the
30-day study period. The results of this study not only provide a baseline of apoptosis in the
mouse liver, which is very low, but also to show the importance of taking into account the
effects of age on such determinations. The significance of the DCA-induced reduction in
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apoptosis reported in this study, from a level that is already inherently low in the mouse, to
account for the mode of action for induction of DCA-induced liver cancer is difficult to discern.
Finally, short-term inhalation studies by Ramdhan et al. (2010) indicate that in wild type,
PPARa-null, and humanized null mice, relatively high exposures to TCE induced increased liver
size after 7 days of inhalation exposure. At the same highest concentration of TCE, although
urinary TCA concentrations were lower in PPARa-null mice than wild type mice, the sum of
urinary TCOH and TCA concentrations were the same, increases in percent liver/body weight
were the same, and liver triglyceride content was much greater in the PPARa-null mice than
wild type mice after TCE exposure. Hepatic steatosis was also greater as a baseline condition
along with hepatic triglyceride content in the PPARa-null mice than wild type mice. These
parameters were more elevated in humanized mice as a background dysregulation and even
more elevated after treatment with TCE. Therefore, the nature of hepatomegally induced by
TCE is complex and dependent on baseline lipid dysregulation states.
E.2.4.3. Summary of TCE Subchronic and Chronic Studies
The results of longer-term (Toraason et al., 1999; Channel etal., 1998; Parrish et al.,
1996) studies of "oxidative stress" for TCE and its metabolites are discussed in
Section E.3.4.2.3. Of note are the findings that the extent of increased enzyme activities
associated with peroxisome proliferation do not appear to correlate with measures of oxidative
stress after longer-term exposures (Parrish et al., 1996) and SSBs (Chang et al., 1992).
Similar to the reports of Melnick et al. (1987) in rats, Merrick et al. (1989) report that
vehicle (aqueous or gavage) affects TCE-induced toxicity in mice. Vehicle type made a large
difference in mortality, extent of liver necrosis, and liver weight gain in male and female
B6C3Fi mice after 4 weeks of exposure. The lowest dose used in this experiment was
600 mg/kg-day in males and 450 mg/kg-day in females. Administration of TCE via gavage
using Emulphor resulted in mortality of all of the male mice and most of the female mice at a
dose in corn oil that resulted in few deaths. However, use of Emulphor vehicle induced little, if
any, focal necrosis in males at concentrations of TCE in corn oil gavage that caused significant
focal necrosis, indicating vehicle effects.
As discussed in Section E.2.4.2, the extent of TCE-induced liver weight increases was
consistent between 4 and 6 weeks of exposure and between 10-day and 4-week exposures at
higher dose levels. In general, the reported elevations of enzymatic markers of liver toxicity and
results for focal hepatocellular necrosis were not consistent and did not reflect TCE dose-
responses observed for induction of liver weight increases (Merrick et al., 1989). Female mice
given corn oil and male and female mice given TCE in Emulphor were reported to have "no to
negligible necrosis," although they had increased liver weight from TCE exposure.
Using a different type of oil vehicle, Goel et al. (1992) exposed male Swiss mice to TCE
in groundnut oil at concentrations ranging from 500 to 2,000 mg/kg for 4 weeks and reported no
E-221
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changes in body weight up to 2,000 mg/kg. There was a 15% decrease at the highest dose and
increased TCE-induced percent liver/body weight ratio. At a dose of 1,000 and 2,000 mg/kg,
liver swelling, vacuolization, and widespread degenerative necrosis of hepatocytes was reported
along with marked proliferation of "endothelial cells" but no quantitation regarding the extent or
location of hepatocellular necrosis was reported, nor whether there was a dose-response
relationship in these events. They reported a TCE-related dose-response in catalase and liver
protein, but a decreased induction at the 2,000 mg/kg level where body weight had decreased.
Three studies were published by Kjellstrand and colleagues that examined effects of
TCE inhalation primarily in mice using whole-body inhalation chambers (Kjellstrand et al.,
1983a: Kjellstrand et al., 1983b: Kjellstrand et al., 1981a). Liver weight changes were used as
the indication of TCE-induced effects. The quantitative results from these experiments had
many limitations due to their experimental design including failure to determine body weight
changes for individual animals and inability to determine the exact magnitude of TCE due to
concurrent oral TCE ingestion from food and grooming behavior. An advantage of this route of
exposure was that there were not confounding vehicle effects. The results from Kjellstrand et al.
(1981a) were particularly limited by experimental design errors showed similar increases in
liver weight gain in gerbils and rats exposed at 150 ppm TCE. For rats, Kjellstrand et al.
(1981a) reported increases in liver/body weight ratios of 1.26- and 1.21-fold of control in male
and female rat 30 days of continuous TCE inhalation exposure.
The unpublished report of Woolhiser et al. (2006) reports 1.05-, 1.07-, and 1.13-fold of
control percent liver/body weight changes in 100, 300, and 1,000 ppm exposure groups that are
exposed for 6 hours/day, 5 days/week for 4 weeks in groups of eight female Sprague-Dawley
rats. At the two highest exposure levels, body weight was reduced by TCE exposure. The
150 ppm continuous exposure concentrations of Kjellstrand were analogous to 750 ppm
exposures using the paradigm of Woolhiser et al. (2006) in terms of total daily dose. Therefore,
the very limited inhalation database for rats does indicate TCE-related increases in liver weight.
The study of Kjellstrand et al. (1983b) employed a more successful experimental design
that recorded liver weight changes in carefully matched control and treatment groups to
determine TCE-treatment related effects on liver weight in seven strains of mice after 30 days of
continuous inhalation exposure at 150 ppm TCE. Individual animal body weight changes were
not recorded so that such an approach cannot take into account the effects of body weight
changes and determine a relative percent liver/body weight ratio. The data presented in this
report were for absolute liver weight changes between treated and nontreated groups with
carefully matched average body weights at the initiation of exposure. A strength of the
experimental design is its presentation of results between duplicate experiments and thus, its
ability to show the differences in results between similar exposed groups that were conducted at
different times. This information gives a measure of variability in response with time. Mouse
strain groups that did not experience TCE-induced decreased body weight gain in comparison
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to untreated groups (i.e., DBA and wild-type mice) represented the most accurate determination
of TCE-induced liver weight changes given that systemic toxicity that affects body weight can
also affect liver weight.
The C57BL, B6CBA, and NZB groups all had at least one group out of two of male mice
with changes in final body weight due to TCE exposure. Only one group of NMRI mice were
reported in this study and that group had TCE-induced decreases in final body weight. The A/sn
group not only had both male groups with decreased final body weight after TCE exposure
(along with differences between exposed and control groups at the initiation of exposure), but
also a decrease in body weight in one of the female groups and thus, appears to be the strain
with the greatest susceptibility to TCE-induced systemic toxicity. In strains of male mice in
which there were no TCE-induced affects on final body weight (wild-type and DBA), the
influence of gender on liver weight induction and variability of the response could be more
readily assessed. In wild-type mice, there was a 1.76- and 1.80-fold of control liver weight in
groups 1 and 2 for female mice, and for males, a 1.84- and 1.62-fold of control liver weight for
groups 1 and 2, respectively. For DBA mice, there was a 1.87- and 1.88-fold of control liver
weight in groups 1 and 2 for female mice, and for males, a 1.45- and 2.00-fold of control liver
weight for groups 1 and 2, respectively. Of note, as described previously, the size of the liver is
under strict control in relation to body size. An essential doubling of the size of the liver is a
profound effect with the magnitude of liver weight size increase physiologically limited.
Overall, the consistency between groups of female mice of the same strain for TCE-
induced liver weight gain, regardless of strain examined, was striking, as was the lack of body
weight changes at TCE exposure levels that induced body weight changes in male mice. In the
absence of body weight changes, the difference in TCE-response in female mice appeared to be
reflective of strain and initial weight differences. Groups of female mice with higher body
weights, regardless of strain, generally had higher increases in TCE-induced liver weight
increases. For the C57BL and As/n strains, female mice starting weights were averaged
17.5 and 15.5 g, while the average liver weights were 1.63- and 1.64-fold of control after TCE
exposure, respectively. For the B6CBA, wild-type, DBA, and NZB female groups, the starting
body weights averaged 22.5, 21.0, 23.0, and 21.0 g, while the average liver weights were 1.70-,
1.78-, 1.88-, and 2.09-fold of control after TCE exposure, respectively. The NMRI group of
female mice, did not follow this general pattern and had the highest initial body weight for the
single group of 10 mice reported (i.e., 27 g) associated with 1.66-fold of control liver weight.
The results of Kjellstrand et al. (1983b) suggested that there was more variability
between male mice than female mice in relation to TCE-induced liver weight gain. More strains
exhibited TCE-induced body weight changes in male mice than female mice, suggesting
increased susceptibility of male mice to TCE toxicity as well as more variability in response.
Initial body weight also appeared to be a factor in the magnitude of TCE-induced liver weight
induction rather than just strain. In general, the strains and groups within strain that had
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TCE-induced body weight decreases had smaller TCE-induced increase in liver weight.
Therefore, only examining liver weight in males as an indication of TCE treatment effects
would not be an accurate predictor of strain sensitivity nor the magnitude or response at doses
that also affect body weight. The results from this study show that comparison of the magnitude
of TCE response, as measured by liver weight increases, should take into account strain, gender,
initial body weight, and systemic toxicity. It shows a consistent pattern of increased liver
weight in both male and female mice after TCE exposure of 150 ppm for 30 days.
Kjellstrand et al. (1983a) presented data in the NMRI strain of mice (a strain that
appeared to be more prone to TCE-induced toxicity in male mice and a smaller TCE-induced
increase in liver weight in female mice) after inhalation exposure of 37-300 ppm TCE. They
used the same experimental paradigm as that reported in Kjellstrand et al. (1983b) except for
exposure concentration.
For female mice exposed to concentrations of TCE ranging from 37 to 300 ppm TCE
continuously for 30 days, only the 300 ppm group experienced a 16% decrease in body weight
between control and exposed animals. Therefore, changes in TCE-induced liver weight
increases were affected by changes in body weight only for that group. Initial body weights in
the TCE-exposed female mice were similar in each of these groups (i.e., range of 29.2-31.6 g,
or 8%), with the exception of the females exposed to 150 ppm TCE for 30 days (i.e., initial body
weight of 27.3 g), reducing the effects of differences in initial body weight on TCE-induced
liver weight induction. Exposure to TCE continuously for 30 days was reported to result in a
linear dose-dependent increase in liver weight in female mice with 1.06-, 1.27-, 1.66-, and
2.14-fold of control liver weights reported at 37, 75, 150, and 300 ppm TCE, respectively.
In male mice, there were more factors affecting reported liver weight increases from
TCE exposure. For male mice, both the 150 and 300 ppm exposed groups experienced a 10 and
18% decrease in final body weight after TCE exposure, respectively. The 37 and 75 ppm
groups did not have decreased final body weight due to TCE exposure but varied by 12% in
initial body weight. TCE-induced increases in liver weight were reported to be 1.15-, 1.50-,
1.69-, and 1.90-fold of control for 37, 75, 150, and 300 ppm TCE exposure in male mice,
respectively. The flattening of the dose-response curve at the two highest doses is consistent
with the effects of toxicity on final body weight.
Kjellstrand et al. (1983a) noted that liver mass increased and the changes in liver cell
morphology were similar in TCE-exposed male and female mice. They report that after
150 ppm exposure for 30 days, liver cells were generally larger and often displayed a fine
vacuolization of the cytoplasm, changes in nucleoli appearance. Kupffer cells of the sinusoid
were reported to be increased in cellular and nuclear size. The intralobular connective tissue
was infiltrated by inflammatory cells. Exposure to TCE in higher or lower concentrations
during the 30 days was reported to produce a similar morphologic picture.
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For mice that were exposed to 150 ppm TCE for 30 days and then examined 120 days
after the cessation of exposure, liver weights were 1.09-fold of control for TCE-exposed female
mice and the same as controls for TCE-exposed male mice. However, the livers were not the
same as untreated liver in terms of histopathology. The authors reported that "after exposure to
150 ppm for 30 days, followed by 120 days of rehabilitation, the morphological picture was
similar to that of the air-exposure controls except for changes in cellular and nuclear sizes." The
authors did not present any quantitative data on the lesions they describe, especially in terms of
dose-response, and most of the qualitative description is for the 150 ppm exposure level in
which there are consistent reports of TCE induced body weight decreases in male mice.
Although stating that Kupffer cells were increased in cellular and nuclear size, no
differential staining was applied to light microscopy sections and used to distinguish Kupffer
from endothelial cells lining the hepatic sinusoid in this study. Without differential staining,
such a determination is difficult at the light microscopic level and a question remains as to
whether these are the same cells as described by Goel et al. (1992) as a proliferation of
sinusoidal endothelial cells after exposures of 1,000 and 2,000 mg/kg-day TCE exposure for
28 days in male Swiss mice. As noted in Section E.2.4.2, the discrepancy in DNA synthesis
measures between hepatocyte examinations of individual hepatocytes and whole liver measures
in several reports of TCE metabolite exposure, is suggestive of increased DNA synthesis in the
nonparenchymal cell compartment of the liver. Thus, nonparenchymal cell proliferation is
suggested as an effect of subchronic TCE exposures in mice without concurrent focal necrosis
via inhalation studies (Kj ell strand et al., 1983a) and with focal necrosis in the presence of TCE
in a groundnut oil vehicle (Goel etal., 1992).
Although Kjellstrand et al. (1983a) did not discuss polyploidization, the changes in cell
size and especially the continued change in cell size and nuclear staining characteristics after
120 days of cessation of exposure are consistent with changes in polyploidization induced by
TCE that were suggested in studies from shorter durations of exposure (Dees and Travis, 1993;
Elcombe etal., 1985) and of longer durations (e.g., Buben and O'Flaherty, 1985). Of note is that
in the histological descriptions provided by Kjellstrand et al. (1983a), there was no mention of
focal necrosis or apoptosis resulting from these exposures to TCE to mice. Vacuolization is
reported and consistent with hepatotoxicity or lipid accumulation, which is lost during routine
histological slide preparation. The lack of reported focal necrosis in mice exposed through
inhalation is consistent with reports of gavage experiments of TCE in mice that do not use corn
oil as the vehicle (Merrick et al., 1989).
Buben and O'Flaherty (1985) reported the effects of TCE via corn oil gavage after
6 weeks of exposure at concentrations ranging from 100 to 3,200 mg/kg-day. This study was
conducted with older mice than those generally used in chronic exposure assays (male Swiss-
Cox outbred mice between 3 and 5 months of age). Liver weight increases, decreases in liver
G6P activity, increases in liver triglycerides, and increases in SGPT activity were examined as
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parameters of liver toxicity. Few deaths were reported during the 6-week exposure period
except at the highest dose and related to CNS depression. TCE exposure caused dose-related
increases in percent liver/body weight with a dose as low as 100 mg/kg-day reported to cause a
statistically significant increase (i.e., 112% of control).
The increases in liver size were attributed to hepatocyte hypertrophy, as revealed by
histological examination and by a decrease in the liver DNA concentration, and although
enlarged, were reported to appear normal. A dose-related trend toward triglyceride
concentration was also noted. A dose-related decrease in glucose-6-phophatase activity was
reported with similar small decreases (-10%) observed in the TCE exposed groups that did not
reach statistical significance until the dose reached 800 mg/kg TCE exposure. SGPT activity
was not observed to be increased in TCE-treated mice except at the two highest doses and even
at the 2,400 mg/kg dose, half of the mice had normal values. The large variability in SGPT
activity was indicative of heterogeneity of this response between mice at the higher exposure
levels for this indicator of liver toxicity. Such variability of response in male mice is consistent
with the work of Kjellstrand and colleagues. Thus, the results from Buben and O'Flaherty
(1985) suggest that hepatomegaly is a robust response that was reported to be observed at the
lowest dose tested, dose-related, and not accompanied by overt toxicity.
In terms of histopathology, Buben and O'Flaherty (1985) reported swollen hepatocytes
with indistinct borders; their cytoplasm was clumped and a vesicular pattern was apparent and
not simply due to edema in TCE-treated male mice. Karyorrhexis (the disintegration of the
nucleus) was reported to be present in nearly all specimens from TCE-treated animals and
suggestive of impending cell death. It was not present in controls, appeared at a low level at
400 mg/kg TCE exposure level, and appeared to be slightly higher at 1,600 mg/kg TCE
exposure level. Central lobular necrosis was present only at the 1,600 mg/kg TCE exposure
level and at a very low level. Buben and O'Flaherty (1985) report increased polyploidy in the
central lobular region for both 400 and 1,600 mg/kg TCE and described it as hepatic cells
having two or more nuclei or enlarged nuclei containing increased amounts of chromatin, but at
the lowest level of severity or occurrence. Thus, the results of this study are consistent with
those of shorter-term studies via gavage, which report hepatocellular hypertrophy in the
centralobular region, increased liver weight induced at the lowest exposure level tested and at a
level much lower than those inducing overt toxicity, and that TCE exposure is associated with
changes in ploidy.
The NTP 13-week study of TCE gavage exposure in 10 F344/N rats (125-2,000 mg/kg
[males] and 62.5-1,000 mg/kg [females]) and in B6C3Fimice (375-6,000 mg/kg) reported that
all rats survived the 13-week study. However, male rat receiving 2,000 mg/kg exhibited a 24%
difference in final body weight. The study descriptions of pathology in rats and mice were not
very detailed and included only mean liver weights. The rats had increased pulmonary
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vasculitis at the highest concentration of TCE and viral liters were positive for Sendai virus. No
liver effects were noted for them in the study.
For mice, liver weights (both absolute and percent liver/body weight) were reported to
increase in a dose-related fashion with TCE exposure and to be increased by >10% in
750 mg/kg TCE-exposed males and > 1,500 mg/kg TCE-exposed females. Hepatotoxicity was
reported as centrilobular necrosis in 6/10 males and 1/10 females exposed to 6,000 mg/kg TCE
and multifocal areas of calcifications scattered throughout 3,000 mg/kg TCE exposed male mice
and only a single female 6,000 mg/kg dose, considered to be evidence of earlier hepatocellular
necrosis. One female mouse exposed to 3,000 mg/kg TCE also had a hepatocellular adenoma,
an extremely rare lesion in female mice of this age (20 weeks). At the lowest dose of exposure,
there was a consistent decrease in liver weight in female and male mice after 13 weeks of TCE
exposure.
Kawamoto et al. (1988b) exposed rats to 2 g/kg TCE subcutaneously for 15 weeks and
reported TCE-induced increases in liver weight. They also reported increase in CYP,
cytochrome b-5, and NADPH cytochrome c reductase. The difficulties in relating this route of
exposure to more environmentally relevant ones is discussed in Section E.2.2.11.
For 2-year or lifetime studies of TCE exposure, a consistent hepatocarcinogenic response
has been observed in mice of differing strains and genders and from differing routes of
exposure. However, for rats, some studies have been confounded by mortality from gavage
error or the toxicity of the dose of TCE administered. In some studies, a relative insensitive
strain of rat has been used. However, in general, it appears that the mouse is more sensitive than
the rat to TCE-induced liver cancer. Three studies give results the authors consider to be
negative for TCE-induced liver cancer in mice, but have either design and/or reporting
limitations, or are in strains and paradigms with apparent low ability for liver cancer induction
or detection.
Fukuda et al. (1983) reported a 104-week inhalation bioassay in female Crj:CD-l (ICR)
mice and female Crj:CD (Sprague-Dawley) rats exposed to 0, 50, 150, and 450 ppm TCE
(n = 50). There were no reported incidences of mice or rats with liver tumors for controls
indicative of relatively insensitive strains used in the study for liver effects. While TCE was
reported to induce a number of other tumors in mice and rats in this study, the incidence of liver
tumors was <2% after TCE exposure. Of note is the report of cystic cholangioma reported in
one group of rats.
Henschler et al. (1980) exposed NMRI mice and WIST random bred rats to 0, 100, and
500 ppm TCE for 18 months (n = 30). This study is limited by short duration of exposure, low
number of animals, and low survival in rats. Control male mice were reported to have one HCC
and one hepatocellular adenoma with the incidence rate unknown. In the 100 ppm TCE exposed
group, two hepatocellular adenomas, and one mesenchymal liver tumor were reported. No liver
tumors were reported at any dose of TCE in female mice or controls. For male rats, only one
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hepatocellular adenomas at 100 ppm was reported. For female rats no liver tumors were
reported in controls, but one adenoma and one cholangiocarcinoma was reported at 100 ppm
TCE and at 500 ppm TCE, two cholangioadenomas, a relatively rare biliary tumor, was
reported. The difference in survival in mice, did not affect the power to detect a response, as
was the case for rats. However, the low number of animals studied, abbreviated exposure
duration, and apparently low sensitivity of this paradigm (i.e., no background response in
controls) suggests a study of limited ability to detect a TCE carcinogenic liver response. Of note
is that both Fukuda et al. (1983) and Henschler et al. (1980) report rare biliary cell derived
tumors in rats in relatively insensitive assays.
Van Duuren et al. (1979) exposed mice to 0.5 mg/mouse to TCE via gavage once a week
in 0.1 mL trioctanion (n = 30). Inadequate design and reporting of this study limit that ability to
use the results as an indicator of TCE carcinogenicity.
The NCI (1976) study of TCE was initiated in 1972 and involved the exposure of
Osborne-Mendel rats and B6C3Fi mice to varying concentrations of TCE. The animals were
co-exposed to a number of other carcinogens as exhalation as multiples studies and control
animals all shared the same laboratory space. Treatment duration was 78 weeks and animals
received TCE via gavage in corn oil at two doses (n = 20 for controls, but n = 50 for treatment
groups). For rats, the high dose was reported to result in significant mortality (i.e., 47/50 high-
dose rats died before scheduled termination of the study). A low incidence of liver tumors was
reported for controls and carbon tetrachloride positive controls in rats from this study. In
B6C3Fi mice, TCE was reported to increase incidence of HCCs in both doses and both genders
of mice (-1,170 and 2,340 mg/kg for males and 870 and 1,740 mg/kg for female mice). HCC
diagnosis was based on histologic appearance and metastasis to the lung. The tumors were
described in detail and to be heterogeneous "as described in the literature" and similar in
appearance to tumors generated by carbon tetrachloride. The description of liver tumors in this
study and tendency to metastasize to the lung are similar to descriptions provided by Maltoni et
al. (1986) for TCE-induced liver tumors in mice via inhalation exposure.
For male rats, noncancer pathology in the NCI (1976) study was reported to include
increased fatty metamorphosis after TCE exposure and angiectasis or abnormally enlarged blood
vessels. Angiectasis can be manifested by hyperproliferation of endothelial cells and dilatation
of sinusoidal spaces. The authors conclude that due to mortality, "the test is inconclusive in
rats." They note the insensitivity of the rat strain used from their data on the positive control of
carbon tetrachloride exposure.
The NTP (1990) study of TCE exposure in male and female F344/N rats, and B6C3Fi
mice (500 and 1,000 mg/kg for rats and 1,000 mg/kg for mice) was limited in the ability to
demonstrate a dose-response for hepatocarcinogenicity. There was also little reporting of non-
neoplastic pathology or toxicity and no report of liver weight at termination of the study.
However, by the end of a 2-year cancer bioassay, liver tumor induction can be a significant
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factor in any changes in liver weight. No treatment-related increases in necrosis in the liver
were observed in mice. A slight increase in the incidence of focal necrosis was noted for
TCE-exposed male mice (8 vs. 2% in control) with a slight reduction in fatty metamorphosis in
treated male mice (0 treated vs. 2 control animals). In female mice, there was a slight increase
in focal inflammation (29 vs. 19% of animals) and no other changes. Therefore, this study did
not show concurrent evidence of liver toxicity but did show TCE-induced neoplasia after 2 years
of TCE exposure in mice. The administration of TCE was reported to cause earlier expression
of tumors as the first animals with carcinomas were reported to have them 57 weeks for
TCE-exposed animals and 75 weeks for control male mice.
The NTP (1990) study reported that TCE exposure was associated with increased
incidence of HCC (tumors with markedly abnormal cytology and architecture) in male and
female mice. Hepatocellular adenomas were described as circumscribed areas of distinctive
hepatic parenchymal cells with a perimeter of normal appearing parenchyma in which there
were areas that appeared to be undergoing compression from expansion of the tumor. Mitotic
figures were sparse or absent but the tumors lacked typical lobular organization. HCCs had
markedly abnormal cytology and architecture with abnormalities in cytology cited as including
increased cell size, decreased cell size, cytoplasmic eosinophilia, cytoplasmic basophilia,
cytoplasmic vacuolization, cytoplasmic hyaline bodies, and variations in nuclear appearance.
Furthermore, in many instances, several or all of the abnormalities were present in different
areas of the tumor and variations in architecture with some of the HCCs having areas of
trabecular organization. Mitosis was variable in amount and location. Therefore, the phenotype
of tumors reported from TCE exposure was heterogeneous in appearance between and within
tumors.
For rats, the NTP (1990) study reported no treatment-related non-neoplastic liver lesions
in males and a decrease in basophilic cytological change reported from TCE-exposure in
females. The results for detecting a carcinogenic response in rats were considered to be
equivocal because both groups receiving TCE showed significantly reduced survival compared
to vehicle controls and because of a high rate (e.g., 20% of the animals in the high-dose group)
of death by gavage error.
The NTP (1988) study of TCE exposure in four strains of rats to "diisopropylamine-
stabilized TCE" was also considered inadequate for either comparing or assessing TCE-induced
carcinogenesis in these strains of rats because of chemically induced toxicity, reduced survival,
and incomplete documentation of experimental data. TCE gavage exposures of 0, 500, or
1,000 mg/kg-day (5 days/week, for 103 weeks) male and female rats was also marked by a large
number of accidental deaths (e.g., for high-dose male Marshal rats, 25 animals were accidentally
killed).
Results from a 13-week study were briefly mentioned in the report and indicated that
exposure levels of 62.5-2,000 mg/kg TCE were not associated with decreased survival (with the
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exception of three male August rats receiving 2,000 mg/kg TCE). Administration of the
chemical for 13 weeks was not associated with histopathological changes.
In regard to evidence of liver toxicity, the 2-year study of TCE exposure reported no
evidence of TCE-induced liver toxicity described as non-neoplastic changes ACT, August,
Marshal, and Osborne-Mendel rats. Interestingly, for the control animals of these four strains,
there was, in general, a low background level of focal necrosis in the liver of both genders. In
summary, the negative results in this bioassay are confounded by the killing of a large portion of
the animals accidently by experimental error but TCE-induced overt liver toxicity was not
reported.
Maltoni et al. (1986) reported the results of several studies of TCE via inhalation and
gavage in mice and rats. A large number of animals were used in the treatment groups but the
focus of the study was detection of a neoplastic response with only a generalized description of
tumor pathology phenotype given and limited reporting of non-neoplastic changes in the liver.
Accidental death by gavage error was reported not to occur in this study. In regards to effects of
TCE exposure on survival, "a nonsignificant excess in mortality" correlated to TCE treatment
was observed only in female rats (treated by ingestion with the compound) and in male B6C3Fi
mice.
TCE-induced effects on body weight were reported to be absent in mice except for one
experiment (BT 306 bis) in which a slight nondose correlated decrease was found in exposed
animals. "Hepatoma" was the term used to describe all malignant tumors of hepatic cells, of
different subhistotypes, and of various degrees of malignancy, and were reported to be unique or
multiple and have different sizes (usually detected grossly at necropsy) from TCE exposure. In
regard to phenotype, tumors were described as usual type observed in Swiss and B6C3Fi mice,
as well as in other mouse strains, either untreated or treated with hepatocarcinogens and to
frequently have medullary (solid), trabecular, and pleomorphic (usually anaplastic) patterns.
Swiss mice from this laboratory were reported to have a low incidence of hepatomas without
treatment (1%). The relatively larger number of animals used in this bioassay (n = 90-100), in
comparison to NTP standard assays, allows for a greater power to detect a response.
TCE exposure for 8 weeks via inhalation at 100 or 600 ppm may have been associated
with a small increase in liver tumors in male mice in comparison to concurrent controls during
the life span of the animals. In Swiss mice exposed to TCE via inhalation for 78 weeks, there a
reported increase in hepatomas associated with TCE treatment that was dose-related in male, but
not female, Swiss mice. In B6C3Fi mice exposed via inhalation to TCE for 78 weeks, the
results from one experiment indicated a greater increase in liver cancer in females than male
mice, but in a second experiment in males, there was a TCE-exposure associated increase in
hepatomas. Although the mice were supposed to be of the same strain, the background level of
liver cancer was significantly different in male mice. The finding of differences in response in
animals of the same strain but from differing sources has also been reported in other studies for
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other endpoints (see Section E.3.1.2). However, for both groups of male B6C3Fi mice, the
background rate of liver tumors over the lifetime of the mice was <20%.
For rats, there were four liver angiosarcomas reported (one in a control male rat, one
each in a TCE-exposed male and female at 600 ppm TCE for 8 weeks, and one in a female rat
exposed to 600 ppm TCE for 104 weeks), but the specific results for incidences of
hepatocellular "hepatomas" in treated and control rats were not given. Although Maltoni et al.
(1986) concluded that the small number of these tumors was not treatment-related, the findings
were brought forward because of the extreme rarity of this tumor in control Sprague-Dawley
rats, untreated or treated with vehicle materials. In rats treated for 104 weeks, there was no
report of a TCE treatment-related increase in liver cancer in rats. This study only presented data
for positive findings, so it did not give the background or treatment-related findings in rats for
liver tumors in this study. Thus, the extent of background tumors and sensitivity for this
endpoint cannot be determined.
Of note is that the Sprague-Dawley strain used in this study was also noted in the Fukuda
et al. (1983) study to be relatively insensitive for spontaneous liver cancer and to also be
negative for TCE-induced hepatocellular liver cancer induction in rats. However, like the
Fukuda et al. (1983) and Henschler et al. (1980) studies, which reported rare biliary tumors in
insensitive strains of rat for hepatocellular tumors, Maltoni et al. (1986) reported a relatively
rare tumor type, angiosarcoma, after TCE exposure in a relatively insensitive strain for
"hepatomas." As noted above, many of the rat studies were limited by premature mortality due
to gavage error or premature mortality (NTP. 1990. 1988: Henschler et al.. 1980: NCL 1976).
which was reported not occur in Maltoni et al. (1986).
There were other reports of TCE carcinogen!city in mice from chronic exposures that
were focused primarily on detection of liver tumors with limited reporting of tumor phenotype
or non-neoplastic pathology. Herren-Freund et al. (1987) reported that male B6C3Fi mice given
40 mg/L TCE in drinking water had increased tumor response after 61 weeks of exposure.
However, concentrations of TCE fell by about half at this dose of TCE during the twice a week
change in drinking water solution, so the actual dose of TCE the animals received was
<40 mg/L. The percent liver/body weight was reported to be similar for control and TCE-
exposed mice at the end of treatment. Despite difficulties in accurately establishing the dose
received, an increase in adenomas per animal and an increase in the number of animals with
HCCs were reported to be associated with TCE exposure after 61 weeks of exposure and
without apparent hepatomegaly.
Anna et al. (1994) reported tumor incidences for male B6C3Fi mice receiving
800 mg/kg-day TCE via gavage (5 days/week for 76 weeks). All TCE-treated mice were
reported to be alive after 76 weeks of treatment. Although the control group contained a
mixture of exposure durations (76-134 weeks) and concurrent controls had a very small number
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of animals, TCE-treatment appeared to increase the number of animals with adenomas and the
mean number of adenomas and carcinomas, but with no concurrent TCE-induced cytotoxicity.
E.2.4.4. Summary of Results for Subchronic and Chronic Effects of DCA and TCA:
Comparisons With TCE
There are no similar studies for TCA and DCA conduced at 6 weeks and with the range
of concentrations examined in Buben and O'Flaherty (1985) for TCE. In general, many studies
of DCA and TCA have been conducted at few and high concentrations, with shortened durations
of exposure, and varying and low numbers of animals to examine primarily a liver tumor
response in mice. However, the analyses presented in Section E.2.4.2 gives comparisons of
administered TCA and DCA dose-responses for liver weight increases for a number of studies in
combination as well as comparing such dose-responses to that of TCE and its oxidative
metabolism. As stated above, many subchronic studies of DCA and TCA have focused on
elucidating a relationship between dose and hypothesized events that may be indicators of
carcinogenic potential that have been described in chronic studies with a focus on indicators of
peroxisome proliferation and DNA synthesis. Many chronic studies have focused on the nature
of the DCA and TCA carcinogenic response in mouse liver through examination of the tumors
induced.
Almost all of the chronic studies for DCA and TCA have been carried out in mice. As
the database for examination of the ability of TCE to induce liver tumors in rats includes several
studies that have been limited in ability determine a carcinogenic response in the liver, the
database for DCA and TCA in rats is even more limited. For TCA, the only available study in
rats (DeAngelo et al., 1997) has been frequently cited in the literature to indicate a lack of
response in this species for TCA-induced liver tumors. Although reporting an apparent dose-
related increase in multiplicity of adenomas and an increase in carcinomas over control at the
highest dose, DeAngelo et al. (1997) use such a low number of animals per treatment group
(n = 20-24) that the abilities of this study to determine a statistically significant increase in
tumor response and to be able to determine that there was no treatment-related effect were
limited. A power calculation of the study shows that the type II error, which should be >50%,
was <8% probability for incidence and multiplicity of all tumors at all exposure TCA
concentrations with the exception of the incidence of adenomas and adenomas and carcinomas
for 0.5 g/L treatment group (58%) in which there was an increase in adenomas reported over
control (15 vs. 4%) that was the same for adenomas and carcinomas combined. Therefore, the
designed experiment could accept a false null hypothesis and erroneously conclude that there is
no response due to TCA treatment. While suggesting a lower response than for mice for liver
tumor induction, it is inconclusive for determination of whether TCA induces a carcinogenic
response in the liver of rats.
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For DCA, there are two reported long-term studies in rats (DeAngelo et al., 1996;
Richmond et al., 1995) that appear to have reported the majority of their results from the same
data set and which consequently were subject to similar design limitations and DCA-induced
neurotoxicity in this species. DeAngelo et al. (1996) reported increased hepatocellular
adenomas and carcinomas in male F344 rats exposed for 2 years. However, the data from
exposure concentrations at a 5 g/L dose had to be discarded and the 2.5 g/L DCA dose had to be
continuously lowered during the study due to neurotoxicity. There was a DCA-induced
increased in adenomas and carcinomas combined reported for the 0.5 g/L DCA (24.1 vs. 4.4%
adenomas and carcinomas combined in treated vs. controls) and an increase at a variable dose
started at 2.5 g/L DCA and continuously lowered (28.6 vs. 3.0% adenomas and carcinomas
combined in treated vs. controls). Only combined incidences of adenomas and carcinomas for
the 0.5 g/L DCA exposure group were reported to be statistically significant by the authors,
although the incidence of adenomas was 17.2 vs. 4% in treated vs. control rats.
Hepatocellular tumor multiplicity was reported to be increased in the 0.5 g/L DCA group
(0.31 adenomas and carcinomas/animal in treated vs. 0.04 in control rats) but was reported by
the authors to not be statistically significant. At the starting dose of 2.5 g/L, continuously
lowered due to neurotoxicity, the increased multiplicity of HCCs was reported by the authors to
be to be statistically significant (0.25 carcinomas/animals vs. 0.03 in control) as well as the
multiplicity of combined adenomas and carcinomas (0.36 adenomas and carcinomas/animals vs.
0.03 in control rats).
Issues that affected the ability to determine the nature of the dose-response for this study
include: (1) the use of a small number of animals (n = 23, n =21, and n = 23 at final sacrifice for
the 2.0 g/L sodium chloride control, 0.05, and 0.5 g/L treatment groups) that limit the power of
the study both to determine statistically significant responses and to determine that there are not
treatment-related effects (i.e., power); (2) apparent addition of animals for tumor analysis not
present at final sacrifice (i.e., 0.05 and 0.5 g/L treatment groups); and (3) most of all, the lack of
a consistent dose for the 2.5 g/L DCA exposed animals.
Similar issues were present for the study of Richmond et al. (1995) that was conducted
by the same authors as DeAngelo et al. (1996) and appeared to be from the same data set. The
Richmond et al. (1995) data for the 2 g/L sodium chloride, 0.05 g/L DCA, and 0.5 g/L DCA
exposure groups were the same data set reported by DeAngelo et al. (1996) for these groups.
Additional data was reported for F344 rats administered and 2.5 g/L DCA that, due to hind-limb
paralysis, were sacrificed 60 weeks (DeAngelo et al., 1996). Tumor multiplicity was not
reported by the authors. There was a small difference in reports of the results between the two
studies for the same data for the 0.5 g/L DCA group in which Richmond et al. (1995) reported a
21% incidence of adenomas and DeAngelo et al. (1996) reported a 17.2% incidence. The
authors did not report any of the results of DCA-induced increases of adenomas and carcinomas
to be statistically significant. The same issues discussed above for DeAngelo et al. (1996) apply
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to this study. Similar to the DeAngelo study of TCA in rats (DeAngelo et al., 1997) the study of
DC A exposure in rats reported by DeAngelo et al. (1996) and Richmond et al. (1995), the use of
small numbers of rats limits the detection of treatment-related effects and the ability to
determine whether there was no treatment related effects (Type II error), especially at the low
concentrations of DC A exposure.
For mice, the data for both DCA and TCA is much more extensive and has shown that
both DCA and TCA induced liver tumors in mice. Many of the studies are for relatively high
concentrations of DCA or TCA, have been conducted for <1 year, and have focused on the
nature of tumors induced to ascertain potential modes of action and to make inferences as to
whether TCE-induced tumors in mice are similar. As shown previously in Section E.2.4.2, the
dose-response curves for increased liver weight for TCE administration in male mice are more
similar to those for DCA administration and TCE oxidative metabolism than for direct TCA
administration. There are two studies in male B6C3Fi mice that attempt to examine multiple
concentrations of DCA and TCA for 2-year studies (DeAngelo et al., 2008; DeAngelo et al.,
1999) at doses that do not induce cytotoxicity and attempt to relate them to subchronic changes
and peroxisomal enzyme induction. However, the DeAngelo et al. (2008) study was carried out
in B6C3Fi mice that were of large size and prone to liver cancer and premature mortality,
limiting its use for the determination of TCA-dose response in a 2-year bioassay. One study in
female B6C3Fi mice describes the dose-response for liver tumor induction at a range of DCA
and TCA concentrations after 51 or 82 weeks (Pereira, 1996) with a focus on the type of tumor
each compound produced.
DeAngelo et al. (1999) conducted a study of DCA exposure to determine a dose
response for the hepatocarcinogenicity of DCA in male B6C3Fi mice over a lifetime exposure
and especially at concentrations that did not illicit cytotoxicity or were for abbreviated exposure
durations. DeAngelo et al. (1999) used 0.05, 0.5, 1.0, 2.0, and 3.5 g/L exposure concentrations
of DCA in their 100-week drinking water study. The number of animals at final sacrifice was
generally low in the DCA treatment groups and variable (i.e., n = 50, n = 33, n = 24, n = 32,
n = 14, and n = 8 for control, 0.05, 0.5, 1, 2.0, and 3.5 g/L DCA exposure groups). It was
apparent that animals that died unscheduled deaths between weeks 79 and 100 were included in
data reported for 100 weeks. Although the authors did not report how many animals were
included in the 100-week results, it appeared that the number was no greater than 1 for the
control, 0.05, and 0.5 exposure groups and varied between 3 and 7 for the higher DCA exposure
groups.
The multiplicity or number of HCCs/animals was reported to be significantly increased
over controls in a dose-related manner at all DCA treatments including 0.05 g/L DCA, and a
NOEL reported not to be observed by the authors (i.e., 0.28, 0.58, 0.68, 1.29, 2.47, and
2.90 HCCs/animal for control, 0.05, 0.5, 1.0, 2.0, and 3.5 g/L DCA). Between the 0.5 and
3.5 g/L exposure concentrations of DCA, the magnitude of increase in multiplicity was similar
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to the increases in magnitude in dose. The incidence of HCCs was reported to be increased at
all doses as well, but not reported to be statistically significant at the 0.05 g/L exposure
concentration. However, given that the number of mice examined for this response (n = 33), the
power of the experiment at this dose was only 16.9% to be able to determine that there was not a
treatment-related effect. The authors did not report the incidence or multiplicity of adenomas
for the 0.05 g/L exposure group in the study and neither did they report the incidence or
multiplicity of adenomas and carcinomas in combination. For the animals surviving from 79 to
100 weeks of exposure, the incidence and multiplicity of adenomas peaked at 1 g/L, while
HCCs continued to increase at the higher doses. This would be expected where some portion of
the adenomas would either regress or progress to carcinomas at the higher doses.
DeAngelo et al. (1999) reported that peroxisome proliferation was significantly
increased at 3.5 g/L DCA only at 26 weeks, not correlated with tumor response, and not
increased at either 0.05 or 0.5 g/L treatments. The authors concluded that DCA-induced
carcinogenesis was not dependent on peroxisome proliferation or chemically sustained
proliferation, as measured by DNA synthesis. DeAngelo et al. (1999) reported not only a dose-
related increase in DCA-induced liver tumors, but also a decrease in time-to-tumor associated
with DCA exposure at the lowest levels examined. In regards to cytotoxicity, there appeared to
be a treatment-related, but not dose-related, increase in hepatocellular necrosis that did not
involve most of the liver from 1 to 3.5 g/L DCA exposures for 26 weeks of exposure. By
52 weeks, this effect was diminished with no necrosis observed at the 0.5 g/L DCA treatment
for any exposure period.
Hepatomegaly was reported to be absent by 100 weeks of exposure at the 0.05 and
0.5 g/L exposures, while there was an increase in tumor burden reported. However, slight
hepatomegaly was present by 26 weeks in the 0.5 g/L group and decreased with time. Not only
did the increase in multiplicity of HCCs increase proportionally with DCA exposure
concentration after 79-100 weeks of exposure, but so did the increases in percent liver/body
weight.
DeAngelo et al. (1999) presented a figure comparing the number of HCCs/animal at
100 weeks compared with the percent liver/body weight at 26 weeks that showed a linear
correlation (r2 = 0.9977), while peroxisome proliferation and DNA synthesis did not correlate
with tumor induction profiles. The proportional increase in liver weight with DCA exposure
was also reported for shorter durations of exposure as noted in Section E.2.4.2. The findings of
the study illustrate the importance of examining multiple exposure levels at lower
concentrations, at longer durations of exposure, and with an adequate number of animals to
determine the nature of a carcinogenic response. Although Carter et al. (1995) suggested that
there is evidence of DCA-induced cytotoxicity (e.g., loss of cell membranes and apparent
apoptosis) at higher levels, the 0.5 g/L exposure concentration was shown by DeAngelo et al.
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(1999) to increase hepatocellular tumors after 100 weeks of treatment without concurrent
peroxisome proliferation or cytotoxicity in mice.
As noted in detail in Section E.2.3.2.13, DeAngelo et al. (2008) exposed male B6C3Fi
mice to neutralized TCA in drinking water to male B6C3Fi mice in three studies. Rather than
using five exposure levels that were generally twofold apart, as was done in DeAngelo et al.
(1999) for DC A, DeAngelo et al. (2008) studied only three doses of TCA that were an order of
magnitude apart, which limits the elucidation of the shape of the dose-response curve. In
addition, DeAngelo et al. (2008) contained two studies, each conducted in a separate
laboratories, for the 104-week data so that the two lower doses were studied in one study and the
highest dose in another. The first study was conducted using 2 g/L sodium chloride, or 0.05,
0.5, or 5 g/L TCA in drinking water for 60 weeks (Study #1), while the other two studies were
conducted for a period of 104 weeks (Study #2 with 2.5 g/L neutralized acetic acid or 4.5 g/L
TCA exposure groups and Study #3 with deionized water, 0.05 and 0.5 g/L TCA exposure
groups). In the studies reported in DeAngelo et al. (2008), a small number of animals has been
used for the determination of a tumor response (~n = 30 at final necropsy), but for the data for
liver weight or PCO activity at interim sacrifices, the number was even smaller (n = 5).
The percent liver/body weight changes at 4 weeks in Study #1 have been included in the
analysis for all TCA data in Section E.2.4.2, and are consistent with that data. Although there
was a 10-fold difference in TCA exposure concentration, there was a 9, 16, and 35% increase in
liver weight over control for the 0.05, 0.5, and 5 g/L TCA exposures. PCO activity varied
2.7-fold as baseline controls, but the increase in PCO activity at 4 weeks was 1.3-, 2.4-, and
5.3-fold of control for the 0.05, 0.5, and 5 g/L TCA exposure groups in Study #1. The incidence
data for adenomas observed at 60 weeks was 2.1-, 3.0-, and 5.4-fold of control values and the
fold increases in multiplicity were similar after 0.05, 0.5, and 5.0 g/L TCA. Thus, in general,
the dose-response for TCA-induced liver weight increases at 4 weeks was similar to the
magnitude of induction of adenomas at 60 weeks. Such a result is more consistent with the
ability of TCA to induce tumors and increases in liver weight at low doses with little change
with increasing dose as shown by this study and the combined data for TCA liver weight
induction by administered TCA presented in Section E.2.4.2.
While the 104-week data from Studies #2 and #3 could have been more valuable for
determination of the dose-response, as it would have allowed enough time for full tumor
expression, serious issues were apparent for Study #3, which was reported to have a 64%
incidence rate of adenomas and carcinomas for controls, while that of Study #2 was 12%. As
stated in Section E.2.3.2.13, the mice in Study #3 were of larger size than those of either
Study #1 or #2 and the large background rate of tumors reported is consistent with mice of these
size (Leakey et al., 2003a). However, the large background rate and increased mortality for
these mice limit their use for determining the nature of the dose-response for TCA liver
carcinogenicity.
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Examination of the data for treatment groups shows that there was no difference in any
of the results between the 0.5 g/L (Study #3) and 5 g/L (Study #2) TCA exposure groups (i.e.,
adenoma, carcinoma, and combinations of adenoma and carcinoma incidence and multiplicity)
for 104 weeks of exposure. For these same exposure groups, but at 60 weeks of exposure
(Study #1), there was a twofold increase in multiplicity for adenomas, and for adenomas and
carcinomas combined between the 0.5 and 5.0 g/L TCA exposure groups. At the two lowest
doses of 0.05 and 0.5 g/L TCA from Study #3 in the large-tumor prone mice, the differences in
the incidences and multiplicities for all tumors were twofold at 104 weeks. These results are
consistent with: the two highest exposure levels reaching a plateau of response after a long
enough duration of exposure for full expression of the tumors (i.e., -90% of animals having
liver tumors at the 0.5 and 5 g/L exposures) with the additional tumors observed in a tumor-
prone paradigm. Thus, without use of the 0.05 and 0.5 g/L TCA data from Study #3, only the
4.5 g/L TCA data from Study #2 can be used for determination of the TCA cancer response in a
2-year bioassay.
To put the 64% incidence data for carcinomas and adenomas reported in DeAngelo et al.
(2008) for the control group of Study #3 in context, other studies cited in this review for male
B6C3Fi mice show a much lower incidence in liver tumors with: (1) NCI (1976) reporting a
colony control level of 6.5% for vehicle and 7.1% incidence of HCCs for untreated male
B6C3Fi mice (n = 70-77) at 78 weeks; (2) Herren-Freund et al. (1987) reporting a 9% incidence
of adenomas in control male B6C3Fi mice with a multiplicity of 0.09 ± 0.06 and no carcinomas
(n = 22) at 61 weeks; (3) NTP (1990) reporting an incidence of 14.6% adenomas and 16.6%
carcinomas in male B6C3Fi mice after 103 weeks (n = 48); and (4) Maltoni et al. (1986)
reporting thatB6C3Fi male mice from the "NCI source" had a 1.1% incidence of "hepatoma"
(carcinomas and adenomas) and those from "Charles River Co." had a 18.9% incidence of
"hepatoma" during the entire lifetime of the mice (n = 90 per group).
The importance of examining an adequate number of control or treated animals before
confidence can be placed in those results in illustrated by Anna et al. (1994), in which at
76 weeks, 3/10 control male B6C3Fi mice that were untreated and 2/10 control animals given
corn oil were reported to have adenomas, but from 76 to 134 weeks, 4/32 mice were reported to
have adenomas (multiplicity of 0.13 ± 0.06) and 4/32 mice were reported to have carcinomas
(multiplicity of 0.12 ± 0.06). Thus, the reported combined incidence of carcinomas and
adenomas of 64% reported by DeAngelo et al. (2008) for the control mice of Study #3, is not
only inconsistent and much higher than those reported in Studies #1 and #2, but also much
higher than reported in a number of other studies of TCE.
Trying to determine a correspondence with either liver weight increases or increases in
PCO activity after shorter periods of exposure will be depend on whether data reported in Study
#3 in the 104-week studies can be used. DeAngelo et al. (2008) reported a regression analyses
that compared "percent of hepatocellular neoplasia," indicated by tumor multiplicity, with TCA
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dose, represented by estimations of the TCA dose in mg/kg-day, and with PCO activity for the
60- and 104-week data. Whether adenomas and carcinomas combined or individual tumor type
were used in these analysis was not reported by the authors. Concerns arise also from
comparing PCO activity at the end of the experiments, when there was already a significant
tumor response, rather than at earlier time points. Such PCO data may not be useful as an
indicator key event in tumorigenesis when tumors are already present.
In addition, regression analyses of these data are difficult to interpret because of the dose
spacing of these experiments as the control and 5 g/L exposure levels will basically determine
the shape of the dose-response curve. The 0.05 and 0.5 g/L exposure levels are close to the
control value in comparison to the 5 g/L exposure level, the dose response appears to be linear
between control and the 5.0 g/L value with the two lowest doses not affect changing the slope of
the line (i.e., "leveraging" the regression). Thus, the value of these analyses is limited by:
(1) use of data from Study #3 in a tumor-prone mouse that is not comparable to those used in
Studies #1 and #2; (2) the appropriateness of using PCO values from later time points and the
variability in PCO control values; (3) the uncertainty of the effects of palatability on the 5 g/L
TCA results, which were reported in one study to reduce drinking water consumption; and
(4) the dose-spacing of the experiment.
DeAngelo et al. (2008) attempted to identify a NOEL for tumorigenicity using tumor
multiplicity data and estimated TCA dose. However, it is not an appropriate descriptor for these
data, especially given that "statistical significance" of the tumor response is the determinant
used by the authors to support the conclusions regarding a dose in which there is no
TCA-induced effect. Due to issues related to the appropriateness of use of the concurrent
control in Study #3, only the 60-week experiment (i.e., Study #1) is useful for the determination
of tumor dose-response. However, there is no allowance for full expression of a tumor response
at the 60-week time point. In addition, a power calculation of the 60-week study shows that the
type II error, which should be >50% and thus, greater than the chances of "flipping a coin," was
41 and 71% for incidence and 7 and 15% for multiplicity of adenomas for the 0.05 and 0.5 g/L
TCA exposure groups. For the combination of adenomas and carcinomas, the power calculation
was 8 and 92% for incidence and 6 and 56% for multiplicity at 0.05 and 0.5 g/L TCA exposure.
Therefore, the designed experiment could accept a false null hypothesis, especially in terms of
tumor multiplicity, at the lower exposure doses and erroneously conclude that there is no
response due to TCA treatment.
Pereira (1996) examined the tumor induction in female B6C3Fi mice and demonstrated
that foci, adenoma, and carcinoma development in mice are dependent on duration of exposure,
(or period of observation in the case of controls) for full expression of a carcinogenic response.
In control female mice, a 360- vs. 576-day observation period showed that at 360 days, no foci
or carcinomas and only 2.5% of animals had adenomas, whereas by 576 days of observation,
11% had foci, 2% adenomas, and 2% had carcinomas. For DC A and TCA treatments, foci,
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adenomas, and carcinoma incidence and multiplicity did not reach full expression until
82 weeks at the three doses employed (2.58 g/L DCA, 0.86 g/L DCA, 0.26 g/L DC A, 3.27 g/L
TCA, 1.1.0 g/L TCA, and 0.33 g/L TCA). Although the numbers of animals were relatively low
and variable at the two highest doses (18-28 mice), there were 50-53 mice studied at the lowest
dose level and 90 animals studied in the control group.
The results of Pereira (1996) showed that not only were the incidences of mice with foci,
adenoma, and carcinomas greatly increased with duration of exposure, but concentration also
affected the nature and magnitude of the response in female mice. At 2.86, 0.86, and 0.26 g/L
DCA exposures and controls, after 82 weeks, the incidence of adenomas in female B6C3Fi mice
was reported to be 84.2, 25.0, 6.0, and 2.2%, respectively, and carcinomas to be 26.3, 3.6, 0, and
2.2%, respectively. For the multiplicity or number of tumors/animal at these same exposure
levels of DCA, the multiplicity was reported to be 5.58, 0.32, 0.06, and 0.02 adenomas/animal,
and 0.37, 0.04, 0, and 0.02 carcinomas/animal. Thus, for DCA exposure in female mice, for
~3-fold increases in DCA exposure concentration, after 82 weeks of exposure, there was a
similar magnitude of increase in adenomas incidence with much greater increases in
multiplicity. For HCC induction, there was no increase in the incidence or multiplicity or
carcinomas between the control and 0.33 g/L DCA dose.
At 3.27, 1.10, and 0.33 g/L TCA and controls, after 82 weeks, the incidence of adenomas
in female B6C3Fi mice was reported to be 38.9, 11.1, 7.6, and 2.2%, respectively, and
carcinomas to be 27.8, 18.5, 0, and 2.2%, respectively. At these same exposure levels of TCA,
the multiplicity was reported to be 0.61, 0.11, 0.08, and 0.02 adenomas/animal, and 0.39, 0.22,
0, and 0.02 carcinomas/animal, respectively. Thus, for TCA, the incidences of adenomas were
lower at the two highest doses than DCA and the ~3-fold differences in dose between the two
lowest doses only resulted in -50% increase in incidences of adenomas. For incidence of
carcinomas, the ~3-fold difference in dose between the two highest doses only resulted in -50%
increase in carcinoma incidence. A similar pattern was reported for multiplicity after TCA
exposure. Foci were also examined and, in general, were similar to adenomas regarding
incidence and multiplicity. Thus, the dose-response curve for tumor induction in female mice
differed between DCA and TCA after 82 weeks of exposure with TCA having a much less steep
dose-response curve than DCA. This is consistent with the pattern of liver weight increases
reported for male B6C3Fi mice in Section E.2.4.2.
DeAngelo et al. (1999) reported a linear increase in incidence and multiplicity of HCCs
that was proportional to dose and as well as proportional to the magnitude of liver weight
increase from subchronic exposure to DCA. However, the studies of DeAngelo et al. (2008)
and Pereira (1996) are suggestive that TCA induced increase in tumor incidence are less
proportional to increases in dose as are liver weight increases from subchronic exposure.
Given that TCE subchronic exposure also induced an increase in liver weight that was
proportional to dose (i.e., similar to DCA but not TCA), it is of interest as to whether the dose-
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response for TCE induced liver cancer in mice was similar. The database for TCE, while
consistently showing a induction of liver tumors in mice, is very limited for making inferences
regarding the shape of the dose-response curve. For many of these experiments, multiplicity
was not given, only liver tumor incidence. NTP (1990), Bull et al. (2002), and Anna et al.
(1994) conducted gavage experiments in which they only tested one dose of-1,000 mg/kg-day
TCE. NCI (1976) tested two doses that were adjusted during exposure to an average of
1,169 and 2,339 mg/kg-day in male mice with only twofold dose spacing in only two doses
tested. Maltoni et al. (1988) conducted inhalation experiments in two sets of B6C3Fi mice and
one set of Swiss mice at three exposure concentrations that were threefold apart in magnitude
between the low and mid-dose and twofold apart in magnitude between the mid- and high-dose.
However, for one experiment in male B6C3Fi mice, the mice fought and suffered premature
mortality and for two the experiments in B6C3Fi mice, although using the same strain, the mice
were obtained from differing sources with very different background liver tumor levels.
For the Maltoni et al. (1988) study, a general descriptor of "hepatoma" was used for liver
neoplasia rather than describing hepatocellular adenomas and carcinomas so that comparison of
that data with those from other experiments is difficult. More importantly, while the number of
adenomas and carcinomas may be the same between treatments or durations of exposure, the
number of adenomas may decrease as the number of carcinomas increase during the course of
tumor progression. Such information is lost by using only a hepatoma descriptor.
Maltoni et al. (1988) did not report an increase over control for 100 ppm TCE for the
Swiss group and one of the B6C3Fi groups and only a slight increase (1.12-fold) in the second
B6C3Fi group. At 300 ppm TCE exposure, the incidences of hepatoma were 2-fold of control
values for the Swiss, 4-fold of control for group of B6C3Fi mice, and 1.6-fold of control for the
other group of B6C3Fi mice. At 600 ppm TCE, the incidences of hepatoma were 3.3-fold of
control for the Swiss group, 6.1-fold of control for one group of B6C3Fi mice, and 1.2-fold for
the other group of B6C3Fi mice. Thus, for each group of TCE exposed mice in the Maltoni et
al. (1988) inhalation study, the background levels of hepatomas and the shape of the dose-
response curve for TCE-hepatoma induction were variable. However, an average of the
increases, in terms of fold of control, between the three experiments gives a ~2.9-fold increase
between the low- and mid-dose (100 and 300 ppm) and ~1.4-fold increase between the mid- and
high-dose (300 and 600 ppm) groups.
Although such a comparison obviously has a high degree of uncertainty associated with
it, it suggests that the magnitude of TCE-induced hepatoma increases over control is similar to
the three- and twofold difference in the magnitude of exposure concentrations between these
doses. Therefore, the increase in TCE-induced liver tumors would roughly be proportional to
the magnitude of exposure dose. This result would be similar to the result for the concordance
of the increases in liver weight and exposure concentration observed at 28-42-day exposures to
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TCE (see Section E.2.4.2) using oral data from B6C3Fi and Swiss mice, and inhalation data
from NMRI mice.
The available inhalation data for TCE-induced liver weight dose-response is from one
study in a strain derived from Swiss mice (Kjellstrand et al., 1983a) and was conducted in male
and female mice with comparable doses of 75 and 300 ppm TCE. However, male mice of this
strain exhibited decreased body weight at the 300 ppm level, which can affect percent liver/body
weight increases. The magnitude of TCE-induced increases in liver weight between the 75 and
300 ppm exposures were ~1.80-fold for males (1.50 vs. 1.90-fold of control liver weights) and
4.2-fold for females (1.27- vs. 2.14-fold of control liver weight) in this strain.
Female mice were examined in one study each of Swiss and B6C3Fi mice by Maltoni et
al. (1988). Both the Swiss and B6C3Fi mice studies reported increases in incidences of
hepatomas over controls only at the 600 ppm TCE level in female mice, indicating less of a
response than males. Similarly, the Kjellstrand et al. (1983a) data also showed less of a
response in females compared to males in terms TCE induction of liver weight at the 37-
150 ppm range of exposure in NMRI strain. While the data for TCE dose-response of liver
tumor induction is very limited, it is suggestive of a correlation of TCE-induced increases in
liver weight correlating liver tumor induction with a pattern that is dissimilar to that of TCA.
Of those experiments conducted at -1,000 mg/kg-day gavage dose of TCE in male
B6C3Fi mice for at least 79 weeks (Bull et al.. 2002: Annaetal.. 1994: NTP, 1990: NCL 1976).
the control values were conducted in varying numbers of animals (some as low as n = 15, i.e.,
(Bull et al., 2002) and with varying results). The incidence of HCCs ranged from 1.2 to 16.7%
(Anna et al., 1994: NTP, 1990: NCI, 1976) and the incidence of adenomas ranged from 1.2 to
14.6% (Annaetal.. 1994: NTP. 1990) in control B6C3Fi mice. After -1,000 mg/kg-day TCE
treatment, the incidence of carcinomas ranged from 19.4 to 62% (Bull et al., 2002: Anna et al.,
1994: NTP. 1990: NCL 1976) with three of the studies (Annaetal.. 1994: NTP. 1990: NCL
1976) reporting a range of incidences between 42.8 and 62.0%). The incidence of adenomas
ranged from 28 to 66.7% (Bull et al.. 2002: Annaetal.. 1994: NTP. 1990). These data are
illustrative of the variability between experiments to determine the magnitude and nature of the
TCE response in the same gender (male), strain (B6C3Fi), time of exposure (3/4 studies were
for 76-79 weeks and 1 was for 2 years duration), and roughly the same dose (800-1,163 mg/kg-
day TCE).
Given that the TCE-induced liver response, as measured by liver weight increase, is
highly correlated with total oxidative metabolism to a number of agents that are hepatoactive
agents and hepatocarcinogens, the variability in response from TCE exposure would be expected
to be greater than studies of exposure to a single metabolite such as TCA or DCA.
Caldwell et al. (2008b) and Caldwell and Keshava (2006) have commented on the
limitations of experimental paradigms used to study liver tumor induction by TCE metabolites
and show that 51-week exposure duration has consistently produced a tumor response for these
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chemicals, but with greater lesion incidence and multiplicity at 82 weeks. As reported by
DeAngelo et al. (1999) and Pereira (1996), full expression of tumor induction in the mouse does
not occur until 78-100 weeks of DC A or TCA exposure, especially at lower concentrations.
Thus, use of abbreviated exposure durations and concurrently high exposure concentrations
limits the ability of such experiments to detect a treatment-related effect with the occurrence of
additional toxicity not necessarily associated with tumor induction. Caldwell et al. (2008b)
present a table that shows that the differences in the ability of the studies to detect treatment-
related effects could also be attributed to a varying and low number of animals in some exposure
groups and that because of the low numbers of animals tested at higher exposures, the power to
detect a statistically significant change is very low and, in fact for many of the endpoints, is
considerably less than "50% chance." Table E-17 from Caldwell et al. (2008b) illustrates the
importance of experimental design and the limitations in many of the studies in the TCE
metabolite database.
Table E-17. Power calculations" for experimental design described in text,
using Pereira and colleagues (1996) as an example
Exposure concentration1" in female
B6C3F! mice
20.0 mmol/L NaCl (control)
(82 wks)
2.58 g/L DCA (82 wks)
0.86 g/L DCA (82 wks)
0.26 g/L DCA (82 wks)
3. 27 g/L TCA (82 wks)
1.10 g/L TCA (82 wks)
0.33 g/L TCA (82 wks)
Number of animals
90
19
28
50
18
27
53
Power
calculation
for foci
Null
hypothesis
0.03
0.74
0.99
0.15
0.60
0.93
Power calculation
for adenomas
Null hypothesis
0.03
0.20
0.98
0.09
0.64
0.91
Power calculation
for carcinomas
Null hypothesis
0.13
0.91
-
0.14
0.3
-
aThe power calculations represent the probability of rejecting the null hypothesis when, in fact, the alternate
hypothesis is true for tumor multiplicity (i.e., the total number of lesions/number of animals). The higher the power
number calculated, the more confidence we have in the null hypothesis. Assumptions made included: normal
distribution for the fraction of tumors reported, null hypothesis represents what we expected the control tumor
fraction to be, the probability of a Type I error was set to 0.05, and the alternate hypothesis was set to 4 times the
null hypothesis value.
bConversion of mmol/L to g/L from the original reports of Pereira (1996) and Pereira and Phelps (1996) is as
follows: 20.0 mmol/L DCA = 2.58 g/L, 6.67 mmol/L DCA = 0.86 g/L, 2.0 mmol/L = 0.26 g/L, 20.0 mmol/L TCA =
3.27 g/L, 6.67 mmol/L TCA = 1.10 g/L, and 2.0 mmol/L TCA = 0.33 g/L.
Bull et al. (1990) examined male and female B6C3Fi mice (age 37 days) exposed from
15 to 52 weeks to neutralized DCA and TCA (1 or 2 g/L) but tumor data were not suitable for
dose response. They reported effects of DCA and TCA exposure on liver weight and percent
liver/body changes that gave a pattern of hepatomegaly generally consistent with short-term
exposure studies. Only 10 female mice were examined at 52 weeks, but the female mice were
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reported to be as responsive as males at the exposure concentration tested. After 37 weeks of
treatment and then a cessation of exposure for 15 weeks, liver weights and percent liver/body
weight were reported to be elevated over controls, which Bull et al. (1990) partially attribute the
remaining increases in liver weight to the continued presence of hyperplastic nodules in the liver.
Macroscopically, livers treated with DCA were reported to have multifocal areas of
necrosis and frequent infiltration of lymphocytes on the surface and interior of the liver. For
TCA-treated mice, similar necrotic lesions were reported but at such a low frequency that they
were similar to controls. Marked cytomegaly was reported from exposure to either 1 or 2 g/L
DCA throughout the liver. Cell size was reported to be increased from TCA and DCA treatment
with DCA producing the greatest change. The 2 g/L TCA exposures were observed to have
increased accumulations of lipofuscin but no quantitative analysis was done. Photographs of
light microscopic sections, that were supposed to be representative of DCA- and TCA-treated
livers at 2 g/L, showed such great hepatocellular hypertrophy from DCA treatment that sinusoids
were obscured. Such a degree of cytomegaly could have resulted in reduction of blood flow and
contributed to focal necrosis observed at this level of exposure.
As discussed in Sections E.3.2 and E.3.4.2.1, glycogen accumulation has been described
to be present in foci in both humans and animals as a result from exposure to a wide variety of
carcinogenic agents and predisposing conditions in animals and humans. Bull et al. (1990)
reported that glycogen deposition was uniformly increased from 2 g/L DCA exposure with
photographs of TCA exposure showing slightly less glycogen staining than controls. However,
the abstract and statements in the paper suggest that there was increased PAS positive material
from TCA treatment that has caused confusion in the literature in this regard. Kato-Weinstein et
al. (2001) reported that in male B6C3Fi mice exposed to DCA and TCA, the DCA treatment
increased glycogen, and TCA decreased glycogen content of the liver by using both chemical
measurement of glycogen in liver homogenates and by using ethanol-fixed sections stained with
PAS, a procedure designed to minimize glycogen loss. Kato-Weinstein et al. (2001) reported
that glycogen rich and poor cells were scattered without zonal distribution in male B6C3Fi mice
exposed to 2 g/L DCA for 8 weeks. For TCA treatments, they reported centrilobular decreases
in glycogen and -25% decreases in whole liver by 3 g/L TCA.
Kato-Weinstein et al. (2001) reported whole-liver glycogen to be increased ~1.50-fold of
control (90 vs. 60 mg glycogen/g liver) by 2 g/L DCA after 8 weeks exposure in male B6C3Fi
mice with a maximal level of glycogen accumulation occurring after 4 weeks of DCA exposure.
Pereira et al. (2004a) reported that after 8 weeks of exposure to 3.2 g/L DCA, liver glycogen
content was 2.20-fold of control levels (155.7 vs. 52.4 mg glycogen/g liver) in female B6C3Fi
mice. Thus, the baseline level of glycogen content reported by (-60 mg/g) and the increase in
glycogen after DCA exposure was consistent between Kato-Weinstein et al. (2001) and Pereira
et al. (2004a). However, the increase in liver weight reported by Kato-Weinstein et al. (2001) of
1.60-fold of control percent liver/body weight cannot be accounted for by the 1.50-fold of
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control glycogen content. Glycogen content only accounts for 5% of liver mass so that 50%
increase in glycogen cannot account for the 60% increase liver mass induced by 2 g/L DCA
exposure for 8 weeks reported by Kato-Weinstein (2001). Thus, DCA-induced increases in liver
weight are occurring from other processes as well.
Carter et al. (2003) and DeAngelo et al. (1999) reported increased glycogen after DCA
treatment at much lower doses after longer periods of exposure (100 weeks). Carter et al. (2003)
reported increased glycogen at 0.5 g/L DCA and DeAngelo et al. (1999) reported increased
glycogen at 0.03 g/L DCA in mice. However, there was no quantitation of that increase.
The issues involving identification of a mode of action through tumor phenotype analysis
are discussed in detail below for the more general case of liver cancer as well as for specific
hypothesized modes of action (see Sections E.3.1.4, E.3.1.8, E.3.2.1, and E.3.4.1.5). For TCE
and its metabolites, c-Jun staining, H-rats mutation, tincture, and heterogeneity in dysplacity
have been used to describe and differentiate liver tumors in the mouse.
Bull et al. (2002) reported 1,000 mg/kg TCE administered via gavage daily for 79 weeks
in male B6C3Fi mice to produce liver tumors and also reported deaths by gavage error
(6/40 animals). The limitations of the experiment are discussed in Caldwell et al. (2008b).
Specifically, for the DCA and TCA exposed animals, the experiment was limited by low
statistical power, a relatively short duration of exposure, and uncertainty in reports of lesion
prevalence and multiplicity due to inappropriate lesions grouping (i.e., grouping of hyperplastic
nodules, adenomas, and carcinomas together as "tumors"), and incomplete histopathology
determinations (i.e., random selection of gross lesions for histopathology examination).
For the TCE results, a high prevalence (23/36 B6C3Fi male mice) of adenomas and HCC
(7/36) was reported. For determinations of immunoreactivity to c-Jun, as a marker of differences
in "tumor" phenotype, Bull et al. (2002) included all lesions in most of their treatment groups,
decreasing the uncertainty of his findings. However, for immunoreactivity results hyperplastic
nodules, adenomas, and carcinomas were grouped and thus, changes in c-Jun expression between
the differing types of lesions were not determined.
Bull et al. (2002) reported lesion reactivity to c-Jun antibody to be dependent on the
proportion of the DCA and TCA administered after 52 weeks of exposure. Given alone, DCA
was reported to produce lesions in mouse liver for which approximately half displayed a diffuse
immunoreactivity to a c-Jun antibody, half did not, and none exhibited a mixture of the two.
After TCA exposure alone, no lesions were reported to be stained with this antibody. When
given in various combinations, DCA and TCA co-exposure induced a few lesions that were only
c-Jun+, many that were only c-Jun-, and a number with a mixed phenotype whose frequency
increased with the dose of DCA. For TCE exposure of 79 weeks, TCE-induced lesions were
reported to also have a mixture of phenotypes (42% c-Jun+, 34% c-Jun-, and 24% mixed) and to
be most consistent with those resulting from DCA and TCA co-exposure but not either
metabolite alone.
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Stauber and Bull (1997) exposed male B6C3Fi mice (7 weeks old at the start of
treatment) to 2.0 g/L neutralized DCA or TCA in drinking water for 38 or 50 weeks,
respectively, and then exposed (n = 12) to 0, 0.02, 0.1, 0.5, 1.0, and 2.0 g/L DCA or TCA for an
additional 2 weeks. Foci and tumors were combined in reported results as "lesions" and
prevalence rates were not reported. The DCA-induced larger "lesions" were reported to be more
"uniformly reactive to c-Jun and c-Fos" but many nuclei within the lesions displaying little
reactivity to c-Jun. Stauber and Bull (1997) stated that while most DCA-induced "lesions" were
homogeneously immunoreactive to c-Jun and C-Fos (28/41 lesions), the rest were stained
heterogeneously. For TCA-induced lesions, the authors reported no difference in staining
between "lesions" and normal hepatocytes in TCA-treated animals. These results are slightly
different that those reported by Bull et al. (2002) for DCA, who report c-Jun positive and
negative foci in DCA-induced liver tumors but no mixed lesions. Because "lesions" comprised
of foci and tumors, different stages of progression reported in these results. The duration of
exposures also differed between DCA and TCA treatment groups that can affect phenotype. The
shorter duration of exposure can also prevent full expression of the tumor response.
Stauber et al. (1998) presented a comparison of in vitro results with "tumors" from
Stauber and Bull (1997) and note that 97.5% of DCA-induced "tumors" were c-Jun+, while none
of the TCA-induced "tumors" were c-Jun+. However, the concentrations used to give tumors in
vivo for comparison with in vitro results were not reported. This appears to differ from the
heterogeneity of result for c-Jun staining reported by Bull et al. (2002) and Stauber and Bull
(1997). There was no comparison of c-Jun phenotype for spontaneous tumors with the authors
stating that because of such short time, no control tumors results were given. However, the
results of Bull et al. (2002) and Stauber and Bull (1997), do show TCA-induced lesions to be
uniformly c-Jun negative and thus, the phenotypic marker was able to show that TCE-induced
tumors were more like those induced by DCA than TCA.
The premise that DCA induced c-Jun positive lesions and TCA induced c-Jun negative
lesions in mouse liver was used as the rationale to study induction of "transformed" hepatocytes
by DCA and TCE treatment in vitro. Stauber et al. (1998) isolated primary hepatocytes from 5-
8-week-old male B6C3Fi mice (n = 3) and subsequently cultured them in the presence of DCA
or TCA. In a separate experiment, 0.5 g/L DCA was given to mice as pretreatment for 2 weeks
prior to isolation. The authors assumed that the anchorage-independent growth of these
hepatocytes was an indication of an "initiated cell." DCA and TCA solutions were neutralized
before use.
After 10 days in culture with DCA or TCA (0, 0.2, 0.5, and 2.0 mM), concentrations of
>0.5 mM DCA and TCA both induced an increase in the number of colonies that was
statistically significant, increased with dose with DCA, and slightly greater for DCA. In a time-
course experiment, the number of colonies from DCA treatment in vitro peaked by 10 days and
did not change through days 15-25 at the highest dose and, at lower concentrations of DCA,
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increased time in culture induced similar peak levels of colony formation by days 20-25 as that
reached by 10 days at the higher dose. Therefore, the number of colonies formed was
independent of dose if the cells were treated long enough in vitro.
However, not only did treatment with DCA or TCA induce anchorage-independent
growth, but untreated hepatocytes also formed larger numbers of colonies with time, although at
a lower rate than those treated with DCA. The level reached by untreated cells in tissue culture
at 20 days was similar to the level induced by 10 days of exposure to 0.5 mM DCA. The time
course of TCA exposure was not tested to see if it had a similar effect with time as did DCA.
The colonies observed at 10 days were tested for c-Jun expression with the authors noting that
"colonies promoted by DCA were primarily c-Jun positive in contrast to TCA promoted colonies
that were predominantly c-Jun negative." Of the colonies that arose spontaneously from tissue
culture conditions, 10/13 (76.9%) were reported to be c-Jun+, those treated with DCA 28/34
(82.3%) were c-Jun+, and those treated with TCA 5/22 (22.7%) were c-Jun+. Thus, these data
show heterogeneity in cell in colonies, but with more c-Jun+ colonies occurring by tissue culture
conditions alone and in the presence of DCA, rather than in the presence of TCA.
The authors reported that with time (24, 48, 72, and 96 hours) of culture conditioning the
number of c-Jun+ colonies was increased in untreated controls. The authors reported that DCA
treatment delayed the increase in c-Jun+ expression induced by tissue culture conditions alone in
untreated controls, while TCA treatment was reported to not affect the increasing c-Jun+
expression that increased with time in tissue culture. These results seems paradoxical given that
DCA induced a higher number of colonies at 10 days of tissue culture than TCA and that most of
the colonies were c-Jun positive. The number of colonies was greater for pretreatment with
DCA, but the magnitude of difference over the control level was the same after DCA treatment
in vitro with and without pretreatment. As to the relationship of c-Jun staining and peroxisome
proliferators as a class, as pointed out by Caldwell and Keshava (2006), although Bull et al.
(2004) have suggested that the negative expression of c-Jun in TCA-induced tumors may be
consistent with a characteristic phenotype shown in general by peroxisome proliferators as a
class, there is no supporting evidence of this.
An approach to determine the potential modes of action of DCA and TCA through
examination of the types of tumors each "induced" or "selected" was to examine H-ras activation
(Bull et al.. 2002: Ferreira-Gonzalez et al.. 1995: Anna et al.. 1994: Nelson et al.. 1990). This
approach has also been used to try to establish an H-ras activation pattern for "genotoxic" and
"nongenotoxic" liver carcinogens compounds and to make inferences concerning peroxisome
proliferator-induced liver tumors.
However, as noted by Stanley et al. (1994), the genetic background of the mice used and
the dose of carcinogen may affect the number of activated H-ras containing tumors that develop.
In addition, the stage of progression of "lesions" (i.e., foci vs. adenomas vs. carcinomas) also has
been linked the observance of H-ras mutations.
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Fox et al. (1990) note that tumors induced by phenobarbital (0.05% drinking water (H^O),
1 year), chloroform (200 mg/kg corn oil gavage, 2 times weekly for 1 year), or Ciprofibrate
(0.0125% diet, 2 years) had a much lower frequency of H-ras gene activation than those that
arose spontaneously (2-year bioassays of control animals) or induced with the "genotoxic"
carcinogen benzidine-2 hydrochloric acid (HC1; 120 ppm, drinking H2O, 1 year) in mice. In that
study, the term "tumor" was not specifically defined, but a correlation between the incidence of
H-ras gene activation and development of either a hepatocellular adenoma or HCC was reported
to be made with no statistically significant difference between the frequency of H-ras gene
activation in the hepatocellular adenomas and carcinomas. Histopathological examination of the
spontaneous tumors, tumors induced with benzidine-2HCL, phenobarbital, and chloroform was
not reported to reveal any significant changes in morphology or staining characteristics.
Spontaneous tumors were reported to have 64% point mutation in codon 61 (n = 50
tumors examined) with a similar response for benzidine of 59% (n = 22 tumors examined),
whereas for phenobarbital, the mutation rate was 7% (n = 15 tumors examined), chloroform 21%
(n = 24 tumors examined), and Ciprofibrate 21% (n = 39 tumors examined). The Ciprofibrate-
induced tumors were reported to be more eosinophilic as were the surrounding normal
hepatocytes.
Hegi et al. (1993) tested Ciprofibrate-induced tumors in the NIH3T3 cotransfection-nude
mouse tumorigenicity assay, which the authors stated is capable of detecting a variety of
activated proto-oncogenes. The tumors examined (Ciprofibrate-induced or spontaneously
arising) were taken from the Fox et al. (1990) study, screened previously, and found to be
negative for H-ras activation. With the limited number of samples examined, Hegi et al. (1993)
concluded that ras proto-oncogene activation or activation of other proto-oncogenes using the
nude mouse assay were not frequent events in Ciprofibrate-induced tumors and that spontaneous
tumors were not promoted with it. Using the more sensitive methods, the H-ras activation rate
was reported to be raised from 21 to 31% for Ciprofibrate-induced tumors and from 64 to 66%
for spontaneous tumors.
Stanley et al. (1994) studied the effect of methylclofenapate (MCP) (25 mg/kg for up to
2 years), a peroxisome proliferator, in B6C3Fi (relatively sensitive) and C57BL/10J (relatively
resistant) mice for H-ras codon 61 point mutations in MCP-induced liver tumors (hepatocellular
adenomas and carcinomas). In the B6C3Fi mice, the number of tumors with codon 61 mutations
was 11/46 and for C57BL/10J mice 4/31. Unlike the findings of Fox et al. (1990). Stanley et al.
(1994) reported an increase in the frequency of mutation in carcinomas, which was reported to be
twice that of adenomas in both strains of mice, indicating that stage of progression was related to
the number of mutations in those tumors, although most tumors induced by MCP did not have
this mutation.
In terms of liver tumor phenotype, Anna et al. (1994) reported that the H-ras codon 61
mutation frequency was not statistically different in liver tumors from DCA and TCE-treated
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mice from a highly variable number of tumors examined. In regard to mutation spectra in H-ras
oncogenes in control or spontaneous tumors, the patterns were slightly different but mostly
similar to that of DCA-induced tumors (0.5% in drinking water). From their concurrent controls
they reported that H-ras codon 61 mutations in 17% (n = 6) of adenomas and 100% (n = 5) of
carcinomas. For historical controls (published and unpublished), they reported mutations in 73%
(n = 33) of adenomas and mutations in 70% (n = 30) of carcinomas. For tumors from TCE
treated animals, they reported mutations in 35% (n = 40) of adenomas and 69% (n = 36) of
carcinomas, while for DCA-treated animals, they reported mutations in 54% (n = 24) of
adenomas and in 68% (n = 40) of carcinomas. Anna et al. (1994) reported more mutations in
TCE-induced carcinomas than adenomas.
The study of Ferreira-Gonzalez et al. (1995) in male B6C3Fi mice has the advantage of
comparison of tumor phenotype at the same stage of progression (HCC), for allowance of the full
expression of a tumor response (i.e., 104 weeks), and an adequate number of spontaneous control
lesions for comparison with DCA or TCA treatments. However, tumor phenotype at an endstage
of tumor progression reflects of tumor progression and not earlier stages of the disease process.
In spontaneous liver carcinomas, 58% were reported to show mutations in H-61 as compared
with 50% of tumor from 3.5 g/L DCA-treated mice and 45% of tumors from 4.5.g/L
TCA-treated mice. Thus, there was a heterogeneous response for this phenotypic marker for the
spontaneous, DCA-, and TCA-treatment induced HCCs and not a pattern of reduced H-ras
mutation reported for a number of peroxisome proliferators.
A number of peroxisome proliferators have been reported to have a much smaller
mutation frequency than spontaneous tumors (e.g., 13-24% H-ras codon 61 mutations after
Methylclofenopate depending on mouse strain, Stanley et al. (1994)): 21-31% for Ciprofibrate-
induced tumors and 64-66% for spontaneous tumors, Fox et al. (1990) and Hegi et al. (1993).
Bull (2000) suggested that "the report by Anna et al. (1994) indicated that TCE-induced
tumors possessed a different mutation spectra in codon 61 of the H-ras oncogene than those
observed in spontaneous tumors of control mice." Bull (2000) stated that "results of this type
have been interpreted as suggesting that a chemical is acting by a mutagenic mechanism" but
went on to suggest that it is not possible to a priori rule out a role for selection in this process and
that differences in mutation frequency and spectra in this gene provide some insight into the
relative contribution of different metabolites to TCE-induced liver tumors. Bull (2000) noted
that data from Anna et al. (1994), Ferreira-Gonzalez et al. (1995), and Maronpot et al. (1995a)
indicated that mutation frequency in DCA-induced tumors did not differ significantly from that
observed in spontaneous tumors. Bull (2000) also noted that the mutation spectra found in
DCA-induced tumors has a striking similarity to that observed in TCE-induced tumors, and
DCA-induced tumors were significantly different than that of TCA-induced liver tumors.
Bull et al. (2002) reported that mutation frequency spectra for the H-ras codon 61 in
mouse liver "tumors" induced by TCE (n = 37 tumors examined) to be significantly different
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than that for TCA (n = 41 tumors examined), with DCA-treated mice tumors giving an
intermediate result (n = 64 tumors examined). In this experiment, TCA-induced "tumors" were
reported to have more mutations in codon 61 (44%) than those from TCE (21%) and DC A
(33%). This frequency of mutation in the H-ras codon 61 for TCA is the opposite pattern as that
observed for a number of peroxisome proliferators in which the number of mutations at H-ras 61
in tumors has been reported to be much lower than spontaneously arising tumors (see
Section E.3.4.1.5). Bull et al. (2002) noted that the mutation frequency for all TCE, TCA, or
DCA tumors was lower in this experiment than for spontaneous tumors reported in other studies
(they had too few spontaneous tumors to analyze in this study), but that this study utilized lower
doses and was of shorter duration than that of Ferreira-Gonzalez et al. (1995). These are
concerns in addition to the effects of lesion grouping in which a lower stage of progression is
grouped with more advanced stages. In a limited subset of tumors that were both sequenced and
characterized histologically, only 8 of 34 (24%) TCE-induced adenomas but 9/15 (60%) of
TCE-induced carcinomas were reported to have mutated H-ras at codon 61, which the authors
suggest is evidence that this mutation is a late event.
Thus, in terms of H-ras mutation, the phenotype of TCE-induced tumors appears to be
more like DCA-induced tumors (which are consistent with spontaneous tumors), or those
resulting from a co-exposure to both DCA and TCA (Bull et al., 2002), than from those induced
by TCA. As noted above, Bull et al. (2002) reported the mutation frequency spectra for the
H-ras codon 61 in mouse liver tumors induced by TCE to be significantly different than that for
TCA, with DCA-treated mice tumors giving an intermediate result and for TCA-induced tumors
to have a H-ras profile that is the opposite than those of a number of other peroxisome
proliferators. More importantly, these data suggest that using measures, other than dysplasticity
and tincture, mouse liver tumors induced by TCE are heterogeneous in phenotype.
With regard to tincture, Stauber and Bull (1997) reported the for male B6C3Fi mice,
DCA-induced "lesions" contained a number of smaller lesions that were heterogeneous and more
eosinophilic with larger "lesions" tending to less numerous and more basophilic. For TCA
results using this paradigm, the "lesions" were reported to be less numerous, more basophilic,
and larger than those induced by DCA.
Carter et al. (2003) used tissues from the DeAngelo et al. (1999) study and examined the
heterogeneity of the DCA-induced lesions and the type and phenotype of preneoplastic and
neoplastic lesions pooled across all time points. Carter et al. (2003) examined the phenotype of
liver tumors induced by DCA in male B6C3Fi mice and the shape of the dose-response curve for
insight into its mode of action. They reported a dose-response of histopathologic changes (all
classes of premalignant lesions and carcinomas) occurring in the livers of mice from 0.05 to
3.5 g/L DCA for 26-100 weeks and suggest that foci and adenomas demonstrated neoplastic
progression with time at lower doses than observed DCA genotoxicity. Preneoplastic lesions
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were identified as eosinophilic, basophilic, and/or clear cell (grouped with clear cell and mixed
cell) and dysplastic.
Altered foci were 50% eosinophilic with about 30% basophilic. As foci became larger
and evolved into carcinomas, they became increasingly basophilic. The pattern held true
throughout the exposure range. There was also a dose and length of exposure related increase in
atypical nuclei in "noninvolved" liver. Glycogen deposition was also reported to be dose-
dependent with periportal accumulation at the 0.5 g/L exposure level. Carter et al. (2003)
suggested that size and evolution into a more malignant state are associated with increasing
basophilia, a conclusion consistent with those of Bannasch (1996) and that there is a greater
periportal location of lesions suggestive as the location from which they arose.
Consistent with the results of DeAngelo et al. (1999), Carter et al. (2003) reported that
DCA (0.05-3.5 g/L) increased the number of lesions per animal relative to animals receiving
distilled water, shortened the time to development of all classes of hepatic lesions, and that the
phenotype of the lesions was similar to those spontaneously arising in controls. Along with
basophilic and eosinophilic lesions or foci, Carter et al. (2003) concluded that DCA-induced
tumors also arose from isolated, highly dysplastic hepatocytes in male B6C3Fi mice chronically
exposed to DCA, suggesting another direct neoplastic conversion pathway other than through
eosinophilic or basophilic foci.
Rather than male B6C3Fi mice, Pereira (1996) studied the dose-response relationship for
the carcinogenic activity of DCA and TCA and characterized their lesions (foci, adenomas, and
carcinomas) by tincture in females (the generally less sensitive gender). Like the studies of TCE
by Maltoni et al. (1986), female mice were also reported to have increased liver tumors after
TCA and DCA exposures. Pereira (1996) pooled lesions for phenotype analyses so the effect of
duration of exposure could not be determined, nor could adenomas be separated from carcinomas
for "tumors."
However, as the concentration of DCA was decreased, the number of foci was reported
by Pereira (1996) to be decreased but the phenotype of the foci to go from primarily eosinophilic
foci (i.e., -95% eosinophilic at 2.58 g/L DCA) to basophilic foci (-57% eosinophilic at
0.26 g/L). For TCA, the number of foci was reported to -40 basophilic and -60 eosinophilic
regardless of dose. Spontaneously occurring foci were more basophilic by a ratio of 7/3. Pereira
(1996) described the foci of altered hepatocytes and tumors induced by DCA in female B6C3Fi
mice to be eosinophilic at higher exposure levels but at lower or intermittent exposures to be half
eosinophilic and half basophilic. Regardless of exposure level, half of the TCA-induced foci
were reported to be half eosinophilic and half basophilic with tumors 75% basophilic. In control
female mice, the limited numbers of lesions were mostly basophilic, with most of the rest being
eosinophilic with the exception of a few mixed tumors. The limitations of descriptions tincture
and especially for inferences regarding peroxisome proliferator from the description of
"basophilia" is discussed in SectionE.3.4.1.5.
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The results appear to differ between male and female B6C3Fi mice in regard to tincture
for DCA and TCA at differing doses. What is apparent is that the tincture of the lesions is
dependent on the stage of tumor progression, agent (DCA or TCA), gender, and dose. Also what
is apparent from these studies is the both DCA and TCA are heterogeneous in their tinctoral
characteristics as well as phenotypic markers such as mutation spectra or expression of c-Jun.
The descriptions of TCE-induced tumors in mice reported by the NCI, NTP, and Maltoni
et al. studies are also consistent with phenotypic heterogeneity as well as consistency with
spontaneous tumor morphology (see Section E.3.4.1.5). As noted in Section E.3.1, HCCs
observed in humans are also heterogeneous. For mice, Maltoni et al. (1986) described malignant
tumors of hepatic cells to be of different subhistotypes, and of various degrees of malignancy and
were reported to be unique or multiple, and have different sizes (usually detected grossly at
necropsy) from TCE exposure. In regard to phenotype, tumors were described as usual type
observed in Swiss and B6C3Fi mice, as well as in other mouse strains, either untreated or treated
with hepatocarcinogens and to frequently have medullary (solid), trabecular, and pleomorphic
(usually anaplastic) patterns.
For the NCI (1976) study, the mouse liver tumors were described in detail and to be
heterogeneous "as described in the literature" and similar in appearance to tumors generated by
carbon tetrachloride. The description of liver tumors in this study and tendency to metastasize to
the lung are similar to descriptions provided by Maltoni et al. (1986) for TCE-induced liver
tumors in mice via inhalation exposure.
The NTP (1990) study reported TCE exposure to be associated with increased incidence
of HCC (tumors with markedly abnormal cytology and architecture) in male and female mice.
Hepatocellular adenomas were described as circumscribed areas of distinctive hepatic
parenchymal cells with a perimeter of normal appearing parenchyma in which there were areas
that appeared to be undergoing compression from expansion of the tumor. Mitotic figures were
sparse or absent but the tumors lacked typical lobular organization. HCCs were reported to have
markedly abnormal cytology and architecture with abnormalities in cytology cited as including
increased cell size, decreased cell size, cytoplasmic eosinophilia, cytoplasmic basophilia,
cytoplasmic vacuolization, cytoplasmic hyaline bodies, and variations in nuclear appearance.
Furthermore, in many instances, several or all of the abnormalities were reported to be present in
different areas of the tumor and variations in architecture with some of the HCCs having areas of
trabecular organization. Mitosis was variable in amount and location. Therefore, the phenotype
of tumors reported from TCE exposure was heterogeneous in appearance between and within
tumors from all three of these studies.
Caldwell and Keshava (2006) reported:
that Bannasch (2001) and Bannasch et al. (2001) describe the early phenotypes of
preneoplastic foci induced by many oncogenic agents (DNA-reactive chemicals,
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radiation, viruses, transgenic oncogenes and local hyperinsulinism) as
insulinomimetic. These foci and tumors have been described by tincture as
eosinophilic and basophilic and to be heterogeneous.
The tumors derived from them after TCE exposure are consistent with the description for
the main tumor lines of development described by Bannasch et al. (2001) (see Section E.3.4.1.5).
Thus, the response of liver to DC A (glycogenosis with emergence of glycogen poor tumors) is
similar to the progression of preneoplastic foci to tumors induced from a variety of agents and
conditions associated with increased cancer risk.
Furthermore, Caldwell and Keshava (2006) noted that Bull et al. (2002) reported
expression of insulin receptor (IR) to be elevated in tumors of control mice or mice treated with
TCE, TCA, and DCA but not in nontumor areas, suggesting that this effect is not specific to
DCA.
There is a body of literature that has focused on the effects of TCE and its metabolites
after rats or mice have been exposed to "mutagenic" agents to "initiate" hepatocarcinogenesis
and this is discussed in Section E.4.2, below. TCE and its metabolites were reported to affect
tumor incidence, multiplicity, and phenotype when given to mice as a co-exposure with a variety
of "initiating" agents and with other carcinogens. Pereira and Phelps (1996) reported that MNU
alone induced basophilic foci and adenomas. MNU and low concentrations of DCA or TCA in
female mice were reported to induce heterogeneous for foci and tumor with a higher
concentration of DCA inducing more eosinophilic and a higher concentration of TCA inducing
more tumors that were basophilic. Pereira et al. (2001) reported that not only dose, but also
gender affected phenotype in mice that had already been exposed to MNU and were then
exposed to DCA. As for other phenotypic markers, Lantendresse and Pereira (1997) reported
that exposure to MNU and TCA or DCA induced tumors that had some commonalities and were
heterogeneous, but for female mice, were overall different between DCA and TCA as co-
exposures with MNU.
Stop experiments, which attempt to ascertain whether progression differences exist
between TCA and DCA, have used higher concentrations at much lower durations of exposure.
A question arises as to whether the differences in results occurred because animals in which
treatment was suspended were not allowed to have full expression of response rather than
"progression" as well as the effects of using large doses.
After 37 weeks of treatment and then a cessation of exposure for 15 weeks, Bull et al.
(1990) reported that liver weight and percent liver/body weight still was statistically significantly
elevated after DCA or TCA treatment. The authors partially attribute the remaining increases in
liver weight to the continued presence of hyperplastic nodules in the liver. In terms of liver
tumor induction, the authors stated that "statistical analysis of tumor incidence employed a
general linear model ANOVA with contrasts for linearity and deviations from linearity to
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determine if results from groups in which treatments were discontinued after 37 weeks were
lower than would have been predicted by the total dose consumed."
The multiplicity of tumors observed in male mice exposed to DCA or TCA at 37 weeks
and then sacrificed at 52 weeks were reported by the authors to have a response in animals that
received DCA very close to that which would be predicted from the total dose consumed by
these animals. The response to TCA was reported by the authors to deviate significantly
(p = 0.022) from the linear model predicted by the total dose consumed. Multiplicity of lesions
per mouse and not incidence was used as the measure. Most importantly, the data used to predict
the dose response for "lesions" used a different methodology at 52 weeks than those at 37 weeks.
Not only were not all animal's lesions examined, but foci, adenomas, and carcinomas were
combined into one measure. Therefore, foci, of which a certain percentage have been commonly
shown to spontaneously regress with time, were included in the calculation of total "lesions."
Pereira and Phelps (1996) note that in MNU-treated mice that were then treated with
DCA, the yield of altered hepatocytes decreases as the tumor yields increase between 31 and
51 weeks of exposure, suggesting progression of foci to adenomas. Initiated and noninitiated
control mice were reported to also have fewer foci/mouse with time. Because of differences in
methodology and the lack of discernment between foci, adenomas, and carcinomas for many of
the mice exposed for 52 weeks, it is difficult to compare differences in composition of the
"lesions" after cessation of exposure in the Bull et al. (1990) study.
For TCA treatment, the number of animals examined for determination of which
"lesions" were foci, adenomas, and carcinomas was 11/19 mice with "lesions" at 52 weeks,
while all 4 mice with lesions after 37 weeks of exposure and 15 weeks of cessation were
examined. For DCA treatment, the number of animals examined was only 10/23 mice with
"lesions" at 52 weeks, while all 7 mice with lesions after 37 weeks of exposure and 15 weeks of
cessation were examined. Most importantly, when lesions were examined microscopically, they
did not all turn out to be preneoplastic or neoplastic. Two lesions appeared "to be histologically
normal" and one necrotic. Not only were a smaller number of animals examined for the
cessation exposure than continuous exposure, but only the 2 g/L exposure levels of DCA and
TCA were studied for cessation. The number of animals bearing "lesions" after 37 weeks and
then 15 cessation weeks was 7/11 (64%), while the number of animals bearing lesions at
52 weeks was 23/24 (96%) after 2 g/L DCA exposure. For TCA, the number of animals bearing
lesions at 37 weeks and then 15 weeks cessation was 4/11 (35%), while the number of animals
bearing lesions at 52 weeks was 19/24 (80%). While suggesting that cessation of exposure
diminished the number of "lesions," conclusions regarding the identity and progression of those
lesion with continuous vs. noncontinuous DCA and TCA treatment are tenuous.
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E.2.5. Studies of CH
Given that total oxidative metabolism appears to be highly correlated with TCE-induced
increases in liver weight in the mouse rather than merely the presence of TCA, other metabolites
are of interest as potential agents mediating the effects observed for TCE. Recently, Caldwell
and Keshava (2006) provided a synopsis of the results of more recent studies involving CH. A
large fraction of TCE oxidative metabolism appears to go through CH, with subsequent
metabolism to TCA and TCOH (Chiu et al.. 2006b). Merdink et al. (2008) demonstrated that CH
administered to humans can be extremely variable and complex in its pharmacokinetic behavior
with a peak plasma concentration of CH in plasma 40-50 times higher than observed at the same
time interval for other subjects. Studies of CH toxicity in rodents are consistent, with the general
presumption that oxidative metabolites are important for TCE-induced liver tumors, but whether
CH and its metabolites are sufficient to explain all of the TCE liver tumorigenesis remains
unclear, particularly because of uncertainties regarding how DC A may be formed (Chiu et al.,
2006b). Studies of CH may enable a comparison between toxicity of TCE and CH and may help
elucidate its role in TCE effects. As with other TCE metabolites, the majority of the studies have
focused on the mouse liver tumor response. For rats, while the limited data suggest that there is
less of a response than mice to CH, those studies are limited in power or reporting.
Daniel et al. (1992) exposed adult male B6C3Fi (C57B1/6JC male mice bred to
C3Heb/Fej female mice) 28-day-old mice to CH, 2-chloroacetaldehyde, or DCA in two different
phases (I and II) with initial weights ranging from 9.4 to 13.6 g. The test compounds were
buffered and administered in drinking water for 30 and 60 weeks (n = 5 for interim sacrifice),
and for 104 weeks (n = 40). The concentration of CH was 1 g/L and the concentration of DCA
was 0.5 g/L; the estimated doses of DCA were 85, 93, and 166 mg/kg-day for the DCA group I,
DCA group II, and CH exposed group, respectively. Microscopic examination of tissues was
conducted for all tissues for five animals of the CH groups with liver, kidneys, testes, and spleen,
in addition to all gross lesions, reported to be examined microscopically in all of the 104-week
survivors.
The initial body weight for drinking water controls was reported to be 12.99 ± 3.04 g for
group I (n = 23) and 10.48 ± 1.70 for group II (n = 10). For DCA-treated animals, initial body
weights were 13.44 ± 2.57 g for group I (n = 23) and 9.65 ± 2.72 g for group II (n = 10). For the
CH-treated group, the initial body weights were reported to be 10.42 ± 2.49 g (n = 40). It is not
clear from the report what control group best matched, if any, the CH group. Thus, the mean
initial body weights of the groups as well as the number of animals varied considerably in each
group (i.e., -40% difference in mean body weights at the beginning of the study).
The number of animals surviving until the termination of the experiment was 10, 10, 16,
8, and 24 for the control group I, control group II, DCA group I, DCA group II, and CH groups,
respectively. An increase in absolute and relative liver weight was reported to be observed at
30 weeks for DCA and CH groups and at 60 weeks for CH but data were not shown in the study.
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At 104 weeks, the data for the surviving control groups were combined as was the data for the
two DCA treatment groups. Of note was that for CH treated survivors (n = 24), water
consumption was significantly reduced in comparison to controls. Absolute liver weight was
reported to be 2.09 ± 0.6, 3.17 ± 1.3, and 2.87 ± 1.1 g for control, DCA, and CH treatment
groups, respectively. The % liver to body weight was reported to be similarly elevated
(1.57-fold of control for DCA and 1.41-fold of control for CH) at 104 weeks.
At 104 weeks, the treatment-related liver lesions in histological sections were reported to
be most prominently hepatocytomegaly and vacuolization in DCA-treated animals. Cytomegaly
was also reported to be in 5, 92, and 79% of control, DCA, and CH treatment groups,
respectively. Cytomegaly in CH-treated mice was described as minimal and associated with an
increased number of basophilic granules (rough endoplasmic reticulum). Hepatocellular necrosis
and chronic active inflammation were reported to be mildly increased in both prevalence and
severity in all treated groups. The histological findings, from interim sacrifices (n = 5), were
considered by the authors to be unremarkable and were not reported.
Liver tumors were increased by DCA and CH treatment. The percent incidence of liver
carcinomas and adenomas combined in the surviving animals was 15, 75, and 71% in control,
DCA, and CH treated mice, respectively. In the CH-treated group, the incidence of HCC was
46%. The number of tumors/animals was also significantly increased with CH treatment. Most
importantly, morphologically, the authors noted that there did not appear to be any discernable
differences in the visual appearance of the DCA- and CH-induced tumors.
George et al. (2000) exposed male B6C3Fi mice and male F344/N rats to CH in drinking
water for 2 years (up to 162.6 mg/kg-day). Target drinking water concentrations were 0, 0.05,
0.5, and 2 g/L CH in rats and 0, 0.05, 0.5, and 1.0 g/L CH in mice. Groups of animals
(n = 6/group) were sacrificed at 13 (rats only), 26, 52, and 78 weeks following the initiation of
dosing with terminal sacrifices at week 104. A complete pathological examination was
performed on five rats and mice from the high-dose group, with examination primarily of gross
lesions except for liver, kidney, spleen, and testes. BrdU incorporation was measured in the
interim sacrifice groups in rats and mice with PCO examined at 26 weeks in mice. In rats, the
number of animals surviving >78 weeks and examined for hepatocellular proliferative lesions
was 42, 44, 44, and 42 for the control, 7.4, 37.4, and 163.6 mg/kg-day CH treatment groups,
respectively. Only 32, 36, 35, and 32 animals were examined at the final sacrifice time.
Only the lowest treatment group had increased liver tumors, which were marginally
significantly increased by treatment. The percent of animals with hepatocellular adenomas and
carcinomas was reported to be 2.4, 14.3, 2.3 and 6.8% in male rats. In mice, preneoplastic foci
and adenomas were reported to be increased in the livers of all CH treatment groups (13.5-
146.6 mg/kg-day) at 104 weeks. The incidences of adenomas were reported to be statistically
increased at all dose levels, the incidences of carcinomas significantly increased at the highest
dose, and time-to-tumor decreased in all CH-treatment groups. The percent incidence of
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hepatocellular adenomas was reported to be 21.4, 43.5, 51.3, and 50% in control, 13.5, 65.0, and
146.6 mg/kg-day treatment groups, respectively. The percent incidence of HCCs was reported to
be 54.8, 54.3, 59.0, and 84.4% in these same groups. The resulting percent incidence of
hepatocellular adenomas and carcinomas was reported to be 64.3, 78.3, 79.5, and 90.6%.
The number of mice surviving >78 weeks was reported to be 42, 46, 39, and 32 and the
number surviving to final sacrifice was 34, 42, 31, and 25 for control, 13.5, 65.0, and
146.56 mg/kg-day, respectively. CH exposure was reported to not alter serum chemistry,
hepatocyte proliferation (i.e., DNA synthesis), or hepatic PCO activity (an enzyme associated
with PPARa agonism) in rats and mice at any of the time periods monitored (all interim sacrifice
periods for BrdU incorporation, 52 or 78 weeks for serum enzymes, and 26 weeks for PCO) with
the exception of 0.58 g/L CH at 26 weeks slightly increasing hepatocyte labeling (~2-3-fold
increase over controls) in rats and mice, but the percent labeling still represented <3% of
hepatocytes.
With regard to other carcinogenic endpoints, only five animals were examined at the high
dose, thereby limiting the study's power to determine an effect. Control mice were reported to
have a high spontaneous carcinoma rate (54%), thereby limiting the ability to detect a treatment-
related response. No descriptions of the foci or tumor phenotype were given. However, of note
is the lack of induction of PCO response with CH at 26 weeks of administration in either rats or
mice.
Leakey et al. (2003b) studied the effects of CH exposure (0, 25, 50, and 100 mg/kg,
5 days/week for 104-105 weeks via gavage) in male B6C3Fi mice with dietary control used to
manipulate body growth (n = 48 for 2-year study and n = 12 for the 15-month interim study).
Dietary control was reported to decrease background liver tumor rates (incidence of 15-20%)
and was reported to be associated with decreased variation in liver-to-body weight ratios, thereby
potentially increasing assay sensitivity. In dietary-controlled groups and groups fed ad libitum,
liver adenomas and carcinomas (combined) were reported to be increased with CH treatment.
With dietary restriction, there was a more discemable CH tumor-response with overall tumor
incidence reduced, and time-to-tumor increased by dietary control in comparison to ad-libitum-
fed mice. Incidences of hepatocellular adenoma and carcinoma overall rates were reported to be
33, 52, 49, and 46% for control, 25, 50, and 100 mg/kg ad-libitum-fed mice, respectively. For
dietary-controlled mice, the incidence rates were reported to be 22.9, 22.9, 29.2, and 37.5% for
controls, 25, 50, and 100 mg/kg CH, respectively. Body weights were matched and carefully
controlled in this study.
After 2 years of CH treatment, the heart weights of ad-libitum-fed male mice
administered 100 mg/kg CH were reported to be significantly less and kidney weights of the
50 and 100 mg/kg were less than vehicle controls. No other significant organ weight changes
due to CH treatment were reported to be observed in either diet group except for liver. The liver
weights of CH treated groups for by dietary groups were reported to be increased at 2 years and
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the absolute liver weights of dosed groups to be generally increased at 15 months, with percent
liver/body weight ratios increased in CH treated dietary-controlled mice at 15 months. There
was 1.0-, 0.87-, and 1.08-fold of control percent liver/body weight for ad-libitum-fed mice
exposed to 25, 50, and 100 mg/kg CH, respectively. For dietary-controlled mice, there was
1.05-, 1.08-, and 1.11-fold of control percent liver/body weight for the same dose groups at
15 months. Thus, there was no corresponding dose-response for percent liver/body weight in the
ad-libitum-fed mice, which were reported to show a much larger variation in liver-to-body-
weight ratios (i.e., the SD and SEs were 2-17-fold lower in dietary-controlled groups than for ad-
libitum-fed groups).
Liver weight increases at 15 months did not correlate with 2-year tumor incidences with
this group. However, for dietary-controlled groups, the increase in percent liver/body weights at
15 months were generally correlated with increases in liver tumors at 2 years.
The incidences of peripheral or focal fatty change were reported to be increased in all
CH-treated groups of ad-libitum-fed mice at 15 months (approximately half the animals showed
these changes for all dose groups, with no apparent dose-response). Of the enzymes associated
with PPARa agonism (total CYP, CYP2B isoform, CYP4A, or lauric acid P-hydroxylase
activity), only CYP4A and lauric acid P-hydroxylase activity were significantly increased at
15 months of exposure in the dietary-restricted group administered 100 mg/kg CH, with no other
groups reported showing a statistically significant increased response (n = 12/group). Although
not statistically significant, the 100 mg/kg CH exposure group of ad-libitum-fed mice also had an
increase in CYP4A and lauric acid P-hydroxylase activity.
The authors reported that the increase in magnitude of CYP4A and lauric acid
P-hydroxylase activity at 100 mg/kg CH at 15 months in dietary controlled mice correlated with
the increase incidence of mice with tumors. However, there was no correlation of tumor
incidence and the increased enzyme activity associated with peroxisome proliferation in the
ad-libitum-fed mice. No descriptions of liver pathology were given other than incidence of mice
with fatty liver changes. Hepatic malondialdehyde concentration in ad-libitum-fed and dietary
controlled mice did not change with CH exposure at 15 months, but the dietary-controlled groups
were all approximately half that of the ad-libitum-fed mice. Thus, while overall increased
tumors observed in the ad libitum diet correlated with increased malondialdehyde concentration,
there was no association between CH dose and malondialdehyde induction for either diet.
Induction of peroxisome-associated enzyme activities was also reported for shorter times
of CH exposure. Seng et al. (2003) described CH toxicokinetics in mice at doses up to
1,000 mg/kg-day for 2 weeks with dietary control and caloric restriction slightly reducing acute
toxicity. Lauric acid P-hydroxylase and PCO activities were reported to be induced only at doses
>100 mg/kg in all groups, with dietary-restricted mice showing the greatest induction.
Differences in serum levels of TCA, the major metabolite remaining 24 hours after dosing, were
reported not to correlate with hepatic lauric acid P-hydroxylase activities across groups.
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Leuschner and Beuscher (1998) examined the carcinogenic effects of CH in male and
female Sprague-Dawley rats (69-79 g, 25-29 days old at initiation of the experiment)
administered 0, 15, 45, and 135 mg/kg CH in unbuffered drinking water 7 days/week
(n = 50/group) for 124 weeks in males and 128 weeks in females. Two control groups were
noted in the methods section without explanation as to why they were conducted as two groups.
The mean survival for males was similar in treated and control groups, with 20, 24, 20,
24, and 20% of Control I, Control II, 15, 45, and 135 mg/kg CH-treated groups, respectively,
surviving until the end of the study. For female rats, the percent survival was 12, 30, 24, 28, and
16% for of Control I, Control II, 15, 45, and 135 mg/kg CH-treated groups, respectively. The
authors reported no substance-related influence on organ weights and no macroscopic evidence
of tumors or lesions in male or female rats treated with CH for 124 or 128 weeks. However, no
data were presented on the incidence of tumors using this paradigm, especially background rates.
The authors reported a statistically significant increase in the incidence of hepatocellular
hypertrophy in male rats at the 135 mg/kg dose (14/50 animals vs. 4/50 and 7/50 in Controls I
and II). For female rats, the incidence of hepatocellular hypertrophy was reported to be
10/50 rats (Control I) and 16/50 (Control II) rats with 18/50, 13/50, and 12/50 female rats having
hepatocellular hypertrophy after 15, 45, and 135 mg/kg CH, respectively. The lack or reporting
in regard to final body weights, histology, and especially background and treatment group data
for tumor incidences, limit the interpretation of this study. Whether this paradigm was sensitive
for induction of liver cancer cannot be determined.
From the CH studies in mice, there is an apparent increase in liver adenomas and
carcinomas induced by CH treatment by either drinking water or gavage with all available
studies performed in male B6C3Fi mice. However, the background levels of hepatocellular
adenomas and carcinomas in the mice in George et al. (2000) and body weight data from this
study show that it is from a tumor-prone mouse model.
Comparisons with concurrent studies of mice exposed to DCA revealed that while both
CH and DCA induced hepatomegaly and cytomegaly, DCA-induced cytomegaly was
accompanied by vacuolization, while that of CH was associated with increased number of
basophilic granules (rough endoplasmic reticulum), which would suggest separate effects.
However, the morphology of the CH-induced tumors was reported to be similar between
DCA- and CH-induced tumors (Daniel et al.. 1992).
Using a similar paradigm (2-year study of B6C3Fi male mice), DeAngelo et al. (1999)
and Carter et al. (2003) described DCA-induced tumors to be heterogeneous. This is the same
description given for TCE-induced tumors in the studies by NTP, NCI, and Maltoni et al. and to
be a common description for tumors caused by a variety of carcinogenic agents. Similar to the
studies cited above for CH, DeAngelo et al. (1999) reported that PCO levels were only elevated
at 26 weeks at 3.5 g/L DCA and had returned to control levels by 52 weeks. Similar to CH, no
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increased tritiated thymidine was reported for DCA at 26 and 52 weeks, with only twofold of
control values reported at 0.05 g/L at 4 weeks.
Leakey et al. (2003b) reported that ad-libitum-fed male mice exhibited a similar degree of
increase in the incidence of peripheral or focal fatty change at 15 months for all CH doses;
however, enzymes associated with peroxisome proliferation were not similarly altered at all CH
doses. While dietary restriction seemed to have decreased background levels of tumors and
increased time-to-tumor, CH-gave a clear dose-response in dietary restricted animals. However,
while the overall level of tumor induction was reduced, there was a greater induction of PPARa
enzymes by CH. Induction of liver tumors by CH observed in ad-libitum-fed mice were not
correlated with PPARa induction, with dietary restriction alone appearing to have greater levels
of lauric acid co-hydrolase activity in control mice at 15 months. Seng et al. (2003) report that
lauric acid p-hydroxylase and PCO were induced only at exposure levels >100 mg/kg CH, again
with dietary restricted groups showing the greatest induction. Such data argues against the role
of peroxisome proliferation in CH-liver tumor induction in mice.
E.2.6. Serum Bile Acid Assays
Serum bile acids (SBA) have been suggested as a sensitive indicator of hepatotoxicity to
a variety of halogenated solvents with an advantage of increased sensitivity and specificity over
conventional liver enzyme tests that primarily reflect the acute perturbation of hepatocyte
membrane integrity and "cell leakage" rather than liver functional capacity (i.e., uptake,
metabolism, storage, and excretion functions of the liver) (Neghab et al., 1997; Bai etal., 1992b).
While some studies have reported negative results, a number of studies have reported elevated
SBA in organic solvent-exposed workers in the absence of any alterations in normal liver
function tests. These variations in results have been suggested to arise from failure of some
methods to detect some of the more significantly elevated SBA and the short-lived and reversible
nature of the effect (Neghab et al., 1997).
Neghab et al. (1997) have reported that occupational exposure to 1,1,2-trichloro-
1,2,2-trifluoroethane and TCE has resulted in elevated SBA and that several studies have
reported elevated SBA in experimental animals to chlorinated solvents such as carbon
tetrachloride, chloroform, hexachlorobutadiene, tetrachloroethylene, 1,1,1-trichloroethane, and
TCE at levels that do not induce hepatotoxicity (Hamdan and Stacey, 1993; Bai et al., 1992b:
Wang and Stacey, 1990). Toluene, a nonhalogenated solvent, has also been reported to increase
SBA in the absence of changes in other hepatobiliary functions (Neghab and Stacey, 1997).
Thus, disturbance in SBA appears to be a generalized effect of exposure to chlorinated solvents
and nonchlorinated solvents and not specific to TCE exposure.
Neghab et al. (1997) reported that 8-hour TWA exposures to TCE of 8.9 ppm, measured
in the breathing zone using a charcoal tube personal sampler for the whole mean duration of
exposure of 3.4 years, do not result in significant changes in albumin, bilirubin, ALP, ALT,
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5'-nucleosidase, y-glutamyltransferase, but do have significantly increased total serum bile acids.
Not only were total bile acids significantly increased in these TCE-exposed workers compared to
controls (approximately twofold of control), but specifically, deoxycholic acid and subtotal of
free bile acids were increased. Neghab et al. (1997) did not show the data, but also reported that
"despite the apparent overall low level of exposure, there was a very good correlations (r = 0.94)
between the degree of increase in serum concentration of total bile acids and level of TCE."
Neghab et al. (1997) noted that while a sensitive indicator or exposure to such solvents in
asymptomatic workers, there is no indication that actual liver injury occurs in conjunction with
SAB increases.
Wang and Stacey (1990) administered TCE in corn oil via i.p. injection to male Sprague-
Dawley rats (300-500 g) at concentrations of 0.01, 0.1, 1, 5, and 10 mmol/kg on 3 consecutive
days (n = 4, 5, or 6) with liver enzymes and SBA examined 4 hours after the last TCE treatment.
At these doses, there were no differences between treated and control animals in regard to ALP
and SDH concentrations, and an elevation of ALT was noted only at the highest dose. However,
there was generally a reported dose-related increase in cholic acid, chenodeoxycholic acid,
deoxycholic acid, taurocholic acid, and tauroursodeoxycholic acid, with cholic acid and
taurochlolic acid increased at the lowest dose. The authors reported that "examination of liver
sections under light microscopy yielded no consistent effects that could be ascribed to
trichloroethylene."
In the same study, rats were also exposed to TCE via inhalation (n = 4) at 200 ppm for
28 days, and 1,000 ppm for 6 hours/day. Using this paradigm, cholic acid and taurocholic acid
were significantly elevated at the 200 ppm level, (-10- and ~5-fold of control, respectively) with
very large SEs. At the 1,000 ppm level (6 hours/day), cholic acid and taurocholic acid were
elevated to approximately twofold of control but neither was statistically significant. The large
variability in responses between rats and the low number of rats tested in this paradigm limit its
ability to determine quantitative differences between groups. Nevertheless, without the
complications associated with i.p. exposure (see Section E.2.2.1), inhalation exposure of TCE at
a relative low exposure level was also associated with increased SBA levels. The authors stated
that "no increases in alanine amino transferase levels were observed in the rats exposed to
trichloroethylene via inhalation." No histopathology results were reported for rats exposed via
inhalation.
As stated by Wang and Stacey (1990), "intraperitoneal injection is not particularly
relevant to humans," which was the rationale given for the inhalation exposure experiments in
the study. They point out that intestinal interactions require consideration because a major
determinant of SBA is that their absorption from the gut and intestinal flora may play a role in
bile acid metabolism. They also noted that grooming done by the experimental rats would
probably result in low exposure via ingestion of TCE as well. However, Wang and Stacey
(1990) reported consistent results in terms of TCE-induced changes in SBA at relatively low
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concentrations by either inhalation or i.p. routes of exposure that were not associated with other
measures of toxicity.
Hamdan and Stacey (1993) administered TCE in corn oil (1 mmol/kg) in male Sprague-
Dawley rats (300-400 g) and followed the time-course of SB A elevation, TCE concentration,
and TCOH in the blood at 2, 4, 8, and 16 hours after dosing (n = 4, 5, or 6 per group). Liver and
blood concentration of TCE were reported to peak at 4 hours, while those of TCOH peaked at
8 hours after dosing. TCE levels were not detectable by 16 hours in either blood or liver, while
those of TCOH were still elevated. Elevations of SB A were reported to parallel those of TCE,
with cholic acid and taurochloate acid reported to show the highest levels of bile acids. The dose
given was based on that reported by Wang and Stacey (1990) to give no hepatotoxicity but an
increase in SB A. The authors stated that liver injury parameters were checked and found
unaffected by TCE exposure but do not show the data. Thus, it was TCE concentration and not
that of its metabolite that was most closely related to changes in SB A and after a single exposure,
the effect was reversible.
In an in vitro study by Bai and Stacey (1993), TCE was studied in isolated rat hepatocytes
with TCE reported to cause a dose-related suppression of initial rates of cholic acid and
taurocholic acid but with no significant effects on enzyme leakage or intracellular calcium
contents, further supporting a role for the parent compound in this effect. The authors noted that
the changes in SB A result from interference with a physiological process rather "than an event
associated with significant pathological consequences."
E.3. STATE OF SCIENCE OF LIVER CANCER MODES OF ACTION
The experimental evidence in mice shows that TCE and its metabolites induce foci,
hepatocellular adenomas, and carcinomas that are heterogeneous in nature as indicated by
phenotypic differences in tincture, mutational markers, or gene expression markers. The tumors
induced by TCE are reflective of phenotypes that are either similar to those induced by mixtures
of DC A and TCA exposure, or more like those induced by DC A. These tumors have been
described to be similar also to those arising spontaneously in mice or from chemically induced
hepatocarcinogenesis and to arise from preneoplastic foci, and in the case of DCA, single
dysplastic hepatocytes as well as foci. HCC observed in humans also has been described to be
heterogeneous and to be associated with formation of preneoplastic nodules. Although several
conditions have been associated with increased risk of liver cancer in humans, the mechanism of
HCC is unknown at this time. A great deal of attention has been focused on predicting which
cellular targets (e.g., "stem-cell" or mature hepatocyte) are associated with HCC as well as on
phenotypic markers in HCC that can provide insight not only into mode of action and origin of
tumor, but also for prediction of clinical course. Examination of pathways and epigenetic
changes associated with cancer and the relationship of these changes to liver cancer are also
discussed below.
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The field of cancer research has been transformed by the recent discoveries of epigenetic
changes and their role in cancer and chronic disease states. The following discussion describes
not only these advances, but also the issues involved with the technologies that have emerged to
describe them (see Section E.3.1.2). Exposure to TCE and its metabolites, like many others,
induces a heterogeneous response, even in a relatively homogeneous genetic paradigm as the
experimental laboratory rodent model. The importance of phenotypic anchoring is a major issue
in the study of any modes of action using these new technologies of gene expression pattern.
Although a large amount of information is now available using microarray technologies and
transgenic mouse models, specifically for TCE and in study of suggested modes of action for
TCE and its metabolites, use of these approaches has limitations that need to be considered in the
interpretation of data and conclusions derived from such data, especially quantitative
conclusions.
For TCE and its metabolites, the extent of acute to subchronic induction of hepatomegaly
correlated with hepatocellular carcinogenicity, although each had differing factors contributing
to that hepatomegaly from periportal glycogen deposition to hepatocellular hypertrophy and
increased polyploidy. The extent of transient DNA synthesis, peroxisome proliferation, or
cytotoxicity was not correlated with carcinogenicity. Hepatomegaly is also a predictor of
carcinogenicity for a number of other compounds in mice and rats. Allen et al. (2004) examined
the NTP database (87 compounds for rat and 83 for mice) and tried to correlate specific
hepatocellular pathology in prechronic studies with carcinogenic endpoints in the chronic 2-year
assays. The best single predictor of liver cancer in mice was hepatocellular hypertrophy.
Hepatocellular cytomegaly and hepatocyte necrosis also contributed, although the numbers of
positive findings were less than hypertrophy.
With regard to genotoxicity studies, there was no evidence of a correlation between
mouse liver tumor chemicals and Salmonella or micronucleus assay outcome. None of the
prechronic liver lesions examined were correlated with either Salmonella or Micronucleus
assays. In rats, no single prechronic liver lesions (when considered individually) was a strong
predictor of liver cancer in rats. The most predictive lesions was hepatocellular hypertrophy.
There was not a significant correlation between liver tumors/toxicity and the two mutagenicity
measures.
Although the lack of correlation with the mutagenicity assays could be interpreted as
rodent assays predominantly identifying nongenotoxic liver carcinogens, this conclusion could
be questioned because it is solely dependent on Salmonella mutagenicity and additional
genotoxic endpoints could conceivably shift the association between liver cancer and
genotoxicity towards a more positive correlation. As to questions of the usefulness of the mouse
bioassay, the two mutagenicity assays did not correlate with rat results either and an important
indicator for carcinogenicity would be lost.
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Examination of tumor phenotype from TCE, DC A, and TCA exposures in mice shows a
large heterogeneity, which is also consistent with the heterogeneity observed in human HCC (see
Section E.3.1.8). The heterogeneity of tumor phenotype has been correlated with survival
outcome and tumor aggressiveness in humans and in transgenic mouse models that share some of
the same perturbations in gene pathway expression (see Sections E.3.1.8 and E.3.2.1, below).
An examination of common pathway disturbances that may be common to all cancers and those
of liver tumors shows that there are pathways in common, but that there is greater heterogeneity
in disturbance of hepatic pathways in cancer that may make is useful as a marker of disturbances
indicative of different targets of carcinogenicity depending on the cellular context and target.
Thus, although primate and human liver may not be as susceptible to HCC as the rodent liver,
the pathways leading to HCC in rodents and humans appear to be similar and heterogeneous,
with some indicative of other susceptible cellular targets for neoplasia in a differing context.
E.3.1. State of Science for Cancer and Specifically Human Liver Cancer
E.3.1.1. Epigenetics and Disease States (Transgenerational Effects, Effects of Aging,
and Background Changes)
Wood et al. (2007) published their work on "genomic landscapes" of human breast and
colorectal cancers that significantly forwards the understanding of "key events" involved with
induction of cancer. They state that there are -80 DNA mutations that alter amino acid in a
typical cancer, but that examination of the overall distribution of these mutations in different
cancers of the same type leads to a new view of cancer genome landscapes: they are composed
of a handful of commonly mutated genes "mountains" but are dominated by a much larger
number of infrequently mutated gene "hills."
Statistical analyses suggested that most of the ~ 80 mutation in an individual
tumor were harmless and that <15 were likely to be responsible for driving the
initiation, progression, or maintenance of the tumor.. .Historically the focus of
cancer research has been on the gene mountains, in part because they were the
only alterations that could be identified with available technologies. However,
our data show that vast majority of mutations in cancers do not occur in such
mountains. This new view of cancer is consistent with the idea that a large
number of mutations, each associated with a small fitness advantage, drive tumor
progression. It is the "hills" and not the "mountains" that dominate the cancer
genomic landscape.
The large number of "hills" actually reflects alterations in a much smaller number of cell
signaling pathways. Indeed, pathways rather than individual genes appear to govern the course
of tumorigenesis.
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It is becoming increasingly clear that pathways rather than individual genes
govern the course of tumorigenesis. Mutations in any of several genes of a single
pathway can thereby cause equivalent increases in net cell proliferation... .This
new view of cancer is consistent with the idea that a large number of mutations,
each associated with a small fitness advantage, drive tumor progression.
Thus, when pathways are altered, the same phenotype can arise from alterations in any of
several genes.
Consistent with the arguments put forth by Wood et al. (2007) for mutations in cancer is
the additional insight into pathway alterations by epigenomic mechanisms, which can act
similarly as mutation. Weidman et al. (2007) report that:
cell phenotype is not only dependent on its genotype but also on its unique
epigenotype, which is shaped by developmental history and environmental
exposures. The human and mouse genome projects identified approximately
15,500 and 29,000 CpG islands, respectively. Hypermethylation of CpG-rich
regions of gene promoters inhibit expression by blocking the initiation of
transcription. DNA methylation is also involved in the allelic inactivation of
imprinted genes, the silencing of genes on the inactive X chromosome, and the
reduction of expression of transposable elements. Because epigenomic
modifications are copied after DNA synthesis by DNMT1, they are inherited
during somatic cell replication.. .Inherited and spontaneous or environmentally
induced epigenetic alterations are increasingly being recognized as early
molecular events in cancer formation. Furthermore, such epigenetic alterations
are potentially more adverse than nucleotide mutations because their effects on
regional chromatin structure can spread, thereby affecting multiple genetic loci.
Although tumor suppressor gene silencing by DNA methylation occurs frequently
in cancer, genome-wide hypomethylation is one of the earliest events to occur in
the genesis of cancer. Demethylation of the genome can lead to the reactivation
of transposable elements, thereby altering the transcription of adjacent genes, the
activation of oncogenes such as H-Ras, and biallelic expression of imprinted loci
(e.g., loss of IGF2 imprinting).
Thus, epigenetic modification may be worse than mutation in terms of cancer induction.
Dolinoy et al. (2007) report on the role of environmental exposures on the epigenome,
especially during critical periods of development and their role in adult disease susceptibility.
They report that:
aberrant epigenetic gene regulation has been proposed as a mechanism of action
for nongenotoxic carcinogenesis, imprinting disorders, and complex disorders
including Alzheimer's disease, schizophrenia, asthma, and autism. Epigenetic
modifications are inherited not only during mitosis but also can be transmitted
transgenerationally (Anway et al., 2005; Rakyan et al., 2003; Rakyan et al.,
2002)). The influence on environmental factors on epigenetic gene regulation
may also persist transgenerationally despite lack of continued exposure in second,
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third, and fourth generations (Anway et al., 2005). Therefore if the genome is
compared to the hardware in a computer, the epigenome is the software that
directs the computer's operation.. .The epigenome is particularly susceptible to
deregulation during gestation, neonatal development, puberty and old age.
Nevertheless, it is most vulnerable to environmental factors during embryogenesis
because DNA synthetic rate is high, and the elaborate DNA methylation pattern
and chromatin structure required for normal tissue development is established
during early development... 83 imprinted genes have been identified in mice and
humans with 29 or about one third being imprinted in both species. Since
imprinted genes are functionally haploid, they are denied the protection from
recessive mutations that diploidy would normally afford. Imprinted genes that
have been linked to carcinogenesis include IGF2 (bladder, lung, ovarian and
others), IGF2R (breast, colon, lung, and others), and Neuronatin (pediatric
leukemia).
Bjornsson et al. (2008) recently reported that not only were there time-dependent changes
in global DNA methylation within the same individuals in two separate populations in widely
separated geographic locations, but also these changes showed familial clustering in both
increased and decreased methylation. These results were suggested not only to support the
relationship of age-related loss of normal epigenetic patterns as a mechanism for late onset of
common human diseases, but also that losses and gains of DNA methylation observed over time
in different individuals could contribute to disease with the example provided of cancer, which is
associated with both hypomethylation and hypermethylation through activation of oncogenes and
silencing of tumor suppressor genes. The study also showed considerable interindividual age
variation, with differences accruing over time within individuals that would be missed by studies
that employed group averaging.
The review by Reamone-Buettner and Borlak (2007) provide insight into the role of
noncoding RNAs in diseases such as cancer. They report that:
a large number of noncoding RNAs (ncRNAs) play important role in regulating
gene expressions, and advances in the identification and function of eukaryotic
ncRNAs, e.g., microRNAs and their function in chromatin organization, gene
expression, disease etiology have been recently reviewed. The regulatory
pathways mediated by small RNAs are usually collectively referred to as RNA
interference (RNAi) or RNA-mediated silencing. RNAi can be triggered by small
double-stranded RNA (dsRNA) either introduced exogenously into cells as small
interfering siRNAs or that have been produced endogenously from small non-
coding RNAs known as microRNAs (miRNAs). The dsRNAs are
characteristically cleaved by the ribonuclease Ill-enzyme Dicer into 21- to 23 nt
duplexes and the resulting fragments base-pair with complementary mRNA to
target cleavage or to repress translation.. .Two mechanisms exist of miRNA-
mediated gene regulation, degradation of the target mRNA, and translational
repression. Whether one or the other of these mechanisms is used depends on the
degree of the complementary between the miRNA and target mRNA. For a near
perfect match, the Argonaute protein in the RNA-induced silencing complex
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(RISC) cleaves the mRNA target, which is destined for subsequent degradation by
ribonucleases. In the situation of a less degree of complimentarity, commonly
occurring in humans, the translational repression mechanism is used to control
gene expression. However, the exact mechanism for translational inhibition is
unclear.
The varying degrees in complimentarity would help explain the large number of genes
that could be affected by miRNA and pleiotropic response.
The review by Feinberg et al. (2006) specifically addresses the epigenetic progenitor
origin of human cancer. They conclude that epigenetic alterations are ubiquitous and serve as
surrogate alterations for genetic change (oncogene activation, tumor-suppressor-gene silencing),
by mimicking the effect of genetic change. They report that:
Advances in characterizing epigenetic alterations in cancer include global
alterations, such as hypomethylation of DNA and hypoacetylation of chromatin,
as well as gene-specific hypomethylation and hypermethylation. Global DNA
hypomethylation leads to chromosomal instability and increased tumour
frequency, which has been shown in vitro and in vivo in mouse models, as well as
gene-specific oncogene activation, such as R-ras in gastric cancer, and cyclin D2
and maspin in pancreatic cancer. In addition, the silencing of tumour-suppressor
genes is associated with promoter DNA hypermethylation and chromatin
hypoacetylation, which affect divergent genes such as retinoblastoma 1 (RBI),
p!6 (also known as cyclin-dependent kinase inhibitor 2A (CDKN2A), von
Hippel-Lindau tumor suppressor (VHL), and MutL protein homologue (MLH1).
Genetic mechanisms are not the only path to gene disruption in cancer.
Pathological epigenetic changes - non-sequence-based alteration that are inherited
through cell division - are increasingly being considered as alternatives to
mutations and chromosomal alterations in disrupting gene function. These
include global DNA hypomethylation, hypermethylation and hypomethylation of
specific genes, chromatin alterations and loss of imprinting. All of these can lead
to aberrant activation of growth-promoting genes and aberrant silencing of
tumour-suppressor genes.
Most CG dinucleotides are methylated on cytosine residues in vertebrate
genomes. CG methylation is heritable, because after DNA replication the DNA
methyltransferase 1, DNMT1, methylates unmethylated CG on the base-paired
strand. CG dinucleotides within promoters within promoters tend to be protected
from methylation. Although individual genes vary in hypomethylation, all
tumours have shown global reduction of DNA methylation. This is a striking
feature of neoplasia.
In addition to global hypomethylation, promoters of individual genes show
increased DNA methylation levels. Hypermethylation of tumour-suppressor
genes can be tumour-type specific. An increasing number of genes are found to
be normally methylated at promoters but hypomethylated and activated in the
corresponding tumours. These include R-RAs in gastric cancer, melanoma
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antigen family A, l(MAGEl) in melanoma, maspin in gastric cancer, S100A4 in
colon cancer, and various genes in pancreatic cancer.
Our genetic material is complexed with proteins in the form of histones in a one-
to-one weight ratio. Core histones H2A, H2B, H3 and H4 form nucleosome
particles that package 147 bp of DNA, and the linker histone HI packages more
DNA between core particles, forming chromatin. It is chromatin and not just
DNA, that is the substrate for all processes that affect genes and chromosomes. In
recent years, it has become increasingly evident that chromatin, like DNA
methylation, can impart memory to genetic activity. There are dozens of post-
translational histone modifications. Studies in many model systems have shown
that particular histone modifications are enriched at sites of active chromatin
(histone H3 and H4 hyperacetylation, lysing at 4 and H3 (H3-K4) dimethylation
and trimethylation, and H3-K79 methylation) and others are enriched at sites of
silent chromatin (H3-K9 and H3-K27 methylation). These and other histone
modifications survive mitosis and have been implicated in chromatin memory.
Overproduction of key histone methyltransferases that catalyze the methylation of
either H3-K4 or H3-K27 residues are frequent events in neoplasia. Global
reductions in monoacetylated H4-K16 and trimethylated H4-K20 are general
features of cancer cells.
Genomic imprinting is parent-of-origin-specific gene silencing. It results from a
germ-line mark that causes reduced or absent expression of a specific allele of a
gene in somatic cells of the offspring. Imprinting is a feature of all mammals
affecting genes that regulate cell growth, behaviour, signaling, cell cycle and
transport; moreover, imprinting is necessary for normal development. Imprinting
is important in neoplasia because both gynogenotes (embryos derived only from
the maternal genetic complement) and androgenotes (embryos derived only from
the paternal genetic complement) form tumours - ovarian teratomas, and
hydtidiform moles/ choriocarcinomas, respectively. Loss of imprinting (LOI)
refers to activation of the normally silenced allele, or silencing of the normally
active allele, of an imprinted gene. LOI of the insulin-like growth factor 2 gene
(IGF2) accounts for half of Wilms tumours in children. LOI of IGF2 is also a
common epigenetic variant in adults and is associated with a fivefold increased
frequency of colorectal neoplasia. LOI of IGF2 might cause cancer by increasing
the progenitor cell population in the kidney in Wilm's tumor and in the
gastrointestinal tract in colorectal cancer.
Feinberg et al. (2006) propose that epigenetic changes can provide mechanistic unity to
understanding cancer, can occur earlier and set the stage for genetic alterations, and have been
linked to the pluripotent precursor cells from which cancers arise. "To integrate the idea of these
early epigenetic events, we propose that cancer arises in three steps; an epigenetic disruption of
progenitor cells, an initiating mutation and genetic and epigenetic plasticity."
The first step involves an epigenetic disruption of progenitor cells in a given
organ or system, which leads to a polyclonal precursor population of neoplasia-
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ready cells. These cells represent a main target of environmental, genetic and
age-dependent exposure that largely accounts for the long latency period of
cancer. Epigenetic disruption might perturb the normal balance between
undifferentiated progenitor cells and differentiated committed cells within a given
anatomical compartment, either in number or in their capacity for aberrant
differentiation, which provides a common mechanism of neoplasia.
All tumours show global changes in DNA methylation, and DNA methylation is
clonally inherited through cell division. Because the conventional genetic
changes in cancer are also clonal, global hypomethylation would have to occur
universally, at the same moment as the mutational changes, which seems unlikely.
This suggests that global DNA hypomethylation (and global reductions of specific
histone modifications) precedes genetic change in cancer. Similarly,
hypermethylation of tumour-suppressor genes has been observed in the normal
tissue of patients in which the same gene is hypermethylated in the tumour tissue.
Recent data demonstrate LOT of IGF2 throughout the normal colonic epithelium
of patients who have LOI-associated colorectal cancer. LOT is associated with
increased risk of intestinal cancers in both humans and mice. A specific change
in the epithelium is seen in mice that are engineered to have biallelic expression
of IGF2 - a shift in the proportion of progenitor to differentiated cells throughout
the epithelium; a similar abnormality was observed in humans with LOT of IGF2.
The proposed existence of the epigenetically disrupted progenitors of cancer
implies that the earliest stages in neoplastic progression occur even before what a
pathologist would recognize as a benign pre-neoplastic lesion. Such alterations
are inherently polyclonal. This is in contrast with the widely accepted model of
cancer as a monoclonal disorder that arises from an initiating mutation- a model
that was proposed and accepted when little was known about epigenetic
phenomena in cancer.
Thus, Feinberg et al. (2006) provide a hypothesis for the latency period of cancer and
suggest that epigenetic changes predate mutational ones in cancer. Tissues that look
phenotypically "normal" may harbor epigenetic changes and predispositions toward neoplasia.
In regard to what cells may be targets or epigenetic changes that can be "progenitor cells" in the
case of cancer, Feinberg et al. (2006) define such cell having "capacity for self-renewal and
pluripotency—over their tendency toward limited replicative potential and differentiation."
Within the liver, there are multiple cell types that would fit such a definition, including those
who are considered "mature" (see Section E.3.1.4). Feinberg et al. (2006) also note that
epigenetic states can be continuously modified to become heterogeneous at all states of the
neoplastic process.
Telomere erosion results in chromosome shortening and uncapped ends that begin
to fuse and the resulting dicentric chromosomes break at anaphase. DNA
palindromes have recently been found to form at high levels in cancer cells. Like
telomere erosion, DNA palindrome formation can lead to genetic instability by
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initiating bridge-breakage-fusion cycles. However, it is not known how or
exactly when palindromes form, although they appear early in cancer progression.
Epigenetic instability can also promote cancer through pleiotropic alterations in
the expression of genes that modify chromatin.
Epigenetic changes are reversible but the changes can initiate irreversible genetic
changes. Permanent epigenetic changes can have an epigenetic basis. On a
background of cancer-associated epigenetic instability, the effects of mutations in
oncogenes and tumour -suppressor genes might be exacerbated. Therefore the
risk of developing malignancy would be much higher for a given mutations event
if it occurred on the background of epigenetic disruption.
The environmental dependence of cancer fits an epigenetic model generally for
human disease - the environment might influence disease onset not simply
through mutational mechanisms but in epigenetically modifying genes that are
targets for either germline or acquired mutation; that is, by allowing genetic
variates to be expressed. Little is known about epigenetic predispositions to
cancer, but a recent twin study indicates that, similar to cancer risk, global
epigenetic changes show striking increase with age.
Environmental insults might affect the expression of tumour-progenitor genes,
leading to both genetic and epigenetic alterations. Liver regeneration after tissue
injury leads to widespread hypomethylation and hypermethylation of individual
genes; both of these epigenetic changes occur in cancer.
In regard to the implications of epigenomic changes and human susceptibility to toxic
insult, the review by Szyf (2007) provided additional insights.
The basic supposition in the field has been that the interindividual variations in
response to xenobiotic are defined by genetic differences and that the main hazard
anticipated at the genomic level from xenobiotic is mutagenesis or physical
damage to DNA. In accordance with this basic hypothesis, the main focus of
attention in pharmacogenetics has been on identifying polymorphisms in genes
encoding drug metabolizing enzymes and receptors. New xenobiotics were
traditionally tested for their genotoxic effects. However, it is becoming clear that
epigenetic programming plays an equally important role in generating
interindividual phenotypic differences, which could affect drug response.
Moreover, the emerging notion of the dynamic nature of the epigenome and its
responsibility to multiple cellular signaling pathways suggest that it is potentially
vulnerable to the effects of xenobiotics not only during critical period in
development but also later in life as well. Thus, non-genotoxic agents might
affect gene function through epigenetic mechanisms in a stable and long-term
fashion with consequences, which might be indistinguishable from the effects of
physical damage to the DNA. Epigenetic programming has the potential to
persist and even being transgenerationally transmitted (Anway et al., 2005) and
this possibility creates a special challenge for toxicological assessment of safety
of xenobiotics. Any analysis of interindividual phenotype diversity should
therefore take into account epigenetic variations in addition to genetic sequence
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polymorphisms. Whereas, a germ-line polymorphism is a static property of an
individual and might be mapped in any tissue at any point in life, epigenetic
differences must be examined at different time points and at diverse cell types.
Karpinets and Foy (2005) proposed that epigenetic alterations precede mutations and that
succeeding mutations are not random, but in response to specific types of epigenetic changes the
environment has encouraged. This mechanism was also suggested as to explain both the delayed
effects of toxicant exposure and the bystander effect of radiation on tumor development, which
are inconsistent with the accepted mechanism of direct DNA damage.
In a study of ionizing radiation, non-irradiated cells acquired mutagenesis through
direct contact with cells whose nuclei had previously been irradiated with alpha-
particles (Zhou et al., 2003). Molecular mechanisms underlying these
experimental findings are not known but it is believed that it may be a
consequence of bystander interactions involving intercellular signaling and
production of cytokines (Lorimore et al., 2003).
Caldwell and Keshava (2006) reported that:
aberrant DNA methylation has emerged in recent years as a common hallmark of
all types of cancers with hypermethylation of the promoter region of specific
tumor suppressor genes and DNA repair genes leading to their silencing (an effect
similar to their mutation), and genomic hypomethylation (Pereira et al., 2004a:
Ballestar and Esteller, 2002; Berger and Daxenbichler, 2002; Rhee et al., 2002;
Herman etal., 1998). Whether DNA methylation is a consequence or cause of
cancer is a long-standing issue(Ballestar and Esteller, 2002). Fraga et. al. (2005;
2004) report global loss of monoacetylation and trimethylation of histone H4 as
common a hallmark of human tumor cells but suggest genomone-wide loss of 5-
methylcytosine (associated with the acquisition of a transformed phenotype) does
not exist as a static predefined value throughout the process of carcinogenesis but
as a dynamic parameter (i.e., decreases are seen early and become more marked in
later stages).
E.3.1.2. Emerging Technologies, DNA and siRNA, miRNA Microarrays—Promise
and Limitations for Modes of Action
Currently, new approaches are emerging for the study of changes in gene expression and
protein production induced by chemical exposure that could be related to their toxicity and serve
as an anchor for determining similar patterns between rodent models and human diseases or risks
of chemically-induced health impacts. Such approaches have the promise to extend the
definitions of "genotoxic" and "nongenotoxic" effects, which with the advent of epigenomic
study have become obsolete as they assume that only alteration of the DNA sequence is
important in cancer induction and progression. However, not only is phenotypic anchoring an
issue in regard to the differing cell types, regions, and lobes of the liver (see Section E.I.2), it is
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also an issue for overall variability of response between animals and is critical for interpretation
of microarray and other genomic database approaches.
As shown in the discussions of TCE effects in animal models, TCE treatment resulted in
a large variability in response between what are supposed to be relatively homogeneous
genetically similar animals, and there was an apparent difference in response between studies
using the same paradigm. It is important that as varying microarray approaches and analyses of
TCE toxicity or of potential modes of action are published, the issue of phenotypic anchoring at
the cellular to animal level is addressed. Several studies of TCE microarray results and those of
PPARa agonists have been reported in the literature in an attempt to discern modes of action.
Issues related to conduct of these experiments and interpretation of their results are listed below.
Perhaps one of the most important studies of this issue has been reported by Baker et al.
(2004). The ILSIHESI formed a hepatotoxicity working group to evaluate and compare
biological and gene expression responses in rats exposed to well-studied hepatotoxins (Clofibrate
and methapyrilene), using standard experimental protocol and to address the following issues:
(1) how comparable the biological and gene expression data are from different laboratories
running identical in vivo studies; (2) how reproducible the data are generated across laboratories
using the same microarray platform; (3) how data compare using different microarray platforms;
(4) how data compare using RNA from pooled and individual animals; and (5) whether the gene
expression changes demonstrate time- and dose-dependent responses that correlate with known
biological markers of toxicity (Baker et al., 2004).
The rat model studied was the male Sprague-Dawley rat (57 or 60-66 days of age)
exposed to 250 or 25 mg/kg-day Clofibrate for 1, 3, or 7 days. Two separate in vivo studies were
conducted: one at Abbott Laboratories and one at GlaxoSmithKline (GSK, in United Kingdom).
There was a difference in biological response between the two laboratories. The high dose
(250 mg/kg-day) group at day 3 had a 15% increase in liver weight relative to body weight in the
GSK study, compared with a 3% liver weight increase in the Abbott study. At 7 days, there was
a 31% liver weight increase in the GSK study and a 15% increase in the Abbott study. Observed
changes in clinical chemistry parameters also indicated differences in the biological response of
the in vivo study concordant with difference in liver weight. A significant reduction in total
cholesterol levels was seen in the GSK study at the high dose for all time points. However, the
Abbott study demonstrated a significant reduction only at one dose and time point. The
incidence of mitotic figures also differed between the labs. In both studies, there was a 2-3
times greater Acyl-CoA enzyme (ACOX) activity at the high dose but no difference from control
in the low dose. Again, the GSK lab gave greater response. For microarrays, GSK and ULR
pooled samples from each treatment group of four animals. U.S. EPA did some of the
microarray analyses as well as GSK and ULR (GSK in United Kingdom). It is apparent that
although the changes in genes were demonstrated by both laboratories, there were quantitative
differences in the fold change values observed between the two sites.
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The U.S. EPA analyzed gene expression in individual RNA samples obtained from day 7
high- and low-dose animals that had been treated at Abbot. GSK (United States) and ULR
analyzed gene expression in pooled RNA from day 7 high- and low-dose animals treated at GSK
(United Kingdom). Gene expression data from individual animal samples indicated that 7 genes
were significantly upregulated (maximum of 7.2-fold) and 12 genes were down regulated
(maximum of 4.3-fold decrease) in the high-dose group. The low-dose group generated only one
statistically significant gene expression change, namely heat shock protein 70 (HSP70). In
comparison, expression changes in the 7-day pooled high-dose samples analyzed by GSK
(United States) ranged from 43.3-fold to a 3.5-fold decrease. Changes in these same samples
analyzed by ULR ranged from a 4.9-fold increase to a 4.3-fold decrease. As an example, the
microarray fold change at 7-day 250 mg/kg-day Clofibrate showed a 3.8-fold increase for U.S.
EPA individual animals sampled, a 2.2-fold increase for pooled samples by ULR, and a 20.3-fold
increase in pooled samples by GSK (United States) for CYP4A1 (Baker et al.. 2004). Thus,
these results show a very large difference not only between treatment groups, but also between
pooled and nonpooled data and between labs analyzing the same RNA.
Not only was there a difference in DNA microarray results but, also a comparison of gene
expression data from day 7 high-dose samples obtained using quantitative realtime PCR vs. data
generated using cDNA microarrays has shown a quantitative difference but qualitative similar
patterns. Although both methods of quantitative real time PCR on the pooled sample showed the
PPARa gene to be downregulated, the GSK (United States) pooled sample microarray analysis
indicated upregulation; the URL pooled and U.S. EPA individual microarray analyses showed no
change. The microarray for PPARa at 7-day 250 mg/kg-day Clofibrate showed no change for
individual animals (U.S. EPA), no change for pooled samples (ULR), and upregulation of
1.8-fold value for pooled samples for GSK (United States). The quantitative real time PCR on
the pooled sample using Taqman gave a 4.5-fold downregulation and using SYBR Green gave a
1.2-fold downregulation of PPARa.
Baker et al. (2004) reported that the pooling of samples for microarray analysis has been
used in the past to defray the cost of microarray experiments, reduce the effect of biological
variation, and in some cases, overcome availability of limiting amounts of tissues.
Unfortunately, this approach essentially produced a sample size (n) of one animal. Repeated
microarray experiments with such pooled RNA produces technical replicates as opposed to true
biological replicates, and thus, does not allow calculation of biologically significant changes in
gene expression between different dose groups or time points. Another possible consequence of
pooling is to mask individual gene changes and leave open the possibility of introducing error
due to individual outlier responses.
Woods et al. (2007b) note that:
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because toxicogenomics is a relatively novel technology, there are a number of
limitations that must be resolved before array data is widely accepted. Microarray
studies have been touted as being highly sensitive for detecting toxic responses at
much earlier time points and/or lower doses than histopathology, clinical
chemistry or other traditional toxicological assays can detect. However, based on
the nature of the assay, measurements of extreme levels of gene expression - low
or high -are thought to be unreliable. Also the reproducibility of microarray
experiments has raised concerns. "Batch effects" based on the day, user, and
laboratory environment have been observed in array datasets. To address these
concerns, confirmation of microarray-derived gene expression profiles is typically
performed using quantitative real time polymerase chain reaction (RT-PCR) or
Northern blot analysis.
In addition to the issues raised above, Waxman and Wurmbach (2007) raise issues
regarding how quantitative real time PCR experiments are conducted. They state that cancer
development affects almost all pathways and genes including the "housekeeping" genes, which
are involved in the cell's common basic functions (e.g., glyceraldehyde-3-phosphate
dehydrogenase [GADPH], beta actin [ACTB], TATA-binding protein, ribosomal proteins, and
many more). However, "many of these genes are often used to normalize quantitative real-time
RT-PCR (qPCR) data to account for experimental differences, such as differences in RNA
quantity and quality, the overall transcriptional activity and differences in cDNA synthesis.
GADPH and ACTB are most commonly used for normalization, including studies of cancer."
Waxman and Wurmbach (2007) suggest that despite the fact that it has been shown that these
genes are differentially expressed in cancers, including colorectal-, prostate-, and bladder-cancer,
some qPCR studies on HCC used GAPDH or ACTB for normalization. Since many
investigations on cancer include multiple comparisons, and analyze different stages of the
disease, such as normal tissue, preneoplasm, and consecutive stages of cancer, "it crucial to find
an appropriate gene for normalization" whose expression is constant throughout all disease
stages and not response to treatment.
For liver cancers associated with exposure to hepatitis C virus (HCV), Waxman and
Wurmbach (2007) reported that differing states, including preneoplastic lesions (cirrhosis and
dysplasia) and consecutive stages of HCC, had differential expression of "housekeeping" genes
and that using them for normalization had an effect on the fold change of qPCR data and on the
general direction (up or down) of differentially expressed genes. For example, GAPDH was
strongly upregulated in advanced and very advanced stages of HCC (in some samples up to
sevenfold) and ACTB was upregulated two- to threefold in many advanced and very advanced
tumor samples. Waxman and Wurmbach (2007) concluded that:
microarray data are known to be highly variable. Due to its higher dynamic range
qPCR is thought to be more accurate and therefore is often used to corroborate
microarray results. Mostly, general direction (up and down-regulation) and rank
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order of the fold-changes are similar, but the levels of the fold changes of
microarray experiments differ compared to qPCR data and show a marked
tendency of being smaller. This effect is more pronounced as the fold change is
very high.
In relation to use of gene expression and indicators of cancer causation, Volgelstein and
Kinzler (2004) made important points regarding their use:
Levels of gene expression are unreliable indicators of causation because
disturbance of any network invariably leads to a multitude of such changes only
peripherally related to the phenotype. Without better ways to determine whether
an unmutated but interesting candidate gene has a causal role in neoplasia, cancer
researchers will likely be spending precious time working on genes only
peripherally related to the disease they wish to study.
This is an important caveat for gene expression studies for mode of action that are
"snapshots in time" without phenotypic anchoring and even more applicable to experimental
paradigms where there is ongoing necrosis or toxicity in addition to gene changes that may or
may not be associated with neoplasia.
For an endpoint that is not as complex as neoplasia, there are issues regarding uses of
microarray data. In regard to the determination of acute liver toxicity caused by one of the most
studied hepatotoxins, acetaminophen, and its correlation with microarray data, Beyer et al.
(2007) also have reported the results of a landmark study examining issues regarding use of this
approach.
The biology of liver and other tissues in normal and disease states increasingly is
being probed using global approaches such as microarray transcriptional profiling.
Acceptance of this technology is based principally on a satisfactory level of
reproducibility of data among laboratories and across platforms. The issue of
reproducibility and reliability of genomics data obtained from similar
(standardized) biological experiments performed in different laboratories is
crucial to the generation and utility of large databases of microarray results.
While several recent studies uncovered important limitation of expression
profiling of chemical injury to cells and tissues (Beekman et al., 2006; Baker et
al., 2004; Ulrich et al., 2004), determining the effects of intralaboratory variables
on the reproducibility, validity, and general applicability of the results that are
generated by different laboratories and deposited into publicly available databases
remains a gap.. .The National Institutes of Environmental Health Sciences
(NIEHS) established the Toxicogenomics Research Consortium to apply the
collective and specialized expertise from academic institutions to address issues in
integrating gene expression profiling, bioinformatics, and general toxicology.
Key elements include developing standardized practices for gene expression
studies and conducting systematic assessments of the reproducibility of traditional
toxicity endpoints and microarray data within and among laboratories. To this
end the consortium selected the classical hepatotoxicant acetaminophen (APAP)
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for its proof of concept experiments. Despite more than 30 years of research on
APAP, we are far from a complete understanding of the mechanisms of liver
injury, risk factors, and molecular markers that predict clinical outcome after
poisoning. APAP-induced hepatotoxicity was performed at seven geographically
dispersed Centers. Parallel studies with N-acetyl-m-aminophenol (AMAP), the
non-hepatotoxic isomer of APAP, provided a method to isolate transcripts
associated with hepatotoxicity (Beyer et al., 2007).
Beyer et al. (2007) identified potential sources of interlaboratory variability when
microarray analyses were conducted by one laboratory on RNA samples generated in different
laboratories but using the same experimental paradigm and source of animals. Toxic injury by
APAP showed variability across Centers and between animals (e.g., percent liver affected by
necrosis [<20-80% at one time period and 0-60% at another], control animal serum ALT
[threefold difference], and in GSH depletion [<5->60%] between centers). There was
concordance between APAP toxicity as measured in individual animals (rather than expressed as
just a mean with SE) and transcriptional response. Of course, the variability between gene
platforms and processing of the microarray s had been reduced by using the same facility to do all
of the microarray analyses. However, the results show that phenotypic anchoring of gene
expression data are required for biologically meaningful meta-analysis of genomic experiments.
Woods et al. (2007b) noted that:
improvements should continue to be made on statistical analysis and presentation
of microarray data such that it is easy to interpret. Prior to the current advances in
bioinformatics, the most common way of reporting results of microarray studies
involved listing differentially expressed genes, with little information about the
statistical significance or biological pathways with which the genes are
associated.
However, there are issues with the use of "Classifiers" or predictive genomic computer
programs based on genes showing altered expression in association with the observed toxicities.
Although these metrics built on different machine learning algorithms could be
useful in estimating the severity of potential toxicities induced by compounds, the
applications of these classifiers in understanding the mechanisms of drug-induced
toxicity are not straightforward. In particular this approach is unlikely to
distinguish the upstream causal genes from the downstream responsive genes
among all the genes associated with an induced toxicity. Without knowledge of
the causal sufficiency order, designing experiments to test predicted toxicity in
animal models remains difficult" (Dai et al., 2007).
Ulrich (2003) stated the limitation of microarray analysis to study nuclear receptors (e.g.,
PPARa).
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Nuclear receptors comprise a large group of ligand-activated transcription factors
that control much of cellular metabolism. Toxicogenomics is the study of the
structure and output of the entire genome as it related and responds to adverse
xenobiotic exposure. Traditionally, the genes regulated by nuclear receptors in
cells exposed to toxins have been explored at the mRNA and protein levels using
northern and western blotting techniques. Though effective when studying the
expression of individual genes, these approaches do not enable the understanding
of the myriad of genes regulated by individual receptors or of the crosstalk
between receptors.. .Discovery of the multiple genes regulated by each receptor
type has thus been driven by technological advances in gene expressional
analysis, most commonly including differential display, RT-PCR and DNA
microarrays., and in the development or receptor transgenic and knockout animal
models. There is much cross talk between receptors and many agonists interact
with multiple receptors. Off target effects cannot be predicted by target
specificity. Though RCR can affect transcription directly, much of its effects are
exerted through heterodimeric binging with other nuclear receptors (PXR, CAR,
PPARa, PPARy, FXR, LXR, TR) (Ulrich. 2003).
Another tool recent developed is gene silencing by introduction of siRNA. Dai et al.
(2007) noted issues involved in the siRNA to change gene expression for exploration of mode of
action etc., to include the potential of off-target effects, incomplete knockdown, and nontargeting
of splice variants by the selected siRNA sequence. Using knockdown of PPARa in mice, Dai et
al. (2007) report "PPARa knockdown was variable between mice ranging from -80%
knockdown to little or no knockdown and that differing siRNAs gave different patterns of gene
expression with some grouped with PPARa -/- null mice but others grouped with expression
patterns of mice injected with control siRNA or Ringers buffer alone and showing no PPARa
knockdown." Dai et al. (2007) concluded that it is possible that it is the change in PPARa levels
that is important for perturbing expression of genes modulated by PPARa rather than the
absolute levels of PPARa.
Not only is the finding of variability in knockdowns by siRNA technologies important,
but the finding that level of PPAR is not necessarily correlated with function and that it could be
the change and not absolute level that matters in modulation in gene expression by PPARa is of
importance as well. How an animal responds to decreased PPARa function may also depend on
its gender. Dai et al. (2007) observed more dramatic phenotypes in female vs. male mice treated
with siRNA. Costet et al. (1998) have reported sexually dimorphic phenotypes including obesity
and increased serum triglyceride levels in females, and steatosis and increased hepatic
triglyceride levels in male PPARa-null mice. Ramdhan et al. (2010) provided extensive date
regarding lipid dysregulation in male PPARa-null mice and humanized mice.
In regard to the emerging science and preliminary reports of the effects of microRNA as
oncogenes and tumor suppressors and of possible importance to hypothesized modes of action
for liver cancer, the same caveats as described for DNA microarray analyses all apply, along
with additional uncertainties. miRNAs repress their targeted mRNAs by complementary base
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pairing and induction of the RNA interference pathway. Zhang et al. (2007) reported Northern
blot detection of gene expression at the mRNA level and its correlation with miRNA expression
in cancer cells as well as realtime PCR. These PCR-based analyses quantify miRNA precursors
and not the active mature miRNAs. However, they reported that the relationship between pri-
miRNA and mature miRNA expression has not been thoroughly addressed and is critical in order
to use real time PCR analysis to study the function of miRNAs in cancers. They go on to state
that:
although Northern Blotting is a widely used method for miRNA analysis, it has
some limitations, such as unequal hybridization efficiency of individual probes
and difficulty in detecting multiple miRNAs simultaneously. For cancer studies,
it is important to be able to compare the expression pattern of all known miRNAs
between cancer cells and normal cells. Thus, it is better to have methods which
detect all miRNA expression at a single time.. .Although Northern blot analysis,
real-time PCR, and miRNA microarray can detect the expression of certain
miRNAs and determine which miRNAs may be associated with cancer formation,
it is difficult to determine whether or not miRNAs play a unique role in cancers.
Also these techniques cannot directly determine the correlation between mRNA
expression levels and whether the up-regulation or down-regulation of certain
miRNAs is the cause of cancer or a downstream effect of the disease.. .Many
miRNA genes have been found that are significantly overexpressed in different
cancers. All of them appear to function as oncogenes; however, only a few of
them have been well characterized.
Zhang et al. (2007) suggested that bioinformatic studies indicate that numerous genes are
the targets of miR-17-92: >600 for miR-19a and miR-20, two members of the miR-17-92 cluster.
Cho (2007) stated that:
though more than 530 miRNAs have been identified in human, much remains to
be understood about their precise cellular function and role in the development of
diseases.. .Although each miRNA can control hundreds of target genes, it remains
a great challenge to identify the accurate miRNA targets for cancer research.
Thus, miRNAs have multiple targets so, like other transcription factors, may have
pleotropic effects that are cell, timing, and context specific.
Vogelstein and Kinzler (2004) stated "in the last decade many important gene responsible
for the genesis of various cancers have been discovered." Most importantly, they and others
suggest that pathways rather than individual gene expression should be the focus of study. As a
specific example, Volgelstein and Kinzler noted:
another example of the reason for focusing on pathways rather than individual
genes has been provided by studies of TP53 tumor-suppressor gene. The p53
protein is a transcription factor that normally inhibits cell growth and stimulates
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cell death when induced by cellular stress. The most common way to disrupt the
p53 pathway is through a point mutation that inactivates its capacity to bind
specifically to its cognate recognition sequence. However, there are several other
ways to achieve the same effects, including amplification of the MDM2 gene and
infection with DNA tumor viruses whose products bind to p53 and functionally
inactivate it.
In regard to cellular anchoring for gene expression or pathway alterations associated with
cancer and the importance of "context" of gene expression changes, Vogelstein and Kinzler
(2004) gave several examples.
In solid tumors the important of the interactions between stroma and epithelium is
becoming increasingly recognized (e.g., the importance of the endothelial
cell).. .One might expect that a specific mutation of a widely expressed gene
would have identical or at least similar effects in different mammalian cell types.
But this is not in general what is observed. Different effects of the same mutation
are not only found in distinct cell types; difference can even be observed in the
same cell types, depending on when the mutation occurred during the tumorigenic
process. The RAS gene mutations provide informative examples of these
complexities. KRAS2 gene mutation in normal pancreatic duct cells seem to
initiate the neoplastic process, eventually leading to the development of
pancreatic cancer. The same mutations occurring in normal colonic or ovarian
epithelial cells lead to self-limiting hyperplastic or borderline lesions that do not
progress to malignancy. In many human and experimental cancers, RAS genes
seem to function as oncogenes. But RAS genes can function as suppressor genes
under other circumstances, inhibiting tumorigenesis after administration of
carcinogens to mice. These and similar observation on other cancer genes are
consistent with the emerging notion that signaling molecules play multiple roles
at multiple time, even in the same cell type. However, the biochemical bases for
such variations among cancer cells are almost unknown.
In regard to the major pathways and mediators involved in cancer, several investigators
have reported a coherent set that are involved in many types of cancers. Vogelstein and Kinzler
(2004) noted that major pathways and mediators include p53, RB, WNT, E-cadherin, GL1, APC,
ERK, RAS:GTP, P13K,SMAD, RTK BAD, BAX, and H1F1. In regard to coherence and site
concordance between animal and human data, the disturbance of a pathway in one species may
result in the different expression of tumor pattern in another, but both linked to a common
endpoint of cancer. Thus, pathways rather than a single mutation should be the focus of mode of
action and cancer as several actions can be manifested by one pathway or change at one time that
lead to cancer.
Vogelstein and Kinzler (2004) also noted that pathways that are common to "cancer" are
also operative in liver cancer where, as a heterogeneous disease, multiple pathways have been
implicated in differing manifestations of this disease. Thus, liver cancer may be an example in
its multiple forms that are analogous to differing sites being affected by common pathways
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leading to "cancer." Pathway concordance may not always show up as site concordance as
expression of cancer between species. Liver cancer may be the example where many pathways
can lead a cancer that is characterized by its heterogeneity.
E.3.1.3. Etiology, Incidence, and Risk Factors for HCC
The review article of Farazi and DePinho (2006) provides an excellent summary of the
current state of human liver cancer in terms of etiology and incidence. The 5-year survival rate
of individuals with liver cancer in the United States is only 8.9% despite aggressive conventional
therapy with lethality of liver cancer due in part from its resistance to existing anticancer agents,
a lack of biomarkers that can detect surgically respectable incipient disease, and underlying liver
disease that limits the use of chemotherapeutic drugs. Chen et al. (2002b) reported that surgical
resection is considered the only "curative treatment" but >80 of patients have widespread HCC at
the time of diagnosis and are not candidates for surgical treatment. Among patients with
localized HCC who undergo surgery, 50% suffer a recurrence. Primary liver cancer is the fifth
most common cancer worldwide and the third most common cause of cancer mortality. HCC
accounts for between 85 and 90% of primary liver cancers (El-Serag and Rudolph, 2007). Seitz
and Stickel (2006) report that epidemiological data from the year 2000 indicate that
>560,000 new cases of HCC occurred worldwide, accounting for 5.6% of all human cancers and
that HCC is the fifth most common malignancy in men and the eighth in women.
Overall, incidence rates of HCC are higher in males compared to females. In almost all
populations, males have higher liver cancer rates than females, with male:female ratios usually
averaging between 2:1 and 4:1 and the largest discrepancies in rates (>4:1) found in medium-risk
European populations (El-Serag and Rudolph, 2007). Experiments showed a 2-8-fold of control
HCC development in male mice as well supporting the hypothesis that androgens influence HCC
progression rather than sex-specific exposure to risk factors (El-Serag and Rudolph, 2007). El-
Serag and Rudolph (2007) also reported that:
in almost all areas, female rates peak in the age group 5 years older than the peak
age group for males. In low risk population (e.g., U.S.) the highest age-specific
rates occur among persons aged 75 and older. A similar pattern is seen among
most high-risk Asian populations. In contrast male rats in high-risk African
populations (e.g., Gambia) ten to peak between ages 60 and 65 before declining,
whereas female rates peak between 65 and 70 before declining.
Age-adjusted incidence rates for HCC are extremely high in East and Southeast Asia and
in Africa, but in Europe, there is a gradually decreasing prevalence from South to North. HCC
incidence rates also vary greatly among different populations living in the same region and vary
by race (e.g., for all ages and sexes in the United States, HCC rates are 2 times higher in Asian
than in African Americans, whose rates are 2 times higher than those in whites); ethnic
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variability is likely to include differences in the prevalence and acquisition time of major risk
factors for liver disease and HCC (El-Serag and Rudolph, 2007).
Worldwide HCC incidence rate doubled during the last two decades and younger age
groups are increasingly affected (El-Serag, 2004). The high prevalence of HCC in Asia and
Africa may be associated with widespread infection with hepatitis B virus (HBV) and HCV but
other risk factors include chronic alcohol misuse, nonalcoholic fatty liver disease (NAFLD),
tobacco, oral contraceptives, and food contamination with aflatoxins (Seitz and Stickel, 2006).
El-Serag and Rudolph (2007) reported HCC to be the fastest growing cause of cancer-related
death in men in the United States with age-adjusted HCC incidence rates increasing more than
twofold between 1985 and 2002 and that, overall, 15-50% of HCC patients in the United States
have no established risk factors.
Although liver cirrhosis is present in a large portion of patients with HCC, it is not always
present. Fattovich et al. (2004) reported that:
differences of geographic area, method of recruitment of the HCC cases (medical
or surgical) and the type of material studied (liver biopsy specimens, autopsy, or
partial hepatectomies) may account for the variable prevalence of HCC without
underlying cirrhosis (7% to 54%) quoted in a series of studies. Percutaneous liver
biopsy specimens are subject to sampling error. However, only a small
proportion of patients with HCC without cirrhosis have absolutely normal liver
histology, the majority of them showing a range of fibrosis intensity from no
fibrosis are all to septal and bridging fibrosis, necroinflammation, steatosis, and
liver cell dysplasia.
Farazi and DePinho (2006) noted that for diabetes, a higher indices of HCC have been
described in diabetic patients with no previous history of liver disease associated with other
factors. El-Serag and Rudolph (2007) reported that in their study of VA patients
(173,643 patients with and 650,620 patients without diabetes), that HCC incidence doubled
among patients with diabetes and was higher among those with a longer follow-up of evaluation.
"Although most studies have been conducted in low HCC rate areas, diabetes also has been
found to be a significant risk factor in areas of high HCC incidence such as Japan. Taken
together, available data suggest that diabetes is a moderately strong risk factor for HCC."
NAFLD and nonalcoholic steatohepatitis contribute to the development of fibrosis and
cirrhosis and therefore, might also contribute to HCC development. The pathogenesis of
NAFLD includes the accumulation of fat in the liver, which can lead to reactive oxygen species
in the liver with necrosis factor a (TNFa) elevated in NAFDL and alcoholic liver disease (Seitz
and Stickel 2006). Abnormal liver enzymes not due to alcohol, viral hepatitis, or iron overload
are present in 2.8-5.5% of the U.S. general population and may be due to NAFLD in 66-90% of
cases (Adams and Lindor, 2007). Primary NAFLD occurs most commonly and is associated
with insulin-resistant states, such as diabetes and obesity, with other conditions associated with
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insulin resistance, such as polycystic ovarian syndrome and hypopituitarism also associated with
NAFLD (Adams and Lindor, 2007). The steatotic liver appears to be susceptible to further
hepatotoxic insults, which may lead to hepatocyte injury, inflammation, and fibrosis, but the
mechanisms promoting progressive liver injury are not well defined (Adams and Lindor, 2007).
Substrates derived from adipose tissue such as FFA, TNF-a, leptin, and adiponectin have been
implicated, with oxidative stress appearing to be important leading to subsequent lipid
peroxidation, cytokine induction, and mitochondrial dysfunction. Liver disease was the 3rd
leading cause of death among NAFLD patients compared to the 13th leading cause among the
general population, suggesting that liver-related mortality is responsible for a proportion of
increased mortality risk among NAFLD patients (Adams and Lindor, 2007).
The RR for HCC in type 2 diabetics has been reported to be approximately 4 and
increases to almost 10 for consumption of >80 g of alcohol per day (Hassan et al., 2002). El-
Serag and Rudolph (2007) reported that:
it has been suggested that many cryptogenic cirrhosis and HCC cases represent
more severe forms of nonalcoholic fatty liver disease (NAFLD), namely
nonalcoholic steato hepatitis (NASH). Studies in the United States evaluating risk
factors for chronic liver disease or HCC have failed to identify HCV, HBV, or
heavy alcohol intake in a large proportion of patients (30-40%). Once cirrhosis
and HCC are established, it is difficult to identify pathologic features of NASH.
Several clinic-based controlled studies have indicated that HCC patients with
cryptogenic cirrhosis tend to have clinical and demographic features suggestive of
NASH (predominance of women, diabetes, and obesity) as compared with age-
and sex-matched HCC patients of well defined vial or alcoholic etiology. The
most compelling evidence for an association between NASH and HCC is indirect
and come from studies examining HCC risk with 2 conditions strongly associated
with NASH: obesity and diabetes. In a large prospective cohort in the US,
followed up for 16 years, liver cancer mortality rates were 5 times greater among
men with the greatest baseline body mass index (range 35-40) compared with
those with a normal body mass index. In the same study, the risk of liver cancer
was not as increase in women, with a relative risk of 1.68. Two other population-
based cohort studies from Sweden and Denmark found excess HCC risk
(increased 2- to 3-fold) in obese men and women compared with those with a
normal body mass index.. .Finally, liver disease occurs more frequently in those
with more severe metabolic disturbances, with insulin resistance itself shown to
increase as the disease progresses. Several developed countries most notably the
United States, are in the midst of a burgeoning obesity epidemic. Although the
evidence linking obesity to HCC is relatively scant, even small increase in risk
related to obesity could translate into a large number of HCC cases.
Thus, even a small increase in risk related to obesity could result in a large number of
HCC cases, and the latency of HCC may make detection of increased HCC risk not detectable
for several years.
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Other factors are involved, as not every cirrhotic liver progresses to HCC. Seitz and
Stickel (2006) suggested that 90-100% of those who drink heavily suffer from alcoholic fatty
liver, 10-35% of those evolve to alcoholic steatohepatitis, 8-20% of those evolve to alcoholic
cirrhosis, and 1-2% of those develop HCC. HCV infects approximately 170 million individuals
worldwide with approximately 20% of chronic HCV cases developing liver cirrhosis and 2.5%
developing HCC.
Infection with HB V, a noncytopathic, partially double-stranded hepatotropic DNA virus
classified as a member of the hepadnaviridae family, is also associated with liver cancer risk with
several lines of evidence supporting the direct involvement of HBV in the transformation process
(Farazi and DePinho. 2006). El-Serag and Rudolph (2007) suggested that:
Epidemiologic research has shown that the great majority of adult-onset HCC
cases are sporadic and that many have at lease 1 established non-genetic risk
factor such as alcohol abuse or chronic HCV or HBV infection. However, most
people with these known environmental risk factors never develop cirrhosis or
HCC, whereas a sizable minority of HCC case develop among individuals without
any known risk factors.. .Genetic epidemiology studies in HCC, similar to several
other conditions, have fallen short of early expectations that they rapidly and
unequivocally would result in identification of genetic variants conveying
substantial excess risk of disease and thereby establish the groundwork for
effective genetic screening for primary prevention.
E.3.1.4. Issues Associated with Target Cell Identification
Another outstanding and important question in HCC pathogenesis involves the cellular
origin of this cancer. The liver is made up of a number of cell types showing different
phenotypes and levels of differentiation. Which cell types are targets of hepatocarcinogens and
are those responsible for human HCC is a matter of intense debate. Studies over the last decade
provide evidence of several types of cells in the liver that can repopulate the hepatocyte
compartment after a toxic insult. "Indeed, although the existence of a liver stem cell is often
debated, most experts agree that progenitor liver cells are activated, in response to significant
exposure to hepatotoxins. Also, progenitor cells derived from nonhepatic sources, such as bone
marrow and pancreas, have been demonstrated recently to be capable of differentiating into
mature hepatocytes under correct microenvironmental conditions" (Gandillet et al., 2003).
At present, analyses of human HCCs for oval cell markers, comparison of their gene-
expression patterns with rat fetal hepatoblasts and the cellular characteristics of HCC from
various animal models have provided contrasting results about the cellular origin of HCC and
imply dual origins from either oval cells or mature hepatocytes. The failure to identify a clear
cell of origin for HCC might stem from the fact that there are multiple cells of origin, perhaps
reflecting the developmental plasticity of the hepatocyte lineage. The resolution of the HCC cell
of origin issue could affect the development of useful preventative strategies to target nascent
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neoplasms, foster an understanding of how HCC-relevant genetic lesions function in that specific
cell-development context, and increase our ability to develop more accurate mouse models in
which key genetic events are targeted to the appropriate cellular compartment (Farazi and
DePinho, 2006). Two reviews by Librecht (2006) and Wu and Chen (2006) provide excellent
summaries of the issues involved in identifying the target cell for HCC and the review by
Roskams et al. (2004) provided a current view of the "oval cell" its location and human
equivalent. Recent reports by Best and Coleman (2007) suggest another type of liver cell is also
capable of proliferation and differentiating into small hepatocytes (i.e., small hepatocyte-like
progenitor cell).
The review by Librecht (2006) provides an excellent description of the controversy and
data supporting different views of the cells of origin for HCC.
In recent years, the results of several studies suggest that human liver tumors can
be derived from hepatic progenitor cells rather than from mature cell types. The
available data indeed strongly suggest that most combined hepatocellular-
cholangiocarcinomas arise from hepatic progenitor cells (HPCs) that retained
their potential to differentiate into the hepatocyte and biliary lineages. Hepatic
progenitor cells could also be the basis for some hepatocellular carcinomas and
hepatocellular adenomas, although it is very difficult to determine the origin of an
individual hepatocellular carcinoma. There is currently not enough data to make
statements regarding a hepatic progenitor cell origin of cholangiocarcinoma. The
presence of hepatic progenitor cell markers and the presence and extent of the
cholangiocellular component are factors that are related the prognosis of
hepatocellular carcinomas and combined hepatocellular-cholangiocarcinomas,
respectively... The traditional view that adult human liver tumors arise from
mature cell types has been challenged in recent decades.. .HPCs are small
epithelial cells with an oval nucleus, scant cytoplasm and location in the bile
ductules and canals of Hering. HPCs can differentiate towards the biliary and
hepatocytic lineages. Differentiation towards the biliary lineage occurs via
formation of reactive bile ductules, which are anastamosing ductules lined by
immature biliary cells with a relatively large and oval nucleus surrounded by a
small rim of cytoplasm. Hepatocyte differentiation leads to the formation of
intermediate hepatocyte-like cells, which are defined as polygonal cells with a
size intermediate between than of HPCs and hepatocytes. In most liver diseases,
hepatic progenitor cells are "activated" which means that they proliferate and
differentiate towards the hepatocytic and/or biliary lineages. The extent of
activation is correlated with disease severity.. .HPCs and their immediate biliary
and hepatocytic progeny not only have a distinct morphology, but they also
express several markers, with many also present in bile duct epithelial cells.
Immunohistochemistry using antibodies against these markers facilitates the
detection of HPCs. The most commonly used markers are cytokeratin (CK) 19
and CK7.. .The proposal that a human hepatocellular carcinoma does not
necessarily arise from mature hepatocyte, but could have HPC origin, has
classically been based on three different observations. Each of them, however,
gives only indirect evidence that can be disputed.. .Firstly, it has been shown that
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HPCs are the cells of origin of HCC in some animal models of
hepatocarcinogenesis, which has led to the suggestion that this might also be the
case in humans. However, in other animal models, the HCCs arise from mature
hepatocytes and not from HPCs or reactive bile ductular cells (Bralet et al 2002;
Lin et al 1995- DEN treated rats). Since it is currently insufficiently clear which
of these animal models accurately mimics human hepatocarcinogenesis, one
should be careful about extrapolating data regarding HPC origin of HCC in
animal models to the human situation... Secondly, liver diseases that are
characterized by the presence of carcinogens and development of dysplastic
lesions also show HPC activation. Therefore, the suggestion has been made that
HPCs form a "target population" for carcinogens, but this is only a theoretical
possibility not supported by experimental data.. .Thirdly, several studies have
shown that a considerable proportion of HCCs express one or more HPC markers
that are not present in normal mature hepatocytes. Due to the fact that most HPC
markers are also expressed in the biliary lineage, the term "biliary marker" has
been used in some of these studies. The "maturation arrest" hypothesis states that
genetic alterations occurring in a HPC, or its immediate progeny, cause aberrant
proliferation and prevent its normal differentiation. Further accumulation of
genetic alterations eventually leads to malignant transformation of these
incompletely differentiated cells. The resulting HCC expresses HPC markers as
evidence of its origin. However, expression of HPC markers can also be
interpreted in the setting of the "dedifferentiation" hypothesis, which suggests that
the expression of HPC markers is acquired during tumor progression as a
consequence of accumulating mutations. For example, experiments in which
human HCC cells lines were transplanted into nude mice have nicely shown that
the expression of HPC marker, CK19, steadily increased when the tumors became
increasingly aggressive and metastasized to the lung, Thus, the expression of
CK19 in a HCC does not necessarily mean that the tumor has a HPC origin, but it
can also be mutation-induced, acquired expression associated with tumor
progression. Both possibilities are not mutually exclusive. For an individual
HCC that expresses a HPC marker, it remains impossible to determine whether
this marker reflects the cellular origin and/or is caused by tumor progression.
This can only be elucidated by determining whether HCC contains cells that are
ultrastructurally identical to HPCs in nontumor liver.
Similarly, the review by Wu and Chen (2006) also presents a valuable analysis of these
issues and stated:
The question of whether hepatocellular carcinomas arises from the differentiation
block of stem cells or dedifferentiation of mature cells remains controversial.
Cellular events during hepatocarcinogenesis illustrate that HCC may arise for
cells at various stages of differentiation in the hepatic stem cell lineage.. .The role
of cancer stem cells has been demonstrated for some cancers, such as cancer of
the hematopoietic system, breast and brain. The clear similarities between normal
stem cell and cancer stem cell genetic programs are the basis of the a proposal
that some cancer stem cells could derived from human adult stem cells. Adult
mesenchymal stem cells (MSC) may be targets for malignant transformation and
undergo spontaneous transformation following long-term in vitro culture,
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supporting the hypothesis of cancer stem cell origin. Stem cells are not only units
of biological organization, responsible for the development and the regeneration
of tissue and organ systems, but are also targets of carcinogenesis. However, the
origin of the cancer stem cell remains elusive.. .Three levels of cells that can
respond to liver tissue renewal or damage have been proved (1) mature liver cells,
as "unipotential stem cells," which proliferate under normal liver tissue renewal
and respond rapidly to liver injury, (2) oval cells, as bipotential stem cells, which
are activated to proliferate when the liver damage is extensive and chronic or if
proliferation of hepatocytes is inhibited; and (3) bone marrow stem cells, as
multipotent liver stem cells, which have a very long proliferation potential. There
are two major nonexclusive hypotheses of the cellular origin of cancer; from stem
cells due to maturation arrest or from dedifferentiation of mature cells. Research
on hepatic stem cells in hepatocarcinogenesis has entered a new era of
controversy, excitement and great expectations.. .The two major hypotheses about
the cellular origination of HCC have been discussed for almost 20 years. Debate
has centered on whether or not HCC originates from the differentiation block of
stem cells or dedifferentiation of mature cells. Recent research suggests that HCC
may originate from the transdifferentiation of bone marrow cells. In fact, there
might be more than one type of carcinogen target cell. The argument about the
origination of HCC becomes much clearer when viewed from this viewpoint:
poorly differentiated HCC originate from bone marrow stem cells and oval cells,
while well-differentiated HCC originates from mature hepatocytes.. .The cellular
events during hepatocarcinogenesis illustrate that HCC may arise from cells at
various stages of differentiation in the hepatocyte lineage. There are four levels
of cells in the hepatic stem cell lineage: bone marrow cell, hepato-pancreas stem
cell, oval cell and hepatocyte. HSC and the liver are known to have a close
relationship in early development. Bone marrow stem cells could differentiate
into oval cells, which could differentiate into heptatocytes and duct cells. The
development of pancreatic and liver buds in embryogenesis suggests the existence
of a common progenitor cells to both the pancreas and liver. All of the four levels
of cells in the stem cell lineage may be targets of hepatocarcinogenesis.
Along with the cell types described as possible targets and participants in HCC, Best and
Coleman (2007) described yet another type of cell in the liver that can respond to hepatocellular
injury, which they term small hepatocyte-like progenitor cells and conclude that they are not the
progeny of oval cells, but represent a distinct liver progenitor cell population. Another potential
regenerative cell is the small hepatocyte-like progenitor cell (SHPC). SHPCs share some
phenotypes with hepatocytes, fetal hepatoblasts, and oval cells, but are phenotypically distinct.
They express markers such as albumin, transferring, and alpha-fetoprotein (AFP) and possess
bile canaliculi and store glycogen.
A recent review by Roskams et al. (2004) provided a current view of the "oval cell" its
location and human equivalent. They concluded that:
while similarities exist between the progenitor cell compartment of human and
rodent livers, the different rodent models are not entirely comparable with the
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human situation, and use of the same term has created confusion as to what
characteristics may be expected in the human ductular reaction. For example, a
defining feature of oval cells in many rodent models of injury is production of
alpha-fetoprotein, whereas ductular reactions in humans rarely display such
expression. Therefore we suggest that the "oval cell" and "oval -like cell" no
longer be used in description of human liver.
In the chronic hepatitis and cancer model of Vig et al. (2006), it is not the oval cells or
SHPCs that are proliferating but the mature hepatocytes, thus supporting theories that it is not
only oval cells that are causing proliferations leading to cancer. Vig et al. (2006) also reported
that studies in mice and humans indicate that oval cells also may give rise to liver tumors and
that oval cells commonly surround and penetrate human liver tumors, including those caused by
hepatitis B. Tarsetti et al. (1993) noted that although some studies have suggested that oval cells
are directly involved in the formation of HCC, others assert that HCC originates from
preneoplastic foci and nodules derived from hepatocytes and report that HCC evolved in their
model of liver damage from hepatocytes, presumably hepatocellular nodules, and not from oval
cells. They also suggested that proliferation alone may not lead to cancer. Recent studies that
follow the progression of hepatocellular nodules to HCC in humans (see Section E.3.1.8) suggest
an evolution from nodule to tumor.
E.3.1.5. Status of Mechanism of Action for Human HCC
The underlying molecular mechanisms leading to hepatocarcinogenesis remain largely
unclear (Yeh et al., 2007). Although HCC is multistep, and its appearance in children suggest a
genetic predisposition exists, the inability to identify most of the predisposing genes and how
their altered expression relates to histological lesions that are the direct precursors to HCC, has
made it difficult to identify the rate limiting steps in hepatocarcinogenesis (Feitelson et al.,
2002). Calvisi et al. (2007) report that although the major etiological agents have been
identified, the molecular pathogenesis of HCC remains unclear and that while deregulation of a
number of oncogenes (e.g., c-Myc, cyclin Dl and p-catenin and tumor suppressor genes
including pi6INK4A, p53, E-cadherin, DLC-1, and pRb) have been observed at different
frequencies in HCC, the specific genes and the molecular pathways that play pivotal roles in
liver tumor development have not been identified. Indeed rather than simple patterns of
mutations, pathways that are common to cancer have been identified through study of tumors
and through transgenic mouse models. Branda and Wands (2006) stated that the molecular
factors and interactions involved in hepatocarcinogenesis are still poorly understood but are
particularly true with respect to genomic mutations, "as it has been difficult to identify common
genetic changes in >20 to 30% of tumors." As well as phenotypically heterogeneous, "it is
becoming clear that HCCs are genetically heterogeneous tumors." The descriptions of
heterogeneity of tumors and of pathway disruptions common to cancer are also shown for liver
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tumors (see Sections E.3.1.6 and E.3.1.8). However, many of these studies focused on the end
process and of examination of the genomic phenotype of the tumor for inferences regarding
clinical course, aggressiveness of tumor, and consistency with other forms of cancer. As stated
above, the events that produce these tumors from patients with conditions that put them at risk,
are not known.
El-Serag and Rudolph (2007) suggested that risk of HCC increases at the cirrhosis stage
when liver cell proliferation is decreased and that acceleration of carcinogenesis at this stage may
result from telomere shortening (resulting in limitations of regenerative reserve and induction of
chromosomal instability), impaired hepatocyte proliferation (resulting in cancer induction by loss
of replicative competition), and altered milieu conditions that promote tumor cell proliferation.
When telomeres reach a critically short length, chromosome uncapping induces
DNA damage signals, cell-cycle arrest, senescence, or apoptosis. Telomeres are
critically short in human HCC and on the single cell level telomere shortening
correlated with increasing aneuploidy in human HCC.. .Chemicals inhibiting
hepatocyte proliferation accelerate carcinogen-induced liver tumor formation in
rats as well as the expansion and transformation of transplanted hepatocytes. It is
conceivable that abnormally proliferating hepatocytes would not expand in
healthy regenerating liver but would expand quickly and eventually transform in
the growth restrained cirrhotic liver... .Liver mass is controlled by growth factors
- mass loss through could provide a growth stimulatory macroenvironment. For
the microenvironment, cirrhosis activates stellate cells resulting in increased
production of extracellular matrix proteins, cytokines, growth factors, and
products of oxidative stress.
Like other cancers, genomic instability is a common feature of human HCC with various
mechanisms thought to contribute, including telomere erosion, chromosome segregation defects,
and alteration in DNA damage-response pathways. In addition to genetic events associated with
the development of HCC (p53 inactivation, mutation in p-catenin, overexpression of ErbB
receptor family members, and overexpression of the MET receptor whose ligand is HGF),
various cancer-relevant genes seem to be targeted on the epigenetic level (methylation) in human
HCC (Farazi and DePinho, 2006). Changes in methylation have been detected in the earliest
stages of hepatocarcinogenesis and to a greater extent in tumor progression (Lee et al., 2003).
Seitz and Stickel (2006) report that aberrant DNA hypermethylation (a silencing effect on genes)
may be associated with genetic instability as determined by the loss of heterozygosity and
microsatellite instability in human HCC due to chronic viral hepatitis and that modifications of
the degree of hepatic DNA methylation have also been observed in experimental models of
chronic alcoholism.
Farazi and DePinho (2006) reported that two of the key molecules that are involved in
DNA damage response, p53 and BRCA2, seem to have roles in destabilizing the HCC genome
(Gollin, 2005). The inactivation of p53 through mutation or viral oncoprotein sequestration is a
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common event in HCC and p53 knock in mouse models containing dominant point mutations
have been shown to cause genomic instability. However, Farazi and DePinho (2006) noted that
despite documentation of deletions or mutations in these and other DNA damage network genes,
their direct roles in the genomic instability of HCC have yet to be established in many genetic
model systems.
Telomere shortening has been described as a key feature of chronic hyperproliferative
liver disease (Rudolf and DePinho. 2001: Miura et al.. 1997: Urabeetal.. 1996: Kitada et al..
1995), specifically occurring in the hepatocyte compartment. These observations have fueled
speculation that telomere shortening associated with chronic liver disease and hepatocyte
turnover contribute to the induction of genomic instability that drives human HCC (Farazi and
DePinho, 2006). Defects in chromosome segregation during mitosis result in aneuploidy, a
common cytogenetic feature of cancer cell including HCC (Farazi and DePinho, 2006).
Several studies have attempted to categorize genomic changes in relation to tumor state.
In general, high levels of chromosomal instability seem to correlate with the de-differentiation
and progression of HCC (Wilkens et al., 2004). Several studies have suggested certain
chromosomal changes to be specific to dysplastic lesions, early-stage and late-stage HCCs, and
metastases. It is important to note that the studies that have attempted to compare genomic
profiles and tumor state are few in number, often did not classify HCCs on the basis of etiology,
and used relatively low-resolution genome-scanning platforms (Farazi and DePinho, 2006).
Farazi and DePinho (2006) noted that it should be emphasized that although genome etiology
correlates reported in some studies are intriguing, several studies have failed to uncover
significant differences in genomic changes between different etiological groups, although the
outcome might related to small sample sizes and the low-resolution, genome scanning platform
used.
E.3.1.6. Pathway and Genetic Disruption Associated with HCC and Relationship to
Other Forms of Neoplasia
In their landmark paper, Hanahan and Weinberg (2000) suggested that the vast catalog of
cancer cell genotypes was a manifestation of six essential alterations in cell physiology that
collectively dictate malignant growth: self-sufficiency in growth signals, insensitivity to growth-
inhibitory (antigrowth signals), elevation of programmed cell death (apoptosis), limitless
replication potential, sustained angiogenesis, and tissue invasion and metastasis. They proposed
that these six capabilities are shared in common by most, and perhaps all, types of human tumors
and, while virtually all cancers must acquire the same six hallmark capabilities, their means of
doing so would vary significantly, both mechanistically and chronologically. It was predicted
that in some tumors, a particular genetic lesions may confer several capabilities simultaneously,
decreasing the number of distinct mutational steps required to complete tumorigenesis. Loss of
the p53 tumor suppressor was cited as an example that could facilitate both angiogenesis and
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resistance to apoptosis and to enable the characteristic of genomic instability. The paths that
cells could take on their way to becoming malignant were predicted to be highly variable, and
within a given cancer type, mutation of a particular target genes such as ras or p53 could be
found only in a subset of otherwise histologically identical tumors. Furthermore, mutations in
certain oncogenes and tumor suppressor genes could occur early in some tumor progression
pathways and late in others. Genes known to be functionally altered in "cancer" were identified
as including Fas,Bcl2, Decoy R, Bax, Smads, TFGpR, pi5, pi6, Cycl D, Rb, human papilloma
virus E7, ARF, PTEN, Myc, Fos, Jun, Ras, Abl, NF1, RTK, transforming growth factor alpha
(TGF-a), Integrins, E-cadherin, Src, p-catenin, APC, and WNT.
Branda and Wands (2006) reported that two signal transduction cascades that appear to be
very important are insulin/IFG-1/IRS-l/MAPK and Wnt/Frizzled/p-catenin pathways, which are
activated in over 90% of HCC tumors (Branda and Wands. 2006). Feitelson et al. (2002)
reported that:
In addition to NF-KB, up-regulated expression of rhoB has been reported in some
HCCs. RhoB is in the ras gene family, is associated with cell transformation, and
may be a common denominator to both viral and non-viral hepatocarcinogenesis.
Activation of ras and NF-KB, combined with down regulation of multiple negative
growth regulatory pathways, then, may contribute importantly to early steps in
hepatocarcinogenesis. Thus viral proteins may alter the patterns of hepatocellular
gene expression by transcriptional trans-regulation.. .Another early event appears
to involve the mutation of P-catenin, which is a component of the Wnt signal
transduction pathway whose target genes include c-myc, c-jun, cyclin Dl,
fibronectin, the connective tissue growth factor WISP, and matrix
metaolloproteinases.
Boyault et al. (2007) reported that:
altogether, the principle carcinogenic pathways known to be deregulated in HCC
are inactivation of TP53, Wnt/wingless activation mainly through CTNNB1
mutations activating P-catenin- and AXIN1-inactivating mutations,
retinoblastoma inactivation through RBI and CDKN2A promoter methylation and
rare gene mutations, insulin growth factor activation through IGF2
overexpression, and IGF2R-inactiving mutations.
El-Serag and Rudolph suggested that "in general, the activation of oncogenic pathways in
human HCC appears to be more heterogeneous compared with other cancer types." El-Serag
and Rudolph (2007) reported that the p53 pathway is a major tumor-suppressor pathway that:
(1) limits cell survival and proliferation (replicative senescence) in response to telomere
shortening; (2) induces cell-cycle arrest in response to oncogene activation (oncogene-induced
senescence); (3) protects genome integrity; and (4) is affected at multiple levels in human HCC.
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"p53 mutations occur in aflatoxin induced HCC (>50%) and with lower frequency (20-40%) in
HCC not associated with aflatoxin." In addition,
the vast majority of human HCC overexpresses gankyrin, which inhibits both Rb
checkpoint and p53 checkpoint function.. .The p!6/Rb checkpoint is another
major pathway limiting cell proliferation in response to telomere shortening,
DNA damage, and oncogene activation. In human HCC the Rb pathway is
disrupted in more than 80% of cases, with repression of p!6 by promoter
methylation being the most frequent alteration. Moreover, expression of gankyrin
(an inhibitor of p53 and Rb checkpoint function) is increased in the vast majority
of human HCCs, indicating that the Rb checkpoint is dysfunctional in the vast
majority of human HCCs.. .The frequent inactivation of p53 in human HCC
indicates that abrogation of p53-dependent apoptosis could promote
hepatocarcinogenesis. The role of impairment of p53-independent apoptosis for
hepatocarcinogenesis remains to be defined.. .Activation of the p-catenin pathway
frequently occurs in mouse and human HCC involving somatic mutations, as well
as transcriptional repression of negative regulators. An activation of the Akt
signaling and impaired expression of phosphatase and tensin homolog (PTEN) (a
negative regulator of Akt) have been reported in 40-60% of Human HCC.
They suggested that although Myc is a potent oncongene inducing hepatocarcinogenesis in
mouse models, the data on human HCC are heterogeneous and further studies are required.
E.3.1.7. Epigenetic Alterations in HCC
The molecular pathogenesis of HCC remains largely unknown, but it is presumed that the
development and progression of HCC are the consequence of cumulative genetic and epigenetic
events similar to those described in other solid tumors (Calvisi et al., 2006). Calvisi et al. (2007)
provided a good summary of DNA methylation status and cancer as well as its status in regard to
HCC:
Aberrant DNA methylation occurs commonly in human cancers in the forms of
genome-wide hypomethylation and regional hypermethylation. Global DNA
hypomethylation (also known as demethylation) is associated with activation of
protooncogenes, such as c-Jun, c-Myc, and c-HA-Ras, and generation of genomic
instability. Hypermethylation on CpG islands located in the promoter regions of
tumor suppressor genes results in transcriptional silencing and genomic
instability. CpG hypermethylation (also known as de novo methylation) acts as
an alternative and/or complementary mechanisms to gene mutations causing gene
inactivation, and it is now recognized as an important mechanism in
carcinogenesis. Although the mechanism(s) responsible for de novo methylation
in cancer are poorly understood, it has been hypothesized that epigenetic silencing
depends on activation of a number of proteins known as DNA methyltransferases
(DNMTs) that posses de novo methylation activity. The importance of DNMTs
in CpG methylation was substantiated by the observation that genetic disruption
of both DNMT1 and DNMT3b genes in HCT116 cell lines nearly eliminated
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methyltransferase activity. However, more recent findings indicate that the
HCT116 cells retain a truncated, biologically active form of DNMT1 and
maintain 80% of their genomic methylation. Further reduction of DNMT1 levels
by a siRNA approach resulted in decreased cell viability, increased apoptosis,
enhanced genomic instability, checkpoint defects, and abrogation of replicative
capacity. These data show that DNTM1 is required for cell survival and suggest
that DNTM1 has additional functions that are independent of its methyltransferase
activity. Concomitant overexpression of DNMT1, -3A, and -3b has been found in
various tumors including HCC. However, no changes in the expression of
DNMTs were found in other neoplasms, such as colorectal cancer, suggesting the
existence of alternative mechanisms. In HCC, a novel DNMT3b splice variant,
known as DNMT3b4 is overexpressed. DNMT3b4 lacks DNMT activity and
competes with DNMT2b3 for targeting of pericentromeric satellite regions in
HCC, resulting in DNA hypomethylation of these regions and induction of
chromosomal instability, further linking aberrant methylation and generation of
genomic alterations.
It is now well accepted that methylation changes occur early and ubiquitously in
cancer development. The case has been made that tumor cell heterogeneity is
due, in part, to epigenetic variation in progenitor cells and that epigenetic
plasticity together with genetic lesions drive tumor progression (Feinberg et al.,
2006).
A growing number of genes undergoing aberrant CpG island hypermethylation in
HCC have been discovered, suggesting that de novo methylation is an important
mechanism underlying malignant transformation in the liver. However, most of
the previous studies have focused on a single or a limited number of genes, and
few have attempted to analyze the methylation status of multiple genes in HCC
and associated chronic liver diseases. In addition, the functional consequence(s)
of global DNA hypomethylation and CpG island hypermethylation in human liver
cancer has not been investigated to date. Furthermore, to our knowledge no
comprehensive analysis of CpG island hypermethylation involving activation of
signaling pathways has been performed.
Calvisi et al. (2007) reported that global gene expression profiles show human HCC to
harbor common molecular features that differ greatly from those of nontumorous surrounding
tissues, and that human HCC can be subdivided into two broad but distinct subclasses that are
associated with length of patient survival. They further suggested that aberrant methylation is a
major event in both early and late stages of liver malignant transformation and might constitute a
critical target for cancer risk assessment, treatment, and chemoprevention of HCC. Calvisi et al.
(2007) conducted analysis of methylation status of genes selected based on their capacity to
modulate signaling pathways (Ras, Jak/Stat, Wingless/Wnt, and RELN) and/or biologic features
of the tumors (proliferation, apoptosis, angiogenesis, invasion, DNA repair, immune response,
and detoxification). Normal livers were reported to show the absence of promoter methylation
for all genes examined. At least 1 of the genes involved in inhibition of Ras (ARH1, CLU,
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DAB2, hDAB21P, HIN-1, HRASL, LOX, NORE1A, PAR4, RASSF1 A, RASSF2, RASSF3,
RASSF4, RIG, RRP22, and SPRY2 and -4), Jak/Stat (ARH1,CIS, SHP1, PIAS-1, PIAS-y,
SOCS1, -2, and -3, SYK, and GRIM-19), and Wnt/p-catenin (APC, E-cadherin, y-catenin,
SFRP1, -2, -4, and -5, DKK-1 and -3, WIF-1 and HDPR1) pathways were affected by de novo
methylation in all HCC. A number of these genes were also reported to be highly methylated in
the surrounding nontumorous liver. In contrast, inactivation of at least one of these genes
implicated in the RELN pathway (DAB1, reelin) was detected differentially in HCC of
subclasses of tumors that had differences in tumor aggressiveness and progression. Epigenetic
silencing of multiple tumor suppressor genes maintains activation of the Ras pathway with a
major finding in the Calvisi et al. (2007) study to be the concurrent hypermethylation of multiple
inhibitors of the Ras pathway with Ras was significantly more active in HCC than in surrounding
or normal livers. Also important was the finding that no significant associations between
methylation patterns and specific etiologic agents (i.e., HVB, HVC, ethanol, etc.) were detected,
further substantiating the conclusion that aberrant methylation is a ubiquitous phenomenon in
hepatocarcinogenesis.
Current evidence suggests that hypomethylation might promote malignant
transformation via multiple mechanisms, including chromosome instability,
activation of protooncogenes, reactivation of transposable elements, and loss of
imprinting.. .The degree of DNA hypomethylation progressively increased from
nonneoplastic livers to fully malignant HCC, indicating that genomic
hypomethylation is an important prognostic factor in HCC, as reported for brain,
breast, and ovarian cancer.
Calvisi et al. (2007) also reported that regional CpG hypermethylation was also enhanced
during the course of HCC disease and that the study of tumor suppressor gene promoters showed
that CpG methylation was frequently detected both in surrounding nontumorous livers and HCC.
E.3.1.8. Heterogeneity of Preneoplastic and HCC Phenotypes
A very important issue for the treatment of HCC in humans is early detection. Research
has focused on identification of lesions that will progress to HCC and to also determine from the
phenotype of the nodule and genetic expression its cell source, likely survival, and associations
with etiologies and modes of action. As with rodent models where preneoplastic foci have been
observed to be associated with progression to adenoma and carcinoma, nodules observed in
humans with high risk for HCC have been observed to progress to HCC. In humans,
histomorphology of HCC is notoriously heterogeneous (Yeh et al., 2007). Although much
progress has been made, there is currently not universally accepted staging system for HCC
partly because of the natural course of early HCC is unknown and the natural progression of
intermediated and advanced HCC are quite heterogeneous (Thorgeirsson, 2006). Nodules are
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heterogeneous as well, with differences in potential to progress to HCC. Chen et al. (2002b)
reported that standard clinical pathological classification of HCC has limited valued in predicting
the outcome of treatment as the phenotypic diversity of cancer is accompanied by a
corresponding diversity in gene expression patterns. There is also histopathological variability in
the presentation of HCC in geographically diverse regions of the world with some slow growing,
differentiated HCC nodules surrounded by a fibrous capsule are common among Japanese but, in
contrast, a "febrile" form of HCC, characterized by leukocytosis, fever, and necrosis within a
poorly differentiated tumor to be common in South African blacks (Feitelson et al., 2002).
A multistep process is suggested histologically, where HCC appears within the context of
chronic hepatitis and/or cirrhosis within regions of the liver cell dysplasia or adenomatous
hyperplasia (Feitelson et al., 2002). Kobayashi et al. (2006) reported that the higher the grade of
the nodule, the higher the percentage that will progress to HCC with 18.8% of all nodules and
regenerative lesions going on to become HCC, 53.3% remaining unchanged, and 27.9%
disappearing in the observation period of 0.1-8.9 years. Borzio et al. (2003) reported that the
rate of liver malignant transformation was 40% in larger regenerative nodules, low-grade
dysplastic, and high-grade dysplastic nodules with higher grade of dysplasia extranodular
detection of large cell change and hyperchronic pattern associated with progression to HCC.
Yeh et al. (2007) reported that nuclear staining for Ki-67 and Topo II-a (a nuclear protein
targeted by several chemotherapeutic agents) significantly increased in the progression from
cirrhosis, through high-grade dysplastic nodules to HCC, whereas the scores for TGF-a in these
lesions showed an inverse relationship. "In comparison with 18 HCC arising in noncirrhotic
livers, the expression of TGF-a is significantly stronger in cirrhotic liver than in noncirrhotic
parenchyma and its expression is also stronger in HCC arising in cirrhosis than in HCC arising in
noncirrhotic patients." They concluded that initiation in cirrhotic and noncirrhotic liver may
have different pathways with transforming growth factor-a (a mitogen activated the EFGR)
playing a relative more important role in HCC from cirrhotic liver. Overexpression of TGF-a in
the liver of transgenic mice induced increased proliferation, dysplasia, adenoma, and carcinoma.
Yeh et al. (2007) concluded that such high-grade dysplastic nodules are precursor lesions in
hepatocarcinogenesis and that TGF-a may play an important role in the early events of liver
carcinogenesis.
Moinzadeh et al. (2005) reported in a meta-analysis of all available (n = 785) HCCs that
gains and losses of chromosomal material were most prevalent in a number of chromosomes and
that amplifications and deletions occurred on chromosomal arms in which oncogenes (e.g., MYC
and 8q24) and tumor suppressor genes (e.g., RBI on 13ql4) are located as well as modulators of
the WNT-signaling pathway. However, in multifocal HCC, nodules arising de novo within a
single liver have a different spectrum of genetic lesions. "Hence, there are likely to be many
paths to HCC, and this is why it has been difficult to assign specific molecular alterations to
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changes in hepatocellular phenotype, clinical, or histopathological changes that accompany
tumor development" (Feitelson et al., 2002).
Serum AFP is commonly used as a tumor marker for HCC. Several reports have linked
HCC to cytokines in an attempt to find more specific markers of HCC. Jia et al. (2007) reported
that AFP marker allows for identification of a small set of HCC patients with smaller tumors,
and these patients have a relatively long-term survival rate following curative treatment.
Presently the only approach to screen for the presence of HCC in high-risk
populations is the combination of serum AFP and ultrasonagraphy. However,
elevated AFP is only observed in about 60 to 70% of HCC patients and to a lesser
extent (33-65%) in patients with smaller HCCs. Moreover, nonspecific elevation
of serum AFP has been found in 15% to 58% of patients with chronic hepatitis
and 11% to 47% of patients with liver cirrhosis.
Soresi et al. (2006) reported that serum IL-6 levels are low in physiological conditions,
but increase considerably in pathological conditions such as trauma, inflammation, and
neoplasia. In tumors, IL-6 may be involved in promoting the differentiation and growth of target
cells. "Many works have reported high serum IL-6 levels in various lifer diseases such as acute
hepatitis, primary biliary cirrhosis, chronic hepatitis (hepatitis C) and HCV-correlated liver
cirrhosis and in hepatocellular carcinoma." Soresi et al. (2006) reported that patients with HCC
group had higher IL-6 values than those with cirrhosis and that "higher-staged" patients had the
highest IL-6 levels. Hsia et al. (2007) also examined IL-6, IL-10 and hepatocyte growth factor
(HGF) as potential markers for HCC.
The expression of IL-6 or IL-10 or higher level of HGF or AFP was observed only
0-3% of normal subjects. Patients with HCC more frequently had higher IL-6 and
IL-10 levels, where as HGF levels in HCC patients were not significantly elevated
compared to patients with chronic hepatitis or non-HCC tumors (but greater than
controls). Among patients with low AFP level, IL-6 or IL-10 expression was
significantly associated with the existence of HCC. Patients with large HCC
(>5 cm) more often had increased IL-6, IL-10 or AFP levels. Serum levels of IL-
6 and IL-10 are frequently elevated in patients with HCC but not in benign liver
disease or non-HCC tumors.
Nuclear DNA content and ploidy have also been the subjects of several studies through
the years for identification of pathways for prediction of survival or origin of tumors. Nakajima
et al. (2004) report that p53 loss can contribute to the propagation of damaged DNA in daughter
cells through the inability to prevent the transmission of inaccurate genetic material, considered
to be one of the major mechanisms for the emergence of aneuploidy in tumors with inactivated
p53 protein and the increasing ploidy in HCC was associated with disturbance in p53. McEntee
et al. (1992) reported that specimens from 74 patients who underwent curative resection for
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primary HCC and analyzed for DNA content, (i.e., tumors were classified as DNA aneuploid if a
separate peak was present from its standard large diploid peak [2C] and tetraploid peak [4C])
33% were DNA diploid, 30% were DNA tetraploid/polyploidy, and 37% were aneuploid of the
primary tumors examined. Nontumor controls were diploid and survival was not different
between patients with diploid vs. nondiploid tumors. Zeppa et al. (1998) reported ploidy in
84 HCCs diagnosed by fine-needle aspiration biopsy to have 68 cases that were aneuploid and
16 euploid (9 diploid and 7 polyploid), with median survival of 38 months for patients with
diploid HCC and 13 months for aneuploid HCC. Lin et al. (2003) reported in their study of fine-
needle aspiration of HCC that:
the ratio of S and G2/M periods of DNA, which reflect cell hyperproliferation, in
the group with HCC tumors>3 cm in diameter were markedly higher than those of
the group with nodules<3 cm in diameter and the group with hyperplastic
nodules.. .DNA analysis of aspiration biopsy tissues acquired from intrahepatic
benign hyperplastic nodules snowed steady diploid (2c) peak that stayed in Gl
period. DNA analysis of aspiration biopsy tissues acquired from HCC nodules
showed S period of hyperproliferation and G2/M period. The DNA analysis of
HCC nodules showed aneuploid peak.
They concluded that in regard to the biological behavior of the cell itself, that the normal
tissue, reactive tissue, and benign tumor all have normal diploid DNA but, like most other
malignant tumors, "HCC appears to have polyploid DNA, especially aneuploid DNA."
Attallah et al. (1999) reported small needle liver biopsy data to show HCC to be 21.4%
diploid, 50% aneuploid, and 28.6% tetraploid and that higher ploidies (aneuploid and tetraploid)
were observed in human liver cancer than residual tissues, although in some cases, there was
increased aneuploidy (cirrhosis, 37%, hepatitis -50%). Of note for the study is the lack of
appropriate control tissue and uncertainty as to how some of their diploid cells could have been
binucleate tetraploid cells. Anti et al. (1994) reported reduction in binuclearity in the chronic
hepatitis and cirrhosis groups that was significantly correlated with a rise in the diploid/
polyploidy ratio and that precancerous and cancerous nodules within cirrhotic liver show an
increased tendency toward diploidy or the emergence of aneuploid populations. They noted that
a number of investigators have reported significantly increased hepatocyte diploidization during
the early stages of chemically induced carcinogenesis in rat liver, but other experimental findings
indicate that malignant transformation can occur after any type of alteration in ploidy
distribution.
On the other hand, Melchiorri et al. (1994) noted that several studies using flow
cytometric or image cytometric methods reported high DNA ploidy values in 50-77% of the
examined HCCs and that the presence of aneuploidy was significantly related to a poor patient
prognosis. They reported that the DNA content of mononucleated and binucleated hepatocytes,
obtained by ultrasound-guided biopsies of 10 macroregenerative nodules without histologic signs
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of atypia from the lesions with the greater fraction of mononucleated hepatocytes were
diagnosed as HCCs during the clinical follow-up, with results also suggesting that diploid and
tetraploid stem cell lines are the main lines of the HCCs as well as a reduction in the percentage
of binucleated hepatocytes in HCC. Gramantieri et al. (1996) reported that the percentage of
binucleated cells was reduced in most of the HCC that they studied (i.e., the mean percentage of
binucleated cells 9% in comparison to 24% found in normal liver) and that most HCC, as many
other solid neoplasms, showed altered nuclear parameters.
Along with reporting pathways that are perturbed in HCC, emerging evidence also shows
that signatures of pathway are predictive of clinical characteristics of HCC. A number of studies
have examined gene expression in tumors to try to determine which pathways may have been
disturbed in an attempt to predict survival and treatment options for the patients and to
investigate possible modes of action for the tumor induction and progression. Chen et al.
(2002b) described a systematic characterization of gene expression patterns in human liver
cancers using cDNA microarrays to study tumor and nontumor liver tissues in HCC patients, and
of note did quality assurance on their microarray chips (many studies do not report that they have
done so), and examined the effects of hepatitis virus on its subject and identified people with it.
Most importantly, Chen et al. (2002b) provided phenotypic anchoring of each tumor with its
genetic profile rather than pooling data.
The hierarchical analysis demonstrated that clinical samples could be divided into two
major clusters, one representing HCC samples and the other with a few exceptions, representing
nontumor liver tissues. Most importantly, expression patterns varied significantly among the
HCC and nontumor liver samples and that samples from HBV-infected, hepatitis C virus
infected, and noninfected individuals were interspersed in the HCC branch. Thus, tumors from
people infected with HVB, HVC, and noninfected people with HCC were interspersed in the
HCC pattern and could be discerned based on etiology. One cluster of genes was highly
expressed in HCC samples compared with nontumor liver tissues included a "proliferation
cluster" comprised of genes whose functions are required for cell-cycle progression and whose
expression levels correlate with cellular proliferation rates with most of the genes in this cluster
are specifically expressed in the G2/M phase. Gene profiles for HCC were consistent with fewer
molecular features of differentiated normal hepatocytes.
Chen et al. (2002b) noted that both normal and liver tumors are complex tissue compose
of diverse cells and that distinct patterns of gene expression seemed to provide molecular
signatures of several specific cell types including expression of two clusters of genes associated
with T and B lymphocytes, presumably reflecting lymphocytic infiltration into liver tissues, and
genes associated with stellate cell activation. This important finding acknowledges that HCC is
not only heterogeneous in hepatocyte phenotype but is also made up of many other
nonparenchymal cell types and that gene expression patterns reflect that heterogeneity. A gene
cluster was also identified at a higher level in HCC that included several genes typically
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expressed in endothelial cells, including CD34, which is expressed in endothelial cells in veins
and arteries but not in the endothelial cells of the sinusoids in nontumor liver and which may
reflect disruption of the molecular program that normally regulate blood vessel morphogenesis in
the liver.
Of great importance was the investigation by Chen et al. (2002b) of whether samples
from multiple sites in a single HCC tumor, or multiple separate tumor nodules in one patient,
would share a recognizable gene expression signature. With a few instructive exceptions, all of
the tumor samples from each patient clustered were reported to cluster together. To further
examine the relationship among multiple tumor samples from individual patients, they calculated
the pairwise comparison for all pairs of samples and samples some primary tumors multiple
times. Tumor patterns of gene expression were more highly correlated with those seen in
samples from the same patient than other patients but every tumor had a distinctive and
characteristic gene expression pattern, recognizable in all samples taken from different areas of
the same tumor.
For multiple discrete tumor masses obtained from six patients, three of these patients had
multiple tumors with a shared distinctive gene expression pattern but in three other patients,
expression patterns varied between tumor nodules and the difference providing new insights into
the sources of variation in molecular and biological characteristics of cancers. Thus, in some
patients, multiple tumors were from the same clone, as demonstrated by a similar gene
expression profile, but for some patients, multiple tumors were arising from differing clones
within the same liver. In regard to whether the distinctive expression patterns characteristic of
each tumor reflect the individuality of the tumor or are determined by the patient in whom the
tumor arose, analysis of the expression patterns observed in the two tumor nodules from one
patient showed that the two tumors were not more similar than those of an arbitrary pair of
tumors from different patients. These results show the heterogeneity of HCC and that "one gene
pattern" will not be characteristic of the disease.
However, HCC did have a pattern that differed from other cancers. Chen et al. (2002b)
analyzed the expression patterns of 10 randomly selected HCC samples and 10 liver metastases
of other cancers and reported that the HCC samples and the metastatic cancers clustered into two
distinct groups, based on difference in their patterns of gene expression. Although some of the
HCC samples were poorly differentiated and expressed the genes of the liver-specific cluster at
very low levels compared to with either normal liver or well-differentiated HCC, the genes of the
liver-specific cluster were reported to be consistently expressed at higher levels in HCC than in
tumors of nonliver origin. Metastatic cancers originating from the same tissue typically clustered
together, expressing gene characteristic of the cell types of origin. Thus, liver cancer was
distinguishable from other cancer even though very variable in expression and differentiation
state.
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In an attempt to create molecular prognostic indices that can be used for identification of
distinct subclasses of HCC that could predict outcome, Lee et al. (2004a) reported two subclasses
of HCC patients characterized by significant differences in the length of survival. They also
identified expression profiles of a limited number of genes that accurately predicted the length of
survival. Total RNAs from the 19 normal livers, including "normal liver in HCC patients," were
pooled and used as a reference for all microarray experiments and thus variations between
patients, and especially differences due to conditions predisposing HCC, were not determined.
DNA microarray data using hierarchical clustering was reported to yield two major clusters, one
representing HCC tumors, and the other representing nontumor tissues with a few exceptions that
were not characterized by the authors. Lee et al. (2004a) reported that, along with two
distinctive subtypes of gene expression patterns in HCC, there was heterogeneity among HCC
gene expression profiles and that one group had an overall survival time of 30.8 months and the
other 83.7 months. Only about half the patients in each group were reported to have cirrhosis.
Expression of typical cell proliferation markers such as PCNA and cell cycle regulators such as
CDK4, CCNB1, CCNA2, and CKS2 was greater in one class than the other of HCC.
The report by Boyault et al. (2007) attempted to compare etiology and genetic
characterization of the tumors they produce and confirmed the heterogeneity of HCC, some
without attendant genomic instability. Boyault et al. (2007) reported that genetic alterations are
indeed closely associated with clinical characteristics of HCC that define two mechanisms of
hepatocarcinogenesis.
The first type of HCC was associated with not only a high level of chromosome
instability and frequent TP53 and AXIN1 mutations but also was closely linked to
HBV infections and a poor prognosis. Conversely, the second subgroup of HCC
tumors was chromosome-stable, having a high incidence of activating p-catenin
alteration and was not associated with viral infection.
Boyault et al. (2007) reported that in a series of 123 tumors, mutations in the CTNNB1
(encoding P-catenin), TP53, ACINI, TCF1, PIK3CA and KRAS genes in 34, 31, 13, 5, 2, and
1 tumors were identified, respectively. No mutations were found in NRAS, HRAS, or EGFR.
Hypermethylation of the CDKN2A and CDH1 promoter was identified in 35 and 16% of the
tumors, respectively. Boyault et al. (2007) grouped tumors by genomic expression as well as
other factors. HCC groups associated with high rate of chromosomal instability were reported to
be enriched with overexpression of cell-cycle/proliferation/DNA metabolism genes. They
concluded that "the primary clinical determinant of class membership is HBV infection and the
other main determinants are genetic and epigenetic alterations, including chromosome instability,
CTNNB1 and TP53 mutations, and parental imprinting. Tumors related to HCV and alcohol
abuse were interspersed across subgroups G3-G6." Boyault et al. (2007) suggested that their
results indicated that HBV infection early in life leads to a specific type of HCC that has
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immature features with abnormal parental gene imprinting selections, possibly through the
persistence of fetal hepatocytes or alternatively through partial dedifferentiation of adult
hepatocytes. "These Gl tumors are related to high-risk populations found in epidemiological
studies."
E.3.2. Animal Models of Liver Cancer
There are obvious differences between rodents and primate and human liver, and there is
a difference in background rates of susceptibility to hepatocarcinogenesis. With strains of mice,
there are large differences in responses to hepatotoxins (e.g., acetaminophen) and to
hepatocarcinogens as well as background rates of hepatocarcinogenecity. Boyault et al. (2007)
reported that modulators of murine hepatocarcinogenesis, such as diet, hormones, oncogenes,
methylation, imprinting, and cell proliferation/apoptosis are among multiple mechanistically
associated factors that impact this target organ response in control as well as in treated mice, and
suggested that there is no one simple paradigm to explain the differential strain sensitivity to
hepatocarcinogenesis. Because of the variety of studies with differing protocols used to generate
susceptibility data, direct comparisons among strains and stocks is problematic but in regard to
susceptibility to carcinogenicity, the C3H/HeJ and C57BL/6J mouse have been reported to have
up to a 40-fold difference in liver tumor multiplicity (Boyault et al., (2007).
However, as noted above, TCE causes liver tumors in C6C3F1 and Swiss mice with
studies of TCE metabolites DCA, TCA, and CH suggesting that both DCA and TCA are
involved in TCE-induced liver tumorigenesis. Many effects reported in mice after DCA
exposure are consistent with conditions that increase the risk of liver cancer in humans and can
involve GST Xi, histone methylation, and overexpression of insulin-like growth factor-II
(IGF-II) (Caldwell and Keshava, 2006). The heterogeneity of liver phenotype observed in
mouse models is also consistent with human HCC. These data lend support to the qualitative
relevance of the mouse model for TCE-induced cancer risk.
Bannasch et al. (2003) made important observations that have implications regarding the
differences in susceptibility between rodent and human liver cancer. They stated that:
Although the classification of such nodular liver lesions in rodents as hyperplastic
or neoplastic has remained controversial, persistent nodules of this type are
considered neoplasms, designated as adenomas. In human pathology, the
situation appears to be paradoxical because adenomas are only diagnosed in the
noncirrhotic liver, yet a confusing variety terms avoiding the clearcut
classification as an adenoma has been created for nodular lesions in liver
cirrhoses, not withstanding that the vast majority hepatocellular carcinomas
develop in cirrhotic livers. Even if a portion of these nodular lesions would be
regarded as adenomas, being integrated into an adenoma-carcinoma sequence as
observed in many animal experiments, clinical and epidemiological records of
liver neoplasms, including both benign and malignant forms, would increase
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considerably. This would not only bring hepatic neoplasia further into focus of
human neoplasia in general, but also shed new light on the classification of some
chemicals producing high incidence of liver neoplasms in rodents, but appearing
harmless to humans according to epidemiological evaluations solely based on the
incidence of hepatocellular carcinoma in exposed populations.
Thus, in humans, only HCCs are recorded, but in animals, adenomas are counted as
neoplasms, indicating that the scope of the problem of liver cancer in humans may be
underestimated.
Tumor phenotype differences have been reported for several decades through the work of
Bannasch et al. The predominant cell line of foci of altered hepatocytes (FAH) have excess
glycogen storage early in development that appears to be similar to that shown by DCA
treatment. Bannasch et al. (2003) reported that "the predominant glycogenotic-basophilic cell
line FAH reveals that there is an overexpression of the insulin receptor, the IGF-1 receptor, the
insulin receptor substrates-1/2 and other components of the insulin-stimulated signal transduction
pathway." Bannasch et al. (2003) stated that foci of this type have increased expression of
GST-Ti and insulin has also been shown to induce the expression of GST-pi, but that
hyperinsulin-induced foci do not show increased GST-u. Cellular dedifferentiation during
progression from glycogenotic to basophilic cell populations is associated with downregulation
in insulin signaling. The amphophilic-basophilic cell lineage of peroxisome proliferators and
hepadnaviridae were reported to have foci that mimic effects of thyroid hormone with
mitochondrial proliferation and activation of mitochondrial enzymes. Bannasch et al. (2003)
stated that:
the unequivocal separation of 2 types of compounds, usually classified as
initiators and promoters, remains a problem at the level of the foci because at least
the majority of chemical hepatocarcinogens seem to have both initiating and
promoting activity, which may differ in quantitative rather than qualitative terms
from one compound to another.. .Whereas genetic mutations have been
predominantly postulated to initiate hepatocarcinogenesis for many years, more
recently epigenetic changes have been increasingly discussed as a plausible cause
of the evolution of preneoplastic foci characterized by metabolic changes
including the expression of GSTpi.
Su and Bannasch (2003) reported that glycogen-storing foci represent early lesions with
the potential to progress to more advance glycogen-poor basophilic lesions through mixed-cell
foci and resulting hyperproliferative lesions and are associated with HCC in man. Small-cell
change (SCC) of liver parenchyma (originally called liver cell dysplasia of small cell size) is
reported to share cytological and histological similarities to early well defined HCC. Close
association between SCC and more advanced (basophilic) foci indicates that foci often progress
to HCC through SCC in humans. SCC was reported to be present in all basophilic foci.
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Previous studies were cited that showed that the biochemical phenotype of human FAH, mainly
including glycogen storing clear cell foci and clear cell-predominated mixed cell foci, were
observed in >50% of cirrhotic livers with or without HCC. FAH of clear and mixed cell types
were observed in almost all livers bearing HCC, and in chronic liver diseases without HCC but at
a lower frequency. Su and Bannasch (2003) reported that:
the finding of mixed cell foci (MCF) mainly in livers with high-risk or
cryptogenenic cirrhosis indicates that these are more advanced precursor lesions
in man, in line with earlier observations in experimental animals. Considering
their preferential emergence in cirrhotic livers of the high-risk group, their
unequivocally elevated proliferative activity, and the resulting large size with
frequent nodular transformation, we suggest that mixed cell populations are
endowed with a high potential to progress to HCC in humans, as previously
shown in rats.
In human HCC, irregular areas of liver parenchyma with marked cytoplasmic
amphophilia, phenotypically similar to the amphophilic preneoplastic foci in rodent liver
exposed to different hepatocarcinogenic chemicals (e.g., DHEA a peroxisome proliferator) or the
hepadnaviruses, were reported to present in 45% of the specimens from cirrhotic livers
examined. "However, more data are needed to elucidate the nature of the oncocytic and
amphophilic lesions regarding their role in HCC development."
With respect to the ability respond to a mitogenic stimulus, differences between primate
and rodent liver response to a powerful stimulus, such as partial hepatectomy, have been noted
that indicate that primate and human liver respond differently (and much more slowly) to such a
stimulus. Gaglio et al. (2002) reported after 60% partial hepatectomy in Rhesus macaques
(Macaca mulatto), the surface area of the liver remnant was restored to its original preoperative
value over a 30-day period. The maximal liver regeneration occurred between days 14 and 21,
with thickening of liver cell plates, binucleation of hepatocytes, Ki-67 and PCNA expression
(occurring in hepatocytes throughout the lobule at a maximum labeling index of 30%), and
mitoses parallel increased most prominently between posthepatectomy days 14 and 30.
However, cytokines associated with inducing proliferation were elevated much earlier.
TGF-a, IL-6, HGF, IL-6, and TNF-a mRNA persisted until Day 14, with peak elevations of IL-6
and TNF-a, occurring 24 hours later surgery, and IL-6 reduced to control levels by day 14.
Gaglio et al. (2002) suggested that their results clearly indicate that the pattern and timing of
liver regeneration observed in this nonhuman primate model are significantly different when
comparing different species (e.g., peak expression of Ki-67 in a 60% partial hepatectomy model
in rats occurs within hours following partial hepatectomy) and that the difference in timing and
pattern of maximal hepatocellular regeneration cannot be explained simply by differences in size
of animals (e.g., 60% partial hepatectomy in dogs produced liver regeneration peaks at 72 hours
with weights approximating the weights of the Rhesus macaques). They noted that previous
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studies in humans, who underwent 40-80% partial hepatectomy, reveal a similar delay in peak
liver regeneration based on changes in serum levels of ornithine decarboxylase and thymidine
kinase, further highlighting significant interspecies differences in liver regeneration.
For C57BL/6 X 129 mice, Fujita et al. (2001) reported that after partial hepatectomy, the
liver had recovered >90% of its weight within 1 week. This difference in response to a
mitogenic stimulus has impacts on the interpretations of comparisons between rodent and
primate liver responses to chemical exposures which give a transient increases in DNA synthesis
or cell proliferation such as PPARa agonists. Also, as stated above, the primate and human liver,
while having a significant polyploidy compartment, do not have the extent of polyploidization
and the early onset of that has been observed in the rodent. However, as noted by Lapis et al.
(1995), exposure to DEN has proven to be a highly potent hepatocarcinogen in nonhuman
primates, inducing malignant tumors in 100% of animals with an average latent period of
16 months when administered at 40 mg/kg i.p. every 2 weeks.
In regard to species extrapolation of epigenomic changes between humans and rodents,
Weidman et al. (2007) cautioned that:
Although we do predict some overlap between mouse and human candidate
imprinted genes identified through our machine-learning approach, it is likely that
the most significant criterion in species-specific identification will differ. This
difference underscored the importance for increased caution when assessing
human risk from environmental agents that alter the epigenome using rodent
models; the molecular pathways targeted may be independent.
Despite species differences, the genome of the mouse has been sequenced and many
transgenic mouse models are being used to study the consequences of gene expression
modulation and pathway perturbation to study human diseases and treatments. However, the use
of transgenic models must be used with caution in trying to determine to determine modes of
action and the background effects of the transgene (including background levels of toxicity) and
specificity of effects must be taken into account for interpretation of mode-of-action data,
especially in cases where the knockout in the mouse causes significant liver necrosis or steatosis
(Caldwell et al.. 2008b: Caldwell and Keshava. 2006: Keshava and Caldwell 2006). For the
determination of effects of pathway perturbation and similarity to human HCC phenotype,
mouse transgenic models have been particularly useful with tumors produced in such models
shown to correlate with tumor aggressiveness and survival to human counterparts.
E.3.2.1. Similarities with Human and Animal Transgenic Models
Mice transgenic for transforming growth factor a (a member of the EGF family and a
ligand for the ErfB receptors) develop HCCs (Farazi and DePinho, 2006). Compound TGFa and
MYC transgenic mice show increase hepatocarcinogenesis that is associated with the disruption
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of TGF-pl signaling and chromosomal losses, some of which are syntenic to those in human
HCCs that include the retinoblastoma (KB) tumor suppressor locus (Sargent et al., 1999).
Lee et al. (2004b) investigated whether comparison of global expression patterns of
orthologous genes in human and mouse HCCs would identify similar and dissimilar tumor
phenotypes, and thus allow the identification of the best-fit mouse models for human HCC. The
molecular classification of HCC on the basis of prognosis in Lee et al. (2004a) was further
compared with gene-expression profiles of HCCs from seven different mouse models (Lee et al.,
2004b). Lee et al. (2004b) characterized the gene expression patters of 68 HCC from seven
different mouse models; two chemically induced (Ciprofibrate and diethylnitrosamine), and four
transgenic (targeted overexpression of Myc, E2F1, Myc and E2F1, and Myc and Tgfa in the
liver). HCCs from some of these mice (MYC, E2F1, and MYC-E2F1 transgenics) showed
similar gene-expression patterns to the ones of HCCs from patients with better survival. Murine
HCCs derived for MYC-TGF-a transgenic model or diethylnitrosamine-treated mice showed
similar gene-expression patterns to HCCs from patients with poor survival. The authors reported
that Myc Tgfa transgenic mice typically have a poor prognosis, including earlier and higher
incident rates of HCC development, higher mortality, higher genomic instability and higher
expression of poor prognostic markers (e.g., AFP) and that Myc and Myc/E2fl transgenic mice
have relatively higher frequency of mutation in p-catenin (Catnb) and nuclear accumulation of
p-catenin that are indicative of lower genomic instability and better prognosis in human HCC.
Lee et al. (2004b) indentified three distinctive HCC clusters, indicating that gene
expression pattern of mouse HCC are clearly heterogeneous and reported that Ciprofibrate-
induced HCCs and HCCs from Acox -/- mice were closely clustered and well separated from
other mouse models. However, there are several issues regarding this study that give limitations
to some of its conclusions regarding the Acox -/- mouse and Ciprofibrate treatment. The
Acox -/- mouse is characterized by profound hepatonecrosis, which confounds conclusions
regarding gene expression related to PPARa agonism made by the authors. There was very
limited reporting of the animal models (DEN and Clofibrate) protocols used. Only three tumors
were examined for Clofibrate treatment and it is unknown if the tumors were from the same
animals. Similarly, only three tumors were examined from DEN treatment, which has been
shown to produce heterogeneous tumors and to produce necrosis in some paradigms of exposure.
Myc/E2Fl and E2F1 mice were split in both clusters that were compared with human HCCs.
The authors used previously published data from Meyer et al. (2003) for tumors from Acoxl"1"
null mice, DENA-treated mice, and Ciprofibrate-treated mice.
Meyer et al. (2003) examined three tumors from two C57BL/6J mice fed Ciprofibrate for
19 months and three tumors from two C57BL/6J mice injected with DEN at 2-3 months, but the
age at which tumors appeared was not given by the authors. Pooled mRNA from animals of
varying age (5-15 months old) was used for controls. mRNAs that differed by twofold in tumors
were reported to have: 60 genes upregulated and 105 genes downregulated in Acoxl"1" null mice
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tumors; 136 genes upregulated and 156 genes downregulated in Ciprofibrate-induced tumors;
and 61 genes upregulated and 105 genes downregulated in DEN-induced tumors. The authors
stated that "Each tumor class revealed a somewhat different unique expression pattern." There
were "genes that were general liver tumor markers in all three types of tumors" with 38 genes
commonly deregulated in all three tumor types. Of note, the cell cycle genes (CDK4,
CDC25Am CDC7, and MAPK3) cited by Lee et al. (2004b) as being more highly expressed in
DEN-induced tumors were not reported to be changed in DEN tumors in Meyer et al. (2003) or
to be altered in the Acoxl"1" null mice or mice treated with Ciprofibrate. Finally, the distinction
between groups may be dominated by gene expression changes in a large number of genes that
are related to PPAR activation, but not related to hepatocarcinogenesis.
Calvisi et al. (2004a) used transgenic mice to study pathway alterations and tumor
phenotype and to further examine the premise that genomic alterations (genetic and epigenetic)
characteristic of HCC can describe tumors into two broad categories, the first category
characterized by activation of the Wnt/Wingless pathway via disruption of p-catenin function
and chromosomal stability and the second by chromosomal instability. Increased coexpression
of c-Myc with TGF-a or E2F-1 transgenic mice was reported to result in a dramatic synergistic
effect on liver tumor development when compared with respective monotransgenic lines,
including shorter latency period, and more aggressive phenotype. P-catenin activation is
relatively common in HCCs developed in c-Myc and c-Myc/TGF-pl transgenic mice and rare in
the c-Myc/TGF-a transgenic line which also has genomic instability.
Calvisi et al. (2004a) also reported that P-catenin staining correlated with histopathologic
type of liver tumors. Eosinophilic tumors with abnormal nuclear staining of P-catenin were
predominant in neoplastic lesions characteristic of c-Myc and c-Myc/E2Fl lesions. Poorly
differentiated HCCs with basophilic or clear-cell phenotypes developed more frequently in
c-Myc/TGF-a and TGF-a mice and often showed a reduction or loss of P-catenin
immunoreactivity. P-catenin mutation was associated with a more benign phenotype. These
observations regarding tincture and aggressiveness are consistent with those of Bannasch (1996)
and Carter et al. (2003). Calvisi et al. (2004a) noted that the relationship between P-catenin
activation, tumor grade, and clinical outcome in human HCC remains controversial.
There are studies that show a significant correlation between P-catenin nuclear
accumulation, a high grade of HCC tumor differentiation, and a better prognosis,
whereas others find that nuclear accumulation of P-catenin may be associated
with poor survival or that it does not affect clinical outcome.
Calvisi et al. (2004b) reported that for E-cadherin, a variety of morphologenetic events,
including cell migration, separation, and formation of boundaries between cell layers and
differentiation of each cell layer into functionally distinct structures. Loss of expression of
E-cadherin was reported to result in dedifferentiation, invasiveness, lymph node, or distant
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metastasis in a variety of human neoplasms including HCC and that the role of E-cadherin might
be more complex than previously believed.
In order to elucidate the role of E-cadherin in the sequential steps of liver
carcinogenesis, we have analyzed the expression patterns of E-cadherin in a
collection of preneoplastic and neoplastic liver lesions from c-Myc, E2F1,
c-Myc/TGF-a and c-Myc/E2Fl transgenic mice. In particular, we have
investigated the relevance of genetic, epigenetic, and transcriptional mechanisms
on E-cadherin protein expression levels. Our data indicate that loss of E-cadherin
contributes to HCC progression in c-Myc transgenic mice by promoting cell
proliferation and angiogenesis, presumably through the upregulation of HIF-la
and VEGF proteins.
The c-Myc line was most like wild-type and lost E-cadherin in the tumors. c-Myc/TGF-a
dysplastic lesions were reported to show overexpression of E-cadherin mainly in pericentral
areas with E2F1 clear cell carcinoma showed intense staining of E-cadherin. Reduction or loss
of E-cadherin expression is primarily determined by loss of heterozygosity at the E-cadherin
locus or by its promoter hypermethylation in human HCC. Calvisi et al. (2004b) determined the
status of the E-cadherin locus and promoter methylation in wild-type livers and tumors from
transgenic mice by microsatellite analysis and methylation specific PCR, respectively.
Wild-type livers and HCCs, regardless of their origins, showed the absence of
LOH at the E-cadherin locus. E-cadherin promoter was not hypermethylated in
wild-type, c-Myc/TGF-a and E2F1 livers. No E-cadherin promoter
hypermethylation was detected in c-Myc and c-Myc/E2Fl HCCs with normal
levels of E-cadherin protein. In striking contrast, seven of 20 (35%) of c-Myc and
two of four (50%) c-Myc/E2Fl HCCs with downregulation of E-cadherin
displayed E-cadherin promoter hypermethylation. These results suggest that
promoter hypermethylation might be responsible for E-cadherin downregulation
in a subset of c-Myc and c-Myc/E2Fl HCCs.. .The molecular mechanisms
underlying down-regulation of E-cadherin in c-Myc tumors remain poorly
understood at present. No LOH at the E-cadherin locus was detected in the
c-Myc HCCs whereas only a subset of c-Myc tumors displayed hypermethylation
of the E-cadherin promoter. Furthermore, no association was detected between
E-cadherin downregulation and protein levels of transcriptional repressers, Snail,
Slug or the tumor suppressor WT1, in disagreement with the finding that
overexpression of Snail suppresses E-cadherin in human HCC.. .E-cadherin might
play different and apparently opposite roles, which depend on specific tumor
requirements in both human and murine liver carcinogenesis.
Importantly, the results of Calvisi et al. (2004b) showed that hypermethylation of
promoters can be associated with downregulation of a gene in mouse liver tumors similar to
human HCC and that tumors can have the same behavior with methylation change as with loss of
hetererozygosity.
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This report also gave evidence of the usefulness of the mouse model to study human liver
cancer as it shows the similarity of dysfunctional regulation in mouse and human cancer and the
heterogeneity within and between mouse lines tumors with differing dysfunctions in gene
expression. These findings parallel human cancer where there is heterogeneity in tumors from
one person and every tumor has its own signature. Finally, this report correlates differing
pathway perturbations with mouse liver phenotypes similar to those reported in experimental
carcinogenesis models and for TCE and its metabolites.
Farazi and DePinho (2006) suggested that:
as comparative array CGH analysis of various murine cancers has shown that such
aberrations often target syntenic loci in the analogous human cancer type, we
further suggest that comparative genomic analysis of available mouse model of
mouse HCC might be particularly helpful in filtering through the complex human
cancer genome. Ultimately, mouse models that share features with human HCCs
could serve as valuable tools for gene identification and drug development.
However, one needs to keep in mind key differences between mice and humans.
For example, as noted in certain human HCC cases, telomere shortening might
drive the genomic instability that enables the accumulation of cancer-relevant
changes for hepatocarcinogenesis. As mice have long telomeres, this aspect of
hepatocarcinogenesis might be fundamentally different between the species and
provide additional opportunities for model refinement and testing of this
mechanism through use of a telomere deficient mouse model. These and other
cross-species difference, and limitations in the use of human cell-culture systems,
must be considered in any interpretation of data from various model systems
(Farazi and DePinho. 2006).
Thus, these mouse models of liver cancer inductions are qualitatively able to mimic
human liver cancer and support the usefulness of mouse models of cancer.
E.3.3. Hypothesized Key Events in HCC Using Animal Models
E.3.3.1. Changes in Ploidy
As stated in Section E.I.I, increased polyploidization has been associated with numerous
types of liver injury and appears to result from exposure to TCE and its metabolites as well as
changes in the number of binucleate cells. Hortelano et al. (1995) reported that cytokines and
NO can affect ploidy and further suggest a role of these changes for carcinogenesis in general.
Vickers and Lucier (1996) noted that while both DEN and 17 a-ethinylestradiol have been
reported to enhance the proportion of diploid hepatocytes, initiators like 7V-nitrosomorpholine are
reported to increase the proportion of hypertrophied and polyploidy hepatocytes. The
relationship of such changes to cancer induction has been studied in transgenic mouse models
and in models involved with mitogens of differing natures.
Melchiorri et al. (1993) reported the response pattern of the liver to acute treatment with
primary mitogens in regard to ploidy changes occurring in rat liver following two different types
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of cell proliferation: compensatory regeneration induced by surgical partial hepatectomy (PH)
and direct hyperplasia induced by the mitogens lead nitrate and Nafenopin (a PPARa agonist) in
8-week-old male Wistar rats. Feulgen stain was used and DNA content was quantified by image
cytometry in mononucleated and binucleated cells. Mitotic index was determined in the same
samples. The term "diploid" was used to identify cells with a single, diploid nucleus and
tetraploid for cells containing two diploid nuclei or one tetraploid nucleus referred (bi- and
mononucleated, respectively). Octoploid cells were identified as either binucleate or
mononucleate.
During liver regeneration following surgical PH an increase in the mitotic index
with a peak at 24 hours was observed. The most striking effect associated with
the regenerative response was the almost complete disappearance of binucleate
cells, tetraploid (2 X 2c) as well as octoploid (4 X 2c) with only < 10% of the
control values being present 3 days after PH... Concomitantly, an increase in
mononucleate tetraploid (4c) as well as mononucleate octoploid (8c) cells was
observed, resulting at 3 days after PH in a population made up of almost entirely
(98%) by mononucleated cells.
Lead nitrate treatment was reported to induce rapid increases in the formation of
binucleated cells occurring 3 days after treatment, their number accounting for 40% of the total
cell population vs. 22% binucleate cells in control rats and 2% in PH animals killed at the same
time point. The increased binuclearity was reported to be observed only in the 4 x 2c cells
(25 vs. 6% of the controls) and in 8 x 2c cells (3.7 vs. 0.1% of controls). The increase in 4 x 2c
and 8 x 2c cells was reported to be accompanied by a concomitant reduction in 2 x 2c cells with
the change induced in cellular ploidy by lead nitrate resulting in 37% of cells being either 8c or
16c. However, at the same time point, cells having a ploidy higher than 4c were reported to
account for only 11% in PH rats and 9% in control animals. Changes in the ploidy pattern were
reported to be preceded by an increased mitotic activity, which was maximal 48 hours after
treatment with lead nitrate. The increase in mitotic index in lead nitrate-treated rats was
associated with a striking increase in the labeling index of hepatocytes (60.1 vs. 3% of control
rats) and to an almost doubling of hepatic DNA content in 3 days after lead nitrate.
Melchiorri et al. (1993) concluded that the entire cell cycle appeared to be induced by
lead nitrate but that the finding of a high increase of binucleated cells suggested that lead nitrate-
induced liver growth, unlike liver regeneration induced by partial hepatectomy, was
characterized by an uncoupling between cell cycle and cytokinesis. This raised questions on
whether lead nitrate-induced liver growth resulted in a true increase in cell number or is only the
expression of an increased hepatocyte ploidy. They reported that part of the increase in DNA
content observed 3 days after lead nitrate was indeed expression of polyploidizing process due to
acytokinetic mitoses, but that a consistent increase in cells number (+26%) was also induced by
lead nitrate treatment.
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After Nafenopin treatment, Melchiorri et al. (1993) reported that the increase in DNA
content was increased 22% over controls and was much lower than induced by lead nitrate and
that Nafenopin did not induce significant changes in binucleated cell number. However, a shift
towards a higher ploidy class (8c) was reported to be observed following Nafenopin and the 21%
increase in DNA content seen after Nafenopin treatment was almost entirely due to increase in
the ploidy state with only 7% increase in cell number.
Melchiorri et al. (1993) examined whether hepatocytes characterized by high ploidy
content (highly differentiated cells) would be preferentially eliminated by apoptosis. An increase
in apoptotic bodies was reported to be associated with the regression phase after lead nitrate
treatment (when liver mass is reduced) but despite the elimination of excess DNA, the changes in
ploidy distribution induced by lead nitrate were found to persist suggested that polyploidy cells
were not preferentially eliminated by apoptosis during the regression phase of the liver.
Melchiorri et al. (1993) noted that other studies in rats exposed to the mitogen, cyproterone
acetate (CPA), and the peroxisome proliferator, MCP, also reported a very strong decline in
binucleated cells with a concomitant increase in mononucleated tetraploid cells in the liver
similar to the pattern described after partial hepatectomy.
Lalwani et al. (1997) reported the results of 1,000 ppm WY-14,643 exposure in male
Wistar rats after 1, 2, and 4 weeks and suggested that an early wave of nuclear division occurred
at the early stages of exposure without cumulative effects on cell proliferation. Consistent with
hepatomegaly, WY-14,643-treated rats were reported to exhibit multifocal hepatocellular
hypertrophy and karyomegaly by routine microscopic analysis. For binucleated hepatocytes,
there were no reported differences between WY-14,643-treated and control groups for days 4
and 11 but an increase in the number at day 25 in WY-14,643-treated animals compared to
controls. Increases in the diameter of nuclei were shown by WY-14,643-treatment from days 11
and 25 with increasing numbers of cells displaying larger nuclear diameters. The mitotic index
was reported not to be significantly changed in WY-14,643-treated rats compared to controls.
Mitotic figures did not appear to survive the treatment necessary for flow cytometric analyses.
PCNA was increased on day 4 in WY-14,643-treated animals compared to controls whereas no
differences were found on days 11 and 25.
However, immunohistochemistry was reported to show remarkable increases in
BrdU-labeled nuclei in liver sections after 4 days of labeling, with the populations of BrdU-
labeled cell declining over the course of treatment. The labeling index was high and
approximately 80% of the BrdU-labeled cells were in periportal areas. PCNA-expressing cells
were increased in the periportal area of the liver. Intense nuclear staining of PCNA was evident
as an indicator of DNA replication in S phase. Microscopic examination showed BrdU labeling
only in periportal hepatocytes, whereas no significant labeling was observed in nonparenchymal
cells, indicating that the replicative activity was confined to the liver cells.
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Lalwani et al. (1997) suggested that their results showed that events related to cell
proliferation occur in the initial phase of WY-14,643 treatment in rats but not followed by
changes in the rate of DNA synthesis as the treatment progressed. They note that Marsman et al.
(1988) observed constant increases in DNA synthesis by [3H]-thymidine authoradiography with
up to 1 year of continuous administration of WY-14,643, whereas the rate of DNA synthesis or
the BrdU labeling index in their study declined after the first 4 weeks of treatment. They suggest
that the increased percentage of cells appearing in G2-M phase and the analysis of liver nuclear
profiles suggest that the progression of these additional cells (i.e., cells that are stimulated to
enter the cell cycle by the test agent) through the cell cycle is arrested in the late stages of the cell
cycle. They state:
Unlike BrdU labeling, which demonstrated DNA synthesis activity over the 4-day
labeling period, the PCNA labeling index represents levels of the protein product
at an interval post treatment. PCNA expression in cells exposed to chemicals or
to WY may not provide true representation of S phase or proliferative activity
because PCNA-expressing nuclei were also found in GO=G1 and G2-M phases.
Lalwani et al. (1997) concluded that cell proliferation alone does not appear to constitute
a determining process leading to tumors in most tissues and sustained cell replication may not be
a primary feature of peroxisome proliferator-induced hepatocarcinogenesis.
Miller et al. (1996) noted that studies with MCP in Alpk: AP rats indicate that DNA
synthesis occurs primarily in one hepatocyte subpopulation as defined by ploidy status, the
binucleated tetraploid (2 x 2N) hepatocytes, and that this preferential hepatocyte DNA synthesis
is manifested by dramatic alterations in hepatocyte ploidy subclasses (i.e., significant increases
in mononucleate tetraploid [4N] hepatocytes concomitant with decreases in 2 x 2N hepatocytes).
They reported results in male F344 rats that were 13 weeks old (an age in which
polyploidization had reached a plateau) exposed to 1,000 ppm WY-14,643 and MCP (gavage via
corn oil at 8 mg/mL or 25 mg/kg MCP once daily) for 2, 5, and 10 days (n = 4). WY-14,643 and
MCP were reported to induce significant increases in the octoploid hepatocyte class that
coincided with decreases in the tetraploid hepatocyte class. However, MCP did not induce this
shift until day 5 of exposure. These results showed an approximate doubling of mononuclear
octoploid (8N) hepatoctyes but still a very low number of the total hepatocyte population that did
not reach >7% and was still only approximately twice that of control values. Thus, this finding
does not indicate a very large target population. There was no real effect on 4N hepatocytes due
to these treatments and the percent of hepatocytes that were 4N stayed -70% and were thus the
major cell type in the liver. Miller et al. (1996) noted the importance of maturation and/or strain
for these analyses; there are maturation-dependent differences in the distribution and mitogenic
sensitivity of hepatoctyes in the various subclasses.
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Hasmall and Roberts (2000) noted that despite their differing abilities to induce liver
cancer, both DCB (a nonhepatocarcinogen in F344 rats) and DEHP, at the doses and routes used
in the NTP bioassays, induced similar profiles of S-phase LI. A large and rapid peak during the
first 7 days (1,115 and 1,151% of control for DEHP and DCB, respectively) was followed by a
return to control levels. They suggested that the size of the S-phase response does not
necessarily determine hepatocarcinogenic risk and that the subpopulation in which S-phase is
induced may be a better correlate with subsequent hepatocarcinogenecity.
They compared the effects on polyploidy/nuclearity and on the distribution of S-phase
labeled cells with ETU, the peroxisome proliferator: MCP and phenobarbitone. Male F334 rats
7-9 weeks old were exposed to MCP (0.1% in diet), ETU (83 ppm in diet), or phenobarbitone
(500 mg/mL in drinking water) for 7 days. The number of rats for the 7-day study was not given
by the authors. Hasmall and Roberts (2000) reported that treatment of rats with MCP, ETU, or
phenobarbitone for 7 days had no significant effect on the ploidy profile as compared with corn
oil controls (data not shown) but that MCP and phenobarbitone did induce significant changes in
nuclearity. MCP reduced the 2 x 2N population and increased the 8N population.
Phenobarbitone similarly increased the proportion of cells in the 4N population. ETU had no
effect on the nuclearity profile as compared with control. However, what the authors describe
for their results in polidy and nuclearity are different than those presented in their figures. There
were significant differences between controls that the authors did not characterize and there
appeared to be a greater difference between controls than some of the treatments.
Gupta (2000) reported that in transgenic mice with overexpression of TGF-a, liver-cell
turnover increases, along with the onset of hepatic polyploidy, whereas HCC originating in these
animals contain more diploid cells. Coexpression of c-Myc and TGF-a transgenes in mouse
hepatocytes was associated with greater degrees of polyploidy as well as increased development
of HCC. Gupta (2000) noted that in the presence of ongoing liver injury and continuous
depletion of parenchymal cells, hepatic progenitor cells (including oval cells) are eventually
activated but what roles polyploid cells play in this process requires further study. In the
working model by Gupta (2000), sustained disease by chronic hepatitis, metabolic disease,
toxins, etc., may lead to hepatocyte polyploidy and loss, and the emergence of rapidly cycling
progenitor or escape cell clones with the onset of liver cancer.
Conner et al. (2003) described the development of transgenic mouse models in which
E2F1 and/or c-Myc was overexpressed in mouse liver. The E2F1 and c-Myc transcription
factors are both involved in regulating key cellular activities including growth and death and,
when overexpressed, are capable of driving quiescent cells into S-phase in the absence of other
mitogenic stimuli and are potent inducers of apoptosis operating at least through one common
pathway involving p53. Deregulation of their expression is also frequently found in cancer cells
(Conner et al., 2003). Conner et al. (2003) reported that although both c-Myc and E2F1 mono-
transgenic mice were prone to liver cancer, E2F1 mice developed HCC more rapidly and with a
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higher frequency and that the combined expression of these two transcription factors
dramatically accelerated HCC growth compared to either E2F1 or c-Myc mono-transgenic mice.
All three transgenic lines were reported to show a low but persistent elevation of hepatocyte
proliferation before an onset of tumor growth. Ploidy was shown to be affected differently by c-
Myc and E2F1, and suggested distinct differences by which these two transcription factors
control liver proliferation/maturation. Both transgenic alterations induced liver cancer but had
differing effects on polyploidization suggestive that liver cancer can arise from either type of
mature hepatocyte.
c-Myc single-transgenic mice showed a continuous high cell proliferation that preceded
the appearance of preneoplastic lesions, which was also true, although to a lesser extent, in the
E2F1 mice. At 15 weeks of age, all of the transgenic mouse lines were reported to have a high
incidence (>60%) of hepatic dysplasia with mitotic indices equivalent in c-Myc/E2Fl, and c-
Myc livers, but twofold higher than the mitotic index in E2F1 and very low in wild-type mice.
Thus, the combination of the two transgenes did not have an additive effect on proliferation. An
analysis of the DNA content in hepatocyte nuclei isolated from 4- to 15-week-old mice was
reported to show that in young wild-type livers, the majority of nuclei had a diploid DNA
content with a smaller proportion of tetraploid nuclei. As the mice aged, the number of
tetraploid and octoploid nuclei increased consistent with the previous findings of others.
However, c-Myc mice were reported to demonstrate a premature polyploidization with
the number of 2N nuclei in c-Myc livers almost 2-fold less, while the proportion of 4N nuclei
increased >2.5-fold at 4 weeks of age. The most prominent ploidy alteration was an increase in
the fraction of hepatocytes with octaploid nuclei (~200-fold higher). The percentage of
polyploidy cells was reported to continue to rise in 15-week-old c-Myc livers. The majority of
hepatocytes had nuclei with 4N and 8N DNA content, with an attendant increase in binucleated
hepatocytes and increase in average cell size.
In striking contrast, E2F1 hepatocytes were reported not to undergo normal
polyploidization with aging. The majority of E2F1 nuclei were reported to remain in the diploid
state and to be almost identical in E2F1 mice at 4 and 15 weeks of age. The percentage of
binucleated hepatocytes was also reduced. In c-Myc/E2Fl mice, the age-related changes in
ploidy distribution were reported to resemble those found in both c-Myc and in E2F1 single
transgenic mice.
At a young age, c-Myc/E2Fl mice, similar to E2F1 mice, were reported to retain
significantly more diploid nuclei than c-Myc mice. However, as mice aged, the majority of
c-Myc/E2Fl hepatocytes, similar to c-Myc cells but in contrast to findings in E2F1 cells, became
polyploid. Consistent with a more progressive polyploidization, the DNA content was
significantly higher in both c-Myc/E2Fl and c-Myc livers. Conner et al. (2003) reported that
other known modulators of ploidy in the liver are the tumor suppressor p53, pRb, and the cell
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cycle inhibitor p21 as well as genes involved in the control of the cell cycle progression such as
cyclin A, cyclin B, cyclin D3, and cyclin E.
Along with increased liver cancer, Conner et al. (2003) noted that the C-Myc mice also
experienced a persistent liver injury as evidenced by significant elevation of circulating levels of
AST, ALT, and ALP along with the appearance of a frequent oval/ductular proliferation.
However, oval cell proliferation may be a marker of hepatocyte damage but not be the cells
responsible for tumor induction (Tarsetti et al., 1993). Conner et al. (2000) reported that if E2F1
is overexpressed in the liver, there is both oncogenic and tumor-suppressive properties. In regard
to liver morphological changes, E2F1 transgenic mice were reported to uniformly develop
pericentral dysplasia and foci adjacent to portal tracts followed by the abrupt appearance of
adenomas and subsequent malignant conversion with all of the animals having foci by 2-
4 months, and by 8-10 months, most having adenomas with dysplastic changes remaining
confined to the pericentral regions of the liver lobule.
In regard to phenotype, the majority of the foci were composed of small round cells, with
clear-cell phenotype but eosinophilic, mixed, and basophilic foci were also seen. In adenomas
with malignant transformation to HCC, there appeared to be high mitotic indices, blood vessel
invasion, and central collection of deeply basophilic cells with large nuclei giving a "nodule-in-
nodule" appearance. Macrovesicular hepatic steatosis was first noted in some E2F1 transgenic
livers at 6-8 months, and by 10-12 months, 60% of animals had developed prominent fatty
change. Hepatic steatosis has been noted in several transgenic mouse models of liver
carcinogenesis (Conner et al., 2000). These results raise interesting points of regional difference
in tumor formation which can be lost in analyses using whole liver and that the phenotype of foci
and tumors are similar to those seen from chemical carcinogenesis. The occurrence of
hepatotoxicity in these transgenic mice is also of note.
E.3.3.2. Hepatocellular Proliferation and Increased DNA Synthesis
Caldwell et al. (2008b) presented a discussion of the role of proliferation in cancer
induction. They stated that:
in the case of CC14 exposure, hepatocyte proliferation may be related to its ability
to induce liver cancer at necrogenic exposure levels, but the nature of this
proliferation is fundamentally different from peroxisome proliferators or other
primary mitogens that cause hepatocyte proliferation without causing cell death
(Columbano and Ledda-Columbano, 2003; Ledda-Columbano et al., 2003;
Ledda-Columbano et al.. 1998: Menegazzi et al., 1997: Conietal., 1993: Ledda-
Columbano et al., 1993). After initiation with a mutagenic agent, the transient
proliferation induced by primary mitogens has not been shown to lead to cancer-
induction, while partial hepatectomy or necrogenic treatments of CC14 result in
the development of tumors (Gelderblom et al., 2001: Ledda-Columbano et al.,
1993).
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Roskams et al. (2003) noted that partial hepatectomy does not cause HCC in normal mice
without initiation. Melchiorri et al. (1993) reported that a series of studies has shown that acute
proliferative stimuli provided by primary mitogens, unlike those of the regenerative type such as
those elicited by surgical or chemical partial hepatectomy, do not support the initiation phase and
do not effectively promote the growth of initiated cells (Columbano et al., 1990; Ledda-
Columbano et al., 1989; Columbano et al., 1987). They noted that the finding that most of these
chemicals, with the exception of WY, induce only a very transient increase in cell proliferation
raises the question whether such a transient induction of liver cell proliferation might be related
to liver cancer appearing 1-2 years later. They noted that mitogen-induced liver growth differs
from compensatory regeneration in several aspects: (1) it does not require an increased
expression of hepatocyte growth factor mRNA in the liver; (2) it is not necessarily associated
with an immediate early genes such as c-Fos and c-Jun; and (3) it results in an excess of tissue
and hepatic DNA content that is rapidly eliminated by apoptotic cell death following
withdrawals of the stimulus.
Other studies have questioned the importance of a brief wave of DNA synthesis in
induction of liver cancer. Chen et al. (1995) noted that Jirtle et al. (1991) and Schulte-Hermann
et al. (1986) reported that during a 2-week period of treatment with lead, DNA synthesis was
increased most in centrolobular hepatocytes and that the predominantly centrilobular distribution
of the labeled nuclei may have been due largely to the brief wave of mitogenic response, because
from the fifth day onward, DNA synthesis activity returned to control level even though lead
nitrate treatment continued. They concluded that sustained cell proliferation may be more
important than a brief wave of increased DNA synthesis. Chen et al. (1995) also noted that a
number of different agents acting via differing modes of action will induce periportal
proliferation.
Vickers and Lucier (1996) reported that mitogenic response induced by acute
17 a-ethinylestradiol administration is randomly distributed throughout the hepatic lobule, while
continuous administration increases the proportion of diploid cells. Richardson et al. (1986)
reported that the lobular distribution of the correlation of hepatocyte initiation and akylation
reported in their model of carcinogenicity did "not support that early proliferation is associated
with cancer as at 7 days there is a transient increase in the lobes least likely to get a tumor and no
difference between the lobes at 14 and 28 days DEN although there is a difference in tumor
formation between the lobes." Thus, cells undergoing DNA synthesis may not be in the same
zone of the liver where other hypothesized "key events" take place.
Tanaka et al. (1992) noted that the distribution of hepatocyte proliferation in the
periportal area was in contrast to the distribution of peroxisome proliferation in the centrilobular
area of Clofibrate-treated rats. Melnick et al. (1996) noted that replicative DNA synthesis
commonly has been evaluated by measurement of the fraction of cells incorporating BrdU or
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tritiated thymidine into DNA during S-phase of the cell cycle (S-phase labeling index), but that
the S-phase labeling index would not be identical to the cell division rate when replication of
DNA does not progress to formation of two viable daughter cells. "The general view at an
international symposium on cell proliferations and chemical carcinogenesis was that although
cell replication is involved inextricably in the development of cancers, chemically enhanced cell
division does not reliably predict carcinogenicity" (Melnick et al., 1993). They noted that the
finding that enzyme-altered hepatic foci were not induced in rats fed WY-14,643 for 3 weeks
followed by partial hepatectomy indicates that early high levels of replicative DNA synthesis and
peroxisome proliferation are not sufficient activities for initiation of hepatocarcinogenesis.
Baker et al. (2004) reported that, similar to the pattern of transient increases in DNA
synthesis reported for TCE metabolites, Clofibrate exposure induced the upregulation of a
variety of cell proliferation-associated genes (e.g., G2/M specific cyclin Bl, cyclin-dependent
kinase 1, DNA topoisomerase II alpha, c-Myc protooncogene, pololike serien-threonine protein
kinase, and cell divisions control protein 20) began on or before day 1 and peaked at some point
between days 3 and 7. By day 7, cell proliferation genes were downregulated. The chronology
of this gene expression agrees with the histologic diagnosis of mitotic figures in the tissue, where
an increase in mitotic figures was detected in the day 1 and most notably day 3 high and low-
dose groups. However, by day 7, the incidence of mitotic figures had decreased. The clustering
of genes associated with the G2/M transition point suggests that in the rats, the polyploid cells
arrested at G2/M are those that are proceeding through the cell cycle.
A dose-response for increased DNA-synthesis also seems to be lacking for the model
PPARa agonist, WY-14,643 suggesting that the transient increases in DNA synthesis reported by
Eacho et al. (1991) for this compound at lower levels that then increase later at necrogenic
exposure levels, are not related to its carcinogenic potential. Wada et al. (1992) reported that in
male F344 rats exposed to a range of WY-14,643 concentrations (5-1,000 ppm), liver weight
gain occurred at the lowest dose that gave a sustained response for many weeks but gave
increased cell labeling only in the first week. Peroxisomes proliferation, as measure by electron
microscopy, increases started at 50 ppm exposures. By enzymatic means, peroxisomal activities
were elevated at the 5 ppm dose. Of note is the reported difference in distribution in
hepatocellular proliferation, which was not where the hypertrophy or where the lipofuscin
increases were observed. The authors noted that these data suggest that 50 and 1,000 ppm
WY-14,643 should give the same carcinogenicity if peroxisome proliferation or sustained
proliferation are the "key events."
The study of (Marsman et al., 1992) is very important in that it not only shows that
clofibric acid (another PPAR a agonist) does not have sustained proliferation, but it also shows
that it and WY-14,643 at 50 ppm did not induce apoptosis in rats. It is probable that use of
WY-14,643 at high concentrations may induce apoptosis in a manner not applicable to other
peroxisome proliferators or to treatment with WY-14,643 at 50 ppm. This study also confirmed
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that exposure to WY-14,643 at 50 ppm and WY-14,643 at 1,000 ppm induces similar effects in
regards to hepatocyte proliferation and peroxisomal proliferation.
The study by Eacho et al. (1991) also gave a reference point for the degree of hepatocytes
undergoing transient DNA synthesis from WY-14,643 and Clofibrate and how much smaller it is
for TCE and its metabolites, which generally involve <1% of hepatocytes.
The labeling index of BrdU was 7.2% on day 3 and 15.5% on day 6 after clofibric
acid but by day 10 and 30 labeling index was the same as controls at -1-2%... .For
WY the labeling index was 34.1% at day 3 and 18.6% at day 6. At day 10 the
labeling index was 3.3% and at day 30 was 6%, representing 6.6- and 15-fold of
respective controls. Control levels were -0.5 to 1%.... The labeling index was
increased to 32% by 0.3% LY171883 and to 52% by 0.05% Nafenopin. The
0.005% and 0.1% dietary doses of WY increased the 7 day labeling index to a
comparable level (55% - 58%).
Yeldandi et al. (1989) reported that until foci appear, cell proliferation has ceased to
increase over controls after the first week for Ciprofibrate-induced hepatocarcinogenesis. The
results also showed the importance of using age-matched controls and not pooled controls for
comparative purposes of proliferation as well as how low proliferative rates are in control
animals.
The results of Barrass et al. (1993) are important in suggesting that age of animals is
important when doing quantitation of labeling indexes. Studies such as that conducted by
Pogribny et al. (2007) that only give the replication rate as a ratio to control will make the
proliferation levels look progressive when, in fact, they are more stable with time as it is just the
controls that change with age as a comparison point.
E.3.3.3. Nonparenchymal Cell Involvement in Disease States Including Cancer
The recognition that not only parenchymal cells but also nonparenchymal cells play a
role in HCC has resulted in studies of their role in initiation as well as progression of neoplasia.
The role of the endothelial cell in controlling angiogenesis, a prerequisite for neoplastic
progression, and the role of the Kupffer cell and its regulation of the cytokine milieu that
controls many hepatocyte functions and responses have been reported. However, as pointed out
by Pikarsky et al. (2004) and by the review by Nickoloff et al. (2005), the roles of inflammatory
cytokines in cancer are context- and timing-specific and not simple. For TCE, nonparenchymal
cell proliferation has been observed after inhalation (Kj ell strand et al., 1983 a) and gavage (Goel
et al., 1992) exposures of-4 weeks duration.
E .3.3.3.1. Epithelial Cell Control of Liver Size and Cancer—Angiogenesis
The epithelium is key in controlling restoration after partial hepatectomy and not
surprisingly HCC growth. Greene et al. (2003) hypothesized that the control of physiologic
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organ mass was similar to the control of tumor mass in the liver and that specifically, the
proliferation of hepatocytes after partial hepatectomy, like the proliferations of neoplastic cells in
tumors, requires the synthesis of new blood vessels to support the rapidly increasing mass. They
reported that a peak in hepatocyte production of vascular endothelial growth factor (VEGF), an
endothelial mitogen, corresponds to an increase of VEGF receptor expression on endothelial
cells after partial hepatectomy and the rate of endothelial proliferation. Fibroblast growth factor
and transforming growth factor-alpha (TGfox), which stimulate endothelial cells, are secreted by
hepatoctyes 24 hours after partial hepatectomy. However, endothelial cells were reported to
secrete hepatocyte growth factor, a potent hepatocyte mitogen, that is also proangiogenic. The
secretion of transforming growth factor-beta by (TGfox) endothelial cells 72 hours after partial
hepatectomy was reported to inhibit hepatocyte proliferation. Thus, Greene et al. (2003)
suggested that endothelial cells and hepatocytes of the regenerating liver influence each other,
and both populations are required for the regulation of the regenerative process.
E .3.3.3.2. Kupffer Cell Control of Proliferation and Cell Signals, Role in Early and
Late Effects
Vickers and Lucier (1996) have reported that Kupffer cells are increased in number in
preneoplastic foci but are decreased in HCC, and that other studies have demonstrated that both
sinusoidal endothelial cells and Kupffer cells within HCC cells in humans stain positive for
mitotic activity although the number of nonparenchymal cells compared to parenchymal cells
may be reduced. Lapis et al. (1995) reported that Kupffer cells contain lysozyme in their
cytoplasmic granules, vacuoles, and phagosomes, some cells show a positive reaction in the
rough endoplasmic reticulum, perinuclear cisternae, and the Golgi zone, and that in human
monocytes, the lysozyme is colocalized with the CD68 antigen and myeloperoxidase. They also
reported that, in rodent hepatocarcinogenesis, increased numbers of Kupffer cells were observed
in preneoplastic foci, whereas abnormally low numbers were present following progression to
HCC. They also noted that "the Kupffer cell count in human HCC has also been shown to be
very low and varies with different histological form." They reported that for monkey HCCs, the
proportion of endothelial elements remained constant (the parenchymal/endothelial cell ratio);
however, there was a striking reduction in the areas occupied by Kupffer cells. While healthy
control livers contained the highest number of Kupffer cells, in the tumor-bearing cases, the
nonneoplastic, noncirrhotic liver adjacent to the HCC nodules had a significantly lower number
of Kupffer cells and the number decreased further in the nonneoplastic portions of cirrhotic
livers. Within HCC nodules, the Kupffer cell count was greatly reduced with no significant
changes observed between the cirrhotic areas and the carcinomas; however, the tumors contained
fewer lysozyme and CD68 positive cells. Lapis et al. (1995) noted that:
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since other cell types within the liver sinusoids (monocytes and polypmorphs) and
portal macrophage were also positive, it was important to identify the star-like
morphology of the Kupffer cells. The results of the two independent observers
assessment of the morphology and enumeration of Kupffer cells were quite
consistent and differed by only 3%." "The loss of Kupffer cells in the HCC may
possibly result from capillarization of the sinusoids, which has been observed
during the process of liver cirrhosis and carcinogenesis. Capillarization entails the
sinusoidal lining endothelial cells losing their fenestrations.
E .3.3.3.3. Nf-kB and TNF-a—Context, Timing and Source of Cell Signaling Molecules
A large body of literature has been devoted to the study of nuclear factor K B for its role
not only in inflammation and a large number of other processes, but also in carcinogenesis.
However, the effects of these cytokines are very much dependent on their cellular context and
the timing of their modulation. As described by Adli and Baldwin (2006):
The classic form of NF-kB is composed of a heterodimer of the p50 and p65
subunits, which is preferentially localized in the cytoplasm as an inactive complex
with inhibitor proteins of the IkB family. Following exposure to a variety of
stimuli, including inflammatory cytokines and LPS, IkBs are phosphorylated by
the IKKa/P complexes then accumulate in the nucleus, where they
transcriptionally regulate the expression of genes involved in immune and
inflammatory responses.
The five members of the mammalian NF-kB family, p65 (RelA), RelB, c-Rel, P50/pl05
(NF-KB 1), and p52/plOO (NF-kB2), exist in unstimulated cells as homo- or heterodimers bound
to IkB family proteins. Transcriptional specificity is partially regulated by the ability of specific
NF-kB dimmers to preferentially associate with certain members of the IkB family. Individual
NF-kB responses can be characterized as consisting of waves of activation and inactivation of
the various NF-kB members (Hayden and Ghosh, 2004). While the function of NF-kB in many
contexts have been established, it is also clear that there is great diversity in the effects and
consequences of NF-kB activation with NF-kB subunits not necessarily regulating the same
genes in an identical manner and in all of the different circumstances in which they are induced.
The context within which NF-kB is activated, be it the cell type or the other stimuli to which the
cell is exposed, is therefore, a critical determinant of the NF-kB behavior (Perkins and Gilmore,
2006).
Balkwill et al. (2005) reported that:
the NF-KB pathway has dual actions in tumor promotion: first by preventing cell
death of cells with malignant potential, and second by stimulating production of
proinflammatory cytokines in cells of infiltrating myeloid and lymphoid cells.
The proinflammatory cytokines signal to initiated and/or otherwise damaged
epithelial cells to promote neoplastic cell proliferation and enhance cell survival.
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However, the tumor promoting role of NF-KB may not always predominate. In
some cases, especially early cancers, activation of this pathway may be tumor
suppressive (2004). Inhibiting NF-KB in keratinocytes promotes squamous cell
carcinogenesis by reducing growth arrest and terminal differentiation of initiated
keratinocytes (SeitzetaL 1998).
Other inflammatory mediators have also been associated with oncogenesis. Balkwill et
al. (2005) reported that TNFa is frequently detected in human cancers (produced by epithelial
tumor cells, as in for instance, ovarian and renal cancer) or stromal cells (as in breast cancer).
They also report that the loss of hormonal regulation of IL-6 is implicated in the pathogenesis of
several chronic diseases, including B cell malignancies, RCC, and prostate, breast, lung, colon,
and ovarian cancers. Over 100 agents, such as antioxidants, proteosome inhibitors, NSAIDs, and
immunosuppressive agents are NF-KB inhibitors with none being entirely specific (Balkwill et
al., 2005). Thus, alterations in these cytokines, and the cells that produce them, are implicated as
features of "cancer" rather than specific to HCC.
Balkwill et al. (2005) reported that:
Two mouse models of inflammation-associated cancer now implicate the gene
transcription factor NF-KB and the inflammatory mediator known as tumor-
necrosis factor a (TNF- a) in cancer progression. Using a mouse model of
inflammatory hepatitis that predisposes mice to liver cancers, Pikarsky et al.
present evidence that the survival of hepatocytes - liver cells - and their
progression to malignancy are regulated by NF-KB. NF-KB is an important
transcription factor that controls cell survival by regulating programmed cell
death, proliferation, and growth arrest. Pikarsky et al. find that the activation state
of NF-KB, and its localization in the cell, can be controlled by TNF-a produced by
neighboring inflammatory cells (collectively known as stromal cells).
Pikarsky et al. (2004) reported that that the inflammatory process triggers hepatocyte
NF-KB through upregulation of TNF-a in adjacent endothelial and inflammatory cells.
Switching off NF-KB in mice from birth to 7 months of age, using hepatocyte-specific inducible
IicB-super represser transgene, had no effect on the course of hepatitis, nor did it affect early
phases of hepatocyte transformation. By contrast, suppressing NF-KB inhibition through anti-
TNF-a treatment or induction of the IicB-super represser in later stages of tumor development
resulted in apoptosis of transformed hepatocytes and failure to progress to HCC. The
Mdr2 knockout hepatocytes in Pikarsky's model of hepatocarcinogenicity were distinguishable
from wild-type cells by several abnormal features: high proliferation rate, accelerated
hyperploidy and dysplasia. Pikarsky et al. (2004) reported that NF-KB knockout and double
mutant mice displayed comparable degrees of proliferation, hyperploidy, and dysplasia, implying
that NF-KB is not required for early neoplastic events. Thus, activation of NF-KB was not
important in the early stages of tumor development, but was crucial for malignant conversion.
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It was noted that:
Greten et al. reporting in Cell, come to a similar conclusion by studying a mouse colitis-
associated cancer model. Their work does not directly implicate TNF-a, but instead
found enhanced production of several pro-inflammatory mediators (cytokines) including
TNF-a, in the tumor microenvironment during the development of cancer. An important
feature of both studies is that NF-KB activation was selectively ablated in different cell
compartments in developing tumor masses, and at different stages of cancer
development.
Balkwill et al. (2005) also noted that TNF-a and NF-KB have many different effects,
depending on the context in which they are called into play and the cell type and environment.
In contrast, El-Serag and Rudolph (2007) noted that "the influence of inflammatory
signaling on hepatocarcinogenesis can be context dependent; deletion of Nf-KB-dependent
inflammatory responses enhanced HCC formation in carcinogen treated mice (Sakurai et al.,
2006)." Similarly, deletion of Nf-KB essential modulator/I kappa P kinase (NEMO/IKK), an
activator of Nf-KB, induced steatohepatitis and HCC in mice (Luedde et al., 2007).
Maeda et al. (2005) reported that hepatocyte-specific deletion of IKKp (which prevents
NF-kB activation) increased DEN-induced hepatocarcinogenesis and that a deletion of IKKp in
both hepatocytes and hematopoietic-derived cells, however, had the opposite effect, decreasing
compensatory proliferation and carcinogenesis. They suggested that these results differ from
previous suggestion that the tumor-promoting function of NF-kB is excreted in hepatocytes
(Pikarsky et al., 2004), and suggest that chemicals or viruses that interfere with NF-kB activation
in hepatocytes may promote HCC development.
Alterations in NF-kB levels have been suggested as a key event for the
hepatocarcinogenicity by PPARa agonists. The event associated with PPAR effects has been the
extent of NF-kB activation as determined through DNA binding. As reported by Tharappel
(2001), NF-kB activity is assayed with electrophoretic mobility shift assay with nuclear extracts
prepared from frozen liver tissue as a measure of DNA binding of NF-kB. Increased
transcription of downstream targets of NF-kB activity has also been measured. It has been
suggested that PPARa may act as a protective mechanism against liver toxicity. Ito et al. (2007)
cite repression of NF-kB by PPARa to be the rationale for their hypothesis that PPARa-null
mice may be more vulnerable to tumorigenesis induced by exposure to environmental
carcinogens. However, as shown in Section E.3.4.1.2, although DEHP was reported to also
induce glomerulonephritis more often in PPARa-null mice, as suggested (Kamijima et al., 2007)
to be due of the absence of PPARa-dependent anti-inflammatory effect of antagonizing the
oxidative stress and NF-KB pathway, there was no greater or lesser susceptibility to
DEHP-induced liver carcinogenicity in the PPARa null mice.
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Because PPARa is known to exert anti-inflammatory effects by inducing expression of
, which antagonizes NFKB signaling, the expression of IicBa has been measured in some
studies (Kamijima et al., 2007), as well as expression of TNR1 mRNA to evaluate the sensitivity
to the inflammatory response. Ito et al. (2007) reported that in wild-type mice, there did not
appear to be a difference between controls and DEHP treatment for p65 immunoblot results.
DEHP treatment was also reported to not induce p65 or p52 mRNA either or influence the
expression levels of TNFa, IkBa, IkBp, and IL-6 mRNA in wild-type mice.
Tharappel et al. (2001) treated rats with WY-14,643, Gemfibrozil, or dibutyl phthalate
and reported elevated NF-kB DNA binding in rats with WY-14,642 to have sustained response
but not others. WY-14,643 increased DNA binding activity of NF-kB at 6, 34, or 90 days.
Gemfibrozil and DEHP increased NF-kB activity to a lesser extent and not at all times in rats.
For Gemfibrozil, there was only a twofold increase in binding at 6 days with no increase at
34 days and an increase only in low dose at 90 days. In rats treated with dibutyl phthalate, there
was no change at 6 days; at 34 days, there was an increase at high and low dose and at 90 days,
only low-dose animals showed a change. In pooled tissue from WY-14,643-treated animals, the
complex that bound the radiolabeled NF-kB fragment did contain both p50 and p65. Both
WY-14,643 and Gemfibrozil were reported to produce tumors in rats with dibutyl phthalate
untested in rats for carcinogenicity. Thus, early changes in NF-kB were not supported as a key
event and WY-14,643 to have a pattern that differed from the other PPARa agonists examined.
In regard to the links between inflammation and cancer, Nickoloff et al. (2005), in their
review of the issue, cautioned that such a link is not simple. They noted that:
dissecting the mediators of inflammation in cutaneous carcinogenic pathways has
revealed key roles for prostaglandins, cyclooxygenase-2, tumor necrosis factor-a,
AP-1, NF-KB, signal transducer and activator of transcription (STAT)3, and
others. Several clinical conditions associated with inflammation appear to
predispose patients to increased susceptibility for skin cancer including discoid
lupus erythematosus, dystrophic epidermolysis bullosa, and chronic wound sites.
Despite this vast collection of data and clinical observations, however, there are
several dermatological setting associated with inflammation that do not
predispose to conversion to lesions into malaignancies such as psoriasis, atopic
dermatitis, and Darier's disease.
Nickoloff et al. (2005) suggested that such a
link may not be as simple as currently portrayed because certain types of
inflammatory processes in skin (and possibly other tissues as well) may also serve
a tumor suppressor function. Over the past few months, several publications in
leading biomedical journals grappled with an important issue in oncology, namely
defining potential links between chronic tissue damage, inflammation, and the
development of cancer. Balkwill and Coussens (2004) reviewed the role of the
NF-KB signal transduction pathway that can regulate inflammation and also
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promote malignancy. Their review summarized the latest findings revealed in a
letter to Nature by Pikarsky et al. (2004). Using Mdr2 knockout mice in which
hepatitis is followed by hepatocellular carcinoma, Pikarsky et al. implicated
TNFa upregulation in tumor promotion of HCC, and suggest that TNFa and NF-
KB are potential targets for cancer prevention in the context of chronic
inflammation. A similar conclusion was reached with respect to NF-KB by an
independent group of investigators using a model of experimental dextran sulfate-
induced colitis, in which inactivation of the 1KB kinase resulted in reduced
colorectal tumors (Greten et al., 2004). Although there are many other clinical
condition supporting the concept of inflammation is a critical component of tumor
progression (e.g., reflux esophagitis/esophageal cancer; inflammatory bowel
disease/colorectal cancer), there is at least one notable example that does not fit
this paradigm. As described below, psoriasis is a chronic cutaneous inflammatory
disease, which is seldom if ever accompanied by cancer suggesting the
relationship between tissue repair, inflammation, and development may not be as
simple as portrayed by the aforementioned reviews and experimental results.
Besides psoriasis, other noteworthy observations pointing to more complexity
include the observation that in the Mdr2 knockout mice, we rarely detect bile duct
tumors despite extensive inflammation, NF-KB activation, and abundant
proliferation of bile ducts in portal spaces (Pikarsky et al., 2004). Moreover, in a
skin-cancer mouse model, NF-KB was shown to inhibit tumor formation (Dajee et
al., 2003). Thus, the composition of inflammatory mediators, or the properties of
the responding epithelial cells (e.g., signaling machinery, metabolic status), may
dictate either tumor promotion or tumor suppression. Chronic inflammation and
tissue repair can trigger pro-oncogenic events, but also that tumor suppressor
pathways may be upregulated at various sites of injury and chronic cytokine
networking.
One cannot easily dismiss the many dilemmas raised by the psoriatic plaque that
confound a simple link between the tissue repair, inflammation, and
carcinogenesis. Since it is easily visible to the naked eye, and patients may suffer
from such lesions for decades, it is difficult to argue that various skin cancers
such as squamous cell carcinoma, basal cell carcinoma, or melanoma actually do
develop within plaques by are being overlooked by patients and dermatologists.
Remarkably, psoriatic plaques are intentionally exposed to mutagenic agents
including excessive sunlight, topical administration of crude coal tar, or parenteral
DNA cross-linking agent -psoralen followed by ultraviolet light. Moreover these
treatments are known to induce skin cancer in nonlesional skin. Thus since
psoriatic skin is characterized by altered differentiation, angiogenesis, increased
telomerase activity, proliferative changes, and apoptosis resistance, one would
expect that each and every psoriatic plaque would be converted to cancer, or at
least serve as fertile soil for the presence of non-epithelial skin cancers over
time... .In conclusion, it would seem prudent to remember the paradigm proposed
by Weiss (1971) in which he suggested that premalignant cells do not comprise an
isolated island, but are a focus of intense tissue interactions. The myriad
inflammatory effects of the tumor microenvironment are important for
understanding tumor development, as well as tumor suppression and senescence,
and for the design for efficacious prevention strategies against inflammation-
associate cancer (Nickoloff et al., 2005).
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E.3.3.4. Gender Influences on Susceptibility
As discussed previously, male humans and rodents are generally more likely to get HCC.
The increased risk of liver tumors from estrogen supplements in women has been documented.
In mice, TCE exposure has been shown not only to have greater variability in response and
greater effects on body weight in males (Kjellstrand et al., 1983a: Kj ell strand et al., 1983b) but
also to induce dose-related increases in liver weight and carcinogenic response in female mice as
well as males (see Section E.2.2). Recent studies have attempted to link differences in
inflammatory cytokines and gender differences in susceptibility.
Lawrence et al. (2007) suggested that:
studies of Naugler et al. (2007) and Rakoff-Nahoum and Medzhitov (2007),
advance our understanding of the mechanisms of cancer-related inflammation.
They describe an important role for an intracellular signaling protein called
MyD88 in the development of experimental liver and colon cancers in mice.
MyD88 function has been well characterized in the innate immune response
(Akira and Takeda, 2004), relaying signals elicited by pathogen-associated
molecules and by the inflammatory cytokine interleukin-1 (IL-1).... The
conclusion from Naugler et al. (2007) and Rakoff-Nahoun and Medzhitov is that
MyD88 may function upstream of NF-KB in cells involved in inflammation-
associated cancer. Immune cells infiltrate the microenvironment of a tumor.
Naugler et al. (2007) and Rakoff-Nahoun and Medzhitov (2007) suggest that the
development of liver and intestinal cancers in mice may depend on a signaling
pathway in infiltrating immune cells that involved the protein MyD88, the
transcription factor NF-KB, and the pro-inflammatory cytokine 11-6. TLR binds a
ligand which acts on MyD88 which acts on NF-KB which leads to secretion of
inflammatory cytokine IL-6 which leads to promotion of tumor cell survival and
proliferation.
Naugler et al. (2007) suggested gender disparity in MyD88-dependent IL-6 production
was linked to differences in cancer susceptibility using the DEN model (a mutagen with
concurrent regenerative proliferation at a single high dose) with a single injection of DEN.
Partial hepatectomy was reported to induce no gender-related difference in IL-6 increase. After
DEN treatment, the male mouse had 275 ng/mL as the peak IL-6 levels 12 hours after DEN, and
for female mice, the peak was reported to be 100 ng/mL 12 hours after DEN administration.
This is only about a 2.5-fold difference between genders. IL-6 mRNA induction was reported
for mice 4 hours after DEN, at a time when there was no difference in serum IL-6 between male
and female mice. It was not established that the 4-hour results in mRNA translated to the
differences in serum at 12 hours between the sexes. The magnitude of mRNA differences does
not necessarily hold the same relationship as the magnitude in serum protein. In fact, there was
not a linear correlation between mRNA induction and IL-6 serum levels.
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A number of issues complicate the interpretation of the results of the study. The study
examined an acute response for the chronic endpoint of cancer and may not explain the
differences in gender susceptibility for agents that do not cause necrosis. The DEN was
administered in 15-day-old mice (which had not reached sexual maturity) for tumor information
at a much lower dose than used in short-term studies of inflammation and liver injury in which
mature mice were used. If large elevations of IL-6 are the reason for liver cancer, why does not
a partial hepatectomy induce liver cancer in itself?
The percentage of proliferation at 36 and 48 hours after partial hepatectomy was the same
between the sexes. If a 2.5-fold difference in IL-6 confers gender susceptibility, it should do so
after partial hepatectomy and lead to cancer. For female mice, partial hepatectomy showed
alterations in a number of parameters. However, partial hepatectomy does not cause cancer
alone. The 5-fold increase 4 hours after DEN induction of IL-6 mRNA in male mice is in sharp
contrast to the 27-fold induction of IL-6 1 hour after partial hepatectomy (in which at 4 hours,
the IL-6 had diminished to 6-fold). There appeared to be variability between experiments. For
example, the difference in males between experiments appears to be the same magnitude as the
difference between male and female in one experiment and the baseline of IL-6 mRNA induction
appeared to be highly variable between experiments as well as absolute units of ALT in serum
24 and 48 hours after DEN treatment that tended to be greater that the effects of treatments. The
experiments used very few animals (n = 3) for most treatment groups. Of note is that the
MyD88 -/- male mice still had a background level of necrosis similar to that of WT mice at
48 hours after DEN treatment, a time, long after the peak of IL-6 mRNA induction and IL-6
serum levels were reported to have peaked.
One of the key issues regarding this study is whether difference in IL-6 reported here lead
to an increase in proliferation and does that difference within 48 hours of a necrotizing dose of a
carcinogen change the susceptibility to cancer? This report shows that male and female mice
have a difference in necrosis after carbon tetrachloride and a difference in proliferation. Are
early differences in IL-6 at 4 hours related to the same kind of stimulus that leads to necrosis and
concurrent proliferation? The amount of proliferation (as measured by DNA synthesis) between
male and female mice 48 hours after DEN was very small and the study was conducted in a very
few mice (n = 3). At 36 hours, the degree of proliferation was almost the same between the
genders and about 0.6% of cells. The baseline of proliferation also differed between genders, but
the variation and small number of animals made it insignificant statistically. At 48 hours the
differences in proliferation between the male and female mouse were more pronounced, but were
still quite low (2% for males and -1% for females). Is the change in proliferation just a change
in damage by the agent? Given the large variation in serum ALT and by inference necrosis, is
there an equal amount of variability in proliferation? This study gives only limited information
for DEN treatment.
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The difference in incidence of HCC was reported to be greater than that of "proliferation"
between genders and of other parameters, although differences in tumor multiplicity or size
between the genders are never given in the paper. Most importantly, comparisons between the
short-term changes in cytokines and indices of acute damage are for adult animals that are
sexually mature and at doses that are 4 times (100 vs. 25 mg/kg) that of the sexually immature
animals that are going through a period of rapid hepatocyte proliferation (15-day-old animals).
It is therefore difficult to extrapolate between the two paradigms to distinguish the effects
of hormones and gender on the response. Finally, the work of Rakoff-Nahoum and Medzhitov
(2007) showed that it is the effect of tumor progression and not initiation that is affected by
MyD88 (a signaling adaptor to Toll-like receptors). Thus, examination of parameters at the
initiation phase at necrotic doses for liver tumors may not be relevant.
E.3.3.5. Epigenomic Modification
There are several examples of chemical exposure to differing carcinogens that have led to
progressive loss of DNA methylation (i.e., DNA hypomethylation) including TCE and its
metabolites. The evidence for TCE and its metabolites is specifically discussed in
Section E.3.4.2.2. Other examples of carcinogen exposures or conditions that have been noted to
change DNA methylation are early stages of tumor development include ethionine feeding,
phenobarbitol, arsenic, dibromoacetic acid, and stress. However, it has not yet been established
whether epigenetic changes induced by carcinogens and found in tumors play a causative role in
carcinogenesis or are merely a consequence of the transformed state (Tryndyak et al., 2006).
Pogribny et al. (2007) reported the effects of WY-14,643 on global mouse DNA
hypomethylation exposed at 1,000 ppm for 1 week, 5 weeks, or 5 months. What is of particular
note in this study is that at this exposure level, one commonly used for mode-of-action studies
using WY-14,643 to characterize the effects of PPARa agonists as a class, there was significant
hepatonecrosis and mortality reported by Woods et al. (2007a).
Both wild-type and PPARa -/- null mice were examined. In wild-type mice DNA
syntheses was elevated 3-, 13-, and 22-fold of time-matched controls after 1 week, 5 weeks, and
5 months of WY 14,543 treatment. Changes in ploidy were not examined. After 5 weeks of
exposure, the ratio of unmethylated CpG cites in whole-liver DNA was the same for WY-14,643
treatment and control but by 5 months, there was an increase in hypomethylation in WY-14,643
treated wild-type mice. The authors did not report whether foci were present or not, which could
have affected this result. The similarity in hypomethylation at 5 days and 5 weeks, a time point
that also had a small probability of foci development, is suggestive of foci affecting the result at
5 months.
For PPAR -/- mice, there was increased hypomethylation reported at 1 and 5 weeks after
WY-14,643 treatment that was not statistically significant with so few animals studied. At
5 months, the null mice had decreased hypomethylation compared to 1 and 5 weeks. The authors
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noted that methylation of c-Myc genes was reported to not be affected by long-term dietary
treatment with WY-14,643 even though WY-14,643-related hypomethylation of c-Myc gene
early after a single dose of WY-14,643 has been observed (Ge et al., 2001a). The authors
concluded "thus, alterations in the genome methylation patterns with continuous exposure to
nongenotoxic liver carcinogens, such as WY, may not be confined to specific cell proliferation-
related genes."
Pogribny et al. (2007) reported Histone H3 and H4 trimethylation status in wild-type and
PPAR null mice to show a rapid and sustained loss of histone H3K9 and histone H4K20
trimethylation in wild-type mice fed WY-14,643 from 1 week to 5 months. There was no
progressive loss in histone hypomethylation, with the same amount of demethylation occurring
at 5 days, 5 weeks, and 5 months in wild-type mice fed WY-14,643. The change from control
was -60% reduction. The control values with time were not reported and all controls were
pooled to give one value (n = 15). For PPAR -/-1 mice, there was a slight decrease with
WY-14,643 treatment (-15%) reported. In wild-type mice, WY-14,643 treatment was reported
to have no effect on the major histone methyltransferase, Suv39hl, while expression of another
(PRDM/Rizl) increased significantly as early as one week of treatment and remained elevated
for up to 5 months. The effect on expression of Suv420h2 (responsible for histone H4K20
trimethylation) was more gradual and the amounts of this protein in livers of mice fed
WY-14,643 were reported to be lower than in control.
The authors did not examine these parameters in the null mice, so the relationship of
these effects to receptor activation cannot be determined. Pogribny et al. (2007) reported
hypomethylation of retroelements (LTRIAP, LINE1, and LINE2 retrotransposons) following
long-term exposure to WY-14,643, which the authors concluded can have effects on the stability
of the genome. Again, these results are for whole liver that may contain foci.
Nevertheless, these findings raise questions about other target organs and a more general
mechanism for WY-14,643 effects than a receptor mediated one. The lack of effects on c-Myc
and the irrelevance of the transient proliferation through it reported here gives more evidence of
the irrelevance of a mode of action dependent on transient proliferation. The authors noted that
studies show that a sustained loss of DNA methylation in liver is an early and indispensable
event in hepatocarcinogenesis induced by long-term exposure of both genotoxic and
nongenotoxic carcinogens in rodents. Thus, this statement argues against making such a
distinction in mode of action for "genotoxic" and "nongenotoxic" carcinogens. Finally, the use
of a dose that Woods et al. (2007a) demonstrate to have significant hepatonecrosis and mortality,
limits the interpretation of these results and their relevance to models of carcinogenesis without
concurrent necrosis.
Strain sensitivity to hepatocarcinogenicity has been investigated in terms of short-term
changes in methylation. Bombail et al. (2004) reported that a tumor-inducing dose of
phenobarbital reduced the overall level of liver DNA methylation in a tumor-sensitive (B6C3Fi)
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mouse strain but that the same dose of phenobarbital did not alter the global methylation level in
a more tumor-resistant strain (C57BL/6), although the compound increased hepatocyte
proliferation as measured by increased DNA synthesis in both strains (Counts et al., 1996).
Bombail et al. reported that "In a similar study, Watson and Goodman (2002) used a PCR-based
technique to measure DNA methylation changes specifically in GC-rich regions of the mouse
genome." Watson and Goodman (2002) found that, that in these areas of the genome, exposure
to phenobarbital caused an increase in methylation in dosed animals compared with control
animals. Again, the change was more pronounced in tumor-prone C3H/He and B6C3Fi strains
than in the less sensitive C57BL/6 strain. They also reported increased DNA synthesis in
C57BL/6 mice but decreased global methylation in the B6C3Fi strain after phenobarbitol
administration for 1-2 weeks. The lifetime spontaneous tumor rates were reported to be <5% in
C57BL/6 mice but up to 80% in C3H/He mice.
Counts et al. (1996) reported cell proliferation and global hepatic methylation status in
relatively liver tumor susceptible B6C3Fi with relatively resistant C57BL6 mice following
exposure to phenobarbitol and/or chlorine/methionine deficient (CMD) diet. Cell proliferation
(i.e., DNA synthesis) was reported to be higher in C57BL/6 mice, while transient
hypomethylation occurred to a greater extent in B6C3Fi mice after phenobarbital treatment.
Dual administration of CMD and phenobarbitol led to enhanced cell proliferation and greater
global hypomethylation with similar trends in terms of strain sensitivities in comparison to with
either treatment alone (i.e., greater increase in cell proliferation in C57BL/6 and greater levels of
hypomethylation in B6C3Fi). Thus, the authors concluded that B6C3Fi mice have relatively low
capacity to maintain the nascent methylation status of their hepatic DNA.
However, on the whole, the control values for methylation for the C57BL/6 mice appear
to be slightly higher than the B6C3Fi mice. Claims that the liver tumor sensitive B6C3Fi had
more global hypomethylation after a promoting stimulus, which could be related to tumor
sensitivity, are tempered by the fact that the resistant strain had a higher control baseline of
methylation. The baseline level of LI or hepatocyte proliferation also appears to be slightly
higher in the C57BL/6 mouse. In addition, the largest strain difference in hypomethylation after
a CMD diet was at week 12 (135% of control for the B6C3Fi strain and 151% of control for the
C57BL/6 strain) and this pattern was opposite that for the 1-week time point. Thus, the
suggestion by Counts et al. (1996), that the inability to maintain methylation status by the
B6C3Fi strain, is also not supported by the longer duration data for CMD diet.
E.3.4. Specific Hypothesis for Mode of Action of TCE Hepatocarcinogenicity in Rodents
E.3.4.1. PPARa Agonism as the Mode of Action for Liver Tumor Induction—The
State of the Hypothesis
PPARa receptor activation has been suggested to be the mode of action for TCA liver
tumor induction and for TCE liver tumor induction to occur primarily as a result of the presence
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of its metabolite TCA (NRC, 2006). However, as discussed previously (see Section E.2.1.10),
TCE-induced increases in liver weight have been reported in male and female mice that do not
have a functional PPARa receptor (Ramdhan et al., 2010; Nakajima et al., 2000). The dose-
response for TCE-induced liver weight increases differs from that of TCA (see Section E.2.4.2).
The phenotype of the tumors induced by TCE have been described to differ from those by TCA
and to be more like those occurring spontaneously in mice, those induced by DC A, or those
resulting from a combination of exposures to both DCA and TCA (see Section E.2.4.4). As to
whether TCA-induced tumors are induced through activation of the PPARa receptor, the tumor
phenotype of TCA-induced mouse liver tumors has been reported to have a pattern of H-ras
mutation frequency that is opposite to that reported for other peroxisome proliferators (see
Section E.2.4.4.; Bull et al.. 2002: Stanley et al.. 1994: Hegietal.. 1993: Fox et al.. 1990). While
TCE, DCA, and TCA are weak peroxisome proliferators, liver weight induction from exposure
to these agents has not correlated with increases in peroxisomal enzyme activity (e.g., PCO
activity) or changes in peroxisomal number or volume. However, liver weight induction from
subchronic exposures appears to be a more accurate predictor of carcinogenic response for DCA,
TCA, and TCE in mice (see Section E.2.4.4). The database for cancer induction in rats is much
more limited than that of mice for determination of a carcinogenic response to these chemicals in
the liver and the nature of such a response.
The mode of action for peroxisome proliferators has been the subject of research and
debate for several decades. It has evolved from an "oxidative damage" due to increased
peroxisomal activity to a mode-of-action framework example developed by Klaunig et al. (2003)
that described causal inferences for hepatocarcinogenesis after a chemical exposure was shown
to activate of the PPAR-a receptor with concurrent perturbation of cell proliferation and
apoptosis, and selective clonal expansion. Of note, although inhibition of apoptosis was
proposed as part of the sequellae of PPARa activation, as noted in Section E.2.4.1, no changes in
apoptosis in mice exposed to TCE have been reported, with the exception of mild enhanced
apoptosis at a 1,000 mg/kg-day dose. More importantly, for mice, the rate of apoptosis decreases
as mice age and appears to be lower than that of rats. While DCA exposure has been noted to
reduce apoptosis, the significance of DCA-induced reduction in apoptosis from a level that is
already inherently low in the mouse, is difficult to apply as the mode of action for DCA-induced
liver cancer.
Klaunig et al. (2003_)based causal inferences on the attenuation of these events in PPAR-
a-null mice in response to the prototypical agonist WY-14,643 with a number of intermediary
events considered to be associative (e.g., expression of peroxisomal and nonperoxisome genes,
peroxisome proliferation, inhibition of gap junction intracellular communication, hepatocyte
oxidative stress as well as Kupffer cell-mediated events). The data set for DEHP was
prominently featured as an example of "PPAR-a induced hepatocarcinogenesis." For DEHP,
PPAR-a activation was described as the initial key event with evidence lacking for a direct effect
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but primarily supported by evidence from PPAR-a-knockout mice treated with WY-14,643.
Klaunig et al. (2003) concluded that".. .all of the effects observed are due only to the activation
of this receptor and the downstream events resulting from this activation and that no other modes
of action are operant"
Although that PPARa receptor activation is the sole mode of action for DEHP has been
cited by several reports (including IARC, 2000), several articles have questioned the adequacy of
this proposed mode of action (Guvton et al.. 2009: Caldwell et al.. 2008b: Melnick et al.. 2008:
Keshava et al.. 2007: Caldwell and Keshava, 2006: Keshava and Caldwell 2006): FIFRA SAP.
2004). New information is now available that also questions several of the assumptions inherent
in the proposed mode of action by Klaunig et al. (2003) and the dismissal of PPARa agonists as
posing a health risk to humans. These issues were recently examined in Guyton et al. (2009) and
are discussed below. Furthermore, IARC has recently concluded that additional mechanistic
information has become available, including studies with DEHP in PPAR-a-null mice, studies
with several transgenic mouse strains, carrying human PPARa or with hepatocyte-specific
constitutively activated PPARa and a study in humans exposed to DEHP from the environment
that has changed its conclusions regarding the relevance of rodent tumor data to human risk
(Grosse et al., 2011). Data from these new studies suggest that many molecular signals and
pathways in several cell types in the liver, rather than a single molecular event, contribute to
cancer development in rodents, with IARC concluding that the human relevance of the molecular
events leading to DEHP-induced cancer in several target tissues (e.g., liver and testis) in rats or
mice could not be ruled out, resulting in the evaluation of DEHP as a Group-2B agent, rather
than Group 3.
Specific questions have been raised about the use of WY-14,643 as a prototype for
PPARa (especially at necrogenic doses) and use of the PPARa -/- null mouse in abbreviated
bioassays to determine carcinogenic hazard.
E .3.4.1.1. Heterogeneity of PPARa Agonist Effects and Inadequacy of WY-14,643
Paradigm as Prototype for Class
Inferences regarding the carcinogenic risk posed to humans by PPARa agonists have
been based on limited epidemiology studies in humans that were not designed to detect such
effects. However, as noted by Nissen et al. (2007), the PPARa receptor is pleiotropic, highly
conserved, has "cross talk" with a number of other nuclear receptors, and plays a role in several
disease states. "The fibrate class of drugs, which are PPARa agonists intended to treat
dyslipidemia and hypercholesterolemia, have recently been associated with a number of serious
side effects." While these reports of clinical side effects are for acute or subchronic conditions
and are not (and would not be expected to be) able to detect liver cancer from fibrate treatment,
they clearly demonstrate that compounds activating the PPAR receptors may produce a spectrum
of effects in humans and the difficulty in studying and predicting the effects from PPAR
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agonism. Graham et al. (2004) recently reported significantly increased incidence of
hospitalized rhabdomyolysis in patients treated with fibrates both alone and in combination with
statins. Even though pharmaceutical companies have spent a great deal of effort to develop
agonists that are selective for desired effects, the pleiotropic nature of the receptor continues to
be an obstacle.
Also, fibrates, WY-14,643, and other PPARa agonists are pan agonists for other PPARs.
Shearer and Hoekstra (2003) noted that fibrates, including Fenofibrate, Clofibrate, Bezafibrate,
Ciprofibrate, Gemfibrozil, and Beclofibrate are all drugs that were discovered prior to the
cloning of PPARa and without knowledge of their mechanism of action but with optimization of
lipid lowering activity carried out by administration of candidates to rodents. They report that
many PPARa ligands, including most of the common fibrate ligands, show only modest
selectivity over the other subtypes with, for example, fenofibric acid and WY-14,643 showing
< 10-fold selectivity for activation of human PPARa compared to PPARy and/or PPAR5. In
human receptor transactivation assays, they report:
Human receptor transactivation assays of median effective concentration (ECso):
WY-14,643 = 5.0 urn for PPARa, 60 urn for PPAR y, 35 urn for PPAR6.
Clofibrate = 55 urn for PPARa, -500 urn for PPAR y, inactive at 100 urn for PPAR6.
Fenofibrate = 30 urn for PPARa, 300 urn for PPAR y, inactive at 100 urn for PPAR6.
Bezafibrate = 50 urn for PPARa, 60 urn for PPAR y, 20 urn for PPAR6.
Murine receptor transactivation assay
WY = 0.63 urn for PPARa, 32 urn for PPAR y, inactive at 100 urn for PPAR6.
Clofibrate = 50 urn for PPARa, -500 urn for PPAR y, inactive at 100 urn for PPAR6.
Fenofibrate = 18 urn for PPARa, 250 urn for PPAR y, inactive at 100 urn for PPAR6.
Bezafibrate = 90 urn for PPARa, 55 urn for PPAR y, 110 urn for PPAR6.
Thus, these data show the relative effective concentrations and "potency for PPAR
activity" of various agonists in humans and rodents, rodent and human responses may vary
depending on agonist, agonists vary in what they activate between the differing receptors, and
there is a great deal of transactivation of these drugs.
For fibrates specifically, a study by Nissen et al. (2007) reported that in current practice,
two fibrates, Gemfibrozil and Fenobibrate, are still widely used to treat a constellation of lipid
abnormalities known as atherogenic dyslipidemia and note that currently available fibrates are
weak ligands for the PPARa receptor and may interact with other PPAR systems. They noted
that the pharmaceutical industry has sought to develop new, more potent and selective agents
within this class but, most importantly, that none of the novel PPARa agonists has achieved
regulatory approval and that according to a former safety officer in the U.S. Food and Drug
Administration (El-Hage, 2007) that >50 PPAR modulating agents have been discontinued due
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to various types of toxicity (e.g., elevations in serum creatinine, rhabdomylosis, "multi-species,
multi-site increases in tumor with no safety margin for clinical exposures," and adverse
cardiovascular outcomes) but without scientific publications describing the reasons for
termination of the development programs. Nissen et al. (2007) reported differences in effect
between a more highly selective and potent PPARa agonist and the less potent and specific one
in humans. They noted:
a recent large study of Fenofibrate in patients with diabetes showed no significant
reduction in morbidity but a trend toward increased all-cause mortality (Keech et
al., 2006; Keech et al., 2005). Whether this potential increase in mortality is
derived from compound specific toxicity of Fenofibrate or is an adverse effect of
PPARa activation remains uncertain."
In addition to the lack of publication of effects from PPAR agonists in human
trials in which toxicity can be examined as noted by Nissen et al., the Keech study
is illustrative of the problem in trying to ascertain liver effects from fibrate
treatment in humans as the focus of the outcomes was coronary events in a study
of 5 years duration in a older diabetic population. As stated above, the challenges
the pharmaceutical industry and the risk assessor face in determining the effects
of PPAR agonists is "that these compounds and drugs modulate the activity of a
large number of genes, some of which produce unknown effects."
Nissen et al. further noted that:
Accordingly, the beneficial effects of PPAR activation appear to be associated
with a variety of untoward effects which may include, oncogenesis, renal
dysfunction, rhabdomylosis, and cardiovascular toxicity. Recently, the FDA
began requiring 2-year preclinical oncogenicity studies for all PPAR-modulating
agents prior to exposure of patients for durations of longer than 6 months (El-
Hage. 2007).
Guyton et al. (2009) further explored the status of the PPARa epidemiological database and
describe its inability to discern a cancer hazard from the available data. Thus, while existing
evidence for liver cancer in humans is null rather than negative, there remains a concern for
oncogenicity and many obstacles for determining such effects through human study. The
heterogeneity in response to PPARa agonists and the heterogeneity of effects they cause
(Keshava and Caldwell, 2006) are evident from these reports.
Many studies have used the effects of WY-14,643 at a very high dose and extrapolated
those findings to PPARa agonists as a class. However, this diverse group of chemicals has
varying potencies and effects for the "key events" described by Klaunig et al. (Keshava and
Caldwell, 2006; 2003). The standard paradigm used with WY-14,643 to induced liver tumors in
all mice exposed to 1 year (an abbreviated bioassay), uses a large dose that has also has been
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reported to produced liver necrosis, which can have an effect of cell proliferation and gene
expression patterns, and to also induce premature mortality (Woods et al., 2007a).
As stated above, WY-14,643 also has a short peak of DNA synthesis that peaks after a
few days of exposure, recedes, and then unlike most PPARa agonists studied (e.g., Clofibrate,
clofibric acid, Nafenopin, Ciprofibrate, DEHP, DCA, TCA and LY-171883), has a sustained
proliferation at the doses studied (David et al., 1999; Carter et al., 1995; Barrass et al., 1993;
LakeetaL 1993: Marsman et al.. 1992: TanakaetaL 1992: EachoetaL 1991: Sanchez and
Bull 1990: Yeldandi etal.. 1989: Marsman et al.. 1988). Clofibrate has been shown to have a
decrease in proliferation gene expression shortly after its peak (see Section E.3.3.2).
As shown above for WY-14,643, hepatocellular increases in DNA synthesis did not
appear to have a dose-response (see Section E.3.3.2), only WY-14,643 had a sustained elevation
of Nf-KB (gem and dibutyl phthalate did not) (see Section E.3.3.3.3). The effects on DNA
methylation occurred at 5 months and not earlier time points (when foci were probably present)
and effects of histone trimethylation were observed to be the same from 1 weeks to 5 months
(see Section E.3.3.5). Such effects on the epigenome suggest that other effects of WY-14,643,
other than receptor activation, are not specific to just WY-14,643 and are found in a number of
conditions leading to cancer and in tumor progression (see Sections E.3.1.1 and E.3.1.7).
In their study of PPARa-independent short-term production of reactive oxygen species
from induced by large concentrations of WY-14,643 and DEHP in the diet, Woods et al. (2007a)
examined short-term exposures to 0.6% w/w DEHP or 0.05% or 500 pm WY-14,643 for 3 days,
1 weeks or 3 weeks and reported that WY-14,643 induced a dramatic increase in bile flow that
was not observed from DEHP exposure. By 1 week of exposure, there was a 5% increase in bile
flow for DEHP treatment but a 240% increase in bile flow for WY-14,643 treatment. By
3 weeks, the difference in bile volume between treated and control was 12% for DEHP and
1,100% for WY-14,643 treated animals.
In this study, oxygen radical formation, as measured by spin trapping in the bile, was
reported to be decreased after 3 days of DEHP and WY-14,643 treatment. However, the large
changes in bile flow by WY-14,643 treatment limit the interpretation of these data along with a
small number of animals examined in this study (e.g., six control and DEHP animals and three
animals exposed to WY-14,643 at 3 days), a 30% variation in percent liver/body weight ratios
between control groups, and the insensitivity of the technique. In an earlier study, oxidative
stress appears to be correlated with neither cell proliferation nor carcinogenic potency (Woods et
al., 2006). Woods et al. (2006) reported WY-14,643 Y or DEHP to induce an increase in free
radicals at 2 hours, a decrease at 3 days then an increase at 3 weeks for both. However, radical
formation did not correlate with the proliferative response, as DEHP fails to produce a sustained
induction of proliferative response in rodent liver but WY-14,643 does, and both WY-14,643 and
DEHP gave a similar pattern of radical formation that did not vary much from controls, which is
in contrast to their carcinogenic potency.
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Although assumed to be a reflection of cell proliferation in many studies of WY-14,643
and by Klaunig et al. (2003), DNA synthesis recorded using the standard exposure paradigm for
WY-14,643 can also be a reflection of hepatocyte, nonparenchymal cell, or inflammatory cell
mitogenesis (in the case of necrosis induced inflammation), from changes in hepatocyte ploidy,
or a combination of all. Other peroxisome proliferators have been shown to have a decrease in
proliferation gene expression shortly after their peaks (e.g., Clofibrate, see Section E.3.3.2) and
both Methylclofenapate and Nafenopin have been shown to increase cell ploidy, with Nafenopin
having the majority of its DNA synthesis as a reflection of increased ploidy with only a small
percentage as increases in cell number (see Section E.3.4.1). Several authors have also noted
increases in ploidy for WY-14,643 (see Section E.3.4.1).
The Tg.AC genetically modified mouse was used to study 14 chemicals administered by
the topical and oral (gavage and/or diet) routes by Eastin et al. (2001). Clofibrate was considered
clearly positive in the topical studies but not WY-14,643, regardless of route of administration.
Based on the observed responses, it was concluded by the workgroup (Assay Working Groups)
that the Tg.AC model was not overly sensitive and possesses utility as an adjunct to the battery
of toxicity studies used to establish human carcinogenic risk. The difference in result between
Clofibrate and WY-14,643 is indicative of a different mode of action for the two compounds.
Similarly, at large exposure concentrations, Boerrigter (2004) investigated the response of
male and female lacZ-plasmid transgenic mice treated at 4 months of age with 6 doses of
2,333 mg/kg DEHP, 200 mg/kg WY-14,643, or 90 mg/kg Clofibrate over a 2-week period.
Mutation frequencies were assayed at 21 days following the last exposure. DEHP and
WY-14,643 were shown to significantly elevate the mutant frequency in both male and female
liver DNA, while Clofibrate, at the dose level studied, was apparently nonmutagenic in male and
female liver (i.e., six-dose exposure to DEHP or WY-14,643 over a 2-week period significantly
increased the mutant frequency in liver of both female and male mice by approximately 40%).
The author noted that:
the laxZ plasmid-based transgenic mouse mutation assay is somewhat unique
among other commercially available models (e.g. mutamouse and big blue), by
virtue of its ability to accurately quantify both point mutations and large deletions
including those which originate in the lacZ plasmid catamer and extend into the 3'
flanking genomic region. It should be noted that to date there is no single, agreed
upon protocol for conducting mutagenicity assays with transgenic rodents
although several aspects have been upon by the Transgenic Mutation Assays
workgroup of the International Workshop on Genotoxicity Procedures.
For several chemicals, both rats and mice demonstrate evidence of receptor activation
through peroxisome proliferation and peroxisome-related gene expression, but only one develops
cancer. The herbicide, 2,4-dichlorophenoxyacetic acid (2,4-D), is a striking example of the
problems that would be associated with only using evidence of PPARa receptor activation to
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make conclusions about the mode of action of liver tumors. 2,4-D is structurally similar to the
PPARa agonist Clofibrate and has been shown at similar concentrations to increase peroxisome
number and size, increase hepatic carnitine acetyltransferase activity and catalase, and decrease
serum triglycerides and cholesterol in rats (Vainio et al., 1983). Peroxisome number was also
increased in Chinese hamsters to a similar level as with Clofibrate at the same exposure
concentration after 9 days of exposure to 2,4-D (Vainio et al., 1982). In mice, Lundgren et al.
(1987) reported that 2,4-D exposure statistically increased the liver-somatic index over controls
after a few days of exposure and increased mitochondrial protein, microsomal protein, carnitine
acetyltransferase, PCO activity, cytochrome oxidase, cytosolic epoxide hydrolase, microsomal
epoxide hydrolase, microsomal P450 content, and hepatic cytosolic epoxide hydrolase in mouse
liver. Thus, 2,4-D activates the PPARa receptor, with associated changes in peroxisome-related
gene expression, in multiple species and at similar doses to Clofibrate. However, Charles et al.
(1996) and Charles and Leeming (1998) reported that in several 2-year studies, there were no
2,4-D-induced increases in liver tumors in F344 rats, CD-I rats, B6C3Fi mice, or CD-I mice.
Another example, is provided by Gemfibrozil, known as (5-2[2,5-dimethylphenoxy]
2,2-dimethylpentanoic acid) and [2,2-dimethyl-5-(2,5-xylyoxy) valeric acid], a therapeutic agent
that activates the PPARa receptor and is a peroxisome proliferator, but is carcinogenic only in
male rats but not female rats, nor in either gender of mouse (Contrera et al., 1997). Gemfibrozil
causes tumors in pancreas, liver, adrenal, and testes of male rats and causes increases in absolute
and relative liver weights in both rats and mice (Fitzgerald et al., 1981). Gemfibrozil is a highly
effective lipid and cholesterol lowering drugs in humans and in mice (Olivier et al., 1988).
However, although Gemfibrozil activates the PPARa receptor and induces peroxisome
proliferation in mice, it does not induce liver tumors in that species.
In the long-term study of Bezafibrate, Hays et al. (2005) noted that the role of this
receptor in hepatocarcinogenesis has only been examined using one relatively specific PPARa
agonist (WY-14,643) and report that Bezafibrate can induce the expression of a number of
PPARa target genes (acyl CoA oxidase and CYP4a) and increased liver weight in PPARa
knockout mice that is not dependent on activation of PPARP or PPARy. As noted by Boerrigter
(2004):
In contrast to DEHP and WY-14,643, Clofibrate produced hepatocellular
carcinomas in rats only while no increase in the incidence of tumors was reported
in mice (Gold and Zeiger, 1997). However, Clofibrate induces peroxisome
proliferation in both rats and mice (Lundgren and DePierre, 1989) but only
produced hepatocellular carcinomas in rats (Gold and Zeiger, 1997).
Melnick et al. (1996) noted that similar levels of peroxisomal induction were observed in
rats exposed to DEHP and di(2-ethylhexyl) adipate (DEHA) at doses comparable to those used in
the bioassays of these chemicals. However, DEHP but not DEHA gave a positive liver tumor
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response in 2-year studies in rats. In an evaluation of the carcinogenicity of tetrachloroethylene,
an expert panel of the IARC concluded that the weak induction of peroxisome proliferation by
this chemical in mice was not sufficient to explain the high incidence of liver tumors observed in
an inhalation bioassay.
In adult animals, apoptosis acts as a safeguard to prevent cells with damaged DNA from
progressing to tumors, but like cell proliferation, alterations in apoptosis are common to many
modes of action. In addition, only short-term data are available on changes in apoptosis due to
PPARa agonists, and long-term changes have not been investigated (Rusyn et al., 2006). For
example, although a decrease in apoptosis has also been suggested to be an important additional
molecular event that may affect the number of cells in rodent liver following exposure to the
peroxisome proliferator DEHP, apoptosis rates have not investigated past 4 days of exposure and
thus, the time-course of this event is uncertain. The antiapoptotic effects of PPAR agonists
appear to be also dependent on nonparenchymal cells (i.e., Kupffer cells), which do not express
PPARa and could be a transient event (Rusyn et al., 2006). Morimura et al. (2006) reported
evidence for exposure to WY-14,643 that does not support a role for PPARa-mediated apoptosis
in tumor formation (see Section E.3.4.1.3) as well as appearing to be specific to WY-14,643 (see
Section E.3.3.3.3).
The lack of a causal relationship of transient DNA synthesis increases and
hepatocarcinogenesis has been raised by many (Caldwell et al., 2008b) and is discussed in
Section E.3.4.2 as well as the changes in ploidy (see Section E.3.4.1). In regard to gene
expression profiles, many studies have focused on gene profiles during the early transient
proliferative phase or have identified genes primarily associated with peroxisome proliferation as
"characteristic" or relevant to those associated with tumor induction. Several have focused on
the number of genes whose expression "goes up" or "goes down" from a small number of
animals. Caldwell and Keshava (2006) presented information on WY-14,643, dibutyl phthalate,
Gemfibrozil, and DEHP, and noted inconsistent results between PPARa agonists, paradoxes
between mRNA and protein expression, strain, gender, and species differences in response to the
same chemical, and time-dependent differences in response for several enzymes and GSH.
E .3.4.1.2. New Information on Causality and Sufficiency for PPARa Receptor
Activation
In its review of the U.S. EPA's draft risk assessment of perfluorooctanoic acid (PFOA),
the Science Advisory Panel (FIFRA SAP, 2004) expressed concerns about whether PPARa
agonism constitutes the sole mode of action for PFOA effects in the liver and the relevance to
exposed fetuses, infants, and children. In part based on uncertainties regarding the Klaunig et al.
(2003) proposed mode of action, they concluded that the tumors induced by PFOA were relevant
to human risk assessment. The hypothesis that activation of the PPARa receptor is the sole
mode-of-action of hepatocarcinogenesis induced by DEHP and many other chemicals is further
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called into question by recent studies. In the case of DEHP, Klaunig et al. (2003) assumed that
WY-14,643 and DEHP would operate through the same key events and that long-term bioassays
of DEHP in PPARa -/- knockout mice would be negative and hence demonstrate the need for
receptor activation for hepatocarcinogenesis from DEHP.
The fallacy of these assumptions is illustrated by the recent report of the first 2-year
bioassay of DEHP in PPARa -/- knockout mice (Sv/129 background strain) that reported
DEHP-induced hepatocarcinogenesis (Ito et al., 2007). Further discussion was provided by
Guyton et al. (Guyton et al., 2009). Similar to other studies, the PPAR -/- mice had slightly
increased liver weights in comparison to controls and treated wild-type mice (-12% increase
over controls). In fact, statistical analysis of the incidence data show that adenomas were
significantly increased in PPARa -/- mice compared with wild-type mice exposed to 500 ppm
DEHP and that a significant dose-response trend for adenomas and adenomas plus carcinomas
was observed in PPARa -/- mice (Figure E-5). Overall, the cancer incidences were consistent
with a previous study of DEHP (David et al., 1999) in B6C3Fi mice at the same doses for nearly
the same exposure duration. A strength of this study is that it was conducted at much lower,
more environmentally relevant doses that did not significantly increase liver enzymes as
indications of toxicity.
Adenomas
I
100
ppm DEHP
500
I Ito-Wfld • no-knockout n David-79wk n David-total
+ - p<=0.05 by 1 -tail Fisher exact test as compared to
control; * - p <=O.OS by 1 - and 2-tail Fisher exact test as
compared to control in same study
Carcinomas
100
|>|>n> DEHP
500
n Ito-Wild • Ito-knockout
n David-79wk n David-total
No statistically-significant differences across all
studies and doses.
Figure E-5. Comparison of Ito et al. and David et al. data for DEHP tumor
induction from (Guvton et al., 2009).
As noted by Kamijo et al. (2007), DEHP was reported also to induce glomerulonephritis
more often in PPARa-null mice because of the absence of PPARa-dependent anti-inflammatory
effect of antagonizing the oxidative stress and NF-KB pathway (Kamijo et al., 2007). Thus, these
data support that hypothesis that there is no difference in liver tumor incidences between
PPARa -/- mice and wild-type mice in a standard nonabbreviated exposure bioassay that does
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not exceed the maximal tolerated doses and that DEHP can induce hepatotoxicity as well as
other effects independent of action of the PPARa receptor.
The study of Yang et al. (2007) informs as to the sufficiency of PPARa receptor
activation and subsequent molecular event for hepatocarcinogenesis in mice. The study used a
VP16PPARa transgene under control of the liver-enriched activator protein (LAP) promoter to
activate constitutively the PPARa receptor in mouse hepatocytes. LAP-VP16PPARa transgenic
mice showed a number of effects associated with PPARa receptor activation including decreased
serum triglycerides and free fatty acids, peroxisome proliferation, enhanced hepatocyte DNA
synthesis, and induction of cell-cycle genes and those described as "PPARa targets" to
comparable levels reported for WY-14,643 exposure. Hepatocyte proliferation, as determined by
the labeling index of hepatocyte nuclei, was increased after 2 weeks of WY-14,643 treatment
over controls (20.5 vs. 1.6% in control livers) with the LAP-VP16PPARa mice giving a similar
results (20.8 vs. 1.0% in control livers).
The authors noted that transgenic mice did not appear to have positive labeling of
nonparenchymal cell nuclei that were present in the WY-14,643 treated animals. The
transferase-mediated dUTP nick end-labeling assay results were reported to show that there was
no difference in apoptosis in wild-type mice treated with WY-14,643, the transgenic mice, or
controls. In a small number of animals, microsomal genes (CYP4A) and peroxisomal (Acox,
BIEN—the bifunctional enzyme) and mitochondrial fatty oxidation genes (LCAD—long chain
acyl CoA dehydrogenase and VLCAD) were expressed in the transgenic mice, with WY-14,643
also increasing expression of these genes in wild-type mice but with less lipoprotein lipase (LPL)
than the transgenic mice. Hepatic CoA oxidation was increased to a similar level in wild-type
mice treated with WY-14,643 and the transgenic mice (n = 3-4) and was statistically different
than controls. LAP-VP16PPARa transgenic mice (8 weeks of age) exhibited hepatomegaly
(-50 increase percent body/liver weight over controls) and an accumulation of lipid due to
triglycerides but not cholesterol.
However, compared to wild-type mice exposed to WY-14,643 for 2 weeks, the extent of
hepatomegaly was reduced (i.e., percent liver/body weight increase of ~2.5-fold with
WY-14,643 treatment), no hepatocellular hypertrophy or eosinophilic cytoplasms were noted,
and no evidence of nonparenchymal cell proliferation was observed in the LAP-VP16PPARa
transgenic mice.
At ~1 year of age, Yang et al. (2007) reported there to be no evidence of preneoplastic
lesions or hepatocellular neoplasia in LAP-VP16PPARa transgenic mice, in contrast to results
after 11 months of exposure to WY-14,643 in wild-type mice. Microscopic examination of liver
sections were consistent with the gross findings, as HCCs and hepatic lesions were observed in
the long-term WY-14,643 treated wild-type mice, but not in >20 LAP-VP16PPARa mice at the
age of over 1 year in the absence of DOX. There was no quantitative information on tumors
given nor of foci development in the WY-14,643 mice. As noted by Yang et al. (2007), PPARa
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activation only in mouse hepatocytes is sufficient to induce peroxisome proliferation and
increased DNA synthesis, but not to induce liver tumors.
Thus, "hepatocyte proliferation" indentified by Klaunig et al. (2003) as a "causal event"
in their PPARa mode of action is not sufficient to induce hepatocarcinogenesis. These data not
only call into question the adequacy of the mode-of-action hypothesis proposed by Klaunig et al.
(2003), but also suggest that multiple mechanisms and multiple cell types may be involved in
hepatocarcinogenicity caused by chemicals that are also PPARa agonists.
E .3.4.1.3. Use of the PPAR -/- Knockout and Humanized Mouse
Great importance has been attached to the results reported for PPARa -/- mice and their
humanized counterparts with respect to inferences regarding the mode of action or peroxisome
proliferators and whether short-term chemical exposures or abbreviated bioassays conducted
with these mice can show that a PPARa mode of action is involved. Consequently, the use of
these models warrants scrutiny.
Compared to untreated wild-type mice, liver weights in knockout mice or humanized
mice have been reported to be elevated (Morimura et al., 2006; Voss et al., 2006; Laughter et al.,
2004) and within 10% of each other (Peters et al., 1997). In order to be able to assign effects to a
chemical tested in knockout mice, a better characterization is needed of the baseline differences
between PPARa -/- knockout and wild-type mice. This is particularly important for examining
weak agonists because the changes they induce may be small and need to be confidently
distinguished from differences due to the loss of the receptor alone. As shown by the Ito et al.
(2007) study and as noted by Maronpot et al. (2004), there is a need for lifetime studies to
characterize background or spontaneous tumor patterns and lifespans (including those of the
background strain). While the original work by Lee et al. (1995) describes "the mice
homozygous for the mutation were viable, healthy, and fertile and appeared normal," the authors
did not describe the survival curves for this model nor their background tumor rate. In fact,
further work has shown that they carry a background of chronic conditions, including:
(1) chronic diseases such as obesity and steatosis (Akiyama et al., 2001; Costet et al., 1998):
(2) altered hepatic of hepatocellular structure and function, such as vacuolated hepatocytes (Voss
et al., 2006; Anderson et al., 2004), also seen in "humanized" mice (Cheung et al., 2004): and (3)
altered lipid metabolism, including reduced glycogen stores, blunted hepatic and cardiac fatty
acid oxidation enzyme system response to fasting, elevated plasma free fatty acids, fatty liver
(steatosis), impaired gluconeogenesis, and significant hepatic insulin resistance (Lewitt et al.,
2001). Howroyd et al. (2004) reported decreased longevity and enhancement of age-dependent
lesions in PPARa -/- mice.
These baseline differences from wild-type mice may render them more susceptible to
toxic responses or shorten their lifespans with chemical exposure. For example, after
administration of 250 uL carbon tetrachloride/kg, all male and 40% of female PPARa knockout
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mice were dead or moribund after 2 days of treatment, whereas 25% of male wild-type mice and
none of the female wild-type mice exhibited outward signs of toxicity (Anderson et al., 2004).
Hays et al. (2005) reported that 100% of PPARa knockout have cholestasis after 1 year of
Bezafibrate treatment with higher bile acid concentration than wild-type mice. As described in
Section E.2.1.15, Ramdhan et al. (2010) have provided data not only that indicated greater
susceptibility of TCE liver toxicity in PPARa-null mice and humanized null mice, but also that
there is a background dysregulation of the number of gene and protein expressions and
triglyceride accumulation in the liver in these strains.
Lewitt et al. (2001) noted that male knockout mice have more marked accumulation of
hepatic fat and hypercholesterolemia and are particularly sensitive to fasting, with some dying if
fasted for >24 hours. Sexual dimorphism, especially increased susceptibility of the male mouse,
has been reported for knockout mice with pure Sv/129 backgrounds (Anderson et al., 2004;
Lewittet al., 2001) as well as those with a suggested C57BL/6N background (Costet et al., 1998;
Djouadi etal., 1998). Akiyama et al. (2001) showed an apparent greater sexual dimorphism in
mice with a pure Sv/129 background than C57BL/6N in regard to weight gain from 2 to
9 months, but not in changes in body weight or liver weight between wild-type and knockout
animals. Adipose tissue, serum triglycerides, and cholesterol were altered in the knockout
animals. Given that the experiment was only carried out for 9 months, changes in body fat, liver
weight, and lipid levels may be greater as the animals get older and steatosis is more prevalent.
The dramatic effect on survival as well as gender difference by the increased expression
of lipoprotein lipase in the PPARa knockout mouse with further genetic modification is
demonstrated by Nohammer et al. (2003), who reported 50% mortality in 6 months and 100%
mortality within 11 months of age while females survived. These differences could affect the
results of tumor induction for PPARa agonists with less potency than WY-14,643 that do not
produce tumors so rapidly.
In addition, these studies suggest the need for careful consideration of the effects of use
of different background strains for the knockout and the need for careful characterization of the
background responses of the mouse model and the effects of the use of different background
strains for the knockout. Morimura et al. (2006) reported that, using the B6 background strain,
there were only foci at time periods but knockouts with the SV129 background had multiple
tumors after WY-14,643 treatment.
PPARa knockout mice have also been used to examine the dependence of PPARa on
changes in cell signaling, protein production, or liver weight. However, to be useful, the changes
incurred just by loss of the PPARa should also be well described. Reported differenced between
PPARa-knockout and wild-type mice can impact the sensitivity and specificity of these markers
of for the hypothesized mode of action.
In regards to altered cell signaling, Wheeler et al. (2003) note that in normal cells, p21waf
and p27klpl inhibit the Cdk/cyclin complexes responsible for cell cycle progression through Gl/S
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transition. While these cellular signaling molecules are downregulated in response to partial
hepatectomy in normal mice, they remain elevated in PPARa knockout mice along with
decreased DNA synthesis.
Fumonisins are hepatocarcinogens that have been associated changes in apoptosis and
tissue generation, and increased acyl-CoA oxidase and CYP4A (markers of PPARa activation)
(Martinez-Larrafiaga et al., 1996). Voss et al. (2006) report that the average number of hepatic
apoptotic foci per mouse induced by Fumonisins were threefold higher and liver mitotic figures
counts were twofold lower in PPARa knockout in comparison to wild-type mice, thus illustrating
a difference in proliferative response in the mice. PPARa-null mice have been reported to have
increased apoptosis and decreased mitosis with fumonisin treatment.
Voss et al. (2006) also report several differences in gene expression in wild-type and
PPARa knockout mice that ranged from 0.3 to 483% of the activity of wild-type mice. The
complex expression patterns of gene expression and determination of their mechanistic
implications in regard to hepatotoxicity and carcinogenicity are difficult. Certainly the large
number of genes whose expression is affected by WY-14,643 (1,012 genes as cited by Voss et
al., 2006) illustrates such complexity. Voss et al. (2006) concluded that studies should consider
dose- and time course-related effects as well as species and strain-related differences in the
expression of gene products.
The "humanized" PPARa mouse has a human copy of PPARa inserted into a PPARa
knockout mouse. It is inserted in a tetracycline response system so that in the absence of DOX,
only human PPARa is transcribed in humanized mouse liver and not in other tissues. A rigorous
examination of newly emerging studies regarding the "humanized" mouse is warranted. The
humanized PPARa mouse has been studied in the reports of Cheung et al. (2004), Morimura et
al. (2006), and Ramdhan et al. (2010) (see Section E.2.1.15). Many of the issues described
above for PPARa -/- mice are of concern for the humanized knockout mouse. In addition, the
placement of the humanized PPAR gene is a potential confounding factor, as discussed by
Morimura et al. (2006):
It also cannot be ruled out that the hPPARa mice are resistant to the hepatotoxic
effects of peroxisome proliferators due to the site of expression of the human
receptor. The cDNA was placed under control of the tetracycline regulatory
system and the liver-specific Cebp/B promoter that is preferentially expressed in
hepatocytes.
In the Cheung (2004) report, the humanized mouse was fed WY-14,643 for 2 or 8 weeks
(age not given for the mice). WY-14,643 and Fenobrate were reported to decrease serum total
triglyceride levels in wild and humanized mice to about the level seen in PPARa -/- mice (which
were already suppressed without treatment). Hepatomegaly and increase in hepatocyte size were
observed in the PPARa-humanized mice fed WY-14,643 for 2 weeks but less than that of wild
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mice. By contrast, Morimura et al. (2006) stated that the humanized mice did not exhibit
hepatomegaly after treatment with WY-14,643.
Cheung et al. (2004) present figures that showed increased vacuolization of hepatocytes
in a control humanized mouse in comparison to wild-type mice. Vacuolization increased with
WY-14,643 treatment in the humanized mouse. Therefore, there was a background level of liver
dysfunction in these mice even with humanized PPARa. Vacuolization is consistent with fatty
liver observed in the nonhumanized PPARa -/- mouse. As reported by Ramdhan et al. (2010),
untreated humanized mice had increased triglyceride levels in their livers in comparison to
untreated wild type mice.
The authors reported that the humanized mouse did not have increased numbers of
peroxi somes after WY treatment. However, they present a figure for genes encoding
peroxisomal, mitochondrial, and microsomal fatty acid oxidation enzymes that shows they were
still markedly increased in PPARa -humanized mice following 8 weeks of exposure to
WY-14,643. Therefore, there is a paradox in these reported results.
Morimura et al. (2006) provided a useful example to illustrate the many issues associated
with interpreting studies with genetically-altered animals. While this study is suggestive of a
difference in susceptibility to tumor induction between wild-type and PPARa humanized mice, a
conclusion that human PPARa is refractory to liver tumor induction is not sufficiently supported.
This study had uneven durations of exposure and follow-up and reported substantial
toxicity or mortality that limit the interpretation of the observed tumor rates. For example, the
6-week-old male "humanized" mice had a 44-week experimental period, but for wild-type mice,
that period was 38 weeks. In addition, for humanized mice, 10 mice were treated with 0.1%
WY-14,643 with 20 controls, but for wild-type mice, 9 mice were given 0.1% WY with
10 controls. Furthermore, wild-type, WY-14,643-treated animals had suppressed growth and
only a 50% survival to 38 weeks, so an effective LDso has been used for this length of exposure.
Specifically, of the 10 wild-type WY-14,643 treated mice, 3 died of toxicity and 2 were killed
due to morbidity and their tissues were examined. Humanized mice had similar growth for
animals treated with WY-14,643 or controls with only one mouse killed because of morbidity.
Therefore, the reported results, including tumor numbers, are for a mixture of different exposure
durations and ages of animals. In addition, the results of the study were reported for only on
exposure level.
Furthermore, it is interesting that while control humanized mice had no adenomas,
WY-14,643 treated humanized mice had one. Morimura et al. (2006) noted that this adenoma
had a morphology "similar to spontaneous mouse liver tumor with basophilic and clear
hepatocytes," whereas the tumors in wild-type mice treated with WY-14,643 were more
diffusely basophilic. If the humanized animals were allowed to live their natural lifespan, this
raises the possibility that WY-14,643 may induce tumors that are similar to other carcinogens
rather than those that have been described as "characteristic" of peroxisome proliferators (see
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Section E.3.4.1.5) when human PPARa is present. Therefore, the humanized PPARa rather than
mouse PPARa may have an association with a tumor phenotype characteristic of other modes of
action but this study needed to be carried out for a longer period of exposure and with more
animals to make that determination.
The baseline tumor response of PPARa humanized mice needs to be characterized as
well as the tumor response following exposure to WY-14,643 or other carcinogens acting
through differing modes of action. The numbers of foci were not reported, but "altered foci"
were detected in one humanized mouse with WY-14,643 treatment and one without treatment.
The phenotypes of the foci were not given by the authors.
As discussed above, changes in liver weights have been associated with susceptibility to
liver tumor induction and the issues regarding baseline differences in PPARa -/- mice are equally
relevant for PPARa humanized mice. Morimura et al. (2006) reported that absolute liver weight
for control humanized mice at 44 weeks was 1.57 g (n = 10). The absolute liver weight for wild
control mice was 1.1 g (n = 9) at 38 weeks. The final body weights differed by 14% but liver
weights differed by 30%. Therefore, even though comparing different aged mice, the control
humanized mice had greater liver size than the wild-type control mice on an absolute and relative
basis. This is consistent with humanized knockout mice having greater sized livers and a
baseline of hepatomegaly. Ramdhan et al. (2010) reported significantly elevated liver/body
weight ratios in untreated humanized mice.
With treatment, Morimura et al. (2006) reported that PPARa humanized mice treated
with WY-14,643 had greater absolute and relative liver weights than controls but less elevations
than wild-type treated animals. However, because half of the wild-type animals died, it is
difficult to discern if liver weights were reported for moribund animals sacrificed as well as
animals that survived to 38 weeks for wild-type mice treated with WY-14,643. However, it
appears that moribund animals were included that were sacrificed early for treated groups and
that values from the animal killed at 27 weeks were added in with those surviving until 45 weeks
in the PPARa humanized mice treated with WY-14,643.
With respect to the gene expression results reported by Morimura et al. (2006), it is
important to note that they are for liver homogenates with a significant portion of the nuclei from
nonparenchymal cell of the liver (e.g., Kupffer and stellate cells). Thus, the results represent
changes resulting from a mixture of cell types and from differing zones of the liver lobule, with
potentially different gene changes merged together. Livers without macroscopic nodules were
used for western blot, but could have contained small foci in the homogenate as well. The gene
expression results were also reported for an exposure level of WY-14,643 that is an LD50 in
wild-type mice and could reflect toxicity responses rather than carcinogenic ones. The samples
were also obtained at the end of the experiment (with a mix of durations of exposure) and may
not reflect key events in the causation of the cancer but events that are downstream.
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These limitations notwithstanding, it is interesting that expression of p53 gene was
reported by Morimura et al. (2006) to be increased in PPARa humanized mice treated with
WY-14,643 compared to all other groups. Furthermore, of the cell cycle genes that were tested
(i.e., CD-I, Cyclin-dependent Kinases 1 and 4, and c-Myc), there was a slightly greater level of
c-Myc and CD-I in control PPARa humanized mice than control wild-type mice as a baseline.
This could indicate that there was already increased cell cycling going on in the control PPARa
humanized mouse and could be related to the increased liver size. Treatment with WY-14,643
induced an increase in cycling genes in wild-type mice in relation to its control, but whether that
induction was greater than control levels for PPARa humanized mice for c-Myc and CDk4 was
not reported by the authors.
Apoptosis genes were reported to have little difference between control PPARa
humanized and wild-type mice but to have a greater response induced by WY-14,643 in
humanized mice for p53 and p21. There was no consistent or large change in apoptosis genes in
response to exposure to WY-14,643 in wild-type mice. The increased response of apoptosis
genes in PPARa humanized mice without corresponding tumor formation does not support that
response as a key event in the mode of action (neither does the lack of response from WY-14,643
in wild-type mice). For genes associated with PPARa peroxisomal (Acox), microsomal
(CYP4a), mitochondrial fatty oxidation (Mead) and especially malic enzyme, there was a greater
response in wild-type than PPARa humanized mouse after treatment with WY-14,643.
However, this is somewhat in contrast to Cheung et al. (2004), who reported increases in some
genes encoding peroxisomal, mitochondrial, and microsomal fatty oxidation enzymes in the
PPARa humanized mouse after treatment with WY-14,643.
The results reported by Yang et al. (2007) use another type of "humanized" mouse to
study PPARa effects. Yang et al. (2007) used a PPARa humanized transgenic mouse on a
PPAR -/- background that has the complete human PPARa (hPPARa) gene on a PAC genomic
clone, introduced onto the mouse PPARa-null background and expressed hPPARa not only in
the liver but also in other tissues. Mice were administered WY-14,643 or Fenofibrate (0.1 or
0.2% [w/w]). The authors showed a figure representing expression of the hPPARa for two mice
with the tissue used for the genotyping exhibiting great variation in expression between the two
cloned mice as indicated by intensity of staining. The authors stated that in agreement with
mRNA expression, hPPARa protein was highly expressed in the liver of hPPARaPAC mice to an
extent similar to the mPPARa in wild-type mice. They reported that following 2 weeks of
Fenofibrate treatment, a robust induction of mRNA expression of genes encoding enzymes
responsible for peroxisomal (Acox), mitochondrial (MCAD and LCAD), microsomal (CYP4A)
and cytosolic (ACOT) fatty acid metabolism were found in liver, kidney, and heart of both wild-
type and hPPARaPAC mice, indicating that hPPARa functions in the same manner as mPPARa to
regulate fatty acid metabolism and associated genes.
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However, the authors did no measures in Fenofibrate-treated animals, only WY-14,643,
raising the issue of whether there was a difference in the relative mRNA expression of genes for
ACOX etc. and lipids between the two peroxisomal proliferator treatments. The expression of
enzymes associated with PPARa induction was presented only for mice treated with Fenofibrate.
However, the lipid results were presented only for mice treated with WY-14,643. Therefore, it
cannot be established that these two agonists give the same response for both parameters. Also
for the enzymes, the relative expressions compared to wild-type controls, the absolute
expression, and variation between animals is not reported.
It appears that the peroxisomal enzyme induction by Fenofibrate is the same in the wild-
type and transgenic mice. However, in Figure 4 of the paper, the mice treated with WY-14,643
instead of Fenofibrate were presented for the peroxisomal membrane protein 70 (PMP70) in total
liver protein gel. There appears to be more PMP70 in the transgenic mice than wild-type mice as
a baseline. The PMP70 appeared to be similar after WY-14,643 treatment. However, only one
gel was given and no other quantitation was given by the authors.
The authors stated that "in addition WY-14,643 and Fenofibrate treatment produced
similar effect to the liver specific humanized PPARa mouse line (Cheung et al., 2004)."
However, the results were not the same between Fenofibrate and WY-14,643 and the mouse line
used by Cheung et al. (Cheung et al., 2004) had background differences in response and
pathology. In one figure in the paper, there appears to be a difference in background level of
serum total triglyceride between the wild-type and hPPARaPAC mice that the authors did not
note. The power of using such few mice does not help discern any significant differences in
background level of triglycerides.
The authors note that WY-14,643 treatment also resulted in decreased serum triglycerides
levels in hPPARaPAC mice, consistent with the induction of expression of genes encoding fatty
acid metabolism, and that the hypolipidemic effects of fibrates are generally explained by
increased expression of LPL and decreased expression of apolipoprotein C- III (Apo C-III)
(Auwerx et al., 1996). However, the alteration of these genes by WY-14,643 treatment was only
observed in wild-type mice and not in hPPARaPAC mice, suggesting that the hypolipidemic effect
observed in hPPARaPAC mice are not through LPL and APO C-III. The authors do not note that
there could be a difference in the regulation of these pathways by the transgene rather than how
the normal gene is regulated and the pathways it affects. The rationale for examining this
question with WY-14,643 treatment rather than with Fenofibrate treatment is not addressed by
the authors, especially since the other "markers" of peroxisomal gene induction appear to be
affected by Fenofibrate in the wild-type and hPPARaPAC mice.
Hepatomegaly was reported to be observed in the hPPARaPAC mice following 2 weeks of
WY-14,643 treatment as revealed by the increase liver to body weight ratio compared to
untreated hPPARaPAC mice, but to be markedly lower when compared to wild-type mice under
the same treatment.
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Histologically, the livers of the wild-type mice treated with WY-14,643 were
hypertrophic with clear eosinophilic regions. These phenotypic effects were observed in both
wild-type and hPPARaPAC mice. The percent liver/body weight was reported to increase from
-4% in wild-type mice to -9% after WY-14,643 treatment and from -4% in hPPARaPAC to little
less that 6% after treatment with WY-14,643.
In wild-type mice treated with WY-14,643, the labeling index was 21.8% compared with
1.1% in untreated wild-type controls. In hPPARaPAC mice, WY-14,643 treatment was reported
to give an average labeling index of 1.0% compared with 0.8% in the untreated control
hPPARaPAC mice. Treatment with WY-14,643 treatment was reported to result in a marked
induction in the expression of CDK4 and cyclin Dl in the livers of wild-type mice but to be
unaffected hPPARaPAC mice treated with WY-14,643. These data were reported to be in
agreement with the liver-specific PPARa-humanized mice that showed no increase in
incorporation of BrdU into hepatocytes upon treatment with WY-14,643 (Cheung et al., 2004)
and further confirmed that activation of hPPARa does not induce hepatocyte proliferation.
However, the authors present a figure as an example with one liver each with no
quantitation given by the authors for BrdU incorporation. It is not clear whether the pictures
were taken from the same area of the liver or how representative they are. The numbers of mice
were never reported for the labeling index. The data presented do suggest that there was
hypertrophy and hepatomegaly in the humanized mice, but not proliferation in this particular
WY-14,643 model. Of interest would be investigation of proliferation by other peroxisome
proliferators besides WY-14,643 at this necrogenic dose, as it is WY-14,643 that is the anomaly
to continue to induce proliferation or DNA synthesis at 2 weeks. The photomicrographs
presented by the authors are so small and at such low magnification that little detail can be
discerned from them. There are no portal triads or central veins to orient the reader as to what
region of the liver has been affected and where, if any, there would be hepatocellular
vacuolization.
To determine whether peroxisome proliferation occurred in the hPPARaPAC mice upon
administration of peroxisome proliferators, Yang et al. (2007) examined by Western Blot
analysis the protein levels of the major PMP70 a marker of peroxisome proliferation). After
2 weeks of treatment with 1,000 ppm WY-14,643, induction of PMP70 was reported to be
observed in the wild-type mice as well as in hPPARaPAC mice. The authors suggested that this
result indicates that peroxisomal proliferator treatment induced peroxisomal proliferation in
hPPARaPAC mice. The results of this study indicate that hepatomegaly and peroxisome
proliferation occur in this humanized mouse model when treated with large concentrations of
WY-14,643. Thus, these results are inconsistent with claims that peroxisome proliferators
cannot cause hepatomegaly or peroxisome proliferation in humans or that humans are refractory
to these effects. Like the lipid effects, they suggest that a broader spectrum of effects may occur
in humans and decreases the specificity of these effects as species specific. However, due to the
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model compound being WY-14,643 at a necrogenic dose of 1,000 ppm, the effect may not be
seen in humans using the lower potency peroxisome proliferators. It would have been useful for
this study to include an examination of these effects with Fenofibrate rather than WY-14,643 and
then attempting to extrapolate such effects to other peroxisome proliferators. The authors often
attributed the effects of peroxisome proliferators to those reactions induced by WY-14,643 and
did not acknowledge that the changes induced by WY-14,643 may be different. This is
especially true in regards to hepatocellular DNA synthesis in which other peroxisome
proliferators can cause liver tumors without the sustained proliferation that WY-14,643 induces,
especially at a necrogenic dose.
Yang et al. (2007) reported the results of induction of various genes by WY-14,643 in
wild-type and hPPARaPAC mice by microarray analysis followed by confirmation and
quantitation by qPCR and report that more genes were induced by WY-14,643 in wild-type mice
than in hPPARaPAC mice. They reported that:
importantly, the oncogene c-myc was not induced in hPPARaPAC mice.
Moreover, genes encoding cell surface proteins such as Anxa2, CD39, CD63,
Ly6D, and CD24a, and several other genes such as Cidea, Cidec, DhrsS and
Hsdllb were also not induced in hPPARaPAC mice. Interestingly, Sult2al was
only induced in hPPARaPAC mice and not in WT mice; this gene is also induced
in human hepatocytes by PP (Fang et al., 2005). The regulation of several of
these genes has previously been demonstrated through a PPARa-dependent
mechanism. Additional studies will be necessary to fully explore the molecular
regulatory mechanism and the functional implication associated with these
differently regulated genes.
The authors did not indicate the context of how the mice were treated, whether these were
pooled results, and when the samples were taken. It is assumed to be whole liver. There are
several limitations for interpretations of the results such as those presented by Yang et al. (2007),
which include the lack of phenotypic anchoring for the results. The authors have shown changes
from whole liver and have listed changes in genes between wild-type and humanized mice on a
PPAR -/- background that, in itself, will bring about changes in gene expression. The authors
acknowledge difficulties in determining what their reported gene changes mean.
Yang et al. (2007) reported that "activation of PPARa alters hepatic miRNA expression
(Shah et al., 2007)." They report that let-7C, a miRNA critical in cell growth and shown to
target c-Myc, was inhibited by WY-14,643 treatment in wild-type mice and that the expression
levels of both pri-let-7C and mature let-7C were significantly higher in hPPARaPAC mice
compared to wild-type mice. Treatment with WY-14,643 was reported to decrease the
expression of Pri-let-7C and mature let-7C in wild-type mice but in hPPARaPAC mice. The
authors noted that:
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in addition, the induction of c-myc by WY-14,643 treatment in wild type mice did
not occur in WY-14,643 treated hPPARaPAC mice. This is in agreement with the
previous observation in liver-specific humanized PPARa (Shah et al., 2007) and
further indicates the activation of human PPARa does not cause a change in
hepatic miRNA and c-myc gene expression.
A qPCR analysis of pri-let-7C following 2 weeks of WY-14,632 treatment was reported
for wild-type and hPPARaPAC mice (n = 3-4). There appeared to be -20 times more let-7C
expression in hPPARaPAC mice than control wild mice as a baseline. The gel given by the
authors showed a very small difference in wild-type mice in let-7C northern blot analysis
between a control wild-type and a WY-14,643-treated wild-type mouse. There appeared to be no
difference in the hPPARaPAC mice between control and WY-14,643 treatment and a larger
stained area than the control wild-type mice. The relative c-Muc expression between the
hPPARaPAC mice and wild-type control mice did not correlate with changes in let-7C expression.
Thus, the amount of decrease by treatment with WY-14,632 in wild-type mice appeared
to be extremely small compared to the much greater baseline expression in the hPPARaPAC mice.
The change brought by WY-14,632 treatment in wild-type mice was a small change compared to
the 20-fold greater baseline expression in the hPPARaPAC mice. The authors stated that the
expression of the c-Myc regulator was higher in the hPPARaPAC mice, indicating overregulation
of cell division and an inability for hepatocytes to proliferate. However, their results showed that
there was a greater difference in regulatory baseline function of the PPAR using this paradigm
and this construct. Are these differences due to human PPAR or to the way PPAR was put back
into PPAR -/- mouse and expected to function? If the experiment included mouse PPAR put
back in this way on a null background, what would such an experiment show? Are these results
representative of the PPAR or how it is now controlled and expressed? In addition, what would
the study of other peroxisome proliferators besides WY-14,643 show in regard to changes in
miRNA. Are these results reflective of a just the transient effect that is prolonged in a special
case?
As discussed in Section E.3.1.2, there are issues with microarray data in addition to the
newly emerging field of miRNA arrays, which include phenotypic anchoring and whether they
are from whole liver or pooled samples. The results given in this report are for relative let-7C
expression given and not absolute values. The changes in baseline let-7C expression between
the wild-type and the hPPARaPAC mice did not correlate with the magnitude of difference in
northern blot analysis and did not correlate at all with c-Myc expression reported in this study.
Thus, a direct correlation between the effect of let-7C expression and function and effects from
WY-14,643 was not supported. The relative expression was reported, but the variation of
baseline expression of the "PPAR controlled genes" was not. Given that one of the first figures
reported a large difference between animals in expression of the human PPAR gene in the
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transgenic animals, how did this difference affect the results given here as relative changes
downstream?
Yang et al. (2007) concluded that the hPPARaPAC mice represent the most relevant model
for humans since, the tissue distribution of PPARa is similar to that observed in wild-type mice
and the hPPARa in hPPARaPAC mice is underregulation of its native promoter. Indeed
upregulation of hepatic mPPARa in wild-type mice by fasting was mirrored by the hPPARa in
hPPARaPAC mice. However, there was no demonstration that the artificial chromosome that is
replicating along with other DNA is controlled sterically by the same control since it is not on
the mouse genome in the same place as the native PPAR. There is also not a demonstration of
how stable the baseline of PPAR DNA expression is in this mouse model—does it vary as much
or more than native PPAR between mice? The authors stated that:
induction of PPARa target genes for fatty acid metabolism and a decrease in
serum triglycerides by PP in hPPARaPAC mice indicates that hPPARa is
functional in the mouse environment with respects to regulation of fatty acid
metabolism. This is in agreement with the liver-specific PPARa humanized mice
that also exhibit these responses (Cheung et al., 2004). Indeed the DNA binding
domain of hPPARa is 100% homologous with that of the mouse suggesting that
both bind to the same PPRE binding site in the promoter region of target genes.
Transfection of hPPAR into murine hepatocytes increased PPs induced
peroxisome proliferation related effects (Macdonald et al., 1999). These results
suggest that hPPARa and mPPARa do not differ in induction of target genes with
known PPRE.
However, replacement with human PPAR in the Cheung et al. (Cheung et al., 2004)
model is not sufficient to prevent the same types of toxicity as seen with PPAR knockouts on the
hepatocytes such as steatosis.
Yang et al. (2007) note that:
the increased LPL and decreased expression of apo C-III are proposed to explain
the hypolipidemic effects of PPS (Auwerx et al.. 1996). However, hPPARaPAC
mice treated with PP exhibit lowered serum triglycerides without alteration of the
expression of LPL and apo C-III. This indicates the hypolipidemic effects in
rodents are mediated via other molecular regulatory mechanisms. It is also
suggested that the activation of PPARa by PPs stimulates hepatic fatty acid
oxidation and thereby diminishing their incorporation into triglycerides and
secretion of VLDL (Fr0yland et al., 1997). Consistent with this idea, a robust
induction of the genes encoding enzymes for fatty acid oxidation by PP in
hPPARaPAC mice were observed. Thus, the exact mechanism by which PPs exert
their hypolipidemic effects needs reexamination.
However, the use of two different peroxisome proliferators (i.e., WY-14,643 and
Fenofibrate) for two types of effects (peroxisomal and lipid) may be the cause of some paradoxes
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here in terms of the mode of action for lipid effects. The baseline differences in the hPPARaPAC
mice for serum total triglycerides was not explored by these authors and the small number of
animals used make conclusions difficult about the magnitude of difference. The differences in
baseline expression for LPL are not discernable in the graphic representation of the results.
Yang et al. (2007) noted that:
on the other hand, the difference in the affinity of ligands for the human and
mouse PPARa receptor was proposed to account for the species difference. The
ligand binding domain of hPPARa is 94% homologous with that of the mouse. In
vitro transactivation assays have previously shown that WY has a higher affinity
for rodent PPARa than human PPARa, while Fenofibrate has similar affinity for
rodent and human PPARa (Shearer and Hoekstra. 2003: Sheretal.. 1993). In the
present study WY and Fenofibrate exhibit the same capacity to induce known
PPARa target genes in the liver, kidney and heart in both wild-type and
hPPARaPAC mice.
The statement by the authors that Fenofibrate and WY-14,643 had the same affinity "as
shown by this study" is not correct. The two treatments were not studied for the same enzymes
or genes in the data reported in the study. Both WY-14,643 and Fenofibrate can induce PPARa
targets, but it was not shown to the same extent. Yang et al. (2007) stated that:
This is in agreement with the liver-specific PPARa humanized mice that also
exhibit a similar capacity to induce PPARa target genes in liver by WY and
Fenofibrate (Cheung et al., 2004). Thus, the ligand affinity difference between
mouse and human PPARa may not be critical under the conditions of these
studies.
Alternatively, these results could reflect that these studies were conducted with two
different agonists with different affinities and responses due to receptor activation.
Finally, a useful comparison to make are the differences between wild-type mice,
PPARa -/- mice that serve as the background for the transgenic human mouse models, and both
transgenic models. The small and variable number of animals examined in these studies is
readily apparent. The results of the Cheung et al. (2004) humanized mouse model and those
reported for Yang et al. (2007) show differences in the study designs including PPARa agonists
studied for particular effects and results reported for similar treatments (see Table E-18).
As shown above, the effect on the PPARa -/- by the knockout included decreased
triglyceride levels and slightly increased liver weight. Although treatment with WY-14,643 and
Fenofibrate were reported to decrease triglyceride levels in wild-type mice, paradoxically, so did
knocking out the receptor. Exposures to WY-14,643 appeared to induce a slight increase and
exposures to Fenofibrate induced a slight decrease in triglyceride levels in PPARa -/- mice, but
the variability of response and small number of animals in the experiments limited the ability to
discern a quantitative difference in the treatments.
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In the study by Cheung et al. (2004), it appears that the insertion of humanized PPARa
restored the baseline and treatment responses for triglyceride levels. Of note is that use of the
same humanized mode in Ramdhan et al. (2010) showed accumulation of triglycerides in the
liver of untreated mice. Overall, the results reported by Yang et al. (2007) appeared to show a
lower level of triglycerides in control wild-type mice that was similar in magnitude to the
treatment effect reported by Fenofibrate by Cheung et al. (2004). However, there also appeared
to be restoration of this effect in the humanized mouse model of Yang et al. (2007).
In regard to DNA synthesis, both Cheung et al. (2004) and Yang et al. (2007) only gave
results for WY-14,643 and for different durations of exposure, so they were not comparable. It
appeared that -60% of hepatocytes were labeled by 8 weeks of WY-14,643 treatment (Cheung
et al., 2004) compared to -20% after 2 weeks of exposure. Again, this highlights the difference
between using WY-14,643 as a model for the PPARa as a class at times when almost all other
PPARa agonists have ceased to increase DNA synthesis or have reductions in this parameter.
The background changes due to the PPARa -/- knockout were not reported so that the effects of
the knockout could not be ascertained. It appeared that insertion of humanized PPARa did not
result in restoration of WY-14,643-induced DNA synthesis. The correlation with this parameter
and any focal areas of necrosis were not discussed by the authors of the study.
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Table E-18. Comparison between results for Yang et al. (2007) and Cheung et al. (2004)a
Effect
Triglycerides
BrdU
incorporation
Wild type mice
Cheung
(n = 6-9)
Control 145 mg/mL
0.1% WY-14,643 60 mg/mL
(2 wks)
0.2% Fenofibrate 85 mg/mL
(2 wks)
Yang
(n = 4-6)
Control 95 mg/mL
0.1 % WY-14,643 55 mg/mL
(2wks)
Cheung
(n = 5)
Control 1.6%
0.1% WY-14,643 57.9%
(8 wks)
Yang
(n = 4-6)
Control 1.1%
0.1% WY-14,643 21.8%
(2 wks)
PPAR -/- knockout mice
Cheung
(n = 6-9)
Control 100 mg/mL
0.1% WY-14,643 115 mg/mL
(2 wks)
0.2% Fenofibrate 85 mg/mL
(2 wks)
Not done
Humanized mice (liver only)
Cheung
(n = 6-9)
Control 175 mg/mL
0.1%WY-14,643 60 mg/mL
(2 wks)
0.2% Fenofibrate 85 mg/mL
(2 wks)
Cheung
(n = 5)
Control 1.6%
0.1% WY-14,643 2.8%
(8 wks)
Humanized PAC mice
Yang
(n = 4-6)
Control 120 mg/mL
0.1%WY-14,643 75 mg/mL
(2 wks)
Yang
(n = 4-6)
Control 0.8%
0.1% WY-14,643 1.0%
(2 wks)
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Table E-18. Comparison between results for Yang et al.
and Cheung et al.
(continued)
Effect
Hepatomegaly1"
(% liver body
weight ratio)
Wild type mice
Cheung
(n = 5-9)
Control 4%
0.1%WY-14,643 11%
(2 wks)
0.2% Fenofibrate 8.5%
(2 wks)
Yang
(n = 4-6)
Control 4%
0.1%WY-14,643 9%
(2 wks)
PPAR -/- knockout mice
Cheung
(n = 5-9)
Control 5%
0.1%WY-14,643 5%
(2 wks)
0.2% Fenofibrate 5.5%
(2 wks)
Humanized mice (liver only)
Cheung
(n = 5-9)
Control 4.5%
0.1%WY-14,643 7%
(2 wks)
0.2% Fenofibrate 7%
(2 wks)
Humanized PAC mice
Yang
(n = 4-6)
Control 4%
0.1% WY 6%
(2 wks)
aThe ages of the humanized knockout mice are not given for Cheung et al. (2004) but are 8-10 weeks for Yang et al.
Percentages are approximate values extrapolated from figures for hepatomegaly.
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In regard to hepatomegaly, Fenofibrate and WY-14,643 appeared to both give an
increase in liver weight in the humanized mouse model of Cheung et al. (2004) with little effect
in the knockout mouse. For Fenofibrate, there was little difference in liver weight gain in the
wild-type mouse and that of the humanized mouse model of Cheung et al. (Cheung et al., 2004).
However, Fenofibrate was not tested in the humanized mouse model of Yang et al. (2007). In
that model, only WY-14,643 was used, but there was still an increase in liver weight. Thus, in
terms of effects on liver weight gain and triglyceride levels, both models gave comparable
results and appeared to indicate that insertion of humanized PPARa would restore some of the
effects of the knockout. However, the results from both experiments highlight the need for
adequate numbers of animals and other PPARa agonists to be tested besides WY-14,463 at such
a high dose and certainly for longer periods of time to ascertain whether such manipulations will
affect carcinogenicity.
The study by Ramdhan et al. (2010) is more definitive in regard to characterization of
the effects of the knockout and insertion of human PPARa (see Section E.2.1.15). From this
study, dysregulation by the knockout and by reinsertion of human PPARa at levels of >10-fold
protein expression can be observed and include alterations in a number of gene and protein
expression levels and an underlying background level of hepatic steatosis and triglyceride
accumulation.
E. 3.4.1.4. NF-KB Activation
NF-KB activation has also been proposed as a key event in the induction of liver cancer
through PPARa activation. As discussed in Sections E.3.1.6 and E.3.3.3.3, activation of the NF-
KB pathway is implicated in carcinogenesis, nonspecific for a particular mode of action for liver
cancer, and is context-dependent on its effects. Its specific actions depend on the cell type and
type of agent or signal that activates translocation of the complex. NF-KB is not only involved in
biological processes other than tumor induction, but also exhibits some apparently contradictory
behaviors (Perkins and Gilmore, 2006). Although many studies point to a tumor-promoting
function of NF-KB subunits, evidence also exists for tumor suppressor functions. NF-KB actions
are associated with TNF and JNK, among many other cell signaling systems and molecules, and
have functions that alter proliferation and apoptosis. NF-KB activation reported in some studies
may be associated with early Kupffer cell responses and be associative but not key events in the
carcinogenic process. However, most assays look at total NF-KB expression in the whole liver
and at the early periods of proliferation and apoptosis. The origin of the NF-KB is crucial as to
its effect in the liver. For instance, hepatocyte specific deletion of IKKp increased DEN-induced
hepatocarcinogenesis, but a deletion of IKKp in both hepatocytes and Kupffer cells, however,
was reported to have the opposite effect (Maeda et al., 2005).
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E .3.4.1.5. Phenotype as an Indicator of a PPARa Mode of Action
As discussed previously (see Sections E.3.1.5, and E.3.1.8), FAH precede both
hepatocellular adenomas and carcinomas in rodents and in humans with chronic liver diseases
that predispose them to HCCs. Striking similarities in specific changes of the cellular phenotype
of preneoplastic FAH are emerging in experimental and human hepatocarcinogenesis,
irrespective of whether this was elicited by chemicals, hormones, radiation, or viruses, or in
animal models, by transgenic oncogenes or Helicobacter hepaticus. Several authors have noted
that the detection of phenotypically similar FAH in various animal models and in humans prone
to developing or bearing HCCs favors the extrapolation from data obtained in animals to humans
(Bannasch et al., 2003; Su and Bannasch, 2003; Bannasch et al., 2001). In regard to phenotype
by tincture, Caldwell and Keshava (2006) stated:
In addition, the term "basophilic" in describing preneoplastic foci or tumors can
be misleading. The different types of FAH have been related to three main
preneoplastic hepatocellular lineages: 1) the glycogenotic-basophilic cell lineage,
2) its xenomorphic-tigroid cell variant, and 3) the amphophilic-basophilic cell
lineage. Specific changes of the cellular phenotype of the first two lineages of
FAHs are similar in experimental and human hepatocarcinogenesis, irrespective
of whether they were elicited by DNA-reactive chemicals, hormones, radiation,
viruses, transgenic oncogenes and local hyperinsulinism as described by the first
two FAHs and this similarity favors extrapolation from data obtained in animals
to humans (Bannasch et al., 2003; Su and Bannasch, 2003; Bannasch et al.,
2001). In contrast, the amphophilic cell lineage of hepatocarcinogenesis has
been observed mainly after exposure of rodents to peroxisome proliferators or to
hepadnaviridae (Bannasch et al., 2001).
Bannasch (1996) describes "amphophilic" FAH and tumors induced by
peroxisome proliferators to maintain the phenotype as the foci progress to
tumors. They are glycogen poor from the start with increased numbers of
mitochondria, peroxisomes and ribosomes. The author further states that the
"homogenous basophilic" descriptions by others of foci induced by WY are
really amphophilic. Agents other than peroxisome proliferators can induce
"acidophilic" or "eosinophilic" (due to increased smooth endoplasmic reticulum)
or glycognotic foci which tend to progress to basophilic stages (due to increased
ribosomes).
Tumors and foci induced by peroxisome proliferators have been suggested to
have a phenotype of increased mitochondrial proliferation and mitochondrial
enzymes (thyromimetic rather than insulinomimetic) (2006).
Tumors from peroxisome proliferators in Kraupp-Grasl et al. (1990) and Grasl-
Kraupp et al. (1993) for rat liver tumors were characterized as weakly basophilic with some
eosinophilia and as similar to the description given by Bannasch et al. as amphophilic.
However, a number of recent studies indicate that other "classic" peroxisome proliferators may
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have a different phenotype than has been attributed to the class through studies of WY-14,643.
A recent study of DEHP, another peroxisome proliferator assumed to induce liver tumors
through activation of the PPARa receptor, reported the majority of liver FAH to be of the first
two types after a lifetime of exposure to DEHP with a dose-related tendency for increased
numbers of amphophilic FAHs in rats (Voss et al., 2005). As stated previously, the mode of
action of DEHP-induced liver tumors in mice also appears not to be dependent on PPARa
activation.
Michel et al. (2007) reported the phenotype of tumors and foci in rats treated with
clofibric acid at a very large dose (5,000 ppm for 20 months) and noted that in controls, the first
type of foci to appear was tigroid on day 264 and their incidence increased with time
representing the most abundant type in this group. They reported no adenomas or carcinomas at
up to 607 days after giving saline injection in the control animals.
DEN treatment was examined up to 377 days only, with tigroid, eosinophilic, and clear
cell foci observed at that time. Clofibric acid was examined up to 607 days, with tigroid and
clear cell foci reported to be the first to appear on day 264, but no other foci class. By day 377,
there were tigroid, eosinophilic, and clear cell foci, but no basophilic foci reported with clofibric
acid treatment and, although only a few animals were examined, 2/5 had adenomas but not
carcinomas. By day 524, all types of foci were seen (including basophilic for the first time) and
there were adenomas and carcinomas in 2/5 animals. By 607 days, a similar pattern was
observed without adenomas, but 3/6 animals had carcinomas.
Although the number of animals examined was very small, these results indicate that
clofibric acid was not inducing primarily "basophilic foci" as reported for peroxisome
proliferators, but that the first foci are tigroid and clear cell foci. Basophilic foci did not appear
until day 524 as similar to control values for foci development and distribution. However,
unlike controls, clofibric acid induced eosinophilic and clear cell foci earlier. This is
inconsistent with the phenotype ascribed to peroxisome proliferators as exemplified by
WY-14,643.
In regard to GST-u and y-transpeptidase (GGT), Rao et al. (1986) fed two male F344 rats
a diet of 0.1% WY-14,643 for 19 months or three F344 rats 0.025% Ciprofibrate for 15-
19 months and reported "altered areas,"(AA) "neoplastic nodules" (NN), and HCCs (HCC). For
WY-14,643 treatment, 107 AA, 75 NN, and 5 HCC were noted, and for Ciprofibrate treatment,
107 AA, 27 NN, and 16 HCC were identified. In the WY-14,643-treated rats, HCC, and NN
were both GGT and GST-u negative (96-100%) with 87% of AA was negative for both. In
Ciprofibrate-treated rats, NN and HCC were negative for both markers (95%) but only 46% of
AA were negative for both markers. Thus, a different pattern for tumor phenotype was reported
for WY-14,643 and another peroxisome proliferator, Ciprofibrate, in this study as well.
In addition, GGT phenotype is reported not to be specific to weakly basophilic foci.
GGT staining was reported to be negative in eosinophilic tumors after initiation and promotion.
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Kraupp-Grasl et al. (1990) noted differences among PPARa agonists in their ability to promote
tumors and suggested they not necessarily be considered a uniform group. Caldwell and
Keshava (2006) suggested that the reports of a simple designation of "basophilic" is not enough
to associate a foci as caused by peroxisome proliferators (Bannasch, 1996; Grasl-Kraupp et al.,
1993; Kraupp-Grasl et al., 1990). Increased basophilia of tumors and increased numbers of
carcinomas is consistent with the progressive basophilia described by Bannasch (1996), as many
adenomas progress to carcinomas.
It should be noted that the amphophilic foci and tumors described by Bannasch et al.
were primarily studied in rats. Morimura et al. (2006) noted that WY-14,643 induced diffusely
basophilic tumors in mice and therefore, identified the WY-14,643 tumors in a way consistent
with the descriptions of amphophilic tumors by Bannasch et al. The tumor induced by
WY-14,643 in their humanized mouse was reported to be similar to those arising spontaneously
in the mouse. However, the mouse response could differ from the rat, especially for PPARa
agonists other than WY-14,643.
H-ras activation and mutation studies have attempted to assign a pattern to peroxisome
proliferator-induced tumors as noted in Section E.2.4.4. However, also as noted in
Section E.2.4.4, the genetic background of the mice used, the dose of carcinogen, and the stage
of progression of "lesions" (i.e., foci vs. adenomas vs. carcinomas) may affect the number of
activated H-ras containing tumors that develop. Fox et al. (1990) noted that tumors induced by
Ciprofibrate (0.0125% diet, 2 years) had a much lower frequency of H-ras gene activation than
those that arose spontaneously (2-year bioassays of control animals) or induced with the
"genotoxic" carcinogen benzidine-2 HC1 (120 ppm, drinking H2O, 1 year) and that the
Ciprofibrate-induced tumors were reported to be more eosinophilic as were the surrounding
normal hepatocytes than spontaneously occurring tumors. Anna et al. (1994) also stated that
mice treated with Ciprofibrate had a markedly lower frequency of tumors with activated H-ras
but that the spectrum of mutations in tumors was similar those in "spontaneous tumors."
Hegi et al. (1993) tested Ciprofibrate-induced tumors from Fox et al. (1990) in the NIH3T3
cotransfection-nude mouse tumorigenicity assay and concluded that ras protooncogene
activation was not a frequent event in Ciprofibrate-induced tumors and that spontaneous tumors
were not promoted with it.
Stanley et al. (1994) studied the effect of MCP, a peroxisome proliferator, in B6C3Fi
(relatively sensitive) and C57BL/10J (relatively resistant) mice for H-ras codon 61-point
mutations in MCP-induced liver tumors (hepatocellular adenomas and carcinomas). In the
B6C3Fi mice, -24% of MCP-induced tumors had codon 61 mutations, and for C57BL/10J
mice, -13%. The findings of an increased frequency of H-ras mutation in carcinomas compared
to adenomas in both strains of mice is suggestive that these mutations were related to stage of
progression. Thus, in mice, the phenotype of tumors did not appear to be readily distinguishable
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from spontaneous tumors based on tincture for peroxisome proliferators other than WY-14,643,
but did have more of a signature in terms of H-ras mutation and activation.
The expression of c-Jun has been used to discern TCE tumors from those of its
metabolites. However, as pointed out by Caldwell and Keshava (2006), although Bull et al.
(2004) have suggested that the negative expression of c-Jun in TCA-induced tumors may be
consistent with a characteristic phenotype shown in general by peroxisome proliferators as a
class, there is no supporting evidence of this. While increased mitochondrial proliferation and
mitochondrial enzymes (thyromimetic rather than insulinomimetic) properties have been
ascribed to peroxisome proliferator-induced tumors, the studies cited in Bull et al. (2004) have
not examined TCA-induced tumors for these properties.
E .3.4.1.6. Human Relevance
In its framework for making conclusions about human relevance, the U.S. EPA Cancer
Guidelines (U.S. EPA, 2005b) asks that critical similarities and differences between test animals
and humans be identified. Humans possess PPARa at sufficient levels to mediate the human
hypolipidemic response to peroxisome-proliferating fibrate drugs. Fenofibrate and Ciprofibrate
induce treatment-related increases in liver weight, hypertrophy, numbers of peroxi somes,
numbers of mitochondria, and smooth endoplasmic reticulum in cynomologous monkeys at
15 days of exposure (Hoivik et al., 2004). Given the species difference in the ability to respond
to a mitogenic stimulus such as partial hepatectomy (see Section E.3.3), lack of hepatocellular
DNA synthesis at this time point is not unexpected, and as Rusyn (2006) noted, examination at
differing time point may produce differing results. It is therefore, generally acknowledged that
"a point in the rat and mouse key events cascade where the pathway is biologically precluded in
humans in principle cannot be identified" (Klaunig et al., 2003): NRC, 2006). Thus, from a
qualitative standpoint, the effects described above are plausible in humans.
As for quantitative differences, there are two key issues. First, as stated in the Cancer
Guidelines, when considering human relevance, "Any information suggesting quantitative
differences between animals and humans is flagged for consideration in the dose-response
assessment." Therefore, while Klaunig et al. (2003) and NRC (2006) go on to suggest that
"this mode of action is not likely to occur in humans based on differences in several key steps
when taking into consideration kinetic and dynamic factors," under the Cancer Guidelines,
such "kinetic and dynamic factors" need to be made explicit in the dose-response assessment,
and should not be part of the qualitative characterization of hazard. Second, the discussion
above points to the lack of evidence supporting associations between the postulated events and
carcinogenic potency. Thus, because interspecies differences in carcinogenicity do not appear
to be associated with interspecies differences in postulated events, they do not provide reliable
metrics with which to make inferences about relative human sensitivity.
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E.3.4.2. Other TCE Metabolite Effects That May Contribute to its
Hepatocarcinogenicity
While the focus of most studies of TCA has been its effects on peroxisomal proliferation,
DCA has been investigated for a variety of effects that are also observed either in early stages of
oncogenesis (glycogen deposition) or conditions that predispose patients to liver cancer. Some
studies have examined microarray profiles in attempt to study the mode of action of TCE (see
Section E.3.1.2 for caveats regarding such approaches). Caldwell and Keshava (2006) have
provided a review of these studies, which is provided below.
E .3.4.2.1. DCA Effects and Glycogen Accumulation Correlations with Cancer
As noted previously, DCA administration has been reported to increase the observable
amount of glycogen in mouse liver via light microscopy and, although to not be primarily
responsible for DCA-induced liver mass increases, to increase whole-liver glycogen as much by
50% (Kato-Weinstein et al., 2001). Given that TCE and DCA tumor phenotypes indicate a role
for DCA in TCE hepatocarcinogenicity (see Section E.2.4.4), Caldwell and Keshava (2006)
described the correlations with effects induced by DCA that have been associated with
hepatocarcinogenicity.
A number of studies suggest DCA-induced liver cancer may be linked to its
effects on the cytosolic enzyme glutathione (GST)-S-transferase-zeta. GST-zeta
is also known as maleylacetoacetate isomerase and is part of the tyrosine
catabolism pathway whose disruption in type 1 hereditary tyrosinemia has been
linked to increased liver cancer risk in humans. GST-zeta metabolizes
maleylacetoacetate (MAA) to fumarylacetoacetate (FAA) which displays
apoptogenic, mutagenic, aneugenic, and mitogenic activities (Bergeron et al.,
2003; Jorquera and Tanguay, 2001; Kim et al., 2000). Increased cancer risk has
been suggested to result from FAA and MAA accumulation (Tanguay et al.,
1996). Cornett et al. (1999) reported DCA exposure in rats increased
accumulation of maleylacetone (a spontaneous decarboxylation product of
MAA), suggesting MAA accumulation. Ammini et al. (2003) report depletion of
the GST-zeta to be exclusively a post-transcriptional event with genetic ablation
of GST-zeta causing FAA and MAA accumulation in mice. Schultz et al. (2002)
report that elimination of DCA is controlled by liver metabolism via GST-zeta in
mice, and that DCA also inhibits the enzyme (and thus its own elimination) with
young mice being the most sensitive to this inhibition. On the other hand, older
mice (60 weeks) had a decreased capacity to excrete and metabolize DCA in
comparison with younger ones. The authors suggest that exogenous factors that
deplete or reduce GST-zeta will decrease DCA elimination and may increase its
carcinogenic potency. They also suggest that, due to suicide inactivation of
GST-zeta, an assumption of linear kinetics can lead to an underestimation of the
internal dose of DCA at high exposure rates. In humans, GST-zeta has been
reported to be inhibited by DCA and to be polymorphic (Blackburn et al., 2001;
Blackburn et al., 2000; Tzeng et al., 2000). Board et al. (2001) report one variant
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to have significantly higher activity with DCA as a substrate than other GST zeta
isoforms, which could affect DCA susceptibility.
Individuals with glycogen storage disease or with poorly controlled diabetes have
excessive storage of glycogen in their livers (glycogenosis) and increased risk of
liver cancer (Rake et al.. 2002: Wideroff et al.. 1997: Adamietal.. 1996: La
Vecchia et al., 1994). In an animal model where hepatocytes are exposed to a
local hyperinsulinemia from transplanted islets of Langerhans and the remaining
tissue is hypoinsulinemic, insulin induces alterations that resemble preneoplastic
foci of altered hepatocytes (FAH) and develop into hepatocellular tumors in later
stages of carcinogenesis (Evert et al., 2003). A number of studies have reported
suppression of apoptosis, decreases in insulin, and glycogenosis in mice liver by
DCA at levels that also induce liver tumors (Bull 2004b: Bull et al.. 2004:
Lingohr et al., 2001). In isolated murine hepatocytes, Lingohr et al. (2002)
reported DCA-induced glycogenosis was dose related, occurred at very low
doses (10 uM), occurred without the presence of insulin, was not affected by
insulin addition, was dependent on phosphatidylinositol 3-kinase (P13K)
activity, and was not a result of decreased glycogen breakdown. The authors
noted that PI3K is also known to regulate cell proliferation and apoptosis in
hepatocytes, and that understanding these mechanisms may be important to
understanding DCA-induced carcinogenesis. They also report insulin receptor
(IR) protein levels decreased to 30% of controls in mice liver after up to 52
weeks of DCA treatment. Activation of the IR is also the principal pathway by
which insulin stimulates glycogen synthetase (the rate limiting enzyme of
glycogen biosynthesis). However, in DCA-induced liver tumors IR protein was
elevated as well as mitogen-activated protein kinase (a downstream target protein
of the IR) phosphorylation. DCA-induced tumors were glycogen poor (Lingohr
et al., 2001). The authors suggest that normal hepatocytes down-regulate
insulin-signaling proteins in response to the accumulation of liver glycogen
caused by DCA and that the initiated cell population, which does not accumulate
glycogen and is promoted by DCA treatment, responds differently from normal
hepatocytes to the insulin-like effects of DCA.
Gene expression studies of DCA show a number of genes identified with cell
growth, tissue remodeling, apoptosis, cancer progression, and xenobiotic
metabolism to be altered in mice liver at high doses (2 g/L DCA) in drinking
water (Thai et al., 2003, 2001). After 4 weeks, RNA expression was altered in 4
known genes (alpha-1 protease inhibitor, cytochrome B5, stearoyl-CoA
desaturase and caboxylesterase) in two mice (Thai etal., 2001). Except for Co-A
desaturase, a similar pattern of gene change was reported in DCA-induced
tumors (10 tumors from 10 different mice) after 93 weeks. Using cDNA
microarray in the same mice, Thai et al. (2003) identified 24 genes with altered
expression, of which 15 were confirmed by Northern blot analysis after 4 weeks
of exposure. Of the 15 genes, 14 revealed expression suppressed two- to fivefold
and included: MHR 23 A, cytochrome P450 (CYP), 2C29, CYP 3A11, serum
paraoxonase/arylesterase 1, liver carboxylesterase, alpha-1 antitrypsin, ERp72,
GST-pi 1, angiogenin, vitronectin precursor, cathepsin D, plasminogen precursor
(contains angiostatin), prothrombin precursor and integrin alpha 3 precursor. An
additional gene, CYP 2A4/5, had a twofold elevation in expression. After 93
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weeks of treatment with 3.5 g/L DC A, Northern blot analyses of total RNA
isolated from DCA-induced hepatocellular carcinomas showed similar alteration
of expression (11 of 15). It was noted that peroxisome proliferator-activated
receptor (PPAR)a and IR gene expression were not changed by DCA treatment.
Genes involved in glycogen or lipid metabolism were not tested.
Although it has not been possible to determine directly whether DCA is produced
from TCE at carcinogenic levels, there is indirect evidence that DCA is formed
from TCE in vivo and contributes to liver tumor development. Pretreatment with
either DCA or TCE inhibits GST-zeta while TCA pretreatment does not (Bull et
al.. 2004: Schultz et al.. 2002). TCE treatment decreased Vmax for DCA
metabolism to 49% of control levels with a 1 g/kg TCE dose resembling effects
those of 0.05 g/L DCA (Schultz et al.. 2002).
E .3.4.2.2. Genetic Profiling Data for TCE: Gene Expression and Methylation Status
Studies
Caldwell and Keshava (2006) and Keshava and Caldwell (2006) reported on both genetic
expression studies and studies of changes in methylation status induced by TCE and its
metabolites (see Sections E.4.1.3 and E.3.3.5) as well as differences and difficulties in the
patterns of gene expression between differing PPARa agonists. In Section E.4.3, the effects of
co-exposures of DCA, TCA, and chloroform on methylation status are discussed. In particular
are concerns for the interpretation of studies that employ pooling of data as well as interpretation
of "snapshots in time of multiple gene changes."
For the Laughter et al. (2004) study in particular, it is not clear whether transcription
arrays were performed on pooled data (no data on variability between individual animals were
provided and the methodology section of the report is not transparently written in this regard).
The issue of phenotypic anchoring also arises as data on percent liver/body weight indicates
significant variability within TCE treatment groups, especially in PPARa-null mice. For studies
of gene expression using microarrays, Bartosiewicz et al. (2001) used a screening analysis of
148 genes for xenobiotic-metabolizing enzymes, DNA repair enzymes, heat shock proteins,
cytokines, and housekeeping gene expression patterns in the liver in response TCE. The
TCE-induced gene induction was reported to be highly selective; only Hsp 25 and 86 and Cyp2a
were upregulated at the highest dose tested. Collier et al. (2003) reported differentially
expressed mRNA transcripts in embryonic hearts from Sprague-Dawley rats exposed to TCE,
with sequences downregulated with TCE exposure appearing to be those associated with cellular
housekeeping, cell adhesion, and developmental processes. TCE was reported to induce
upregulated expression of numerous stress-response and homeostatic genes.
Laughter et al. (2004) reported transcription profiles using macroarrays containing
approximately 1,200 genes in response to TCE exposure. Forty-three genes were reported to be
significantly altered in the TCE-treated wild-type mice and 67 genes were significantly altered
in the TCE-treated PPARa knockout mice. Out of the 43 genes expressed in wild-type mice
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upon TCE exposure, 40 genes were reported by the authors to be dependent on PPARa and
included genes for CYP4al2, epidermal growth factor receptor, and additional genes involved in
cell growth. However, the interpretation of this information is difficult because in general,
PPARa knockout mice have been reported to be more sensitive to a number of hepatotoxins,
partly because of defects in the ability to effectively repair tissue damage in the liver (Shankar et
al., 2003; Mehendale, 2000) and because a comparison of gene expression profiles between
controls (wild-type and PPARa knockout) were not reported.
As stated previously, knockout mice in this study also responded to TCE exposure with
increased liver weight, had increased background liver weights, and had higher baseline levels
of hepatocyte proliferation than wild-type mice. Nakajima et al. (2000) reported that the
number of peroxisomes in hepatocytes increased by twofold in wild-type mice but not in PPARa
knockout mice. However, TCE induced increased liver weight in both male and female wild-
type and knockout mice, suggesting hepatic effects independent of PPARa activation. Ramdhan
et al. (2010) also reported increased liver weight after TCE exposure in male wild type,
PPARa-null, and PPARa humanized mice to a similar extent.
In regards to toxicity, after 3 weeks of TCE treatment (0-1,500 mg/kg via gavage),
Laughter et al. (2004) reported toxicity at the 1,500 mg/kg level in the knockout mice that was
not observed in the wild-type mice—all knockout mice were moribund and had to be removed
from the study. Differences in experimental protocol made comparisons between TCE effects
and those of its metabolites difficult in this study (see Section E.2.1.13). After short-term
inhalation exposure, Ramdhan et al. (2010) reported increased TCE induction of toxicity in
PPARa-null and humanized mice in terms of hepatic steatosis and minimal levels of necrosis.
As reported by Voss et al. (2006), dose-, time course-, species-, and strain-related
differences should be considered in interpreting gene array data. The comparison of differing
PPARa agonists presented in Keshava and Caldwell (2006) illustrate the pleiotropic and varying
liver responses of the PPARa receptor to various agonists, but did imply that these responses
were responsible for carcinogenesis.
As discussed in Section E.3.3.5 and in Caldwell and Keshava (2006),
Aberrant DNA methylation has emerged in recent years as a common hallmark of
all types of cancers, with hypermethylation of the promoter region of specific
tumor suppressor genes and DNA repair genes leading to their silencing (an effect
similar to their mutation) and genomic hypomethylation (Pereira et al., 2004a:
Ballestar and Esteller, 2002; Berger and Daxenbichler, 2002; Rhee et al., 2002;
Herman etal., 1998). Whether DNA methylation is a consequence or cause of
cancer is a long-standing issue (Ballestar and Esteller, 2002). Fraga et al. (2005;
2004) reported global loss of monoacetylation and trimethylation of histone H4 as
a common hallmark of human tumor cells; they suggested, however, that
genomewide loss of 5-methylcytosine (associated with the acquisition of a
transformed phenotype) exists not as a static predefined value throughout the
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process of carcinogenesis but rather as a dynamic parameter (i.e., decreases are
seen early and become more marked in later stages).
Although little is known about how it occurs, a hypothesis has also been proposed that
that the toxicity of TCE and its metabolites may arise from its effects on DNA methylation status.
In regard to methylation studies, many are co-exposure studies as they have been conducted in
initiated animals, and as stated above, some are very limited in regard to the reporting and
conduct of the study.
Cal dwell and Keshava (2006) reviewed the body of work regarding TCE, DC A, and TCA
for this issue. Methionine status has been noted to affect the emergence of liver tumors. As
noted by Counts et al. (1996), a choline/methionine-deficient diet for 12 months did not increase
liver tumor formation in C3H/HeN mice, but was tumorigenic to B6C3Fi mice. Tao et al. (2000)
and Pereira et al. (2004a) have studied the effects of excess methionine in the diet to see if it has
the opposite effects as a deficiency (i.e., and reduction in a carcinogenic response rather than
enhancement). As noted above for Tao et al. (2000), the administration of excess methionine in
the diet is not without effect. The data of Tao et al. (2000) suggested that percent liver/body
weight ratios are affected by short-term methionine exposure (300 mg/kg) in female B6C3Fi
mice.
Pereira et al. (2004a) reported that very high levels of methionine supplementation to an
AIN-760A diet affected the number of foci and adenomas after 44 weeks of co-exposure to
3.2.g/L DC A. While the highest concentration of methionine (8.0 g/kg) was reported to decrease
both the number of DCA-induce foci and adenomas, the lower level of methionine co-exposure
(4.0 g/kg) increased the incidence of foci. Co-exposure of methionine (4.0 or 8.0 g/kg) with
3.2 g/L DC A was reported to decrease by -25% DCA-induced glycogen accumulation and
increase mortality, but not to have much of an effect on peroxisome enzyme activity (which was
not elevated by >33% over control for DCA exposure alone).
Methionine treatment alone at the 8 g/kg level was reported to increase liver weight,
decrease lauroyl-CoA activity, and increase DNA methylation. The authors suggested that their
data indicate that methionine treatment slowed the progression of foci to tumors. Given that
increasing hypomethylation is associated with tumor progression, decreased hypomethylation
from large doses of methionine are consistent with a slowing of progression. Whether these
results would be similar for lower concentrations of DCA and lower concentrations of
methionine that were administered to mice for longer durations of exposure cannot be ascertained
from these data. It is possible that in a longer-term study, the number of tumors would be
similar. Whether methionine treatment co-exposure had an effect on the phenotype of foci and
tumors was not presented by the authors in this study. Such data would have been valuable to
discern if methionine co-exposure at the 4.0 mg/kg level that resulted in an increase in
DCA-induced foci also resulted in foci of a differing phenotype or a more heterogeneous
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composition than DCA treatment alone. Finally, a decrease in tumor progression by methionine
supplementation is not shown to be a specific event for the mode of action for DCA-induced liver
carcinogenicity.
Tao et al. (2000) reported that 7 days of gavage dosing of TCE (1,000 mg/kg in corn oil),
TCA (500 mg/kg, neutralized aqueous solution), and DCA (500 mg/kg, neutralized aqueous
solution) in 8-week-old female B6C3Fi mice resulted in not only increased liver weight but also
increased hypomethylation of the promoter regions of c-Jun and c-Myc genes in whole-liver
DNA (data shown for 1-2 mice per treatment). Treatment with methionine was reported to
abrogate this response only at a 300 mg/kg i.p. dose with 0-100 mg/kg doses of methionine
having no effect. Ge et al. (200Ib) reported DCA- and TCA-induced DNA hypomethylation and
cell proliferation in the liver of female mice at 500 mg/kg and decreased methylation of the
c-Myc promoter region in liver, kidney, and urinary bladder. However, increased "cell
proliferation" preceded hypomethylation. Ge et al. (2002) also reported hypomethylation of the
c-Myc gene in the liver after exposure to the peroxisome proliferators 2,4-dichlorophenoxyacetic
acid (2,4-D)(l,680 ppm), dibutyl phthalate (20,000 ppm), Gemfibrozil (8,000 ppm), and
WY-14,643 (50-500 ppm, with no effect at 5 or 10 ppm) after 6 days in the diet. Caldwell and
Keshava (2006) concluded that hypomethylation did not appear to be a chemical-specific effect
at these concentrations. As noted in Section E.3.3.5, chemical exposure to a number of differing
carcinogens have been reported to lead to progressive loss of DNA methylation.
Caldwell and Keshava (2006) also noted similar changes in methylation after initiation
and treatment with DCA or TCA.
After initiation by N-methyl-N-nitrosourea (25 mg/kg) and exposure to 20 mmL/L
DCA or TCA (46 weeks), Tao et al. (2004a) report similar hypomethylation of
total mouse liver DNA by DCA and TCA with tumor DNA showing greater
hypomethylation. A similar effect was noted for region-2 (DMR-2) of the
insulin-like growth factor-II (IGF-II) gene. The authors suggest that
hypomethylation of total liver DNA and the IGF-II gene found in non-turnorous
liver tissue would appear to be the result of a more prolonged activity and not cell
proliferation, while hypomethylation of tumors could be an intrinsic property of
the tumors. Over expression of IGF-II gene in liver tumors and preneoplastic foci
has been shown in both animal models of hepatocarcinogenesis and humans, and
may enhance tumor growth, acting via the over-expressed IGF-I receptor (Scharf
etal., 2001; Werner and Le Roith, 2000). IGF-I is the major mediator of the
effects of the growth hormone; it thus has a strong influence on cell proliferation
and differentiation and is a potent inhibitor of apoptosis (Fiirstenberger and Senn,
2002). Normally, expression of IGF-II in liver is greater during the fetal period
than the adult, but is over-expressed in human hepatocarcinomas due to activation
of fetal promoters (Scharf et al., 2001) and loss of imprinting (Khandwala et al.,
2000). Takeda et al. (1996) report IGF-II expression in the liver is monoallelic
(maternally imprinted) in the fetal period is relaxed during the postnatal period,
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(resulting in biallelic expression), and is imbalanced in human hepatocarcinomas
(leading to restoration of monoallelic IG-II expression).
However, Bull (2004b) and Bull et al. (2004) have recently suggested that
hypomethylation and peroxisome proliferation occur at higher exposure levels than those that
induce liver tumors for TCE and its metabolites. They reported that a direct comparison in the
no-effect level or low-effect level for induction of liver tumors in the mouse and several other
endpoints shows that, for TCA, liver tumors occur at lower concentrations than peroxisome
proliferation in vivo, but that PPARa activation occurs at a lower dose than either tumor
formation or peroxisome proliferation. A similar comparison for DCA shows that liver tumor
formation occurs at a much lower exposure level than peroxisome proliferation, PPARa
activation, or hypomethylation. In addition, they reported that these chemicals are effective as
carcinogens at doses that do not produce cytotoxicity.
E.3.4.2.3. Oxidative Stress
Several studies have attempted to study the possible effects of "oxidative stress" and
DNA damage resulting from TCE exposures. The effects of induction of metabolism by TCE, as
well as through co-exposure to ethanol, have been hypothesized in itself to increase levels of
"oxidative stress" as a common effect for both exposures (see Section E.4.3.4). Oxidative stress
has been hypothesized to be the mode of action for peroxisome proliferators as well, but has
been found to be correlated with neither cell proliferation nor carcinogenic potency of
peroxisome proliferators (see Section E.3.4.1.1). As a mode of action, it is not defined or
specific, as the term "oxidative stress" is implicated as part of the pathophysiologic events in a
multitude of disease processes and is part of the normal physiologic function of the cell and cell
signaling.
In regard to measures of oxidative stress, Rusyn (2006) noted that although an
overwhelming number of studies draw a conclusion between chemical exposure, DNA damage,
and cancer based on detection of 8-OHdG, a highly mutagenic lesion, in DNA isolated from
organs of in vivo treated animals, a concern exists as to whether increases in 8-OHdG represent
damage to genomic DNA, a confounding contamination with mitochondrial DNA, or an
experimental artifact. As described in Section E.2.2.8, the study by Channel et al. (1998)
demonstrated that corn oil as vehicle had significant effects on measures of "oxidative stress"
such as TEARS. Also as noted previously (see Sections E.2.1.1 and E.2.2.11), studies of TCE
that employ the i.p. route of administration can be affected by inflammatory reactions resulting
from routes of administration and subsequent toxicity that can involve oxygen radical formation
from inflammatory cells.
The issues with interpretation of the Channel et al. (1998) study of TCE administered via
corn oil gavage to mice have already been discussed in Section E.2.2.8. The TEARS results
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indicated suppression of TEARS with increasing time of exposure to corn oil alone with data
presented in such a way for 8-OHdG and total free radical changes that the pattern of corn oil
administration was obscured. It was not apparent from that study that TCE exposure induced
oxidative damage in the liver.
Toraason et al. (1999) measured 8-OHdG and a "free radical-catalyzed isomer of
arachidonic acid and marker of oxidative damage to cell membranes, 8-Epi-prostaglandin F2a
(SepiPGF)," excretion in the urine and TEARS (as an assessment of malondialdehyde and marker
of lipid peroxidation) in the liver and kidney of male Fischer rats (150-200 g) exposed to single
0, 100, 500, or 1,000 mg/kg TCE i.p. injections in Alkamuls vehicle (n = 6/group). Two
sequential urine samples were collected 12 hours after injection and animals were sacrificed at
24 hours with DNA collected from liver tissues and TEARS measured in liver homogenates. The
mean body weights of the rats were reported to vary by 13%, but the liver weights varied by 44%
after the single treatments of TCE. In contrast to the large volume of the literature that reports
TCE-induced increases in liver weight, the 500 and 1,000 mg/kg exposed rats were reported to
have reduced liver weight by 44% in comparison to the control values.
Using this paradigm, 500 mg/kg TCE was reported to induce stage II anesthesia and a
1,000 mg/kg TCE to induce Level III or IV (absence of reflex response) anesthesia and burgundy
colored urine with 2/6 rats at 24 hours comatose and hypothermic. The animals were sacrificed
before they could die and the authors suggested that they would not have survived another
24 hours. Thus, using this paradigm, there was significant toxicity and additional issues related
to route of exposure. Urine volume declined significantly during the first 12 hours of treatment
and while water consumption was not measured, it was suggested by the authors to be decreased
due to the moribundity of the rats. Given that this study examined urinary markers of "oxidative
stress," the effects on urine volume and water consumption, as well as the profound toxicity
induced by this exposure paradigm, limit the interpretation of the study.
The authors noted that because both using volume and creatinine excretion were affected
by experimental treatment, urinary excretion of 8-OHdG changed significantly based on the
mode of data expression. Excretion of SepiPGF was reported to be no different from controls at
12-24 hours and was decreased 24 hours after TCE exposure at the two highest levels. Excretion
of 8-OHdG was reported to not be affected by any exposure level of TCE and, if expressed on the
basis of 24-hours, decreased. TEARS concentration per g of liver was reported to be increased at
the 500 and 1,000 mg/kg TCE exposure levels (-2-3-fold). The effects of decreased liver size in
the treated animals for this measure in comparison to control animals, was not discussed by the
authors. For 8-OHdG measures in the liver and lymphocytes, the authors reported that "cost
prohibited analysis of all of the tissues samples" so that a subset of animals was examined
exhibiting the highest TEARS levels. The number of animals used for this determination was not
given nor were the data reported, except for 500 mg/kg TCE exposure level. TCE was reported
to increase 8-OHdG/dG in liver DNA relative to controls to about the same extent in
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lymphocytes from blood and liver (approximately twofold) with the results for liver reported to
be significant. The issues of bias in selection of the data for this analysis, as well as the issues
already stated for this paradigm limit interpretation of these data, while the authors suggest that
evidence of oxidative damage was equivocal.
DCA and TCA have also been investigated using similar measures. Larson and Bull
(1992b) exposed male B6C3Fi mice (26 ±3 g [SD]) to a single dose of 0, 100, 300, 1,000, or
2,000 mg/kg-day TCA or 0, 100, 300, or 1,000 mg/kg-day DCA in distilled water by gavage
(n = 4). F344 rats (237 ± 4 g) received a single oral dose of 0, 100, or 1,000 mg/kg DCA or TCA
(n = 4 or 5) TEARS was measured from liver homogenates and assumed to be malondialdehyde.
The authors stated that a preliminary experiment had shown that maximal TEARS was increased
6 hours after a dose of DCA and 9 hours after a dose of TCA in mice (data shown) and that by
24 hours, TEARS concentrations had declined to control values (data not shown). However,
time-course information in rats was not presented and the same times used for both species (i.e.,
6- and 9-hour time periods after administration of DCA and TCA) for examination of TEARS
activity. A dose of 100 mg/kg DCA (rats or mice) or TCA (mice) did not elevate TEARS
concentrations over that of control liver, with this concentration of TCA not examined in rats.
For TCA, there was a slight dose-related increase in TEARS over control values starting
at 300 mg/kg in mice (i.e., 1.68-, 2.02-, and 2.70-fold of control for 300, 1,000, and 2,000 mg/kg
TCA). For DCA, there were similar increases over control for both the 300 and 1,000 mg/kg
dose levels in mice (i.e., 3.22- and 3.45-fold of control, respectively).
For rats, the 1,000 and 2,000 mg/kg levels of TCA were reported to show a statistically
significant increase in TEARS over control (i.e., 1.67- and 2.50-fold, respectively) with the
300 and 1,000 mg/kg level of DCA showing similar increases, but with only the 300 mg/kg-
induced change statistically significant different than control values (i.e., 3- and 2-fold of control,
respectively). Of note is the report that the induction of TEARS in mice is transient and had
subsided within 24 hours of a single dose of DCA or TCA, that the response in mice appeared to
be slightly greater with DCA than TCA at similar doses, and that for DCA, there was similar
TEARS induction between rats and mice at similar dose levels.
A study by Austin et al. (1996) appears to a follow-up publication of the preliminary
experiment cited in Larson and Bull (1992b). Male B6C3Fi mice (8 weeks old) were treated
with single doses of DCA or TCA in buffered solution (300 mg/kg) with liver examined for
8-OHdG. The authors stated that in order to conserve animals, controls were not employed at
each time point. For DCA, the time course of 8-OHdG was studied at 0, 4, 6, and 8 hours after
administration, and for TCA, at 0, 6, 8, and 10 hours after of a 300 mg/kg dose (n = 6). There
was a statistically significant increase over controls in 8-OHdG for the 4- and 6-hour time points
for DCA (-1.4- and 1.5-fold of control, respectively) but not at 8 hours in mice. For TCA, there
was a statistically significant increase in 8-OHdG at 8 and 10 hours for TCA (-1.4- and 1.3-fold
of control, respectively).
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The results for PCO and liver weight for Parrish et al. (1996) are discussed in
SectionE.2.3.2.3 for male B6C3Fi mice exposed to TCA or DCA (0, 0.01, 0.5, and 2.0 g/L) for
3 or 10 weeks (n = 6). The study focused on an examination of the relationship with measures of
peroxisome proliferation and oxidative stress. The dose-related increase in PCO activity at
21 days (-1.5-, 2.2-, and -4.1-fold of control, for 0.1, 0.5, and 2.g/L TCA) was reported not to be
increased similarly for DCA. Only the 2.0 g/L dose of DCA was reported to induce a statistically
significant increase at 21 days of exposure of PCO activity over control (-1.8-fold of control).
After 71 days of treatment, TCA induced dose-related increases in PCO activities that were
approximately twice the magnitude as that reported at 21 days (i.e., -9-fold greater at 2.0 g/L
level). Treatments with DCA at the 0.1 and 0.5 g/L exposure levels produced statistically
significant increase in PCO activity of-1.5- and 2.5-fold of control, respectively. The
administration of 1.25 g/L clofibric acid in drinking water, used as a positive control, gave -6-
7-fold of control PCO activity at 21 and 71 days exposure.
Parrish et al. (1996) reported that laurate hydroxylase activity was reported to be elevated
significantly only by TCA at 21 days and to approximately the same extent (-1.4-1.6-fold of
control) increased at all doses tested. At 71 days, both the 0.5 and 2.0 g/L TCA exposures
induced a statistically significant increase in laurate hydroxylase activity (i.e., 1.6- and 2.5-fold of
control, respectively) with no change reported after DCA exposure. The actual data rather than
percent of control values were reported for laurate hydroxylase activity with the control values
varying 1.7-fold between 21- and 71-day experiments. Levels of 8-OHdG in isolated liver nuclei
were reported to not be altered from 0.1, 0.5, or 2.0 g/L TCA or DCA after 21 days of exposure
and this negative result was reported to remain even when treatments were extended to 71 days of
treatment.
The authors noted that the level of 8-OHdG increased in control mice with age (i.e.,
approximately twofold increase between 71- and 21-day control mice). Clofibric acid was also
reported not to induce a statistically significant increase of 8-OHdG at 21 days, but to produce an
increase (-1.4-fold of control) at 71 days. Thus, the increases in PCO activity noted for DCA
and TCA were not associated with 8-OHdG levels (which were unchanged) and, also, not with
changes in laurate hydrolase activity observed after either DCA or TCA exposure. Of note is the
variability in both baseline levels of PCO and laurate hydrolase activity. Also of note is that the
authors report taking steps to minimize artifactual responses for their 8-OHdG determinations.
The authors concluded that their data do not support an increase in steady-state oxidative damage
to be associated with TCA initiation of cancer and that extension of treatment to time periods
sufficient to insure peroxisome proliferation failed to elevate 8-OHdG in hepatic DNA. The
increased 8-OHdG at 10 weeks after Clofibrate administration but lack of 8-OHdG elevation at
similar levels of PCO induction were also noted by the authors to suggest that peroxisome
proliferative properties of TCA were not linked to oxidative stress or carcinogenic response.
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As noted above for the study of Leakey et al. (2003b) (see Section E.2.5), hepatic
malondialdehyde concentration in ad-libitum-fed and dietary-controlled mice did not change
with CH exposure at 15 months, but the dietary-controlled groups were all approximately half
that of the ad-libitum-fed mice. Thus, while overall increased tumors observed in the ad libitum
diet correlated with increased malondialdehyde concentration, there was no association between
CH dose and malondialdehyde induction for either diet.
E.4. EFFECTS OF CO-EXPOSURES ON MODE OF ACTION—INTERNAL AND
EXTERNAL EXPOSURES TO MIXTURES INCLUDING ALCOHOL
Caldwell et al. (2008b) published a review of the issues and studies involved with the
effects of co-exposures to TCE metabolites that could be considered internal (i.e., an internal co-
exposure for the liver) and co-exposures to metabolites and other commonly occurring
chemicals that are present in the environment. As they stated:
Human exposure to a pollutant rarely occurs in isolation. EPA's Cumulative
Exposure project and subsequent National Air Toxics Assessment have
demonstrated that environmental exposure to a number of pollutants, classified
as potential human carcinogens, is widespread [U.S. EPA, 2006:(Woodruff et al.,
1998)1. Interactions between carcinogens in chemical mixtures found in the
environment have been a concern for several decades. Furthermore, how these
interactions affect the mode of action (MOA) by which these chemicals operate
and how such effects may modulate carcinogenic risk is of concern as well.
Thus, an understanding of the MOA(s) of a pollutant can help elucidate its
potential carcinogenic risk to humans, and can also help identify susceptible
subpopulations through their intrinsic factors (e.g., age, gender, and genetic
polymorphisms of key metabolic and clearance pathways) and extrinsic factors
(e.g. co-exposures to environmental contaminants, ethanol consumption, and
pharmaceutical use). Trichloroethylene (TCE) can be a useful example for
detailing the difficulties and opportunities for investigating such issues because,
for TCE, there is both internal exposure to a "chemical mixture" of multiple
carcinogenic metabolites (Chiu et al., 2006a: Chiu et al., 2006b) and co-
exposures from environmental contamination of TCE metabolites, and from
pollutants that share common metabolites, metabolic pathways, MO As, and
targets of toxicity with TCE.
Typically, ground water or contaminated waste sites can have a large number of
pollutants that vary in regard to information available to support the
characterization of their potential hazard, and that have differing MO As and
targets. For example, Veeramachaneni et al. (2001) reported reproductive effects
in male rabbits, resulting from exposure to drinking water containing
concentrations of chemicals typical of ground water near hazardous waste sites.
The drinking water exposure mixture contained arsenic, chromium, lead,
benzene, chloroform, phenol, and TCE. Even at 45 weeks after the last
exposure, mating desire/ability, sperm quality, and Leydig cell function were
subnormal. However, while the exposure levels are relevant to human
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environmental exposures, design of this study precludes a conclusion as to which
individual toxicant, or combination of the seven toxicants, caused the effects.
Thus, this study exemplifies he problems associated with studying a multi-
mixture milieu. Studies of the interactions of TCE metabolites or common co-
exposures that report the interactions of 2 or 3 chemicals at one time are easier to
interpret.
Since EPA published its 2001 draft assessment, several approaches have been
reported that include examination of tumor phenotype, gene expression, and
development of physiologically-based pharmacokinetic (PBPK) models to assess
possible effects of co-exposure. They attempt to predict whether such co-
exposures would produce additivity of response or if co-exposure would change
the nature of responses induced by TCE or its metabolites. In addition, new
studies on co-exposure to DBA may help identify a co-exposure of concern.
These studies may give potential insights into possible MO As and modulators of
TCE toxicity. More recent information on the toxicity of individual metabolites
of TCE (Caldwell and Keshava, 2006) may be helpful in trying to identify which
are responsible for TCE toxicity, but may also identify the effects of
environmental co-exposures.
Recently, EPA sought advice from the National Academy of Sciences (NAS)
(Chiu et al., 2006a) with the NAS charge questions including the following.
(1) What TCE metabolites, or combinations of metabolites, may be plausibly
involved in the toxicity of TCE? (2) What chemical co-exposures may plausibly
modulate TCE toxicity? (3) What can be concluded about the potential for
common drinking water contaminants such as other solvents and/or haloacetates
to modulate TCE toxicity? (4) What can be concluded about the potential for
ethanol consumption to modulate TCE toxicity? Thus, the understanding of the
effects of co-exposure, in the context of MO A, is an important element in
understanding the risk of a potential human carcinogen.
U.S. EPA's draft TCE risk assessment (U.S. EPA. 2001) identified several
factors involving co-exposure to TCE metabolites, environmental contaminants,
and ethanol that could lead to differential sensitivity to TCE toxicity. Research
needs identified there, as well as in previous reviews (Bull, 2000; Pastino et al.,
2000), included further elucidation of the interaction of TCA and DCA in TCE-
induced liver tumors and a better understanding of the functional relationships
among risk factors. The complexity of TCE's potential interactions with
chemical co-exposures from either common environmental co-contaminants or
common behaviors such as alcohol consumption mirrors the complexity of the
metabolism and the actions of TCE metabolites. Thus, TCE presents a good case
study for further exploration of the effects of co-exposure on MO A.
The following sections first reiterate the findings of Bull et al. (2002) in regard to simple
co-exposures of DCA and TCA that can be experienced as an internal co-exposure after TCE
exposure. A number of studies have examined the effects of TCE or its metabolites after
previous exposure to presumably genotoxic carcinogen to not only determine the effect of the
co-exposure on liver carcinogenicity but also to use such paradigms to distinguish between the
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effects of TCA and DC A. Finally, not only is TCE a common co-exposure with its own
metabolites, but is also a common co-exposure with other solvents, and the brominated
analogues of TCA and DCA. The available literature is examined for potential similarities in
target and effects that may cause additional concern. The effects of ethanol on TCE toxicity is
examined as well as the potential pharmacokinetic modulation of risk using recently published
reports of PBPK models that may be useful in predicting co-exposure effects.
E.4.1. Internal Co-exposures to TCE Metabolites: Modulation of Toxicity and
Implications for TCE Mode of Action
Exposure to TCE will produce oxidative metabolites in the liver as an internal co-
exposure. As stated above, the phenotypic analysis of TCE-induced tumors have similarities to
combinations of DCA and TCA and in some reports to resemble more closely DCA-induced
tumors in the mouse. Results from Bull et al. (2002) are presented in Section E.2.2.22 for the
treatment of mice to differing concentrations of DCA and TCA in combination and the
resemblance of tumor phenotype to that of TCE. In regard to cancer dose-response, the most
consistent treatment-related increase in response occurred with combinations of exposure to
DCA and TCA that appeared to increase lesion multiplicity when compared to effects from
individual chemicals separately. Bull et al. (2002) presented results for "selected" lesions
examined for pathology characterization that suggest co-exposure of 0.5 g/L DCA with either
0.5 or 2 g/L TCA had a greater-than-additive effect on the total number of hyperplastic nodules.
In addition, co-exposure to 0.1 g/L DCA and 2 g/L TCA was reported to have a greater-than-
additive effect on the total number of adenomas, but not carcinomas, induced. The random
selection of lesions for the determination of potential treatment-related effects on incidence and
multiplicity, rather than characterization of all lesions, increases the uncertainty in this finding.
E.4.2. Initiation Studies as Co-exposures
There is a body of literature that has focused on the effects of TCE and its metabolites
after rats or mice have been exposed to "mutagenic" agents to "initiate" hepatocarcinogenesis.
Given that most of these "initiating agents" have many effects that are not only mutagenic but
also epigenetic, that the dose and exposure paradigm modify these effects, that "initiators" can
increased tumor responses alone, and that the tumors that arise from these protocols are
reflective of simultaneous actions of both "initiator" and "promoter," paradigms that first expose
rats or mice to a "mutagen" and then to other carcinogenic agents can be described as a co-
exposure protocols.
As stated previously, DEN and TV-nitrosomorpholine have been reported to increase
differing populations of mature hepatocytes with DEN not only being a mutagen but also being
able to induce concurrent hepatocyte regeneration at a high dose. Thus, the effects of the TCE
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or its metabolites are hard to discern from the effects of the "initiating" agent in terms of mode
of action.
As demonstrated in the studies of Pereira et al. (1997) below, the gender also
determines the nature of the tumor response using these protocols. In addition, when the
endpoint for examination is tumor phenotype the consequences of tumor progression are hard to
discern from the mode of action of the agents using paradigms of differing concentrations,
different durations of exposure, lesions counted as "tumors" to include different stages of tumor
progression (foci to carcinoma), and highly variable and low numbers of animals examined.
However, differences in phenotype of tumors resulting from such co-exposures, like the co-
exposure studies cited above for just TCE metabolites, can help determine that exposure to TCE
metabolites results in differing actions as demonstrated by differing effects in the presence of
cocarcinogens. As stated above, Kraupp-Grasl et al. (1990) use the same approach and note
differences among PPARa agonists in their ability to promote tumors suggest that they should
not necessarily be considered a uniform group.
E.4.2.1. Herren-Freund et al. (1987)
The results of TCE exposure alone were reported previously (Section E.2.2.17) for this
study. This study's focus was on the effect of TCE, TCA, DC A, and phenobarbital on
hepatocarcinogenicity in male B6C3Fi mice after "initiation" at 15 days with 2.5 or 10 ug/g
body weight of ethylnitrosourea (ENU) and then subsequent exposure to TCE and other
chemicals in drinking water begging at 4 weeks of age (an age when the liver is already
undergoing rapid growth). DCA and TCA were given in buffered solutions and sodium chloride
was given in the water of control animals. The experiment was reported to be terminated at
61 weeks because the "mice started to exhibit evidence of tumors." Concentrations of TCE
were 0, 3 and 40 mg/L, of DCA and TCA 0, 2 and 5 g/L, and of phenobarbital 0 and 500 mg/L.
The number of animals examined in each group ranged from 16 to 32. ENU alone in this
paradigm was reported to induce statistically significant increases in adenomas and HCCs (39%
incidence of adenomas and 39% incidence of carcinomas vs. 9 and 0% for controls) at the
10 ug/g dose (n = 23), but not at 2.5 ug/g dose (n = 22).
The effects of high doses of DCA and TCA alone have already been discussed for other
studies, as well as the lack of statistical power using a paradigm with so few and variable
numbers of animals, the limitations of an abbreviated duration of exposure that does not allow
for full expression of a carcinogenic response, and problems of volatilization of TCE in drinking
water. DCA and TCA treatments at these levels (5 g/L) were reported to increase adenomas and
carcinomas irrespective of ENU pretreatment and to approximately the same extent with and
without ENU. TCE at the highest dose was reported to increase the number of animals with
adenomas (37 vs. 9% in control) and carcinomas (37 vs. 0% in controls) but only the number of
adenomas/animal was statistically significant as the number of animals examined was only 19 in
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the TCE group. Phenobarbital was reported to have no effect on ENU tumor induction using
this paradigm.
E.4.2.2. Parnell et al. (1986)
This study used a rat liver foci bioassay (GOT) for hepatic foci after at 3 and 6 month
using protocols that included partial hepatectomy, DEN (10 mg/kg) or TCA (1,500 ppm in
drinking water) treatment, and then promotion with 5,000 ppm TCA (i.e., 5 g/L) for 10, 20, or
30 days and phenobarbital (500 ppm) in male Sprague-Dawley rats (5-6 weeks old at partial
hepatectomy). The number of animals per group ranged from 4 to 6. PCO activities were given
for various protocols involving partial hepatectomy, DEN, TCA, and phenobarbital treatments,
but there were no control values given that did not have a least one of these treatments.
Overall, it appeared that there was a slight decrease of PCO activity in rats treated with
partial hepatectomy/DEN/phenobarbital treatments and a slight increase over other treatments
for rats treated with partial hepatectomy/DEN/5,000 ppm TCA or just TCA from 2 weeks to
6 months of sampling. In regard to GGT-positive foci, the partial hepatectomy/DEN/
phenobarbital group (n = 6) was reported to have more positive foci at 3 or 6 months than rats
"initiated" with TCA and phenobarbitol after partial hepatectomy or partial hepatectomy/
phenobarbital treatment alone (2.05 foci/cm2 vs. -0.05-0.10 foci/cm2 for all other groups). The
number of GGT-positive foci in rats without any treatment were not studied or presented by the
authors. For "promotion" protocols, the number of GGT-positive foci induced by the partial
hepatectomy/DEN/phenobarbital protocol at 3 and 6 months, appeared to be reduced when
phenobarbital exposure was replaced by TCA co-exposure, but there was no dose-response
between the 50, 500, and 5,000 ppm. However, TCA treatment along with partial hepatectomy
and DEN treatment did increase the levels of foci (means of 0.71-0.39 foci/cm2 at 3 months and
1.83-2.45 foci/cm2 at 6 months) over treatment of just partial hepatectomy and DEN (0.05 ±
0.20 foci/cm2 at 3 months and 0.30 ± 0.39 foci/cm2 at 6 months).
For the TCA animals treated only with 5,000 ppm TCA, the number of GGT-positive
foci at 3 months was 0.23 ±0.16 foci/cm2 and at 6 months 0.03 ± 0.32 foci/cm2 with no values
for untreated animals presented. For the positive control (partial hepatectomy/DEN/
phenobarbital), the number of GGT-positive foci increased from 3 to 6 months (1.65 ±
0.23 foci/cm2 and at 6 months 7.61 ± 0.72 foci/cm2). The authors concluded that:
although TCA is reported to cause hepatic peroxisomal stimulation in rats and
mice, the results of this study indicate that it is unlikely TCA's effects are related
to the promoting ability seen here. The minimal stimulation of, 10 to 20% over
controls of peroxisomal associated, PCO activity in TCA exposed rats was seen
only at the 5000 ppm level and only within the promotion protocol. This finding
is in contrast to the promoting activity seen at all three concentrations of TCA.
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E.4.2.3. Pereira and Phelps (1996)
The results for mice that were not "initiated" by exposure to MNU, but exposed to DCA
or TCA, are discussed in Section E.2.3.2. However, differences in responses after initiation are
useful for showing differences between single and co-exposures as well as differences between
DCA and TCA effects. On day 15 of age, female B6C3Fi mice received an i.p. injection of
MNU (25 mg/kg) and at 7 weeks of age, received DCA (2.0, 6.67, or 20 mmol/L), TCA (2.0,
6.67 mmol, or 20 mmol/L) or sodium chloride continuously for 31 or 51 weeks of exposure.
The number of animals studied ranged from 6 to 10 in 31-week groups and from 6 to 39 in the
52-week groups. There was a "recovery group" in which mice received either 20 mmol/L
DCA (2.58 g/L DCA) (n = 12) or TCA (3.27 g/L TCA) (n = 11) for 31 weeks and then
switched to saline for 21 weeks until sacrifice at 52 weeks. Strengths of the study included the
reporting of hepatocellular lesions as either foci, adenomas, or carcinomas and the presentation
of incidence and multiplicity of each separately reported for the treatment paradigms.
Limitations included the low and variable number of animals in the treatment groups.
MNU was reported to not "significantly" induce foci or altered hepatocytes, adenomas,
or carcinomas at 31 (n = 10) or 51 weeks (n = 39). However, MNU did increase the incidence
and number/mouse of foci, adenomas, and carcinomas at the 52-week sacrifice time in
comparison to saline controls, albeit at lower levels than observed in DCA or TCA
cotreatments groups (e.g., 10 vs. 0% foci, 17.5 vs. 2.5% adenomas, and 10 vs. 0% incidence of
carcinomas at 52 weeks for MNU-treated mice vs. saline control). Co-exposure of DCA
(20.0 mmol/L) for 52 weeks in MNU-treated mice increased the number of foci and
hepatocellular adenomas with the authors reporting "the yield of total lesions/mouse increased
as a second order function of the concentration of DCA (correlation coefficients >0.998)."
TCA co-exposure in MNU-treated mice was reported not to result in a significant difference in
yield of foci or altered hepatocytes with either continuous 52- or 31-week exposure, but
exposures to 20.0 or 6.67 mmol/L TCA did result in increased yield of liver tumors with both
exposure protocols (see below).
For TCA treatment in MNU-treated mice, the incidences of foci were similar (12.5 vs.
18.2%), but the number of foci/mouse was ~3-fold greater in the cessation protocol than with
continuous exposure. The incidence of adenomas was reported to be the same (-66%) as well
as the number of adenomas/animal between continuous and cessation exposures. For
carcinomas, there was a greater incidence for mice with continuous TCA exposure (83 vs.
36%) as well as a greater number of carcinomas/mouse (~4-fold) than for those initiated mice
with cessation of TCA exposure. As noted above, the number of animals treated with TCA
was low and variable (e.g., 23 mice studied at 52 weeks 20.0 mmol/L TCA, and 6 mice at
52 weeks 6.67 mmol/L TCA), limiting the ability to discern a statistically significant effect in
regard to dose-response. The concentration-response relationship for tumors/mouse after
31 and 51 weeks was reported to be best represented by linear progression.
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A comparison of results for animals treated with MNU and 20.0 mmol/L DCA or TCA
for 31 weeks and sacrificed at 31 weeks and those that were treated with MNU and DCA or
TCA for 31 weeks and then sacrificed at 52 weeks is limited by the number of animals exposed
(n = 10 for 31-week sacrifice DCA or TCA, n = 12 for DCA recovery group, and n = 11 for
TCA recovery group). No carcinoma data were reported for animals exposed at 31 weeks and
sacrificed at 31 weeks, making comparisons with recovery groups impossible for this
parameter and thus, determinations about progression from adenomas to carcinomas. For the
MNU- and DCA-treated animals, the incidence or number of animals reported to have foci at
31 weeks was reported to be 80% but 38.5% for in the recovery group. For adenomas, the
incidence was reported to be 50% for DCA-treated animals at 31 weeks and 46.2% for the
recovery group. For MNU- and TCA-treated animals, the incidence of foci at 31 weeks was
reported to the 20 and 18.2% for the recovery group. For adenomas, the incidence was
reported to be 60% for the TCA-treated animals at 31 weeks and 63.6% for the recovery group.
Thus, this limited data set shows a decrease in incidence of foci for the MNU and DCA-treated
recovery group but no change in incidence of foci for TCA or for adenomas for DCA or TCA
treatment between those sacrificed at 31 weeks and those sacrificed 21 weeks later.
In regard to multiplicity, the number of foci/mouse was reported to be 2.80 ± 0.20 for
the 31-week DCA group and 0.46 ± 0.18 for the recovery group (mean ± SEM). The number
of adenomas/mouse was reported to be 1.80 ± 0.83 for the 31-week group and 0.69 ± 0.26 for
the recovery group. Thus, both the number of foci and adenomas per mouse was reported to be
decreased after the recovery period for MNU- and DCA-treated mice. Given that the number
of animals with foci was decreased by half, the concurrent decrease in foci/mouse is not
surprising. For TCA treatments, the numbers of foci/mouse were reported to be 0.20 ±0.13 for
the 31-week group and 0.45 ± 0.31 for the recovery group. The number of adenomas/mouse
for TCA-treatment groups was reported to be 1.30 ± 0.45 for the 31-week group and 0.91 ±
0.28 for the recovery group. For the MNU- and TCA-treated mice, the numbers of foci/mouse
were reported to be increased and the number of adenomas/mouse reported to be slightly lower.
Because carcinoma data are not presented for the 31-week group, it is impossible to determine
whether the TCA adenomas regressed to foci or the TCA adenomas progressed to carcinomas
and more foci apparent with increased time.
For the comparison of the numbers of foci, adenomas, or carcinomas per mouse that
were reported for the mice exposed at 31 weeks and sacrificed and those exposed for 52 weeks,
issues arise as to the impact of such few animals studied at 31 weeks, and the differing
incidences of lesions reported for these mice on tumor multiplicity estimates. The number of
animals studied who treated with MNU and 20.0 mmol/L DCA or TCA for 31 weeks and then
sacrificed was n = 10, while the number of animals exposed to 20.0 mmol/L DCA or TCA for
52 weeks was 24 for the DCA group and 23 for the TCA group. The number of animals treated
at lower concentrations of DCA or TCA were even lower at the 31-week sacrifice (e.g., n = 6
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for MNU and 6.67 mmol/L DCA at 31 weeks) and also for the 52-week durations of exposure
(e.g., n = 6 for MNU and 6.6.7 mmol/L TCA).
At 31 weeks, 80% of the animals were reported to have foci and 50% to have foci after
52 weeks of exposure to 20.0 mmol/L DCA and MNU treatment. Thus, similar to the
"recovery" experiment, the number of animals with foci decreased even with continuous
exposure between 31 and 52 weeks. For adenomas, 20.0 mmol DCA exposure for 31 weeks
was reported to induce adenomas in 50% of mice and after 52 weeks of exposure to induce
adenomas in 73% of mice. For TCA, the number of animals with foci was reported to be 20%
at 31 weeks and 12% at 52 weeks after exposure to 20.0 mmol/L TCA after MNU treatment
and similar to the incidence of foci reported for the TCA-recovery group. For 20.0 mmol TCA,
adenomas reported in 60% of mice after 31 weeks and in 67% of mice after 52 weeks of
exposure and also similar to the incidence of adenomas reported for the TCA-recovery group.
In regard to multiplicity, the number of foci/mouse was decreased from 2.80 ± 0.20 to
1.46 ± 0.48 between 31 and 52 weeks of 20.0 mmol DCA in MNU exposed mice. The number
of adenomas/mouse was reported to be increased from 1.80 ± 0.83 to 3.62 ± 0.70 between
31 and 52 weeks of 20.0 mmol DCA and MNU exposed mice. For 20.0 mmol/L TCA, the
number of foci/mouse was 0.20 ±0.13 and 0.13 ± 0.7 for 31- and 52-week exposures. The
number of adenomas/mouse was reported to be 1.30 ± 0.45 and 1.29 ± 0.24 for 31- and
52-week exposures. Thus, by only looking at foci and adenoma multiplicity data, there would
not appear to be a change between 31 and 52 weeks.
However, during progression, a shift may occur such that foci become adenomas with
time and adenomas become carcinomas with time. For carcinomas, there were no data reported
for 31-week exposure in MNU and DCA- or TCA-treated mice. However, at 52 weeks,
20.0 mmol DCA was reported to induce carcinomas in 19.2% of mice and 20.0 mmol TCA to
induce carcinomas in 83% of mice. The corresponding numbers of carcinomas/mouse was
0.23 ±0.10 for 20.0 mmol/L DCA treatment and 2.79 ± 0.48 for 20.0 mmol/L TCA treatment
at 52 weeks in MNU treated mice. Thus, although fewer than 20% of MNU-treated mice were
reported to have foci at 20.0 mmol TCA, by 52 weeks, almost all had carcinomas with -67%
also having adenomas. For DCA, many more mice had foci at 31 weeks (80%) than for TCA
and by 52 weeks -70% had adenoma with only -20% reported to have carcinomas. The
incidence data are suggestive that as these high doses of DCA and TCA, TCA was more
efficient inducing progression of a carcinogenic response than DCA in MNU-treated mice.
The authors interpreted the decrease in foci and adenomas between animals treated with
MNU and 20.0 mmol/L DCA for 31 weeks and sacrificed and those sacrificed 21 weeks later
to indicate that these lesions were dependent on continued exposure. However, the total
number of lesions cannot be ascertained because carcinoma data were not reported for 31-week
exposures. Carcinomas were reported in the recovery group at 52 weeks (0.15 ± 0.10
carcinomas/mouse in 15.4% of animals). Of note is that not only did the number of foci/mouse
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and incidence decrease between the 31-week group and the recovery group, but also between
31 and 52 weeks of continuous exposure for the MNU and 20.0 mmol/L DCA treated groups.
Although derived from very few animals, the 6.67 mmol/L DCA group reported no change for
foci/mouse but a decrease in the incidence of foci between 31- and 52 weeks of exposure in
MNU treated mice (i.e., 0.67 ± 0.18 foci/mouse in 50% of the animals at 31 weeks and 0.50 ±
0.34 foci/mouse in 20% of mice treated for 52 weeks). The numbers of foci/mouse for both
MNU-treated and untreated control mice were reported to be decreased between 31 and
51 weeks as well.
As noted in Section E.3.1.8, the number of "nodules" in humans, which may be
analogous to foci and adenomas, can spontaneously regress with time rather than becoming
HCCs. Also, as tumors get larger with progression, the number of tumors/mouse can decrease
due to coalescence of tumors and difficulty distinguishing between them. While data are
suggestive of a decrease in the number of adenomas/mouse after cessation of DCA exposure,
the incidence data are similar between the 31-week exposure and recovery groups.
Of note is that the number of carcinomas/mouse and the incidence of carcinomas was
reported to be similar between the MNU-treated mice exposed continuously to 20.0 mmol/L
DCA for 52 weeks and those that were treated for 31 weeks and then sacrificed at 52 weeks.
Also of note is that, although incidences and multiplicities of foci and adenomas were reported
to be relatively low in the 2.0 mmol/L DCA exposure groups, at 52 weeks, 40% of the mice
tested had carcinomas with 0.70 ± 0.40 carcinomas/mouse. This was a greater percentage of
animals with carcinomas and multiplicity than that reported for the highest dose of DCA. This
result suggests that the effects in regard to tumor progression, and specifically for carcinoma
induction, differ between the lowest and highest doses used in this experiment. However, the
low numbers of animals examined for the lower doses, 31-week exposures, and in the recovery
group decrease the confidence in the results of this study in regard to the effects of cessation of
exposure on tumor progression.
In regard to tumor phenotype, in MNU-treated female mice that were not also exposed
to either DCA or TCA, all four foci and 86.7% of 15 adenomas were reported to be basophilic
and 13.3% eosinophilic at the end of the 52-week study. However, when MNU-treated female
mice were also exposed to DCA, the number eosinophilic foci and tumors increased with
increasing dose after 52 weeks of continuous exposure. At the 20.0 mmol/L level, all 38 foci
examined were eosinophilic and 99% of the tumors (almost all adenomas) were eosinophilic.
At the 2.0 mmol/L DCA exposure, there were no foci examined but about five of nine tumors
examined (~2:1 carcinoma:adenoma ratio) were basophilic and the other four were
eosinophilic.
For TCA co-exposure in MNU-treated mice, the 20 mmol/L TCA treatment was
reported to give results of one of the three foci examined to be basophilic and two that were
eosinophilic. For the 98 tumors examined (-2:1 carcinoma/adenoma ratio), 71.4% were
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reported to be basophilic and 28.6% were eosinophilic. At the 2.0 mmol/L TCA exposure
level, the two foci examined were reported to be basophilic, while the six tumors (all
adenomas) were reported to be 50% eosinophilic and 50% basophilic. Thus, after 52 weeks,
female mice treated with MNU and a high dose of DC A had eosinophilic foci and adenomas
and those treated with the high dose of TCA had a mixture of basophilic and eosinophilic foci
and tumors with a 3:1 ratio of tumors (mostly carcinomas) being basophilic. At the lower
doses of either DC A or TCA, the tumors tended to be mostly carcinomas for DCA and
adenomas for TCA, but both were -50% basophilic and 50% eosinophilic. The tumors
observed from MNU treatment alone were all adenomas and mostly 87% basophilic. Thus, not
only did treatment concentrations of DCA and TCA give a different result for tumor
multiplicity and incidence, but also for tumor phenotype in MNU treated female mice.
Eosinophilic foci and tumors were reported to be consistently GST-u positive while basophilic
lesions "did not contain GST-u, except for a few scattered cells or very small area comprising
less than 5% of the tumor."
Thus, exposure to either DCA or TCA increased the incidence and number of animals
with lesions (foci, adenomas, or carcinomas) in MNU-treated vs. nontreated mice (see
Section E.2.3.2). These results suggest that the pattern of foci, adenoma and carcinoma
incidence, multiplicity, and progression appeared to differ between TCA and DCA in
MNU-treated female mice. However, the low and variable number of animals used in this
study, make quantitative inferences between DCA and TCA exposures in "initiated" animals,
problematic.
E.4.2.4. Tao et al. (2000)
The source of liver tumors for this analysis was reported to be the study of Pereira and
Phelps (1996). Samples of liver "tumors" and "noninvolved" liver were homogenized for
protein expression for c-Jun and c-Myc and therefore, contained homogeneous cell types for
study. The term "liver tumors" was not defined, so it cannot be ascertained as to whether the
lesions studied were altered foci, hepatocellular adenomas, or carcinomas. Liver tissues were
reported to be frozen prior to study which raises issues of m-RNA quality. Although this study
reports that there were no MNU-induced "tumors," the original paper of Pereira and Phelps
(1996) reports that there were 4 foci and 15 adenomas in MNU-only treated mice. The authors
reported no difference in c-Jun and c-Myc m-RNA from DCA or TCA-induced tumors from
mice "initiated" with MNU. DNA methyltransferase was reported to be decreased in
noninvolved liver in MNU-only treated mice in comparison to that from TCA- and
DCA-treated mice. For a comparison between noninvolved liver and tumors, tumors were
reported to have a greater level than did noninvolved liver.
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E.4.2.5. Latendresse and Pereira (1997)
This study used the tumors from Pereira and Phelps (1996), except for the MNU-treated
only groups and those groups treated with either DCA or TCA but not MNU initiation, to further
study various biomarkers. The omissions were cited as to be due to insufficient tissue. For
immunohistochemical evaluation of the molecular biomarkers other than GST-u, liver
specimens from seven MNU/20.0 mmol DCA- (i.e., 2.58 g/L DCA) treated and six MNU/20.0
mmol TCA- (i.e., 3.27 g/L TCA) treated female mice randomly selected. For GST-u, the
number of animals from which lesion specimens were derived, was 24 MNU/DCA-treated and
23 MNU/TCA-treated mice.
The DCA-treated mice were reported to have 1-9 lesions/mouse and TCA-treated mice
had 1-3 lesions/mouse. The number of lesions examined for each biomarker varied greatly. For
TCA-induced foci, no foci were examined for any biomarker except 3 lesions for GST-u, while
for DCA, 12-15 foci were examined for each biomarker and 38 lesions were examined for
GST-u. Similarly for TCA-induced adenomas, there were 8-10 lesions examined for all
biomarkers with 32 lesions examined GST-u, while for DCA, there were 12 lesions for all
biomarkers with 94 lesions examined for GST-u. Finally, for TCA-induced carcinomas, there
were 3-4 lesions examined per group with 64 lesions examined for GST-u, while for
DCA-induced carcinomas, there were no lesions examined for any biomarker except 3 examined
for GST-u. The biomarkers used were: GST-u, TGF-a, TGF-P, c-Jun, c-Fos, c-Myc,
cytochrome oxidase CYP2E1, and cytochrome oxidase CYP4A1.
MNU/DCA treatment was reported to produce "predominantly eosinophilic lesions"
with:
in general, the hepatocytes of DCA-promoted foci and tumors were less
pleomorphic and uniformly larger and had more distinctive cell borders than the
hepatocytes in lesions caused by TCA. Parenchymal hepatocytes of DCA-
promoted mice were uniformly hypertrophied, with prominent cell borders, and
the cytoplasm was markedly vacuolated, which was morphologically consistent
with the previous description of glycogen deposition in these lesions. In contrast,
TCA-promoted proliferative lesions tended to be basophilic, as previously
reported, and were composed of hepatocytes with less distinct cell borders, slight
cytoplasmic vacuolization, and greater variability in nuclear size and cellular size.
The hepatocytes of altered foci and hepatocellular adenomas from MNU-treated female
mice also treated with DCA were reported to stain positively for TGF-a, c-Jun, c-Myc,
CYP2E1, CYP4A1, and GST-u. The authors do not present the data for foci and adenomas
separately, but as an aggregate, and as the number of lesions with <50% cells stained or the
number of lesions with >50% cells stained either "minimally to mildly" or "moderately to
densely" stained.
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Because no carcinomas for DCA were examined and especially because no foci for TCA
analyses were included in the aggregates, it is difficult to compare the profile between TCA and
DCA exposure in initiated animals and to separate these results from the effects of differences in
tumor progression. Thus, any differences seen in these biomarkers due to progression from foci
to adenoma in DCA-induced lesions or from progression of adenoma to carcinoma in TCA-
induce lesions, was lost. If the results for adenomas had been reported separately, there would
have been a common stage of progression from which to compare the DCA and TCA effects on
initiated female mice liver tumors. For DCA-induced "lesions" (-50% foci and -50%
adenomas), most lesions had >50% cells staining with moderate to dense levels for TGF-a, and
CYP2E1, CYP4A1, and GST-u and most lesions had <50% cells staining for even minimally to
mild staining for TGF-P and c-Fos. For c-Jun and c-Myc, the aggregate DCA-induced "lesions"
were heterogeneous in the amount of cells and the intensity of cell staining for these biomarkers
in MNU-treated female mice.
For the TCA "lesions" (-60% adenomas and -30% carcinomas) the authors note that:
in general, the hepatocytes of tumors promoted by TCA demonstrated variable
immunostaining. With the exception of c-Jun, greater than 50% of the
hepatocytes in TCA lesions were essentially negative or stained only minimally to
mildly for the protein biomarkers studies. In some instances, particularly in TCA-
promoted tumors, there was regional staining variability within the lesions,
including immunoreactivity for c-Jun and c-Myc proteins, consistent with clonal
expansion or tumor progression.
As stated above, the term "lesion" refers to foci and adenomas for DCA, but for
adenomas and carcinomas for TCA, making inferences as to differences in the actions of the two
compounds through the comparisons of biomarkers confounded by the effects of tumor
progression. The largest differences in patterns between TCA induced "lesions" and those by
DCA appeared to be TGF-a (with no lesions having >50% cells stained mildly or
moderately/densely for TCA-induced lesions), CYP2E1 (with few lesions having >50% stained
moderately/densely for TCA-induced lesions), CYP4A1 (with no lesions having >50% stained
mildly or moderately/densely for TCA-induced lesions), and GST-u (with all lesions having
<50% cells stained even mildly for TCA-induced lesions). However, as shown by these data,
while the "lesions" induced by TCA and DCA had some commonalities within each treatment,
there was heterogeneity of lesions produced by both treatments in female mice already exposed
to MNU. Overall, the tumor biomarker pattern suggests differences in the effects of DCA and
TCA through differences in tumor phenotype they induce as co-exposures with MNU treated
female mice.
The authors noted that nonlesion parenchymal hepatocytes in DCA-treated initiated mice
stained mostly negative for CYP2E1 and CYP4A1, while in TCA-treated mice, staining patterns
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in parenchymal nonlesions hepatocytes were centrilobular for CYP2E1 and panlobular for
CYP4A1 (a pattern for CYP4A1 that is opposite of that found in the TCA-induced lesions).
E.4.2.6. Pereira et al. (1997)
This study used a similar paradigm as that of Pereira and Phelps (1996) to study co-
exposures of TCA and DCA to female B6C3Fi mice already exposed to MNU. At 15 days, the
mice received 25 mg/kg MNU and starting at 6 weeks of age neutralized solutions of either 0,
7.8, 15.6, or 25.0 mmol/L DCA (n = 30 for control and 25 mmol/L DCA and n = 20 for 7.8 and
15.6 mmol/L DCA), 6.0 or 25.0 mmol/L TCA (n = 30 for 25.0 mmol/L TCA and n = 20 for
6.0 TCA), or combinations of DCA and TCA that included 25.0 mmol/L TCA + 15.6 mmol/L
DCA (n = 20), 7.8 mmol/L DCA + 6.0 mmol/L TCA (n = 25), 15.6 mmol/L DCA + 6.0 mmol/L
TCA (45), and 25.0 mmol/L DCA + 6.mmol/L TCA (n = 25). The corresponding
concentrations of DCA and TCA in g/L are 25 mmol = 3.23 g/L, 15.6 mmol = 2.01 g/L and
7.8 mmol = 1.01 g/L DCA and 25 mmol = 4.09 g/L, and 6.0 mmol = 0.98 g/L TCA.
Accordingly, the number of animals at the beginning of the study varied between 20 and 45. At
terminal sacrifice (after 44 weeks of exposure), the numbers of animals examined were less with
the lowest number examined to be 17 mice in the 7.8 mmol/L DCA group and the largest to be
42 mice in the 15.6 mmol/L DCA + 6.0 mmol/L TCA exposed group.
The authors reported that only a total of eight HCCs were found in the study (i.e.,
25.0 mmol/L DCA induced three carcinomas, 7.8 mmol DCA + 6.0 mmol TCA induced one
carcinoma, and 25.0 mmol/L TCA induced four carcinomas). Thus, they presented data for
foci/mouse, adenomas/mouse, and their sum of both as "total lesions." The incidences of
lesions (i.e., how many mice in the groups had lesions) were not reported. The shortened
duration of exposure (i.e., 44 weeks), the omission of carcinomas from total "lesion" counts
(precluding consideration of progression of adenomas to carcinomas), the lack of reporting of
tumor incidences between groups, and the variable and low numbers of animals examined in
each group make quantitative inferences regarding additivity of these treatments difficult.
MNU-treated mice did have a neoplastic response, albeit low using this paradigm.
For mice that were only exposed to MNU (n = 30 at terminal sacrifice), the mean
numbers of foci, adenomas, and "lesions" per mouse were 0.21, 0.07, and 0.28, respectively.
No data were given for mice without MNU treatment but few lesions would be expected in
controls. Pereira and Phelps (1996) reported that saline-only treatment in 40 female mice for
51 weeks resulted in 0% foci, 0.03 adenomas/mouse in 2.5% of mice, and 0% carcinomas. In
general, it appeared that the numbers of foci, adenomas, and the combination of both reported as
"lesions" per mouse that would have been predicted by the addition of multiplicities given for
DCA, TCA, and MNU treatments alone, were similar to those observed as co-exposure
treatments. The largest numbers of foci and adenomas/mouse were reported for the
25.0 mmol/L DCA and 6.0 mmol/L TCA treatments in MNU-treated mice (mean of
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6.57 "lesions'Vmouse) with the lowest number reported for 7.8 mmol/L DC A and 6 mmol/L
TCA (mean of 1.16 "lesions'Vmouse).
The authors reported that the foci of altered hepatocytes were predominantly eosinophilic
in DCA-treated female mice initiated with MNU, while those observed after MNU and TCA
treatment were basophilic. MNU treatment alone induced four basophilic and two eosinophilic
foci, and two basophilic adenomas. MNU and DCA treatment was reported to produce only
eosinophilic foci and adenomas at the 25.0 mmol/L DCA exposure level. At the 7.8 mmol/L
DCA level of treatment in MNU-treated mice, two foci were basophilic, four were eosinophilic,
and the one adenoma observed was reported to be eosinophilic. Thus, the concentration of
exposure appeared to alter the tincture of the foci observed after MNU and DCA exposure using
this paradigm. In this study, MNU and TCA treatment was reported to induce foci and
adenomas that were all basophilic at both 25.0 mmol/L TCA and 6.0 mmol/L TCA exposures.
After 7.8 mmol/L DCA + 6.0 mmol/L TCA exposure, 2/23 foci were basophilic and 21/23 foci
were reported to be eosinophilic, while all four adenomas reported for this group were
eosinophilic.
Irrespective of treatment, eosinophilic foci for were reported to be GST-u positive and
basophilic foci to be GST-7i negative. An exception was the four carcinomas in the group
treated with 25 mmol/L TCA, which were reported to be predominantly basophilic but
contained small areas of GST-7i positive hepatocytes.
It should be noted that the increased dose (up to 3.23 g/L DCA and 4/09 g/L TCA) raises
issues of toxicity and effects on water consumption, as other studies have noted toxicity at
highly doses of DCA and TCA. The use of an abbreviated duration of exposure in the study
raises issues of sensitivity of the bioassay at the lower doses used in the experiment. In
particular, was enough time provided to observe the full development of a tumor response?
Finally, a question arises as to what can be concluded from the low numbers of foci examined in
the study and the effect of such low numbers on the ability to discern differences in these foci by
treatment. As with Pereira and Phelps (1996), there appeared to be a difference the nature of the
response induced by co-exposure of MNU to relatively high vs. low DCA concentrations. Of
note is that while this experiment reported no HCCs at the lowest dose of DCA at 44 weeks
(7.8 mmol DCA), Pereira and Phelps (1996) reported that in nine mice treated with MNU and
2.0 mmol DCA for 52 weeks, there were no foci, but 20% of mice had adenomas
(0.20 adenomas/mouse) and 40% of mice had carcinomas (0.70 carcinomas/mouse).
These results suggest that DCA co-exposure affects TCA-induced lesions. The authors
concluded that mixtures of DCA and TCA appear to be at least additive and likely synergistic
and similar to the pathogenesis of DCA.
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E.4.2.7. Tao et al. (1998)
The focus of this study was an examination of tumors resulting from MNU and DCA or
TCA exposure in mice with the source of tumors was reported to be the study of Pereira et al.
(1997). Thus, similar concerns discussed above for that study paradigm are applicable to the
results of this study. The authors stated that there were also two recovery groups in which
exposure was terminated 1 week prior to euthanization at week 44. The Pereira et al. (1997)
study does not report a cessation group in the study. In this study, the number of animals treated
in the cessation group, the incidences of tumors in the mice, and the number of tumors examined
were not reported. Another group of female B6C3Fi mice (7-8 weeks old) were reported to not
be administered MNU but given 25 mmol/L DCA (3.23 g/L DCA), 25 mmol TCA (4.09 g/L
TCA), or control drinking water for 11 days (n = 7).
Hepatocellular adenomas in DCA-treated mice and adenomas and carcinomas in
TCA-treated mice were reported to be analyzed for percent-5-methylcytosine in the DNA of
tumor tissues. The levels of 5-methylcytosine in liver DNA of mice administered DCA or TCA
for 11 days were reported to be reduced in comparison to control tissues (reduced to -36% of
control for DCA and -41% of control for TCA with the control value reported to be -3.5% of
DNA methylated). The number of animals examined was reported to be 7-10 animals per
group.
For control liver from mice that had received MNU but not DCA or TCA, and
noninvolved liver after 44 weeks of exposure to either, the levels of 5-methylcytosine were
similar and not different from the -3.5% of DNA methylated in untreated mice in the 11-day
experiment. Thus, initial decreases in methylated DNA shown by exposure to DCA or TCA
alone for 11 days, were not observed in "noninvolved" liver of animals exposed to either DCA
or TCA and MNU.
In regard to tumor tissues, the level of 5-methylcytosine in DNA of hepatocellular
adenomas receiving DCA and MNU was reported to be decreased by 36% in comparison to
noninvolved liver from the same animals. When exposure to DCA was terminated for 1 week
prior to sacrifice, the level of 5-methylcytosine in the adenomas was reported to be higher and
no longer differed statistically from the noninvolved liver from the same animal or liver from
control animals only administered MNU. The number of samples was reported to be 9-
16 samples without identification as to how many samples were used for each tumor analysis or
how many animals provided the samples (i.e., were most of the adenomas from on animal?)
For TCA, the 5-methylcytosine level was reported to be reduced by 40% in
hepatocellular adenomas and 51% reduction in HCCs in comparison to noninvolved liver from
the same animals. These levels were also reported to be less than that the control animals
administered only MNU.
Termination of exposure to TCA 1 week prior to sacrifice was reported to not produce a
statistically significant change in the level of 5-methylcytosine in either adenomas or
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carcinomas. The levels of 5-methylcytosine were reported to be lower in carcinomas than
adenomas (-20% reduction) and to be lower in the "recovery" carcinomas than continuous
carcinomas (-25%), but were not reported as statistically significant. The results are reported to
have been derived from 8 to 16 "samples each." Again, information on the number of animals
with tumors, whether the tumors were from primarily from one animal, and which DNA results
are from 8 vs. 16 samples, was not provided by the authors.
Given that Pereira et al. (1997), the source for material of this study, reported that
treatment of MNU and 25.0 mmol/L TCA treatment for 44 weeks induced only four carcinomas,
a question arises as to how many carcinomas were used for the 44-week 5-methylcytosine
results in this study for carcinomas (i.e., how can 8-16 samples arise from four carcinomas?).
In addition, a question arises as to whether there was a difference in tumor-response in those
animals with and without 1 week of cessation of exposure, which cannot be discerned from this
report. The use of highly variable number of samples between analysis groups and lack of
information as to how many tumors were analyzed adds uncertainty to the validity of these
findings. There did not appear to be a difference in methylation activity from short-term
exposure to either DCA or TCA alone in whole-liver DNA extracts. However, the authors
conclude that the difference in methylation status between tumors resulting from MNU and
DCA or TCA exposures supports differences in the action between DCA and TCA.
E.4.2.8. Stauber et al. (1998)
In this study, 5-8-week-old male B6C3Fi mice were used for isolation of primary
hepatocytes, which were subsequently isolated and cultured in DCA or TCA. In a separate
experiment, 0.5 g/L DCA was given to mice as pretreatment for 2 weeks prior to isolation. The
authors note that an indication of an "initiated cell" is anchorage-independent growth. DCA and
TCA solutions were neutralized before use. The primary hepatocytes from three mice per
concentration were cultured for 10 days with DCA or TCA colonies (eight cells or more)
determined in quadruplicate. The levels of DCA used were 0, 0.2, 0.5, and 2.0 mM DCA or
TCA. At concentrations of >0.5 mM, DCA and TCA both induced an increase in the number of
colonies that was statistically significant and increased with dose, with DCA giving a slightly
greater effect. The authors noted that concentrations >2.0 mM were cytotoxic, but did not show
data on toxicity for this study.
Of great interest is the time-course experiment from this study in which the number of
colonies from DCA treatment in vitro peaked by 10 days and did not change through days 15-
25 at the highest dose. For the lower concentrations of DCA, increased time in culture induced
similar peak levels of colony formation by days 20-25 as that reached by 10 days at the higher
dose. Therefore, the number of colonies formed was independent of dose if the cells were
treated long enough in vitro. The number of colonies that formed in control hepatocyte cultures
also increased with time but at a lower rate than those treated with DCA (2.0 mM DCA gave
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approximately twofold of control by 25 days of exposure to hepatocytes in culture). However,
the level reached by cells untreated in tissue culture alone by 20 days was similar to the level
induced by 0.5 mM DCA by 10 days of exposure. This finding raises the issue of what these
"colonies" represent, as tissue culture conditions alone transform these cells to what the authors
suggest is an "initiated" state. TCA exposure was not tested with time to see if it had a similar
effect to DCA.
At 10 days, colonies were tested for c-Jun expression with the authors noting that
"colonies promoted by DCA were primarily c-Jun positive in contrast to TCA promoted
colonies that were predominantly c-Jun negative." For colonies that arose spontaneously from
tissue culture conditions, 10/13 (76.9%) were reported to be c-Jun+, those treated with DCA
28/34 (82.3%) were c-Jun+, and those treated with TCA 5/22 (22.7%) were c-Jun+. These data
show heterogeneity in cell in colonies, although more were c-Jun+ with DCA than TCA. The
number of colonies reported in the c-Jun labeling results represent sums between experiments
and thus, present total numbers of the control and the of colonies derived from doses of DCA
and TCA at 0.2-2.0 mM at 10 days. Thus, changes in colony c-Jun+ labeling due to increasing
dose cannot be determined.
The authors reported that with time (24, 48, 72, and 96 hours) of culture conditioning,
the number of c-Jun+ colonies was increased in untreated controls. DCA treatment was reported
to delay the increase in c-Jun+ expression induced by tissue culture conditions alone in
untreated controls. TCA treatment was reported to not affect the increasing c-Jun+ expression
that increased with time in tissue culture. In this instance, tissue culture environment alone was
shown to transform cells and can be viewed as a "co-exposure." DCA pretreatment in vivo was
reported to increase the number of colonies after plating, which reached a plateau at 0.10 mM
and gave changes as at low a concentration of 0.02mM DCA administered in vitro. The
background level of colony formation varied between controls (i.e., twofold different in
pretreatment experiments and nonpretreatment experiments). Therefore, although the number of
colonies was greater for pretreatment with DCA, the magnitude of difference over the control
level was the same after DCA treatment in vitro with and without pretreatment.
The authors presented a comparison of "tumors" from Stauber and Bull (1997) and state
that DCA tumors were analyzed after 38 weeks of treatment but that TCA tumors were analyzed
after 52 weeks. They note that 97.5% of DCA-induced "tumors" were c-Jun+, while none of the
TCA-induced "tumors" were c-Jun+. The concentrations used to give tumors in vivo for
comparison with in vitro results were not reported. What was considered to be "tumors" from
the earlier report for this analysis was also not noted. Stauber and Bull (1997) reported results
for combination of foci and tumors raising issues as to what was examined in this report. The
authors stated that because of such short time, no control tumors results were given. The short
and variable time of duration of exposure increases the possibility of differences between the in
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vivo data resulting from differences in tumor progression as well as a decreased ability by the
shortened time of observation for full expression of the tumor response.
E.4.3. Co-exposures of Haloacetates and Other Solvents
As noted by Caldwell et al. (2008b), drinking water exposure data suggest that co-
exposure of TCE and its haloacetic acid metabolites, TCA and DCA, is not an uncommon event,
as DCA and TCA are the two most abundant haloacetates in most water supplies (Boorman,
1999; Weisel et al., 1999). Dibromoacetic acid (DBA) concentrations have also been reported
to range up to approximately 20 ug/L in finished water and distribution systems (U.S. EPA,
2002a). Caldwell et al. (2008b) have also noted that co-exposure in different media also occurs
with solvents like perchloroethylene (PERC) that may share some modes of action, targets of
toxicity, and common metabolites that can, therefore, potentially affect TCE health risk (Wu and
Schaum, 2000). Some of the information contained in the following sections has been excerpted
from the discussions by Caldwell et al. (2008b) regarding the implications for the risk of TCE
exposure as modulated by co-exposures to haloacetates and other solvents that have been
studied and reported in the literature.
E.4.3.1. Carbon tetrachloride, DCA, TCA: Implications for Mode of Action from Co-
exposures
Studies of specific combinations of TCE and chemicals colocated in contaminated areas
have been reported by Caldwell et al. (2008b). For carbon tetrachloride:
Pretreatment with TCE in drinking water at levels as low as 15 mM for three days
has been reported to increase susceptibility to liver damage to subsequent
exposure to a single IP injection of 1 mM/kg carbon tetrachloride (CCU) in
Fischer 344 rats (Steup et al., 1991). Potential mechanistic explanations for this
observation included altered metabolism, decreased hepatic repair capability,
decreased detoxification ability, or combination of one or more of the above
activities. Simultaneous administration of an oral dose of TCE (0.5ml/kg) has
also been reported to increase the liver injury induced by an oral dose of 0.05
ml/kg CCU (Steup et al., 1993). The authors suggested that TCE appeared to
impair the regenerative activity in the liver, thus leading to increased damage
when CCU is given in combination with TCE.
As discussed in Section E.4.2, initiation studies are in themselves a co-exposure. The
study of Bull et al. (2004) is included here as it not only used a co-exposure of vinyl carbamate
with TCE metabolites, but also used carbon tetrachloride as a co-exposure. The rationale for
this approach was that co-exposure of TCE (and therefore, to its metabolites) and carbon
tetrachloride are likely to occur as they are commonly found together at contaminated sites.
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Bull et al. (2004) hypothesized that modification of tumor growth rates is an indication
of promotion rather than effects on tumor number, and that by studying tumor growth rates, they
could classify carcinogens by their modes of action. B6C3Fi male mice were initiated with
vinyl carbamate (3 mg/kg) at 2 weeks of age and then treated with DCA, TCA, or carbon
tetrachloride (0.1, 0.5, or 2.0 g/L for DCA and TCA; 50, 100 or 500 mg/kg carbon tetrachloride
in 5% Alkamuls via gavage) in pair-wise combinations of the three for 18-36 weeks. The
exposure level of carbon tetrachloride to 5, 20, and 50 mg/kg was reported to be reduced at
week 24 due to toxicity for carbon tetrachloride. The number of mice in each group was
reported to be 10 with the study divided into five segments. There were evidently differences
between treatment segments as the authors state that "because of some significant quantitative
differences in results that were obtained with replicate experiments treated in different time
frames, the simultaneous controls have been used in the analysis and presentation of these data."
As with Bull et al. (2002), the interpretation of the results of the study is limited by a low
number of animals per group, short duration time of exposure, and limited examination and
reporting of results. For example, a sample of 100/8,000 lesions identified in the study was
examined to verify that the general descriptor of neoplastic and nonneoplastic lesion was
correctly labeled with "tumors" describing a combination of hyperplastic nodules, adenomas,
and carcinomas. No incidence data were reported by the authors, but general lesion growth
information included mean lesion volume and multiplicity of lesions (numbers of
lesions/mouse). Using these reported indices, there appeared to be differences in treatment-
related effects.
As discussed in Caldwell et al. (2008b):
Each treatment was examined alone and then in differing combinations with each
other. Mice initiated with vinyl-carbamate, but without further exposure to the
other toxicants, were reported to have a few lesions that were of small size during
the examination period (20-36 weeks). At 30 weeks of CC14 exposure, there was
a dose-related response reported for multiplicity but mean lesion size was smaller
at the highest dose in initiated animals. At 36 weeks, DCA exposure was reported
to increase multiplicity at the two highest exposure levels and increased lesion
size at all levels compared to initiated-only animals. However, at a similar level
of induction, multiplicity and mean size of those lesions resulting from DCA
treatment were reported to be much smaller in comparison with CC14 treatment
(i.e., a 20-fold difference for lesion volume). At 36 weeks, treatments with the
same concentration of TCA or DCA induced similar multiplicity, but the mean
lesion volume was reported to be approximately 4-fold greater in tumors induced
by DCA as compared to TCA, and in animals treated with DCA multiplicity had
reached a plateau by 24 weeks rather than 36 for those treated with TCA.
Thus, using multiplicity of lesions and lesion volume as indicators of differences in mode
of action, exposure to carbon tetrachloride, DCA, and TCA appeared to produce distinct
differences in results in animals previously treated with vinyl carbamate.
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As discussed in Caldwell et al. (2008b):
Simultaneous coexposure of differing combinations of CC14, DCA, and TCA were
reported to give more complex results between 24 and 36 weeks of observation
but to show that coexposure had effects on lesion multiplicity and volume in
initiated animals. At 36 weeks, TCA coexposure appeared to reduce the lesion
volume of either DCA- or CCl4-induced lesions after vinyl carbamate treatment.
Similarly, DCA coexposure was reported to reduce the lesion volume of either
TCA- or CCU-induced lesions when each was given alone after vinyl carbamate
treatment. With regard to multiplicity, TCA coexposure was reported to reduce
DCA-induced multiplicity only at the lowest dose of TCA while coexposure with
DCA increased multiplicity of CCU-induced lesions at all exposure levels. At 24
weeks, there appeared to be little effect on mean lesion volume by any of the
coexposures but DCA coexposure decreased multiplicity of TCA-induced lesions
(up to 3-fold) while TCA treatment slightly increased the number of CCl4-induced
multiplicity (1.6-fold). This study confirms that short duration of exposure to all
three of these chemicals can cause lesions in already exposed to vinyl carbamate,
and suggests that combinations of these agents differentially influence lesion
number and growth rates. The authors have interpreted their results to indicate
differences in MOA between such treatments. However, the limitations of the
study limit conclusions regarding how such coexposure may be able to affect
toxicity and tumor induction and what the MOA is for each of these agents. This
is especially true at lower and more environmentally relevant concentrations
given for longer durations to uninitiated animals.
E.4.3.2. Chloroform, DCA, and TCA Coexposures: Changes in Methylation Status
In Section E.3.4.2.2, information on the effects of TCE and its metabolites was presented
in regard to effects on methylation status. After 7 days of gavage dosing, TCE, TCA, and DCA
were reported to increased hypomethylation of the promoter regions of c-Jun and c-Myc genes
in mouse whole-liver DNA; however, Caldwell and Keshava (2006) concluded that
hypomethylation did not appear to be a chemical-specific effect at the concentration used. Bull
et al. (2004) suggested that hypomethylation occurs at higher exposure levels than those that
induce liver tumors for TCE and its metabolites. Along with studies of methylation changes
induced by a exposure to a single agent, Pereira et al. (2001) have attempted to examine the
effects on methylation changes from co-exposures. This study was also reviewed by Caldwell et
al. (2008b).
Pereira et al. (2001) hypothesized that changes in the methylation status of DNA can be a
key event for the mode of action for DCA- and TCA-induced liver carcinogen!city through
changes in gene regulation, and that chloroform (CHCb) co-exposure may result in modification
of DNA methylation. As discussed in Caldwell et al. (2008b),
After 17 days of exposure of exposure to CHC13 (0, 400, 800, 1,600 mg/L, n = 6
mice per treatment group) female B6C3Fi mice were coexposed to DCA or TCA
(500 mg/kg) during the last 5 days of exposure to chloroform. As noted by
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Caldwell et al. (2008b). Pereira et al. (2001) reported the effects of
hypomethylation of the promoter region of c-Myc gene and on expression of its
mRNA in the whole livers of female B6C3Fi mice and thus, these results
represent composite changes in DNA methylation status from a variety of cell
types and for hepatocytes lumped from differing parts of the liver lobule. When
given alone, DCA, TCA, and to a lesser extent, the highest concentration of
CHCb (1,600 mg/L), was reported to decrease methylation of the c-myc promoter
region. Coadministration of CHCls (at 800 and 1,600 mg/L) was reported to
decrease DCA-induced hypomethylation while exposure to CHCb had no effect
on TCA-induced hypomethylation. Treatment with DCA, TCA, and, to a lesser
extent CHCls, was reported to increase levels of c-myc mRNA. While expression
of c-myc mRNA was increased by DCA or TCA treatment, increasing
coexposures to CHCb were reported to attenuate the actions of DCA but not
TCA. Thus, differences in methylation status and expression of the c-myc gene
induced by DCA or TCA exposure was reported to be differentially modulated by
coexposure to CHQs The authors suggest these differences support differing
actions by DCA and TCA. However, whether these changes represent key events
in the induction of liver cancer is a matter of debate, especially as a "snapshot in
time" approach for such a nonspecific endpoint.
In a co-exposure study in which an "initiating agent" was used as a co-exposure along
with other co-exposure, Pereira et al. (2001) treated male and female 15-day-old B6C3Fi mice
with MNU (a cause of liver and kidney tumors) and then, starting at 5 weeks of age, treated
them with DCA (3.2 g/L) or TCA (4.0 g/L) along with co-exposure to CHC13 (0, 800, or
1,600 mg/L) for 36 weeks. Mice were reported to be examined for evidence of promotion of
liver and kidney tumors. The numbers of animals in the exposure groups were highly variable,
ranging from 25 (female-initiated mice exposed to DCA) to 6 (female-initiated mice exposed to
DCA and 1,600 mg/L CHCb), thus limiting the power of the study to ascertain treatment-related
changes. However, unlike Bull et al. (2004), all liver tissues were examined with incidences of
foci, adenomas, carcinomas, and both adenoma and carcinoma reported separately for treatment
groups. Multiplicity for a combination of adenomas and carcinomas were reported as well as
the tincture of foci and tumors.
Although as noted by Cal dwell et al. (2008b):
[T]he statistical power of the study to detect change was very low, an examination
of the pattern of tumors induced by coexposure to MNU and TCE metabolites in
female mice suggested that: (1) DCA exposure increased the incidence of
adenomas but not carcinomas; (2) TCA increased incidence of carcinomas with
little change in adenoma incidence; (3) coexposure to 800 and 1,600 mg/L of
CHCls decreased adenoma incidence by DCA treatment but not TCA; and
(4) CHCb coexposure decreased multiplicity of TCA-induced tumors and foci,
but not for DCA.
Caldwell et al. (2008b) also note that this study suggests:
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[A] gender-related effect on tumor induction from this study with: (1) adenoma
incidences similar in male and female mice treated with DCA, but carcinoma
incidence greater in males; (2) adenoma and carcinoma incidences greater in
males than females treated with TCA; (3) tumor multiplicity similar in both
genders for DCA treatments, but lower in females mice for TCA; and (4) less of
an inhibitory effect by CHCb on adenoma incidence from DCA exposure in male
mice.
Pereira et al. (2001) also described the tinctural characteristics of the specific lesions
induced by their co-exposure treatments. Both foci and tumors induced by DCA exposure in
"initiated" mice were reported to be over 95% eosinophilic in females, while in males, 89% of
the foci were eosinophilic and 91% of tumors were basophilic. Thus, not only was there a
gender-related difference in the incidences of tumors and foci but also foci and tumor
phenotype. CHCb co-exposure was reported to change the DCA-induced foci from primarily
eosinophilic to basophilic (i.e., 11 vs. 75% basophilic) in male mice coexposed to MNU. In
male and female mice, TCA-induced tumors and foci were basophilic with no effect of CHCb
on phenotype in MNU treated mice.
E.4.3.3. Co-exposures to Brominated Haloacetates: Implications for Common Modes
of Action and Background Additivity to Toxicity
As noted by Caldwell et al. (2008b), along with chlorinated haloacetates and other
solvents, "co-exposures with TCE and brominated haloacetates may occur through drinking
water. These compounds may affect TCE toxicity in a similar fashion to their chlorinated
counterparts. As bromide concentrations increase, brominated haloacetates increase in the water
supply."
Kato-Weinstein et al. (2001) administered dibromoacetate (DBA), bromochloroacetate
(BCA), bromodichloroacetate (BDCA), TCA, and DCA in drinking water at concentrations of
0.2-3 g/L for 12 weeks to B6C3Fi male mice. The focus of the study was to determine the
similarity in action between the brominated and chlorinated haloacetates. Each of the
haloacetates, given individually, were reported to increase liver/body weight ratios in a dose-
dependent manner.
The dihaloactates, DCA, BCA, and DBA, caused liver glycogen accumulation both by
chemical measurements in liver homogenates and in ethanol-fixed liver sections (to preserved
glycogen) stained with PAS. For DCA, a maximal level of glycogen increase was observed at
4 weeks of exposure at a 2 g/L exposure concentration. They report a 1.60-fold of control
percent liver/body weight and 1.50-fold of control glycogen content after 8 weeks of exposure to
2 g/L DCA in male B6C3Fi mice. The baseline level of glycogen content (-60 mg/g) and the
increase in glycogen after DCA exposure was consistent with the results reported by Pereira et
al. (2004a). The percent liver/body weight data for control mice was for animals sacrifice at
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20 weeks of age. The 4-12-week exposure to DCA were staggered so that all animals would be
20 weeks of age at sacrifice. Thus, the animals were at differing ages at the beginning of DCA
treatments between the groups.
However, as with Pereira et al. (2004a), the -10% increase in liver mass that the
glycogen increases represent are lower than the total increase in liver mass reported for DCA
exposure. The authors noted possible contamination of BCA with small percentages of DCA
and DBA in their studies (i.e., 84% BCA, 6% DCA and 8% DBA). The trihaloacetates (TCA
and low concentrations of BDCA) were reported to produce slight decreases in liver glycogen
content, especially in the central lobular region in cells that tended to accumulate glycogen in
control animals. These effects on liver glycogen were reported at the lowest dose examined
(i.e., 0.3 g/L). At the highest concentration, BDCA was reported to induce a pattern of glycogen
distribution similar to that of DCA in mice.
All dihaloacetates were reported to reduce serum insulin levels at high concentrations.
Conversely, trihaloacetates were reported to have no significant effects on serum insulin levels.
For the study of peroxisome proliferation and DNA synthesis, mice were treated with BCA,
DBA, and BDCA for 2, 4, or 26 weeks. The effects on DNA synthesis were small for all
brominated haloacetates with only DBA reported to show a significant increase in DNA
synthesis at 2 and 4 weeks but not at 26 weeks (the increase in DNA synthesis was threefold of
the highest control level). Of note is the highly variable level of DNA synthesis reported for
control values that varied to a much higher degree (~3-6-fold variation within control groups at
the same time points) than did treatment-related changes. DBA was the only brominated
haloacetate that was reported to consistently increased PCO activity as a percentage of control
values (actual values and variability between controls were not reported) with a 2-3-fold
increase in PCO activity at 0.3-3.0 g/L DBA. DBA-induced PCO activity increases were
reported to be limited to 2-4 weeks of treatment in contrast to TCA, which the authors reported
to increase PCO activity consistently over time.
Tao et al. (2004a) reported DNA methylation, glycogen accumulation, and peroxisome
proliferation after exposure of female B6C3Fi mice and male F344 rats exposed to 1 or 2 g/L
DBA in drinking water for 2-28 days. DBA was reported to induce dose-dependent DNA
hypomethylation in whole mouse and rat liver after 7 days of exposure with suppression
sustained for the 28-day exposure period. The expression of mRNA for c-Myc in mice and rats
and mRNA expression of the IGF-II gene in female mice were reported to be increased during
the same period. Both rats and mice were reported to exhibit increased glycogen with mice
having increased levels at 2 day and rats at 4 days. DBA was reported to cause an increase in
lauroyl-CoA oxidase activity (a marker of peroxisome proliferation) in both mice (after 7 days)
and rats (after 4 days) that was sustained for 28 days.
Methylation changes reported here for DBA exposure in rats and mice are consistent
with those reported for TCA and DCA by Pereira et al. (2001) in mice. The pattern of glycogen
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accumulation was also similar to that reported for DCA by Kato-Weinstein et al. (2001) and
suggests that the brominated analogues of TCE metabolites exhibited similar actions as their
chlorinated counterparts. In regard to peroxisomal enzyme activities, Kato-Weinstein et al.
(2001) reported PCO activity to be limited to 2-4 weeks with Tao et al. (2004b) reporting
lauroyl-CoA oxidase activity to be sustained for the lengths of the study (28 days) for DBA.
As noted by Caldwell et al. (2008b):
"given the similarity of DCA and DBA effects, it is plausible that DBA exposure
also induces liver cancer. Melnick (2008) reported the results of DBA exposure
to F344/N rats and B6C3Fi mice exposed to DBA for 3 months or 2 years in
drinking water (0, 0.05, 0.5, or 1.0 g/L DBA for 2 years). Neoplasms at multiple
sites were reported in both species exposed to DBA for 2 years with no effects on
survival and little effect on mean body weight in either species. Similar to TCE,
DCA and TCA, the liver was reported to be a target of DBA exposure. After
3-months of exposure, there were dose-related increases in hepatocellular
vacuolization and liver weight reported in rats and mice described as 'glycogen-
like.'"
The authors report that the major neoplastic effects of DBA in rats were induction of
malignant mesotheliomas in males and increased incidence of mononuclear cell leukemia in
males and females. For mice, the major neoplastic effect of DBA exposure was reported to be
the increased incidence of hepatocellular adenomas and carcinomas at all exposure levels.
In addition to these liver tumors, hepatoblastomas were also reported to be increased in
all exposure groups of male mice and exceeded historical control rates. The incidence of
alveolar/bronchiolar adenoma and carcinoma was reported to be increased in the 0.5 g/L group
of male mice along with marginal increases in alveolar hyperplasia in DBA-treated groups. The
authors reported that the increases in hepatocellular neoplasms were not associated with
hepatocellular necrosis or regenerative hyperplasia and concluded that an early increase in
hepatocyte proliferation was not likely involved in the mode of action for DBA because no
increases in hepatocyte DNA labeling index were observed in mice exposed for 26 days and the
slight increase that occurred in male F344 rats was not accompanied by an increase in liver
tumor response.
As noted by Caldwell et al. (2008b),
[T]he results of Kato-Weinstein et al. (2001). Tao et al. (2004b), and Melnick et
al. (2008) are generally consistent for DBA and show a number of activities that
may be common to TCE metabolites (i.e., brominated and chlorinated haloacetate
analogues generally have similar effects on liver glycogen accumulation, serum
insulin levels, peroxisome proliferation, hepatocyte DNA synthesis, DNA
methylation status, and hepatocarcinogenicity). It is therefore, plausible that these
effects may be additive in situations of coexposure. However, as noted by
(Melnick et al., 2008), methylation status, events associated with PPARa
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agonism, hepatocellular necrosis, and regenerative hyperplasia are not established
as key events in the MO A of these agents, and the MO As for DC A- and DBA-
induced liver tumors are unknown.
E.4.3.4. Co-exposures to Ethanol: Common Targets and Modes of Action
As noted in the U.S. EPA's draft TCE assessment (U.S. EPA. 2001). alcohol
consumption is a common co-exposure that has been noted to affect TCE toxicity with TCE
exposure cited as potentially increasing the toxicity of methanol and ethanol, not only by
altering their metabolism to aldehydes, but also by altering their detoxification (e.g., similar to
the "alcohol flush" reported for those who have an inactive aldehyde dehydrogenase allele). As
noted by Caldwell et al. (2008b) "chemical co-exposures from both the environment and
behaviors such as alcohol consumption may have effects that overlap with TCE in terms of
active agents, pharmacokinetics, pharmacodynamics, and/or target tissue toxicity."
Caldwell et al. (2008b) also noted:
In their review of solvent risk (including TCE), Brautbar and Williams (2002)
suggest that laboratory testing that is commonly used by clinicians to detect liver
toxicity may not be sensitive enough to detect early liver hepatotoxicity from
industrial solvents and that the final clinical assessment of hepatotoxicity and
industrial solvents must take into account synergism with medications, drugs of
use and abuse, alcohol, age-dependent toxicity, and nutrition. Although many of
these factors may be important, the focus in this section is on the effects of
ethanol. Contemporary literature reports effects similar to those of TCE's and
previous reports indicate ethanol consumption impacts TCE toxicity in humans,
affects the pharmacokinetics and toxicity of TCE in rats, and is also a risk factor
for cancer.
The association between malignant tumors of the upper gastrointestinal tract and
liver and ethanol consumption is based largely on epidemiological evidence, and
thought to be causally related (Bradford et al.. 2005: Badger et al.. 2003).
Studies of the mechanisms of ethanol carcinogenicity have suggested the
importance of its metabolism, including induction of CYP2E1 associated
increases in production of reactive oxygen species and enhanced activation of a
variety of pro-carcinogens, alteration of retinol and retinoic acid metabolism, and
the actions of the metabolite acetaldehyde (Badger et al., 2003). While ethanol is
primarily metabolized by alcohol dehydrogenase, it undergoes simultaneous
oxidation to acetate by hepatic P450s, primarily CYP2E1. Both chronic ethanol
consumption as well as TCE treatment induces CYP2E1 (U.S. EPA. 2001).
Oneta et al. (2002) report that even at moderate chronic ethanol consumption,
hepatic CYP2E1 is induced in humans, which they suggest, may be of
importance in the pathogenesis of alcoholic liver disease; of ethanol, drug, and
vitamin A interactions; and in alcohol-associated carcinogenesis. Induction of
CYP2E1 can cause oxidative stress to the liver from nicotinamide dinucleotide
phosphate (NADPH)-dependent reduction of dioxygen to reactive products even
in the absence of substrate, and subsequent apoptosis (Badger et al., 2003).
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Bradford et al. (2005) suggest that CYP2E1, and not NADPH oxidase, is
required for ethanol-induced oxidative DNA damage to rodent liver but that
NADPH oxidase-derived oxidants are critical for the development of ethanol-
induced liver injury.
There is increasing evidence that acetaldehyde, which is toxic, mutagenic, and
carcinogenic, rather than alcohol is responsible for its carcinogenicity (Badger et
al., 2003). Mitochondrial aldehyde dehydrogenase (ALDH2) disposes of
acetaldehyde generated by the oxidation of ethanol, and ALDH2 inactivity
through mutation or polymorphism has been linked to esophageal cancer in
humans (everyday drinkers and alcoholics) (Badger et al., 2003). For instance,
increased esophageal cancer risk was reported for patients with the ALDH3*1
polymorphism as well as increased acetaldehyde in their saliva. TCE exposure
has also been reported to induce a similar alcohol flush in humans which may be
linked to its ability to decrease ALDH activities at relatively low concentrations
and thus conferring a similar status to individuals with inactive ALDH2 allele
(Wangetal., 1999). Whether the MOA for the buildup of acetaldehyde after
ethanol and TCE co-exposure is: (1) the induction of CYP2E1 by TCE resulting
in increased metabolism to acetaldehyde; (2) inhibition of ALDH and thus
reduced clearance of acetaldehyde, or (3) a number of other actions are
unknown. Crabb et al. (2001) reported 20-30% reductions in ALDH2 protein
level by PPARa agonists (Clofibrate treatment in rats and WY treatment in both
wild and PPARa-null mice). This could be another pathway for TCE-induced
effects on ethanol metabolism. It is an intriguing possibility that the reported
association between the increased risk of human esophageal cancer and TCE
exposure (Scott and Chiu, 2006) could be related to TCE effects on
mitochondrial ALDH, given a similar association of this endpoint with ethanol
consumption or ALDH polymorphism.
Finally, ethanol ingestion may have significant effects on TCE pharmacokinetics.
Baraona et al. (2002a; 2002b) reported that chronic, but not acute, ethanol
administration increased the hepatotoxicity of peroxynitrite, a potent oxidant and
nitrating agent, by enhancing concomitant production of nitric oxide and
superoxide. They also reported that nitric oxide mediated the stimulatory effects
of ethanol on blood flow. Ethanol markedly enhanced portal blood flow (2-fold
increase), with no changes in the hepatic, splenic, or pancreatic arterial blood
flows in rats.
E.4.3.5. Co-exposure Effects on Pharmacokinetics: Predictions Using PBPK Models
Along with experimental evidence that has focused on chronic and acute experiments
using rodents, the potential pharmacokinetic modulation of risk has also been recently published
reports using PBPK models that may be useful in predicting co-exposure effects. Caldwell et al.
(2008b) also examined and discussed these approaches and noted:
An important issue for prediction of the effects and relationship on MO As by co-
exposure is the degree to which modulation of TCE toxicity by other agents can
be quantified. Pharmacokinetics or the absorption, distribution, metabolism, and
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elimination of an agent, can be affected by internal and external co-exposure.
Such information can help to identify the chemical species that may be causally
associated with observed toxic responses, the MO A, and ultimately identify
potentially sensitive subpopulations for an effect such as carcinogenicity.
Physiologically based pharmacokinetic (PBPK) models are often used to
estimate and predict the lexicologically relevant dose of foreign compounds in
the body and have been developed to predict effects on pharmacokinetics that are
additive or less or greater than additive. One of the first such models was
developed for TCE (Andersen et al.. 1987b). Given that TCE, PERC, and
methyl chloroform (MC) are often found together in contaminated groundwater,
Dobrev et al. (2001) attempted to investigate the pharmacokinetic interactions
among the three solvents to calculate defined "interaction thresholds" for effects
on metabolism and expected toxicity. Their null hypothesis was defined as
competitive metabolic inhibition being the predominant result for TCE given in
combination with other solvents. Gas uptake inhalation studies were used to test
different inhibition mechanisms. A PBPK model was developed using the gas
uptake data to test multiple mechanisms of inhibitory interactions (i.e.,
competitive, noncompetitive, or uncompetitive) with the authors reporting
competitive inhibition of TCE metabolism by MC and PERC in simulations of
pharmacokinetics in the rat. Occupational exposures to chemical mixtures of the
three solvents within their Threshold Limit Value (TLV)/TWA limits were
predicted to result in a significant increase (22%) in TCE blood levels compared
with single exposures.
Dobrev et al. (2002) extended this work to humans by developing an interactive
human PBPK model to explore the general pharmacokinetic profile of two
common biomarkers of exposure, peak TCE blood levels, and total amount of
TCE metabolites generated in rats and humans. Increases in the TCE blood
levels were predicted to lead to higher availability of the parent compound for
GSH conjugation, a metabolic pathway that may be associated with kidney
toxicity/carcinogenicity. A fractional change in TCE blood concentration of
15% for combined TLV exposures of the three chemicals (25/50/350 ppm of
PERC/TCE/MC) resulted in a predicted 27% increase of the S-(l, 2-
dichlorovinyl)-L-cysteine (DCVC) metabolites, indicating a nonlinear risk
increase due to combined exposures to TCE. Binary combinations of the
solvents produced GST-mediated metabolite levels almost twice as high as the
expected rates of increase in peak blood levels of the parent compound. The
authors suggested that using parent compound peak blood levels (a less sensitive
biomarker) would result in two to three times higher (i.e., less conservative)
estimates of potentially safe exposure levels. In regard to the detection of
metabolic inhibition by PERC and MC, the simulations showed TCE blood
concentrations to be the more sensitive dose-metric in rats, but the total of TCE
metabolites to be the more sensitive dose measure in humans. Finally,
interaction thresholds were predicted to be occurring at lower levels in humans
than rats.
Thrall and Poet (2000) investigated the pharmacokinetic impact of low-dose co-
exposures to toluene and TCE in male F344 rats in vivo using a real-time breath
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analysis system coupled with PBPK modeling. The authors report that, using the
binary mixture to compare the measured exhaled breath levels from high- and
low-dose exposures with the predicted levels under various metabolic interaction
simulations (competitive, noncompetitive, or uncompetitive inhibition), the
optimized competitive metabolic interaction description yielded an interaction
parameter Ki value closest to the Michaelis-Menten affinity parameter (KM) of
the inhibitor solvent. This result suggested that competitive inhibition is the
most plausible type of metabolic interaction between these two solvents.
Isaacs et al. (2004) have reported gas uptake co-exposure data for CHC13 and
TCE. The question as to whether it is possible to use inhalation data in
combination with PBPK modeling to distinguish between different metabolic
interactions was addressed using sensitivity analysis theory. Recommendations
were made for design of optimal experiments aimed at determining the type of
inhibition mechanisms resulting from a binary co-exposure protocol. This paper
also examined the dual nature of inhibition of each chemical in the pair to each
other, which is that TCE and CHCb were predicted to interact in a competitive
manner. Even though as stated by Dobrev et al. (2001), other solvents inhibit
TCE metabolism, it is also possible to quantify the synergistic interaction that
TCE has on other solvents, using techniques such as gas uptake inhalation
exposures.
Haddad et al. (2000) has developed a theoretical approach to predict the
maximum impact that a mixture consisting of co-exposure to dichloromethane,
benzene, TCE, toluene, PERC, ethylbenzene, m-, p-, and o-xylene, and styrene
would have on venous blood concentration due to metabolic interactions in
Sprague-Dawley rats. Two sets of experimental co-exposures were conducted.
The first study evaluated the change in venous blood concentration after a 4 hour
constant inhalation exposure to the 10 chemical mixtures. This experiment was
designed to examine metabolic inhibition for this complex mixture. The second
study was designed to study the impact of possible enzyme induction by using
the same inhalation co-exposure after a 3 day pretreatment with the same 10
chemical mixture. The resulting venous concentration measurements for TCE
from the first study were consistent with metabolic inhibition theory. The 10-
chemical mixture was the most complex co-exposure used in this study. The
authors stated that as mixture complexity increased, the resulting parent
compound concentration time courses changed less, an observation which is
consistent with metabolic inhibition. For the pretreatment study, the authors
found a systematic decrease in venous concentration (due to higher metabolic
clearance) for all chemicals except PERC. Overall, these studies suggest a
complex metabolic interaction between TCE and other solvents.
A PBPK model for TCE including all its metabolites and their interactions can be
considered a mixtures model where all metabolites have a common starting point
in the liver. An integrated approach taking into account TCE metabolites and
their metabolic inhibition and interactions among each other is suggested in Chiu
et al. (2006b).
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E.5. POTENTIALLY SUSCEPTIBLE LIFE STAGES AND CONDITIONS THAT
MAY ALTER RISK OF LIVER TOXICITY AND CANCER
As described in Sections E.I.2 and E.3.1, there are a number of conditions that are
associated with increased risk of liver cancer and toxicity that include age, use of a number of
prescription medications including fibrates and statins, disease state (e.g., diabetes, NALD, viral
infections), and exposure to external environmental contaminants that have an effect on TCE
toxicity and targets. Obviously, epigenetic and genetic factors play a role in determining the
risk to the individual. In terms of liver cancer, there is general consensus that despite the
associations that have been made with etiological factors and the risk of liver cancer, the
mechanism is still unknown. The mode of action of TCE toxicity is also unknown, but exposure
to TCE and its metabolites have shown in rodent models to induce liver cancer and in a fashion
that is not consistent with only a hypothesized mode of action of PPARa receptor activation that
is in need of revision. However, multiple TCE metabolites have been shown to also induce liver
cancer with varying effects on the liver that have also been associated with early stages of
neoplasia (glycogen storage) or other actions associated with risk of hepatocarcinogenicity. The
growing epidemic of obesity has been suggested to increase the risk of liver cancer and may
reasonably increase the target population for TCE effects on the liver.
Lifestyle factors such as ethanol ingestion have not only been shown to increase liver
cancer risk in those who already have fatty liver, but also to increase the toxicity of TCE.
However, as noted by Caldwell et al. (2008b), while there is evidence to suggest that TCE
exposure may increase the risk of liver toxicity and cancer, there are no data to support a
quantitative estimate of how co-exposures may modulate that risk.
These findings can also serve to alert the risk manager to the possibility that
multiple internal and external exposures to TCE that may act via differing MO As
for the production of liver effects. This information suggests a possible lack of
"zero" background exposures and can help identify potential susceptible
populations.
Background levels of haloacetates in drinking water may add to the cumulative
exposure an individual receives via the metabolism of TCE. The brominated
haloacetates apparently share some common effects and pathways with their
chlorinated counterparts. Thus, concurrent exposure of TCE, its metabolites, and
other haloacetates may pose an additive response as well as an additive dose.
However, personal exposures are difficult to ascertain and the effects of such co-
exposures on toxicity are hard to quantify. EPA's guidance on cumulative risk
assessments directs "each office to take into account cumulative risk issues in
scoping and planning major risk assessments and to consider a broader scope that
integrates multiple sources, effects, pathways, stressors, and populations for
cumulative risk analyses in all cases for which relevant data are available" [U.S.
EPA, 1997]. Widespread exposure to possible background levels of TCE
metabolites or co-contaminants and other extrinsic factors have the potential to
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affect TCE toxicity. However, the available data for co-exposures on TCE
toxicity appears inadequate for quantifying these effects, particularly at
environmental levels of contamination and exposure. Thus, the risk manager and
assessor are going to be limited by not having information regarding either (1)
the type of exposure data necessary to assess the magnitude of co-exposures that
may affect toxicity, or (2) the potential quantitative impacts of these co-
exposures that would enable specific adjustments to risk. Nonetheless, the risk
manager should be aware that qualitatively a case can be made that extrinsic
factors may affect TCE toxicity.
E.6. UNCERTAINTY AND VARIABILITY
Along with general conclusions about the coherence of data that enable conclusions
about effects on the liver shown through experimental studies of TCE, there have also been
extensive discussions throughout this report regarding the specific limitations of experimental
studies whose design was limited by small and varying groups of animals and variability in
control responses as well as reporting deficiencies. Section E.3.1.5 has brought forward the
uncertainty in the mode of action for liver cancer in general. The consistency of different
animal models with human HCC is described in Section E.3.3, with Section E.3.1.2 providing a
discussion of the promise and limitations of emerging technologies to study the modes of action
of liver cancer in general and for TCE specifically. Issues regarding the target cell for HCC and
the complexities of studying the mode of action for a heterogeneous disease are described in
Sections E.3.1.4 and E.3.1.8, respectively. Finally, the uncertainty regarding key events in how
activation of the PPARa receptor my lead to hepatocarcinogenesis and the problems with
extrapolation of results using the common paradigm to study them (exposure to high levels of
WY-14,643 in abbreviated bioassays in knockout mice) are outlined in Section E.3.4.1. As such
uncertainties are identified, future research can focus on resolving them.
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F. NONCANCER DOSE-RESPONSE ANALYSES
F.I. DATA SOURCES
Data sources are cited in the body of this report in the section describing dose-response
analyses (see Chapter 5).
F.2. DOSIMETRY
This section describes some of the more detailed dosimetry calculations and adjustments
used in Section 5.1.
F.2.1. Estimates of TCE in Air From Urinary Metabolite Data Using Ikeda et al. (1972)
F.2.1.1. Results for Chia et al. (1996)
Chia et al. (1996) demonstrated a dose-related effect on hyperzoospermia in male
workers exposed to TCE, lumping subjects into four groups based on range of TCA in urine (see
Table F-l).
Table F-l. Dose-response data from Chia et al. (1996)
TCA, mg per g creatinine"
0.8-<25
50-<75
75-<100
>100-136.4
Number of subjects
37
18
8
5
Number with hyperzoospermia
6
8
4
3
"Minimum and maximum TCA levels are reported in the text of Chia et al. (1996), the other data, in their Table 5.
Data from Ikeda et al. (1972) were used to estimate the TCE exposure concentrations
corresponding to the urinary TCA levels reported by Chia et al. (1996). Ikeda et al. (1972)
studied 10 workshops, in each of which TCE vapor concentration was "relatively constant."
They measured atmospheric concentrations of TCE and concentrations in workers' urine of
TTCs, TCA, and creatinine, and demonstrated a linear relation between TTC/creatinine (mg/g) in
urine and TCE in the work atmosphere. Their data are tabulated as geometric means (the last
column was calculated by U.S. EPA, as described in Table F-2).
F-l
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Table F-2. Data on TCE in air (ppm) and urinary metabolite concentrations
in workers reported by Ikeda et al. (1972)
n
9
5
6
4
4
5
5
5
4
4
TCE (ppm)
3
5
10
25
40
45
50
60
120
175
TTC (mg/L)
39.4
45.6
60.5
164.3
324.9
399
418.9
468
915.3
1,210.9
TCA (mg/L)
12.7
20.2
17.6
77.2
90.6
138.4
146.6
155.4
230.1
235.8
TTC (mg/g creatinine)
40.8
42.4
47.3
122.9
221.2
337.7
275.8
359
518.9
1,040.1
TCA (mg/g creatinine)
13.15127
18.78246
13.76
57.74729
61.68273
117.137
96.52012
119.2064
130.4478
202.5399
These data were used to construct the last column as follows: TCA (mg/g creatinine) =
TCA (mg/L) x TTC (mg/g creatinine)/TTC (mg/L). The regression relation between TCE (ppm)
and TCA (mg/g creatinine) was evaluated using these data. Ikeda et al. (1972) reported that the
measured values are lognormally distributed and exhibit heterogeneity of variance, and that the
reported data (above) are geometric means. Thus, the regression relation between loglO(TCA
[mg/g creatinine]) and loglO(TCE [ppm]) was used, assuming constant variances and using
number of subjects "«" as weights. Figure F-l shows the results.
F-2
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log10(TCA, mg/g.creatinine in urine) = 0.7098 + 0.7218 * log10(TCE, ppm)
o
o
o
in
8.
D)
5
10
50
100
TCE, ppm
Coefficients:
Value Std. Error t value Pr(>|t|)
(Intercept) 0.7098 0.1132 6.2688 0.0002
loglO(TCE.ppm) 0.7218 0.0771 9.3578 0.0000
Residual standard error: 0.3206 on 8 degrees of
freedom
Multiple R-Squared: 0.9163
F-statistic: 87.57 on 1 and 8 degrees of freedom,
the p-value is 0.0000139
Figure F-l. Regression of TCE in air (ppm) and TCA in urine (nig/g
creatinine) based on data from Ikeda et al. (1972).
Next, a Berkson setting for linear calibration was assumed, in which one wants to predict
X(TCE, ppm) from means for 7 (TCA, mg/g creatinine), with substantial error in 7 (Snedcor and
Cochran, 1980). Thus, the inverse prediction for the data of Chia et al. (1996) was used to infer
their mean TCE exposures. The relation based on data from Ikeda et al. (1972) is:
loglO(TCA, mg/g creatinine) = 0.7098 + 0.7218 x loglO(TCE, ppm)
(Eq. F-l)
F-3
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and the inverse prediction is
loglO(TCE) = [loglO(TCA) - 0.7098]/0.7218
TCE, ppm = 10A( [loglO(TCA) - 0.7098]/0.7218)
(Eq. F-2)
Because of the lognormality of data reported by Ikeda et al. (1972), the means of the
logarithms of the ranges for TCA (mg/g creatinine) in Chia et al. (1996), which are estimates of
the median for the group, were used. The results are shown in Table F-3.
Table F-3. Estimated urinary metabolite and TCE air concentrations in dose
groups from Chia et al. (1996)
TCA, mg per g Creatinine
0.8-<25
50-<75
75-<100
>100-136.4
Estimated TCA median"
4.47
61.2
86.6
117
LoglO(TCA median)
0.650515
1.787016
1.937531
2.067407
Estimated ppm TCEb
0.827685
31.074370
50.226119
76.008668
a!0A(mean[loglO(TCA limits in first column)]).
b!0A([loglO(TCA median)] - 0.7098)70.7218.
Dose-response relations for the data of Chia et al. (1996) were modeled using both the
estimated medians for TCA (mg/g creatinine) in urine and estimated TCE (ppm in air) as doses.
The TCE-TCA-TTC relations are linear up to about 75 ppm TCE (Figure 1 of Ikeda et al.
(1972)), and certainly in the range of the BMD. As noted (see Section F.2.2), the occupational
exposure levels are further adjusted to equivalent continuous exposure for deriving the POD.
F.2.1.2. Results for Mhiri et al. (2004)
The LOAEL group for abnormal trigeminal nerve somatosensory evoked potential
reported in Mhiri et al. (2004) had a urinary TCA concentration of 32.6 mg TCA/mg creatinine.
Using Eq. F-2, above gives an occupational exposure level = 10A([loglO(32.6) - 0.7098]/0.7218)
= 12.97404 ppm. As noted below (see Section F.2.2), the occupational exposure levels are
further adjusted to equivalent continuous exposure for deriving the POD.
F.2.2. Dose Adjustments to Applied Doses for Intermittent Exposure
The nominal applied dose was adjusted for exposure discontinuity (e.g., exposure for
5 days/week and 6 hours/day reduced the dose by the factor [5/7] x [6/24]). The PBPK dose-
metrics took into account the daily and weekly discontinuity to produce an equivalent average
dose for continuous exposure. No dose adjustments were made for duration of exposure or a
less-than-lifetime study, as is typically done for cancer risk estimates, though in deriving the
F-4
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candidate reference values, an UF for subchronic-to-chronic exposure was applied where
appropriate.
For human occupational studies, inhalation exposures (air concentrations) were adjusted
by the number of work (vs. nonwork) days and the amount of air intake during working hours as
a fraction of the entire day (10m3 during work/20 m3 for entire day). For the TCE ppm in air
converted from urinary metabolite data using Ikeda et al. (1972), the work week was 6 days, so
the adjustment for number of work days is 6/7.
F.2.3. Estimation of the Applied Doses for the Oral Exposures via Drinking Water and
Feed
When oral doses were not reported in mg/kg/day and when study-specific data were not
available for body weight and/or consumption rate, standard generic sex/strain-specific values
from U.S. EPA (1988) were used to convert doses (e.g., in ppm in water) to doses in mg/kg/day.
For the feed study of George et al. (1986), study-specific data were used to estimate the
applied dose. Female F334 rats were exposed for 19 weeks in their feed. Average body weights
(Wt) are reported (Table A2, p. 53) for time periods having durations (dt) of 1-4 weeks.
Proportions of the 19 weeks of feeding were calculated for each time period as
Pt = dt/( Y dt)
Average daily feed consumed (Ft) is reported (Table A3) for the same time periods as body
weight. Concentration (%w/w) of TCE in feed (Table 1, p.31) is reported for weeks 1, 6, 12, and
18.13 Two determinations are reported, which we averaged. The grouping of TCE feed
concentrations into time periods (Table 1) differs from that used for body weight and feed
consumption (Tables A2, A3). This was reconciled by linear interpolation of feed concentrations
to produce concentrations (denoted Ct) for the time periods presented in Tables A2 and A3. We
then calculated mg TCE consumed per kg-day, for each time period, as the product of:
Ct/100 feed concentration, %w/w, divided by 100 to give a fraction
Ft feed consumed (grams)
1,000 1,000 (conversion of grams to mg)
l/Wt l/[ body weight, kg ]
And found the TWA of these for each dose group:
13"Study Week 1" is repeated in the table, which is a typo for week 6, confirmed positively by the text on pages 19-
20: "Analysis of Task 2 feed formulations at six week intervals ... Similarly, during week 6 of Task 2, the 0.15%,
0.30%, and 0.60% TCE formulations assayed at 27%, 71% and 82% of the theoretical concentration, respectively
(Table 1)".
F-5
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V(Ft x ((Ct x Ft x 1000)/VKt)}
The results were:
Nominal %w/w concentration in feed Calculated mg/kg/day
0 0
0.15 72
0.30 186
0.60 389
F.2.4. PBPK Model-Based Internal Dose-Metrics
PBPK modeling was used to estimate levels of dose-metrics corresponding to different
exposure scenarios in rodents and humans (see Section 3.5). The selection of dose-metrics for
specific organs and endpoints is discussed under Section 5.1.
The PBPK model requires an average body weight. For most of the studies, averages
specific to each species, strain, and sex were used. Where these were not reported in the text of
an article, data were obtained by digitizing the body weight graphics (Maltoni etal., 1986) or by
finding the median of weekly averages from graphs (NTP, 1990, 1988; NCI, 1976). Where
necessary, default adult body weights specific to the strain were used (U.S. EPA, 1988).
F.3. DOSE-RESPONSE MODELING PROCEDURES
Where adequate dose-response data were available, models were fitted with the BMDS
(http://www.epa.gov/ncea/bmds) using the applicable applied doses or PBPK model-based dose-
metrics for each combination of study, species, strain, sex, endpoints, and BMR under
consideration.
F.3.1. Models for Dichotomous Response Data
F.3.1.1. Quantal Models
For dichotomous responses, the log-logistic, multistage, and Weibull models were fitted.
These models adequately describe the dose-response relationship for the great majority of data
sets, specifically in past TCE studies (Filipsson and Victorin, 2003). If the slope parameter of
the log-logistic model was <1, indicating a supralinear dose-response shape, then the model with
the slope constrained to 1 was also fitted for comparison. For the multistage model, an order one
less than the number of dose groups was used, in addition to the 2nd-order multistage model if it
differed from the preceding model, and the first-order ('linear') multistage model (which is
F-6
-------
identical to a Weibull model with power parameter equal to 1). The Weibull model with the
power parameter unconstrained was also fitted t.
F.3.1.2. Nested Dichotomous Models
In addition, nested dichotomous models were used for developmental effects in rodent
studies to account for possible litter effects, such maternal covariates or intralitter correlation.
The available nested models in BMDS are the nested log-logistic model, the Rai-VanRyzin
models, and the NCTR model. Candidates for litter-specific covariates (LSC) were identified
from the studies and considered legitimate for analysis if they were not significantly dose-related
(determined via regression, ANOVA). The need for a LSC was indicated by a difference of at
least 3 in the AIC for models with and without a LSC. The need to estimate intralitter
correlations (1C) was determined by presence of a high correlation coefficient for at least one
dose group and by AIC. The fits for nested models were also compared with the results from
quantal models.
F.3.2. Models for Continuous Response Data
For continuous responses, the distinct models available in BMDS were fitted: power
model (power parameter unconstrained and constrained to >1), polynomial model, and Hill
model. Both constant variance and modeled variance models were fit; but constant variance
models were used for model parsimony unless the/?-value for the test of homogenous variance
was <0.10, in which case the modeled variance models were considered. For the polynomial
model, model order was selected as follows. A model of order 1 was fitted first. The next higher
order model (up to order w-1) was accepted if AIC decreased >3 units and the/>-value for the
mean did not decrease.
F.3.3. Model Selection
After fitting these models to the data sets, the recommendations for model selection set
out in U.S. EPA''s Benchmark Dose Technical Guidance Document (External Review Draft,
(U.S. EPA, 2000b) were applied. First, models were generally rejected if the/>-value for
goodness of fit was <0.10. In a few cases in which none of the models fit the data with/? > 0.10,
linear models were selected on the basis of an adequate visual fit overall. Second, models were
rejected if they did not appear to adequately fit the low-dose region of the dose-response
relationship, based on an examination of graphical displays of the data and scaled residuals. If
the BMDL estimates from the remaining models were "sufficiently close" (a criterion of within
twofold for "sufficiently close" was used), then the model with the lowest AIC was selected.
The AIC is a measure of information loss from a dose-response model that can be used to
compare a set of models. Among a specified set of models, the model with the lowest AIC is
considered the "best." If two or more models share the lowest AIC, the draft Benchmark Dose
F-7
-------
Technical Guidance Document (U.S. EPA, 2000b) suggests that an average of the BMDLs could
be used, but averaging was not used in this assessment (for the one occasion in which models
shared the lowest AIC, a selection was made based on visual fit). If the BMDL estimates from
the remaining models are not sufficiently close, some model dependence is assumed. With no
clear biological or statistical basis to choose among them, the lowest BMDL was chosen as a
reasonable conservative estimate, as suggested in the draft Benchmark Dose Technical Guidance
Document, unless the lowest BMDL appeared to be an outlier, in which case further judgments
were made.
F.3.4. Additional Adjustments for Selected Data Sets
In a few cases, the dose-response data necessitated further adjustments in order to
improve model fits.
The behavioral/neurological endpoint "number of rears" from Moser et al. (1995)
consisted of counts, measured at five doses and four measurement times (with eight observations
each). The high dose for this endpoint was dropped because the mean was zero, and no
monotone model could fit that well. Analysis of means and SDs for these counts suggested a
Box-Cox power transform (Box et al., 1978) of 0.5 (i.e., square root) to stabilize variances (i.e.,
the slope of the regression of log[SD] on log[mean] was 0.46, and the relation was linear and
highly significant). This information was helpful in selecting a suitable variance model with
high confidence (i.e., variance constant, for square-root transformed data). Thus, the square root
was taken of the original individual count data, and the mean and variance of the transformed
count data were used in the BMD modeling.
The high-dose group was dropped due to supra-linear dose-response shapes in two cases:
fetal cardiac malformations from Johnson et al. (2003) and decreased PFC response from
Woolhiser et al. (2006). Johnson et al. (2003) is discussed in more detail below (see
Section F.4.2.1). For Woolhiser et al. (2006), model fit near the BMD and the lower doses as
well as the model fit to the variance were improved by dropping the highest dose, a procedure
suggested in U.S. EPA (2000b).
In some cases, the supralinear dose-response shape could not be accommodated by these
measures, and a LOAEL or NOAEL was used instead. These include NCI (1976) (toxic
nephrosis, >90% response at lowest dose), Keil (2009) (autoimmune markers and decreased
thymus weight, only two dose groups in addition to controls), and Peden-Adams et al. (2006)
(developmental immunotoxicity, only two dose groups in addition to controls).
F.4. DOSE-RESPONSE MODELING RESULTS
F.4.1. Quantal Dichotomous and Continuous Modeling Results
Supplementary data files show the fitted model curves ("Supplementary data for TCE
assessment: Non-cancer plots contin," 2011; "Supplementary data for TCE assessment: Non-
F-8
-------
cancer plots dichot" 2011). The graphics include observations (group means or proportions), the
estimated model curve (solid red line), and estimated BMD, with a BMDL. Vertical bars show
95% CIs for the observed means. Printed above each plot are some key statistics (necessarily
rounded) for model goodness of fit and estimated parameters. Printed in the plots in the upper
left are the BMD and BMDL for the rodent data, in the same units as the rodent dose.
More detailed results, including alternative BMRs, alternative dose-metrics, quantal
analyses for endpoints for which nested analyses were performed, etc. are documented in the
several spreadsheets. Input data for the analyses are in other supplementary data files
("Supplementary data for TCE assessment: Non-cancer input data contin," 2011;
"Supplementary data for TCE assessment: Non-cancer input data dichot" 2011). Additional
supplementary data files ("Supplementary data for TCE assessment: Non-cancer results contin,"
2011; "Supplementary data for TCE assessment: Non-cancer results dichot" 2011) present the
data and model summary statistics, including goodness-of-fit measures (%2 goodness-of-fit/>-
value, AIC), parameter estimates, BMD, and BMDL. The group numbers "GRP" are arbitrary
and are the same as GRP in the plots. Finally, note that not all plots are shown in the documents
above, since these spreadsheets include many "alternative" analyses.
F.4.2. Nested Dichotomous Modeling Results
F.4.2.1. Johnson et al. (2003) Fetal Cardiac Defects
F .4.2.1.1. Results using applied dose.
The biological endpoint was frequency of rat fetuses having cardiac defects, as shown in
Table F-4. Individual animal data were kindly provided by Dr. Johnson (personal
communication from Paula Johnson, University of Arizona, to Susan Makris, U.S. EPA,
26 August 2009). Cochran-Armitage trend tests using number of fetuses and number of litters
indicated significant increases in response with dose (with or without including the highest dose).
One suitable candidate for a LSC was available: female weight gain during pregnancy.
Based on goodness of fit, this covariate did not contribute to better fit and was not used. Some
ICs were significant and these parameters were included in the model.
Table F-4. Data on fetuses and litters with abnormal hearts from Johnson et
al. (2003)
Dose group (mg/kg/d):
0
0.00045
0.048
0.218
129
Fetuses
Number of pups:
Abnormal heart:
606
13
144
0
110
5
181
9
105
11
Litters
Number of litters:
Abnormal heart:
55
9
12
0
9
4
13
5
9
6
F-9
-------
With the high dose included, the x goodness of fit was acceptable, but some residuals
were large (1.5 to 2) for the control and two lower doses. Therefore, models were also fitted
after dropping the highest dose. For these, goodness of fit was adequate, and scaled residuals
were smaller for the low doses and control. Predicted expected response values were closer to
observed when the high dose was dropped, as shown in Table F-5:
Table F-5. Comparison of observed and predicted numbers of fetuses with
abnormal hearts from Johnson et al. (2003), with and without the high-dose
group, using a nested model
Dose group (mg/kg/d):
Observed:
Abnormal hearts (pups)
0
13
0.00045
0
0.048
5
0.218
9
129
11
Predicted expected:
With high dose
Without high dose
19.3
13.9
4.5
3.3
3.5
3.4
5.7
10
11
-
Accuracy in the low-dose range is especially important because the BMD is based upon
the predicted responses at the control and the lower doses. Based on the foregoing measures of
goodness of fit, the model based on dropping the high dose was used.
The nested log-logistic and Rai-VanRyzin models were fitted; these gave essentially the
same predicted responses and POD. The former model was used as the basis for a POD; results
are in Table F-6 and Figure F-2.
Table F-6. Results of nested log-logistic model for fetal cardiac anomalies
from Johnson et al. (2003) without the high-dose group, on the basis of
applied dose (mg/kg/day in drinking water)
Model
NLOG
NLOG
NLOG
NLOG
NLOG
NLOGb
LSC?a
Y
Y
N
N
N
N
1C?
Y
N
N
Y
Y
Y
AIC
246.877
251.203
248.853
243.815
243.815
243.815
Pval
NA(df=0)
0.0112
0.0098
0.0128
0.0128
0.0128
BMR
0.01
0.01
0.01
0.1
0.05
0.01
BMD
0.252433
0.238776
0.057807
0.71114
0.336856
0.064649
BMDL
0.03776
0.039285
0.028977
0.227675
0.107846
0.020698
aLSC analyzed was female weight gain during pregnancy.
blndicates model selected (Rai-VanRyzin model fits are essentially the same).
NLOG = "nested log-logistic" model
F-10
-------
Nested Logistic Model with 0.95 Confidence Level
0.12
0.1
"§0.08
t5
^0.06
'•6 0.04
2
LJ_
0.02
0
tested Logistic
BMDL
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
dose
13:3608/272008
Nested Logistic Model with 0.95 Confidence Level
0.
Nested Logistic
0.1
"§0.08
"G
£
<0.06
c
••§0.04
TO
LL
0.02
0
BIVDL
BIVD
0 0.05
13:3708/272008
0.1
dose
0.15
0.2
Figure F-2. BMD modeling of Johnson et al. (2003) using nested log-logistic
model, with applied dose, without LSC, with 1C, and without the high-dose
group, using a BMR of 0.05 extra risk (top panel) or 0.01 extra risk (bottom
panel).
F .4.2.1.2. x2 Goodness-of-Fit Test for nested log-logistic model.
The BMDS choice of subgroups did not seem appropriate given the data. The high-dose
group of 13 litters was subdivided into three subgroups having sums of expected counts 3, 3, and
2. However, the control group of 55 litters could have been subdivided because expected
F-ll
-------
response rates for controls were relatively high. There was also concern that the goodness of fit
might change with alternative choices of sub groupings.
An R program was written to read the BMDS output, reading parameters and the table of
litter-specific results (dose, covariate, estimated probability of response, litter size, expected
response count, observed response count, scaled %2 residual). The control group of 55 litters was
subdivided into three subgroups of 18, 18, and 19 litters. Control litters were sampled randomly
without replacement 100 times, each time creating 3 subgroups (i.e., 100 random assignments of
the 55 control litters to three subgroups were made). For each of these, the goodness-of-fit
calculation was made and the/>-value saved. Within these 100 ^-values, >75% were >0.05 and
>50% had/>-values >0.11; this indicated that the model is acceptable based on goodness-of-fit
criteria.
F .4.2.1.3. Results using PBPK model-based dose-metrics.
The nested log-logistic model was also run using the dose-metrics in the dams of total
oxidative metabolism scaled by body weight to the 3/4-power (TotOxMetabBW34) and the AUC
of TCE in blood (AUCCBld). As with the applied dose modeling, LSC (maternal weight gain)
was not included, but 1C was included, based on the criteria outlined previously (see
Section F.3.1.2). The results are summarized in Table F-7 and Figure F-3 for TotOxMetabBW34
and Table F-8 and Figure F-4 for AUCCBld.
Table F-7. Results of nested log-logistic model for fetal cardiac anomalies
from Johnson et al. (2003) without the high-dose group, using the
TotOxMetabBW34 dose-metric
Model
NLOG
NLOG
NLOG
NLOGb
NLOG
LSC?a
Y
Y
N
N
N
1C?
Y
N
Y
Y
N
AIC
246.877
251.203
243.815
243.815
248.853
Pval
NA(df=0)
0.0112
0.0128
0.0128
0.0098
BMR
0.01
0.01
0.1
0.01
0.01
BMD
0.174253
0.164902
0.489442
0.0444948
0.0397876
BMDL
0.0259884
0.0270378
0.156698
0.0142453
0.0199438
aLSC analyzed was female weight gain during pregnancy.
blndicates model selected. BMDS failed with the Rai-VanRyzin and NCTR models.
F-12
-------
Nested Logistic Model with 0.95 Confidence Level
0.1
§5.08
CD
-------
Table F-8. Results of nested log-logistic model for fetal cardiac anomalies
from Johnson et al. (2003) without the high-dose group, using the AUCCBld
dose-metric
Model
NLOG
NLOG
NLOGb
NLOGb
NLOG
LSC?a
Y
Y
N
N
N
1C?
Y
N
Y
Y
N
AIC
246.877
251.203
243.816
243.816
248.853
Pval
NA(df=0)
0.0112
0.0128
0.0128
0.0098
BMR
0.01
0.01
0.1
0.01
0.01
BMD
0.00793783
0.00750874
0.0222789
0.00202535
0.00181058
BMDL
0.00118286
0.00123047
0.00712997
0.000648179
0.000907513
aLSC analyzed was female weight gain during pregnancy.
blndicates model selected. BMDS failed with the Rai-VanRyzin and NCTR models.
Nested Logistic Model with 0.95 Confidence Level
Nested Logistic
0.1
|J0.08
$
<0.06
c
'•§0.04
CO
^0.02
0
BMDL
BMP
0 0.001 0.002 0.003 0.004 0.005 0.006 0.007
dose
12:4202/062009
Figure F-4. BMD modeling of Johnson et al. (2003) using nested log-logistic
model, with AUCCBld dose-metric, without LSC, with 1C, and without the
high-dose group, using a BMR of 0.01 extra risk.
F-14
-------
F.4.2.2. Narotsky et al. (1995)
Data were combined for the high doses in the single-agent experiment and the lower
doses in the 'five-cube' experiment. Individual animal data were kindly provided by Dr.
Narotsky (personal communications from Michael Narotsky, U.S. EPA, to John Fox, U.S. EPA,
19 June 2008, and to Jennifer Jinot U.S. EPA, 10 June 2008). Two endpoints were examined:
frequency of eye defects in rat pups and prenatal loss (number of implantation sites minus
number of live pups on PND 1).
Two LSCs were considered, with analyses summarized in Table F-9. The number of
implants is unrelated to dose, as inferred from regression and ANOVA, and was considered as a
LSC for eye defects. As number of implants is part of the definition for the endpoint of prenatal
loss, it is not considered as a LSC for prenatal loss. A second LSC, the dam body weight on
GD 6 (damBW6) was significantly related to dose and is unsuitable as a litter-specific covariate.
Table F-9. Analysis of LSCs with respect to dose from Narotsky et al. (1995)
Relation of litter-specific covariates to dose
Implants:
damBW6:
none
significant
TCE
0
10.1
32
101
320
475
633
844
1,125
Mean
Implants
9.5
10.1
9.1
7.8
10.4
9.7
9.6
8.9
9.6
Mean
damBW6
176.0
180.9
174.9
170.1
174.5
182.4
185.3
182.9
184.2
Using expt as covariate, e.g., damBW6 ~ TCE.mg.kgd + expt
Linear regression
AoV (ordered factor)
p = 0.7486
p = 0.1782
p = 0.0069
p = 0.0927
Two LSCs were considered, with analyses summarized in Table F-9. The number of
implants is unrelated to dose, as inferred from regression and ANOVA, and was considered as a
LSC for eye defects. As number of implants is part of the definition for the endpoint of prenatal
loss, it is not considered as a LSC for prenatal loss. A second LSC, the dam body weight on
GD 6 (damBW6) was significantly related to dose and is unsuitable as a litter-specific covariate.
F .4.2.2.1. Fetal eye defects
The nested log-logistic and Rai-VanRyzin models were fitted to the number of pups with eye
defects reported by Narotsky et al. (1995), with the results summarized in Table F-10.
F-15
-------
Table F-10. Results of nested log-logistic and Rai-VanRyzin model for fetal
eye defects from Narotsky et al. (1995), on the basis of applied dose
(mg/kg/day in drinking water)
Model
NLOG
NLOG
NLOG
NLOG
NLOG
NLOG
RAI
RAI
RAI
RAI
RAI
RAI
LSC?a
Y
Y
N
N
N
N
Y
Y
N
N
N
N
1C?
Y
N
Y
N
N
N
Y
N
Y
N
N
N
AIC
255.771
259.024
270.407
262.784
262.784
262.784
274.339
264.899
270.339
262.481
262.481
262.481
Pval
0.3489
0.0445
0.2281
0.0529
0.0529
0.0529
0.1047
0.0577
0.2309
0.0619
0.0619
0.0619
BMR
0.05
0.05
0.05
0.10
0.05
0.01
0.05
0.05
0.05
0.10
0.05
0.01
BMD
875.347
830.511
622.342
691.93
427.389
147.41
619.849
404.788
619.882
693.04
429.686
145.563
BMDL
737.328b
661.629
206.460
542.101
264.386
38.7117C
309.925
354.961
309.941
346.52
214.843
130.938C
aLSC analyzed was implants.
bGraphical fit at the origin exceeds observed control and low-dose responses and slope is quite flat (see Figure F-5),
fitted curve does not represent the data well.
Indicates model selected.
RAI = Rai-VanRyzin model
Results for the nested log-logistic model suggested a better model fit with the inclusion of
the LSC and 1C, based on AIC. However, the graphical fit (see Figure F-5) is strongly sublinear
and high at the origin where the fitted response exceeds the observed low-dose responses for the
control group and two low-dose groups. An alternative nested log-logistic model without either
LSC or 1C (see Figure F-6), which fits the low-dose responses better, was selected. Given that
this model had no LSC and no 1C, the nested log-logistic model reduces to a quantal log-logistic
model. Parameter estimates and the ^-values were essentially the same for the two models (see
Table F-l 1). A similar model selection can be justified for the Rai-Van Ryzin model (see
Figure F-7). Because no LSC and no 1C were needed, this endpoint was modeled with quantal
models, using totals of implants and losses for each dose group, which allowed choice from a
wider range of models (those results appear with quantal model results in this appendix).
F-16
-------
Nested Logistic Model with 0.95 Confidence Level
0.5
0.4
T3
CD
"d
£0.3
•| 0.2
tc
LJ_
0.1
0
tested Logistic
BMDL
BMD
0 200
17:2708/042008
400 600
dose
800
1000
Figure F-5. BMD modeling of fetal eye defects from Narotsky et al. (1995)
using nested log-logistic model, with applied dose, with both LSC and 1C,
using a BMR of 0.05 extra risk.
Nested Logistic Model with 0.95 Confidence Level
0.5
Nested Logistic
0.4
T3
CD
"d
0.3
<
c
o
CO
0.1
0
BMPL
BMP
0 200
17:2808/042008
400
600
dose
800
1000
Figure F-6. BMD modeling of fetal eye defects from Narotsky et al. (1995)
using nested log-logistic model, with applied dose, without either LSC or 1C,
using a BMR of 0.05 extra risk.
F-17
-------
Table F-ll. Comparison of results of nested log-logistic (without LSC or 1C)
and quantal log-logistic model for fetal eye defects from Narotsky et al.
(1995)
Model
Nested
Quantal
Parameter
Alpha
0.00550062
0.00549976
Beta
-12.3392
-12.3386
Rho
1.55088
1.55079
BMDos
427.4
427.4
BMDLos
264.4
260.2
RaiVR Model with 0.95 Confidence Level
0.5
0.4
£0.3
•-02
t3
03
LL
0.1
0
RaiVR
BMDL
BMD
0 200
17:2508/042008
400
600
dose
800
1000
Figure F-7. BMD modeling of fetal eye defects from Narotsky et al. (1995)
using nested Rai-VanRyzin model, with applied dose, without either LSC or
1C, using a BMR of 0.05 extra risk.
F .4.2.2.2. Narotsky et al. (1995) prenatal loss
The nested log-logistic and Rai-VanRyzin models were fitted to prenatal loss reported by
Narotsky et al. (1995), with the results summarized in Table F-12.
F-18
-------
Table F-12. Results of nested log-logistic and Rai-VanRyzin model for
prenatal loss from Narotsky et al. (1995), on the basis of applied dose
(mg/kg/day in drinking water)
Model
NLOG
NLOG
NLOG
NLOG
NLOG
NLOG
RAI
RAI
RAI
RAI
RAI
RAI
LSC?a
Y
Y
N
N
N
N
Y
Y
N
N
N
N
1C?
Y
N
N
Y
Y
Y
Y
N
N
Y
Y
Y
AIC
494.489
627.341
628.158
490.766
490.766
490.766
491.859
626.776
626.456
488.856
488.856
488.856
Pval
0.2314
0.0000
0.0000
0.2509
0.2509
0.2509
0.3044
0.0000
0.0000
0.2983
0.2983
0.2983
BMR
0.10
0.10
0.10
0.10
0.05
0.01
0.10
0.10
0.10
0.10
0.05
0.01
BMD
799.723
790.96
812.92
814.781
738.749
594.995
802.871
819.972
814.98
814.048
726.882
562.455
BMDL
539.094
694.673
725.928
572.057
447.077
252.437b
669.059
683.31
424.469
678.373
605.735
468.713b
aLSC analyzed was dam body weight on GD 6.
blndicates model selected.
The BMDS nested models require a LSC, so dam body weight on GD6 ("damBW6") was
used as the LSC. However, damBW6 is significantly related to dose and, so, is not a reliable
LSC. Number of implants could not be used as a LSC because it was identified as number at risk
in the BMDS models. These issues were obviated because the model selected did not employ
the LSC.
For the nested log-logistic models, the AIC is much larger when the 1C is dropped, so the
1C is needed in the model. The LSC can be dropped (and is also suspect because it is correlated
with dose). The model with 1C and without LSC was selected on the basis of AIC (shown in
Figure F-8). For the Rai-VanRyzin models, the model selection was similar to that for the nested
log-logistic, leading to a model with 1C and without LSC, which had the lowest AIC (shown in
Figure F-9).
FAS. Model Selections and Results
The final model selections and results for noncancer dose-response modeling are
presented in Table F-13.
F-19
-------
Nested Logistic Model with 0.95 Confidence Level
0.8
0.7
"S0.6
J0.5
^0.4
o
tS 0.3
CO
"- 0.2
0.1
0
NG31GU I_VJVJIOIIO :
r ^
r / \
/
• / ]
: ./ :
! ^ I
BMDL BMD ;
0
200 400
16:4408/202008
600
dose
800
1000
Nested Logistic IVbdel with 0.95 Confidence Level
0.
tested
£
o
cc
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
BIN/PL
0
200
400
16:4508/202008
600
dose
800
1000
Figure F-8. BMD modeling of prenatal loss reported in Narotsky et al.
(1995) using nested log-logistic model, with applied dose, without LSC, with
1C, using a BMR of 0.05 extra risk (top panel) or 0.01 extra risk (bottom
panel).
F-20
-------
RaiVR Model with 0.95 Confidence Level
0.9,
0.8
0.7
£0.5
o
'•S0.3
CD
it 0.2
0.1
0
[aiVR
BMPL
BMP
0
200
400
16:4608/202008
600
dose
800
1000
RaiVR Model with 0.95 Confidence Level
0.9
0.8
0.7
| 0.5
c 0.4
o
t> 0.3
"t 0.2
0.1
0
BIVDL
BIVD
0 200
16:4608/202008
400
600
dose
800
1000
Figure F-9. BMD modeling of prenatal loss reported in Narotsky et al.
(1995) using nested Rai-VanRyzin model, with applied dose, without LSC,
with 1C, using a BMR of 0.05 extra risk (top panel) or 0.01 extra risk (bottom
panel).
F-21
-------
Table F-13. Model selections and results for noncancer dose-response analyses
GRP
Study/run
abb rev.
Species
Sex
Strain
Exposure
route
Endpoint
Dose-metric
BMR
type
BM
R
BMD/
BMD
L
BMDL
Model
Rep.
BMD
Notes
Dichotomous models
3
7
13
13
13
14
36
38
38
38
38
39
39
39
39
49
49
49
49
Chia et al.
(1996)
Narotsky
et al. (1995)
Narotsky
etal. (1995)
Narotsky
et al. (1995)
Narotsky
et al. (1995)
Johnson et al.
(2003).drophi
Griffin et al.
(2000b)
Maltoni et al.
(1986)
Maltoni et al.
(1986)
Maltoni et al.
(1986)
Maltoni et al.
(1986)
Maltoni et al.
(1986)
Maltoni et al.
(1986)
Maltoni et al.
(1986)
Maltoni et al.
(1986)
NTP (1988)
NTP (1988)
NTP (1988)
NTP (1988)
Human
Rat
Rat
Rat
Rat
Rat
mice
Rat
Rat
Rat
Rat
Rat
Rat
Rat
Rat
Rat
Rat
Rat
Rat
M
F
F
F
F
F
F
M
M
M
M
M
M
M
M
F
F
F
F
workers, elec . factory
F344
F344
F344
F344
Sprague.Dawley
MRL++
Sprague.Dawley
Sprague.Dawley
Sprague.Dawley
Sprague.Dawley
Sprague.Dawley
Sprague.Dawley
Sprague.Dawley
Sprague.Dawley
Marshall
Marshall
Marshall
Marshall
inhal
oral.gav
oral.gav
oral.gav
oral.gav
oral.dw
oral.dw
inhal
inhal
inhal
inhal
oral.gav
oral.gav
oral.gav
oral.gav
oral.gav
oral.gav
oral.gav
oral.gav
N.hyperzoospermia
N.pups.eye.defects
N.dams.w.resorbed.litters
N.dams.w.resorbed.litters
N.dams.w.resorbed.litters
N. litters, abnormal.hearts
portal.infiltration
megalonucleocytosis
megalonucleocytosis
megalonucleocytosis
megalonucleocytosis
megalonucleocytosis
megalonucleocytosis
megalonucleocytosis
megalonucleocytosis
toxic nephropathy
toxic nephropathy
toxic nephropathy
toxic nephropathy
appl.dose
appl.dose
appl.dose
AUCCBld
TotMetabBW34
appl.dose
appl.dose
appl.dose
ABioactDCVCBWS
4
AMetGSHBW34
TotMetabBW34
appl.dose
ABioactDCVCBWS
4
AMetGSHBW34
TotMetabBW34
appl.dose
ABioactDCVCBWS
4
AMetGSHBW34
TotMetabBW34
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
extra
0.1
0.01
0.01
0.01
0.01
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.05
0.05
0.05
0.05
2.14
1.46
5.47
5.77
1.77
2.78
2.67
1.22
1.18
1.19
1.13
1.53
1.60
1.65
1.41
1.45
1.45
1.46
1.45
1.43
60.1
32.2
17.5
77.5
0.0146
13.4
40.2
0.0888
0.086
53.8
33.8
0.0594
0.0605
20.5
9.45
0.0132
0.0129
2.13
loglogistic.l
multistage
multistage.2
multistage.2
weibull
loglogistic.l
loglogistic.l
multistage
loglogistic
loglogistic
weibull
multistage.2
multistage.2
multistage.2
multistage.2
loglogistic.l
loglogistic.l
loglogistic.l
loglogistic.l
3.06
806
570
327
156
0.0406
35.8
49.2
0.105
0.102
61
51.8
0.0948
0.0977
29
28.9
0.0404
0.0397
6.5
a
b
c
d
e
e
F-22
-------
Table F-13. Model selections and results for noncancer dose-response analyses (continued)
GRP
Study/run
abb rev.
Species
Sex
Strain
Exp.
route
Endpoint
Dose-metric
BMR
type
BM
R
BMD/
BMD
L
BMDL
Model
Rep.
BMD
Notes
Nested dichotomous models
NA
NA
NA
NA
Johnson
etal.
(2003).dro
phi
Johnson
etal.
(2003).dro
phi
Johnson
etal.
(2QQ3).dro
phi
Narotsky
etal.
(1995)
rat
rat
rat
rat
F
F
F
F
Sprague.Dawley
Sprague.Dawley
Sprague.Dawley
F344
oral.dw
oral.dw
oral.dw
oral.gav
N.pups.abnormal.hearts
N.pups.abnormal.hearts
N.pups.abnormal.hearts
N.prenatal.loss
appl.dose
TotOxMetabBW34
AUCCBld
appl.dose
extra
extra
extra
extra
0.01
0.01
0.01
0.01
3.12
3.12
3.12
1.2
0.0207
0.0142
0.000648
469
loglogistic.IC
loglogistic.IC
loglogistic.IC
RAI.IC
0.711
814
b
b
b
Continuous models
2
6
8
19
21
23
26
Land et al.
(1981)
Carney
etal.
(2006)
Narotsky
etal.
(1995)
Crofton
and Zhao
(1997)
George
etal.
(1986)
George
etal.
(1986)
George
etal.
(1986)
mouse
rat
rat
rat
rat
rat
rat
M
F
F
M
F
F
F
(C57BlxC3H)Fl
Sprague-Dawley
(Crl:CD)
F344
Long-Evans
F344
F344
F344
inhal
inhal
oral.gav
inhal
oral. food
oral. food
oral. food
pct.abnormal. sperm
gm. wgt. gain. GD6 . 9
gm.wgt.gain.GD6.20
dB.auditory.threshold(16kHz)
litters
live.pups
Foffspring.BWgm.day21
appl.dose
appl.dose
appl.dose
appl.dose
appl.dose
appl.dose
appl.dose
standard
relative
relative
absolute
standard
standard
relative
0.5
0.1
0.1
10
0.5
0.5
0.05
1.33
2.5
1.11
1.11
1.69
1.55
1.41
46.9
10.5
108
274
179
152
79.7
polynomial, constvar
hill
polynomial, constvar
polynomial, constvar
polynomial, constvar
polynomial, constvar
polynomial, constvar
125
62.3
312
330
604
470
225
F-23
-------
Table F-13. Model selections and results for noncancer dose-response analyses (continued)
GRP
34 sq
49
51
51
51
58
58
58
60. Rp
60.Rp
60.Rp
63
62
Study/run
abb rev.
Moser
etal.
(1995>fper
scorn
George
etal.
(1986)
Buben and
O'Flaherty
(19851
Buben and
O'Flaherty
(19851
Buben and
O'Flaherty
(19851
Kjell strand
etal.
(1983a1
Kjell strand
etal.
(1983a1
Kjell strand
etal.
(1983a)
Kjell strand
etal.
(1983a)
Kjell strand
etal.
(1983a1
Kjell strand
etal.
(1983a1
Woolhiser
etal.
(20061
Woolhiser
etal.
(20061
Species
rat
rat
mouse
mouse
mouse
mouse
mouse
mouse
mouse
mouse
mouse
rat
rat
Sex
F
F
M
M
M
M
M
M
M
M
M
F
F
Strain
F344
F344
SwissCox
SwissCox
SwissCox
NMRI
NMRI
NMRI
NMRI
NMRI
NMRI
CD (Sprague-
Dawley)
CD (Sprague-
Dawleyl
Exp.
route
oral.gav
oral. food
oral.gav
oral.gav
oral.gav
inhal
inhal
inhal
inhal
inhal
inhal
inhal
inhal
Endpoint
no. rears
traverse.time.21do
Liverwt.pctBW
Liverwt.pctBW
Liverwt.pctBW
LiverwtpctBW
LiverwtpctBW
Liverwt.pctBW
Kidneywt.pctBW
Kidneywt.pctBW
Kidneywt.pctBW
Antibody .Forming Cells
Antibody .Forming Cells
Dose-metric
appl.dose
appl.dose
appl.dose
AMetLivlBW34
TotOxMetabBW34
appl.dose
AMetLivlBW34
TotOxMetabBW34
appl.dose
AMetGSHBW34
TotMetabBW34
appl.dose
AUCCBld
BMR
type
standard
relative
relative
relative
relative
relative
relative
relative
relative
relative
relative
standard
standard
BM
R
1
1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
1
1
BMD/
BMD
L
1.64
1.98
1.26
1.08
1.08
1.36
1.4
1.3
1.17
1.18
1.17
1.94
1.44
BMDL
248
72.6
81.5
28.6
37
21.6
111
73.4
34.7
0.17
71
31.2
149
Model
polynomial, constvar
power
hill. constvar
polynomial, constvar
polynomial, constvar
hill
hill
hill
polynomial
polynomial
polynomial
power, constvar
polynomial
Rep.
BMD
406
84.9
92.8
28.4
36.7
30.4
32.9
97.7
47.1
0.236
95.2
60.6
214
Notes
b,f
b
F-24
-------
Table F-13. Model selections and results for noncancer dose-response analyses (continued)
GRP
62
65
65
65
65
67
67
67
Study/run
abb rev.
Woolhiser
etal.
(2006)
Woolhiser
etal.
(2006)
Woolhiser
etal.
(2006)
Woolhiser
etal.
(2006)
Woolhiser
etal.
(2006)
Woolhiser
etal.
(2006)
Woolhiser
etal.
(2006)
Woolhiser
etal.
(2006)
Species
rat
rat
rat
rat
rat
rat
rat
rat
Sex
F
F
F
F
F
F
F
F
Strain
CD (Sprague-
Dawley)
CD (Sprague-
Dawley)
CD (Sprague-
Dawley)
CD (Sprague-
Dawley)
CD (Sprague-
Dawley)
CD (Sprague-
Dawley)
CD (Sprague-
Dawley)
CD (Sprague-
Dawley)
Exp.
route
inhal
inhal
inhal
inhal
inhal
inhal
inhal
inhal
Endpoint
Antibody .Forming Cells
kidney. wt.perlOOgm
kidney. wt.perlOOgm
kidney. wt.perlOOgm
kidney .wt.perl OOgm
liver.wt.perlOOgm
liver.wt.perlOOgm
liver.wt.perlOOgm
Dose-metric
TotMetabBW34
appl.dose
ABioactDCVCBWS
4
AMetGSHBW34
TotMetabBW34
appl.dose
AMetLivlBW34
TotOxMetabBW34
BMR
type
standard
relative
relative
relative
relative
relative
relative
relative
BM
R
1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
BMD/
BMD
L
1.5
4.29
4.27
4.28
1.47
4.13
1.53
1.53
BMDL
40.8
15.7
0.0309
0.032
40.8
25.2
46
48.9
Model
polynomial
hill.constvar
hill.constvar
hill.constvar
polynomial, constvar
hill.constvar
polynomial, constvar
polynomial, constvar
Rep.
BMD
61.3
54.3
0.103
0.107
52.3
70.3
56.1
59.8
Notes
aEight-stage multistage model.
bDropped highest dose.
Three-stage multistage model.
dWeibull selected over log-logistic with the same AIC on basis of visual fit (less extreme curvature).
eSecond-order MS selected on basis of visual fit (less extreme curvature).
fSquare-root transformation of original individual count data.
Applied dose BMDLs are in units of ppm in air for inhalation exposures and mg/kg/day for oral exposures. Internal dose BMDLs are in dose-metric units. Reporting BMD is
BMD using a BMR of 0.1 extra risk for dichotomous models, and 1 control SD for continuous models.
Log-logistic = unconstrained log-logistic; log-logistic. 1 = constrained log-logistic; multistage = multistage with #stages=dose groups-1; multistage.n = n-stage multistage; log-
logistic.1C = nested log-logistic with 1C, without LSC; RAI.IC = Rai-VanRyzin model with 1C, without LSC; zzz.constvar = continuous model zzz with constant variance
(otherwise variance is modeled).
Rep. = reporting
F-25
-------
F.5. DERIVATION OF POINTS OF DEPARTURE
F.5.1. Applied Dose Points of Departure
For oral studies in rodents, the POD on the basis of applied dose in mg/kg/day was taken
to be the BMDL, NOAEL, or LOAEL. NOAELs and LOAELs were adjusted for intermittent
exposure to their equivalent continuous average daily exposure (for BMDLs, the adjustments
were already performed prior to BMD modeling).
For inhalation studies in rodents, the POD on the basis of applied dose in ppm was taken
to be the BMDL, NOAEL, or LOAEL. NOAELs and LOAELs were adjusted for intermittent
exposure to their equivalent continuous average daily exposure (for BMDLs, the adjustments
were already performed prior to BMD modeling). These adjusted concentrations are considered
HECs, in accordance with U.S. EPA (1994a), as TCE is considered a Category 3 gas
(systemically acting) and has a blood-air partition coefficient in rodents greater than that in
humans.14
F.5.2. PBPK Model-Based Human Points of Departure
As discussed in Section 5.1.3, the PBPK model was used for simultaneous interspecies
(for endpoints in rodent studies), intraspecies, and route-to-route extrapolation based on the
estimates from the PBPK model of the internal dose points of departure (idPOD) for each
candidate critical study/endpoints. The following supplementary data files contain figures
showing the derivation of the FtEDs and FtECs for the median (50th percentile) and sensitive (99th
percentile) individual from the (rodent or human) study idPOD. In each case, for a specific
study/endpoint(s)/sex/species (in the figure main title), and for a particular dose-metric (Y-axis
label), the horizontal line shows the original study idPOD (a BMDL, NOAEL, or LOAEL as
noted) and where it intersects with the human 99th percentile (open square) or median (closed
square) exposure-internal-dose relationship:
1. FtECs from human inhalation studies ("Supplementary data for TCE assessment:
Non-cancer FffiCs plots from human inhalation studies," 2011)
2. FtECs from rodent inhalation studies ("Supplementary data for TCE assessment: Non-
cancer HECs plots from rodent inhalation studies," 2011)
3. HECs from rodent oral studies ("Supplementary data for TCE assessment: Non-
cancer FffiCs plots from rodent oral studies," 2011)
4. HEDs from human inhalation studies ("Supplementary data for TCE assessment:
Non-cancer FffiDs plots from human inhalation studies," 2011)
5. FtEDs from rodent inhalation studies ("Supplementary data for TCE assessment:
Non-cancer FffiDs plots from rodent inhalation studies," 2011)
14 The posterior population median estimate for the TCE blood:air partition coefficient was 14 in the mouse [Table
3-37], 19 in the rat [Table 3-38], and 9.2 in the human [Table 3-39].
F-26
-------
6. HEDs from rodent oral studies ("Supplementary data for TCE assessment: Non-
cancer HEDs plots from rodent oral studies," 2011)
The original study internal doses are based on the median estimates from about
2,000 "study groups" (for rodent studies) or "individuals" (for human studies), and
corresponding exposures for the human median and 99th percentiles were derived from a
distribution of 2,000 "individuals." In both cases, the distributions reflect combined uncertainty
(in the population means and variances) and population variability.
In addition, as part of the uncertainty/variability analysis described in Section 5.1.4.2, the
POD for studies/endpoints for which BMD modeling was done was replaced by the LOAEL or
NOAEL. This was done to because there was no available tested software for performing BMD
modeling in such a context and because of limitations in time and resources to develop such
software. However, the relative degree of uncertainty/variability should be adequately captured
in the use of the LOAEL or NOAEL. The graphical depiction of the HECgg or HEDgg using
these alternative PODs is shown in the following supplementary data files:
1. HECs from rodent inhalation studies ("Supplementary data for TCE assessment: Non-
cancer HECs altPOD plots from rodent inhalation studies," 2011)
2. HECs from rodent oral studies ("Supplementary data for TCE assessment: Non-
cancer HECs altPOD plots from rodent oral studies," 2011)
3. HEDs from rodent inhalation studies ("Supplementary data for TCE assessment:
Non-cancer HEDs altPOD plots from rodent inhalation studies," 2011)
4. HEDs from rodent oral studies ("Supplementary data for TCE assessment: Non-
cancer HEDs altPOD plots from rodent oral studies," 2011)
F.6. SUMMARY OF POINTS OF DEPARTURE (PODs) FOR STUDIES AND
EFFECTS SUPPORTING THE INHALATION RfC AND ORAL RfD
This section summarizes the selection and/or derivation of PODs from the critical and
supporting) studies and effects that support the inhalation RfC and oral RfD. In particular, for
each endpoint, the following are described: dosimetry (adjustments of continuous exposure,
PBPK dose-metrics), selection of BMR and BMD model (if BMD modeling was performed),
and derivation of the HEC or dose for a sensitive individual (if PBPK modeling was used).
Section 5.1.3.1 discusses the dose-metric selection for different endpoints.
F.6.1. NTP (NTP, 1988)—BMD Modeling of Toxic Nephropathy in Rats
The supporting endpoint here is toxic nephropathy in female Marshall rats (NTP, 1988),
which was the most sensitive sex/strain in this study, although the differences among different
sex/strain combinations was not large (BMDLs differed by threefold or less).
F-27
-------
F.6.1.1. Dosimetry and BMD Modeling
Rats were exposed to 500 or 1,000 mg/kg/day, 5 days/week, for 104 weeks. The primary
dose-metric was selected to be average amount of DCVC bioactivated/kgyVday, with median
estimates from the PBPK model for the female Marshall rats in this study of 0.47 and 1.1.
Figure F-10 shows BMD modeling for the dichotomous models used (see Section F.5.1,
above). The log-logistic model with slope constrained to >1 was selected because: (1) the log-
logistic model with unconstrained slope yielded a slope estimate <1 and (2) it had the lowest
AIC.
The idPOD of 0.0132 mg DCVC bioactivated/kgyVday was a BMDL for a BMR of 5%
extra risk. This BMR was selected because toxic nephropathy is a clear toxic effect. This BMR
required substantial extrapolation below the observed responses (about 60%); however, the
response level seemed warranted for this type of effect and the ratio of the BMD to the BMDL
was not large (1.56 for the selected model).
F.6.1.2. Derivation of HEC99 and HED99
The F£EC99 and F£ED99 are the lower 99th percentiles for the continuous FtEC and
continuous human ingestion dose that lead to a human internal dose equal to the rodent idPOD.
The derivation of the HEC99 of 0.0056 ppm and HED99 of 0.00338 mg/kg/day for the 99th
percentile for uncertainty and variability are shown in Figure F-l 1. These values are used as this
supporting effect's POD to which additional UFs are applied.
F-28
-------
NTP.1988 kidney toxic nephropathy rat Marshall F oral.g
BMR: 0.05 extra
0)
"o
o
CD
Orf
Orf
CM
loglogistic, Pval = 1, AIC =
background 0, intercept 0.74, sl<
BMDandBMDL, 6.7I
i
i
i
i
0.0 0.2 0.4 0.6 0.8 1.0
ABioactDCVCBW34
o
tj
CO
loglogistic, Pval = 0.44, Al(
background 0, intercept 1, slope
\ i i i r
0.0 0.2 0.4 0.6 0.8 1.0
ABioactDCVCBW34
0)
"o
o
CD
multistage-1, Pval = 0.05, / 3
Background 0,Beta(1) 1.4, Beta(;
o
tj
TO
n i i r
0.0 0.2 0.4 0.6 0.8 1.0
weibull, Pval = 1, AIC = 12
Background 0, Slope 1.1, Power
BMDandBMDL, 9.1Be-08, NA
i i i i r
0.0 0.2 0.4 0.6 0.8 1.0
ABioactDCVCBW34
ABioactDCVCBW34
Figure F-10. BMD modeling of NTP (1988) toxic nephropathy in female
Marshall rats.
F-29
-------
CD
O
o
Q
-G
(D
g
CD
NTP.1988
BMDL for systemic kidney toxic.nephre
F Marshall rat
10™ 10°3 10°2 10D1
1 101 102 103 104
TCE inhalation (ppm)
NTP. 1988
BMDL for systemic kidney toxic.nephrc
F Marshall rat
O -
10M10D310D210D1
1 101 102 103 104
TCE oral (mg/kg-d)
Figure F-ll. Derivation of HECgg and HEDgg corresponding to the rodent
idPOD from NTP (1988) toxic nephropathy in rats.
F.6.2. Woolhiser et al. (2006)—BMD Modeling of Increased Kidney Weight in Rats
The endpoint here is increased kidney weights in female Sprague-Dawley (Sprague-
Dawley) rats (Woolhiser et al., 2006), which was considered a supporting effect for the RfD.
F.6.2.1. Dosimetry and BMD Modeling
Rats were exposed to 100, 300, and 1,000 ppm, 6 hours/day, 5 days/week, for 4 weeks.
The primary dose-metric was selected to be average amount of DCVC bioactivated/kg3/4/day,
with median estimates from the PBPK model for this study of 0.038, 0.10, and 0.51.
Figure F-12 shows BMD modeling for the continuous models used (see Section F.5.2,
above). The Hill model with constant variance was selected because it had the lowest AIC and
because other models with the same AIC either were a power model with power parameter <1 or
had poor fits to the control data set.
F-30
-------
Woolhiser.etal.2006 Kidney kidney.wt.perlOOgm rat CD
BMR: 0.1 relative
ra
-------
The idPOD of 0.0309 mg DCVC bioactivated/kg/4/day was a BMDL for a BMR of 10%
weight change, which is the BMR typically used by U.S. EPA for body weight and organ weight
changes. The response used in each case was the organ weight as a percentage of body weight,
to account for any commensurate decreases in body weight, although the results did not differ
much when absolute weights were used instead.
F.6.2.2. Derivation of HEC99 and HED99
The HECgg and F£ED99 are the lower 99th percentiles for the continuous HEC and
continuous human ingestion dose that lead to a human internal dose equal to the rodent idPOD.
The derivation of the HEC99 of 0.0131 ppm and HED99 of 0.00791 mg/kg/day for the 99th
percentile for uncertainty and variability are shown in Figure F-13. These values are used as this
effect's POD to which additional UFs are applied, and the resulting candidate RfD value is
supportive of the RfD.
Woolhiser.etal.2006
BMDL for systemic kidney weight.incre
FSprague-Dawlev rat
10™ 10D3 10°2 10D1 1 101 102 103 104
TCE inhalation (ppm)
m
O
o
ro
o
o -
WDOIhiser.etal.2006
BMDL for systemic kidney weight.incre
FSpraaue-Dawlev r
O -
10M10D310D210m 1 101 102 103 104
TCE oral (mg/kg-d)
Figure F-13. Derivation of HEC99 and HED99 corresponding to the rodent
idPOD from Woolhiser et al. (2006) for increased kidney weight in rats.
F.6.3. Keil et al. (2009)—LOAEL for Decreased Thymus Weight in Mice
The critical endpoint here is decreased thymus weight in female B6C3Fi mice (Keil et al.
2009).
F-32
-------
F.6.3.1. Dosimetry
Mice were exposed to 1,400 and 14,000 ppb of TCE in drinking water, with an average
dose estimated by EPA to be 0.35 and 3.5 mg/kg/day, for 30 weeks, based on the average of
subchronic and chronic values for generic body weight and water consumption rates for female
B6C3F1 mice (U.S. EPA, 1988). The dose-response relationships were sufficiently supralinear
that BMD modeling failed to produce an adequate fit. The primary dose-metric was selected to
be the average amount of TCE metabolized/kg Vday. The lower dose group was the LOAEL,
and the median estimate from the PBPK model at that exposure level was 0.139 mg TCE
metabolized/kg Vday, which is used as the rodent idPOD.
F.6.3.2. Derivation of HEC99 and HED99
The F£EC99 and F£ED99 are the lower 99th percentiles for the continuous FIEC and
continuous human ingestion dose that lead to a human internal dose equal to the rodent idPOD.
The derivation of the HEC99 of 0.0332 ppm and HED99 of 0.0482 mg/kg/day for the 99th
percentile for uncertainty and variability are shown in Figure F-14. These values are used as this
critical effect's POD to which additional UFs are applied.
-5T
CQ
Keil.etal.2009
rrouse B
-------
F.6.4. Johnson et al. (2003)—BMD Modeling of Fetal Heart Malformations in Rats
The critical endpoint here is increased fetal heart malformations in female Sprague-
Dawley rats (Johnson et al., 2003).
F.6.4.1. Dosimetry and BMD Modeling
Rats were exposed to 2.5, 250, 1.5, or 1,100 ppm TCE in drinking water for 22 days
(GDs 1-22). The primary dose-metric was selected to be average amount of TCE metabolized
by oxidation/kg Vday, with median estimates from the PBPK model for this study of 0.00031,
0.033, 0.15, and 88.
As discussed previously in Section F.4.2.1, from results of nested log-logistic modeling
of these data, with the highest dose group dropped, the idPOD of 0.0142 mg TCE metabolized
by oxidation/kgyVday was a BMDL for a BMR of 1% increased in incidence in pups. A 1%
extra risk of a pup having a heart malformation was used as the BMR because of the severity of
the effect; some of the types of malformations observed could have been fatal.
F.6.4.2. Derivation of HEC99 and HED99
The HECgg and F£ED99 are the lower 99th percentiles for the continuous HEC and
continuous human ingestion dose that lead to a human internal dose equal to the rodent idPOD.
The derivation of the HEC99 of 0.00365 ppm and HED99 of 0.00515 mg/kg/day for the 99th
percentile for uncertainty and variability are shown in Figure F-15. These values are used as this
critical effect's POD to which additional UFs are applied.
F-34
-------
CD
.0
X
e
o
o -
o -
o -=
Johnson.etal.2003
BMDL for developmental heart malform
F Spraaue-Dawlev rat
10™ 10D3 10D2 10m
1 101 102 103 104
TCE inhalation (ppm)
CD
.a
e
o
o -
Johnson.etal. 2003
BMDL for developmental heart malform
FSprague-Davy lev rat
10D510M10D310D210m 1 101 102 103 104
TCE oral (mg/kg-d)
Figure F-15. Derivation of HECgg and HEDgg corresponding to the rodent
idPOD from Johnson et al. (2003) for increased fetal cardiac malformations
in female Sprague-Dawley rats using the total oxidative metabolism dose-
metric.
F.6.5. Peden-Adams et al. (2006)—LOAEL for Decreased PFC Response and Increased
Delayed-Type Hypersensitivity in Mice
The critical endpoints here are decreased PFC response and increased delayed-type
hypersensitivity in mice exposed pre- and postnatally (Peden-Adams et al., 2006).
Mice were exposed to 1,400 and 14,000 ppb in drinking water, with an average dose in
the dams estimated by the authors to be 0.37 and 3.7 mg/kg/day, from GD 0 to postnatal ages of
3 or 8 weeks. The dose-response relationships were sufficiently supralinear that BMD modeling
failed to produce an adequate fit. In addition, because of the lack of an appropriate PBPK model
and parameters to estimate internal doses given the complex exposure pattern (placental and
lactational transfer, and pup ingestion postweaning), no internal dose estimates were made.
Therefore, the LOAEL of 0.37 mg/kg/day on the basis of applied dose was used as the critical
effect's POD to which additional UFs are applied.
F-35
-------
G. TCE CANCER DOSE-RESPONSE ANALYSES WITH RODENT CANCER BIOASSAY
DATA
G.I. DATA SOURCES
TCE cancer endpoints were identified in Maltoni et al. (1986), NCI (1976), NTP (1990.
1988), Fukuda et al. (1983), and Henschler et al. (1980). These data were reviewed and
tabulated in spreadsheets, and the numbers were verified. All endpoint data identified by authors
as having a statistically significant response to dose were tabulated, and data that had marginally
significant trends with dose were also reviewed. For all endpoints for which dose-response
model estimates were presented, trends were verified using the Cochran-Armitage or the Poly-3
test.
G.I.I. Numbers at Risk
The numbers of animals at risk are not necessarily those used by the authors; instead, the
number alive at 52 weeks was used (if the first cancer of the type of interest was observed at later
than 52 weeks) or the number alive at the week when the first cancer of the type of interest was
observed. In general, the data of Maltoni et al. (1986) were presented in this way, in their tables
titled "Incidence of the different types of tumors referred to specific corrected numbers." In a
few cases in Maltoni et al. (1986), the time of first occurrence was later than 52 weeks, so an
alternative number at risk was used from another column (for another cancer) in the same table
having a first occurrence close to 52 weeks. For NTP (1990, 1988) and NCI (1976), the week of
the first observation and the numbers alive at that week were determined from the appendix
tables. For Fukuda et al. (1983), the reported "effective number of mice" in their Table 2 was
used, which is consistent with numbers alive at 40-42 weeks (when the first tumor, a thymic
lymphoma, was observed) in their mortality curve. For Henschler et al. (1980), the number of
mice alive at week 36 (from their Figure 1), which is when the first tumor was observed
(according to their Figure 2), was used.
In cases in which there is high early mortality or differential mortality across dose groups
and the individual animal data are available, a more involved analysis that takes into account
animals at risk at different times (ages) is preferred (e.g., the poly-3 approach or time-to-tumor
modeling; see Section G.7). The more rudimentary approach of adjusting the denominator to
account for animals alive at the time of the first tumor entails some inaccuracy (bias) in
estimating the animals at risk compared to a more involved analysis accounting more completely
for time. However, it is generally agreed that it is better to use such an adjustment than to use no
adjustment at all (Haseman et al., 1984: Gartetal., 1979: Hoel and Walburg, 1972).
G-l
-------
G.1.2. Cumulative Incidence
Maltoni et al. (1986) conducted a lifetime study, in which rodents were exposed for
104 weeks (rats) or 78 weeks (mice), and allowed to live until they died "naturally." Maltoni
et al. (1986) reported cumulative incidence on this basis, and it was not possible to determine
incidence at any fixed time, such as 104 weeks on study. For Henschler et al. (1980), the number
of mice with tumors observed by week 104 (their Figure 2) was used. The cumulative incidence
reported by Fukuda et al. (1983) at 107 weeks (after 104 weeks of exposure) was used. For the
NCI (1976) and NTP (1990. 1988) studies, the reported cumulative incidence at 103-107 weeks
(study time varied by study and species) was used.
G.2. INTERNAL DOSE-METRICS AND DOSE ADJUSTMENTS
PBPK modeling was used to estimate levels of dose-metrics corresponding to different
exposure scenarios in rodents and humans (see Section 3.5). The selection of dose-metrics for
specific organs and endpoints is discussed under Section 5.2. Internal dose-metrics were
selected based on applicability to each major affected organ. The dose-metrics used with our
cancer dose-response analyses are shown in Table G-l.
Table G-l. Internal dose-metrics used in dose-response analyses, identified
by "X"
Dose-metric units
ABioactDCVCBW34 (mg/wk-kg3/4)
AMetGSHBW34 (mg/wk-kg3/4)
AMetLivlBW34 (mg/wk-kg374)
AMetLngBW34 (mg/wk-kg3/4)
AUCCBld (mg-hr/L-wk)
TotMetabBW34 (mg/wk-kg3/4)
TotOxMetabBW34 (mg/wk-kg3/4)
Liver
0
0
X
0
0
0
X
Lung
0
0
0
X
X
0
X
Kidney
X
X
0
0
0
X
0
Other
0
0
0
0
X
X
0
The PBPK model requires the rodent body weight as an input. For most of the studies,
central estimates specific to each species, strain, and sex (and substudy) were used. These were
estimated by medians of body weights digitized from graphics in Maltoni et al. (1986), by
medians of weekly averages in NTP (1990, 1988), and by averages over the study duration of
weekly mean body weights tabulated in NCI (1976).
For the studies by Fukuda et al. (1983) and Henschler et al. (1980), mouse body weights
were not available. After reviewing body weights reported for similar strains by two
laboratories15 and in the other studies reported for TCE, it was concluded that a plausible range
15http://phenome.jax.org/pub-
cgi/phenome/mpdcgi?rtn=meas%2Fdatalister&req=Cbodv+weight&pan=2&noomit=&datamode=measavg.
http://www.hilltoplabs.com/public/growth.html.
G-2
-------
for lifetime average body weight is 20-35 g, with a median near 28 g. For these two studies,
internal dose-metrics for these three average body weights (20, 28, and 35 g) were computed.
The percentage differences between the internal dose-metrics for the intermediate body weight of
28 g and the low and high average body weight of 20 and 35 g were then evaluated. Internal
dose-metrics were little affected by choice of body weight. For all dose-metrics, the differences
were less than ±13%. A body weight of 28 g was used for these two studies.
The medians (from the Markov chain Monte Carlo posterior distribution) for each of the
dose-metrics for the rodent were used in quantal dose-response analyses. The median is
probably the most appropriate posterior parameter to use as a dose-metric, as it identifies a
"central" measure and it is also a quantile, making it more useful in nonlinear modeling. The
"multistage" dose-response functions are nonlinear. One is interested in estimating the expected
response. The expected value of a nonlinear function of dose is under- or overestimated when
the mean (expected value) of the dose is used, depending on whether the function is concave or
convex. (This is Jensen's Inequality: for a real convex function f(X), f[E(X)] < E[f(X)].) For the
dose-response function, one is interested in E[f(X)], so using E(X) (estimated by the posterior
mean) as the dose-metric will not necessarily predict the mean response. Using the posterior
median rather than the mean as the dose-metric should lead to a response function that is closer
to the median response. However, if the estimated dose-response function is close to linear, this
source of distortion may be small, and the mean response might be predicted reasonably well by
using the posterior mean as the dose-metric. The mean and median are expected to be rather
different because the posterior distributions are skewed and approximately lognormal.
Therefore, results based on the posterior median and the posterior mean dose-metrics were
compared before deciding to use the median.
G.3. DOSE ADJUSTMENTS FOR INTERMITTENT EXPOSURE
The nominal applied dose was adjusted for exposure discontinuity (e.g., exposure for
5 days/week and 6 hours/day reduced the dose by the factor [(5/7) x (6/24)]), and for exposure
durations less than full study time (up to 2 years) (e.g., the dose might be reduced by a factor
[78 week/104 week]). The PBPK dose-metrics took into account the daily and weekly
discontinuity to produce an equivalent dose for continuous exposure. The NCI (1976) gavage
study applied one dose for weeks 1-12 and another, slightly different dose for weeks 13-78;
PBPK dose-metrics were produced for both dose regimes and then time-averaged (e.g., average
dose = (12/78) x Dl + (66/78) x D2). For Henschler et al. (1980). Maltoni et al. (19861 and NCI
(1976), a further adjustment of (exposure duration/study duration) was made to account for the
fact that exposures ended prior to terminal sacrifice, so that the dose-metrics reflect average
weekly values over the exposure period. Finally, for NCI (1976), the dose-metrics were then
G-3
-------
adjusted for early sacrifice16 (at 91 weeks rather than 104 weeks) by a factor of (91 wk/
104wk)3.17
G.4. RODENT TO HUMAN DOSE EXTRAPOLATION
Adjustments for rodent-to-human extrapolation were applied to the final results—the
BMD, BMDL, and cancer slope factor (potency), which is calculated as BMR/BMDL, e.g.,
0.10/BMDLio.
For the PBPK dose-metrics, a ratio between human and laboratory animal internal dose
was determined by methods described in Section 3.5. The cancer slope factor is relevant only for
very low extra risk (typically on the order of 10"4-10"6), thus very low dose, and it was
determined that the relation between human and animal internal dose was linear in the low-dose
range for each of the dose-metrics used, hence this ratio was multiplied by the animal dose (or
divided into the cancer slope factor) to extrapolate animal to human dose or concentration.
For the experimentally applied dose, default interspecies extrapolation approaches were
used. These are provided for comparison to results based on PBPK metrics. To extrapolate
animal inhalation exposure to human inhalation exposure, the "equivalent" HEC (i.e., the
exposure concentration in humans that is expected to give the same level of response that was
observed in the test species) was assumed to be identical to the animal inhalation exposure
concentration (i.e., "ppm equivalence"). This assumption is consistent with U.S. EPA
recommendations (U.S. EPA, 1994a) for deriving a FtEC for a Category 3 gas for which the
blood:air partition coefficient in laboratory animals is greater than that in humans.18 To
extrapolate animal oral exposure to equivalent human oral exposure, animal dose was scaled up
by body weight to the 3/4-power using the factor (BWHuman/BWAnimai)0'75. To extrapolate animal
inhalation exposure to human oral exposure, the following equation (Eq. G-l) was used;19
Animal, equivalent oral intake, mg/kg/day =
ppm x [A/[WTCE/24A5]20 x MV * (60 minutes/hour) x (103 mg/g) x [24 hour/BWkg](Eq. G-l)
with units
16For studies of <2 years (i.e., with terminal kills before 2 years), the doses are generally adjusted by the study
length ratio to a power of 3 (i.e., a factor [length of study in week/104 week]3) to reflect the fact that the animals
were not observed for the full standard lifetime (1980).
17For studies of <2 years (i.e., with terminal kills before 2 years), the doses are generally adjusted by the study
length ratio to a power of 3 (i.e., a factor [length of study in week/104 week]3) to reflect the fact that the animals
were not observed for the full standard lifetime (1980).
18 The posterior population median estimate for the TCE blood:air partition coefficient was 14 in the mouse [Table
3-37], 19 in the rat [Table 3-38], and 9.2 in the human [Table 3-39].
19ToxRisk version 5.3, © 2000-2001 by the KS Crump Group, Inc.
20Molecular weight of TCE is 131.39; there are 24.45 Lof perfect gas per g-mol at standard temperature and
pressure.
G-4
-------
ppm x [g/mol + L/mol ] x L/minute x (minutes/hour) x (mg/g) x [hour/day + kg](Eq. G-2)
which reduces to
ppm x [7.738307 x MV/BWkg] (Eq. G-3)
where
ppm = animal inhalation concentration, 1/106, unitless
MV = minute volume (breathing rate) at rest, L/minute.
Minute volume (MV) was estimated using equations from U.S. EPA (1994b, p. 4-27),
Mouse ln(MV) = 0.326 + 1.05 x ln(BWkg) (Eq. G-4)
Rat ln(MV) = -0.578 + 0.821 x ln(BWkg). (Eq. G-5)
Animal equivalent oral intake was converted to human equivalent oral intake by
multiplying by the rodent to human ratio of body weights to the power +0.25.21
To extrapolate animal oral exposure to equivalent human inhalation exposure, the
calculation above was reversed to extrapolate the animal inhalation exposure.
G.5. COMBINING DATA FROM RELATED EXPERIMENTS IN MALTONI ET AL.
(1986)
Data from Maltoni et al. (1986) required decisions regarding whether to combine related
experiments for certain species and cancers.
In experiment BT306, which used B6C3Fi mice, males experienced unusually low
survival, reportedly because of the age of the mice at the outset and resulting aggression. The
protocol was repeated (for males only), with an earlier starting age, as experiment BT306bis, and
male survival was higher (and typical for such studies). The rapid male mortality in experiment
BT306 apparently censored later-developing cancers, as suggested by the low frequency of liver
cancers for males in BT306 as compared to BT306bis. Data for the two experiments clearly
cannot legitimately be combined. Therefore, only experiment BT306bis males were used in the
analyses.
Experiments BT304 and BT304bis, on rats, provide evidence in male rats of leukemia,
carcinomas of the kidney, and testicular (Leydig cell) tumors, and provide evidence in female
rats for leukemia. Maltoni et al. (1986) stated "Since experiments BT 304 and BT 304bis on
Find whole-animal intake from mg/kg/d x B WAnJmai- Scale this allometrically by (BWHuman/B WAnimai) to
extrapolate whole-human intake. Divide by human body weight to find mg/kg/d for the human. The net effect is
Animal mg/kg/d x (BWAnimai/B WHuman)0 25 = Human mg/kg/d.
G-5
-------
Sprague-Dawley rats were performed at the same time, exactly in the same way, on animals of
the same breed, divided by litter distribution within the two experiments, they have been
evaluated separately and comprehensively." The data were also analyzed separately and in
combination.
The data and modeling results for these tumors in the BT304 and BT304bis experiments
are tabulated in Tables G-2 through G-5. It was decided that it was best to combine the data for
the two experiments. There were no consistent differences between experiments, and no firm
basis for selecting one of them. Our final analyses are, therefore, based on the combined
numbers and tumor responses for these two experiments.
Table G-2. Experiments BT304 and BT304bis, female Sprague-Dawley rats,
Maltoni et al. (1986). Number alive is reported for week of first tumor
observation in either males or females.a These data were not used for dose-
response modeling because there is no consistent trend (for the combined data,
there is no significant trend by the Cochran-Armitage test, and no significant
differences between control and dose groups by Fisher's exact test).
Exposure
concentration
(ppm)
0
100
300
600
0
100
300
600
0
100
300
600
Number
alive
Number of
rats with this
cancer
Proportion
with cancer
Multistage model fit statistics1"
Model
order
/7-Value
AIC
BMD10
BMDL10
Experiment BT304, female rats, leukemias, N alive at 7 wks
105
90
90
90
7
6
0
7
0.067
0.067
0.000
0.078
No adequately fitting model
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Experiments BT304 and BT304bis, female rats, leukemias, combined data
145
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was estimated for extra risk of 0.10 and confidence level 0.95. Exposure concentrations were multiplied by (7/24) x
(5/7) = 0.20833 before fitting the models, to adjust for exposure periodicity (i.e., the time-averaged concentrations
were about 20% of the nominal concentrations).
G-6
-------
Table G-3. Experiments BT304 and BT304bis, male Sprague-Dawley rats,
Maltoni et al. (1986): leukemias. Number alive is reported for week of first
tumor observation in either males or females.a
Exposure
concentration
(ppm)
0
100
300
600
0
100
300
600
0
100
300
600
Number
alive
Number of
rats with
this cancer
Proportion
with cancer
Multistage model fit statistics1"
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order
/7-Value
AIC
BMD10
BMDL10
Experiment BT304, male rats, leukemias, N alive at 7 wks
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71.9
iltSBf Ifssl
Vi&'K, S'fffSS:
Combined data for BT304 and BT304bis, male rats, leukemias
134
130
130
129
9
13
14
15
0.067
0.100
0.108
0.116
1
:S;i|;.g:|j:;;|j
SK^*J;:^':i*j^
:v-?i^;-:-^:^v^I
0.715
siiiiiss
lilSSIill
?S;i?lfi^S;'WS
337
Slllill
:••!?•:=! v.'.::'--:.:'1;-.1';'^-' '''.-••
269
iillfiS; S
:v>-' '^$U,!-v^;'<;v:'; '^
111
ffiSlSfefSjfl
Sliittlslif
fllIitfff|S|
aFirst tumor occurrences were not reported separately by sex.
bModels of orders 3 were fitted; the highest-order nonzero coefficient is reported in column "Model order." BMDL
was estimated for extra risk of 0.10 and confidence level 0.95. Exposure concentrations were multiplied by (7/24) x
(5/7) = 0.20833 before fitting the models, to adjust for exposure periodicity (i.e., the time-averaged concentrations
were about 20% of the nominal concentrations). "NA" indicates the BMD or BMDL could not be solved because it
exceeded the highest dose.
G-7
-------
Table G-4. Experiments BT304 and BT304bis, male Sprague-Dawley rats,
Maltoni et al. (1986): kidney adenomas + carcinomas. Number alive is
reported for week of first tumor observation in either males or females.a
Exposure
concentration
(ppm)
0
100
300
600
0
100
300
600
0
100
300
600
Number
alive
Number of
rats with
this cancer
Proportion
with cancer
Multistage model fit statistics1"
Model
order
/7-Value
AIC
BMD10
BMDL10
Experiment BT304 male rats, kidney adenomas + carcinomas, N alive at 47 wks
87
86
80
85
0
1
0
4
0.000
0.012
0.000
0.047
3
iflftfltl
iSSfSSi
tflff'ljS'fSf-
0.318
|14afi?S®;
^'SsysiffiA-
Hi??«SA£*S;M
Ki'SMSftS?
50.1
*ISaSft®|S
ir '-;-.•'.'.. /'-'A;!-! =v.v •;>:••'
*•!' ';'•'•••':,*' <'''•:•<''?;'•!•'•
'si'-':'-'&(:':'-!'jfi':''S-''':'
173
!f;l8f?%iSp
SpsSSSSftf
^•'i^:I^C^$W^:
134
lililiiSi
iiiiiiiiss
Experiment BT304bis, male rats, kidney adenomas + carcinomas, N alive at 53 wks
34
32
36
38
0
0
0
1
0.000
0.000
0.000
0.027
3
Ki'-PiSJSfl
i«K:&?fi:?f'S
©jKiftjftWfi
0.988
ifeSIfSili
tjjjjjW: Hi
:'''• x'.''''.V',v •*'*!;•>'• •«• >;i-j" '•'>''•
•j.'.''''V'i!i;'-Xi;J-i>>:,'::?-':-,v '•*;'•
13.0
'•:&*&&&<
wss-^m
W$^SK&
^',^ir;~* M-
•^V'^-:^S?v:-^
266
;tt;:!C;}:i-s;*iss
'i|i;SSSSS2iS
SWSWteM;
173
^v-^M^^/^^/^
vt?;ii;SS;il-vsSiS
>';¥s*;:s:fBi};!'J«.'
jilt!
0.292
;^:%;l?^'-'i •/•i^-^^.-;
'•;->^'^:..>-V!--V.£or!V^^^
SllSSIiill
e,s^^'''''.,r\:^'-V;jV!<}^i>.S.?;;:',''i
,"..i •:'.:;•-. /•x.K.^iHiViS;;,;/^;!-.; ; •;.;•
60.5
S;;Siiil
Illllfl
'?i&x!M%
telSSe
181
|||;;|3ffS|
'"•/siV;.'/'.}','. •(;V.v-'?.'.,i?s.
:,:'• >;>,•• •~:j-;-;>?;--'; '[A ': ,J" \
!S||l?s|Sill
144
'X'fi'f'y-flf'KK
&'5;ai!?SvS>)S«
-------
Table G-5. Experiments BT304 and BT304bis, male Sprague-Dawley rats,
Maltoni et al. (1986): testis, Leydig cell tumors. Number alive is reported for
week of first tumor observation.11
Exposure
concentration
(ppm)
0
100
300
600
0
100
300
600
0
100
300
600
Number
alive
Number of
rats with this
cancer
Proportion
with cancer
Multistage model fit statistics1"
Model
order
/7-Value
AIC
BMD10
BMDL10
Experiment BT304, male rats, Leydig cell tumors, N alive at 47 wks
87
86
80
85
5
11
24
22
0.057
0.128
0.300
0.259
1
Z'i''r?"'?'"^<.'$t :-:>.V-;.>',
"i'^'V^;:'^;-''^^;;*'''.:.
:'-$'?i:! $•*.'$ •'£$''•&£
0.0494
f;:!;'' 'i-M'l'V Cf-J'---''} '•'•'!'•" ^r'-1
,.,''•••." ' "•:".$£ -".'.-, -:.-"t •','&
B:;VV-i'* .. ';i ;'."• ?S*\ •'.',;"£ -X'i ••: ; £
'.''•''^•f^^'f-'i'.^.'v-'^,^]-
''^M'&'-J-^"^!^''^
309
'•^•"'W^'*^^
'.;. ; • •••/•;•:.•'•.;,• l-j.--f'
.v^5>^:;:;^'pv^fh'
f!j:1(«:iS;:;J?.;j.
41.5
iSillil
S|ftj;;:j|gl;|:|
SSl|;sfiP|l
29.2
illliliiliii;
?:iS;":|;«jf;^i'!^;ji;;i!:-;;j
•i &VWS K9'- '««.«•>»'
Experiment BT304bis, male rats, Leydig cell tumors, N alive at 53 wks
34
32
36
o o
38
1
5
6
9
0.029
0.156
0.167
0.237
i
^u^'>^'=^'^^
feSBSsfc*;;
;fli§S|g|
0.369
Si;:;!H3IS«
IJliillfiS
117
SSftS; SiS
linn
(-.!;V^-^'---r^
/i^V^.--Cr''^-/W-r^
54.5
SSlill
•'>•;.' •^•^f\^-'-'-?s :.';.,;'-;-':'
^silliii
30.9
KlWvy^f!-;-*!-;*':;.1
;!>;?» .^B-«Jy a; Kf;
S*4rfiSS>sW3f
»": -'.^ •; :• '^-- • . .;:••-< ' "'••• .•; i'^ ^v'.^
:• \." "::'; ••••^^•^•i—.--:^r'
iSliCSISiiiflSI
Combined data for BT304 and BT304bis, male rats, Leydig cell tumors
121
116
116
122
6
16
30
31
0.050
0.138
0.259
0.254
1
|il>ft|gfe
B||;|||||
;j4jjf|s:H;;?;S?::
0.0566
|f;?*tfifff|
tPStill
tf;*F;feft»?2
421
dill
iliiiiii
Jrtt;;;"ss«g:s;
Si'.'S©-?^
44.7
••;^::j"*Ai'^::^^^:'^::;^;';^
;.vr^c';;^?!JH^v;^.^
illiil
32.7
ijj||J:3Sj;|P»5|i;
|;?p|g||||g
:V'VVVVi'/'v/'''!.^'^''V.'''-:v::-jJ1/-;!:';v.';
"Numbers alive reported for weeks as close as possible to week 52 (first tumors observed at weeks 81 and 62,
respectively, for the two experiments).
bModels of orders three were fitted; the highest-order nonzero coefficient is reported in column "Model order."
BMDL was estimated for extra risk of 0.10 and confidence level 0.95. Exposure concentrations were multiplied by
(7/24) x (5/7) = 0.20833 before fitting the models, to adjust for exposure periodicity (i.e., the time-averaged
concentrations were about 20% of the nominal concentrations). "NA" indicates the BMD or BMDL could not be
solved because it exceeded the highest dose.
G-9
-------
G.6. DOSE-RESPONSE MODELING RESULTS
Using BMDS, the multistage quantal model was fitted using the applicable dose metrics
for each combination of study, species, strain, sex, organ, and BMR (extra risk) value under
consideration. A multistage model of order one less than the number of dose groups (g) was
fitted. This means that, in some cases, the fitted model could be strictly nonlinear at low dose
(estimated coefficient "bl" was zero), and in other cases, higher-order coefficients might be
estimated as zero so the resulting model would not necessarily have order (#groups-l). Because
more parsimonious, lst-order models often fit such data well, based on our extensive experience
and that of others (Nitcheva et al., 2007), if the resulting model was not a lst-order multistage,
then lower-order models were also fitted, down to a lst-order multistage model. This permitted
us to screen results efficiently.
A supplementary data file ("Supplementary data for TCE assessment: Cancer rodents
plots," 2011) shows the fitted model curves. The graphics include observations (as proportions
[i.e., cumulative incidence divided by number at risk]), the estimated multistage curve (solid red
line), and estimated BMD, with a BMDL. Vertical bars show 95% CIs for the observed
proportions. Printed above each plot are some key statistics (necessarily rounded) for model
goodness of fit and estimated parameters. Printed in the plots at upper left are the BMD and
BMDL for the rodent data, in the same units as the rodent dose. Within the plot at lower right
are human exposure values (BMDL and cancer slope factor for continuous inhalation and oral
exposures) corresponding to the rodent BMDL. For applied doses, the human equivalent values
were calculated by "default" methods,22 as discussed above, and then only for the same route of
exposure as the rodent, and they are in units of rodent dose. For internal dose-metrics, the
human values are based upon the PBPK rodent-to-human extrapolation, as discussed in Section
5.2.1.2.
Another supplementary data file ("Supplementary data for TCE assessment: Cancer
rodents results," 2011) presents the data and model summary statistics, including goodness-of-fit
measures (%2 goodness-of-fit^-value, AIC), parameter estimates, BMD, BMDL, and "cancer
slope factor" ("CSF"), which is the extra risk divided by the BMDL. Much more descriptive
information appears also, including the adjustment terms for intermittent exposure, and the doses
before applying those adjustments. The group "GRP" numbers are arbitrary, and are the same as
GRP numbers in the plots. There is one line in this table for each dose-response graph in the
preceding document. Input data for the analyses are in a separate supplementary data file
("Supplementary data for TCE assessment: Cancer rodents input data," 2011). Finally, the
values and model selections for the results used in Section 5.2 are summarized in another
supplementary data file (primary dose-metrics in bold) ("Supplementary data for TCE
assessment: Cancer rodents model selections," 2011).
22For oral intake, dose (BMDL) is multiplied by the ratio of animal to human body weight (60 kg female, 70 kg
male) taken to the 1A power. For inhalation exposures, ppm equivalence is assumed.
G-10
-------
G.7. MODELING TO ACCOUNT FOR DOSE GROUPS DIFFERING IN SURVIVAL
TIMES
Differential mortality among dose groups can potentially interfere with (i.e., censor) the
occurrence of late-appearing cancers. Usually the situation is one of greater mortality rates at
higher doses, caused by toxic effects, or, sometimes, by cancers other than the cancer of interest.
Statistical methods of estimation (for the cancer of interest) in the presence of competing risks
assume uninformative censoring.
For bioassays with differential early mortality occurring primarily before the time of the
1st tumor or 52 weeks (whichever came first), the effects of early mortality were largely
accounted for by adjusting the tumor incidence for animals at risk, as described above, and the
dose-response data were modeled using the multistage model.
If, however, there was substantial overlap between the appearances of cancers and
progressively differential mortality among dose groups, it was necessary to apply methods that
take into account individual animal survival times. Two such methods were used here: time-to-
tumor modeling and the poly-3 method of adjusting numbers at risk. Three such studies were
identified, all with male rats (see Table 5-34). Using both survival-adjustment approaches,
BMDs and BMDLs were obtained and unit risks derived. Section 5.2.1.3 presents a comparison
of the results for the three data sets and for various dose-metrics.
G.7.1. Time-to-Tumor Modeling
The first approach used to take into account individual survival times was application of
the multistage Weibull (MSW) time-to-tumor model. This model has the general form
P(d, 0=1- exp[-(^o + qid + q2d + ...+ <&) x (t - to)z], (Eq. G-6)
where P(d,t) represents the probability of a tumor by age t for dose d, and parameters z > 1,
to > 0, and qt > 0 for /' = 0, !,...,&, where k = the number of dose groups; the parameter to
represents the time between when a potentially fatal tumor becomes observable and when it
causes death. The MSW model likelihood accounts for the left-censoring inherent in
"incidental" observations of nonfatal tumors discovered upon necropsy and the right-censoring
inherent in deaths not caused by fatal tumors. All of our analyses used the model for incidental
tumors, which has no to term, and which assumes that the tumors are nonfatal (or effectively so,
to a reasonable approximation). This seems reasonable because the tumors of concern appeared
relatively late in life and there were multiple competing probable causes of death (especially
toxic effects) operating in these studies (also note that cause of death was not reported by the
studies used). It is difficult to formally evaluate model fit with this model because there is no
G-ll
-------
applicable goodness-of-fit statistic with a well-defined asymptotic distribution. However, plots
of fitted vs. observed responses were examined.
A computer program ("MSW") to implement the multistage Weibull time-to-tumor
model was designed, developed and tested for U.S. EPA by Battelle Columbus (Ohio). The
MSW program obtains maximum likelihood estimates for model parameters and solves for the
BMDL (lower confidence limit for BMD) using the profile-likelihood method. The model, with
documentation for methodology (statistical theory and estimation, and numerical algorithms) and
testing, was externally reviewed by experts in June 2007. Reviews were generally positive and
confirmed that the functioning of the computer code has been rigorously tested. (U.S. EPA and
Battelle confirmed that MSW gave results essentially identical to those of "ToxRisk," a program
no longer commercially issued or supported.) U.S. EPA's BMDS Web site provided reviewers'
comments and U.S. EPA's responses.23 The MSW program and reports on statistical and
computational methodology and model testing are available on U.S. EPA's BMDS Web site
(www.epa.gov/ncea/bmds).
Results of this modeling are shown in a supplementary data file ("Supplementary data for
TCE assessment: Rodents time to tumor results, 2011").
G.I.2. Poly-3 Calculation of Adjusted Number at Risk
To obtain an independent estimate of a POD using different assumptions, it was thought
desirable to compare time-to-tumor modeling to an alternative survival-adjustment technique,
"poly-3 adjustment" (Portier and Bailer, 1989), applied to the same data. This technique was
used to adjust the tumor incidence denominators based on the individual animal survival times.
The adjusted incidence data then served as inputs for U.S. EPA's BMDS multistage model, and
multistage model selection was conducted as described in Section 5.2.
A detailed exposition is given in Section 6.3.2 of Piegorsch and Bailer (Bailer and
Piegorsch, 1997). Each tumor-less animal is weighted by its fractional survival time (survival
time divided by the duration of the bioassay) raised to the power of 3 to reflect the fact that
animals are at greater risk of cancer at older ages. Animals with tumors are given a weight of 1.
The sum of the weights of all of the animals in an exposure group yields the effective survival-
adjusted denominator. The "default" power of 3 (thus, "poly-3") was assumed, which was found
to be representative for a large number of cancer types (Portier et al., 1986). Algebraically,
l (Eq.G-7)
23At http://www.epa.gov/ncea/bmds/response.html under title "2007 External Review of New Quanta! Models;" use
links to comments and responses.
G-12
-------
where
Wj = 1 if tumor is present
Wj = (tj/T)3 if tumor is absent at time of death (t,)
T = duration of study. N was rounded to the nearest integer.24
Calculations are reproduced in the time-to-tumor supplementary data file ("Supplementary data
for TCE assessment: Rodents time to tumor results," 2011).
G.8. COMBINED RISK FROM MULTIPLE TUMOR SITES
For bioassays that exhibited more than one type of tumor response in the same sex and
species (these studies have a row for "combined risk" in the "Endpoint" column of Table 5-34,
Section 5.2), the cancer potency for the different tumor types combined was estimated. The
combined tumor risk estimate describes the risk of developing tumors for any (not all together)
of the tumor types that exhibited a TCE-associated tumor response; this estimate then represents
the total excess cancer risk. The model for the combined tumor risk is also multistage, with the
sum of the stage-specific multistage coefficients from the individual tumor models serving as the
stage-specific coefficients for the combined risk model (i.e., for each
q, q = a + a + ... + q . where the q s are the coefficients for the powers of dose and k is
;' i[combined] i\ fl ik i
the number of tumor types being combined) (NRC, 1994; Bogen, 1990). This model assumes
that the occurrences of two or more tumor types are independent. The resulting model equation
can be readily solved for a given BMR to obtain a maximum likelihood estimate (BMD) for the
combined risk. However, the confidence bounds for the combined risk estimate are not
calculated by available modeling software. Therefore, a Bayesian approach was used to estimate
confidence bounds on the combined BMD. This approach was implemented using the freely
available WinBUGS software (Spiegelhalter et al., 2003), which applies Markov chain Monte
Carlo computations. Use of WinBUGS has been demonstrated for derivation of a distribution of
BMDs for a single multistage model (Kopylev et al., 2007) and can be straightforwardly
generalized to derive the distribution of BMDs for the combined tumor load.
G.8.1. Methods
G.8.1.1. Single Tumor Sites
Cancer dose-response models were fitted to data using BMDS. These were multistage
models with coefficients constrained to be non-negative. The order of model fitted was (g- 1),
where g is the number of dose groups. For internal dose-metrics, the values shown in tables
above were used.
24Notice that the assumptions required for significance testing and estimating variances of parameters are changed
by this procedure. The Williams-Bieler variance estimator is described by Piegorsch and Bailer (1997). Our
multistage modeling did not take this into account, so the resulting BMDL may be somewhat lower than could be
obtained by more laborious calculations.
G-13
-------
The multistage model was modified for U.S. EPA NCEA by Battelle (under contract
EPC04027) to provide model-based estimates of extra risk at a user-specified dose and profile-
likelihood CIs for that risk. Thus, CIs for extra risk in addition to BMDs could be reported.
G.8.1.2. Combined Risk From Multiple Tumor Sites
The multistage model identified by BMDS25 was used in a WinBUGS script to generate
posterior distributions for model parameters, the BMD and extra risk at the same dose specified
for the BMDS estimates. The prior used for multistage parameters was the positive half of a
normal distribution having a mean of zero and a variance of 10,000, effectively a very flat prior.
The burn-in was of length 10,000, then 100,000 updates were made and thinned to every 10th
update for sample monitoring. From a WinBUGS run, the sample histories, posterior
distribution plots, summary statistics, and codas were archived.
Codas were then imported to R and processed using R programs to compute BMD and
the extra risk at a specific dose for each tumor type. BMD and extra risk for the combined risk
function (assuming independence) were also computed following Bogen (NRC, 1994, Chapter
11, Appendix 1-1, Appendix 1-2; 1990, Chapter IV). Results were summarized as percentiles,
means, and modes (modes were based upon the smoothed posterior distributions). The extra
risks across tumor types at a specific dose (10 or 100 was used) were also summed.
BMDLs for rodent internal doses, reported below, were converted to human external
doses using the conversion factors in Tables G-6 and G-7 (based on PBPK model described in
Section 3.5).
Table G-6. Rodent to human conversions for internal dose-metric
TotOxMetabBW34
Route
Inhalation, ppm
Oral, mg/kg/d
Sex
F
M
F
M
Human (mean)
9.843477
9.702822
15.72291
16.4192
Table G-7. Rodent to human conversions for internal dose-metric
TotMetabBW34
Route
Inhalation, ppm
Oral, mg/kg/d
Sex
F
M
F
M
Human (mean)
11.84204
11.69996
18.76327
19.6
25The highest-order model was used, e.g., if BMDS estimates were gamma = 0, beta.l > 0, beta.2 = 0, beta.3 > 0, the
model in WinBUGS allowed beta.2 to be estimated (rather than being fixed at zero).
G-14
-------
The application of rodent to human conversion factors is as follows:
Given rodent internal dose/) in some units of TotOxMetabBW34, divide by tabled value Y
above to find human exposure in ppm or mg/kg/day.
Example: ppm (human) = Z)(rodent)/7
ppm (human female mean) = 500 (internal units)/9.843477
= 50.80 ppm (Eq. G-8)
G.8.2. Results
The results follow in this order:
Applied doses
NCI (1976), Female B6C3Fi mice, gavage, liver and lung tumors and lymphomas (see
Tables G-8 through G-10 and Figures G-l and G-2)
Maltoni (1986), Female B6C3Fi mice, inhalation (expt. BT306), liver and lung tumors
(see Tables G-l 1 through G-l3 and Figures G-3 and G-4)
Maltoni (1986), Male Sprague-Dawley rats, inhalation (expt. BT304), kidney tumors,
testis Leydig Cell tumors, and lymphomas (see Tables G-14 through G-16 and
Figures G-5 and G-6)
Internal Doses
NCI (1976) Female B6C3Fi mice, gavage, liver and lung tumors and lymphomas (see
Tables G-l7 through G-l9 and Figures G-7 and G-8)
Maltoni (1986), Female B6C3Fi mice, inhalation (expt. BT306), liver and lung tumors
(see Tables G-20 through G-22 and Figures G-9 and G-10)
Maltoni (1986), Male Sprague-Dawley rats, inhalation (expt. BT304), kidney tumors,
Testis Leydig Cell tumors, and lymphomas (see Tables G-23 through G-25 and
Figures G-11 and G-l2)
G-l 5
-------
Table G-8. Female B6C3Fi mice—applied doses: data
Dose3
0
356.4
713.3
Nb
18
45
41
Liver HCCs
0
4
11
Lung adenomas +
carcinomas
1
4
7
Hematopoietic
lymphomas +
sarcomas
1
5
6
aDoses were adjusted by a factor 0.41015625, accounting for exposure 5/7 days/week, exposure duration
78/91 weeks, and duration of study (91/104)3. Averaged applied gavage exposures were low-dose 869 mg/kg/day,
high dose 1,739 mg/kg/day.
^Numbers at risk are the smaller of (a) time of first tumor observation or (b) 52 weeks on study.
Source: NCI (1976).
Table G-9. Female B6C3Fi mice—applied doses: model selection
comparison of model fit statistics for multistage models of increasing order
Tumor site
Liver
Lung
Lymphomas + sarcomas
Model order,
selected
2
1"
2
1"
2
1"
Coefficient
estimates
equal zero
Y
Y
NA
NA
(32
NA
AIC
78.68
77.52
78.20
76.74
77.28
77.28
Largest"
scaled
residual
0
-0.711
0
-0.551
0.113
0.113
Goodness of
fit/7-value
1
0.6698
1
0.4649
0.8812
0.8812
"Largest in absolute value.
Source: NCI (1976).
G-16
-------
Table G-10. Female B6C3Fi mice—applied doses: BMD and risk estimates
(inferences for BMR of 0.05 extra risk at 95% confidence level)
Parameters used in model
p- Value for BMDS model
BMD05 (from BMDS)
BMD05 (median, mode— WinBUGS)
BMDL (BMDS)a
BMDL (5th percentile, WinBUGS)
BMD05 for combined risk (median, mode, from
WinBUGS)
BMDL for combined risk (5th percentile,
WinBUGS)
Liver HCCs
qO,ql
0.6698
138.4
155.5, 135.4
92.95
97.48
Lung adenomas +
carcinomas
qO,ql
0.6611
295.2
314.5,212.7
144.3
150.7
Hematopoietic
lymphomas +
sarcomas
qO,ql
0.8812
358.8
352.3,231.7
151.4
157.7
84.99, 78.95
53.61
BMDS maximum likelihood risk estimates
Risk at dose 100
Upper 95% confidence limit
Sum of risks at dose 100
0.03640
0.05749
0.01722
0.03849
0.01419
0.03699
0.06781
WinBUGS Bayes risk estimates
Risk at dose 100: mean, median
Upper 95% confidence limit
Combined risk at dose 100 mean, median
Combined risk at dose 100, upper 95%
confidence limit
0.0327, 0.0324
0.0513
0.0168,0.0161
0.0334
0.0152,0.0143
0.0319
0.06337, 0.0629
0.09124
"All CIs are at 5% (lower) or 95% (upper) level, one-sided.
Source: NCI (1976).
G-17
-------
Vt
5
X
UJ
CM
T*
o
- vertical solid, BMDe
I ~ - vertical dash, BMXc
0
50
100
Dose
150
200
Figure G-l. Female B6C3F! mice
tumor extra-risk functions.
-applied doses: combined and individual
o
CM
CD
O
o
o
to o
C T~
-------
Table G-ll. B6C3Fi female mice inhalation exposure—applied doses
Dose"
0
15.6
46.9
93.8
Liver hepatomas/Nb
3/88
4/89
4/88
9/85
Lung adenomas + carcinomas/Nb
2/90
6/90
7/89
14/87
"Doses adjusted by a factor 0.133928571, accounting for exposure 7/24 hours/day x 5/7 days/week, and exposure
duration 78/104 weeks. Applied doses were 100, 300, and 600 ppm.
^Numbers at risk are the smaller of (a) time of first tumor observation or (b) 52 weeks on study.
Source: Maltoni Q986).
Table G-12. B6C3Fi female mice—applied doses: model selection
comparison of model fit statistics for multistage models of increasing order
Tumor site
Liver
Lung
Model order,
selected
3
2
la
3
2
r
Coefficient
estimates equal
zero
(32
PI
NA
(32
(32
NA
AIC
154.91
153.02
153.47
195.91
193.91
193.91
Largest" scaled
residual
0.289
0.330
-0.678
0.741
0.714
0.714
Goodness of fit 77-
value
0.7129
0.8868
0.7223
0.3509
0.6471
0.6471
"Largest in absolute value.
Source: Maltoni (1986).
G-19
-------
Table G-13. B6C3Fi female mice inhalation exposure—applied doses
(inferences for 0.05 extra risk at 95% confidence level)
Parameters used in model
p- Value for BMDS model
BMD05(fromBMDS)
BMD05 (median, mode— WinBUGS)
BMDL (BMDS)a
ms combo.exe BMDosC, BMDLc
BMD05 (S^percentile, WinBUGS)
BMD05 for combined risk (median, mode, from
WinBUGS)
BMDL for combined risk (5th percentile,
WinBUGS)
Liver hepatomas
qO,ql
0.7223
72.73
71.55,56.79
37.13
Lung adenomas + carcinomas
qO,ql
0.06471
33.81
34.49,31.65
21.73
32.12, 16.22
37.03
22.07
23.07, 20.39
15.67
BMDS maximum likelihood risk estimates
Risk at dose 10
Upper 95% confidence limit
Sum of risks at dose 10
0.0070281
0.0151186
0.0150572
0.0250168
0.0220853
WinBUGS Bayes risk estimates: means (medians)
Risk at dose 10: mean, median
Upper 95% confidence limit
Combined risk at dose 10: mean, median
Combined risk at dose 10: upper 95% confidence
limit
0.007377, 0.007138
0.01374
0.01489, 0.01476
0.02
0.02216,0.02198
0.03220
aAll CIs are at 5% (lower) or 95% (upper) level, one-sided.
Source: Maltoni (1986).
G-20
-------
o
CO
(O
a:
s
"><
uu
— vertical solid, BMDe
t - - vertical dash, BMDLe
0
I
50 100 150 200
Dose
Figure G-3. B6C3Fi female mice inhalation exposure—applied doses:
combined and individual tumor extra-risk functions.
CD
p
o
* +
§ §
Q
CM
q
d
o
p
o
1 I I I
0 100 200 300
N = Bandwidth = 0.4731
Figure G-4. B6C3Fi female mice inhalation exposure—applied doses:
posterior distribution of BMDc for combined risk.
G-21
-------
Table G-14. Maltoni Sprague-Dawley male rats—applied doses
Dose"
0
20.8
62.5
125
Kidney adenomas +
carcinomas/Nb
0/121
1/118
0/116
5/123
Leukemias/Nb
9/134
13/130
14/130
15/129
Testis, Leydig cell
tumors/Nb
6/121
16/116
30/116
31/122
"Doses adjusted by a factor 0.208333333, accounting for exposure 7 hours/day x 5/7 days/week. Applied doses
were 100, 300, and 600 ppm.
^Numbers at risk are the smaller of (a) time of first tumor observation or (b) 52 weeks on study.
Table G-15. Maltoni Sprague-Dawley male rats—applied doses: model
selection comparison of model fit statistics for multistage models of
increasing order
Tumor site
Kidney
Leukemia
Dropping high dose
Testis
Dropping high dose
Model
order"
o
J
2
1"
o
J
2
1
2
la
o
J
2
1
2
la
Coefficient
estimates
equal zero
P1,P2
Y
Y
P2,(33
P2
NA
P2
NA
P2,P3
P2
NA
P2
NA
AIC
60.55
61.16
59.55
336.8
336.8
336.8
243.7
243.7
421 .4
421 .4
421 .4
277.6
277.6
Largest+
scaled
residual
1.115
-1.207
-1.331
0.537
0.537
0.537
0.512
0.512
-1.293
-1.293
-1.293
0.291
0.291
Goodness of
fit/7-value
0.292
0.253
0.4669
0.715
0.715
0.715
0.529
0.529
0.057
0.057
0.057
0.728
0.728
"Model order selected + largest in absolute value.
G-22
-------
Table G-16. Maltoni Sprague-Dawley male rats—applied doses
Parameters used in models
p- Value for BMDS model
BMD01 (from BMDS)
BMD01 (median, mode— WinBUGS)
BMDL (BMDS)3
BMDL (S^percentile, WinBUGS)
BMDoi for combined risk (median, mode,
from WinBUGS)
BMDL for combined risk (5th percentile,
WinBUGS)
Kidney adenomas +
carcinomas
qO,ql
0.4669
41.47
46.00, 35.71
22.66
23.23
Leukemia (high
dose dropped)
qO,ql
0.5290
14.5854
12.32, 8.021
5.52597
5.362
Testis, Leydig cell
tumors (high dose
dropped)
qO,ql
0.7277
2.46989
2.497, 2.309
1.77697
1.789
1.960, 1.826
1.437
BMDS maximum likelihood risk estimates
Risk at dose 10
Upper 95% confidence limit
Sum of risks at dose 10
Risk at dose 1
Upper 95% confidence limit
Sum of risks at dose 1
0.0024208
0.0048995
0.0068670
0.0202747
0.0398747
0.0641010
0.0002423
0.0004911
0.0006888
0.0020462
0.0040609
0.0066029
WinBUGS Bayes risk estimates: means (medians)
Risk at dose 10: mean, median
Upper 95% confidence limit
Combined risk at dose 10, mean, median
Combined risk at dose 10, upper 95%
confidence limit
Risk at dose 1 : mean, median
Upper 95% confidence limit
Combined risk at dose 1, mean, median
Combined risk at dose 1, upper 95%
confidence limit
0.002302, 0.002182
0.004316
0.008752, 0.008120
0.01860
0.03961, 0.03945
0.05462
0.05020, 0.04998
0.06757
2.305 x ID'4,
2.184 x ID'4
4.325 x ID'4
8.800 x 10-4,
8.150 x 104
1.876 x ID'3
0.004037,0.004017
0.005601
0.005143,0.005114
0.006971
aAll CIs are at 5% (lower) or 95% (upper) level, one-sided.
G-23
-------
o _
vertical BMDc
dash,
20 40 60 80
Dose
Figure G-5. Maltoni Sprague-Dawley male rats—applied doses: combined
and individual tumor extra-risk functions.
Q
00
o
o
Csl
O
p
o
I
I
I
2 4 Q 8 10 12 14
N = =
Figure G-6. Maltoni Sprague-Dawley male rats—applied doses: posterior
distribution of BMDc for combined risk.
G-24
-------
Table G-17. Female B6C3F! mice
metabolism): data
-internal dose-metric (total oxidative
Internal dose"
0
549.8
813.4
N*
18
45
41
Liver HCCs
0
4
11
Lung adenomas +
carcinomas
1
4
7
Hematopoietic
lymphomas +
sarcomas
1
5
6
"Internal dose, Total Oxidative Metabolism, adjusted for body weight, units [mg/(wk-kg3/4)]. Internal doses were
adjusted by a factor 0.574219, accounting for exposure duration 78/91 weeks, and duration of study (91/104)3.
Before adjustment, the median internal doses were 957.48 and 1416.55 (mg/wk-kg374).
^Numbers at risk are the smaller of (a) time of first tumor observation or (b) 52 weeks on study.
Source: NCI (1976).
Table G-18. Female B6C3Fi mice—internal dose: model selection
comparison of model fit statistics for multistage models of increasing order
Tumor site
Liver
Lung
Lymphomas + sarcomas
BMD,
BMDL
505, 284
367, 245
742, 396
780, 380
870, 389
839, 390
Model
order"
2a
1
2a
1
2
la
Coefficient
estimates
equal zero
Y,P1
Y
PI
NA
NA
NA
AIC
77.25
78.86
76.33
76.74
79.26
77.27
Largest+
scaled
residual
-0.594
-1.083
-0.274
-0.551
0
-0.081
Goodness of
fit/7-value
0.7618
0.3542
0.7197
0.4649
1
0.9140
aModel order selected + largest in absolute value.
Source: NCI (1976).
G-25
-------
Table G-19. Female B6C3Fi mice—internal dose-metric (total oxidative
metabolism): BMD and risk estimates (values rounded to 4 significant
figures) (inferences for BMR of 0.05 extra risk at 95% confidence level)
Parameters used in models
p- Value for BMDS model
BMD05 (from BMDS)
BMD05 (median, mode from WinBUGS)
BMDL (BMDS)3
BMDL (5th percentile, WinBUGS)
BMD05 for Combined Risk (median, mode, from
WinBUGS)
BMDL for Combined Risk (5th percentile,
WinBUGS)
Liver HCCs
qO, ql, q2
0.7618
352.4
284.8, 292.5
138.1
162.6
Lung adenomas +
carcinomas
qO, ql, q2
0.7197
517.8
414.3,299.9
193.0
195.4
Hematopoietic
lymphomas +
sarcomas
qO,ql
0.9140
423.8
409.8, 382.6
189.5
226.2
136.1, 121.1
85.65
BMDS maximum likelihood risk estimates
Risk at dose 100
Upper 95% confidence limit
Sum of risks at dose 100
0.004123
0.04039
0.001912
0.02919
0.0120315
0.0295375
WinBUGS Bayes risk estimates
Risk at dose 100: mean, median
Upper 95% confidence limit
Combined risk at dose 100 mean, median
Combined risk at dose 100, upper 95% confidence
limit
0.01468,0.01311
0.03032
0.01284, 0.01226
0.02590
0.009552, 0.008286
0.021410
0.03663, 0.03572
0.05847
aAll CIs are at 5% (lower) or 95% (upper) level, one-sided.
Source: NCI (1976).
G-26
-------
52
Of
S
4«-*
X
LJJ
CO
O
p
d>
0
200
400
600
800
Dose
Figure G-7. Female B6C3Fi mice—internal dose-metric (total oxidative
metabolism): combined and individual tumor extra-risk functions.
to
c
Q
Q
oo
o
CD
O
o
O
CD
O
O
O
O
1 I I I I I
100 200 300 400 500 600
N = Bandwidth = 3.023
Figure G-8. Female B6C3Fi mice—internal dose-metric (total oxidative
metabolism): posterior distribution of BMDc for combined risk.
G-27
-------
Table G-20. B6C3Fi female mice inhalation exposure
(total oxidative metabolism)
-internal dose-metric
Internal dose"
0
280.946
622.530
939.105
Liver hepatomas/7Vb
3/88
4/89
4/88
9/85
Lung adenomas + carcinomas/TV1"
2/90
6/90
7/89
14/87
Internal dose, Total Oxidative Metabolism, adjusted for body weight, units (mg/[wk-kg374]). Internal doses were
adjusted by a factor 0.75, accounting for exposure duration 78/104 weeks. Before adjustment, median internal doses
were 374.5945, 830.0405, 1,252.14 (mg/[wk-kg3/4]).
^Numbers at risk are the smaller of (a) time of first tumor observation or (b) 52 weeks on study
Source: Maltoni (1986).
Table G-21. B6C3Fi female mice—internal dose: model selection
comparison of model fit statistics for multistage models of increasing order
Tumor site
Liver
Lung
Model order,
selected"
y
2
i
o
J
2
la
Coefficient
estimates
equal zero
Pl,(32
PI
NA
(32
NA
NA
AIC
153.1
153.4
154
195.8
195.9
194
Largest+
scaled
residual
-0.410
-0.625
-0.816
-0.571
-0.671
-0.776
Goodness of
fit/7-value
0.8511
0.7541
0.5571
0.3995
0.3666
0.6325
aModel order selected + largest in absolute value.
Source: Maltoni (1986).
G-28
-------
Table G-22. B6C3Fi female mice inhalation exposure—internal dose-metric
(total oxidative metabolism) (inferences for 0.05 extra risk at 95% confidence
level)
Parameters used in models
p- Value for BMDS model
BMD05(fromBMDS)
BMD05 (median, mode— WinBUGS)
BMDL (BMDS)3
ms combo BMDosC, BMDLc
BMDL (5th percentile, WinBUGS)
BMD05 for combined risk (median, mode, from WinBUGS)
BMDL for combined risk (5th percentile, WinBUGS)
Liver hepatomas
qO, ql, q2, q3
0.5571
813.7
672.9, 648.0
419.7
Lung adenomas +
carcinomas
qO,ql
0.6325
366.7
382.8,372.1
244.6
412.76, 189.23
482.7
251.1
286.7,263.1
199.5
BMDS maximum likelihood risk estimates
Risk at dose 100
Upper 95% confidence limit
Sum of risks at dose 100
0.006284
0.01335
0.01389
0.02215
0.02017
WinBUGS Bayes risk estimates: means (medians)
Risk at dose 100: mean, median
Upper 95% confidence limit,
Combined risk at dose 100 mean, median
Combined risk at dose 100, upper 95% confidence limit
0.003482,
0.002906
0.008279
0.01337,
0.01331
0.02022
0.01637,0.01621
0.02455
aAll CIs are at 5% (lower) or 95% (upper) level, one-sided.
Source: Maltoni (1986).
G-29
-------
CC.
S
"x
800
Figure G-9. B6C3Fi female mice inhalation exposure
combined and individual tumor extra-risk functions.
-internal dose-metric:
.
05
C
fl>
Q
CD
o
p
o
o
o
o
CM
o
p
O _
O
o
CD
O
200 400 600 800 1000 1400
N = Bandwidth = 5.053
Figure G-10. B6C3Fi female mice inhalation exposure—internal dose-
metric: posterior distribution of BMDc for combined risk.
G-30
-------
Table G-23. Maltoni Sprague-Dawley male rats—internal dose-metric (total
metabolism)
Internal dose"
0
214.6540
507.0845
764.4790
Kidney adenomas +
carcinomas/TV1"
0/121
1/118
0/116
5/123
Leukemias/7Vb
9/134
13/130
14/130
15/129
Testis, Leydig cell
tumors/TV6
6/121
16/116
30/116
31/122
Internal dose, Total Oxidative Metabolism, adjusted for body weight, units (mg/(wk-kg3/4)].
^Numbers at risk are the smaller of (a) time of first tumor observation or (b) 52 weeks on study.
Table G-24. Maltoni Sprague-Dawley male rats—internal dose model
selection comparison of model fit statistics for multistage models of
increasing order
Tumor site
Kidney
Leukemias
Testis, Leydig cell tumors
Model
order,
selected
3
2
r
3
2
1"
o
J
2
la
Coefficient
estimates equal
zero
Y,P2
Y
Y
P2,(33
(32
NA
P2,P3
(32
NA
AIC
61.35
61.75
60.32
336.5
336.5
336.5
417.7
417.7
417.7
Largest"
scaled
residual
-1.264
-1.343
-1.422
0.479
0.479
0.479
1.008
1.008
1.008
Goodness of fit
/7-value
0.262
0.246
0.370
0.828
0.828
0.828
0.363
0.363
0.363
aLargest in absolute value.
G-31
-------
Table G-25. Maltoni Sprague-Dawley male rats—internal dose-metric (total
metabolism) (inferences for 0.01 extra risk at 95% confidence level)
Parameters used in models
p- Value for BMDS model
BMDoi (from BMDS)
BMDoi (median, mode— WinBUGS)
BMDL (BMDS)3
BMDL (5th percentile, WinBUGS)
BMDoi for combined risk (median, mode, from
WinBUGS)
BMDL for combined risk (5th percentile,
WinBUGS)
Kidney adenomas +
carcinomas
qO,ql
0.3703
295.1
161.3
Leukemias
qO,ql
0.8285
145.8
65.29
Testis, Leydig cell
tumors
qO,ql
0.3626
26.65
20.32
20.97, 19.73
16.14
BMDS maximum likelihood risk estimates
Risk at dose 100
Upper 95% confidence limit
Sum of risks at dose 100
Risk at dose 10
Upper 95% confidence limit
Sum of risks at dose 10
0.003400
0.0068784
0.0068694
0.0169134
0.0370162
0.0504547
0.04729
0.0003406
0.0006900
0.0006891 0.0037648
0.0017044 0.0051638
0.004795
WinBUGS Bayes risk estimates: means (medians)
Risk at dose 100: mean, median
Upper 95% confidence limit
Combined risk at dose 100 — mean, median
Combined risk at dose 100, upper 95%
confidence limit
Risk at dose 100 — mean, median
Upper 95% confidence limit
Combined risk at dose 10 — mean, median
Combined risk at dose 10, upper 95% confidence
limit
0.003191,0.003028
0.006044
7.691 x ID'3,
7.351 x ID'3
1.539 x ID'2
0.03641,0.03641
0.04769
0.04688, 0.04680
0.060380
3.196 x ID'4, 3.032 x 104
6.060000 x ID'4
7.726 x 1Q-4, 0.003705,
7.376 x 104 0.003703
1.550000 x ID'3 0.004874000
0.004793, 0.0047820
0.006208
aAll CIs are at 5% (lower) or 95% (upper) level, one-sided.
G-32
-------
£2
'{£
I
x
vertical solid, BMte
vertical dash, BMDLc
I I I I I
0 100 200 300 400 500
Dose
Figure G-ll. Maltoni Sprague-Dawley male rats—internal dose-metric:
combined and individual tumor extra-risk functions.
Distribution of BMDc for combined risk
Cft
c
CO
O
o _
s
o
o
O
20 40 60 80 100
N = 300000 Bandwidth = 0,2732
Figure G-12. Maltoni Sprague-Dawley male rats—internal dose-metric:
posterior distribution of BMDc for combined risk.
G-33
-------
G.9. PBPK-MODEL UNCERTAINTY ANALYSIS OF UNIT RISK ESTIMATES
As discussed in Section 5.2, an uncertainty analysis was performed on the unit risk
estimates derived from rodent bioassays to characterize the impact of pharmacokinetic
uncertainty. In particular, two sources of uncertainty are incorporated: (a) uncertainty in the
rodent internal doses for each dose group in each chronic bioassay and (b) uncertainty in the
relationship between exposure and the human population mean internal dose at low exposure
levels.
A Bayesian approach provided the statistical framework for this uncertainty analysis.
Rodent bioassay internal dose-response relationships were modeled with the multistage model,
with general form:
P(id) = 1 - exp[-(<7o + qiid+ q2id2 + ... + 0, for / = 0, 1, ..., k. Since the BMD (in internal dose units) for a given BMR can
be derived from the multistage model parameters qt, it is sufficient to estimate the posterior
distribution of qt given the combined bioassay data (for each dose group/ the number
responding^, the number at risk «,, and the administered dose 4) and the rodent
pharmacokinetic data, for which the posterior distribution can be derived using the Bayesian
analysis of the PBPK model described in Section 3.5. In particular, the posterior distribution of
qt can be expressed as:
P(q[i\ \Db,oaSsay Dpk) oc P(q[i}) P(ym | qm nv]) P(idm \dm, Dpk) (Eq. G- 1 0)
Here, the first term after the proportionality P(q\i]) is the prior distribution of the
multistage model parameters (assumed to be noninformative), the second term P(y^\q^ nV]) is
the likelihood of observing the bioassay response given a particular set of multistage parameters
and the number at risk (the product of binomial distributions for each dose group), and P(id^\d^
Dpk) is the posterior distribution of the rodent internal doses id[j\, given the bioassay doses and
the pharmacokinetic data used to estimate the PBPK model parameters.
The distribution of unit risk (URtd = BMRIBMD) estimates in units of "per internal dose"
is then derived deterministically from the distribution of multistage model parameters:
P(URld\Dbloassay Dpk.rodent) = \P(q^\Dbloassay Dpk.rodent) 5[UR - BMR/BMD(q[l])] dqv] (Eq. G-l 1
Here 6 is the Dirac delta-function. Then, the distribution of unit risk estimates in units of
"per human exposure" (per mg/kg/day ingested or per continuous ppm exposure) is derived by
converting the unit risk estimate in internal dose units:
G-34
-------
1 \Ul\human\l-' bioassay -L'pk-rodent) i-f^\'J-'^id\J-Jbioassay -L'pk-rodent) -i(jMConversion\-L'pk-human)
x idconversion) didconversion (Eq. G-12)
Here, idconversion is the population mean of the ratio between internal dose and administered
exposure at low dose (0.001 ppm or 0.001 mg/kg/day), and P(idconversion\Dpk-human) is its posterior
distribution from the Bayesian analysis of the human PBPK model.
This statistical model was implemented via Monte Carlo as follows. For each bioassay,
for a particular iteration r (r= 1 . ..nr\
(1) A sample of rodent PBPK model population parameters (\a£\odent:r was drawn from the
posterior distribution. Using these population parameters, a single set of group rodent
PBPK model parameters Qrodent,r was drawn from the population distribution. As
discussed in Section 3.5, for rodents, the population model describes the variability
among groups of rodents, and the group-level parameters represent the "average"
toxicokinetics for that group.
(2) Using Qrodent.r, the rodent PBPK model was run to generate a set of internal doses /'<%r for
the bioassay.
(3) Using this set of internal doses /<%r, a sample q^r was selected from the distribution
(conditional on /<%r) of multistage model parameters, generated using the WinBUGS,
following the methodology of Kopylev et al. (2007).
(4) The unit risk in internal dose units URia,r = BMR/BMD(q^r} was calculated based on the
multistage model parameters.
(5) A sample of human PBPK model population parameters (u,2)toma«,r was drawn from the
posterior distribution. Using these population parameters, multiple sets of individual
human PBPK model parameters Qhuman,r,[s] (s= l...ns) were generated. A continuous
exposure scenario at low exposure was run for each individual, and the population mean
internal dose conversion was derived by taking the arithmetic mean of the internal dose
conversion for each individual: idconversion,r = Sum(idconversion^s)/ns.
(6) The sample for the unit risk in units per human exposure was calculated by multiplying
the sample for the unit risk in internal dose units by the sample for the population internal
dose conversion: URhuman,r - UR,d,r x idconversion,r.
In practice, samples for each of the above distributions were "precalculated," and
inferences were performed by re-sampling (with replacement) according to the scheme above.
For the results described in Section 5.2, a total of nr = 15,000 samples was used for deriving
summary statistics. For calculating the unit risks in units of internal dose, the BMDs were
derived by re-sampling from a total of 4.5x 106 multistage model parameter values (1,500 rodent
PBPK model parameters from the Bayesian analysis described in Section 3.5, for each of which
there were conditional distributions of multistage model parameters of length 3,000 derived
G-35
-------
using WinBUGS). The conversion to unit risks in units of human exposure was re-sampled from
500 population mean values, each of which was estimated from 500 sampled individuals.
A supplementary data file ("Supplementary data for TCE assessment: Cancer rodents
uncertainty analysis," 2011) contains summary statistics (mean, and selected quantiles from 0.01
to 0.99) from these analyses, and is the source for the results presented in Chapter 5 (see Tables
5-41 and 5-42). Histograms of the distribution of unit risks in per unit human exposure are in
separate supplementary data files for the rodent inhalation bioassays ("Supplementary data for
TCE assessment: Cancer rodents uncertainty CSF-inhalation histograms, inhalation bioassays,")
and for the rodent oral bioassays ("Supplementary data for TCE assessment: Cancer rodents
uncertainty CSF-oral histograms, oral bioassays," 2011). Route-to-route extrapolated unit risks
are in other supplementary data files for inhalation unit risks extrapolated from oral bioassays
("Supplementary data for TCE assessment: Cancer rodents uncertainty CSF-inhalation
historams, oral bioassays," 2011) and for oral unit risks extrapolated from inhalation bioassays
("Supplementary data for TCE assessment: Cancer rodents uncertainty CSF-oral histograms,
inhalation bioassay," 2011)). Each figure shows the uncertainty distribution for the male and
female combined population risk per unit exposure (transformed to base-10 logarithm), with the
exception of testicular tumors, for which only the population risk per unit exposure for males is
shown.
G-36
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H. LIFETABLE ANALYSIS AND WEIGHTED LINEAR REGRESSION BASED ON
RESULTS FROM CHARBOTEL ET AL. (2006)
H.l. LIFETABLE ANALYSIS
A spreadsheet illustrating the extra-risk calculation for the derivation of the lower 95%
bound on the effective concentration associated with a 1% extra risk (LECoi) for RCC incidence
is presented in Table H-l.
H.2. EQUATIONS USED FOR WEIGHTED LINEAR REGRESSION OF RESULTS
FROM CHARBOTEL ET AL. (2006) [SOURCE: ROTHMAN (1986), P. 343-344]
Linear model: RR = 1 + bX
where RR = risk ratio, X = exposure, and b = slope.
b can be estimated from the following equation:
b =
(Eq. H-l)
where y specifies the exposure category level and the reference category (j = 1) is ignored.
The standard error of the slope can be estimated as follows:
(Eq. H-2)
.7=2
The weights, w7, are estimated from the CIs (as the inverse of the variance):
Var(RR.) ~ RR 2Var[\n(RR
\n(RR,)-\n(RR,}
2 xl.96
(Eq. H-3)
where RR} is the 95% upper bound on the RRj estimate (for they'th exposure category) and RR^ is
the 95% lower bound on the RR, estimate.
H-l
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Table H-l. Extra-risk calculation" for environmental exposure to 1.82 ppm TCE (the LECoi for RCC incidence) using
a linear exposure-response model based on the categorical cumulative exposure results of Charbotel et al. (2006), as
described in Section 5.2.2.1.2.
A
Interval
number
(0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
B
Age
interval
<1
1^
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40^4
45^9
50-54
55-59
60-64
65-69
70-74
75-59
80-84
C
All cause
mortality
(x 105/yr)
685.2
29.9
14.7
18.7
66.1
94
96
107.9
151.7
231.7
352.3
511.7
734.8
1,140.1
1,727.4
2,676.4
4,193.2
6,717.2
D
RCC
incidence
(x 105/yr)
0
0
0
0.1
0.1
0.2
0.7
1.6
3.2
6.3
11
17.3
26.2
36.2
44.6
49
51.6
44.4
E
All
cause
hazard
rate
(h*)
0.0069
0.0012
0.0007
0.0009
0.0033
0.0047
0.0048
0.0054
0.0076
0.0116
0.0176
0.0256
0.0367
0.0570
0.0864
0.1338
0.2097
0.3359
F
Prob. of
surviving
interval
(
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Column A: interval index number (/').
Column B: 5-year age interval (except <1 and 1-4) up to age 85.
Column C: all-cause mortality rate for interval / (x 105/year) [2004 data from CDC (2007)1.
Column D: RCC incidence rate for interval / (x 105/year) (2001-2005 SEER data [http://seer.cancer.gov]).
Column E: all-cause hazard rate for interval /' (h*,) [= all-cause mortality rate x number of years in age interval].0
Column F: probability of surviving interval /' without being diagnosed with RCC (q,) [= exp(-/z*,)].
Column G: probability of surviving up to interval / without having been diagnosed with RCC (S,) [Sj = 1; St = S^i x qt_^ for i > 1].
Column H: RCC incidence hazard rate for interval / (h,) [= RCC incidence rate x number of years in interval].
Column I: conditional probability of being diagnosed with RCC in interval /' [= (A//**,-) x Sf x (l-q,)] (i.e., conditional upon surviving up to interval /' without having been
diagnosed with RCC) [Ro, the background lifetime probability of being diagnosed with RCC = the sum of the conditional probabilities across the intervals].
Column J: exposure duration (in years) at mid-interval (xtime).
Column K: cumulative exposure mid-interval (xdose) [= exposure level (i.e., 1.82 ppm) x 365/240 x 20/10 x xtime] (365/240 x 20/10 converts continuous environmental
exposures to corresponding occupational exposures).
Column L: RCC incidence hazard rate in exposed people for interval / (hx,) [= h,• x (l + p x Mose), where (3 = 0.001205 + (1.645 x 0.0008195) =0.002554] [0.001205
per ppm x year is the regression coefficient obtained from the weighted linear regression of the categorical results (see Section 5.2.2.1.2). To estimate the
LECoi (i.e., the 95% lower bound on the continuous exposure giving an extra risk of 1%), the 95% upper bound on the regression coefficient is used (i.e.,
MLE+1.645 xSE).
Column M: all-cause hazard rate in exposed people for interval /' (h*x,) [= h*t + (hxt - h,)].
Column N: probability of surviving interval /' without being diagnosed with RCC for exposed people (qx,) [= exp(-h*x,)].
Column O: probability of surviving up to interval /' without having been diagnosed with RCC for exposed people (Sx,) [Sx, = 1; Sxt = Sx^ x qXj_^ for i > 1].
Column P: conditional probability of being diagnosed with RCC in interval /' for exposed people [= (hxj/h*x,) x Sxt x (l-qx,)] (Rx, the lifetime probability of being
diagnosed with RCC for exposed people = the sum of the conditional probabilities across the intervals).
aUsing the methodology of BEIRIV (1988).
bThe estimated 95% lower bound on the continuous exposure level of TCE that gives a 1% extra lifetime risk of RCC.
Tor the cancer incidence calculation, the all-cause hazard rate for interval /' should technically be the rate of either dying of any cause or being diagnosed with the specific
cancer during the interval, i.e., the all-cause mortality rate for the interval + the cancer-specific incidence rate for the interval—the cancer-specific mortality rate for the
interval [so that a cancer case isn't counted twice, i.e., upon diagnosis and upon death]) x number of years in interval. This adjustment was ignored here because the RCC
incidence rates are small compared with the all-cause mortality rates.
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I. EPA RESPONSE TO MAJOR PEER REVIEW AND PUBLIC COMMENTS
1.1. PBPK MODELING (SAB REPORT SECTION 1): COMMENTS AND EPA
RESPONSE
1.1.1. SAB Overall Comments:
The Panel commended the updated PBPK model (Chiu et al.. 2009: Evans et al.. 2009)
for dose-response assessment. The Panel found that while the PBPK model was generally well
presented, its description was incomplete in that mass-balance equations were not presented.
The Panel provided suggestions to improve model documentation and clarity, including clearer
descriptions of the strategy behind the model structure and the biological relevance of each
model equation. Model assumptions need to be more clearly described and the consequences of
potential violations of these assumptions should be discussed. In addition, a more detailed
justification was needed for the handling of between-animal variability in the model. The Panel
agreed that use of the Bayesian framework for estimation and characterization of the PBPK
model parameter uncertainties was appropriate. However, a more thorough description was
needed for the choice of prior distributions, the Bayesian fitting methodology, and the fit of the
posterior distribution for each model parameter. The Panel also generally endorsed the
hierarchical calibration approach that uses the posterior results in mice to establish the rat priors,
and the rat posterior results to set the human priors. The Panel also recommended performance
of a local sensitivity analysis to identify key model parameters that drive changes in modeling
results.
1.1.2. Major SAB Recommendations and EPA Response:
1.1.2.1. PBPK Model Structure (SAB Report Section la)
• Provide a better description of the final model structure and, in particular, provide a
revised model structure diagram that identifies model parameters with model states and
pathways (flows).
EPA response: EPA accepts this recommendation and has provided revised model structure
diagrams in Appendix A, Section A.4.1.
• Clarify the strategy behind the model structure and describe the biological relevance of
each model equation.
EPA response: EPA accepts this recommendation and has clarified the model structure and
equations, and their biological relevance, in Appendix A, Section A.4.1.
• Document model assumptions and discuss the consequences of potential violations of
these assumptions (e.g., impacts on bias and accuracy).
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EPA response: EPA accepts this recommendation and has expanded the discussion of
limitations of the model to include added discussion of model assumptions and the consequences
of potential violations in Section 3.5.7.4.
• Provide a more detailed justification for how between animal variability is accounted for
in the model.
EPA response: EPA accepts this recommendation and has expanded the discussion of how
between animal variability is addressed in the model in Section 3.5.5.2.
1.1.2.2. Bayesian Statistical Approach (SAB Report Section Ib)
• Present better descriptions and/or details on the choice of prior distributions, the Bayesian
fitting methodology and fit of the posterior distribution for each model parameter.
EPA response: EPA accepts this recommendation and has added a description of the choice of
prior distribution functions in Section 3.5.5.2; presented a description of the overall Bayesian
posterior distribution function used in the parameter fitting in Section A.4.4; and added graphical
presentation to Section A.5.1 of the posterior distributions, in comparison with the prior
distribution, for each model parameter. In addition, the use of the terms "population" and
"group" have been clarified throughout Chapter 3 and Appendix A.
• Provide some information on correlations around posterior medians for species-specific
parameters.
EPA response: EPA accepts this recommendation and provided tables of correlation coefficients
in Appendix A, Section A.5.1.
• Supply more information on the model ordinary differential equations and on the
likelihood function used in the Bayesian estimation.
EPA response: EPA accepts this recommendation and has supplied more information on the
model ordinary differential equations in Appendix A, Section A.4.1, and more information on
the likelihood function in Appendix A, Section A.4.3.4.
1.1.2.3. Parameter Calibration (SAB Report Section Ic)
• Improve the quality and the description of the assumptions underlying the use of the
hierarchical approach to parameter calibration. Help the reader to understand the extent
to which these assumptions are used consistently throughout the parameter calibration
process.
EPA response: EPA accepts this recommendation and revised Table A-4 to clarify the scaling
assumptions consistently used throughout the parameter calibration process, and revised
Section 3.5.5.3 to clarify the description of the assumptions underlying the hierarchical approach.
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1.1.2.4. Model Fit Assessment and Dose-Metric Projections (SAB Report Section Id)
• Move some graphical presentations from the linked graphics documents into the body of
the report or into Appendix A.
EPA response: EPA accepts this recommendation and has moved (in a more condensed form)
graphical presentations of the PBPK model predictions as compared to the in vivo data to the
body of Appendix A.
• Incorporate more discussion on model fit and in particular indicate areas where the model
fits well and areas where it did not fit well. Tie this discussion somehow to Table 3-41.
EPA response: EPA accepts this recommendation and has incorporated more discussion of
model fit in Section 3.5.6.3 indicating areas where the model fits well and areas where it did not
fit well. This discussion is tied to the Table previously labeled "3-41," as recommended. In
addition, the interpretation of the residual error GSD is more closely tied to this revised
discussion.
• Include graphs that show predicted vs. observed values for all data points used in the
analysis (one graph per endpoint).
EPA response: EPA accepts this recommendation and has added graphics showing predicted vs.
observed values for all data points used in the analysis (one graph per endpoint) to
Section 3.5.6.3. The width of the residual error GSDs are also included on these graphs for
comparison. In addition, this is tied to the revised discussion on model fit and the Table
previously labeled "3-41."
• To help readers identify which parameters are better specified than others, provide a table
of model parameters listed in reverse order by the width of their posterior variability
(width of the IQR or width of 95% CI).
EPA response: EPA accepts this recommendation and has added a table to Section 3.5.6.2 of
model parameters listed in reverse order by the width of their posterior variability, indicated by
the width of 95% CI.
• Identify those parameters with very different prior and posterior distributions and discuss
why this might be a reasonable result of the parameter calibration process. An alternative
would be to provide a table where parameters are ranked based on the percent change of
the posterior from the prior.
EPA response: EPA accepts this recommendation and has included a table in Section 3.5.6.2
that indicates the fold-change between the prior and posterior medians. This table is already
sorted by reverse order of the width of the posterior variability (see previous recommendation).
In order to identify those parameter with more different priors and posteriors, the fold-change
was bolded if the change was greater than threefold. It is noted in the revised text for
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Section 3.5.6.2 that those parameters with shifts >3-fold had prior CIs greater (sometimes
substantially) than 100-fold, so that such shifts are reasonable in that context.
• Clarify which parameters are related to variability and which address parameter
uncertainty. Separate the discussion of the two types of parameters.
EPA response: EPA accepts this recommendation and has replaced the tables in Section 3.5.6.2
that previously showed combined uncertainty and variability with tables that separately
summarize parameter uncertainty and variability. This separation of uncertainty and variability
has the added benefit of removing the appearance that posterior parameter distributions appear
flatter than prior distributions, since posterior parameter uncertainty should always be less than
or equal to prior parameter uncertainty. In addition, the text of Section 3.5.6.2 has been revised
to discuss separately estimates of the central tendency of the population from estimates of
population variability.
1.1.2.5. Lack of Adequate Sensitivity Analysis (SAB Report Section le)
• Perform a local sensitivity analysis, starting from the final fitted PBPK model, to assess
how small changes in model parameter estimates impact predictions. Provide graphical
presentations of the sensitivity of the model to changes in key model parameters in the
final documentation.
EPA response: EPA accepts this recommendation and has conducted a local sensitivity analysis
starting from the final fitted PBPK model, and assessing how small changes (5% increase or
decrease) in model parameter estimates impact predictions. Two types of model predictions are
analyzed. First, in Section 3.5.6.4, the sensitivity of predictions of calibration data is assessed,
including a graphical presentation of the number of data points that are sensitive to each
parameter. Second, in Section 3.5.7.2, the sensitivity of prediction of dose-metrics is assessed,
including a graphical presentation of the sensitivity coefficient for each parameter and dose-
metric. The results of these local sensitivity analyses confirms that the calibration data inform
the value of most model parameters, with the remaining parameters either informed by
substantial prior information or having little sensitivity with respect to dose metric predictions.
1.1.3. Summary of Major Public Comments and EPA Responses:
• Some public commenters disagreed with the extent and degree of variability of GSH
conjugation in humans predicted by the PBPK model.
EPA response: In accordance with SAB recommendations (see response below in
Section 1.5.2.3), EPA has revised the discussions in Sections 3.3 and 3.5 to reflect the uncertainty
in GSH conjugation predictions in humans.
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• Some public commenters disagreed with the extent of population variability predicted by
the PBPK model for some parameters.
EPA response: The External Review Draft reported posterior distributions as lumped
uncertainty and variability. For the parameters raised as a concern in the comments, the high
apparent variability is actually predominantly uncertainty, so the extent of population variability
is not exceedingly high. In accordance with SAB recommendations (see response above in
Section 1.1.2.4), EPA has revised the description of posterior parameters to separate uncertainty
and variability, providing additional clarity to the posterior predictions.
• Some public commenters recommended that EPA perform a sensitivity analysis on the
PBPK model.
EPA response: In accordance with SAB recommendations (see response above in
Section 1.1.2.5), EPA has conducted a local sensitivity analysis of the PBPK model.
• Some public commenters recommended that EPA incorporate additional data in its PBPK
model.
EPA response: In accordance with SAB recommendations (see response below in
Section 1.5.2.2), EPA incorporated additional data on TCA bioavailability in the TCA submodel
of the PBPK model. Other data cited by the commenters were evaluated in Appendix A for the
purposes of additional validation, but were not directly incorporated in the PBPK model.
1.2. META-ANALYSES OF CANCER EPIDEMIOLOGY (SAB REPORT
SECTION 2): COMMENTS AND EPA RESPONSE
1.2.1. SAB Overall Comments:
The Panel agreed that EPA's updated meta-analyses for kidney cancer, lymphoma and
liver cancer followed the NRC (2006) recommendations. The Panel agreed with EPA's
conclusions that TCE increased the risk for the three cancers studied, based on appropriate
inclusion criteria for studies, the methods of conducting the meta-analysis that included
consideration of bias and confounding, and the robustness of the findings based on the tests for
heterogeneity and sensitivity. The Panel also suggested performing a meta-analysis for lung
cancer to further support the absence of smoking as a possible confounder.
1.2.2. Major SAB Recommendations and EPA Response:
• Provide a rationale for the three cancer sites selected for the meta-analysis. The rationale
could be nicely summarized in a table.
EPA Response: EPA accepts this recommendation and has added text to Section 4.1 and
Appendix C.
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• Consider including meta-analysis for lung cancer for confounding purposes or other sites
for comparison for which some association with TCE exposure has been reported in
epidemiologic studies, such as childhood leukemia and cervical cancer. It might also be
possible to provide this information without a formal meta-analysis.
EPA Response: EPA accepts this recommendation and has included a meta-analysis for lung
cancer in Appendix C. Additionally, in the discussion in Chapter 4 of the possible role of
smoking in confounding the association between TCE exposure and kidney cancer, EPA
compares the RR estimates for lung and kidney cancers in five smoking cohorts and discusses
the expected contribution by smoking to kidney cancer in Raaschou-Nielsen et al. (2003), which
was estimated as 1-6%, far smaller than the 20-40% excess reported in this study. Meta-
analyses were not conducted for other cancer types for which there may have been suggestive
associations because there was inadequate reporting in the cohort studies, and for childhood
leukemia, there were too few studies of sufficient quality.
• Provide measures of heterogeneity such as the I2 statistic for each meta-analysis.
Although this information was provided and accurately explained in Appendix C, it was
mischaracterized at several points in the primary document. For example, the summary
of the kidney cancer meta-analysis on p. 4-167 of the primary document states that "there
was no observable heterogeneity across the studies for any of the meta-analyses," but
Appendix C indicates "the I2 value of 38% suggested the extent of the heterogeneity was
low-to-moderate." Non-significant heterogeneity is indeed observed heterogeneity.
EPA Response: EPA accepts this recommendation and has provided measures of heterogeneity
in the primary document. EPA has also corrected this sentence in Section 4.4.2.5; it now reads
"there was no observable heterogeneity for any of the meta-analyses of the 15 studies and no
indication of publication bias."
• Evaluate the likely impact of converting ORs to RR estimates [i.e., using the method of
Greenland (2004) or Zhang and Yu (1998)], and decide if necessary to perform these
conversions for the meta-analysis.
EPA Response: The papers cited by the SAB describe methods for correcting ORs in studies of
common outcomes. Each of the cancer types for which EPA did meta-analyses has a
background incidence <10% and is thus considered a rare disease, so no correction should be
necessary. In the case of rare diseases, only high ORs might notably overestimate RRs. In the
TCE studies, only Hardell et al. (1994) reported an OR high enough to be of potential concern, a
Mantel-Haenszel-adjusted OR of 7.2 for NHL. According to Zhang and Yu (1998), the Mantel-
Haenszel adjustment is a suitable way to estimate the RR; in fact, in the example they provide,
the Mantel-Haenszel adjustment outperforms the adjustment they are proposing. Furthermore,
according to McNutt et al. (2003), the Zhang and Yu method is incorrect when applied to an
adjusted OR and will produce a biased estimate when confounding is present. Additionally, the
model-based methods for estimating a RR from a case-control study described by Greenland
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(2004) are only applicable when one has the raw data. Thus, neither of the papers cited by the
SAB provides a satisfactory way to convert the Hardell et al. (1994) OR. However, any
overestimation that might occur by treating the Hardell et al. (1994) OR as an RR estimate is
negligible in the overall analysis. Removing the study all together only decreases the RRm from
1.23 to 1.21, and the latter result is still statistically significant (p = 0.004).
• Change the terminology regarding the meta-analysis results for 'lymphoma' to 'non-
Hodgkin lymphoma' throughout the document.
EPA Response: EPA accepts this recommendation and has revised the terminology throughout
the document.
1.2.3. Summary of Major Public Comments and EPA Responses:
• Some public commenters requested a glossary of epidemiology terms be included in the
document.
EPA response: EPA did not implement this recommendation, as definitions of epidemiologic
terms can be easily found from authoritative sources on the internet.
• Some public commenters suggested that EPA examine the TCE subregistry for
information about the association between TCE and cancer.
EPA response: EPA did not implement this recommendation with respect to cancer, as the
ATSDR TCE subregistry provides only limited information on cancer outcomes, as analyses are
for total cancers and less informative than for cancer types. EPA did consider, however,
observations on neurotoxicity and other noncancer outcomes.
• Some public commenters disagreed with the meta-analysis conclusions from the External
Review Draft, noting heterogeneity of findings, lack of consistent exposure-response, and
other methodological problems. These comments noted that while EPA's meta-analysis
methods and summaries are generally consistent with recent published summaries of this
literature, the commenters did not agree with EPA's interpretation. These comments
asserted that it is more accurate to report the epidemiologic evidence as "mixed" rather
than "consistent" or "robust."
Other public commenters agreed with the meta-analysis conclusions from the External
Review Draft, noting that epidemiologic studies are usually biased towards the null,
making it harder to detect a true causal relationship.
EPA response: In accordance with the SAB review, EPA maintains its meta-analysis
conclusions. EPA agrees with the public commenters that the characterization of the general
association between overall TCE exposure and cancer is "modest"; this was one of the points
explicitly brought out in the discussion in Section 4.11.2.1.2 concerning the strength of the
association. EPA also carefully considered the questions raised by the public commenters
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regarding consistency of the results and regarding alternative explanations for these findings.
This consideration is discussed in detail in Section 4.11.2.1. A strength of the meta-analytic
approach is its ability to assess heterogeneity among studies, which is of particular importance in
situations in which the overall RR estimate is modest and in situations in which results from
individual studies may be quite imprecise because of sample size limitations. In reviewing the
available data, including the results of the meta-analyses, EPA noted that chance was not
supported as an explanation for the findings, nor was there support for confounding by other
known or suspected risk factors as an explanation for the results.
1.3. NONCANCER HAZARD ASSESSMENT (SAB REPORT SECTION 3):
COMMENTS AND EPA RESPONSE
1.3.1. SAB Overall Comments:
EPA has provided a comprehensive synthesis of the available evidence regarding the
effects of TCE and its major metabolites on the CNS, kidney, liver, immune system, male
reproductive system, and developing fetus. One issue of concern was the inconsistencies
between reported levels of GSH conjugation pathway metabolites. The Panel recommended that
the impact of these divergent levels be more transparently presented. The Panel recommended
inclusion of the potential for TCE-induced immune dysfunctions (i.e., immunosuppression,
autoimmunity, inappropriate and/or excessive inflammation) to mechanistically underlie other
adverse health endpoints.
1.3.2. Major SAB Recommendations and EPA Response:
• If additional endpoints of renal dysfunction (e.g., diuresis, increased glucose excretion)
were present in the reported studies, they should be included in the report. Often, only
one or two parameters of renal function and histopathology were presented. A better
overall description of renal dysfunction should be presented if available (especially for
animal studies).
EPA Response: EPA accepts this recommendation, and has added the information to all studies
where such data are available.
• There should be a better description of the location of the renal lesion, including nephron
segment, if known. For example, TCE and DCVC appeared to affect the proximal tubule
at the level of the outer stripe of the medulla (S3 segment of proximal tubule). Is this the
site of lesions seen with other TCE metabolites? Explaining the role (or lack of a role) of
any other TCE metabolites in TCE nephrotoxicity could be strengthened by comparing
the sites of the renal lesion.
EPA Response: EPA accepts this recommendation, and has added the information to all studies
where such data are available.
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• On page 4-338, please clarify the use of the phrase, "subpopulation levels," on lines 31
and 33.
EPA Response: EPA accepts this recommendation, and has clarified the use of
"subpopulations."
• A statement should be added that the spectrum of TCE-induced immune dysfunctions
(immunosuppression, autoimmunity, inappropriate and/or excessive inflammation)
included in this EPA draft report has the potential to produce adverse effects that are seen
well beyond lymphoid organs and involving several other physiological tissues and
systems. The types of immune-inflammatory dysfunctions described in this report have
been observed to affect function and risk of disease in the nervous system (e.g., loss of
hearing), the skin, the respiratory system, the liver, the kidney, the reproductive system
(e.g., male sterility), and the cardiovascular system (e.g., heart disease, atherosclerosis).
EPA Response: EPA accepts this recommendation, and has added statements to Sections 4.6
and 4.6.3.1 that immume-related and inflammatory effects, particularly cell-mediated immunity,
may influence a broader range of disease, including cancer.
• A statement should be added to emphasize the cell-mediated immune effects of TCE as
some of this has been supported by the human epidemiology data and the issue is
pertinent to risk of cancer.
EPA Response: See previous response.
1.3.3. Summary of Major Public Comments and EPA Responses:
• Some public commenters disagreed with EPA's draft conclusion that TCE poses a human
health hazard for developmental cardiac effects due to limitations in the available data.
EPA response: In accordance with the SAB review, EPA acknowledges the limitations of the
available data, but maintains its conclusion that TCE poses a human health hazard for
developmental cardiac effects.
• Some public commenters disagreed with EPA's draft conclusion TCE poses a human
health hazard for immunotoxicity because additional confirmatory studies are needed.
EPA response: In accordance with the SAB review, EPA concludes that adequate data are
available to conclude that TCE poses a human health hazard for immunotoxicity.
1.4. CARCINOGENIC WEIGHT OF EVIDENCE (SAB REPORT SECTION 4):
COMMENTS AND EPA RESPONSE
1.4.1. SAB Overall Comments:
The Panel agreed with EPA's conclusion that TCE is "carcinogenic to humans" by all
routes of exposure. This is based on convincing evidence of a causal association between TCE
exposure and kidney cancer, compelling evidence for lymphoma, and more limited evidence for
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liver cancer as presented in the draft document. The epidemiologic data, in the aggregate, were
quite strong. The summary risk estimates from the meta-analyses provided a clear indication of
a cancer hazard from TCE. In addition, both animal data and toxicokinetic information provide
biological plausibility and support the epidemiologic data.
1.4.2. Major SAB Recommendations and EPA Response:
• The immune effects as highlighted in the hazard assessment should be referred to in the
conclusion, especially in the criteria of biological plausibility and coherence because of
the relationship between immune system dysfunction and cancer risk.
EPA Response: EPA accepts this recommendation, and has added a statement to
Section 4.11.2.1.6 that immune-related effects should also be considered in the biologic
plausibility of TCE carcinogenicity.
• Although the summary evaluation focused on the scientific evidence and meta-analysis
for kidney, lymphoma, and liver cancers, there is also some suggestive evidence for TCE
as a risk factor for cancer at other sites including bladder, esophagus, prostate, cervix,
breast, and childhood leukemia. This evidence that also supports the conclusion should
be mentioned in the summary evaluation (Section 4.11.2.1).
EPA Response: EPA accepts this recommendation, and has added a statement mentioning the
suggestive evidence of association between TCE and other types of cancer in
Section 4.11.2.1.10.
• Add a paragraph describing the definition of lymphoma as used in IRIS. Change the
terminology regarding the meta-analysis to 'non-Hodgkin lymphoma' instead of
'lymphoma', throughout the document. The term 'NHL' more accurately describes the
intent of the analysis as well as the overwhelming majority of cases in the analysis,
despite changing classification schemes. The focus of the meta-analysis on NHL and the
exact classifications the meta-analysis includes where it may diverge from classical NHL
(as in studies that included CLL) should be clearly explained in both Appendix C and in
the Hazard Characterization document (Section 4.6.1.2.2).
EPA Response: EPA accepts this recommendation and has added text describing the definition
of NHL as used in the assessment, in addition to revising the terminology and indicating
divergent case definitions in both Appendix C and Section 4.6.1.2.2.
• To assist the reader, please include references in the summary section (Section 4.11.2).
For example, "The other 13 high-quality studies [note: besides Hardell and Hansen]
reported elevated RR estimates with overall TCE exposure that were not statistically
significant." References for statements like this would be helpful. The Panel counted
fewer than 13 studies in the meta-analysis after subtracting out Hardell and Hansen, and
not all of these showed elevated risk estimates, so it would be helpful for the reader to
know which 13 studies this statement refers to.
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EPA Response: EPA accepts this recommendation and has added references to conclusions in
Section 4.11.2.1.
1.4.3. Summary of Major Public Comments and EPA Responses:
• Some public commenters disagreed with EPA's draft conclusion in the External Review
Draft that TCE is "carcinogenic to humans," judging the epidemiologic evidence to be
inadequate due to limitations of the body of evidence. Limitations cited by these
comments include exposure assessment limitations, potential unmeasured confounding,
potential selection biases, and inconsistent findings across groups of studies. Comments
also cited limitations in the experimental animal data, such as conflicting or negative
experimental animal data for kidney and immune tumors. These comments suggested
that a classification of "likely to be carcinogenic in humans" or "suggestive evidence of
carcinogen!city" would be more appropriate. Some of these comments cited the
NAS/NRC Contaminated Water Supplies at Camp Lejeune: Assessing Potential Health
Effects (NEC. 2009) as support.
Other public commenters supported EPA's draft conclusion in the External Review Draft
that TCE is "carcinogenic to humans."
EPA response: In accordance with the SAB review, EPA concludes that TCE is "Carcinogenic
to humans." EPA also notes that the NRC (2009) Camp Lejeune report was discussed during the
SAB review meetings. The meeting minutes from the June 24, 2010 teleconference call state
that "Panelists discussed the extent to which the EPA draft risk assessment document should
discuss or compare its findings and conclusions to those of the 2009 NAS Report on Camp
Lejuene. It was generally agreed that it was not necessary to compare EPA conclusions to all of
the other reviews, particularly in view of the different criteria applied across reviews, different
studies used across assessments and different scopes of each review and the fact that the current
draft risk assessments carries out a meta-analysis that was not considered in the 2009 NAS
review" (SAB, 2010a). In the meeting minutes from the December 15, 2010 SAB quality review
teleconference call, the Panel chair stated that "the material reviewed by the Panel was different
from what the NAS had reviewed" (SAB. 2010a. b).
1.5. ROLE OF METABOLISM (SAB REPORT SECTION 5): COMMENTS AND EPA
RESPONSE
1.5.1. SAB Overall Comments:
The Panel agreed with EPA's conclusion that oxidative metabolites of TCE were likely
responsible for mediating the liver effects. The Panel recommended that EPA examine studies
that provided quantitative assessment of TCA and DCA formation after TCE exposure. Dose-
response modeling, similar to that performed for tetrachloroethylene, may be considered by EPA
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to provide scientifically-based information on relative contribution, or lack thereof, of TCA
and/or DCA to the liver carcinogenesis effect of TCE.
EPA has provided a clear and comprehensive summary of the available evidence that
metabolites derived from GSH conjugation of TCE mediate kidney effects. The Panel noted that
uncertainties exist with regard to the extent of formation of the dichlorovinyl metabolites of TCE
between humans and rodents. The issue of quantitative assessment of the metabolic flux of TCE
through the GSH pathway vs. the oxidative metabolism pathway needs to be considered
carefully. A more complete discussion of the strengths and limitations of the analytical
methodologies used should be provided to address the large discrepancies in estimates of DCVG
formation.
1.5.2. Major SAB Recommendations and EPA Response:
1.5.2.1. Mediation of TCE-Induced Liver Effects by Oxidative Metabolism (SAB Report
Section 5a)
• No major recommendations in this section.
1.5.2.2. Contribution of TCA to Adverse effects on the Liver (SAB Report Section 5b)
• The EPA should examine studies that provide quantitative assessment of TCA and DCA
formation after TCE exposure in vivo and draw conclusions with regards to the relative
amount and kinetics of the oxidative metabolites of interest for liver toxicity.
EPA response: Most studies of TCA following TCE exposure have already been incorporated
into the PBPK model-based analyses, and previous studies of DCA following TCE exposure are
limited by the rapid clearance of DCA at low concentrations and analytical artifacts in DCA
detection. Section 4.5.6.1 has been revised to include discussion of the studies by Delinksy et al.
(2005) and Kim et al. (2009), which use more reliable analytic methods to quantify DCA
formation after TCE exposure in vivo.
• A careful evaluation of the concentration-time kinetics is needed to achieve certainty in
the comparisons of liver effects and the conclusions drawn by the EPA, which suggest
that TCA-induced adverse liver effects do not explain those observed with TCE. Equally
important is to fully consider the bioavailability of TCE itself with regards to the vehicle
effects between studies.
EPA response: EPA assumes that the first part of this comment refers to the issue of TCA
bioavailability, which is mentioned in the narrative text preceding this recommendations. EPA
has incorporated into Section 4.5.6.2.1 an updated analysis of TCA bioavailability and its impact
on conclusions regarding the role of TCA in TCE-induced hepatomegaly (Chiu, 2011). With
respect to TCE vehicle effects, there are not enough kinetic data using different vehicles to
quantitatively address vehicle effects. However, it is noted that if they are important, they may
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be a significant contributor to the residual variability in the combined analysis of TCE-induced
hepatomegaly.
• The body of the document could be further strengthened by reporting EPA's evaluation
on the strength of the specific criteria used for phenotypic classification described in each
study discussed, and noting where specific criteria were not reported. While most of this
information was included in the appendix, the EPA may consider constructing a summary
table for Section 4.5.6.
EPA response: Section 4.5.6.3.3.1 has been revised to note that no specific criteria are usually
given as to what constitutes "basophilic" or "eosinophilic," with the one exception of Carter et
al. (2003) noted. For immunochemical staining, it is noted that some studies use negative
controls as a comparison.
• Dose-response modeling, similar to that performed for PERC, may be considered by the
EPA to provide science-based information on relative contribution, or lack thereof, of
TCA and/or DCA to the apical liver carcinogenesis effect of TCE. While data gaps exist
and there are limitations in the comparisons between independent cancer bioassays, the
document should clearly state what the limitations are should such analysis be deemed
futile.
EPA response: EPA agrees that a quantitative analysis of the relative contributions of TCA
and/or DCA to TCE-induced liver carcinogenesis would be useful if feasible. However, as noted
in the revised Section 4.5.6.3.2.5, such an analysis is precluded by the high degree of
heterogeneity both within and across the databases for TCE and its oxidative metabolites. The
revised section gives notes substantial variability across bioassays in characteristics such as study
duration, control group incidences, and apparent carcinogenic potency, thus precluding either
quantitative analysis.
• The draft assessment may be strengthened by including information from human use of
DCA in clinical practice.
EPA response: Human data on use of DCA in clinical practice is summarized in EPA's
Toxicological Review of Dichloroacetic Acid (2003_b), and reference has been made to this
document in Section 4.5.6. In particular, it is noted that data on DCA in humans are scarce and
complicated by the fact that available studies have predominantly focused on individuals who
have a pre-existing (usually severe) disease.
1.5.2.3. Role of GSH-Conjugation Pathway on TCE-induced Kidney Effects (SAB Report
Section 5c)
• The issue of quantitative assessment of the metabolic flux of TCE through the GSH
pathway vs. the oxidative metabolism pathway should be considered carefully since
uncertainties exist with regard to the extent of formation of the dichlorovinyl metabolites
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of TCE between humans and rodents. EPA may need to provide appropriate reservations
to the conclusions based on the limited data for GSH metabolites.
• The discussion of how each of the in vitro and in vivo data sets were used to estimate
DCVG formation parameters for the PBPK model should be more transparent indicating
strengths and weaknesses in the database.
EPA responses: EPA accepts these two related recommendations. EPA has revised
Section 3.3.3.2 to articulate additional reservations as to its conclusions regarding the except of
formation of dichlorovinyl metabolites of TCE between rodents and humans, and to be more
transparent regarding the strengths and weaknesses in vitro and in vivo datasets. In addition,
cross-references to this discussion have been added in the context of the PBPK model parameters
and predictions to Sections 3.5.4.3, 3.5.5.1, 3.5.6.3.3, 3.5.7.3.1, 3.5.7.3.2, 3.5.7.4, and 3.5.7.5.
1.5.3. Summary of Major Public Comments and EPA Responses:
• Some public commenters disagreed with EPA's conclusion that DCA may play a role in
TCE-induced liver effects. These commenters also recommended that EPA take into
account the bioavailability of TCA in its evaluation of liver effects.
EPA response: In accordance with SAB recommendations, EPA has re-examined the data on
the contributions of TCA and/or DCA to TCE-induced liver effects, including incorporation of
data on TCA bioavailability, in Section 4.4. However, EPA's conclusion remains that TCA
cannot adequately account for account the liver effects of TCE.
• Some public commenters disagreed with EPA's conclusion that GSH-conjugation-
derived metabolites play the primary role in TCE-induced nephrotoxicity and
nephrocarcinogenicity.
EPA response: EPA maintains its conclusions, and notes that both the SAB review and the NRC
(2006) report support the conclusion that the GSH pathway is primarily responsible for
TCE-induced kidney effects.
1.6. MODE OF ACTION (SAB REPORT SECTION 6): COMMENTS AND EPA
RESPONSE
1.6.1. SAB Overall Comments:
The Panel agreed that the weight of evidence supports a mutagenic mode of action for
TCE-induced kidney tumors. However, the Panel concluded that the weight of evidence also
supported an mode of action involving cytotoxicity and compensatory cell proliferation and
including these may more accurately reflect kidney tumor formation than does a mutagenic
mechanism alone. The combination of cytotoxicity, proliferation and DNA damage together
may be a much stronger mode of action than any individual components.
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The Panel agreed that the data are inadequate to conclude that any of the TCE-induced
cancer and noncancer effects in rodents are not relevant to humans.
The Panel agreed that there is inadequate support for peroxisome proliferator activated
receptor alpha (PPARa) agonism and its sequellae being key events in TCE-induced human liver
carcinogenesis. Recent data from animal models (Yang et al., 2007) suggest that activation of
PPARa is an important, but not limiting, factor for the development of mouse liver tumors, and
additional molecular events may be involved. The Panel viewed the mode of action for liver
carcinogenicity in rodents as complex rather than unknown. It is likely that key events from
several pathways may operate leading to acute, subchronic, and chronic liver toxicity of TCE.
1.6.2. Major SAB Recommendations and EPA Response:
1.6.2.1. Hazard Assessment and Mode of Action (SAB Report Section 6a)
• The impact of the inconsistencies in data on the quantity of GSH pathway metabolites
formed in humans and rodents should be presented more transparently.
EPA Response: EPA accepts this recommendation, and has added discussion and/or mention of
the quantitative uncertainties with respect to GSH conjugation wherever relevant throughout the
document.
• In the body of the document, mode-of-action information should be systematized and
broken down into key events for each proposed mode of action. The EPA may consider
using a tabular format to facilitate the ease of evaluation. Information on
supporting/refuting (if any) evidence (with appropriate references indicated), human
relevance (if available), and "strength" of each line of evidence/study should be included.
EPA Response: EPA accepts this recommendation, and has added tables summarizing the
proposed modes of action and conclusions for kidney and liver carcinogenesis.
• EPA should consider tabular summaries by specific metabolites when studies used
metabolite exposure rather than the parent compound.
EPA Response: EPA considered this recommendation, but decided against adding the tables for
the metabolites because in most cases (TCA, DC A, and CH), those studies are described and
tabulated in detail in other toxicological reviews.
• Data gaps should be clearly identified to help guide future research.
EPA Response: EPA considered this recommendation, and decided to focus on data gaps related
to dose-response, as these will have the greatest impact on any future revision to the
Toxicological Review. These research needs are now included as a separate section at the end of
Chapters.
• Key conclusions supporting/refuting each key event should be presented in bullet form
indicating where in the document a more detailed narrative/tables can be found.
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EPA Response: EPA accepts this recommendation, and has included key conclusions in the
summary mode-of-action tables described above for kidney and liver carcinogenesis.
1.6.2.2. Mode of Action for TCE-Induced Kidney Tumors (SAB Report Section 6b)
• Modify the relevant text to reflect that the available data do, in fact, provide support for
TCE-induced kidney tumors involving cytotoxicity and compensatory cell proliferation,
possibly in combination with a mutagenic mode of action, although not to the extent that
support for a mutagenic mode of action was provided.
EPA Response: EPA accepts this recommendation and has included additional discussion along
the lines suggested to the section on kidney tumor mode of action.
1.6.2.3. Inadequate Support for PPARa Agonism and its Sequellae Being Key Events in
TCE-Induced Liver Carcinogenesis (SAB Report Section 6c)
• Graphical or tabular presentation of these data to strengthen the comparative analysis
between metabolites and chemicals.
• Including some of the analyses that compare the receptor transactivation potency and the
carcinogenic potential of TCA, DCA and other model peroxisome proliferators from
Guy ton et al. (2009) to strengthen the arguments.
EPA Response: EPA accepts these recommendations, and has added the tabular presentation of
quantitative differences among PPARa agonists and the quantitative analyses comparing
carcinogenic potential and the receptor transactivation potency or other short-term markers of
PPARa activation from Guyton et al. (2009) to strengthen the comparative analysis and
arguments.
1.6.2.4. Inadequate Data to specify Key Events and Modes of Action Involved in Other
TCE-Induced Cancer and Noncancer Effects (SAB Report Section 6d)
• No major recommendations in this section.
1.6.2.5. Human Relevance of TCE-Induced Cancer and Noncancer Effects in Rodents
(SAB Report Section 6e)
• The impact of potential overestimation of the extent of the GSH pathway in humans in
Section 4.4.7 (Kidney) must be transparent
EPA Response: EPA accepts this recommendation, and has added discussion and/or mention of
the quantitative uncertainties with respect to GSH conjugation wherever relevant throughout the
document.
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• The mode of action for carcinogen!city should be described as complex rather than
unknown in Section 4.5.7.4. Mode of Action. With respect to conclusions regarding the
liver, while the complete mode of action in animals may not be clear at this time,
complex is a more appropriate descriptor since it is likely that key events from several
pathways may operate leading to acute, subchronic, and chronic liver toxicity of TCE.
EPA Response: EPA accepts this recommendation, and has rephrased the liver mode of action
conclusions in Section 4.5.7.4 along the lines suggested.
1.6.3. Summary of Major Public Comments and EPA Responses:
• Some public commenters disagreed with EPA's conclusion that a mutagenic mode of
action is operative for TCE-induced kidney tumors, recommending instead that a mode of
action involving cytotoxicity is involved.
EPA response: EPA maintains its conclusion, in accordance with the SAB review (see
Section 1.6.1, above), that a mutagenic mode of action is operative for TCE-induced kidney
tumors. However, in accordance with the SAB recommendations (see Section 1.6.2.2, above)
and in partial response to this public comment, EPA has added additional discussion of the data
supporting a mode of action involving cytotoxicity.
• Some public commenters disagreed with EPA's conclusion that there is inadequate
support for PPARa agonism and its sequellae being key events in TCE-induced
hepatocarcinogenesis. Other public commenters agreed with EPA's conclusions.
EPA response: In accordance with the SAB recommendations (see Section 1.6.2.3, above), EPA
has provided additional analysis to support its conclusions.
• Some public commenters disagreed with EPA's conclusion that a cytotoxic mode of
action was inadequately supported for TCE-induced lung tumors, citing analogies to
other chemicals and other indirect data.
EPA response: EPA has added discussion of data from other compounds hypothesized to have
the same mode of action for inducing mouse lung tumors. However, in accordance with the SAB
review, EPA still concludes that there are inadequate data to specify the key events and modes of
action involved in TCE-induced lung cancer and noncancer effects.
1.7. SUSCEPTIBLE POPULATIONS (SAB REPORT SECTION 7): COMMENTS
AND EPA RESPONSE
1.7.1. SAB Overall Comment:
The Panel found that EPA's hazard assessment provided a good review of potentially
susceptible populations, and identified factors (genetics, lifestage, background, co-exposures,
and pre-existing conditions) that may modulate susceptibility to TCE carcinogenicity and
noncancer effects. However, the Panel disagreed with EPA's conclusion that toxicokinetic
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variability can be adequately quantified using existing data. The Panel recommended that
exposure to solvent mixtures should be considered for potential co-exposures, since exposure to
more than one chemical with the same target organ likely increases risk.
1.7.2. Major SAB Recommendations and EPA Response:
• The Panel disagreed with the statement that "toxicokinetic variability in adults can be
quantified given the existing data," as the main study characterizing toxicokinetic
variability in adults was small (n < 100) and was composed of subjects selected non-
randomly. The Hazard Assessment document should note the limitations of the adult
data for toxicokinetic modeling in terms of uncertainty and possible bias in
Section 4.10.3, and elsewhere in the document where these data are used for hazard
characterization modeling.
EPA response: EPA accepts this recommendation and has added a statement in Section 4.10.3
noting the limitations of the toxicokinetic database.
• Section 4.10 of the Hazard Assessment should discuss explicitly the lack of data
demonstrating modulation of health effects from TCE by the identified factors (genetics,
lifestage, background, co-exposures, and pre-existing conditions), and the need for such
data in risk assessment.
EPA response: A statement has been added to the introduction of Section 4.10 noting the lack of
data on susceptible populations and the need for such data. A statement on the need for
additional data to address uncertainties regarding susceptible populations has been added to
Section 4.10.3. The title of Section 4.10.3 has been amended to now read "Uncertainty of
Database and Research Needs for Susceptible Populations."
• EPA should make specific recommendations for studies that would fill the data gap for
susceptible groups. For example, epidemiologic studies in which TCE exposure is well-
characterized and in which internal comparisons can be made to determine whether there
is effect modification, and animal studies comparing subgroups (e.g., based on genetics,
obesity, multiple solvent exposures).
EPA response: Where appropriate, statements on the need for additional research to fill data
gaps regarding susceptible populations have been added where appropriate throughout
Section 4.10.
• Modulation of TCE exposure-related hypersensitivity dermatitis by genetic variation may
be relevant for future study, given results of the study of hypersensitivity dermatitis in
Asian workers reported in Li et al. (2007) and increasing industrial chemical exposures in
China.
EPA response: The need for future research on the relationship between genetic variation and
generalized hypersensitivity skin diseases is now highlighted in Section 4.10.3.
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• The wording in Section 4.10 was often not clear about whether it was describing results
for a study that looked at effect modification of the TCE effect or not, as opposed to
direct effects of age, gender, etc. Also, the draft document needs to state explicitly where
effects of TCE within one subgroup were stated, whether the other subgroup was also
examined in the same study.
EPA response: Additional clarification was added throughout Section 4.10 where necessary to
address when the results were unrelated to TCE exposure or related to TCE exposure.
Additional information was also added regarding the comparison group.
• The Panel recommended that exposure to solvent mixtures should be added as a potential
susceptibility factor (co-exposures) to Section 4.10, since exposure to more than one
chemical to the same target organ likely increases risk.
EPA response: A new Section 4.10.2.6 has been added on mixtures. This text is broader than
solvent mixtures, as there are available studies that address exposure to TCE together with non-
solvents.
• Section 4.10.2.4.1 (page 4-585) should be more accurately titled 'Obesity', rather than
'Obesity and metabolic syndrome'. As presently written, Section 4.10.2.4.1 gives no
clear message as to how obesity affected the kinetics of TCE, and the section should be
revised to provide clarification.
EPA response: As recommended, Section 4.10.2.4.1 has been retitled as "Obesity," and the text
has been amended to more clearly present the data on toxicokinetics of TCE as it relates to
obesity.
1.7.3. Summary of Major Public Comments and EPA Responses:
• Some comments noted that there is widespread exposure to TCE, including potentially
vulnerable subpopulations.
EPA response: No response needed.
• Some comments questioned why EPA was not basing its assessment on in utero
exposures.
EPA response: For noncancer effects, studies with in utero exposures were considered and, in
one case used, for the basis of the RfC or RfD. No data on in utero exposures and cancer effects
were located that were adequate for dose-response analysis.
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1.8. NONCANCER DOSE-RESPONSE ASSESSMENT (SAB REPORT SECTION 8):
COMMENTS AND EPA RESPONSE
1.8.1. SAB Overall Comments
1.8.1.1. Selection of Critical Studies and Effects
The Panel supported the selection of an RfC and RfD based on multiple candidate
reference values that lie within a narrow range at the low end of the full range of candidate
reference values developed, rather than basing these values on the single most sensitive critical
endpoint. The Panel expressed concerns about the use of several candidate critical studies and
effects, specifically NTP (1988) (toxic nephropathy), NCI (1976) (toxic nephrosis), and
Woolhiser et al. (2006) (increased kidney weights). However, the Panel noted that uncertainties
about the quantitative risk assessment based on kidney effects in NTP (1988), NCI (1976), and
Woolhiser et al. (2006) did not indicate that there was uncertainty that TCE caused renal toxicity.
As discussed previously, the three PBPK model-based candidate RfCs/RfDs (p-cRfCs/RfDs) for
renal endpoints were based on an uncertain dose-metric, especially in regard to the relative rate
of formation of the toxic metabolite in humans and animals. Additional issues related to choice
of toxic nephropathy in female Marshall rats from NTP (1988) included excessive mortality due
to dosing errors and possibly other causes, and a high level of uncertainty in the extrapolation to
the BMD due to the use of very high doses and a high incidence (>60%) of toxic nephropathy at
both dose levels used. With respect to toxic nephrosis in mice from NCI (1976), the BMD
analysis was not supported because the effect occurred in nearly 100% of animals in both dose
groups, and because a high level of uncertainty is associated with extrapolation from the LOAEL
at which nearly 100% animals were affected. Renal cytomegaly and toxic nephropathy, which
were not selected as critical effects, occurred at high frequency in all treated groups.
The Panel recommended that the two endpoints for immune effects from Keil et al.
(2009) and the cardiac malformations from Johnson et al. (2003) be considered the principal
studies supporting the RfC. The Panel also recommended that the endpoints for immune effects
from Keil et al. (2009) and Peden-Adams et al. (2008) and the cardiac malformations from
Johnson et al. (2003) be considered as the principal studies supporting the RfD.
1.8.1.2. Derivation of RfD and RfC
The screening, evaluation, and selection of candidate critical studies and effects used for
the development of the RfC and RfD were sound. The derivation of the PODs was generally
appropriate. However, the BMD modeling results were uncertain for some datasets. For
example, the log-logistic BMD analysis for toxic nephropathy in female Marshall rats in NTP
(1988), shown in Figure F-10 in Appendix F, may greatly overestimate the risks at low doses.
As discussed above, this modeling involved extrapolation from a high LOAEL at which a high
percentage of the animals were affected.
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EPA used PBPK-based dose-metrics for interspecies, intraspecies, and route-to-route
extrapolation. The Panel supported this approach for development of the RfC and RfD. The
Panel noted that the candidate RfDs/RfCs for kidney endpoints were highly sensitive to the rate
of renal bioactivation of the cysteine conjugate, DCVC, in humans relative to rodents. Candidate
RfDs/RfCs developed using this dose-metric were several hundred-fold lower than RfD/RfCs for
the same endpoints based on applied dose with standard UFs. The Panel noted that the
uncertainties about the in vitro and in vivo data used to estimate the rate of renal bioactivation of
DCVC were much greater than for other dose-metrics (e.g. there are large discrepancies in the
rates of human GSH conjugation reported by Lash et al. (1999a) and Green et al. (1997a)]).
These uncertainties should be clarified and should be the basis of a sensitivity analysis in the
next update of the TCE draft risk assessment. The Panel also recommended that the rationale for
scaling the dose-metric to body weight374, in conjunction with the interspecies extrapolation
based on PBPK modeling, should be presented in a clearer and more transparent way.
1.8.1.3. UFs
The Panel agreed that, in general, the selection of UFs was clearly and transparently
described and appropriate. EPA developed equivalent doses and concentrations for sensitive
humans to replace standard UFs for inter- and intra-species toxicokinetics. The Panel concluded
that the approach used, including the selections of PODs and the extrapolations from rodent to
human, followed by consideration of the 99th percentile human estimates, was acceptable to
address the sensitive population. In future work, the variability and uncertainty could be better
characterized by considering other quantiles of the distribution.
1.8.2. Major SAB Recommendations and EPA Response:
1.8.2.1. The Screening, Evaluation, and Selection of Candidate Critical Studies and Effects
(SAB Report Section 8a)
• Chapter 5 should include a list of all noncancer health effects and studies discussed in
Chapter 4, noting those that were considered candidate critical effects and studies.
EPA Response: EPA considered this recommendation and concluded that a list of all of the
noncancer health effects and studies discussed in Chapter 4 would be overly long and redundant.
As an alternative, first, EPA has ensured that each section of Chapter 4 includes tables of the
relevant noncancer health effects and studies discussed, with studies and effects in bold
designating those considered in Chapter 5. Second, EPA has added to Chapter 5 tables with the
experimental details (e.g., which species, doses, effects) of the candidate studies for each
endpoint type, with cross-references back to the tables in Chapter 4 that contain all of the studies
for each type of effect. Therefore, there is now a transparent trace-back from the PODs used in
Chapter 5 (tables in the external review draft), to the experimental details from which the POD
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was derived (new tables in Chapter 5), to the larger set of studies considered for each effect type
(tables in Chapter 4).
• Tables 5-1-5-5 should provide cross-references to the table or page in Chapter 4 and/or to
the Appendices (such as Appendix E for hepatic studies) where the listed study was
discussed, and should include more details (e.g., gender, strain, duration) of the studies
selected as the basis for cRfDs and cRfCs when these details were needed to prevent
ambiguity.
EPA Response: EPA accepts this recommendation and has addressed it as part of its response to
the previous recommendation for a table in Chapter 5 listing all of the studies.
• Consistent dose units should be used in discussing the same study in different places in
the document.
EPA Response: EPA accepts this recommendation and has checked the dose units used as it
developed the new tables for Chapter 5.
1.8.2.2. The PODs, Including those Derived from BMD Modeling (e.g., Selection of Dose-
Response Models, BMR Levels) (SAB Report Section 8b)
• Chapter 5 should include the information on POD derivation from Table F-13 of
Appendix F, including approach, selection criterion and decision points.
EPA Response: After reviewing Chapter 5, EPA did not implement this suggestion. Chapter 5
describes the modeling approaches and selection criteria and important decisions in sufficient
detail, and on page 5-3, the reader is directed to Appendix F for further details. The succeeding
pages of Chapter 5 describe studies and effects by effect domain quite extensively, and the tables
and footnotes contain sufficient detail on BMRs, PODs, and reasons for study selection. We
think that it is appropriate to provide the mass of numerical modeling details in Appendix F, and
that the modeling decisions are well described therein. Integrating this material into Chapter 5
would greatly increase the length of Chapter 5 and make it unwieldy for the reader.
1.8.2.3. The Selected PBPK-Based Dose-Metrics for Inter-Species, Intra-Species, and
Route-to-Route Extrapolation, Including the Use of Body Weight to the % Power Scaling
for Some Dose-Metrics (SAB Report Section 8c)
• The uncertainty about the rate of human GSH conjugation found in Lash et al. (1999a)
vs. Green et al. (1997a) should be highlighted in the current assessment.
EPA Response: EPA accepts this recommendation and has added discussion and/or mention of
the quantitative uncertainties with respect to GSH conjugation wherever relevant throughout the
document.
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• The basis for the renal bioactivation dose-metric should be more clearly and transparently
presented and discussed in Chapter 3 and other appropriate sections. If this dose-metric
was derived indirectly from data on other metabolic pathways leading to and/or
competing with bioactivation, this should be more clearly discussed.
EPA Response: EPA accepts this recommendation and has revised Section 3.5.7.3.1 to more
clearly discuss the basis of the renal bioactivation dose-metric. In other sections of the document
where the dose-metric is discussed, reference is made to Section 3.5.7.3.1.
• The rationale for scaling the dose-metric to body weight374, in conjunction with the
interspecies extrapolation based on PBPK modeling, should be presented in a clearer and
more transparent way (e.g., on pp. 5-33-5-36).
EPA Response: EPA accepts this recommendation and has revised the discussion of this
rationale substantially.
• The discussion of "empirical dosimetry" vs. "concentration equivalence dosimetry"
should be made clearer and more transparent (pp. 5-33-5-36).
EPA Response: As noted by the SAB in the narrative preceding this recommendation, it is not
necessary to include an extensive discussion of the two dosimetry approaches in these sections.
EPA accepts this recommendation and has replaced this discussion with a clearer and more
transparent rationale for the body weight374 scaling.
1.8.2.4. UFs (SAB Report Section 8d)
• The definitions of chronic and subchronic studies should be provided in the document
and a citation given.
EPA Response: EPA accepts this recommendation and has added a footnote with this
information on page 5-6 in the paragraph describing UFs for subchronic-to-chronic
extrapolation.
• The discussion of the subchronic to chronic UF on p. 5-6 should be clarified as far as
durations of studies considered suitable as the basis of a chronic risk assessment.
EPA Response: There is no hard and fast rule in this area. Longer studies are generally
preferred as the basis for a chronic risk assessment; however, in any given case, the basis of an
RfC or RfD, or whether one is derived at all, will depend on the studies available and an
assessment of their relevance for extrapolation to longer durations.
• The draft document should include discussion of whether studies in the lower end of the
range defined as subchronic (e.g., 4 weeks) are of sufficient duration to be used as the
basis for a chronic (lifetime) risk assessment.
EPA Response: EPA notes that studies of this duration have been evaluated for other risk
assessments. For any study and endpoint that is used as a basis for a POD in this and previous
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assessments, EPA has explained its applicability in the light of alternative studies of the same
endpoint having longer and shorter duration and alternative studies and endpoints within the
same domain having various durations.
• Studies only slightly longer than the minimum needed to be considered chronic should be
noted as such, and the use of an UF to account for less than lifetime exposure (of less
than the full UF of 10) could be considered for studies of such durations, especially for
endpoints thought to progress in incidence or severity with time.
EPA Response: If there is evidence suggesting there might be further progression with increased
exposure duration, a subchronic-to-chronic UF of 3 might be considered for a nominally chronic
study. The example given by the panel could merit special justification of an UF of 3 if there
were evidence that the response continued to increase with exposure durations longer than 18
weeks. No such evidence was found. For the study of Kulig et al. (1987), severity didn't
progress beyond week 9 for the two-choice response, and in the 1,000 ppm exposure group, it
didn't progress much in those first 9 weeks; thus, it is not anticipated that the 500 ppm response,
which was flat over the 18 weeks, would become significant over an extended duration of
exposure.
1.8.2.5. The Equivalent Doses and Concentrations for Sensitive Humans Developed from
PBPK Modeling to Replace Standard Ufs for Inter- and Intra-Species Toxicokinetics,
Including Selection of the 99th Percentile for Overall Uncertainty and Variability to
Represent the Toxicokinetically-Sensitive Individual (SAB Report Section 8e)
• The Panel noted variability/uncertainty for the toxicokinetically-sensitive individual
could be quantified in future work by considering distributions in addition to the
distribution of the 99th percentile, such as the 95th percentile.
EPA Response: EPA agrees that this could be the subject of future work.
• A quantile regression looking simultaneously at several quantiles could be developed in
the future and presented in future refinements of this assessment.
EPA Response: EPA agrees that this could be developed in the future and presented in future
refinements of this assessment.
1.8.2.6. The Qualitative and Quantitative Characterization of Uncertainty and Variability
(SAB Report Section 8f)
• The quantitative uncertainty analysis of PBPK model-based dose-metrics for LOAEL or
NOAEL based PODs (Section 5.1.4.2) should be revised to clarify the objective of this
2-D type analysis, as well as the methodology used.
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EPA Response: EPA accepts this recommendation and has revised the discussion, clarifying its
objective and methodology.
• In future work, EPA could develop an approach using distribution to characterize
uncertainty in a Bayesian framework.
EPA Response: EPA agrees that such an approach could be developed in future work.
1.8.2.7. The Selection of NTP (1988) [Toxic Nephropathy], NCI (1976) [Toxic Nephrosis],
Woolhiser et al. (2006) [Increased Kidney Weights], Keil et al. (2009) [Decreased Thymus
Weights and Increased Anti-dsDNA and Anti-ssDNA Antibodies], Peden-Adams et al.
(2008) [Developmental Immunotoxicity], and Johnson et al. (2003) [Fetal Heart
Malformations] as the Critical Studies and Effects for Noncancer Dose-Response
Assessment (SAB Report Section 8g)
EPA Response: See recommendation in Section 1.8.2.8, below.
1.8.2.8. The Selection of the Draft RfC and RfD on the Basis of Multiple Critical Effects
for Which Candidate Reference Values are in a Narrow Range at the Low End of the Full
Range of Candidate Critical Effects, Rather than on the Basis of the Single Most Sensitive
Critical Effect (SAB Report Section 8h)
• The two endpoints for immune effects from Keil et al. (2009) and the cardiac
malformations from Johnson et al. (2003) should be considered the principal studies
supporting the RfC.
EPA Response: EPA accepts this recommendation and has revised Chapter 5 accordingly.
• The endpoints for immune effects from Keil et al. (2009) and Peden-Adams et al. (2008)
and the cardiac malformations from Johnson et al. (2003) should be considered as the
principal studies supporting the RfD.
EPA Response: EPA accepts this recommendation and has revised Chapter 5 accordingly.
1.8.3. Summary of Major Public Comments and EPA Responses:
• Some public commenters disagreed with the choices of critical studies for dose-response
analyses of noncancer endpoints.
EPA response: In accordance with SAB recommendations (see Section 1.8.2.8), EPA has
selected the immune effects from Keil et al. (2009) and the cardiac malformations from Johnson
et al. (2003) as the principal studies supporting the RfC, and the immune effects from Keil et al.
(2009) and Peden-Adams et al. (2008) and the cardiac malformations from Johnson et al. (2003)
as the principal studies supporting the RfD.
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• Some public commenters recommended that EPA not rely on PBPK model-based
estimates of DCVC bioactivation in conducting dose-response analysis for kidney
endpoints.
EPA response: In accordance with SAB recommendations (see Section 1.8.2.3), EPA has noted
the uncertainties in the PBPK model-based DCVC bioactivation dose-metrics and considers the
kidney effects as supporting, rather than as principal bases for, the RfC and RfD.
• Some public commenters recommended that EPA provide a more concise and
consolidated characterization of the RfC and RfD determination, particularly in the
context of kidney effects.
EPA response: A concise and consolidated characterization of the RfC and RfD determination
appears in Sections 5.1.5.2 and 5.1.5.3. EPA has added discussion of the uncertainties related in
kidney effects to these summary characterizations.
• Some public commenters recommended that EPA provide more discussion of the
proportionality between applied and internal dose and its impact on the quantitative
analysis.
EPA response: The impact of the proportionality of applied and internal dose, as well as its
impact both dose-response analysis, is discussed in Section 5.1.3.3 and shown graphically in
Appendix F.
• Some public commenters viewed the use of PBPK modeling as "double counting"
variability, based on the idea that the observed dose-response is in part due to
pharmacokinetic variability.
EPA response: In accordance with the SAB review, the methodology that EPA used is
consistent with existing practice in the derivation of RfDs and RfCs. The methodology used is
also consistent with previous applications of PBPK modeling in noncancer risk assessment. The
comments highlight some uncertainties and ambiguities inherent in the RfD/RfC methodology,
but disaggregating the multiple contributions to dose-response assessment—including effects of
TK variation, TD variation, experimental error, stochasticity, and other factors in both the
experimental animal and human population—requires development of new approaches that are
beyond the scope of the assessment. While some published literature have addressed some of
these issues, further research and development are needed, as no alternative approach has been
generally accepted at the current time. EPA agrees with the SAB (see Section 1.8.2.6, above)
that a future research need is the development of an approach using distributions to characterize
uncertainty in a Bayesian framework. Such an approach, when developed, could also help
address the commenters' concerns.
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1.9. CANCER DOSE-RESPONSE ASSESSMENT (INHALATION UNIT RISK AND
ORAL UNIT RISK) (SAB REPORT SECTION 9): COMMENTS AND EPA RESPONSE
1.9.1. SAB Overall Comment:
In this assessment, EPA developed an inhalation unit risk and oral unit risk for the
carcinogenic potency of TCE in accordance with the approach outlined in the U.S. EPA Cancer
Guidelines (2005e, b). The unit risks for RCC were based on a case-control study published by
Charbotel et al. (2006). The Panel found that the analysis of the Charbotel et al. (2006) data was
well described and that the selection of this study to estimate unit risks was appropriate.
However, more discussion is needed on whether or not it is necessary to adjust for exposure to
cutting oils when computing an OR or RR relating TCE exposure to kidney cancer. The Panel
recommended that EPA take a closer look at the literature to determine if there are other studies
that suggest that exposure to cutting oils is a risk factor for kidney cancer. EPA should also
provide a more detailed discussion on the implication of assumptions made in their analysis. In
addition, background kidney cancer rates in the United States were used in constructing the life
table, although the Charbotel et al. (2006) data were based on a French cohort. A comparison of
background cancer rates in France and the United States would be helpful in supporting their
conclusions. The Panel supported the adjustment of the RCC unit risks to account for the added
risk of other cancers, using the meta-analysis results and Raaschou-Nielsen et al. (2003).
The Panel agreed that human data, when available, should be preferred over rodent data
when estimating unit risk since within species uncertainty is easier to address than between
species uncertainty. The Panel supported the use of linear extrapolation from the POD for cancer
dose-response assessment of TCE as a default approach. The Panel agreed that characterization
of uncertainty and variability was appropriate, and was exceptionally strong in the PBPK
models.
1.9.2. Major SAB Recommendations and EPA Response:
1.9.2.1. Estimation of Unit Risks for RCC (SAB Report Section 9a)
• The Panel believed that more discussion was needed on whether or not it is necessary to
adjust for exposure to cutting oils when computing an OR or RR relating TCE exposure
to kidney cancer. The Panel recommended that EPA take a closer look at the literature to
determine if there are other studies that suggest that exposure to cutting oils is a risk
factor for kidney cancer.
EPA Response: EPA accepts this recommendation and has discussed other studies examining
cutting fluids (Section 4.4.2.3). These studies suggest that potential confounding by cutting
fluids is of minimal concern, and thus, including these exposures in the logistic regression may
over-adjust because of the correlation with TCE exposure. Nonetheless, EPA has included, as a
sensitivity analysis, the derivation of a unit risk estimate based on the Charbotel et al. (2006)
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RCC ORs further adjusted for cutting fluids and petroleum oils, and this estimate is essentially
the same as the original estimate (Section 5.2.2.1.3).
• The Panel believed that the EPA should provide a more detailed discussion of the
limitations of their analysis. In particular, the model described on p. 5-131 made some
very restrictive assumptions: linear dose-response and exposure was measured without
error. In addition, the life table analysis applied the same estimated RR to each age
interval; another restrictive assumption. While the Panel understood that these
assumptions were necessary due to limited data, there was inadequate discussion of how
violations of these assumptions may affect the results.
EPA Response: EPA accepts the recommendation and has added text pertaining to these
assumptions. Note, too, that the uncertainties in the unit risk estimate, including uncertainties
about the exposure assessment, are discussed in some detail in the uncertainty section
(Section 5.2.2.1.3).
• Finally, in constructing the life table, the EPA used background kidney cancer rates in the
United States though the Charbotel et al. (2006) data were based on a French cohort.
Hence, a comparison of background cancer rates in France and the United States would
be helpful in supporting their conclusions.
EPA Response: EPA accepts this recommendation, and has added additional information to
Section 5.2.2.1.2. In particular, this section now notes that the usual assumption is that RR
transfers across populations independent of background rates. In addition, this section now
contains information comparing background kidney cancer rates in France and the United States.
1.9.2.2. Adjustment of RCC Unit Risks (SAB Report Section 9b)
• No major recommendations in this section.
1.9.2.3. Estimation of Human Unit Risks from Rodent Bioassays (SAB Report Section 9c)
• The Panel agreed that the analysis and results were appropriate but recommended that the
EPA provide more details about their implementation and potential biases. For instance,
in bioassays in which mortality occurred before time to first tumor, the authors simply
adjusted their denominators to equal the number alive at time to first tumor. This
approach assumed that drop-out prior to time to first tumor was unrelated to future risk of
a tumor which could result in biased estimates.
EPA Response: EPA accepts this recommendation and has added a paragraph discussing the
potential biases of this approach, along with citations to relevant literature, to Section G. 1.1.
• In addition, more information was needed on the priors used in their Bayesian analysis of
combined risk across tumor types.
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EPA Response: EPA accepts this recommendation and has added this information to
Section G.8.1.2.
1.9.2.4. Use of Linear Extrapolation for Cancer Dose-Response Assessment (SAB Report
Section 9d)
• No major recommendations in this section.
1.9.2.5. Application of PBPK Modeling (SAB Report Section 9e)
• No major recommendations in this section.
1.9.2.6. Qualitative and Quantitative Characterization of Uncertainty and Variability
(SAB Report Section 9f)
• No major recommendations in this section.
1.9.2.7. Conclusion on the Consistency of Unit Risk Estimates Based on Human
Epidemiologic Data and Rodent Bioassay Data (SAB Report Section 9g)
• No major recommendations in this section.
1.9.2.8. Preference for the Unit Risk Estimates based on Human Epidemiologic Data (SAB
Report Section 9h)
• No major recommendations in this section.
1.9.3. Summary of Major Public Comments and EPA Responses:
• Some public commenters stated that the time courses of kidney cancer, liver and biliary
cancer, and NHL do not support the hypothesis that TCE poses a great risk of cancer in
the human population. These comments recommended that EPA perform a "validation"
exercise to determine if the draft cancer classification and quantitative risk estimates are
consistent with the observable facts concerning human cancer rates and other known risk
factors for the tumor types listed.
EPA response: The analysis suggested by this comment is beyond the scope of the
Toxicological Review. Moreover, such an analysis would require data that do not currently
exist, including detailed historical population estimates not only of TCE exposure, but also of all
other exposures and risk factors associated with each cancer, as well as quantitative estimates as
to how each risk factor modulates the risk of cancer. It is noted, however, that limited
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"validation" was performed by comparing qualitative and quantitative inferences based on
epidemiologic data to those based on animal bioassay data. Further quantitative "validation"
may be possible in the future if epidemiologic studies with quantitative exposure information are
conducted.
• Some public commenters disagreed with the use of epidemiologic data as the primary
basis for the cancer dose-response analysis.
EPA response: EPA maintains its conclusion in accordance with the SAB review (see
Section 1.9.1, above), that the epidemiological data are appropriate to use for estimating cancer
risks. In response to recommendations by the SAB, EPA has provided more detailed discussions
as to the limitations of the analysis.
• Some public commenters disagreed with the use of linear low-dose extrapolation for
estimating cancer risks at levels below the POD, recommending instead the use of
nonlinear extrapolation.
EPA response: EPA maintains its conclusion in accordance with the SAB review (see
Section 1.9.1, above), that the linear low-dose extrapolation is appropriate to use given the
available data.
1.10. ADAFs (SAB REPORT SECTION 10): COMMENTS AND EPA RESPONSE
1.10.1. SAB Overall Comment:
The Panel agreed that application of ADAFs in the TCE analysis consistently followed
recommendations in the U.S. EPA Cancer Guidelines (2005b). All of the steps were clearly
presented for inhalation exposure. However, the discussion for the oral exposure route was
shortened and referred back to the inhalation section, making understanding of the example
difficult to follow. Currently, EPA's IRIS assessment provides lifetime cancer risk drinking
water concentrations for adults only. The Panel recommended that drinking water
concentrations for specified cancer risk levels should also be derived for various age groups.
1.10.2. Major SAB Recommendations and EPA Response:
• The Panel recommended that the statement on page 5-151, lines 14-18, be expanded to
better explain why ADAFs were used for <16 years of age, but not for the elderly, and
why EPA did not directly produce age dependent unit risks per mg/kg-day.
EPA Response: EPA accepts these recommendations. Section 5.2.3.3 notes that due to lack of
appropriate data, no ADAFs are used for other life-stages, such as the elderly. ADAF-adjusted
unit risks per ppm and per mg/kg-day are now presented in each sample calculation table in
Sections 5.2.3.3.1 and 5.2.3.3.2.
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• Include all details presented for the inhalation sample calculations as was done for the
oral exposure sample calculations.
EPA Response: EPA accepts this recommendation and has revised Sections 5.2.3.3.1 and
5.2.3.3.2 to include all of the details for each sample calculation.
• IRIS assessments in which ADAFs are applied, such as TCE, should include estimated
drinking water concentrations for specified lifetime cancer risk levels (10"4, 10"5, 10"6),
using representative drinking water intakes for various age groups, while noting that
other drinking water estimates may be used if preferred.
EPA Response: EPA accepts this recommendation and has added drinking water concentrations
for specified lifetime cancer risks under the assumptions used in the drinking water example
calculation to Section 5.1.3.3.2. Similarly, EPA has added air concentrations for specified
lifetime cancer risks under the assumptions used in the inhalation example calculation to
Section 5.1.3.3.1.
• Include in the documentation a discussion of the perceived conflict between the use of
ADAFs and the assumptions underlying the life table analysis of the Charbotel et al.
(2006) data.
EPA Response: EPA accepts this recommendation and has added a discussion addressing the
use of the ADAFs and the assumptions underlying the life table analysis.
1.10.3. Summary of Major Public Comments and EPA Responses:
• None
1.11. ADDITIONAL KEY STUDIES (SAB REPORT SECTION 11) AND EDITORIAL
COMMENTS: COMMENTS AND EPA RESPONSE
• The Panel has identified additional studies to be considered in the assessment, as well as
a number of editorial comments.
EPA Response: EPA has incorporated the additional studies in the appropriate sections, and
addressed the editorial comments.
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